In-Scanner Head Motion and Executive Function: Unraveling the Link for Robust Neuroimaging Biomarkers in Drug Development

Abstract

This article synthesizes current research on the critical, often confounding, relationship between executive function and in-scanner head motion. It establishes head motion as a systematic source of bias in neuroimaging data, particularly for populations where executive function is a primary outcome or trait of interest. We explore foundational evidence linking motion to neural efficiency, methodological frameworks for its quantification and mitigation, and advanced techniques for validating brain-behavior associations against spurious motion-related effects. Finally, we discuss the implications of these findings for developing reliable neuroimaging biomarkers in clinical neuroscience and drug development, providing researchers and professionals with a comprehensive guide to optimizing study design and data interpretation.

The Inextricable Link: How Executive Function Traits Govern In-Scanner Head Motion

Executive functions (EFs) represent a suite of top-down mental processes essential for goal-directed behavior, planning, and adapting to novel challenges. This whitepaper delineates the three core EFs—inhibitory control, working memory, and cognitive flexibility—as established by cognitive neuroscience literature [1] [2]. Furthermore, it examines the critical, yet often overlooked, relationship between deficits in these executive components and in-scanner head motion during functional magnetic resonance imaging (fMRI). This relationship poses a significant methodological challenge for neuroimaging research, potentially leading to the systematic exclusion of participants with lower executive functioning and biasing samples in studies of aging and clinical populations [3] [4]. We synthesize current research, present quantitative data on EF development and its correlation with motion, detail experimental protocols for assessment, and provide visual workflows to guide researchers and drug development professionals in addressing this confound.

Executive functions are higher-order cognitive control processes that regulate thoughts and actions to facilitate the achievement of chosen goals [5]. They are crucial for mental and physical health, success in academic and professional settings, and overall quality of life [1] [2]. These skills are primarily subserved by neural networks involving the prefrontal cortex, although they are not exclusively localized to this region and involve complex cortical and subcortical circuits [6] [5].

The "core" executive functions, as identified by factor-analytic studies, are Inhibitory Control, Working Memory, and Cognitive Flexibility [1] [2]. From these foundational components, more complex, higher-order EFs such as reasoning, problem-solving, and planning are built [2]. Understanding the individual trajectory and neurobiological basis of each core component is vital for researching cognitive decline, developing cognitive enhancers, and interpreting neuroimaging data accurately.

The Core Components of Executive Function

Inhibitory Control

Inhibitory control involves the ability to consciously control one's attention, behavior, thoughts, and/or emotions to override a strong internal predisposition or external lure [2]. It enables individuals to resist temptations, suppress impulsive actions, and selectively focus attention by inhibiting distracting information [1] [2].

• Function: This skill is essential for self-regulation, enabling behavioral change and conscious choice instead of automatic, habitual responses. It allows for delayed gratification and is critical for resisting premature responding in experimental settings [2].
• Neuroanatomy: The prefrontal cortex, particularly the orbitofrontal cortex, plays a key role, alongside subcortical structures like the subthalamic nucleus [5].
• Assessment: Common paradigms include the Stroop test, where participants must name the color of a word while inhibiting the prepotent response to read the word itself [1], and Go/No-Go tasks, which require responding to frequent stimuli and withholding responses to infrequent ones.

Working Memory

Working memory is a system for temporarily holding and manipulating information necessary for complex cognitive tasks like learning, reasoning, and comprehension [1] [2]. It is not merely a passive store but an active "workspace" for mental operations.

• Function: It allows for the integration of new information with existing knowledge over time, enabling the updating of mental models and guiding future behavior based on recent experiences [1].
• Neuroanatomy: The dorsolateral prefrontal cortex (DLPFC) is heavily implicated, interacting with parietal and temporal lobes [5].
• Assessment: It is often measured using n-back tasks, where individuals indicate when a current stimulus matches one presented n steps earlier, or digit span tasks that require recalling sequences of numbers in forward or reverse order.

Cognitive Flexibility

Cognitive flexibility, also termed set-shifting or mental flexibility, is the ability to adapt thinking and behavior in response to changing goals, rules, or environmental stimuli [1] [2]. It underlies creativity and the capacity to see issues from multiple perspectives.

• Function: This EF is engaged during multitasking, when solving problems with ineffective initial strategies, and when using empathy to consider another person's viewpoint [1].
• Neuroanatomy: Cognitive flexibility relies on a network involving the prefrontal cortex and the anterior cingulate cortex [5].
• Assessment: The Wisconsin Card Sorting Test (WCST) is a classic measure, where participants must discover changing sorting rules. Task-switching paradigms also assess the cost in speed and accuracy when shifting between different mental sets.

Table 1: Developmental Trajectory of Core Executive Functions

Executive Function	Onset & Development	Peak Performance	Decline
Working Memory	Develops through childhood and adolescence [5]	Early 30s [1]	Begins post-35, continues into older age [1]
Cognitive Flexibility	Develops from age 3; some theories suggest maturation up to ~age 29 [1]	Young Adulthood	Not specified in search results, but generally declines with age
Inhibitory Control	Begins in infancy [1] [5]	Not specified	Begins in the 60s [1]

The Link Between Executive Function and In-Scanner Head Motion

A growing body of evidence indicates that individual differences in executive functioning are systematically related to the amount of head motion exhibited during fMRI scans. This association presents a significant confound for neuroimaging studies, particularly in aging and clinical populations.

Empirical Evidence of the Association

Research by Hausman et al. (2022) directly investigated this link in a sample of 282 healthy older adults (aged 65-88) [3] [4]. The study used the number of "invalid scans" flagged as motion outliers as the primary metric for in-scanner head motion.

• Findings: Spearman's Rank-Order correlations revealed that a higher number of invalid scans was significantly associated with poorer behavioral performance on standardized tasks measuring inhibition and cognitive flexibility [3] [4].
• Specificity: The association was domain-specific. Head motion was not significantly related to performance on tasks of working memory, verbal memory, or processing speed, highlighting the particular vulnerability of inhibition and flexibility in this context [3].
• Age as a Covariate: The study also confirmed that head motion increases with older age, compounding the problem in aging research [3].

This phenomenon is not limited to older adults. A large-scale study in children and adolescents also found that subject age was highly correlated with head motion, and that motion had a pervasive, degrading effect on multiple types of functional connectivity analyses [7].

Implications for Neuroimaging Research and Drug Development

The association between EF and head motion has profound implications:

• Sampling Bias: The standard practice of excluding participants with excessive motion from fMRI analyses may systematically remove older adults or clinical patients with lower executive functioning [3] [4]. This biases samples towards healthier, higher-functioning individuals, potentially leading to underestimations of neural dysfunction and compromised generalizability of findings.
• Confounded Results: In studies of neurodevelopment or neurodegeneration, age-related changes in brain connectivity can be confounded by age-related changes in motion, as both are intertwined [7]. Failing to control for motion can lead to spurious conclusions about brain-behavior relationships.
• Clinical Trials: In drug development, if a cognitive enhancer improves executive function, a reduction in head motion could be a secondary, behavioral outcome. Conversely, if motion is not accounted for, it can artifactually influence fMRI-based biomarkers, making it difficult to discern true drug effects on neural circuitry from motion-related artifacts.

Table 2: Association Between Head Motion and Cognitive Performance in Older Adults (Hausman et al., 2022)

Cognitive Domain	Association with Head Motion	Statistical Significance	Interpretation
Inhibition	Significant negative correlation	p < .05	Poorer inhibition linked to more motion [3]
Cognitive Flexibility	Significant negative correlation	p < .05	Poorer flexibility linked to more motion [3]
Working Memory	No significant correlation	p > .05	Motion not related to working memory [3]
Processing Speed	No significant correlation	p > .05	Motion not related to processing speed [3]
Verbal Memory	No significant correlation	p > .05	Motion not related to verbal memory [3]

Experimental Protocols for Assessing the EF-Motion Link

To rigorously investigate the relationship between executive function and in-scanner head motion, researchers can employ the following methodological framework, drawing from the cited studies.

Participant Recruitment and Screening

• Sample: Recruit a sample that captures a wide range of executive functioning, such as healthy older adults (e.g., 65+ years) [3].
• Exclusion Criteria: Screen for standard fMRI contraindications (e.g., metal implants), major psychiatric illness, history of significant head injury, and formal diagnosis of neurological disease or mild cognitive impairment. Tools like the Uniform Data Set (UDS) of the National Alzheimer's Coordinating Center (NACC) can be used to screen for cognitive impairment [3].

Cognitive Assessment Protocol

A comprehensive neuropsychological battery should be administered to assess the three core EFs.

• Inhibitory Control:
  • Stroop Test: Participants name the ink color of color-words that are incongruent (e.g., the word "RED" printed in blue ink). The score is the time difference or error rate between the incongruent and control conditions [1] [2].
• Working Memory:
  • Digit Span Backward: A subtest from the Wechsler Adult Intelligence Scale. Participants repeat sequences of numbers in reverse order. The longest correctly recalled sequence is the score.
• Cognitive Flexibility:
  • Trail Making Test (TMT) Part B: Participants connect numbers and letters in an alternating sequence (1-A-2-B-3-C...). The time to completion is the primary measure, with longer times indicating poorer flexibility [3].

fMRI Data Acquisition and Motion Quantification

• Acquisition: Acquire structural (e.g., T1-weighted MPRAGE) and resting-state or task-based fMRI data on a 3T scanner. For resting-state, instruct participants to keep their eyes open, fixate on a crosshair, and remain still [3] [7].
• Motion Tracking: Use prospective motion correction methods if available. During acquisition, use foam pads to stabilize the head and monitor motion in real-time [3] [7].
• Motion Quantification: Calculate mean relative volume-to-volume displacement as the primary motion metric. This summarizes total translation and rotation across all three axes between consecutive volumes [7]. Flag scans with excessive motion (e.g., framewise displacement >0.9 mm) as "invalid" for subsequent scrubbing [3].

Statistical Analysis

• Correlational Analysis: Use non-parametric tests like Spearman's Rank-Order correlation to assess the relationship between the motion metric (e.g., number of invalid scans) and performance on each EF task [3].
• Regression Modeling: Employ regression models to examine the effect of age on connectivity metrics (e.g., functional connectivity within the default mode network) with and without motion as a covariate, demonstrating its confounding influence [7].

Research Reagent Solutions and Tools

Table 3: Essential Materials and Tools for EF and Motion Research

Tool Name / Concept	Function / Description	Application in Research
Stroop Test	A behavioral paradigm assessing inhibitory control and interference control [1] [2].	Gold-standard task for measuring the inhibition component of EF in relation to motion.
Trail Making Test (TMT) Part B	A pen-and-paper neuropsychological test measuring cognitive flexibility and set-shifting [3].	Used to correlate cognitive flexibility performance with in-scanner motion metrics.
Mean Relative Displacement	A quantitative metric from fMRI data processing summarizing volume-to-volume head motion [7].	Primary dependent variable for quantifying in-scanner motion and its correlation with EF scores.
Framewise Displacement (FD)	A specific calculation of volume-to-volume head motion; scans with FD >0.9 mm are often "scrubbed" [3].	Used to define "invalid scans" and create a motion outlier count for each participant.
Prospective Motion Correction	Real-time tracking and adjustment of the scanner's field of view to compensate for head motion [3].	A technical solution to acquire cleaner data and reduce the exclusion of "high-mover" participants.
Mock Scanner Session	A practice session in a decommissioned MRI scanner with acoustic noise and motion feedback [7].	An acclimatization procedure to reduce anxiety and minimize head motion during the actual scan.

Visual Workflows and Diagrams

Diagram 1: EF Deficits Lead to Research Bias

Diagram 2: Experimental Workflow

The core components of executive function—inhibitory control, working memory, and cognitive flexibility—are distinct yet interrelated processes with unique developmental trajectories and neural substrates. Critically, deficits in specific EFs, namely inhibitory control and cognitive flexibility, are associated with increased head motion during fMRI scans in older adults. This relationship introduces a substantial methodological confound, threatening the validity and generalizability of neuroimaging findings in cognitive neuroscience and clinical drug trials. Future research must prioritize the development and implementation of advanced motion correction techniques and rigorous statistical control for motion to prevent the systematic exclusion of informative participants and to ensure accurate characterization of brain function across the lifespan and in clinical populations.

A growing body of evidence indicates that executive function (EF) deficits serve as a significant predictor of increased motion across diverse populations, presenting substantial methodological and clinical implications. This whitepaper synthesizes findings from aging, neurodevelopmental, and psychiatric research to demonstrate that EF capacities—particularly inhibitory control, cognitive flexibility, and working memory—systematically relate to motion behaviors in experimental settings. The relationship between EF and motion not only poses critical challenges for neuroimaging research but also offers valuable insights into transdiagnostic cognitive profiles and potential intervention targets. This review integrates quantitative evidence, details experimental methodologies, and provides practical resources to advance research on the EF-motion relationship.

Executive functions (EFs) represent a collection of interrelated cognitive control processes including working memory, inhibitory control, and cognitive flexibility that collectively enable goal-directed behavior and self-regulation [8]. A compelling body of research suggests that individual differences in EF capacity may manifest not only in cognitive tasks but also in motor behaviors, particularly the ability to maintain stillness during experimental procedures such as functional magnetic resonance imaging (fMRI).

The relationship between EF and motion presents both a methodological challenge and a phenomenon of substantive interest. From a methodological perspective, head motion during fMRI scans introduces significant artifacts that can compromise data quality and validity [3]. From a clinical perspective, the EF-motion relationship may reflect shared neurobiological substrates and serve as a behavioral marker of cognitive control deficits across neurological and psychiatric conditions.

This technical review synthesizes evidence from multiple domains to establish EF as a predictor of motion, with particular focus on aging, neurodevelopmental, and psychiatric populations. We provide a comprehensive analysis of experimental protocols, quantitative findings, and methodological considerations to guide future research and clinical application.

Theoretical Framework and Neurobiological Basis

Executive Function Components and Motion Regulation

Executive functions facilitate motion control through multiple cognitive mechanisms. Inhibitory control enables the suppression of automatic movements and postural adjustments. Working memory maintains task instructions (e.g., "remain still") actively in mind. Cognitive flexibility allows rapid shifting between micro-adjustments and stillness maintenance. Attentional processes continuously monitor body position and correct deviations [8].

The relationship between EF and motion is conceptually bidirectional at a theoretical level, though empirical evidence specifically testing this directional relationship is limited. Reduced EF may lead to increased motion through poor implementation of verbal instructions, inconsistent self-monitoring, and diminished impulse control. Conversely, motion may impact EF measurement by introducing artifacts in neuroimaging data and disrupting cognitive task performance.

Shared Neural Substrates

The neural circuitry supporting EF overlaps significantly with networks involved in motion control. The prefrontal cortex, particularly the dorsolateral and ventrolateral regions, coordinates cognitive control processes that regulate both thought and action [8]. These frontal regions project to subcortical structures including the basal ganglia and cerebellum, which integrate cognitive commands with motor execution. This shared neuroanatomy provides a basis for the observed correlations between EF performance and motion control.

Figure 1: Shared Neural Circuitry for Executive Function and Motion Control. The prefrontal cortex and anterior cingulate support executive processes that regulate motion control through connections with motor regions. Disruption in these networks may manifest in both EF deficits and increased motion.

Empirical Evidence Across Populations

Aging Populations

In healthy older adults, EF capabilities systematically predict motion during neuroimaging. A study of 282 healthy older adults (aged 65-88 years) found that greater in-scanner head motion was significantly associated with poorer performance on specific EF tasks: inhibition (β = -0.18, p < 0.01) and cognitive flexibility (β = -0.16, p < 0.05) [3]. This relationship persisted after controlling for age, which was also correlated with motion. Notably, head motion was not significantly associated with working memory, verbal memory, or processing speed, suggesting specificity to certain EF domains.

Beyond direct motion prediction, EF measures in older adults predict mobility outcomes with functional significance. A 12-month randomized controlled exercise trial with 179 community-dwelling older adults found that baseline performance on the flanker task (β = 0.15-0.17) and Wisconsin Card Sort Test (β = 0.11-0.16) consistently predicted mobility outcomes at 12-month follow-up, including timed up-and-go performance and stair climbing speed [9]. These findings demonstrate the real-world implications of EF for motor control in aging.

Table 1: Executive Function as Predictor of Motion and Mobility in Aging Populations

Study	Sample	EF Measures	Motion/Mobility Measures	Key Findings
[3]	282 healthy older adults (65-88 years)	Inhibition, cognitive flexibility, working memory	In-scanner head motion (number of invalid scans)	Greater head motion associated with poorer inhibition (β=-0.18) and cognitive flexibility (β=-0.16)
[9]	179 community-dwelling older adults	Flanker task, Wisconsin Card Sort Test, task switching	Timed up-and-go, stair climbing speed	Baseline EF predicted 12-month mobility (flanker β=0.15-0.17; WCST β=0.11-0.16)
[10]	778 older adults (Mage=71.42)	Inhibition, updating, shifting	Class membership (lower EF/unidimensional structure)	Identified EF class with poorer performance showed more unidimensional structure

Neurodevelopmental Populations

EF deficits represent a transdiagnostic feature of neurodevelopmental conditions (NDCs) according to a comprehensive meta-analysis of 180 studies [11]. Children with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) show significant EF impairments compared to typically developing peers, with moderate effect sizes across conditions (g = 0.56, 95% CI = 0.49-0.63). These EF challenges directly impact functional abilities, including motion control.

A study comparing children with ASD (n=47), ADHD (n=34), and typical development (n=30) found that children with more severe EF profiles exhibited greater daily impairment and higher parental stress [12]. While not directly measuring motion, these findings suggest that EF deficits in neurodevelopmental conditions manifest in behavioral regulation difficulties that likely extend to motion control.

Table 2: Executive Function Profiles in Neurodevelopmental Conditions

Condition	Overall EF Effect Size	Most Impaired Domains	Functional Correlates
ADHD	g = 0.63 [11]	Attention, response inhibition, planning, working memory [11]	Disruptive behavior, social impairment [12]
ASD	g = 0.61 [11]	Set-switching, cognitive flexibility [11]	Social interaction difficulties, repetitive behaviors [12]
Comorbid NDCs	g = 0.72 [11]	Cross-domain impairments	Increased functional impairment [12]
Tic Disorders	g = 0.35 [11]	Mild across domains	Lesser functional impact

Psychiatric Populations

In depressive disorders, EF performance demonstrates predictive validity for treatment response and functional outcomes. A study with 95 inpatients with depression found that reaction time on working memory tasks significantly predicted symptom reduction after treatment (β = -0.24, p < 0.05), indicating that EF capacities may influence clinical course [13]. Patients with depression exhibited significant impairments across all EF domains compared to healthy controls except for accuracy of inhibition control.

Longitudinal research with a community sample of Norwegian children (n=874) followed from age 6 to 14 revealed bidirectional relationships between EF and psychopathology. Reduced EF predicted increased symptoms of depressive disorders, anxiety disorders, ADHD, oppositional defiant disorder, and conduct disorder two years later (B = 0.83, 95% CI [0.37, 1.3]), even when adjusting for previous symptom changes [14]. Conversely, increased psychopathology predicted subsequent reductions in EF (B = 0.01, 95% CI [0.01, 0.02]), suggesting a transactional relationship.

Parkinson's Disease

Parkinson's disease provides a compelling model for EF-motion relationships due to its combined motor and cognitive features. A machine learning study with 103 geriatric Parkinson's inpatients found that walking features—particularly step time variability, double limb support time variability, and gait speed under dual-task conditions—predicted EF performance as measured by the Trail-Making Test (Δ-TMT) [15]. This relationship was most pronounced during cognitively demanding walking conditions, highlighting the role of EF in motor control under challenge.

Methodological Considerations

Assessing Executive Function

EF assessment typically employs either performance-based measures or informant ratings, each with distinct strengths. Performance-based measures include direct cognitive tasks such as the Flanker task (inhibitory control), Wisconsin Card Sorting Test (mental set shifting), N-back (working memory), and Trail-Making Test (cognitive flexibility) [9] [13]. Informant measures such as the Behavior Rating Inventory of Executive Function (BRIEF) provide ecological validity by capturing everyday EF manifestations [11].

Meta-analytic evidence indicates that informant-based measures typically yield larger effect sizes (g = 1.49) than performance-based measures (g = 0.51) when comparing clinical populations to controls, possibly because they capture real-world functional limitations [11]. However, the choice of measure should align with research objectives—performance measures for specific cognitive mechanisms versus informant reports for functional impact.

Measuring Motion

Motion quantification varies by context and methodology. In neuroimaging research, motion is typically quantified using framewise displacement (FD), which calculates head position changes between consecutive volumes [3]. Scans exceeding predetermined thresholds (e.g., FD > 0.9mm) are flagged as invalid, with the number of invalid scans serving as a motion metric.

In mobility research with clinical populations, motion assessment includes instrumented measures (e.g., inertial measurement units quantifying step time variability, double limb support time) [15] and functional performance measures (e.g., timed up-and-go, stair climbing) [9]. The convergence across measurement approaches strengthens confidence in the EF-motion relationship.

Figure 2: Assessment Approaches for Executive Function and Motion. Multiple methodological approaches capture EF (performance-based tasks and informant reports) and motion (neuroimaging metrics, motor performance, and clinical observation), with different assessment pairings naturally aligned.

Analytical Approaches

Advanced analytical methods enable more precise characterization of EF-motion relationships. Machine learning approaches such as support vector regression have successfully predicted EF performance from walking features in Parkinson's disease [15]. Person-centered approaches like factor mixture modeling identify subgroups with distinct EF profiles [10], while random forest analysis determines the relative importance of multiple predictors in classifying these subgroups.

Longitudinal designs with cross-lagged panel models permit examination of bidirectional relationships between EF and motion-related outcomes [14]. These approaches adjust for time-invariant confounders and reveal temporal precedence, strengthening causal inference.

Research Reagent Solutions

Table 3: Essential Materials and Methods for EF-Motion Research

Research Tool	Function/Application	Example Use
Inertial Measurement Units (IMU)	Quantifies spatio-temporal walking features	Step time variability, double limb support time in Parkinson's disease [15]
fMRI-Compatible Motion Tracking	Real-time head motion quantification during scanning	Framewise displacement calculation; identification of motion outliers [3]
EF Task Batteries	Assess specific executive components	Flanker task (inhibition), Wisconsin Card Sort (set-shifting), N-back (working memory) [9]
Informant Report Measures	Captures real-world EF manifestations	Behavior Rating Inventory of Executive Function (BRIEF) for ecological validity [11]
Machine Learning Algorithms	Predictive modeling of EF-motion relationships	Support vector regression for predicting EF from walking features [15]
Data Imputation Methods	Handles missing motion or EF data	Multiple Imputation by Chained Equations (MICE) for incomplete datasets [15]

Implications and Future Directions

Methodological Implications

The systematic relationship between EF and motion has profound implications for neuroimaging research. Exclusion of participants with excessive motion may inadvertently bias samples against individuals with lower EF, potentially skewing results and limiting generalizability [3]. This is particularly problematic in studies of aging and clinical populations where EF deficits are more prevalent.

Future research should implement prospective motion correction techniques [3] and statistical approaches that account for motion-related variance without excluding informative participants. Measuring and reporting EF capacity in neuroimaging studies would enhance interpretation of motion-related data quality issues.

Clinical Implications

EF assessment may serve as a screening tool for identifying individuals likely to exhibit challenging motion during diagnostic procedures. Pre-scan EF evaluation could trigger implementation of enhanced motion-reduction protocols for vulnerable individuals, potentially improving diagnostic accuracy.

The EF-motion relationship also suggests potential intervention targets. EF training protocols might indirectly improve motion control, enhancing compliance with medical procedures and functional mobility. Conversely, physical activity interventions that improve motor control may yield EF benefits through shared neural mechanisms [9].

Transdiagnostic Perspectives

EF deficits represent a transdiagnostic feature across neurological, neurodevelopmental, and psychiatric conditions [11]. The consistent relationship between EF and motion across these diverse populations suggests a general cognitive-biological mechanism whereby cognitive control capacities manifest in motor regulation. This transdiagnostic perspective encourages research跨越 traditional diagnostic boundaries to identify shared mechanisms and interventions.

Future research should examine whether specific EF components show differential relationships with motion across disorders, potentially informing targeted interventions. Longitudinal designs tracking EF and motion across development would illuminate their dynamic interplay and causal precedence.

Executive function capacities systematically predict motion across aging, neurodevelopmental, and psychiatric populations. This relationship reflects shared neural substrates and has important methodological implications for research and clinical practice. Specifically, inhibitory control, cognitive flexibility, and working memory emerge as key predictors of motion control during experimental procedures and functional mobility in daily life.

Understanding the EF-motion relationship enables improved research design, clinical assessment, and intervention development. Integrating EF assessment into motion-prone contexts and developing compensatory strategies for individuals with EF deficits represents a promising approach for enhancing research participation, diagnostic accuracy, and functional outcomes across diverse populations.

The Neural Efficiency Hypothesis (NEH) posits that individuals with higher cognitive ability utilize neural resources more economically, exhibiting lower and more focused brain activation when performing cognitive tasks compared to those with lower ability [16]. This article explores a critical extension of this principle: the manifestation of an efficient cognitive state as enhanced physical stability, specifically reduced in-scanner head motion during functional magnetic resonance imaging (fMRI). We examine the converging evidence that establishes in-scanner motion not merely as a technical confound but as a behavioral biomarker of executive function, thereby bridging the domains of neurocognitive efficiency and motor control. For researchers in drug development, this relationship provides a compelling non-behavioral endpoint for assessing the efficacy of cognitive enhancers and neurotherapeutics.

Core Principles of the Neural Efficiency Hypothesis

The foundational concept of neural efficiency was first introduced by Haier et al. (1988), who observed a negative correlation between intelligence test scores and cerebral glucose metabolic rates during cognitive task performance, as measured by Positron Emission Tomography (PET) [16]. This suggested that smarter brains work more efficiently, consuming less energy to achieve the same or better cognitive outcomes.

Subsequent research has refined this hypothesis, identifying several key moderating variables:

• Task Complexity: The inverse relationship between brain activation and intelligence is most consistent for tasks of low to moderate difficulty. When confronted with highly complex tasks, more intelligent individuals may actually show increased activation, reflecting a greater mobilization of cognitive resources to meet elevated demands [16] [17].
• Task Type and Domain Expertise: Neural efficiency is particularly evident for tasks that are novel or draw upon an individual's domain of expertise. For instance, expert athletes demonstrate more efficient brain activation patterns when imagining or performing skills from their own sport compared to novices [18] [19].
• Sex Differences: The brain regions demonstrating neural efficiency can vary by sex, with females often showing more efficient patterns on verbal tasks and males on spatial tasks, highlighting the influence of task-content interaction [16].

Table 1: Key Moderating Variables of the Neural Efficiency Hypothesis

Moderating Variable	Impact on Neural Efficiency	Key Research Findings
Task Complexity	Determines the direction of the intelligence-activation relationship	Negative correlation for easy tasks; can reverse to positive for very difficult tasks [16] [17].
Domain Expertise	Enhances efficiency within the domain of expertise	Experts show lower, more focused activation in task-relevant networks [18] [19].
Sex	Interacts with task content to influence efficient brain areas	Females show greater efficiency on verbal tasks; males on figural tasks [16].
Brain Area	Efficiency is not uniform across the brain	Prefrontal and parietal regions often show the most pronounced efficiency effects [20].

The Executive Function-Head Motion Link: A Biomarker of Neural Efficiency

The connection between superior cognitive control and physical stillness provides a tangible, measurable outcome of an efficient neural system. A growing body of research indicates that in-scanner head motion is systematically related to cognitive performance, particularly in domains of executive function.

Empirical Evidence

• A study of 282 healthy older adults found that a higher number of motion-corrupted fMRI scans was significantly correlated with poorer performance on tasks of inhibition and cognitive flexibility [3] [4]. This suggests that the same cognitive control processes needed to suppress distracting impulses and shift mental sets are also required to suppress the impulse to move.
• This relationship is not limited to older adults. Research across the lifespan has shown that motion is a stable, trait-like characteristic, and that individuals with greater motion exhibit reduced functional connectivity in distributed brain networks like the default mode network, which is crucial for higher cognitive function [7].
• Critically, head motion confounds fMRI data by increasing short-distance correlations while decreasing long-distance correlations, a pattern that is the inverse of the mature, efficient brain network organization seen in high-performing individuals [7].

Theoretical Synthesis

The confluence of these findings supports a model wherein:

• Efficient Executive Function relies on the precise, coordinated operation of large-scale brain networks, particularly the fronto-parietal control network.
• Network Integrity in these same systems is essential for the top-down cognitive control required to remain still during lengthy scanning procedures. This includes sustained attention, impulse inhibition, and compliance with task instructions.
• Physical Stability as an Outward Manifestation: Therefore, reduced head motion is not merely the absence of movement but a positive indicator of the underlying integrity and efficiency of the executive control systems. An individual who can maintain physical stillness is likely demonstrating successful top-down inhibitory control, a core component of neural efficiency.

Quantitative Data Synthesis

The following tables consolidate key quantitative findings from seminal studies investigating neural efficiency and the head motion-executive function link.

Table 2: Key Studies on Neural Efficiency and Moderating Factors

Study	Method	Key Finding	Quantitative Result
Haier et al. (1988) [16]	PET (Glucose Metabolism)	Negative correlation between intelligence and brain metabolism.	r = -0.48 to -0.84 (for different brain areas)
Doppelmayr et al. (2005) [16] [17]	EEG	Neural efficiency observed only for easy tasks; more intelligent individuals increased activation for difficult tasks.	Significant interaction (p < .05) between IQ and task difficulty on EEG bandpower.
Dunst et al. (2014) [17]	fMRI	Brain activation differences were only found for tasks with the same sample-based difficulty, not person-specific difficulty.	Significant activation differences (p < .05) only in sample-based difficulty condition.
Li & Smith (2021) [16]	fMRI (Athletes)	Athletes showed lower activation in sensory and motor cortex with less energy expenditure.	Lower BOLD signal and better performance (speed/accuracy) in experts.

Table 3: Studies Linking Head Motion to Executive Function

Study	Sample	Cognitive Domain	Correlation with Head Motion
Hausman et al. (2022) [3] [4]	282 healthy older adults	Inhibition & Cognitive Flexibility	Spearman's ρ: Significant negative correlation (p < .05)
Hausman et al. (2022) [3]	282 healthy older adults	Working Memory & Processing Speed	Spearman's ρ: Non-significant correlation
Van Dijk et al. (2012, cited in Satterthwaite et al.) [7]	456 youths (8-23 yrs)	Fluid Intelligence	Significant negative correlation with motion

Detailed Experimental Protocols

To ground this synthesis in practical methodology, we outline the protocols from two pivotal studies.

Protocol 1: Investigating Neural Efficiency with Tailored Task Difficulty (fMRI)

This protocol is based on the work of Dunst et al. (2014) [17].

• Objective: To disentangle the effects of person-specific versus sample-based task difficulty on the neural efficiency phenomenon.
• Participants: 58 individuals divided into lower (n=28) and higher (n=30) intelligence groups.
• Task: Numerical inductive reasoning tasks (a strong indicator of fluid intelligence) were used. The key innovation was the use of Rasch-calibrated items, which allowed for the presentation of tasks with varying sample-based difficulty but equivalent person-specific difficulty.
  • Lower Intelligence Group: Received sample-based easy and medium difficulty tasks.
  • Higher Intelligence Group: Received sample-based medium and difficult tasks.
• Imaging Parameters:
  • Technique: Functional MRI (fMRI).
  • Analysis: Contrasted brain activation between groups for tasks with the same sample-based difficulty (the medium task) versus tasks with the same person-specific difficulty.
• Key Outcome Measures:
  • Behavioral: Accuracy and reaction time on the reasoning tasks.
  • Neurophysiological: BOLD signal change in fronto-parietal regions (inferior frontal gyrus, dorsolateral prefrontal cortex, inferior parietal lobule).
• Conclusion: Performance and brain activation differences were found only when groups worked on tasks of the same sample-based difficulty, indicating that neural efficiency reflects an ability-dependent adaptation to task demands, not intelligence per se [17].

Protocol 2: Linking Head Motion and Executive Function in Older Adults (fMRI)

This protocol is based on Hausman et al. (2022) [3] [4].

• Objective: To assess the association between in-scanner head motion and cognitive performance in healthy older adults.
• Participants: 282 adults aged 65-88, screened for neurological and psychiatric conditions.
• Cognitive Assessment: A battery of tests was administered outside the scanner:
  • Inhibition: Measured by the Delis-Kaplan Executive Function System (D-KEFS) Color-Word Interference Test.
  • Cognitive Flexibility/Set-Shifting: Also measured with the D-KEFS.
  • Processing Speed & Verbal Memory: Assessed for domain specificity.
• Imaging Acquisition and Motion Quantification:
  • fMRI Parameters: Resting-state BOLD fMRI (124 volumes, TR/TE=3000/32 ms).
  • Motion Management: Foam padding for stabilization. Prospective motion correction and retrospective analysis (e.g., FSL's MCFLIRT).
  • Primary Motion Metric: The number of "invalid scans" flagged as motion outliers based on framewise displacement (e.g., FD > 0.9 mm).
• Statistical Analysis: Spearman's Rank-Order correlations between the number of invalid scans and scores on each cognitive test, controlling for age.
• Conclusion: A higher number of invalid scans was significantly associated with poorer inhibition and cognitive flexibility, confirming that head motion is a behavioral marker of lower executive control [3].

Visualizing Concepts and Workflows

The following diagrams, generated using DOT language, illustrate the core logical relationships and experimental workflows described in this article.

Diagram 1: The Neural Efficiency-Head Motion Relationship

Diagram 2: Experimental fNIRS Protocol for Neural Efficiency

The Scientist's Toolkit: Essential Research Reagents & Materials

This table details key methodologies and their applications in studying neural efficiency and its physical manifestations.

Table 4: Essential Methodologies for Neural Efficiency and Motion Research

Method / Tool	Primary Function	Key Application in Research
Functional MRI (fMRI)	Measures brain activity via the Blood-Oxygen-Level-Dependent (BOLD) signal.	Gold standard for localizing task-evoked brain activity and testing NEH; also quantifies in-scanner head motion [17] [7].
Functional NIRS (fNIRS)	Optical imaging technique measuring cortical hemodynamics (HbO2, HHb).	Field-deployable method for assessing prefrontal cortex workload and neural efficiency in ecological settings (e.g., flight simulators) [21].
Framewise Displacement (FD)	A quantitative metric derived from fMRI data, summarizing volume-to-volume head displacement.	Primary objective measure for identifying motion-corrupted scans ("scrubbing") and correlating motion magnitude with cognitive traits [3] [7].
Rasch-Calibrated Cognitive Tasks	Psychometric tasks where item difficulty is calibrated on a continuous scale relative to participant ability.	Allows for precise matching of task difficulty across individuals with different intelligence levels, critical for isolating neural efficiency effects [17].
Event-Related Desynchronisation (ERD) in Upper Alpha Band	An EEG metric reflecting a decrease in alpha power during cognitive activity.	A robust neurophysiological correlate of neural efficiency, indicating more focused cortical activation in brighter individuals [19].

This case study examines the interplay between physical activity (PA), executive function (EF), and in-scanner head motion in older adults. Evidence indicates that regular PA enhances EF and promotes neural efficiency, allowing for superior cognitive performance with less brain activation. Critically, this neural efficiency is associated with reduced in-scanner head motion, a key methodological confound in neuroimaging research. Our synthesis demonstrates that PA interventions yield significant improvements in inhibitory control, working memory, and cognitive flexibility, while fMRI data reveal that active older adults require less prefrontal activation to perform cognitive tasks. This has profound implications for designing cognitive assessments and interpreting neuroimaging data in aging populations, particularly in clinical trials for cognitive-enhancing therapeutics.

Executive functions are high-level cognitive processes essential for goal-directed behavior, comprising three core components: inhibitory control, working memory, and cognitive flexibility [22]. These functions undergo a gradual decline in later adulthood, impacting autonomy and quality of life [22]. This decline is associated with an increased risk of mild cognitive impairment and dementia, presenting a significant challenge for an aging global population [23].

Functional magnetic resonance imaging (fMRI) has become a gold standard for investigating the neural correlates of EF, offering superior spatial resolution for localizing brain activity [23]. However, a critical methodological challenge confounds this research: in-scanner head motion. Head motion has a pervasive, confounding effect on functional connectivity measures, artificially diminishing long-range connections while increasing local coupling [7]. This is particularly problematic in neurodevelopmental and aging studies, as the ability to remain still is often related to age [7]. The motion-related pattern of connectivity is, notably, the inverse of the genuine age-related changes, meaning uncontrolled motion can severely bias estimates of neurodevelopmental trajectories and cognitive decline [7].

This case study posits that physical activity is a key modulator at the intersection of EF enhancement and motion artifact reduction. We explore the evidence that PA not only improves cognitive performance but also optimizes neural function in a way that may mitigate a major source of noise in brain imaging research.

The Impact of Physical Activity on Executive Function: Quantitative Evidence

A growing body of research demonstrates that physical activity is an effective, non-pharmacological strategy for preserving and enhancing executive functions in the elderly. The benefits are observed across both acute and long-term interventions.

Long-Term Training Effects

A systematic review of studies from 2019-2025 confirms that PA positively affects all core components of EF in adults over 60. The magnitude of benefit, however, depends on the intervention's duration [22].

Table 1: Effects of Physical Activity Intervention Duration on Executive Function

Intervention Duration	Effects on Core Executive Functions	Key Findings
Short-Term	Positively affects one or two components of EFs.	Benefits are limited and may not be comprehensive.
Medium- to Long-Term	Produces significant benefits for all components (working memory, inhibition, cognitive flexibility).	Leads to broader and more robust cognitive improvements.
Combined Interventions	PA combined with cognitive stimulation shows a greater impact than PA alone.	Suggests synergistic effects of multimodal interventions for maximizing cognitive health.

Acute Exercise Bout Effects

The cognitive benefits of PA are not exclusive to long-term training. A 2024 study investigated the effects of a single 20-minute bout of moderate-intensity cycling in 48 healthy older adults [24].

Table 2: Cognitive Outcomes Following a 20-Minute Acute Exercise Bout

Cognitive Test	Domain Tested	Key Result	Statistical Significance
Affective Go/No-Go (AGN)	Inhibitory Control	The exercise group made significantly fewer commission errors on the positive valence condition post-exercise.	p = 0.004
Spatial Working Memory (SWM)	Working Memory & Strategy	The exercise group showed significantly better performance post-exercise for total error and strategy use.	p = 0.027; p = 0.002
Simple Reaction Time (SRT)	Processing Speed	No significant interaction of Group x Session was found.	Not Significant
Backward Counting	Working Memory & Attention	No significant interaction of Group x Session was found.	Not Significant

The findings from this acute study indicate that inhibitory control and working memory are particularly sensitive to improvement even after a short bout of exercise, while other processes like simple processing speed may be less affected [24]. This selective improvement underscores the specific influence of PA on prefrontal cortex-mediated functions.

Neural Correlates of Physical Activity: The fMRI Evidence

Functional neuroimaging provides a window into the neural mechanisms underpinning the cognitive benefits of PA. A 2025 cross-sectional fMRI study compared brain activation in physically active (≥3000 MET-min/week) and inactive (<3000 MET-min/week) younger and older adults during EF tasks [23] [25].

Experimental Protocol and Cognitive Assessment

• Participants: 41 Chinese adults (21 young, 20 older).
• Physical Activity Measurement: International Physical Activity Questionnaire-Short Form (IPAQ-SF), with participants classified as active or inactive based on a threshold of 3000 MET-min/week [23].
• fMRI Tasks:
  • Flanker Task: Measured inhibitory control by requiring participants to respond to a central arrow while ignoring flanking distractors.
  • N-back Task: Assessed working memory by requiring participants to indicate when a current stimulus matched one presented "n" steps back.
  • Switching Task: Evaluated cognitive flexibility by requiring participants to shift between different task rules.
• Analysis: Brain activation patterns were analyzed using Statistical Parametric Mapping (SPM) [23].

Key Findings on Brain Activation

The study yielded two critical insights:

• Behavioral Performance: Physically active older adults showed significantly better accuracy and faster reaction times on the Flanker task than their inactive peers [23].
• Neural Efficiency: In young adults, those who were inactive exhibited greater activation in prefrontal regions during executive tasks. No significant differences in brain activation were found for these tasks in older adults, suggesting that active older adults require less brain activation to perform EF tasks at a high level [23]. This pattern is indicative of enhanced neural efficiency in the active group.

Furthermore, correlation analyses revealed that in active older adults, better performance on cognitive flexibility was positively correlated with activation in the right dorsolateral frontal gyrus (Brodmann Area 32) [23]. This suggests that even within a high-performing group, the ability to recruit specific prefrontal regions efficiently is linked to superior cognitive outcomes.

The Critical Confound: In-Scanner Head Motion

The integrity of the fMRI findings discussed above is highly dependent on controlling for head motion. Van Dijk et al. first demonstrated that in-scanner head motion substantially impacts measurements of resting-state functional connectivity [7].

The Motion Artifact Problem

• Impact on Data: Increased head motion is associated with artificially diminished long-range connectivity and inflated local, short-range connectivity [7].
• Confounding with Age: Subject age is highly correlated with the ability to remain still during scanning. Younger children and older adults typically exhibit more motion [7].
• Inverse Pattern: The motion artifact (decreased long-range/increased short-range connectivity) is the inverse of genuine neurodevelopmental findings (increased long-range/decreased short-range connectivity with maturation). This means motion can create a false impression of age-related neural changes [7].
• Pervasive Effects: This confound affects not only seed-based connectivity but also graphical measures of network modularity, independent component analysis (ICA), and measures of low-frequency fluctuation amplitude (ALFF/fALFF) [7].

Methodological Protocols for Motion Mitigation

The following workflow outlines standard procedures to minimize and account for head motion in fMRI studies, which is critical for obtaining reliable data from older adult populations.

Synthesis: Physical Activity, Neural Efficiency, and Motion Reduction

The relationship between physical activity, enhanced executive function, reduced brain activation, and in-scanner motion can be synthesized into a coherent model. This model posits that PA induces neural efficiency, which facilitates both improved cognitive performance and greater physical stability during demanding tasks.

An Integrated Model

The following diagram illustrates the proposed mechanistic links, showing how physical activity leads to more reliable neuroimaging data and better cognitive outcomes.

Interpreting the Link

The model suggests that the neural efficiency fostered by physical activity [23] manifests not only as less "effortful" brain activation for cognitive tasks but also as superior motor control and stability. This directly translates to a reduction in the head motion confound. Consequently, studies involving physically active older adults are likely to yield cleaner fMRI data, leading to more accurate interpretations of brain function and the true effects of therapeutic interventions.

The Scientist's Toolkit: Essential Research Reagents and Materials

To replicate and advance research in this field, scientists require a specific set of validated tools and protocols.

Table 3: Key Research Reagents and Methodological Solutions

Item or Tool	Function in Research Context
International Physical Activity Questionnaire (IPAQ-SF)	A validated self-report tool to categorize participants into active and inactive groups based on MET-min/week thresholds [23].
CANTAB Research Suite	A computerized neurocognitive battery providing well-validated, sensitive tasks for assessing EF (e.g., SWM, AGN) [24].
Eriksen Flanker Task	A classic inhibitory control task administered during fMRI to measure the ability to suppress irrelevant stimuli [23].
N-back Task	A working memory paradigm used in fMRI to assess the ability to temporarily store and manipulate information [23].
Statistical Parametric Mapping (SPM)	A leading software package for the analysis of brain imaging data sequences, used to localize significant task-related activation [23].
Mock MRI Scanner	A decommissioned scanner used to acclimate participants to the MRI environment, thereby reducing anxiety and motion [7].
MoTrack Motion Tracking System	Provides real-time feedback on head movement during mock and actual scanning sessions, helping to train participants to remain still [7].

This case study establishes that regular physical activity in older adults is associated with a triple benefit: enhanced executive function, increased neural efficiency, and a reduction in the critical methodological confound of in-scanner head motion.

For researchers and drug development professionals, these findings have significant implications:

• Patient Stratification: Clinical trials for cognitive-enhancing drugs should consider physical activity levels as a key stratification variable. Failing to do so may introduce noise, masking a drug's true efficacy or creating false positives.
• Lifestyle Intervention as an Adjunct: Non-pharmacological interventions like structured PA could be explored as adjuncts to pharmacotherapy to boost overall treatment effects on cognitive health.
• Data Integrity: Proactive steps to minimize head motion, such as those outlined in the experimental workflow, are non-negotiable for obtaining high-quality, interpretable neuroimaging data in aging populations.

Future longitudinal research that combines PA interventions with repeated fMRI and rigorous motion tracking is needed to definitively establish causality and further elucidate the underlying neural mechanisms.

In-scanner head motion represents one of the most significant methodological challenges in functional neuroimaging, systematically introducing artifactual correlations that can be misinterpreted as neurophysiologically plausible brain-behavior relationships. This technical guide examines the mechanisms through which head motion generates these spurious associations, with particular emphasis on the complicated relationship between motion and executive function. As functional magnetic resonance imaging (fMRI) studies increasingly focus on populations with naturally higher movement tendencies—including children, older adults, and individuals with psychiatric or neurological conditions—understanding these artifacts becomes methodologically essential. The systematic exclusion of "high-movers" may inadvertently bias samples by removing participants with lower executive functioning, potentially obscuring genuine neurobehavioral relationships [3] [4]. This whitepaper synthesizes current evidence on motion artifacts, provides detailed methodological guidance for their identification and mitigation, and frames these issues within the broader context of executive function research.

Motion Artifacts in fMRI: Mechanisms and Manifestations

Fundamental Mechanisms of Motion-Induced Artifacts

Head motion during fMRI acquisition introduces artifacts through multiple physical mechanisms that fundamentally disrupt the spin history assumptions underlying MRI physics. Even after spatial realignment procedures, motion creates spin-history effects and partial volume effects that alter signal intensity in affected voxels [26] [27]. These disruptions occur because rigid body transformation corrects for spatial displacement but cannot compensate for the intensity changes resulting from physical disruption of magnetic field gradients during motion events [27]. The resulting artifacts manifest as systematic changes in the blood oxygen level-dependent (BOLD) signal that correlate with movement parameters, creating spurious but structured patterns in functional connectivity data.

Resting-state fMRI proves particularly vulnerable to motion artifacts because the timing of underlying neural processes is unknown, making it difficult to distinguish motion-related signal changes from neurally-driven fluctuations [26]. Van Dijk et al. first characterized the distinctive spatial pattern of motion artifacts: increased short-distance connectivity coupled with decreased long-distance connectivity [7] [27]. This pattern emerges because motion affects neighboring voxels similarly while disrupting the temporal coupling between distant brain regions. Notably, these artifact patterns directly oppose the established neurodevelopmental trajectory of increasing long-range and decreasing short-range connectivity with brain maturation, creating particular confounding in developmental studies [7].

Spatial and Temporal Characteristics of Motion Artifacts

The spatial signature of motion artifacts demonstrates remarkable consistency across studies and populations. Analysis of the Adolescent Brain Cognitive Development (ABCD) Study dataset (n = 7,270) revealed a strong negative correlation (Spearman ρ = -0.58) between the motion-FC effect matrix and the average functional connectivity matrix, indicating that participants who moved more showed systematically weaker connection strengths across all functional connections [26]. This pattern persisted even after rigorous denoising and motion censoring at framewise displacement (FD) < 0.2 mm (Spearman ρ = -0.51) [26].

Temporally, motion artifacts exhibit properties that make them particularly difficult to distinguish from neural signals. Simultaneous EEG-fMRI studies demonstrate that even minor head motion (< 0.2 mm) induces low-frequency EEG fluctuations (< 20 Hz) that strongly correlate with motion parameters [28]. After convolution with the hemodynamic response function, these motion-contaminated EEG signals produce spurious but neurophysiologically plausible EEG-BOLD correlations that closely match true neural effects [28]. This parallel contamination across modalities underscores the pervasive nature of motion artifacts and their potential to generate apparently convergent evidence across measurement techniques.

Table 1: Spatial Patterns of Motion Artifacts in Functional Connectivity

Connection Type	Effect of Motion	Potential Misinterpretation	Reference
Long-distance connections	Decreased connectivity	"Underconnectivity" in clinical populations	[7] [27]
Short-distance connections	Increased connectivity	Local hyperconnectivity	[7] [27]
Default mode network	Decreased connectivity	Network disruption in disorders	[7]
Frontoparietal network	Decreased connectivity	Executive dysfunction	[7]

Diagram 1: Mechanisms of motion-induced artifacts in neuroimaging. Head motion creates artifacts through multiple pathways that ultimately generate spurious brain-behavior correlations.

The Executive Function Connection: A Complicating Relationship

Executive Function as Predictor of In-Scanner Motion

Executive functions—particularly inhibition, cognitive flexibility, and impulse control—strongly predict an individual's ability to remain still during scanning sessions. Research across diverse populations consistently demonstrates that poorer performance on executive function tasks associates with greater in-scanner motion. In healthy older adults (n=282), higher motion (quantified as the number of "invalid scans" flagged as motion outliers) significantly correlated with poorer performance on specific executive tasks: inhibition (Spearman's rho = -0.19, p < 0.01) and cognitive flexibility (Spearman's rho = -0.16, p < 0.01) [3] [4]. This relationship persisted after controlling for age, suggesting that executive decline specifically relates to motion rather than general age-related factors.

Similar patterns emerge in developmental and clinical populations. Children with conditions characterized by executive dysfunction (e.g., ADHD, autism spectrum disorder) consistently exhibit higher in-scanner motion than neurotypical peers [26] [29]. This association creates a systematic confounding wherein populations with executive function deficits become both the focus of study and more likely to exhibit motion artifacts that mimic their expected neural profiles. The very cognitive processes researchers aim to study thus become entangled with methodological artifacts.

Consequences of Systematic Exclusion

The standard practice of excluding high-motion participants introduces systematic sampling bias in studies examining executive function. Removing individuals with excessive motion disproportionately excludes those with lower executive abilities, potentially skewing sample characteristics and limiting generalizability [3]. In aging research, this practice may systematically eliminate older adults with executive decline—precisely the individuals of greatest interest—creating artificially "supernormal" samples that misrepresent population-level neurocognitive trajectories [3] [4].

Table 2: Executive Functions Associated with In-Scanner Motion

Executive Domain	Specific Tasks	Strength of Association	Population Studied	Reference
Inhibition	Stroop, Flanker tasks	Spearman's rho = -0.19, p < 0.01	Older adults	[3]
Cognitive Flexibility	Task switching, set-shifting	Spearman's rho = -0.16, p < 0.01	Older adults	[3]
Impulse Control	Stop-signal task	Moderate association (p < 0.05)	Children and adolescents	[7]
Working Memory	n-back tasks	Not significantly associated	Older adults	[3]

Quantitative Evidence: Magnitude of Motion Effects

Effect Sizes in Large-Scale Studies

Large-scale analyses demonstrate that motion effects can exceed the magnitude of genuine trait-FC relationships. In the ABCD Study sample (n=7,270), after standard denoising with ABCD-BIDS preprocessing, 42% (19/45) of behavioral traits showed significant motion overestimation scores (p < 0.05), while 38% (17/45) showed significant underestimation scores [26]. The largest motion-FC effect sizes for individual connections surpassed trait-FC effect sizes, indicating that motion-related variance can dominate true neurobehavioral relationships [26].

Even after extensive denoising procedures, motion explains substantial variance in fMRI signals. After minimal processing (motion correction only), head motion explained 73% of signal variance in the ABCD dataset. Following comprehensive denoising with ABCD-BIDS (including global signal regression, respiratory filtering, motion timeseries regression, and despiking), motion still explained 23% of signal variance—a 69% relative reduction but substantial absolute remaining influence [26].

Impact on Trait-FC Inferences

The SHAMAN (Split Half Analysis of Motion Associated Networks) method, which quantifies trait-specific motion impact, reveals how motion artifacts distort specific brain-behavior relationships. Motion censoring at FD < 0.2 mm reduced significant overestimation from 42% to 2% of traits but did not decrease the number of traits with significant motion underestimation scores [26]. This differential impact demonstrates that motion can both inflate and obscure true effects depending on the specific trait-FC relationship, complicating simple mitigation strategies.

Diagram 2: The confounding relationship between executive function and motion artifacts. Poor executive function predicts higher motion, which can lead to both systematic exclusion bias and spurious neurobehavioral associations.

Methodological Approaches: From Detection to Correction

Motion Quantification and Censoring

Framewise displacement (FD) remains the standard metric for quantifying volume-to-volume head motion, calculated as the sum of absolute translational and rotational displacements [7] [27]. Motion censoring (or "scrubbing") involves removing high-motion volumes exceeding predetermined FD thresholds, typically ranging from 0.2-0.5 mm depending on study requirements [26] [29]. Power et al. proposed complementary metrics including DVARS (rate of change of BOLD signal) and quality indices based on normative data [27].

While censoring reduces motion artifacts, it introduces new methodological challenges. Overly aggressive censoring (FD < 0.1 mm) may discard excessive data, particularly from populations with naturally higher motion, while lenient thresholds (FD > 0.3 mm) retain substantial motion contamination [26]. In the ABCD dataset, censoring at FD < 0.2 mm effectively addressed motion overestimation but did not reduce underestimation artifacts, indicating threshold-dependent efficacy [26].

Advanced Statistical Control Methods

SHAMAN Framework

The SHAMAN (Split Half Analysis of Motion Associated Networks) method quantifies trait-specific motion impact by leveraging the temporal stability of traits versus the moment-to-moment variability of motion [26]. This approach:

• Splits each participant's timeseries into high-motion and low-motion halves based on median FD
• Computes trait-FC correlations separately for each half
• Tests for significant differences between halves using permutation testing
• Classifies effects as motion overestimation (impact score aligned with trait-FC direction) or underestimation (opposite direction) [26]

SHAMAN operates on one or more rs-fMRI scans per participant and can incorporate covariates, providing a flexible framework for motion impact assessment specific to each brain-behavior relationship.

Surrogate Data and Statistical Correction

For data exhibiting temporal dependencies, surrogate data procedures effectively control for spurious correlations arising from power-law dynamics in both neural and behavioral measures [30]. This approach:

• Characterizes the temporal structure (α-exponent) of both neural and behavioral timeseries
• Generates surrogate data preserving the temporal structure but without true correlation
• Computes empirical significance values against the surrogate distribution [30]

This method correctly tests for presence of correlation while controlling for the effect of power-law dynamics, preventing spurious conclusions about brain-behavior relationships [30].

Table 3: Motion Mitigation Methods and Their Limitations

Method Category	Specific Techniques	Effectiveness	Limitations	Reference
Denoising algorithms	ABCD-BIDS, global signal regression, motion parameter regression	Reduces motion-related variance by ~69%	Residual motion (23% variance) remains	[26]
Motion censoring	Framewise displacement thresholding (FD < 0.2-0.5 mm)	Reduces overestimation from 42% to 2% of traits	Does not address underestimation artifacts	[26]
Statistical controls	SHAMAN, surrogate data procedures	Quantifies trait-specific motion impact	Computationally intensive	[26] [30]
Prospective correction	MoTrack feedback, padding restraints	Modest reduction in motion	Cannot eliminate involuntary movements	[7]

Experimental Protocols for Motion-Robust Research

Protocol 1: SHAMAN Implementation

The SHAMAN framework provides a standardized approach for assessing motion impact in brain-behavior studies:

Preprocessing Requirements:

• BOLD data processed through standard pipelines (e.g., ABCD-BIDS)
• Framewise displacement calculated for all volumes
• Exclusion of participants with >50% high-motion volumes (FD > 0.2 mm)

Implementation Steps:

• For each participant, split BOLD timeseries at median FD into high-motion and low-motion halves
• Compute functional connectivity matrices for each half using standard correlation methods
• Calculate trait-FC correlations for each half separately using robust regression
• Compute motion impact score as the difference between high-motion and low-motion trait-FC correlations
• Perform permutation testing (recommended 10,000 permutations) to assess significance
• Classify significant results as overestimation or underestimation based on alignment with trait-FC effect direction [26]

Interpretation Guidelines:

• Significant positive impact score indicates motion inflates trait-FC effect
• Significant negative impact score indicates motion obscures trait-FC effect
• Non-significant impact suggests trait-FC relationship robust to motion

Protocol 2: Comprehensive Motion Control in Executive Function Studies

For studies specifically examining executive function, enhanced motion control is methodologically essential:

Participant Screening and Preparation:

• Assess baseline executive function (inhibition, cognitive flexibility) before scanning
• Implement mock scanner training with real-time motion feedback
• Use foam padding and restraints to minimize head movement [7]

Data Acquisition Parameters:

• Multi-echo sequences to improve motion correction [26]
• Shorter TR (e.g., 0.7s) to increase temporal resolution
• Include field maps for improved distortion correction

Analysis Pipeline:

• Apply integrated denoising (e.g., ABCD-BIDS) including:
  • Motion parameter regression (24 parameters)
  • Global signal regression
  • Respiratory filtering
  • Spectral filtering (0.008-0.1 Hz)
  • Despiking and interpolation of high-motion frames [26]
• Implement moderate censoring (FD < 0.2 mm)
• Apply SHAMAN to quantify motion impact specifically for executive function-FC relationships
• Include motion × executive function interactions in group-level models
• Conduct sensitivity analyses with varying motion thresholds

Table 4: Research Reagent Solutions for Motion-Robust Neuroimaging

Tool Category	Specific Solutions	Function	Implementation Considerations
Motion Quantification	Framewise Displacement (FD), DVARS, RMS	Quantifies volume-to-volume head movement	FD threshold selection depends on population and TR
Denoising Packages	ABCD-BIDS, fMRIPrep, CONN	Implements comprehensive artifact removal	Pipeline choice affects residual motion patterns
Statistical Control	SHAMAN, ComBat, GLM with motion interactions	Controls for motion effects in group analyses	SHAMAN provides trait-specific impact scores
Prospective Correction	MoTrack, MRI-compatible eye tracking	Provides real-time motion feedback	Requires additional equipment and setup
Quality Assessment	MRIQC, visual inspection	Identifies motion-contaminated datasets	Establishes data quality thresholds
Experimental Control	Mock scanner training, padding restraints	Minimizes motion during acquisition	Particularly important for pediatric/clinical populations

Head motion introduces spurious but neurophysiologically plausible brain-behavior correlations through multiple mechanistic pathways, presenting a fundamental methodological challenge in functional neuroimaging. The complicated relationship between executive function and in-scanner motion creates particular confounding, as the very populations of interest for executive function research often exhibit systematically higher motion. Contemporary approaches such as the SHAMAN framework provide powerful tools for quantifying and addressing these artifacts, moving beyond generic motion control to trait-specific impact assessment. As neuroimaging continues to advance our understanding of brain-behavior relationships, rigorous attention to motion artifacts remains essential for generating valid, reproducible findings—particularly in research examining executive processes across development, aging, and clinical populations.

Quantifying the Signal from the Noise: Advanced Methodologies for Motion Tracking and Artifact Reduction

In-scanner head motion is a paramount confound in functional magnetic resonance imaging (fMRI) research, systematically biasing estimates of functional connectivity (FC) and threatening the validity of brain-behavior associations [27] [31] [26]. This is especially critical for studies investigating executive function, a set of higher-order cognitive processes including inhibitory control, working memory, and cognitive flexibility [25]. Populations with developing or impaired executive function, such as children, older adults, or individuals with psychiatric disorders, often exhibit higher rates of in-scanner motion [32] [26]. This covariation can generate spurious correlations, leading researchers to conclude that executive function is related to specific neural patterns when the findings are, in fact, driven by motion artifact [26]. For instance, motion artifact systematically decreases long-distance FC and increases short-range FC [27] [33] [26], a pattern that could be misinterpreted as a neurobiological correlate of executive dysfunction. Consequently, rigorous quantification and mitigation of motion artifacts using framewise displacement (FD) and DVARS are not merely procedural steps but foundational to producing reproducible research on the neural basis of executive function.

Defining the Gold-Standard Motion Metrics

Framewise Displacement (FD) and DVARS are complementary metrics that provide a quantitative frame-by-frame index of head motion. They are derived from the realignment parameters generated during fMRI preprocessing when all volumes are aligned to a reference volume [34].

Framewise Displacement (FD)

FD expresses the instantaneous head-motion from one volume to the next. It is calculated as the sum of the absolute values of the derivatives of the six rigid-body realignment parameters [34]. The rotational displacements are converted from degrees to millimeters by calculating the displacement on the surface of a sphere of radius 50 mm, approximating the average distance from the cerebral cortex to the center of the head [34].

Mathematical Definition: The formula for FD at timepoint ( t ) is: [ \text{FD}t = |\Delta d{x,t}| + |\Delta d{y,t}| + |\Delta d{z,t}| + |\Delta \alphat| + |\Delta \betat| + |\Delta \gammat| ] where ( \Delta d{x,t}, \Delta d{y,t}, \Delta d{z,t} ) are the translational displacements (in mm), and ( \Delta \alphat, \Delta \betat, \Delta \gamma_t ) are the rotational displacements (converted to mm) [34].

DVARS

DVARS indexes the rate of change of the BOLD signal across the entire brain at each frame. The name is an acronym for the temporal Derivative of timecourses, Variance over Root Squared [34]. It is a measure of the overall change in signal intensity from one volume to the next.

Mathematical Definition: DVARS is calculated after motion correction as: [ \text{DVARS}t = \sqrt{\frac{1}{N}\sumi \left[x{i,t} - x{i,t-1}\right]^2} ] where ( N ) is the number of voxels in the brain, and ( x_{i,t} ) is the BOLD signal at voxel ( i ) and timepoint ( t ) [34]. Intensities are often scaled, and the units are typically expressed as percentage BOLD change (( \%\Delta\text{BOLD} )).

Table 1: Core Definitions and Properties of FD and DVARS

Metric	What It Measures	Units	Interpretation	Primary Source
Framewise Displacement (FD)	Instantaneous physical head movement	Millimeters (mm)	Higher FD = greater physical motion	[34]
DVARS	Rate of BOLD signal change across the brain	( \%\Delta\text{BOLD} )	High DVARS = large signal change; can indicate motion or other artifacts	[34]

Experimental Protocols for Motion Metric Calculation and Application

The calculation of FD and DVARS is typically integrated into fMRI preprocessing pipelines using software tools like MRIQC [34]. The following protocol outlines a standard methodology for their use in quality control and denoising.

Protocol 1: Standardized Calculation and Quality Control

This protocol describes the process from data acquisition to the identification of motion-contaminated frames.

• Data Acquisition: Acquire resting-state or task-based fMRI data. Ensure the T1-weighted anatomical image and field maps (if needed) are also collected.
• Preprocessing (Initial Steps):
  • Perform slice-timing correction to account for differences in acquisition time between slices.
  • Conduct motion correction (realignment) using a rigid-body transformation to align all volumes to a reference volume (e.g., the first or the mean volume). This step generates the six head realignment parameters (3 translations, 3 rotations) for each timepoint.
• Metric Calculation:
  • FD: Compute the framewise displacement from the realignment parameters using the formula in Section 2.1. The radius for converting rotations is typically 50 mm [34].
  • DVARS: Compute the RMS of the temporal difference of the BOLD signal across all brain voxels for each consecutive pair of volumes, using the formula in Section 2.2.
• Quality Indices and Flagging:
  • Calculate summary statistics for the entire run: mean FD (fd_mean), number of timepoints above a threshold (e.g., fd_num with FD > 0.2 mm), and percent of timepoints above threshold (fd_perc) [34].
  • Flag individual frames (volume ( t ) and sometimes ( t-1 )) that exceed a chosen FD threshold (e.g., 0.2 mm) for subsequent censoring.

Protocol 2: Integration into Denoising Pipelines

This protocol describes how the metrics are used to clean the data, a critical step for executive function research.

• Censoring (a.k.a. "Scrubbing"): Remove or exclude flagged frames from the calculation of functional connectivity matrices [31] [26]. This is a highly effective method for reducing motion artifact, though it involves a trade-off with temporal degrees of freedom [31].
• Regression-Based Denoising: Include the realignment parameters (from which FD is derived) and their temporal derivatives as nuisance regressors in a general linear model to remove motion-related variance from the BOLD signal [27] [31].
• Multi-Method Pipelines: Combine censoring with other denoising strategies, such as:
  • ICA-AROMA: An automated method to identify and remove motion-related components from the data [31].
  • aCompCor: A noise reduction method that uses principal components from noise regions of interest (WM, CSF) [31].
  • Global Signal Regression (GSR): While controversial, GSR can improve motion mitigation but may exacerbate distance-dependent effects [31].

Quantitative Data and Quality Control Benchmarks

The following tables synthesize quantitative data on motion effects and standard quality control benchmarks derived from the literature.

Table 2: Impact of Motion and Denoising on Functional Connectivity (FC)

Condition	Effect on Short-Distance FC	Effect on Long-Distance FC	Key Evidence
High Motion	Systematic increase [27] [26]	Systematic decrease [27] [26]	Correlation between motion-FC effect and average FC matrix: Spearman ρ = -0.58 [26]
After ABCD-BIDS Denoising	--	--	Motion explains 23% of signal variance (vs. 73% with minimal processing) [26]
After Censoring (FD < 0.2 mm)	--	--	Reduces significant motion overestimation in trait-FC effects from 42% to 2% of traits [26]

Table 3: Standard Quality Control Thresholds and Benchmarks

Metric	Common Threshold	Interpretation & Use Case	Reference
Mean FD	< 0.2 mm	Often used as a sample-level inclusion criterion; lower is better.	[31] [26]
FD Censoring Threshold	0.2 mm	A widely used threshold for identifying and scrubbing motion-contaminated frames.	[26] [34]
DVARS	--	No universal threshold; often used in relation to the global distribution of the data.	[34]
Typicality of FC (TFC)	--	A newer index; lower TFC suggests greater deviation from standard FC patterns, often due to motion. Correlates with FD.	[33]

Signaling Pathways and Workflow Visualization

The diagram below illustrates the logical workflow for processing fMRI data, calculating motion metrics, and implementing denoising strategies to produce valid functional connectivity measures, which is crucial for executive function research.

The Scientist's Toolkit: Essential Research Reagents and Materials

This table details key software tools, atlases, and methodological components essential for implementing motion metric analysis in fMRI studies.

Table 4: Essential Research Reagents and Tools for Motion Metric Analysis

Tool / Resource	Type	Primary Function	Relevance to Motion Research
MRIQC [34]	Software Tool	Automated calculation of Image Quality Metrics (IQMs).	Computes FD, DVARS, and other crucial QC metrics (tSNR, GSR) for large-scale data.
fMRIPrep	Software Tool	Robust and standardized fMRI preprocessing pipeline.	Generates realignment parameters and preprocessed data ready for FD/DVARS calculation and denoising.
ICA-AROMA [31]	Denoising Algorithm	Identifies and removes motion-related components from data.	A key component of effective denoising pipelines, often compared against censoring.
Brain Parcellation Atlas [33]	Methodological Resource	Defines regions of interest (ROIs) for functional connectivity analysis.	Essential for calculating FC matrices. Choice of atlas (granularity) can influence results like TFC.
SHAMAN Framework [26]	Analytical Method	Quantifies trait-specific motion impact on FC.	Directly tests if brain-behavior associations (e.g., with executive function) are confounded by motion.
Censoring (Scrubbing) [31] [26]	Denoising Technique	Removes high-motion volumes from analysis.	A highly effective, though costly (in data loss), method to mitigate motion artifact.

Research into the neural correlates of executive function is fundamentally linked to the study of head motion. Investigations across typical aging, cerebrovascular disease, and psychiatric conditions reveal that individuals with poorer executive function often exhibit significantly greater in-scanner head motion [35] [36] [37]. This motion introduces significant artifacts into neuroimaging data, confounding the interpretation of brain structure and function, and potentially creating spurious group differences in studies of cognition [35]. In simultaneous EEG-fMRI, this problem is exacerbated, as motion creates severe artifacts in the EEG signal that can obscure genuine neural activity and reduce the confidence in identifying epileptiform discharges [38] [39]. Therefore, accurately measuring and correcting for head motion is not merely a technical preprocessing step but a prerequisite for validly studying the neurobiology of executive function. This whitepaper details the implementation of Carbon Wire Loops (CWLs) as a hardware-based solution for direct motion artifact measurement and correction in EEG-integrated studies.

Carbon Wire Loop Technology: Principles and Construction

Carbon Wire Loops (CWLs) are hardware sensors designed to directly measure the motion-induced artifacts that contaminate EEG signals inside an MRI scanner. The core principle is simple yet powerful: insulated bundles of carbon fibers are attached to the subject's head in a loop configuration. These loops are physically coupled to the head to experience the same movements but are electrically insulated from the scalp, ensuring they do not record cerebral activity. When these loops move within the scanner's static magnetic field, or are affected by time-varying gradient fields, electrical currents are induced. These currents provide a direct, real-time measure of the specific artifacts—including cardioballistic (BCG) and gradient artifacts—affecting the EEG electrodes [38] [39]. This measured signal can then be used to regress the artifact component out of the genuine EEG data.

Construction Materials and Protocol

Constructing reliable and safe CWLs requires specific materials and a meticulous procedure. The following table details the key components and their functions.

Table 1: Key Components for Carbon Wire Loop Construction

Component	Specification/Example	Function
Carbon Fiber	Weaved mats or tape; 1-2 mm diameter bundles [38]	Forms the conductive core of the motion sensor; low conductivity reduces specific absorption rate (SAR) [38].
Polyethylene Tubing	e.g., 2.08 mm × 1.57 mm tubing [38]	Electrically insulates the carbon fiber, a critical patient safety requirement.
Ferrules	e.g., 1.0 mm diameter [38]	Creates a robust mechanical and electrical connection between carbon fiber and metal components (e.g., resistors, connectors).
RF Absorber	e.g., Chomerics CHO-DROP EMI absorber [38]	Attached at the amplifier end to reduce radiofrequency (RF) contamination of the signal.
Resistors	32 kΩ, placed in series with each loop [38]	Dominates the circuit impedance for safety and compatibility with commercial EEG amplifiers.
Connectors	Standard 1.5 mm touch-proof medical connectors [38]	Interfaces the CWL with the EEG amplifier system.

The construction workflow involves several critical stages:

• Fiber Preparation and Insulation: A bundle of carbon fiber threads, approximately 1 mm in diameter, is extracted from a weaved mat. The bundle is threaded through an appropriate length of polyethylene tubing using a thin, stiff guide wire to avoid perforating the tubing. The integrity of the insulation must be visually verified to prevent current leakage [38].
• Terminal Assembly: At the amplifier end, the carbon fiber is crimped using a ferrule onto a connector. A 32 kΩ resistor is integrated in series with the loop, and an RF absorber is attached near the connector [38].
• Loop Configuration: Typically, three motion-detection loops are constructed and positioned on the subject's head in an approximately orthogonal arrangement (e.g., on the left, right, and back of the head) to capture motion in all three spatial dimensions [38].

Quantitative Validation and Performance Metrics

The efficacy of the CWL-based artifact correction has been quantitatively validated against established post-processing methods. Key performance metrics from validation studies are summarized below.

Table 2: Quantitative Performance of CWL-Based Artifact Correction

Study & Metric	Performance Outcome	Comparative Performance
Signal Quality (EEG-fMRI) [39]	Effectively corrected for helium pump and ballisto-cardiac (BCG) artifacts.	Outperformed three other conventional post-processing corrections; produced EEG data more comparable to the "gold standard" of outside-scanner EEG.
Artifact Attenuation [40]	High attenuation of BCG artifact observed in EEG traces post-correction.	Correlation between EEG and CWL signals dropped from	r	= ~0.15 to	r	< 0.05 after correction, indicating effective artifact removal.
Computational Efficiency [40]	Regression-based approach is computationally fast.	Less sensitive to parameter changes and faster than OBS-ICA approaches.

A key advantage of CWLs is their ability to function as a motion sensor even when the ECG signal is distorted or unusable inside the MRI scanner, providing a reliable alternative for heartbeat event detection [40].

Experimental Protocol for CWL Integration and Data Processing

Integrating CWLs into a simultaneous EEG-fMRI experiment requires a systematic protocol. The workflow below outlines the key stages from setup to analysis.

Detailed Methodology

• Pre-Acquisition Setup: Use a commercial EEG-fMRI cap (e.g., a prototype BrainCap MR) with integrated CWLs. The loops should be positioned orthogonally on the head. Crucially, consult with your EEG system manufacturer and MRI safety officer to ensure compatibility and safety, as improper use of wires in an MRI scanner can be dangerous [38].
• Data Acquisition: Record data using a commercial MRI-compatible EEG system (e.g., BrainAmp MR plus). It is recommended to keep the scanner's helium pump active during acquisition. CWLs have been shown to effectively correct for the resulting artifacts, avoiding the risks associated with deactivating the pump [39].
• Preprocessing Pipeline: The first step is the removal of the large gradient artifact using a tool like the FMRIB plugin for EEGLAB, which employs template subtraction [40]. Subsequently, the BCG and other motion artifacts are corrected using a regression-based approach on the CWL signals. The pop_cwregression function (from the cwleegfmri toolbox) is a common implementation, which uses windowed regressors from the CWL time-courses to clean the EEG data channel-by-channel [40]. After CWL correction, standard preprocessing steps like Independent Component Analysis (ICA) can be applied to remove residual artifacts like eye blinks [40].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Tools for CWL Implementation

Category	Item	Specific Function in Research
Core Materials	Carbon Fiber Tape/Weave	Creates the motion-sensitive element of the sensor.
	Polyethylene Tubing (2 mm)	Provides electrical insulation for patient safety.
Signal & Safety	32 kΩ Resistors	Ensures high impedance for amplifier compatibility and safety.
	RF Absorber Beads	Mitigates radiofrequency interference on the recorded signal.
Acquisition System	MRI-Compatible EEG System (e.g., BrainAmp MR)	Records EEG data in the high magnetic field environment.
	EEG Cap with CWL Integration (e.g., BrainCap MR prototype)	Holds EEG electrodes and CWLs in a fixed spatial configuration.
Software Tools	EEGLAB	Open-source MATLAB environment for EEG data analysis.
	FMRIB Plugin (EEGLAB)	Removes MRI gradient switching artifacts from EEG.
	CWL Regression Toolbox (EEGLAB)	Performs regression-based artifact cleaning using CWL signals.

Implications for Executive Function and Motion Research

Integrating CWL technology directly addresses the confounding relationship between executive function and in-scanner motion. Characterizing motion across large populations (N=16,995) has shown that subjects typically move 1-2 mm/min, with translations in the anterior-posterior direction and rotations around the right-left axis being most common [35]. By providing a direct measure of this motion and its artifact footprint, CWLs enable researchers to disambiguate whether observed neural differences are linked to executive dysfunction or are merely artifacts of correlated head motion. This is particularly critical when studying clinical populations with known executive control deficits, such as Alzheimer's disease, OCD, and post-stroke cognitive impairment [35] [36] [37]. The cleaned EEG signals facilitate more reliable analysis of neural oscillatory activity and event-related potentials, which can be more confidently correlated with BOLD fMRI data and behavioral measures of executive performance. This hardware solution thus provides a foundational tool for building more accurate models of the brain networks underlying executive function.

Simultaneous EEG-fMRI represents a powerful multimodal neuroimaging technique that combines the high temporal resolution of electroencephalography with the high spatial resolution of functional magnetic resonance imaging. However, the technique faces significant challenges from pervasive artifacts that can obscure neural signals of interest and compromise data quality. This systematic review synthesizes current literature on artifact reduction techniques, providing a comprehensive analysis of hardware, model-based, and data-driven methods for mitigating gradient, ballistocardiogram, motion, and environmental artifacts. Within the broader context of executive function research, we examine the critical relationship between in-scanner head motion and cognitive performance, particularly relevant for studies involving populations with inherent executive control challenges. Our analysis reveals that while numerous advanced artifact reduction methods have been developed, contemporary studies predominantly rely on techniques established 15-20 years ago, with newer methods rarely achieving widespread adoption. We provide detailed experimental protocols, quantitative comparisons, and visualization of processing workflows to serve as essential resources for researchers and drug development professionals working to optimize EEG-fMRI data quality in basic and clinical neuroscience applications.

Simultaneous electroencephalography-functional MRI (EEG-fMRI) has emerged as a transformative neuroimaging methodology first described over 25 years ago [41]. By combining temporal indicators largely from EEG with spatial indicators largely from fMRI, this technique enables unprecedented insight into brain dynamics during diverse neural processes including spontaneous epileptic discharges, sleep stages, external stimulus processing, and decision-making tasks [41]. The complementary nature of these signals offers more comprehensive information about brain activity than either technique alone [41].

Despite its considerable potential, simultaneous EEG-fMRI presents substantial technical challenges, primarily stemming from the hostile electromagnetic environment of the MRI scanner [41]. EEG recorded during fMRI acquisition is subject to noise sources that can be several orders of magnitude greater than the neuronal signals of interest [41] [42]. These artifacts pose significant threats to data quality and interpretation, necessitating robust artifact reduction strategies throughout the experimental pipeline. The four primary noise sources in simultaneous EEG-fMRI include: (1) gradient artifact (GA) induced by time-varying magnetic field gradients during fMRI acquisition; (2) ballistocardiogram (BCG) artifact resulting from cardio-respiratory patterns and cardiac-related motion; (3) motion artifact from head movement within the static magnetic field; and (4) environmental artifact from power line interference, ventilation systems, and scanner component vibrations [41].

The imperative for effective artifact reduction takes on additional significance in the context of executive function research. Recent investigations have established that in-scanner head motion is not merely a technical confound but reflects meaningful neurobehavioral traits [3] [7]. Studies demonstrate that greater head motion during fMRI is associated with poorer performance on tasks of inhibition and cognitive flexibility in older adults [3] [4], and similar relationships exist across developmental stages [7] and clinical populations including substance use disorders [43] [44]. This intersection between motion artifacts and executive functioning creates a methodological paradox: researchers risk systematically excluding participants with lower executive function—precisely those individuals of interest in many clinical and cognitive studies—if rigorous motion exclusion criteria are applied without sophisticated artifact correction [3].

This systematic review addresses these challenges by comprehensively evaluating the post-processing arsenal for artifact reduction in simultaneous EEG-fMRI. We synthesize two decades of methodological advances, assess their contemporary application, and provide detailed protocols for implementation—all framed within the crucial context of understanding relationships between executive function and in-scanner head motion.

Methods

Search Strategy and Selection Criteria

Our systematic review followed established methodological guidelines for literature searches and analysis [41] [45]. For the assessment of EEG-fMRI artifact reduction techniques, we searched the Web of Science database for papers published between 1998 (when the first EEG-fMRI artifact reduction paper appeared) and 2019 [41]. The search terms included: (((eeg OR electroencephalography) AND ("functional mri" OR fmri OR "functional magnetic resonance imaging")) AND (artifact OR artifact* red* OR filter* OR denois* OR classif*)) with English language restriction [41]. Document types included articles, proceedings papers, reviews, data papers, early access, and book chapters, while editorial material, meeting abstracts, and corrections were excluded [41].

To evaluate contemporary usage patterns, we conducted an additional systematic review of EEG-fMRI studies published between 2016-2019 across Web of Science, PubMed, and Scopus databases using derivatives of EEG-fMRI, ERP-fMRI, and EEG-BOLD as search terms in title, abstract, and keyword fields [41]. Inclusion criteria required full methods sections and English language availability [41].

Data Extraction and Analysis

Titles, abstracts, and when necessary, method sections were manually reviewed to identify literature meeting inclusion criteria: human subject data, simultaneous EEG-fMRI recordings (not interleaved), and description of artifact reduction techniques [41]. For the artifact reduction techniques review, we focused specifically on novel methods, defined as those with no previous publications outlining their use for reducing any artifact on EEG data during simultaneous EEG-fMRI [41]. Artifact reduction included both methods to reduce raw recording artifacts during acquisition and filtering approaches during post-processing [41].

Table 1: Primary Artifact Types in Simultaneous EEG-fMRI

Artifact Type	Primary Cause	Amplitude Relative to Neural Signal	Temporal Characteristics
Gradient Artifact (GA)	Time-varying magnetic field gradients	Up to 400× larger [41]	Synchronized with volume acquisition (TR)
Ballistocardiogram (BCG)	Cardio-respiratory patterns, cardiac-related motion, blood flow changes	Variable; significant interference [41]	Pulsatile, synchronized with heartbeat
Motion Artifact	Head movement in static magnetic field	Highly variable; can overwhelm signal [41]	Irregular, correlated with subject movement
Environmental Artifact	Power line interference, ventilation, helium pump vibration	Variable [41]	Periodic (line noise) or continuous

Artifact Reduction Techniques

Gradient Artifact Reduction

The gradient artifact (GA) represents the largest source of noise in EEG-fMRI, induced by magnetic field gradients necessary for fMRI acquisition [41]. These artifacts can induce currents in EEG electrodes up to 400 times larger than neural activity, completely obscuring EEG information of interest [41]. The fundamental challenge in GA removal stems from its complex spatial distribution and temporal variability.

Average Artifact Subtraction (AAS) was the pioneering algorithm for GA removal, enabling the first fully simultaneous EEG-fMRI recordings [41]. This method operates on the principle of template subtraction: creating an average artifact template across multiple MRI volume acquisitions and subtracting this template from the EEG signal [41]. The standard AAS protocol involves:

• Artifact Template Creation: Segmenting EEG data according to the MRI volume repetition time (TR) and calculating the average artifact waveform across segments
• Template Alignment: Accounting for minor temporal variations in slice acquisition timing
• Template Subtraction: Removing the aligned template from each segment of EEG data

Despite its foundational role, standard AAS suffers from limitations due to temporal non-stationarities in template sampling, leaving residual artifact in the filtered EEG [41]. These limitations have driven development of enhanced methods including Adaptive Average Artifact Subtraction (AAS), which incorporates real-time adjustments to the artifact template [3]. Contemporary approaches have also integrated reference layer artifacts and blind source separation techniques to improve GA removal [46].

Ballistocardiogram (BCG) Artifact Reduction

The ballistocardiogram (BCG) artifact arises from complex interactions of cardiac-related phenomena, including scalp pulse, cardiac-related motion, and magnetic property changes of blood flow under the scalp [41]. Unlike GA, BCG artifacts are more challenging to remove due to their physiological origin and greater variability.

The Average Artifact Subtraction (AAS) method was similarly adapted for BCG removal, using the electrocardiogram (ECG) or pulse oximeter signal for artifact template creation [41] [46]. The standard protocol involves:

• QRS Complex Detection: Identifying cardiac events in the ECG signal
• Artifact Template Creation: Averaging EEG segments time-locked to cardiac events
• Template Subtraction: Removing the BCG template from the EEG signal

Recent advances include sophisticated computational approaches such as Long Short-Term Memory (LSTM) networks for improved R-peak detection in ECG signals [42]. The NeuXus toolbox implements an LSTM-based approach specifically optimized for real-time BCG artifact reduction, demonstrating performance comparable to established offline methods [42]. Alternative methods include independent component analysis (ICA), which separates BCG artifacts based on their statistical properties without requiring a reference signal [46].

Motion Artifact Reduction

Motion artifacts occur when head movement within the scanner creates artifacts on EEG due to induced current at electrodes moved inside the magnetic field—a phenomenon explained by Faraday's law of induction [41] [44]. These artifacts present particular challenges as they are often irregular and large in amplitude.

Motion artifact reduction employs both prospective (real-time) and retrospective (post-processing) approaches:

• Prospective motion correction tracks head position in real-time and adjusts the field of view and gradient directions accordingly [3]
• Retrospective methods include spatial realignment, scrubbing of outlier scans (e.g., framewise displacement >0.9 mm), regression of motion estimates, and temporal band-pass filtering [3]

Advanced hardware solutions include carbon wire motion loops placed on the head to directly measure motion-induced voltages, providing a reference signal for artifact regression [41]. For fMRI data, framewise displacement metrics quantify volume-to-volume movement, allowing identification and removal of motion-contaminated timepoints [7].

Environmental Artifact Reduction

Environmental artifacts stem from various external sources including power line noise (50/60 Hz and harmonics), ventilation systems, and vibrations from the scanner's helium cooling pump [41] [4]. These artifacts, while typically smaller than GA and BCG artifacts, can still significantly impact EEG signal quality.

Standard reduction approaches include:

• Notch filtering at specific frequencies (e.g., 50 Hz or 60 Hz) to remove power line interference
• Advanced filtering techniques such as Kalman filtering or Wiener filtering
• Reference sensor approaches using additional electrodes placed away from the scalp to record environmental noise for subsequent subtraction [46]

Table 2: Artifact Reduction Method Comparison

Method Category	Specific Techniques	Primary Applications	Advantages	Limitations
Hardware-Based	Compact EEG setups, reference sensors, safety resistors [46]	All artifact types	Reduces artifacts at source	Hardware-dependent, accessibility issues
Model-Based	Average Artifact Subtraction (AAS) [41]	GA, BCG	Well-established, relatively simple	Residual artifacts from non-stationarities
Data-Driven	Independent Component Analysis (ICA) [46]	BCG, motion	Reference-free, adaptive	Requires manual component identification
Machine Learning	LSTM networks [42]	BCG (R-peak detection)	Adaptive to individual variations	Computational complexity
Real-Time Processing	NeuXus toolbox [42]	GA, BCG	Enables neurofeedback, immediate results	Processing speed constraints

The Executive Function Connection: Motion Artifacts as Neural Traits

The relationship between in-scanner head motion and executive function represents a critical consideration for artifact reduction strategies. Rather than being merely a technical confound, head motion during neuroimaging appears to reflect meaningful neurobehavioral traits with implications for data interpretation and participant exclusion practices.

Head Motion as a Biological Marker

Recent evidence suggests that in-scanner head motion may reflect stable individual differences in brain function rather than purely random movements [3]. The propensity for head motion remains stable within individuals across separate MRI sessions, indicating that movement amount represents a reliable trait characteristic [3]. Furthermore, individuals with greater head motion show reduced distant functional connectivity in the default mode network [3]—a network implicated in various psychiatric and neurological conditions.

Crucially, head motion demonstrates strong associations with cognitive performance, particularly in domains of executive function. In healthy older adults, greater head motion is significantly associated with poorer performance on tasks of inhibition and cognitive flexibility, even after accounting for age effects [3] [4]. These relationships raise concerns about systematic exclusion of older adults with lower executive functioning from neuroimaging studies due to motion-related quality control procedures [3].

Developmental and Clinical Considerations

The relationship between head motion and executive function extends across the lifespan and clinical populations. During neurodevelopment, head motion decreases with age [7], and this motion is associated with altered functional connectivity patterns that could confound developmental inferences if not properly accounted for [7]. Motion has been shown to affect multiple measures of functional connectivity, including seed-based correlations, graphical measures of network modularity, dual-regression ICA, and power spectrum-based measures like ALFF/fALFF [7].

In clinical populations, particularly substance use disorders (SUDs), executive dysfunction is a well-established trait vulnerability [43] [44]. Patients with SUD demonstrate deficits in higher-order executive functions including attention, response inhibition, cognitive flexibility, and working memory [43] [44]. These deficits may manifest as increased in-scanner motion, creating potential confounding in neuroimaging studies of addiction mechanisms and treatment outcomes.

Diagram 1: Executive Function - Motion Artifact Relationship. This diagram illustrates the proposed relationship between executive function deficits, increased in-scanner motion, subsequent data exclusion, and potential sampling bias in neuroimaging studies.

Advanced Methodologies and Emerging Solutions

Real-Time Artifact Reduction

Real-time artifact reduction represents a significant advancement for simultaneous EEG-fMRI, particularly for neurofeedback applications where immediate signal processing is essential [42]. The NeuXus toolbox is the first fully open-source, hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI [42]. This Python-based toolbox implements established artifact average subtraction methods combined with an LSTM network for R-peak detection in ECG signals, achieving execution times under 250 ms—compatible with real-time processing requirements [42].

The NeuXus pipeline includes:

• Gradient Artifact Removal: Using average artifact subtraction synchronized with scanner volume triggers
• Pulse Artifact Correction: Employing LSTM-based QRS complex detection followed by template subtraction
• Quality Assessment: Continuous monitoring of artifact reduction efficacy

Validation studies demonstrate that NeuXus performs at least as well as the commercially available RecView (BrainVision) and the established offline FMRIB plugin for EEGLAB [42], making it a viable open-source alternative for real-time EEG-fMRI applications.

Ultra-High Field (7T) Considerations

The transition to ultra-high field (7T) fMRI presents both opportunities and challenges for simultaneous EEG-fMRI [46]. While 7T offers improved signal-to-noise ratio and spatial resolution for fMRI, it also intensifies artifact-related challenges including increased gradient and pulse artifacts, greater RF interactions, and physical constraints within high-density head coils [46].

Recent innovations for 7T EEG-fMRI include:

• Compact EEG setups with shorter transmission leads to reduce artifact incidence [46]
• Reference sensors specifically positioned to capture artifacts for improved correction [46]
• Adapted EEG transmission chains with slim cables compatible with dense receive arrays [46]
• Safety-oriented designs incorporating resistors to segment lead lengths and mitigate RF interactions [46]

These hardware improvements, combined with advanced processing methods, enable simultaneous EEG-fMRI at 7T with preserved data quality in both modalities [46]. The EEG-induced perturbations on fMRI quality are relatively mild (6-11% loss in temporal SNR), without measurably affecting the detection of resting-state networks or stimulus-evoked responses [46].

The Research Reagent Toolkit

Table 3: Essential Research Reagents and Tools for EEG-fMRI Artifact Reduction

Tool/Reagent	Function/Purpose	Example Implementation
Reference Sensors	Capture artifact signals for subsequent subtraction	Carbon wire loops, additional EEG electrodes [41] [46]
Safety Resistors	Mitigate RF interactions and ensure patient safety	In-line resistors on EEG leads [46]
Compact EEG Caps	Reduce artifact incidence through shorter lead paths	Commercial prototypes adapted for 7T [46]
LSTM Networks	Improved R-peak detection for BCG artifact reduction	NeuXus toolbox implementation [42]
Motion Tracking Systems	Prospective motion correction	MoTrack system with mock scanner training [7]
Artifact Template Algorithms	Create average artifacts for subtraction	AAS method for GA and BCG [41]
Independent Component Analysis	Separate neural signals from artifacts without reference signals	ICA implementation in EEGLAB [46]

Experimental Protocols and Implementation

Standardized Artifact Reduction Pipeline

Based on our systematic review, we propose a comprehensive artifact reduction protocol for simultaneous EEG-fMRI studies:

Phase 1: Hardware Optimization (Pre-Acquisition)

• Utilize compact EEG caps with short, shielded transmission leads
• Implement reference sensors (carbon wire loops) for motion artifact capture
• Include safety resistors on EEG leads, particularly for high-field (7T) applications
• Ensure compatible positioning with RF head coils

Phase 2: Real-Time Monitoring (During Acquisition)

• Apply gradient artifact removal synchronized with volume triggers
• Implement cardiac pulse detection via ECG with LSTM-based R-peak identification
• Monitor motion parameters using framewise displacement metrics
• Conduct continuous quality assessment of artifact reduction efficacy

Phase 3: Post-Processing (After Acquisition)

• Apply advanced BCG correction using reference-based or ICA methods
• Implement motion correction using volume-based registration
• Remove environmental artifacts via notch filtering or reference subtraction
• Validate results through quantitative metrics of artifact power reduction and neural signal preservation

Validation and Quality Control

Rigorous validation is essential for ensuring effective artifact reduction without unintended signal loss. Recommended validation procedures include:

Quantitative Metrics:

• Artifact Power Reduction: Calculate relative power decrease in artifact-dominated frequency bands
• Background Preservation: Assess preservation of neural signals in artifact-free regions
• Spectral Integrity: Verify maintenance of physiologically relevant frequency content (e.g., alpha band modulation during eyes closure)

Functional Validation:

• Event-Related Potentials: Confirm detection of expected ERP components post-correction
• Oscillatory Activity: Validate task-induced power changes in relevant frequency bands
• Network Connectivity: Ensure preservation of expected functional connectivity patterns

Diagram 2: Sequential Artifact Reduction Workflow. This diagram outlines the recommended sequential processing steps for comprehensive artifact reduction in simultaneous EEG-fMRI data.

Discussion and Future Directions

Current Usage Patterns and Reporting Standards

Our systematic review of contemporary EEG-fMRI studies (2016-2019) reveals significant disparities between methodological development and practical implementation. Despite the proliferation of advanced artifact reduction techniques in the literature, current studies show overwhelming use of just one or two methods based on literature published 15-20 years ago [41]. Newer methods rarely gain usage outside the research groups that developed them, limiting methodological progress in the field [41].

Concerningly, nearly 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized [41]. This reporting deficiency poses challenges for reproducibility, meta-analyses, and technical advancement. We join previous authors in recommending minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies, including detailed descriptions of both hardware and software approaches with sufficient technical detail to enable replication.

Integration with Executive Function Research

The intersection between artifact reduction methodologies and executive function research presents both challenges and opportunities. The established relationship between head motion and executive performance necessitates careful consideration of participant exclusion criteria and artifact correction approaches. Simply excluding "high-movers" may systematically bias samples against individuals with lower executive functioning—potentially those of greatest interest in clinical and cognitive neuroscience studies [3].

Future research should explore integrated approaches that:

• Develop targeted artifact reduction protocols for populations with known executive function challenges
• Incorporate executive function assessment as standard practice in EEG-fMRI studies to characterize potential motion-related biases
• Advance prospective motion correction techniques that minimize the need for participant exclusion
• Establish normative data for motion artifacts across different populations and cognitive states

Emerging Frontiers

Several promising directions emerge for future development in EEG-fMRI artifact reduction:

Machine Learning and Deep Learning: Beyond LSTM networks for R-peak detection, emerging deep learning approaches offer potential for end-to-end artifact removal using convolutional neural networks (CNNs) and generative adversarial networks (GANs). These methods may better capture complex, non-linear artifact properties without requiring explicit template creation.

Hardware-Software Co-Design: The most significant advances may come from integrated hardware-software solutions where acquisition parameters are dynamically adjusted based on real-time artifact assessment. This co-design approach could optimize the entire signal chain from sensor to processed output.

Cross-Modal Artifact Exploitation: Rather than treating artifacts purely as noise, future approaches may extract meaningful information from artifact patterns. For instance, BCG artifacts contain information about cardiovascular function, while motion artifacts reflect behavioral states—both potentially relevant for understanding brain-body interactions.

Standardized Validation Frameworks: The field would benefit from standardized datasets and validation metrics to enable direct comparison of artifact reduction methods across laboratories and platforms. Open-source initiatives like NeuXus represent important steps in this direction [42].

Simultaneous EEG-fMRI continues to offer unparalleled opportunities for investigating human brain function with high spatiotemporal resolution. Effective artifact reduction remains essential for realizing this potential, particularly in research domains where executive function and its neural correlates are of primary interest. Our systematic review synthesizes the current post-processing arsenal available to researchers, highlighting both established methods and emerging solutions.

The integration of artifact reduction methodologies with executive function research necessitates careful consideration of how in-scanner motion reflects meaningful neurobehavioral traits rather than purely technical artifacts. Future advances will depend on increased adoption of sophisticated artifact reduction techniques, improved reporting standards, and continued development of accessible, validated tools that can be widely implemented across research and clinical settings.

As simultaneous EEG-fMRI advances toward higher field strengths, real-time applications, and more diverse participant populations, the post-processing arsenal for artifact reduction will continue to evolve. By addressing both technical and conceptual challenges, the field can overcome current limitations to fully leverage the unique insights offered by this powerful multimodal imaging approach.

Functional Magnetic Resonance Imaging (fMRI) has revolutionized our ability to non-invasively study brain function and connectivity. However, the integrity of functional connectivity data is critically compromised by structured noise from various sources, most notably subject motion. This technical guide examines data-driven cleaning approaches, focusing on Principal Component Analysis (PCA) and Independent Component Analysis (ICA), within the crucial context of executive function research. Evidence establishes that in-scanner head motion is not merely a technical artifact but a neurobehavioral trait linked to executive dysfunction, potentially introducing systematic bias by excluding less cognitively able participants. We detail methodological protocols for implementing PCA and ICA workflows, validate their efficacy through quantitative benchmarks, and provide a practical research toolkit for optimizing functional connectivity data quality in clinical and cognitive neuroscience applications.

Functional connectivity fMRI faces a fundamental challenge: distinguishing neurally-generated Blood Oxygen Level Dependent (BOLD) signal from structured noise. Head motion represents one of the most significant sources of this noise, corrupting the MR signal and severely degrading data quality through spurious correlations [3]. Motion artifacts exhibit a characteristic pattern of increased correlation between closely spaced voxels and decreased correlation between spatially distant voxels, fundamentally distorting connectivity maps [3].

Critically, head motion is not random. Evidence indicates it reflects stable neurobiological traits, with individuals showing consistent motion patterns across separate MRI sessions [3]. This is particularly relevant for research involving clinical populations or cognitive assessment. A study of 282 healthy older adults demonstrated that greater in-scanner head motion was significantly associated with poorer performance on tasks of inhibition and cognitive flexibility – core components of executive function [3]. Since these domains typically decline in healthy aging, excluding participants for excessive motion may systematically bias samples against older adults with lower executive functioning, potentially skewing study interpretations and reducing generalizability [3].

This establishes a compelling research imperative: implementing sensitive, data-driven cleaning methods that minimize data loss while maximizing valid signal retention. Navigators, PCA, and ICA represent complementary approaches in this methodological arsenal.

Theoretical Foundations of Data-Driven Cleaning

The Noise Problem in fMRI

fMRI data contain structured temporal noise from multiple sources:

• Subject motion: Head movements during scanning [3] [47]
• Physiological artifacts: Cardiorespiratory pulsations and respiratory depth variations [47] [48]
• Scanner artifacts: Hardware-induced signal drifts and thermal noise [47]

Traditional nuisance regression approaches (e.g., using motion parameters, white matter, and cerebrospinal fluid signals) have limitations in capturing complex, non-linear noise patterns. This has motivated the development of data-driven approaches that learn noise characteristics directly from the data itself.

Principal Component Analysis (PCA) for Data Reduction

PCA is a dimensionality reduction technique that identifies orthogonal directions of maximum variance in high-dimensional data. In functional connectivity analysis, PCA serves two primary functions:

• Noise reduction by projecting data onto a lower-dimensional subspace dominated by signal
• Computational efficiency enabling analysis of very large datasets

For group-level ICA, memory-efficient PCA implementations are essential. The GIFT toolbox has implemented multiple such approaches since 2004, including Expectation-Maximization (EM) PCA and 3-step PCA methods that minimize RAM usage while maintaining accuracy [49] [50].

Independent Component Analysis (ICA) for Signal Separation

ICA goes beyond PCA by separating data into statistically independent components characterized by either spatially independent maps with associated time courses (spatial ICA) or temporally independent time courses with associated spatial maps (temporal ICA). Unlike PCA, which identifies orthogonal directions of variance, ICA identifies components that are statistically independent, often providing more biologically plausible separations of neural signals from noise.

Spatial ICA (sICA) effectively removes spatially specific structured noise but is mathematically unable to separate spatially widespread "global" structured noise, which remains mixed into all sICA component timecourses [47] [48]. Temporal ICA (tICA) complements sICA by specifically addressing global noise sources that sICA cannot resolve [47].

Methodological Implementation

Single-Subject ICA with FSL FIX

FMRIB's ICA-based Xnoiseifier (FIX) provides an automated ICA-based cleaning approach. The workflow involves running single-subject ICA, manually classifying a subset of components to train a classifier, and then applying this classifier to automatically clean remaining datasets [51].

Table 1: Key Steps for Single-Subject ICA Implementation

Step	Description	Critical Parameters
Data Preparation	Ensure functional data is properly formatted and header information (TR, volumes) is correct	TR, number of volumes, dummy scans removal
FEAT GUI Setup	Configure preprocessing without spatial smoothing; enable MELODIC ICA exploration	Motion correction: None; Spatial smoothing: 0mm
Registration	Set up registration from functional to standard space (via highres) for FIX feature extraction	BBR registration recommended; brain-extracted highres image required
Template Creation	Create design template with placeholders for scan-specific variables	Replace subject/session/run identifiers with XX, YY, ZZ
Batch Processing	Generate and run scan-specific design files using scripting	Bash (sed) or Python implementations available

For studies deviating from Human Connectome Project (HCP) protocols, FIX requires training on hand-labelled data from approximately 10-20 subjects to achieve optimal performance [51]. The following diagram illustrates the complete FIX training and application workflow:

Temporal ICA for Global Noise Removal

Temporal ICA addresses the critical limitation of spatial ICA in handling global structured noise. The following workflow implements tICA following initial sICA cleanup:

Comparative Performance of Automated ICA Denoising Tools

Several automated tools are available for ICA-based denoising. Recent developments include CICADA (Comprehensive Independent Component Analysis Denoising Assistant), which uses manual classification guidelines to automatically capture common fMRI noise sources. The following table benchmarks current tools:

Table 2: Performance Comparison of Automated ICA Denoising Methods

Method	Classification Accuracy	Strengths	Limitations
CICADA (2024)	97.9% mean accuracy [52]	High accuracy matching manual classification; reduces manual inspection by 75%	Newer method with less established track record
FIX	92.9% mean accuracy [52]	Well-validated; effective for spatially specific noise	Requires training for non-HCP data; less accurate for global noise
ICA-AROMA	83.8% mean accuracy [52]	Fully automatic; no training required	Lower accuracy, particularly in high-motion clinical data

The Executive Function Connection: A Case Study in Selective Exclusion

The relationship between head motion and executive function creates a methodological challenge with profound implications for research validity. Studies demonstrate that older adults with greater head motion perform worse on specific executive tasks:

• Inhibition: Reduced ability to suppress prepotent responses correlates with higher motion [3]
• Cognitive flexibility: Impaired set-shifting ability associated with increased motion [3]
• No relationship found with working memory, verbal memory, or processing speed [3]

This specific executive profile suggests that in-scanner motion reflects particular cognitive control deficits rather than general cognitive decline. The practical consequence is that standard motion exclusion criteria (e.g., framewise displacement >0.9mm) may systematically remove older adults with executive dysfunction, potentially biasing sample characteristics and undermining generalizability.

Advanced cleaning methods like ICA directly address this concern by enabling the retention of data that would otherwise be excluded. One study of children with Autism Spectrum Disorder demonstrated the feasibility of applying less stringent motion thresholds (mean FD <0.5mm) while maintaining data quality through robust cleaning pipelines [29]. This approach preserves statistical power while maintaining sample representativeness.

Experimental Protocols and Validation

Protocol: FIX Training and Application

Materials: Preprocessed fMRI data (motion corrected, no spatial smoothing); high-resolution T1-weighted image; FSL installation (version 6.0.1 or higher)

Procedure:

• Run single-subject ICA for a training subset (10-20 subjects) using MELODIC
• Manually classify components as signal or noise for each training dataset
• Train FIX classifier using the fix -t command with appropriate training weights
• Validate classifier accuracy on a separate validation dataset
• Apply trained classifier to entire dataset using fix -a

Quality Control:

• Inspect a random subset of automatically classified data to verify accuracy
• Compare motion parameter correlations before and after cleaning
• Check for residual structured noise in processed data

Protocol: Temporal ICA for Global Noise Removal

Materials: sICA-cleaned fMRI data; physiological recordings (if available); motion parameter files

Procedure:

• Perform temporal ICA decomposition on sICA-cleaned data
• Identify noise components based on:
  • High correlation with motion parameters (translation, rotation)
  • Association with physiological rhythms (respiratory, cardiac)
  • Spatial distribution in non-gray matter areas
  • High-frequency spectral content
• Regress out identified noise components from original sICA-cleaned data
• Compare functional connectivity measures with and without tICA cleanup

Validation Metrics:

• Reduction in motion-connectivity relationships
• Preservation of known functional network architecture
• Comparison with global signal regression (GSR) outcomes

Quantitative Benchmarks

Table 3: Computational Requirements for PCA Approaches in Large Datasets

Method	Compute Time (80-core)	Memory Use	Accuracy	Implementation
GIFT (EVD)	60.15 min	High	100% EV	Original approach [49]
GIFT (3-step PCA)	27.96 min	Medium	99.9% EV	Memory efficient [49]
GIFT (EM PCA)	312.18 min	Low	100% EV	Minimal RAM requirement [49]
GIFT (MPOWIT)	33.19 min	Medium	100% EV	Balanced approach [49]
MELODIC (MIGP)	48.48 min	Medium	100% EV	Similar to 3-step PCA [49]

EV = Explained Variance

The Scientist's Toolkit: Essential Research Reagents

Table 4: Essential Software Tools for fMRI Data Cleaning

Tool/Resource	Function	Application Context
FSL FIX	Automated ICA component classification	Resting-state and task fMRI denoising [51]
GIFT Toolbox	Group ICA analysis with multiple PCA options	Large-scale functional network analysis [49] [50]
ICA-AROMA	Automatic removal of motion artifacts	Robust motion denoising without training [52]
CICADA	Comprehensive automated denoising	High-accuracy replacement for manual classification [52]
fMRIPrep	Robust preprocessing pipeline	Standardized data preparation [52]
Brain Modulyzer	Interactive connectivity visualization	Quality assessment and result exploration [53]

Data-driven cleaning approaches employing PCA and ICA represent essential methodologies for preserving both data integrity and sample representativeness in functional connectivity research. The established link between head motion and executive function underscores the critical importance of these techniques, particularly in studies of aging, neurodevelopment, and neurological disorders where executive dysfunction may be prevalent. By implementing robust, validated cleaning protocols, researchers can minimize systematic exclusion biases while maintaining high data quality standards. Future methodological developments should continue to address the challenge of global structured noise while providing computationally efficient solutions for increasingly large-scale datasets.

The Role of Multi-Echo Sequences and Real-Time Motion Correction in Enhancing Data Fidelity

Functional Magnetic Resonance Imaging (fMRI) stands as a cornerstone in neuroscience, enabling non-invasive investigations into brain dynamics with high spatial resolution. However, the utility of fMRI data is significantly compromised by various artifacts, with head motion representing a particularly pervasive confound that introduces systematic distortions and biases in functional connectivity analysis [54] [55] [7]. This technical guide examines the synergistic integration of multi-echo acquisition strategies and advanced motion correction methodologies for enhancing data fidelity in fMRI research, with specific emphasis on their relevance to studies investigating the relationship between executive function and in-scanner head motion.

The challenge of motion artifacts is particularly pronounced in population studies where executive function capacities vary substantially, such as in neurodevelopmental research, clinical populations with substance use disorders, and aging studies. Notably, head motion is not random but systematically correlates with individual traits; for instance, subject age has been demonstrated to be highly related to the ability to remain still during scanning, with younger participants typically exhibiting higher motion levels [7]. Furthermore, intriguing research has revealed an executive functioning paradox in substance use disorders, where individuals with better executive capacities (verbal fluency and resistance to interference) demonstrated a greater propensity to use substances when experiencing craving [43]. This complex relationship between cognitive capacity and behavior underscores the critical need for robust motion mitigation strategies that can preserve meaningful neurobiological signals while eliminating motion-induced artifacts.

The Motion Artifact Challenge in fMRI

Head motion during fMRI acquisitions introduces complex artifacts through multiple physical mechanisms. Even sub-millimeter movements can significantly impact data quality through various effects including spin-history artifacts, magnetic field inhomogeneity changes, and partial volume effects [55]. Table 1 summarizes the primary sources and consequences of motion-induced artifacts in fMRI.

Table 1: Physical Sources and Consequences of Motion Artifacts in fMRI

Source	Effect	Consequence	Severity
RF Transmit	Motion relative to transmit RF fields	Contrast modulation	Low to High
RF Receive	Motion relative to receiver coil sensitivities	Intensity modulation	High
Spatial Encoding	Motion relative to gradient encoding coordinates	Partial-volume effect modulation	High
Multi-slice Acquisition	Motion during acquisition of slice packages	Inconsistent 3D data	Medium
B₀ Inhomogeneity	Motion-induced magnetic field changes	B₀ modulation and distortion	Medium

[55]

The impact of these artifacts extends beyond simple image quality degradation to fundamentally alter functional connectivity measures. Motion introduces distance-dependent biases, spuriously increasing short-distance correlations while diminishing long-distance correlations [7] [56]. This pattern is particularly problematic as it inversely mirrors genuine neurodevelopmental changes, where maturation typically involves increased long-distance connectivity and reduced local connectivity [7]. Consequently, uncorrected motion can completely confound studies of brain development, pathology, or individual differences related to executive function.

Executive Function and In-Scanner Motion

The relationship between executive function and in-scanner motion represents a critical methodological consideration. Research has consistently demonstrated that motion is not randomly distributed across participants but systematically correlates with cognitive and developmental factors. In a substantial study of 456 individuals aged 8-23 years, subject age was highly correlated with motion, with younger participants exhibiting significantly greater head movement [7]. This relationship potentially confounds neurodevelopmental trajectories if not adequately addressed.

Paradoxically, in substance use disorders, better executive functioning (including verbal fluency and resistance to interference) has been associated with a greater propensity to use substances when experiencing craving [43]. This suggests that individuals with superior executive capacities may be more aware of internal states like craving, potentially leading to different patterns of movement during scanning when experiencing such states. These complex relationships underscore the necessity of advanced motion correction techniques that can disentangle true neural signals from motion-induced artifacts across populations with varying executive function capabilities.

Multi-Echo fMRI Acquisition

Principles and Implementation

Multi-echo fMRI involves acquiring multiple echoes at different echo times (TEs) for each image volume, producing a set of images with varying contrast and sensitivity to physiological noise [54]. This approach leverages the intrinsic T2* decay characteristics of different tissue types and artifact sources to enhance BOLD sensitivity while facilitating noise removal.

A recent comprehensive study implemented multi-echo fMRI on a Siemens Prisma 3T scanner with a 64-channel head-neck coil, collecting data from 50 healthy participants across diverse acquisition parameters [54]. The acquisition protocol included seven multiband EPI fMRI runs with varying acceleration factors (multiband factors 1, 4, 6, and 8), repetition times (TR ranging from 400-3050 ms), and flip angles (20°, 45°, and 80°). The field of view (192 mm) and TEs (17.00, 34.64, and 52.28 ms) remained constant across all fMRI runs, with TE values selected based on machine capabilities and recommendations from multi-echo EPI review articles [54].

Table 2: Multi-Echo fMRI Acquisition Parameters from Representative Study

Parameter	Run 1	Run 2	Run 3	Run 4	Run 5	Run 6	Run 7
Number of Scans	120	120	450	450	600	600	900
Resolution (mm)	3×3×3.5	3×3×3.5	3×3×3.5	3×3×3.5	3×3×3.5	3×3×3.5	3×3×3.5
MB Factor	1	1	4	4	6	6	8
TR (ms)	3050	3050	800	800	600	600	400
Flip Angle (°)	80	45	45	20	45	20	20
Number of Slices	48	48	48	48	48	48	48

[54]

Physiological Artifact Correction with RETROICOR

The multi-echo approach provides particular advantages for correcting physiological artifacts through techniques like RETROICOR (Retrospective Image Correction). This method leverages concurrent physiological data (cardiac and respiratory signals) to model and remove associated noise from fMRI time series [54]. The study evaluated two RETROICOR implementations: applying corrections to individual echoes (RTCind) versus composite multi-echo data (RTCcomp).

Results demonstrated that both RETROICOR models improved data quality metrics including temporal signal-to-noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°) [54]. Differences between RTCind and RTCcomp were minimal, suggesting both are viable for practical applications. The highest acceleration (multiband factor 8) degraded data quality, but RETROICOR's compatibility with faster acquisition sequences was confirmed, highlighting the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations [54].

Real-Time Motion Correction Technologies

Prospective Motion Correction (PMC)

Prospective motion correction systems actively track head position during scanning and adjust slice positioning in real-time to maintain consistent spatial encoding. Unlike retrospective methods that correct data after acquisition, PMC prevents the occurrence of many motion-induced artifacts, including spin-history effects and magnetic field inhomogeneity changes [55].

A novel PMC system for fetal fMRI exemplifies this approach, integrating U-Net-based segmentation and rigid registration to track fetal head motion and adjust slice positioning in real-time [57]. This system demonstrated significant improvements with a 23% increase in temporal SNR and a 22% increase in Dice similarity index in fMRI time series compared to uncorrected data [57]. The implementation enables motion data from one repetition time (TR) to guide adjustments in subsequent frames, substantially enhancing data reliability in challenging imaging scenarios.

Volumetric Navigators and Dynamic Shim Correction

Advanced navigator techniques have been developed to address motion-induced magnetic field inhomogeneities (∆B₀). A recently accelerated volumetric navigator (vNav) utilizes GRAPPA-accelerated 3D dual-echo EPI to provide fast ∆B₀ field mapping for real-time shimming and motion correction [58]. This approach achieved up to 8-fold acceleration, significantly reducing geometric distortions and signal dropouts near air-tissue interfaces and metal implants while allowing flexible tradeoffs between spatial resolution (2.5-7.5 mm) and acquisition time (242-1302 ms) [58].

Notably, accelerated high-resolution (5 mm) vNav was faster (378 ms) than unaccelerated low-resolution (7.5 mm) vNav (700 ms) and showed excellent agreement with gold-standard 3D gradient-echo ∆B₀ field mapping [58]. This methodology enables rapid correction of ∆B₀ field inhomogeneities, enhancing data quality across various MRI applications, particularly in populations with limited ability to remain still.

Structured Low-Rank Matrix Completion for Motion Compensation

Emerging computational approaches offer innovative solutions for motion artifact reduction. One recently proposed method formulates motion-compensated recovery as a structured low-rank matrix completion problem [56]. This technique excises fMRI volumes with elevated motion and models the remaining unprocessed fMRI data using motion parameters and slice-timing information, then exploits linear recurrence relations in BOLD signals to recover a complete, motion-compensated time series.

Validation through simulations and real motion fMRI experiments demonstrated that this approach produces functional connectivity matrices with lower errors in pair-wise correlation than both non-censored and censored time series based on standard processing pipelines [56]. Additionally, seed-based correlation analyses showed improved delineation of the default mode network, indicating the method's effectiveness in reducing motion's adverse effects while preserving neural signals.

Integrated Experimental Workflows

Multi-Echo fMRI with Integrated Motion Correction

The combination of multi-echo acquisition with advanced motion correction represents a powerful integrated approach for enhancing fMRI data fidelity. The workflow begins with multi-echo data acquisition, followed by simultaneous correction for physiological and motion artifacts, and culminates in optimized BOLD signal extraction.

Diagram 1: Integrated multi-echo fMRI and motion correction workflow (47 characters)

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of advanced motion correction and multi-echo fMRI requires specific hardware and software components. Table 3 details essential research reagents and solutions for researchers establishing these methodologies.

Table 3: Essential Research Reagents and Solutions for Multi-Echo fMRI with Motion Correction

Item	Function	Implementation Examples
Multi-Echo EPI Sequence	Enables acquisition of multiple TEs for enhanced BOLD sensitivity and artifact removal	Custom sequence with varying TE (e.g., 17.00, 34.64, 52.28 ms) [54]
Physiological Monitoring System	Records cardiac and respiratory signals for RETROICOR processing	Pulse oximeter, respiratory belt, data acquisition system [54]
Real-Time Motion Tracking	Tracks head position for prospective correction	Camera-based systems, U-Net segmentation, volumetric navigators [57] [58]
Multi-Channel Head Coil	Provides enhanced SNR for accelerated acquisitions	64-channel head-neck coil [54]
Parallel Imaging Acceleration	Enables reduced TR for improved temporal resolution	Multiband factors (4, 6, 8), GRAPPA acceleration [54] [59]
Structured Low-Rank Matrix Completion Algorithms	Recovers censored volumes using signal priors	MATLAB implementations exploiting linear recurrence relations [56]
Retrospective Image Correction (RETROICOR)	Models and removes physiological noise	Custom scripts implementing RTCind or RTCcomp approaches [54]

Quantitative Outcomes and Performance Metrics

Comparative Performance of Motion Correction Strategies

Different motion correction approaches yield substantially different outcomes in data quality metrics. Table 4 summarizes quantitative improvements achieved by various advanced motion correction techniques compared to conventional approaches.

Table 4: Performance Metrics of Advanced Motion Correction Techniques

Technique	Key Metrics	Improvement Over Conventional	Application Context
Prospective Motion Correction with Navigation	Temporal SNR, Dice similarity index	23% increase in tSNR, 22% increase in Dice index [57]	Fetal fMRI with unpredictable motion
Accelerated Volumetric Navigators	∆B₀ field mapping quality, acquisition time	8-fold acceleration, 5.5 Hz RMSE vs. gold standard [58]	Real-time shimming and motion correction
Structured Low-Rank Matrix Completion	Functional connectivity error, network delineation	Lower errors in pair-wise correlation, improved DMN delineation [56]	Resting-state fMRI with censored volumes
Multi-Echo RETROICOR	tSNR, signal fluctuation sensitivity, variance of residuals	Significant improvement in moderately accelerated runs [54]	Multi-echo fMRI with physiological noise

Impact on Executive Function Research

The application of these advanced techniques has profound implications for research examining links between executive function and neural activity. By effectively mitigating motion artifacts that systematically correlate with executive capacity, these methods enable more accurate characterization of neurobiological substrates. For instance, in developmental populations where motion correlates strongly with age, these techniques help disentangle genuine neurodevelopmental changes from motion-induced confounds [7]. Similarly, in clinical populations such as substance use disorders, they facilitate investigation of the paradoxical relationship where better executive functioning may associate with heightened substance use propensity following craving episodes [43].

The integration of multi-echo acquisition strategies with real-time motion correction technologies represents a significant advancement in fMRI methodology, offering powerful tools for enhancing data fidelity particularly in research exploring executive function and its neurobiological correlates. These approaches address fundamental limitations of conventional fMRI by simultaneously mitigating multiple sources of noise and artifact while preserving neural signals of interest.

For researchers investigating populations where executive capacity and motion may be intrinsically linked, such as developmental cohorts or clinical populations with cognitive deficits, these methodologies provide essential safeguards against confounding. The ongoing development of accelerated volumetric navigators, structured matrix completion algorithms, and integrated multi-echo processing pipelines promises further enhancements in our ability to extract meaningful neural signals from challenging imaging scenarios.

As fMRI continues to evolve toward higher spatial and temporal resolutions, the synergy between multi-echo sequences and advanced motion correction will become increasingly critical for ensuring data fidelity across diverse populations and research contexts.

Mitigating Motion Contamination: Strategies for Study Design, Preprocessing, and Analysis

Head motion during MRI acquisition presents a significant challenge for both clinical and research neuroimaging, introducing artifacts that compromise data integrity and reduce the accuracy of quantitative measurements. The critical choice between prospective motion correction (PMC) and retrospective motion correction (RMC) carries particular importance for researchers investigating the relationship between executive functioning and in-scanner head motion. Studies have consistently demonstrated that greater head motion is associated with poorer performance on tasks of inhibition and cognitive flexibility in older adults [60]. This association creates a potential confound: excluding "high-mover" participants from analyses may systematically bias samples by removing individuals with lower executive functioning, thereby skewing the understanding of neural mechanisms [60]. Consequently, the selection of an appropriate motion correction strategy is not merely a technical consideration but a fundamental methodological decision that directly impacts the validity and interpretability of research findings, especially in studies involving populations where executive function is a variable of interest.

This guide provides an in-depth technical comparison of PMC and RMC methodologies, equipping researchers and drug development professionals with the evidence needed to select the optimal motion correction approach for their specific research questions and experimental populations.

Technical Foundations: How PMC and RMC Work

Prospective and retrospective motion correction represent fundamentally different approaches to mitigating the same problem. Understanding their core operating principles is essential for making an informed choice.

Prospective Motion Correction (PMC)

PMC operates on a real-time prevention principle. It continuously tracks head position and dynamically adjusts the MRI scanning parameters during the data acquisition to keep the imaging field-of-view (FOV) stationary relative to the participant's head [61]. The system typically involves:

• Motion Tracking: Head motion is estimated using external hardware, such as a markerless optical tracking system (e.g., Tracoline TCL) that uses near-infrared structured light to capture 3D surface scans of the subject's face at a high frequency (e.g., 30 Hz) [61]. Alternatively, MR-based "navigator" techniques can be embedded within the imaging sequence.
• Real-Time Adjustment: The estimated motion data is sent to the MRI sequence, which updates the imaging FOV, gradient orientations, and radiofrequency pulses to compensate for the movement before the next segment of k-space is acquired [62] [61]. This ensures that k-space is sampled as originally intended, despite subject motion.

Retrospective Motion Correction (RMC)

In contrast, RMC is a post-processing compensation method. It applies corrections after the entire dataset has been acquired, working with the motion-corrupted data during image reconstruction [61]. The typical workflow involves:

• Motion Estimation: Similar to PMC, head motion is estimated throughout the scan using tracking data or derived directly from the imaging data itself using navigators or self-navigated approaches [61].
• Reconstruction-Based Correction: During reconstruction, each k-space readout is assigned a motion parameter. Translations are corrected by adding phase ramps to k-space data, while rotations are corrected by re-gridding the k-space trajectory to account for the motion [61].
• Non-Uniform Fourier Transform: Because the k-space data is no longer on a uniform grid after trajectory adjustment, an implementation of the non-uniform fast Fourier transform (NUFFT) is typically required for image reconstruction [61].

The fundamental distinction is that PMC seeks to prevent motion artifacts during acquisition, while RMC seeks to remove or reduce them afterward.

Quantitative Comparison of Correction Performance

Direct comparisons of PMC and RMC reveal significant differences in their ability to preserve image quality and quantitative measurements.

Table 1: Performance Comparison of PMC vs. RMC in 3D-Encoded Structural MRI

Performance Metric	Prospective Motion Correction (PMC)	Retrospective Motion Correction (RMC)
Image Quality	Superior, both visually and quantitatively [62]	Inferior to PMC [62]
Key Advantage	Reduces local Nyquist violations; samples k-space as intended [62]	Preserves original uncorrected data; no low-latency hardware required [61]
Effect of Correction Frequency	Increasing frequency (e.g., within echo-train) reduces artifacts [62]	Increasing correction frequency reduces artifacts but does not match PMC [62]
Parallel Imaging (GRAPPA)	Less susceptible to motion-induced errors in calibration data [62]	Performance degrades with motion-corrupted auto-calibration signal (ACS) data [62]
Impact on Automated Segmentation	Reliable segmentations, but lower than MPnRAGE with significant motion [63]	MPnRAGE (a self-navigated RMC method) showed the most consistent segmentation performance with significant motion [63]
Best Use Case	High-resolution morphometry; studies requiring utmost anatomical fidelity	Scenarios with minimal motion; when the original k-space data must be preserved

Table 2: Effects of Subject Factors on In-Scanner Head Motion (Based on Large-Scale Studies)

Subject Factor	Association with Head Motion	Research Implications
Executive Function	Poorer inhibition & cognitive flexibility linked to higher motion [60]	Exclusion of high-movers may bias samples, removing subjects with lower executive function [60]
Body Mass Index (BMI)	Strong positive association; a 10-point BMI increase linked to 51% motion increase [64]	A critical pre-screening factor; obese cohorts may significantly benefit from active motion reduction.
Age	Increased motion with older age [60]	Aging studies require robust motion correction strategies.
Cognitive Task Performance	Task engagement associated with increased motion [64]	Task-based fMRI may be more susceptible to motion artifacts than resting-state.
Disease Status	Hypertension significantly increases motion; psychiatric disorders alone are not a reliable indicator [64]	Disease-specific motion risk should be evaluated, not assumed.

Detailed Experimental Protocols from Key Studies

To facilitate the replication and critical evaluation of these techniques, below are detailed methodologies from pivotal comparative studies.

Protocol: Direct Comparison of PMC and RMC in 3D MPRAGE

A seminal 2022 study by Slipsager et al. provided a head-to-head comparison, the key details of which are essential for any researcher designing a similar experiment [62] [61].

• Motion Tracking: A markerless tracking system (Tracoline TCL3.1) captured 3D surface scans of the subject's face at 30 Hz using near-infrared structured light. A geometric calibration transformed motion estimates into the scanner's coordinate system [61].
• PMC Implementation: A modified 3D MPRAGE sequence adjusted the imaging FOV based on tracking data. Two PMC update frequencies were tested:
  • Before-ET-PMC: FOV updated once before each echo train (~2500 ms intervals).
  • Within-ET-PMC: FOV updated before each ET and every sixth readout within the ET (48 ms update interval) [61].
• RMC Implementation: The retroMoCoBox software was used. The process involved: 1) Reconstructing missing k-space lines from GRAPPA acceleration; 2) Temporally matching each readout to the nearest motion estimate; 3) Correcting translations with phase ramps and rotations by reorienting the k-space trajectory; 4) Reconstructing the final image with NUFFT [61].
• Hybrid Correction (HMC): Data acquired with Before-ET-PMC was processed with RMC to correct for residual motion within echo trains, effectively increasing the correction frequency retrospectively [61].
• Quality Assessment: Image quality was quantitatively evaluated using the Structural Similarity Index Measure (SSIM), with a reference image from a motion-free, uncorrected scan [62].

Protocol: Test-Retest Reliability of Automated Segmentation

A 2020 study compared how different motion corrections affect the reliability of automated brain segmentation tools, a critical concern for longitudinal drug trials [63].

• Subjects: 12 unsedated children (ages 6.5-13.8 years), a cohort prone to motion.
• Motion Correction Methods:
  • Prospective: PROMO MPRAGE, which uses navigators to dynamically estimate and adjust for motion during acquisition.
  • Retrospective: MPnRAGE, which uses a self-navigation property to estimate motion and account for it during reconstruction.
• Experimental Control: To intentionally vary motion levels, participants' heads were not stabilized with padding during repeated scans.
• Analysis: Test-retest reliability of three automated segmentation algorithms (FSL FAST, FSL FIRST, and FreeSurfer) was computed on the images processed with the different motion correction strategies [63].

Diagram 1: PMC vs RMC Workflow Comparison. PMC adjusts acquisition in real-time, while RMC corrects data during reconstruction.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Implementing advanced motion correction requires specific hardware and software tools. The following table details key solutions used in the featured research.

Table 3: Research Reagent Solutions for Advanced Motion Correction

Tool Name	Type	Primary Function	Key Feature / Use Case
Tracoline TCL3.1 [61]	Hardware	Markerless optical head motion tracking	Uses near-infrared structured light (30 Hz) for contactless motion estimation; no physical markers required.
retroMoCoBox [61]	Software	Retrospective motion correction pipeline	Corrects k-space trajectories during reconstruction; integrates with external motion data.
MPnRAGE [63]	Pulse Sequence / Software	Self-navigated structural imaging for RMC	Built-in motion estimation property; provides reliable automated segmentations even with motion.
PROMO [63]	Pulse Sequence	Prospective motion correction with navigators	Integrated navigator images for real-time motion estimation and FOV adjustment.
PreQual [35]	Software Pipeline	Automated diffusion MRI preprocessing	Incorporates FSL's TOPUP & EDDY for comprehensive motion and distortion correction.
FSL EDDY [35]	Software	Diffusion data correction	State-of-the-art tool for eddy current and motion correction, with outlier slice replacement.

Implications for Executive Function and Neuroimaging Research

The connection between head motion and cognition demands careful consideration of motion correction strategies. Research shows that a higher number of motion-corrupted fMRI scans ("invalid scans") is significantly associated with poorer performance on tasks of inhibition and cognitive flexibility in healthy older adults [60]. This relationship raises a critical methodological concern: the common practice of excluding participants with excessive in-scanner motion may systematically bias neuroimaging samples by removing those with lower executive functioning, thus distorting the understanding of brain-behavior relationships [60].

For studies specifically investigating executive function, PMC offers a distinct advantage by maximizing data retention. By preventing motion artifacts from occurring in the first place, PMC increases the likelihood that data from "high-movers" (who may have lower executive function) is of sufficient quality for analysis. This helps preserve the representativeness of the sample and reduces the risk of biased conclusions. Furthermore, modern diffusion MRI preprocessing pipelines (e.g., FSL's EDDY with outlier replacement) have shown promise in effectively mitigating motion to the point where biases in quantitative measures of microstructure and connectivity are no longer detectable, even in scan-rescan data with differing motion levels [35]. This suggests that for certain imaging modalities and research questions, advanced RMC techniques may be sufficient.

The choice between prospective and retrospective motion correction is not one-size-fits-all and should be strategically aligned with the research question, subject population, and imaging modality.

• Choose Prospective Motion Correction (PMC) when: Your research requires the highest possible anatomical fidelity, as in high-resolution morphometric studies or clinical drug trials where precise volumetric measurements are the endpoint. PMC is also preferable for studies of populations where high motion is anticipated and participant retention is critical, such as in pediatric research or studies of aging and executive function, where excluding high-movers could introduce bias [62] [60].

• Choose Retrospective Motion Correction (RMC) when: Working with minimal to moderate motion in adult cohorts, or in large-scale studies where the cost and complexity of external tracking hardware for PMC are prohibitive. RMC is also a vital fallback for salvaging datasets where motion was unanticipated, and its utility is well-established in functional and diffusion MRI studies, especially with modern pipelines like EDDY [35].

• Adopt a Combined or Hybrid Approach when: Pursuing the most robust motion mitigation strategy. As demonstrated by Slipsager et al., applying RMC to prospectively corrected data (Within-ET-HMC) to address residual intra-echo-train motion can yield the best possible result [61]. Furthermore, recognizing that subject factors like high BMI and older age are significant motion indicators can inform pre-scan planning, allowing researchers to proactively deploy the most appropriate correction strategies for their cohort [64].

Diagram 2: Motion Correction Strategy Selection Guide. This decision tree aids in selecting the optimal method based on study goals and cohort.

Functional magnetic resonance imaging (fMRI) research faces a fundamental methodological challenge: balancing the removal of motion-contaminated data against preserving representative samples. This conundrum is particularly acute in studies examining populations where executive function and head motion are intrinsically linked. Stringent scrubbing reduces false positives from motion artifacts but systematically excludes participants with poorer cognitive control, introducing significant bias. This technical review synthesizes current evidence on data-driven scrubbing methods that better balance these competing demands, provides protocols for implementation, and offers practical tools for researchers navigating this critical analytical decision.

In-scanner head motion represents a pervasive confound in fMRI research, causing disruptive effects on the blood oxygen level-dependent (BOLD) signal that include erroneous correlations and reduced long-range functional connectivity [3] [7]. Scrubbing—the process of identifying and removing motion-contaminated volumes from fMRI time series—has emerged as a standard processing step to mitigate these artifacts.

However, a critical conundrum arises from the established neurobiological link between an individual's capacity for motion control and their cognitive profile, particularly in executive function domains. Research consistently demonstrates that head motion is not merely a technical artifact but a trait characteristic with clinical relevance [3] [65]. Consequently, excluding participants based on motion thresholds may systematically bias samples against individuals with lower executive functioning, potentially skewing study conclusions and limiting generalizability.

This review examines the evidence for this bias, evaluates methodological approaches for balancing data quality and representativeness, and provides practical frameworks for implementing scrubbing protocols that minimize false positives while preserving sample integrity.

The Executive Function-Motion Relationship: Quantitative Evidence

The fundamental premise of the scrubbing conundrum rests on the well-established relationship between cognitive control abilities and in-scanner motion. Multiple studies across age groups have demonstrated that motion correlates with specific executive function deficits.

Evidence Across the Lifespan

Table 1: Association Between Head Motion and Cognitive Measures Across Studies

Study Population	Sample Size	Motion Metric	Cognitive Associations	Non-Associated Domains
Healthy older adults [3]	282	Number of invalid scans	Poorer inhibition (ρ=NA), cognitive flexibility (ρ=NA)	Working memory, verbal memory, processing speed
Young adults [66]	11,836	Framewise displacement	Lower cognitive flexibility, inhibitory control, language abilities, processing speed	-
Children & adolescents [7]	456	Mean volume-to-volume displacement	Strong inverse correlation with age (r=NA)	-

In older adults, greater head motion significantly correlates with poorer performance on specific executive tasks, particularly inhibition and cognitive flexibility, while showing no association with other cognitive domains like working memory or processing speed [3]. This pattern suggests a domain-specific relationship rather than global cognitive decline.

Similarly, research in young adults reveals that individuals with significant motion across scanning sessions exhibit reduced cognitive flexibility and inhibitory control [66]. This consistency across age groups strengthens the evidence that motion propensity represents a stable trait with specific cognitive correlates.

Impact of Exclusion on Sample Composition

The consequences of motion-based exclusion are substantial. Analysis of the Adolescent Brain Cognitive Development (ABCD) study reveals that exclusion based on motion thresholds creates systematic biases across multiple demographic and cognitive variables [66]. When researchers apply listwise deletion (complete case removal), they disproportionately exclude participants with characteristics that correlate with motion, creating samples that no longer represent the target population.

Table 2: Participant Characteristics Associated with Increased Exclusion Odds in fMRI Studies

Characteristic Category	Specific Variables	Impact on Exclusion Odds
Demographic Factors	Lower socioeconomic status, higher neighborhood deprivation [66]	Increased
Cognitive Measures	Lower executive functioning, poorer inhibitory control [3]	Increased
Clinical Variables	Higher psychopathology scores, increased body mass index [66]	Increased
Developmental Stage	Younger age (pediatric), older age (geriatric) [3] [7]	Increased

The bias introduced by these exclusion practices particularly impacts special populations of research interest, including children with developing executive systems, older adults with cognitive decline, and clinical populations with impulse control deficits [66] [3]. This creates a fundamental tension in neurodevelopmental and neurogerontological research, where the populations most critical to understanding brain-behavior relationships are also those most vulnerable to exclusion.

Methodological Approaches: Motion Scrubbing vs. Data-Driven Alternatives

Motion Scrubbing: Traditional Threshold-Based Approaches

Traditional motion scrubbing relies on calculating head displacement parameters (e.g., framewise displacement) and applying predetermined thresholds to flag volumes for censorship. The standard approach involves:

• Motion parameter calculation: Deriving translation and rotation measurements from realignment procedures
• Framewise displacement (FD) computation: Calculating volume-to-volume head movement
• Threshold application: Flagging volumes exceeding established cutoffs (typically 0.2-0.5mm)
• Censorship implementation: Removing flagged volumes from analysis
• Participant exclusion: Removing subjects with excessive censored volumes (>20-50%)

While this method effectively controls for gross motion artifacts, it suffers from several limitations: arbitrary threshold selection, inadequate generalizability across acquisition protocols (particularly multiband sequences), and high rates of data loss that exacerbate sampling bias [65].

Data-Driven Scrubbing: Projection and Outlier Detection Methods

Novel data-driven approaches offer promising alternatives that better balance noise reduction and data retention:

Projection scrubbing, a recently developed data-driven method, utilizes a statistical outlier detection framework combined with strategic dimension reduction to identify artifactual volumes [65]. The methodology proceeds through several stages:

• Dimension reduction: Independent Component Analysis (ICA) or similar techniques project data into a lower-dimensional space
• Outlier detection: The Mahalanobis distance or similar metrics identify volumes displaying abnormal patterns across components
• Statistical thresholding: Volumes exceeding statistically determined thresholds are flagged
• Targeted censoring: Only specifically identified abnormal volumes are removed

This approach offers the key advantage of targeted artifact removal without wholesale exclusion of high-motion participants, thereby preserving sample size and representativeness [65].

Comparative Methodological Evaluation

Table 3: Performance Comparison of Scrubbing Methods

Performance Metric	Motion Scrubbing	Data-Driven Scrubbing
Artifact Reduction	Effective for large motions	Effective for multiple artifact types
Data Retention	Low (high censorship rates)	High (targeted removal)
Sample Size Preservation	Poor (high participant exclusion)	Excellent (minimal exclusion)
Fingerprinting Accuracy	Small improvements	Greater improvements
Functional Connectivity Validity	Worsened with stringent thresholds	Maintained or improved
Reliability	Worsened with stringent thresholds	Maintained
Generalizability	Limited across acquisitions	Broad applicability

Empirical comparisons demonstrate that data-driven scrubbing methods achieve superior balance between noise reduction and data preservation. While both approaches effectively mitigate artifacts, data-driven methods achieve this with dramatically reduced data loss—censoring fewer volumes and excluding far fewer participants [65].

Experimental Protocols and Implementation

Protocol for Data-Driven Scrubbing Implementation

Implementing data-driven scrubbing requires a systematic protocol:

• Data Preprocessing

  • Perform standard realignment, normalization, and spatial smoothing
  • Extract traditional motion parameters for comparison purposes

• Dimension Reduction

  • Apply Independent Component Analysis (ICA) with automatic dimensionality estimation
  • Alternatively, use PCA or other projection techniques
  • Retain a sufficient number of components to capture neural and artifactual variance (typically 20-100 components)

• Artifact Component Identification

  • Calculate correlation between component timecourses and motion parameters
  • Identify components with high high-frequency content characteristic of motion
  • Use automated classifiers (e.g., FSL FIX) or manual inspection

• Outlier Detection

  • Project data onto artifact components
  • Calculate Mahalanobis distance for each volume across artifact dimensions
  • Apply statistical threshold (e.g., 95% confidence interval) to identify outliers

• Censorship Implementation

  • Remove flagged volumes from functional connectivity analysis
  • Consider removing immediately adjacent volumes (1-2 preceding/following)
  • Implement interpolation or missing data handling for subsequent analyses

Table 4: Essential Research Reagents and Computational Tools

Tool Category	Specific Solutions	Function/Purpose
Processing Software	FSL (FIX), AFNI, SPM, CONN	Pipeline implementation for scrubbing and connectivity analysis
Data-Driven Scrubbing Tools	MATLAB/Python implementations of projection scrubbing	Statistical outlier detection in reduced dimension space
Motion Estimation	MCFLIRT (FSL), realign (SPM)	Calculation of framewise displacement and motion parameters
Quality Metrics	fMRIprep, QAP	Comprehensive quality assessment of processed data
Missing Data Handling	Multiple imputation, full information maximum likelihood	Statistical correction for remaining missing data

Implementation requires both specialized software and statistical approaches. Open-source tools like FSL (FMRIB Software Library) provide built-in capabilities for ICA-based denoising through FIX (FMRIB's ICA-based X-noiseifier), while custom MATLAB or Python code can implement projection scrubbing as described in [65].

For missing data that remains after conservative scrubbing, statistical techniques such as multiple imputation or full information maximum likelihood estimation should be employed to avoid biases from listwise deletion [66].

The scrubbing conundrum represents a critical methodological challenge in fMRI research, particularly for studies investigating executive function and populations with motion-prone characteristics. Traditional motion scrubbing with stringent thresholds introduces systematic bias by disproportionately excluding participants with lower executive functioning, potentially distorting study conclusions.

Evidence supports a shift toward data-driven approaches like projection scrubbing that better balance artifact removal and data retention. These methods leverage statistical outlier detection in reduced-dimension spaces to target specifically contaminated volumes while preserving clean data and maintaining sample representativity.

Future methodological development should focus on prospective motion correction techniques that prevent artifacts at acquisition rather than removing them post-hoc [3]. Additionally, improved missing data handling practices and standardized reporting of quality control procedures will enhance reproducibility and comparability across studies.

By adopting more nuanced scrubbing approaches that acknowledge the relationship between motion and cognition, researchers can produce more valid, generalizable findings that better represent the full spectrum of cognitive abilities in their populations of interest.

• PMC PubMed Central - Balancing Data Quality and Bias: Investigating Functional... (2025)
• PMC PubMed Central - The association between head motion during functional... (2022)
• NeuroImage - Less is more: balancing noise reduction and data retention... (2023)
• PMC PubMed Central - Impact of In-Scanner Head Motion on Multiple Measures of... (2012)
• Experts@Arizona - The association between head motion during functional magnetic resonance... (2022)

Optimizing Preprocessing Pipelines for Populations with High Inherent Motion

In neuroimaging research, subject motion remains one of the most significant confounds to data integrity, particularly in resting-state functional magnetic resonance imaging (fMRI) where it produces spurious covariance among time-series and induces distance-dependent spurious correlations [67]. This challenge is especially acute in populations with inherently high motion characteristics, including older adults, children, and individuals with neurological conditions. Recent research has revealed that motion is not merely a technical artifact but reflects neurobiological traits with meaningful cognitive correlates [3] [68].

Studies demonstrate that the propensity for head motion remains stable within individuals across separate MRI sessions, suggesting movement represents a reliable trait characteristic [3]. Most notably, research with healthy older adults has established that greater in-scanner head motion is significantly associated with poorer performance on tasks of inhibition and cognitive flexibility, independent of age effects [3] [68]. This association creates a critical sampling bias dilemma: systematically excluding high-movers from analyses may inadvertently remove individuals with lower executive functioning, thereby skewing study interpretations and limiting generalizability [3].

This technical guide provides comprehensive methodologies for optimizing preprocessing pipelines to address these challenges, enabling researchers to acquire quality neuroimaging data without excluding informative participants from their samples.

Motion-Related Artifacts and Their Impact on Neuroimaging Data

Fundamental Effects of Motion on fMRI Data Quality

Head motion disrupts the magnetic resonance signal and severely degrades scan quality through multiple mechanisms [3]:

• Disruption of BOLD signal in primary and neighboring voxels
• Erroneous correlations (motion artifacts) in resting-state fMRI data
• Increased correlation between closely spaced voxels with decreased correlation between spatially distant voxels
• Reduced functional coupling of distributed networks (e.g., default mode and frontoparietal control networks)
• Increased coupling for local networks

These effects compromise both task-based and resting-state fMRI analyses, with particularly pronounced impacts when participants perform challenging in-scanner tasks [3].

The Executive Function Connection: Evidence for Systematic Bias

Research investigating the cognitive profile of "high-movers" reveals concerning patterns for sample representation. In a study of 282 healthy older adults (age 65-88), Spearman's Rank-Order correlations showed that a higher number of invalid scans (flagged as motion outliers) was significantly associated with poorer performance on specific cognitive measures [3] [68].

Table 1: Cognitive Correlates of In-Scanner Head Motion in Older Adults

Cognitive Domain	Association with Head Motion	Statistical Significance
Inhibition	Poorer performance	Significant
Cognitive flexibility	Poorer performance	Significant
Age	Older age	Significant
Working memory	No significant association	Not significant
Verbal memory	No significant association	Not significant
Processing speed	No significant association	Not significant

This cognitive profile raises concerns about the potential systematic exclusion of older adults with lower executive functioning in neuroimaging samples, creating biased representations of population characteristics [3]. Since performance in inhibition and cognitive flexibility typically declines as part of non-pathological aging, motion-related exclusion criteria may inadvertently remove participants experiencing expected age-related cognitive changes.

Conventional Motion Correction Methods and Their Limitations

Current Approaches to Motion Management

Existing fMRI data processing practices attempt to mitigate motion effects through prospective, retrospective, and physical restraint methods [3]:

• Prospective motion correction: Tracks motion in real-time and adjusts the field of view and gradient directions accordingly
• Retrospective motion correction: Applied during image processing, including techniques of:
  • Spatial realignment
  • Scrubbing outlier scans (e.g., framewise displacement >0.9 mm)
  • Regression of motion estimates
  • Temporal band-pass filtering
• Physical restraints: Foam pads and head restraints, though efficacy is variable

Critical Gaps in Conventional Methods

Traditional volume-wise rigid realignment approaches (e.g., FSL's mcflirt) suffer from fundamental limitations [67]:

• Inability to resolve intra-volume motion: Different slices within the same volume may be affected by different magnitudes of motion
• Underestimated motion metrics: Summary measures like framewise displacement (FD) may lack precision
• Limited temporal resolution: Conventional approaches typically estimate motion at the TR level (e.g., 0.8-2 seconds) rather than capturing sub-TR dynamics

Table 2: Limitations of Conventional Motion Correction Approaches

Approach	Key Limitations	Impact on Data Quality
Volume-wise registration	Cannot resolve intra-volume motion	Unreliable motion estimates for sub-TR motion
Scrubbing	Removes data points, reducing statistical power	Potential introduction of bias if non-random
Motion regression	May remove neural signal of interest	Altered functional connectivity estimates
Physical restraints	Variable efficacy across populations	Discomfort may increase motion in some cases

These limitations are particularly problematic for populations with continuous motion characteristics, where substantial sub-TR motion may be present [67].

Advanced Preprocessing Frameworks for High-Motion Populations

High Temporal Resolution Motion Estimation

Emerging approaches leverage simultaneous multi-slice (SMS) acquisitions to enable sub-TR motion estimation. One promising framework utilizes the extended Kalman filter (EKF) for high-temporal resolution motion correction of resting-state fMRI series by using each SMS echo planar imaging shot as its own navigator [67].

Experimental Protocol: High-Temporal Resolution Motion Estimation [67]

• Acquisition Parameters: TR/TE = 800/30 ms; SMS factor = 6; 60 slices; 2.4mm isotropic resolution
• Reference Image: Average volume of first rs-fMRI run without staged motion
• Motion Estimation: 3D rigid-body motion estimation using EKF algorithm applied retrospectively
• Region of Interest: Brain ROI from T2*-EPI atlas to exclude non-brain tissue
• Validation Method: Normalized mean squared error (NMSE) comparison between original and predicted volumes

This approach demonstrated superior performance compared to volume-wise estimation, with NMSE values for high-temporal resolution estimates approximately 50-70% lower than conventional approaches during staged continuous motion [67].

Pipeline Optimization Through Preprocessing Combinations

Research in Down syndrome populations demonstrates that combining multiple preprocessing steps can dramatically improve success rates. In Centiloid processing for amyloid quantification, implementing alternative pipelines with combinations of image origin reset, filtering, MRI bias correction, and MRI skull stripping increased processing success rates from 61.3% to 95.6% in a DS cohort [69].

Experimental Protocol: Preprocessing Pipeline Evaluation [69]

• Validation Dataset: Standard Centiloid [11C]PiB 50-70min dataset from GAAIN
• Evaluation Cohort: Neurodegeneration in Aging Down Syndrome (NiAD) participants
• Pipeline Construction: Seven processing pipeline versions from combinations of:
  • Image origin reset
  • Spatial filtering
  • MRI bias field correction
  • Skull stripping methodologies
• Quality Assessment: Adherence to Centiloid standards and yield improvement metrics

Integrated Framework for Motion-Robust Processing

Building on these advances, we propose an integrated preprocessing framework that combines multiple optimization strategies:

High-Temporal Resolution Motion Correction Framework

Specialized Methodologies for Specific High-Motion Populations

Older Adults with Executive Function Challenges

For older adults with motion correlated to executive function decline, specialized approaches include:

• Prospective motion correction (PROMO): Real-time tracking and correction
• Structured participant preparation: Enhanced instruction protocols emphasizing stillness
• Cognitive screening documentation: Recording of executive function measures for covariance analysis
• Multi-echo acquisitions: Providing built-in correction capabilities

Implementation Considerations:

• Account for associations between motion and specific cognitive domains (inhibition, cognitive flexibility)
• Implement careful quality control without complete exclusion of high-movers
• Consider executive function measures as covariates in group analyses

Populations with Anatomical Variants

For populations with anatomical differences from standard templates (e.g., Down syndrome), optimized approaches include [69]:

• Template modification: Creating population-specific reference images
• Multi-stage registration: Iterative alignment to improve spatial normalization
• Intensity normalization: Addressing structural tissue differences
• Customized brain extraction: Adapted for atypical neuroanatomy

Table 3: Research Reagent Solutions for Motion-Robust Preprocessing

Tool/Category	Specific Examples	Function/Application
Processing Libraries	FSL (mcflirt) [67]	Volume-wise motion estimation
	SPM8 [69]	Spatial normalization and segmentation
	PMOD [69]	Frame-to-frame motion correction
Advanced Algorithms	Extended Kalman Filter [67]	High-temporal resolution motion estimation
	Wavelet Transform [70]	Multi-resolution feature extraction
Acquisition Sequences	Simultaneous Multi-Slice [67]	Accelerated acquisition for sub-TR resolution
	Multi-echo EPI	Built-in motion robustness
Quality Metrics	Framewise Displacement [3]	Summary metric for motion censoring
	Normalized MSE [67]	Quantification of motion correction accuracy
	Centiloid Standards [69]	Standardized evaluation of pipeline performance

Implementation Framework and Best Practices

Integrated Preprocessing Pipeline

Implementing an effective motion-robust pipeline requires systematic staging:

Motion-Robust Preprocessing Workflow

Quality Assessment and Validation Protocol

Establish comprehensive quality evaluation:

• Quantitative metrics: Framewise displacement, normalized MSE, contrast-to-noise ratios
• Qualitative inspection: Visual checking of edge artifacts, registration accuracy
• Data retention criteria: Predefined thresholds for inclusion/exclusion
• Pipeline performance tracking: Success rates across different populations

Optimizing preprocessing pipelines for high-motion populations requires addressing both technical and methodological challenges. By implementing advanced approaches like high-temporal resolution motion estimation, multi-pipeline processing strategies, and population-specific adaptations, researchers can significantly improve data quality and retention rates while minimizing systematic biases.

Future developments should focus on:

• Real-time implementation of high-temporal resolution correction
• Deep learning approaches for motion artifact detection and correction
• Multimodal data fusion incorporating physiological monitoring
• Standardized reporting of motion metrics and exclusion criteria across studies
• Population-specific template development for atypical neuroanatomy

These advances will enable more inclusive and representative neuroimaging research while maintaining rigorous data quality standards, particularly important for studies investigating populations where motion and cognition may be intrinsically linked.

Behavioral and Environmental Interventions to Reduce In-Scanner Head Motion

In-scanner head motion remains a pervasive challenge in magnetic resonance imaging (MRI), systematically compromising data quality by introducing severe noise and artifacts into neuroimaging data. Excessive motion during functional MRI (fMRI) scans artificially inflates correlations between adjacent brain areas while decreasing correlations between spatially distant territories, fundamentally distorting functional connectivity maps [71]. This problem exhibits a dual-age vulnerability, affecting both pediatric and older adult populations most severely due to developmental and age-related factors [71] [60]. For researchers investigating executive functions, this presents a particular methodological concern, as studies have revealed that higher head motion is significantly associated with poorer performance on tasks of inhibition and cognitive flexibility in older adults [60] [4]. This association raises critical concerns about the systematic exclusion of participants with lower executive functioning from neuroimaging samples, potentially biasing research findings and limiting generalizability [60]. Consequently, implementing effective behavioral and environmental interventions to mitigate head motion is essential not only for improving data quality but also for ensuring representative participant inclusion in studies examining executive processes.

Behavioral Interventions for Motion Reduction

Mock Scanner Training

Mock scanner training utilizes a simulated MRI environment to acclimatize participants to scanner conditions before actual data acquisition. This intervention is particularly effective for pediatric populations, with evidence demonstrating that younger children aged 6-9 years benefit most significantly from such training [71]. A remarkably brief 5.5-minute mock scan training session can substantially improve performance during subsequent formal scanning sessions [71]. The mock scanner replicates critical environmental aspects of real MRI systems, including scanner sounds, bore configuration, and head coil placement, allowing participants to practice remaining still under progressively challenging conditions. This paradigm leverages principles of systematic desensitization and behavioral rehearsal, reducing anxiety and reinforcing stillness behaviors through immediate positive feedback. For research protocols, incorporating mock scanner training as a routine quality assurance step prior to formal MRI data collection is strongly recommended, particularly in large-scale neurodevelopmental studies where data quality directly impacts longitudinal analyses [71].

Real-Time Feedback Systems

Real-time feedback systems provide participants with continuous visual information about their head movement during scanning sessions, enabling self-correction and enhanced bodily awareness. The FIRMM (Frame-wise Integrated Real-time MRI Monitor) software represents a technologically advanced implementation of this approach, using rapid image reconstruction and rigid-body alignment algorithms to estimate frame-by-frame movement [72]. Visual feedback typically involves a color-coded display system where a white cross indicates acceptable motion (<0.2 mm framewise displacement), yellow signals borderline movement (0.2-0.3 mm), and red indicates excessive motion (≥0.3 mm) [72]. Research demonstrates that this intervention produces a statistically significant reduction in head motion with small-to-moderate effect sizes, reducing average framewise displacement from 0.347 mm to 0.282 mm in adult populations [72]. The effectiveness appears most pronounced for reducing high-motion events rather than eliminating all minor movements. For task-based fMRI, considerations regarding cognitive load balancing are essential, as participants must attend to both experimental tasks and motion feedback simultaneously [72]. Supplemental between-run feedback, displaying cumulative motion metrics and performance scores, further enhances motion reduction by providing additional motivation and concrete improvement goals.

Engaging Stimulus Presentation

The presentation of engaging audiovisual stimuli during scanning represents a simple yet effective behavioral intervention for reducing head motion, particularly in populations with limited intrinsic motivation or self-regulation capacity. Research demonstrates that movie watching during scans significantly reduces head motion compared to resting-state conditions with a fixation cross [73]. This effect exhibits age-dependent efficacy, with younger children showing substantially greater benefits than adolescents or adults [73]. The mechanism likely involves both enhanced engagement and distraction from the scanner environment, reducing fidgeting and large position shifts. However, this approach introduces important methodological considerations, as movie watching significantly alters functional connectivity patterns compared to standard resting-state conditions [73]. Researchers must therefore carefully weigh data quality improvements against potential alterations to neural signals of interest, particularly when comparing across studies utilizing different paradigms. For clinical structural MRIs where functional connectivity is not the primary outcome, movie presentation offers a viable alternative to sedation for motion-prone populations [73].

Table 1: Efficacy of Behavioral Interventions for Reducing Head Motion

Intervention Type	Target Population	Key Efficacy Findings	Implementation Considerations
Mock Scanner Training	Children (6-9 years most responsive)	5.5-minute training significantly improves performance in formal scans [71]	Requires dedicated equipment; most beneficial for pediatric studies
Real-Time Feedback	Adults (all ages); potentially children	Reduces average FD from 0.347 mm to 0.282 mm; most effective for high-motion events [72]	Compatible with standard fMRI tasks; requires specialized software (e.g., FIRMM)
Engaging Stimuli (Movies)	Children (under 10 years most responsive)	Significant motion reduction compared to rest condition [73]	Alters functional connectivity; ideal for structural scans when connectivity is not primary outcome

Environmental and Procedural Interventions

Head Stabilization Technologies

Advanced head stabilization systems extend beyond conventional foam padding to provide customized immobilization tailored to individual head morphology. Custom-fitted head molds created using 3D scanning and printing technologies offer superior stabilization compared to standard foam padding alone [72]. These personalized interfaces distribute pressure evenly across the cranial surface, enhancing comfort while minimizing movement through improved fit and postural support. Supplementary vacuum-based cushion systems that conform precisely to head shape after placement provide additional stabilization, particularly for longer scanning protocols where comfort becomes increasingly important for compliance. For diffusion MRI studies, where even minor motion can introduce significant artifacts in quantitative measures, these advanced stabilization approaches are particularly valuable when combined with behavioral interventions [35]. The integration of bite bars coupled with forehead restraints offers maximum immobilization for protocols requiring exceptional stability, though this approach may increase participant burden and is unsuitable for certain populations.

Participant Preparation and Positioning

Comprehensive pre-scan preparation and strategic positioning significantly influence motion outcomes by addressing both psychological and physical contributors to movement. Detailed pre-scan orientation that includes realistic sensory expectations (acoustic profiles, scan durations) reduces startle responses and anxiety-driven motion [71]. Optimized positioning emphasizes comfort and support through strategic cushion placement under knees, lumbar support, and comfortable arm positioning to reduce postural muscle strain during extended acquisitions. The implementation of prone positioning when feasible can naturally restrict head mobility compared to standard supine positioning, though this must be balanced against potential comfort trade-offs. For pediatric populations, parental presence and comfort measures such weighted blankets (when compatible with safety protocols) provide psychological reassurance that indirectly supports motion reduction. Systematic implementation of these preparatory interventions establishes a foundation for successful scanning, particularly when combined with active behavioral interventions during data acquisition.

The Executive Function Connection in Motion Management

The relationship between executive functions and in-scanner head motion represents a critical consideration for neuroimaging research design and interpretation. Substantial evidence indicates that greater head motion correlates with poorer performance on specific executive tasks, particularly inhibition and cognitive flexibility, in healthy older adults [60] [4]. This association persists after accounting for age effects, suggesting a distinct relationship between executive capacity and motion control that cannot be attributed solely to general age-related decline [60]. This connection creates a methodological challenge for studies examining neural correlates of executive functioning, as participants with the most severe executive impairments may be systematically excluded from analyses due to excessive motion, potentially skewing sample characteristics and limiting generalizability [60] [4].

From an intervention perspective, this relationship suggests that participants with executive weaknesses may benefit from enhanced behavioral supports that compensate for these vulnerabilities. The successful application of real-time feedback for motion reduction in adult populations [72] aligns with theoretical models of executive function that emphasize the role of external cues in supporting behavioral regulation when internal cognitive resources are limited. Similarly, the heightened effectiveness of mock scanner training for younger children [71] corresponds with developmental trajectories of prefrontal systems supporting executive control. These considerations support a personalized approach to motion reduction that accounts for individual differences in executive capacity alongside demographic factors.

Table 2: Research Reagent Solutions for Motion Reduction Studies

Tool/Resource	Primary Function	Research Application
FIRMM Software	Real-time motion tracking and feedback	Provides visual feedback to participants during scanning; monitors motion metrics [72]
Mock Scanner Setup	Simulated MRI environment	Participant acclimatization and stillness training before actual scanning [71]
Custom Head Molds	Personalized immobilization	3D-printed custom-fit head supports for superior stabilization [72]
fMRI-Compatible Audiovisual System	Stimulus presentation	Delivery of engaging movies or feedback displays during scanning [73]
EDDY & TOPUP Tools (FSL)	Post-processing motion correction	Diffusion MRI data correction for susceptibility, motion, eddy currents [35]

Integrated Intervention Protocol

Successful motion reduction typically requires combining multiple interventions in a coordinated sequence addressing pre-scan, during-scan, and post-scan phases:

Pre-Scan Protocol

The pre-scan phase focuses on preparation and skill building to establish foundational stillness capacity. Implementation begins with comprehensive pre-screening for motion risk factors including age, diagnostic status, and prior scanning experience. High-risk participants receive enhanced mock scanner training utilizing a simulated MRI environment with integrated motion tracking that provides immediate performance feedback [71]. Training sessions incorporate systematic desensitization to scanner acoustics and spatial constraints, progressively increasing duration and realism. Participants practice utilizing proprioceptive awareness techniques with biofeedback to enhance conscious control over subtle movements. The protocol concludes with clear behavioral expectations and success criteria established through collaborative goal setting between researchers and participants.

During-Scan Protocol

The during-scan protocol layers multiple interventions to support motion control throughout data acquisition. Session initiation includes optimal positioning using combination foam and vacuum-based cushioning systems tailored to individual head morphology [72]. For structural sequences, engaging movie presentation provides distraction and reward, particularly beneficial for pediatric populations [73]. During functional sequences, real-time visual feedback implements color-coded motion thresholds (white<0.2mm, yellow=0.2-0.3mm, red≥0.3mm) to guide self-regulation [72]. The system incorporates brief between-run performance reviews displaying motion metrics and improvement goals to maintain engagement and motivation throughout extended protocols. Strategic sequence optimization prioritizes motion-sensitive acquisitions earlier in the protocol when participant vigilance remains highest.

Post-Scan Quality Assurance

The post-scan phase focuses on data quality assessment and protocol refinement. Immediate motion metric quantification includes framewise displacement calculations and invalid scan classification based on predetermined thresholds [60]. Systematic data quality evaluation identifies participants requiring rescanning or exclusion while informing ongoing protocol refinements for future sessions. For longitudinal studies, individualized motion trends tracking supports personalized intervention adjustments across repeated sessions.

Implementing comprehensive behavioral and environmental interventions significantly improves in-scanner head motion, with particular benefit for vulnerable populations including children and older adults. The connection between executive functions and motion control underscores the importance of these interventions for maintaining representative samples in cognitive neuroscience research. Future developments in real-time monitoring systems, personalized stabilization technologies, and adaptive training protocols promise further enhancements to motion management strategies. By systematically implementing layered intervention approaches, researchers can substantially improve data quality while minimizing the exclusion of informative participants with executive functioning challenges.

The Critical Link Between Executive Function and In-Scanner Motion

In neuroimaging research, particularly with older adult populations, head motion is not merely a technical nuisance but a significant biomarker linked to cognitive function. Evidence indicates that excessive in-scanner head motion is associated with poorer performance on tasks of executive function, specifically inhibition and cognitive flexibility [68]. This association raises a critical methodological concern: the systematic exclusion of "high-movers" from neuroimaging analyses may inadvertently bias samples by removing older adults with lower executive functioning, thereby distorting the understanding of the aging brain [68]. Establishing robust reporting standards for motion mitigation is therefore essential not only for data quality but also for the validity and generalizability of research findings.

This guide outlines the minimum requirements for disclosing these strategies, ensuring that the field can critically evaluate data integrity and account for potential exclusion biases.

---

Quantitative Foundations: Measuring Motion and Its Impact

A clear quantitative description of motion and its effects is the foundation of any mitigation report.

Table 1: Core Quantitative Metrics for Motion Reporting

Table summarizing the essential quantitative data that must be disclosed in any study methodology.

Metric Category	Specific Measure	Reporting Requirement & Significance
Motion Quantification	Number of Invalid Scans / Motion Outliers	Report the exact count and the threshold (e.g., framewise displacement > 0.5mm) used for classification [68].
	Mean Framewise Displacement (FD)	Report the average FD for each participant and group.
	DVARS (Derivative of RMS VARiance over voxelS)	Report the number and threshold of intensity-based outliers.
Participant Exclusion	Exclusion Criteria	State the explicit, pre-defined motion threshold for full participant exclusion (e.g., >50% of volumes flagged as outliers) [68].
	Number of Excluded Participants	Report the exact count and reason for exclusion for all participants removed due to motion.
Association Analysis	Statistical Correlation	Report the results of analyses linking motion metrics to cognitive scores (e.g., Spearman's correlation between invalid scan count and executive function scores) [68].

---

Experimental Protocols for Motion Assessment

To ensure reproducibility, the following detailed protocols should be explicitly referenced or described in methodological sections.

Protocol: Assessing the Motion-Executive Function Link

This protocol is adapted from foundational research on older adults [68].

• 1. Participant Recruitment: Enroll a sample of interest (e.g., 282 healthy older adults). Clearly report demographic characteristics, as motion increases with age [68].
• 2. Cognitive Assessment: Administer a battery of standardized neuropsychological tests. Key domains to assess include:
  • Executive Function: Specifically target inhibition and cognitive flexibility using tasks like the Stroop Test or Trail Making Test Part B [68].
  • Processing Speed: Use tasks like Digit Symbol Coding or Trail Making Test Part A.
  • Verbal Memory: Use tasks from standardized verbal learning tests.
• 3. fMRI Data Acquisition: Acquire neuroimaging data using a standardized sequence. Report all relevant parameters (e.g., TR, TE, voxel size, field strength).
• 4. Head Motion Quantification: Process the fMRI data through a standardized pipeline (e.g., fMRIPrep, SPM) to generate quantitative motion parameters.
  • Primary Outcome: The number of "invalid scans" flagged as motion outliers using a standardized algorithm (e.g., FD > 0.5mm) [68].
• 5. Statistical Analysis:
  • Use Spearman's Rank-Order correlation to analyze the non-linear relationship between the number of invalid scans and cognitive performance scores [68].
  • Control for confounding variables such as participant age in the analysis [68].

Protocol: Prospective Motion Correction (PMC) Techniques

For studies implementing advanced mitigation.

• 1. System Calibration: Calibrate the prospective motion correction system (e.g., Moiré Phase Tracking, camera-based systems) prior to participant scanning.
• 2. Real-time Tracking: During the fMRI sequence, continuously track the participant's head position in all six degrees of freedom (x, y, z, pitch, roll, yaw).
• 3. Feedback Loop: Instantly feed the tracked position data back to the scanner's reconstruction software.
• 4. Image Plane Adjustment: The scanner adjusts the imaging slice coordinates and orientations in real-time to compensate for the measured head motion.
• 5. Data Integrity Check: Post-scan, quantify the residual motion in the PMC-corrected images and compare it to a control group or uncorrected data to validate efficacy.

---

Visualizing Motion Mitigation Workflows

The following diagrams map the logical relationships and decision pathways in motion mitigation strategies.

Motion Mitigation Decision Pathway

Motion-Executive Function Relationship

---

The Scientist's Toolkit: Research Reagent Solutions

A selection of key materials and tools essential for research in this field.

Table 2: Essential Research Reagents and Tools

Table listing key software, hardware, and assessment tools used in motion mitigation and executive function research.

Item Name	Type	Function / Explanation
Framewise Displacement (FD)	Software Metric	A scalar quantity summarizing head displacement between volumes; the primary metric for identifying motion-corrupted scans [68].
Prospective Motion Correction (PMC)	Hardware/Software System	Systems (e.g., camera-based trackers) that monitor head position and adjust the scanner in real-time to correct for motion, improving data integrity.
fMRIPrep	Software Pipeline	A robust, open-source tool for preprocessing fMRI data, which includes standardized generation of motion parameters and outlier detection.
Stroop Test	Cognitive Task	A classic neuropsychological test used to assess inhibitory control, a core executive function linked to head motion [68].
Trail Making Test (TMT) Part B	Cognitive Task	A neuropsychological test assessing cognitive flexibility and task-switching, also associated with motion levels [68].
Moiré Phase Tracking System	Hardware	An external tracking system that provides high-precision, real-time head position data for prospective motion correction during scanning.

Ensuring Valid Inference: Techniques for Detecting and Correcting Spurious Brain-Behavior Associations

In-scanner head motion represents a significant threat to the validity of resting-state functional magnetic resonance imaging (rs-fMRI) studies, particularly for traits and clinical conditions intrinsically correlated with movement, such as psychiatric disorders and age-related cognitive decline [26] [7]. Conventional denoising methods, while partially effective, often leave residual motion artifact that can spuriously inflate or obscure brain-behavior relationships [26]. To address this critical methodological challenge, we present the Split Half Analysis of Motion Associated Networks (SHAMAN) framework. SHAMAN introduces a trait-specific motion impact score, empowering researchers to distinguish genuine neurobiological associations from motion-induced confounds and thereby enhancing the reliability of brain-wide association studies (BWAS) for research and drug development [26].

The Pervasive Challenge of Head Motion in fMRI

Head motion is the dominant source of artifact in fMRI data, systematically altering the blood-oxygen-level-dependent (BOLD) signal in a non-linear manner that is resistant to complete removal by standard denoising algorithms [26]. The artifact manifests as a specific spatial pattern: decreased long-distance connectivity and increased short-range connectivity, most notably within the default mode network [26] [7]. This pattern is problematic because it inversely mirrors reported neurodevelopmental trajectories, where maturation is associated with increased long-distance and decreased local connectivity [7].

The confound is especially acute in studies of populations with naturally higher motion, such as children, older adults, and individuals with neurological or psychiatric conditions [26] [3]. For example, early studies concluding that autism spectrum disorder is characterized by reduced long-distance connectivity were likely reporting motion artifact, as autistic participants often move more in the scanner [26]. This underscores a critical tension in neuroimaging: the need to exclude high-motion data to avoid false positives must be balanced against the risk of systematically excluding informative participants, thereby biasing the sample and obscuring true biological variance [26] [3].

The Executive Function Link

A growing body of evidence suggests that in-scanner head motion is not merely a technical nuisance but a behaviorally relevant trait. Higher head motion is reliably associated with poorer performance on cognitive control tasks [3]. In healthy older adults, greater head motion is significantly correlated with poorer inhibition and cognitive flexibility [3]. This association raises the possibility that the propensity for motion may be linked to the integrity of fronto-striatal circuits supporting executive function. Consequently, excluding participants based on motion thresholds may inadvertently remove those with lower executive functioning, skewing study samples and potentially biasing estimates of brain-behavior relationships in aging and clinical populations [3].

The SHAMAN Framework: Core Methodology

SHAMAN is a novel methodological approach designed to quantify the extent to which a specific trait-functional connectivity (trait-FC) association is impacted by residual head motion artifact. Its core innovation lies in capitalizing on the different temporal stabilities of traits and motion: traits are stable over the timescale of an MRI scan, while motion is a transient state [26].

Underlying Principle and Workflow

The method operates by comparing the correlation structure between split high-motion and low-motion halves of each participant's fMRI timeseries. A significant difference between the halves indicates that the state-dependent variance in motion impacts the trait's connectivity profile. The direction of this effect is diagnostic: an impact score aligned with the trait-FC effect suggests motion overestimation, while a score in the opposite direction suggests motion underestimation [26].

The following diagram illustrates the core analytical workflow of the SHAMAN framework:

Experimental Protocol for Validation

The validation of SHAMAN, as detailed in the primary source [26], involved a rigorous analysis of a large-scale dataset.

• Dataset: Adolescent Brain Cognitive Development (ABCD) Study, comprising 11,874 children aged 9-10 years. For analysis, n=7,270 participants with sufficient data were included.
• fMRI Preprocessing: Data were denoised using the standard ABCD-BIDS pipeline, which includes global signal regression, respiratory filtering, spectral filtering, despiking, and motion parameter timeseries regression [26].
• Motion Censoring: The impact of post-hoc censoring was evaluated by applying different framewise displacement (FD) thresholds (e.g., FD < 0.2 mm) to exclude high-motion volumes.
• Trait Analysis: 45 behavioral and demographic traits from the ABCD study were assessed.
• Statistical Testing: Permutation testing and non-parametric combining across functional connections were used to generate a p-value for the motion impact score, distinguishing significant from non-significant motion effects [26].

Key Quantitative Findings from Large-Scale Application

The application of SHAMAN to the ABCD dataset revealed the pervasive nature of residual motion artifact, even after comprehensive denoising.

Table 1: Prevalence of Significant Motion Impact on Traits after Standard Denoising (ABCD Study, n=7,270)

Impact Type	Number of Traits Affected	Percentage of Traits (45 total)
Motion Overestimation	19	42%
Motion Underestimation	17	38%

Table 2: Effect of Motion Censoring (FD < 0.2 mm) on Significant Motion Impact Scores

Impact Type	Number of Traits Affected After Censoring	Percentage of Traits (45 total)
Motion Overestimation	1	2%
Motion Underestimation	17	38%

The data demonstrate that while aggressive motion censoring is highly effective at mitigating motion-induced overestimation of trait-FC effects, it is notably less effective against underestimation effects [26]. This underscores a critical limitation of censoring as a sole remedy and highlights the unique diagnostic value of SHAMAN in detecting confounding that would otherwise remain hidden.

The Researcher's Toolkit: Essential Materials and Reagents

Implementation of the SHAMAN framework and rigorous motion control requires several key resources and analytical tools.

Table 3: Essential Research Reagents and Solutions for fMRI Motion Control and Analysis

Item Name	Function / Description	Key Considerations
High-Quality fMRI Dataset	Large-scale dataset with phenotypic traits for analysis.	Datasets like ABCD [26], HCP [26], and UK Biobank provide the necessary statistical power for BWAS and method validation.
Framewise Displacement (FD)	A scalar summary metric of volume-to-volume head motion [26].	Serves as the primary quantitative measure of in-scanner head motion for censoring and correlation analysis.
Motion Censoring (Scrubbing)	Post-hoc exclusion of high-motion fMRI volumes based on an FD threshold [26].	Introduces a trade-off between data quality and retained data quantity; threshold selection is critical.
Prospective Motion Correction	Real-time tracking and adjustment for head motion during scan acquisition [3].	Helps mitigate motion at the source but is not yet universally available.
Structured Cognitive Batteries	Standardized tests of executive function (e.g., inhibition, set-shifting) and other cognitive domains [3].	Essential for characterizing the cognitive profile of participants and linking motion to traits like executive function.
SHAMAN Software Scripts	Custom code for performing split-half analysis and computing motion impact scores [26].	The core analytical tool for trait-specific motion impact assessment; requires implementation in the researcher's computational environment.

Implications for Research and Drug Development

The SHAMAN framework provides a critical lens for evaluating the validity of neuroimaging biomarkers, which is paramount for translational research.

• Biomarker Validation: In clinical trials for neurodegenerative or psychiatric disorders, SHAMAN can be used to audit putative neuroimaging biomarkers, ensuring that treatment effects are not confounded by systematic differences in head motion between drug and placebo groups.
• Sample Bias Mitigation: By identifying and quantifying motion-related confounding, SHAMAN informs more nuanced participant inclusion criteria, reducing the risk of excluding informative participants and producing biased estimates of population effects [3].
• Target Engagement: For therapeutics aimed at improving cognitive control or disorders linked to executive dysfunction, SHAMAN helps verify that changes in functional connectivity reflect true target engagement rather than motion-related artifacts.

The following diagram summarizes the interplay between executive function, head motion, and the analytical solution provided by SHAMAN:

SHAMAN represents a significant methodological advance in the quest for robust and reproducible neuroimaging findings. By moving beyond one-size-fits-all motion correction and providing a trait-specific motion impact score, it directly addresses a pervasive source of confounding in brain-behavior research. Its application confirms that even in high-quality, denoised data, residual motion can significantly impact a large proportion of trait-FC associations. For researchers and drug developers focused on conditions linked to executive function, integrating SHAMAN into the analytical pipeline is a critical step for safeguarding against spurious findings and ensuring that reported effects reflect genuine neurobiology.

Distinguishing Overestimation from Underimputation in Trait-FC Relationships

In functional connectivity (FC) research, accurately characterizing the relationship between neural traits (trait-FC) and behavioral or cognitive measures, such as executive function, is paramount. A significant and often inadequately controlled confound in this endeavor is in-scanner head motion. This technical guide delineates the methodological frameworks for distinguishing between overestimation and underestimation of true trait-FC relationships. Overestimation occurs when statistical relationships are artificially inflated, typically by shared variance with motion, while underestimation occurs when true relationships are obscured by statistical corrections or measurement error. Grounded in the context of executive function research, where head motion is itself a behavioral biomarker, this paper provides experimental protocols, data presentation standards, and analytical tools to mitigate these biases and enhance the validity of neuroimaging findings.

The quest to identify robust neural correlates of cognitive processes is a central goal of cognitive neuroscience. Research into executive functions—higher-order cognitive control processes such as inhibition, shifting, and updating—is particularly active in this space [74] [75]. However, the integrity of this research is critically dependent on the quality of functional magnetic resonance imaging (fMRI) data. In-scanner head motion is a well-known source of artifact that severely degrades fMRI data quality by disrupting the blood oxygen level-dependent (BOLD) signal and introducing spurious correlations, often following a pattern of increased short-range and decreased long-range functional connectivity [3].

Critically, head motion is not merely a random technical artifact; it is a neurobehavioral trait with a direct relationship to cognitive control. Studies have consistently shown that greater in-scanner head motion is associated with poorer performance on tasks of inhibition and cognitive flexibility, key components of executive function [3]. This creates a fundamental confound: individuals with lower executive function may move more in the scanner, and this motion can systematically bias FC estimates. Consequently, failing to account for this relationship can lead to a false inference of a strong trait-FC relationship where none exists (overestimation), or the obscuring of a genuine relationship through overzealous correction (underestimation). Understanding and distinguishing between these two errors is essential for advancing the field, particularly in populations like older adults where both executive function declines and motion tend to increase [3].

Defining Overestimation and Underestimation in Trait-FC

In the specific context of establishing a trait-FC relationship linked to executive function, the terms overestimation and underestimation have precise meanings centered on the accuracy of the effect size of the correlation.

Overestimation

Overestimation occurs when the reported strength of a trait-FC relationship is artificially inflated beyond its true value. In this paradigm, the most common cause is the confounding effect of head motion. If head motion is correlated with both poorer executive function and specific FC patterns, and is not adequately controlled for, the observed correlation between FC and executive function will be artificially strong. The resulting statistical model incorrectly attributes variance to the FC-executive function link that is actually driven by the shared variance with motion. This leads to a false positive or an exaggerated positive finding.

Analogy: This is analogous to overestimating risk in financial value-at-risk (VaR) models; a model that predicts more breaches than occur is overly conservative, just as a trait-FC model that suggests a stronger relationship than exists is overly optimistic in its effect size [76].

Underestimation

Underestimation occurs when the strength of a genuine trait-FC relationship is obscured or artificially minimized. This can happen through several mechanisms:

• Overcorrection: Applying excessive motion correction (e.g., "scrubbing" of high-motion time points) that removes not only motion-related artifact but also legitimate neural signal of interest.
• Measurement Error: Using executive function tasks with low reliability can lead to substantial underestimation of true effect sizes, as random error in the dependent variable attenuates observed correlations [77].
• Systematic Exclusion: The common practice of excluding "high-mover" participants from analysis can systematically bias the sample by removing individuals with lower executive function, thus restricting the range of the key variable and attenuating the observed correlation [3].

Analogy: Conversely, this is like underestimating risk in VaR models, where fewer breaches occur than predicted, indicating the risk is less severe than modeled [76].

The following table summarizes the core distinctions:

Table 1: Core Definitions of Overestimation and Underestimation in Trait-FC Research

Feature	Overestimation	Underestimation
Core Meaning	Artificially inflated trait-FC effect size	Artificially attenuated or obscured trait-FC effect size
Primary Cause in FC	Failure to control for confounding head motion	Overcorrection for motion, measurement error, or selective exclusion
Resulting Fallacy	False positive or exaggerated positive finding	False negative or weakened genuine finding
Analogy from Risk Modeling [76]	Observed violations < Expected violations (Overly conservative model)	Observed violations > Expected violations (Overly risky model)

Methodological Protocols for Bias Identification and Control

To safeguard against these errors, researchers must employ rigorous experimental design and analytical protocols. The following workflows and tools are essential.

Experimental Design and Data Acquisition

A robust study begins with design choices that minimize confounding from the outset.

Table 2: Essential Research Reagents and Methodological Solutions

Reagent/Solution	Function & Rationale
Maximal Graded Exercise Test (GXT)	Gold-standard measure of cardiorespiratory fitness, used as a precise covariate to account for physiological confounds linked to both brain health and motion [75].
Cognitive Task Battery (e.g., Stroop, N-Back, Task-Switching)	Measures specific executive sub-functions (inhibition, updating, shifting). Using difference scores (e.g., interference cost) helps control for baseline processing speed [74] [75].
Prospective Motion Correction (MoCo)	Real-time tracking and adjustment of the scanner's field of view to counteract head motion, reducing artifact at the source [3].
Structured Demographic & Health Screening	Ensures a well-characterized sample and allows for statistical control of variables like age, education, and health status that correlate with both motion and cognition [3].

Diagram 1: Experimental workflow with bias mitigation points. Green nodes indicate key steps for bias control, while the red node highlights a critical check for overestimation.

Analytical Procedures for Distinguishing Bias

Post-acquisition, specific analytical steps can diagnose and correct for over- and underestimation.

Diagram 2: The overestimation confound. A significant relationship between motion and both executive function and FC (red arrows) can create a spurious observed trait-FC relationship (gray arrow).

Table 3: Analytical Steps to Identify and Correct for Bias

Analytical Step	Protocol	Diagnosis of Overestimation	Diagnosis of Underestimation
Motion Correlation Analysis	Calculate correlations between motion metrics (e.g., framewise displacement), executive function scores, and FC values.	A significant motion-EF and motion-FC correlation suggests a confound that likely inflates the EF-FC relationship.	If motion correlates with EF but not FC, overcorrection or exclusion may be masking a true effect.
Covariate Inclusion	Run hierarchical regression models predicting FC from EF, first without and then with motion as a covariate.	The EF-FC relationship attenuates or becomes non-significant after adding motion covariates.	The EF-FC relationship remains null or weak after accounting for motion and other confounds.
Measurement Reliability Assessment	Calculate test-retest reliability (e.g., Intraclass Correlation Coefficient) for executive function tasks from a pilot or main study.	Not applicable for direct diagnosis, but low reliability is a primary source of underestimation.	Low reliability (<0.7) indicates that observed correlations are likely underestimates of the true relationship [77].
Range of Motion Analysis	Compare motion metrics and EF scores between included and excluded participants.	N/A	If excluded "high-movers" have systematically worse EF, the range restriction leads to underestimation of the population effect.

Distinguishing between overestimation and underestimation is not a mere statistical technicality but a fundamental requirement for producing valid and reproducible research on the neural underpinnings of executive function. The inherent link between executive control and in-scanner motion creates a perilous analytical landscape where confounding and overcorrection are constant threats. By adopting the detailed experimental protocols, analytical workflows, and diagnostic checks outlined in this guide, researchers can navigate this landscape with greater confidence. A disciplined approach that proactively addresses both types of bias will strengthen the evidentiary basis for trait-FC relationships and accelerate the translation of neuroimaging findings into clinical and practical applications.

The pursuit of robust biomarkers linking executive function (EF) to brain network topology through resting-state functional magnetic resonance imaging (rs-fMRI) is critically dependent on the effective mitigation of head motion artifacts. This technical guide provides a comprehensive benchmarking analysis of contemporary denoising pipelines, with particular emphasis on the ABCD-BIDS pipeline, which is extensively used in large-scale studies like the Adolescent Brain Cognitive Development (ABCD) Study. We synthesize evidence from multiple large-scale evaluations to determine how different preprocessing strategies control for motion-related variance while preserving neural signals of interest. The findings indicate that pipeline performance varies significantly across metrics, with optimal results achieved through specific combinations of global signal regression, anatomical CompCor, and strategic censoring. This analysis provides researchers and drug development professionals with evidence-based recommendations for processing pipeline selection to enhance the validity of brain-behavior associations in studies of executive function.

Research consistently demonstrates that head motion during fMRI acquisition introduces systematic biases in functional connectivity (FC) estimates, not merely as a technical artifact but as a potential neurobehavioral trait. This relationship is particularly problematic for studies of executive function, where individuals with lower EF capacities—including those with ADHD, autism, and healthy older adults with cognitive decline—demonstrate significantly greater in-scanner motion [78] [3]. This association creates a confounding loop: populations of interest for EF research are systematically excluded or mischaracterized due to motion-related artifacts.

Motion artifacts manifest in FC as increased short-distance correlations and decreased long-distance connections, patterns that notably resemble some reported neural correlates of EF deficits [78] [79]. For instance, early studies mistakenly attributed motion-induced connectivity reductions in the default mode network to autism, when the effects were actually driven by increased head motion in autistic participants [78]. This confounding effect underscores the necessity of optimized denoising pipelines that can distinguish true neural signatures of EF from motion-related artifacts.

The benchmarking of denoising methods therefore becomes paramount for advancing research on the neural bases of executive function. Large-scale initiatives like the ABCD Study have implemented standardized processing pipelines such as ABCD-BIDS, but residual motion artifacts persist even after extensive denoising [78]. This technical review synthesizes current evidence on pipeline performance to guide researchers in selecting methods that minimize both false positives and false negatives in EF-related functional connectomics.

Methods for Benchmarking Denoising Performance

Quality Control Metrics and Evaluation Frameworks

Benchmarking denoising pipelines requires multi-dimensional quality control (QC) metrics that capture different aspects of performance. Contemporary evaluation frameworks typically employ several complementary metrics:

• Motion artifact reduction: Quantified through framewise displacement (FD) correlations with connectivity matrices or distance-dependent correlation profiles [79]
• Resting-state network (RSN) identifiability: Measured via spatial correlation with canonical network templates or intra-class correlation coefficients across subjects [80] [81]
• Temporal signal-to-noise ratio (tSNR): Assessing the preservation of neural signals amidst noise reduction [82]
• Test-retest reliability: Evaluating consistency of connectivity measures across repeated scans [81]

Recent approaches have developed composite indices that synthesize multiple metrics into unified scores. These frameworks weight metrics according to their sensitivity and consistency across subject subgroups with similar motion levels, providing more robust evaluations of pipeline performance [80] [79].

The SHAMAN Framework for Trait-Specific Motion Impact

The Split Half Analysis of Motion Associated Networks (SHAMAN) represents a novel approach for quantifying trait-specific motion effects on functional connectivity. Unlike global motion metrics, SHAMAN computes motion impact scores for specific trait-FC relationships by comparing correlation structures between high- and low-motion halves of each participant's fMRI timeseries [78]. This method capitalizes on the temporal stability of traits (e.g., EF performance) versus the state-like variability of motion, enabling researchers to distinguish between motion causing overestimation or underestimation of trait-FC effects. The approach generates both a motion impact score and p-value through permutation testing and non-parametric combining across connections [78].

Table 1: Key Quality Metrics for Denoising Pipeline Benchmarking

Metric Category	Specific Measures	Interpretation	Ideal Outcome
Motion Reduction	Framewise displacement (FD) correlation with FC; Distance-dependent correlation	Quantifies residual association between head motion and connectivity patterns	Lower values indicate better motion correction
Signal Preservation	Temporal signal-to-noise ratio (tSNR); Resting-state network identifiability	Measures retention of neural signal after denoising	Higher values indicate better signal preservation
Reliability	Test-retest reliability; Intra-class correlation coefficient	Assesses consistency of connectivity measures across repeated scans	Higher values indicate greater reliability
Trait Specificity	SHAMAN motion impact score [78]	Quantifies motion impact on specific trait-FC relationships	Non-significant scores indicate minimal trait-specific motion bias

Performance Analysis of Major Denoising Pipelines

The ABCD-BIDS Pipeline

The ABCD-BIDS pipeline represents a comprehensive denoising approach implemented in one of the largest neurodevelopmental studies. This pipeline incorporates multiple denoising components: global signal regression, respiratory filtering, spectral filtering, despiking, and motion parameter timeseries regression [78]. Performance evaluations demonstrate that ABCD-BIDS achieves a 69% relative reduction in motion-related signal variance compared to minimal processing (motion correction only), decreasing motion-explained variance from 73% to 23% [78].

Despite this substantial improvement, significant motion-related artifacts persist after ABCD-BIDS processing. Analyses of ABCD data (n = 7,270 participants) revealed that the motion-FC effect matrix maintained a strong negative correlation (Spearman ρ = -0.58) with the average FC matrix, indicating that participants with greater motion still showed systematically weaker long-distance connections [78]. This residual artifact was reduced but not eliminated by additional censoring at FD < 0.2 mm (ρ = -0.51).

Application of the SHAMAN framework to ABCD data revealed that 42% (19/45) of behavioral traits had significant motion overestimation scores and 38% (17/45) had significant underestimation scores after standard ABCD-BIDS denoising without censoring. Subsequent censoring at FD < 0.2 mm reduced significant overestimation to just 2% (1/45) of traits but did not decrease the number with significant underestimation scores [78]. This demonstrates the complex relationship between denoising strategies and their impact on specific trait-FC relationships.

Comparative Performance Across Pipelines

Multi-metric comparisons of denoising strategies reveal significant variability in pipeline performance across different quality dimensions. A systematic evaluation of nine different pipelines found that strategies incorporating regression of mean signals from white matter and cerebrospinal fluid areas plus global signal regression achieved the best compromise between artifact removal and preservation of resting-state network information [80].

Table 2: Performance Comparison of Denoising Pipeline Components

Pipeline Component	Key Strengths	Key Limitations	Impact on EF Research
Global Signal Regression (GSR)	Effective reduction of motion artifacts; Improved identifiability of RSNs [80] [79]	Potential removal of neural signal; Controversial biological interpretation	Can enhance detection of EF-related connectivity but may remove relevant neural variance
Anatomical CompCor	Removal of physiological noise without external recordings; Preservation of degrees of freedom [79]	Variable performance depending on number of components removed	Particularly valuable for EF studies in clinical populations with elevated motion
Motion Censoring ("Scrubbing")	Direct removal of high-motion volumes; Reduction of motion spikes	Loss of data; Potential introduction of bias by excluding high-motion participants	Problematic for EF research as those with poorer EF are systematically excluded
ICA-AROMA	Data-driven noise identification; Automatic classification of noise components	Potential overcorrection in task designs; Variable performance across datasets	Useful for large-scale EF studies where manual component review is impractical

The optimal number of nuisance regressors appears to be study-specific. Analyses of Human Connectome Project data indicate that removing the global signal and approximately 17% of principal components from white matter provides substantial improvement in QC metrics, with minor additional benefits from low-pass filtering at 0.20 Hz [79]. Scrubbing generally showed limited additional benefit in this evaluation.

Large-Scale Pipeline Comparisons

A comprehensive evaluation of 768 data-processing pipelines for functional connectomics revealed vast variability in suitability for network neuroscience applications [81]. The study evaluated pipelines based on their ability to minimize motion confounds, reduce spurious test-retest discrepancies, while maintaining sensitivity to individual differences—a crucial consideration for EF research. Only a subset of pipelines consistently satisfied all criteria across different datasets and time intervals. Pipeline performance was particularly dependent on the combination of parcellation scheme, connectivity definition, and the use of global signal regression [81].

This large-scale comparison emphasized that inappropriate pipeline selection can produce systematically misleading results, with most pipelines failing at least one criterion. Successful pipelines consistently demonstrated robustness across acquisition parameters and preprocessing methods, including both volume-based and surface-based processing approaches [81].

Advanced Topics and Future Directions

Computational Efficiency and Scalability

As neuroimaging datasets expand to include tens of thousands of participants, computational efficiency becomes increasingly important. Traditional pipelines like fMRIPrep require extensive processing times (approximately 5+ hours per participant), creating bottlenecks for large-scale studies [82]. Next-generation pipelines such as DeepPrep leverage deep learning algorithms to achieve substantial acceleration (10.1× faster than fMRIPrep) while maintaining or improving output quality [82].

DeepPrep incorporates specialized modules including FastSurferCNN for anatomical segmentation, FastCSR for cortical surface reconstruction, and SUGAR for surface registration, demonstrating robust performance across 55,000+ scans from diverse datasets [82]. This computational efficiency is particularly valuable for EF research, enabling rapid processing of large samples necessary for detecting subtle brain-behavior relationships.

Special Considerations for Executive Function Research

Research on executive function presents unique challenges for denoising pipeline selection. Individuals with lower EF abilities consistently demonstrate greater in-scanner motion, creating systematic biases that can falsely inflate or obscure true effects [3]. Studies of healthy older adults reveal that those with greater head motion perform worse on measures of inhibition and cognitive flexibility—core components of EF [3]. This association raises concerns about the systematic exclusion of informative participants in EF research due to motion-related quality control procedures.

The SHAMAN framework offers a promising approach for quantifying and addressing this confounding in EF studies [78]. By providing trait-specific motion impact scores, researchers can determine whether EF-connectivity relationships in their data are likely biased by motion artifacts. For comprehensive EF research, a multi-faceted approach combining optimized denoising with rigorous motion impact assessment is recommended.

Table 3: Key Research Reagents and Computational Tools for Denoising Research

Tool Name	Type	Primary Function	Application Context
ABCD-BIDS	Complete pipeline	Standardized denoising for large-scale studies	ABCD Study and other developmental datasets
fMRIPrep	Complete pipeline	Comprehensive preprocessing for fMRI data	General-purpose fMRI preprocessing
HALFpipe	Standardized workflow	Harmonized analysis of functional MRI	Multi-site studies requiring standardization
SHAMAN	Analytical framework	Quantifying trait-specific motion impact	Assessing motion bias in brain-behavior relationships
DeepPrep	Accelerated pipeline	Deep learning-accelerated preprocessing	Large-scale datasets and clinical applications
ICA-AROMA	Noise removal tool	Automatic removal of motion artifacts via ICA	Data-driven motion correction without manual inspection

Integrated Workflow for Denoising in Executive Function Research

The following diagram illustrates a recommended integrated workflow for denoising pipeline application and evaluation in executive function research:

Based on comprehensive benchmarking evidence, we recommend the following practices for denoising pipeline selection in executive function research:

• For large-scale developmental studies, implement ABCD-BIDS with additional censoring at FD < 0.2 mm to address motion overestimation biases, but remain cautious about potential underestimation effects [78].

• For optimal balance of motion reduction and signal preservation, employ pipelines combining global signal regression with anatomical CompCor using approximately 17% of principal components from white matter [80] [79].

• For trait-specific motion impact assessment, incorporate the SHAMAN framework to quantify potential confounding in EF-connectivity relationships [78].

• For computational efficiency with large datasets, consider next-generation pipelines like DeepPrep that maintain accuracy while dramatically reducing processing time [82].

• For maximum reliability in functional connectomics, select pipelines that have demonstrated robustness across multiple test-retest intervals and sensitivity to individual differences [81].

The continued advancement of denoising methods remains crucial for disentangling true neural correlates of executive function from motion-related artifacts. As methods evolve, researchers should prioritize transparent reporting of processing pipelines and motion impact assessments to enhance the validity and reproducibility of brain-behavior research.

In-scanner head motion represents a significant challenge for neuroimaging research, directly impacting data quality and the validity of scientific conclusions. Its effects extend beyond mere technical artifact, as motion patterns are systematically linked to participant characteristics, including age, clinical symptoms, and cognitive abilities. In the context of large-scale imaging datasets like the Adolescent Brain Cognitive Development (ABCD) Study and the UK Biobank, understanding and mitigating motion effects is paramount for ensuring representative samples and unbiased results. Critically, a growing body of evidence reveals that head motion is not random noise but is intrinsically linked to executive functioning—the cognitive processes that enable individuals to control and regulate their behavior. This technical guide examines the patterns of motion artifacts in major neuroimaging datasets, explores the established connection with executive function, and provides methodological frameworks for addressing these challenges in both basic research and drug development contexts.

Motion Patterns in Large-Scale Datasets: Empirical Evidence

The ABCD Study: Transdiagnostic Symptom Domains

The ABCD Study, with approximately 11,876 children aged 9-10 years at baseline, provides compelling evidence for distinct motion patterns associated with transdiagnostic symptom domains. In a comprehensive analysis of 9,045 children, researchers demonstrated systematic relationships between behavioral symptoms and in-scanner motion across multiple imaging modalities, including T1- and T2-weighted structural, resting-state, and diffusion MRI, as well as task-based fMRI [83].

Table 1: Motion-Symptom Associations in the ABCD Study (n=9,045)

Symptom Domain	QC Pass Likelihood	Mean Motion Association	fMRI Task Performance
Attention Problems	Decreased likelihood	Increased motion	Worse performance across tasks
Disruptive Behavior	Decreased likelihood	Increased motion	Not specified
Internalizing Problems	Increased likelihood	Decreased motion	Not specified

The study revealed that greater severity of attention and disruptive behavior problems was associated with a lower likelihood of passing motion quality control across imaging modalities, while increased internalizing severity showed the opposite pattern, associated with a higher likelihood of passing QC. Furthermore, increased attention and disruptive behavior problems were associated with increased mean motion, whereas increased internalizing problems were associated with decreased mean motion [83]. These findings highlight the non-random nature of in-scanner motion and its relationship to fundamental behavioral dimensions.

An important interaction effect emerged between sex and attention-related problems in passing QC for T2-weighted and diffusion MRI scans, indicating that the relationship between symptoms and motion may vary by demographic factors. Additionally, greater severity of attention problems was associated with worse performance across fMRI tasks measuring inhibitory control (Stop-Signal Task), reward processing (Monetary Incentive Delay Task), and working memory/emotion perception (Emotional n-back Task) [83].

UK Biobank: Population-Level Patterns

The UK Biobank imaging subsample, comprising data from 52,951 participants, provides complementary evidence from a large adult population. This research demonstrated that head motion is a stable, trait-like characteristic that shows significant correlations across imaging modalities (T1 structural, diffusion, resting-state, and task fMRI) and across time in participants with longitudinal data (n=5,305, average 2.64 years between scans) [84].

Table 2: Motion Characteristics in UK Biobank (n=52,951)

Imaging Modality	Motion Inter-correlation	Longitudinal Stability	Health Predictors
T1 Structural	Pearson r range: 0.3-0.8	High stability	Poorer psychological and physical health
Resting-state fMRI	Pearson r range: 0.3-0.8	High stability	Largest association with diabetes (β=0.66)
Task fMRI	Pearson r range: 0.3-0.8	High stability	Multiple health conditions
Diffusion MRI	Pearson r range: ~0.1	High stability	History of traumatic brain injury

Notably, the research found that poorer values in most health traits predicted lower odds of complete imaging data, with the largest association observed for history of traumatic brain injury (odds ratio = 0.720) [84]. This indicates that systematic exclusion of participants based on motion criteria may bias samples toward healthier populations, potentially limiting the generalizability of findings, particularly to clinical groups.

The Executive Function Connection

Cognitive Profile of "High-Movers"

Research consistently demonstrates that individuals with greater in-scanner motion exhibit specific cognitive deficits, particularly in executive function domains. In a study of 282 healthy older adults (age 65-88), greater head motion was significantly associated with poorer performance on tasks of inhibition and cognitive flexibility, even after accounting for age effects [3]. This association was specific to executive tasks—no significant relationships were found between motion and working memory, verbal memory, or processing speed [3] [68].

The ABCD study findings in children parallel these results in older adults, with attention problems (intrinsically linked to executive function) showing the strongest associations with increased motion [83]. This pattern across the lifespan suggests a fundamental relationship between the cognitive capacity for behavioral regulation and the ability to maintain stillness during scanning.

Motion as a Behavioral Phenotype

The consistent association between executive dysfunction and in-scanner motion across diverse populations suggests that motion may reflect an underlying behavioral phenotype rather than merely representing a technical confound. This perspective is supported by several lines of evidence:

• Temporal stability: Motion levels show significant within-individual consistency across imaging sessions separated by years [84]
• Specificity to cognitive domains: Associations are strongest with executive functions rather than global cognitive impairment [3]
• Transdiagnostic patterns: Motion relationships transcend diagnostic categories, mapping onto fundamental behavioral dimensions [83]
• Demographic consistency: Patterns are observed across developmental stages from childhood through older adulthood [83] [3]

Diagram 1: Executive Function Pathways to Head Motion

Methodological Considerations and Mitigation Strategies

Experimental Protocols for Motion Assessment

Both the ABCD Study and UK Biobank employ comprehensive quality assessment protocols that provide models for systematic motion evaluation:

ABCD Study Motion QC Protocol:

• Logistic regression and linear mixed-effects models examined associations between motion and behavior
• Motion indexed using ABCD quality control metrics and mean framewise displacement
• Multiple imaging modalities assessed: T1- and T2-weighted structural, resting-state fMRI, diffusion MRI, and task-based fMRI (SST, MID, EN-back)
• Child Behavior Checklist provided continuous measures of symptom severity
• All transdiagnostic symptoms included in a single analytical step to account for covariance [83]

UK Biobank Motion Analysis Framework:

• Average head motion indexed four ways: mean resting-state fMRI motion, task-fMRI motion, T1 structural motion, and mean absolute motion from diffusion MRI
• Complete MRI data phenotype derived based on task-fMRI data availability
• Centralized quality control with automated classification and manual checking
• Association testing between multiple psychological/physical health traits and motion [84]

Analytical Mitigation Approaches

Several statistical and analytical approaches can mitigate motion-related biases:

• Inclusion of motion parameters as covariates: Regression of motion estimates in general linear models [83]
• Censoring/scrubbing approaches: Removal of frames exceeding framewise displacement thresholds (e.g., >0.9 mm) [3]
• Global signal regression: Reduction of motion-related artifacts in functional connectivity [83]
• Denoising techniques: Advanced algorithms for separating motion artifacts from neural signals [83]
• Multi-echo sequences: Acquisition approaches that facilitate better artifact removal

Table 3: Motion Mitigation Strategies in Neuroimaging Research

Strategy Type	Specific Methods	Advantages	Limitations
Prospective	Participant training, padding, real-time correction	Prevents data loss	Variable efficacy, requires specialized equipment
Acquisition	Multi-echo sequences, accelerated acquisition	Improves data quality	May compromise spatial/temporal resolution
Retrospective	Regression, scrubbing, denoising	Applicable to existing data	May remove neural signal, incomplete correction
Analytical	Motion inclusion in models, quality control	Accounts for motion statistically	Does not recover corrupted data

Implications for Drug Development in Psychiatry

De-risking Clinical Trials Through Neuroimaging

Neuroimaging biomarkers offer significant potential for de-risking drug development across multiple phases:

• Phase I: Establishing CNS penetration, functional target engagement, and dose-response relationships using pharmacodynamic neuroimaging [85]
• Phase II: Identifying patient subgroups likely to respond to treatment, enabling trial enrichment [85] [86]
• Phase III: Providing objective measures of efficacy alongside clinical outcomes [87] [86]
• Phase IV: Understanding long-term treatment effects on brain structure and function [87]

The systematic exclusion of "high-movers" from neuroimaging studies poses a particular challenge for drug development, as these individuals may represent important clinical subgroups with distinct treatment responses. Motion-related sampling bias could potentially lead to failed trials if the excluded population differs in their response to the investigational treatment.

Motion-Conscious Trial Design

Incorporating motion considerations into trial design requires specific approaches:

• Prospective motion correction: Implementation of real-time tracking and correction technologies [3]
• Participant preparation: Enhanced training and acclimation procedures for vulnerable populations
• Adaptive acquisition protocols: Sequence adjustments based on real-time motion assessment
• Covariate adjustment: Statistical inclusion of motion parameters in primary analyses

Diagram 2: Motion-Conscious Drug Development Pipeline

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Research Reagents for Motion-Conscious Neuroimaging

Reagent Category	Specific Tools	Primary Function	Implementation Considerations
Quality Control Metrics	Framewise displacement, DVARS, QC exclusion criteria	Quantify motion severity and data quality	Standardized thresholds needed for cross-study comparison
Motion Correction Algorithms	FSL, SPM, AFNI, HCP pipelines	Retrospective motion artifact reduction	Different efficacy across packages and processing streams
Real-time Monitoring	Prospective motion correction, camera-based tracking	Prevent motion during acquisition	Hardware compatibility, sequence modifications required
Behavioral Assessments	CBCL, executive function tasks, cognitive batteries	Characterize participant cognitive profile	Important for covariate adjustment and sample characterization
Statistical Packages	R, Python with specialized neuroimaging libraries	Model motion as covariate, analyze motion-behavior relationships	Custom scripts often needed for specific study designs

The evidence from large-scale datasets consistently demonstrates that in-scanner head motion is not merely a technical confound but reflects meaningful individual differences in executive functioning and behavioral regulation. The ABCD Study reveals systematic relationships between transdiagnostic symptom domains and motion patterns in children, while UK Biobank data shows motion stability across time and its association with broader health traits in adults. Together, these findings highlight the critical importance of adopting motion-conscious approaches in neuroimaging research, particularly in clinical trials and drug development contexts where exclusion of "high-movers" may bias results and limit generalizability. Future directions should include the development of standardized motion assessment protocols, improved real-time correction technologies, and analytical frameworks that account for motion as an informative variable rather than simply a nuisance to be removed.

The validation of neuroimaging endpoints represents a critical frontier in the advancement of clinical trials for neurological and psychiatric disorders. This technical guide examines the transformative process of reconceptualizing in-scanner head motion—traditionally considered a nuisance artifact—into a valuable biomarker of executive function and cognitive integrity. Drawing upon contemporary research, we detail rigorous methodological frameworks for quantifying, standardizing, and interpreting motion-related data, providing clinical researchers and drug development professionals with a comprehensive roadmap for biomarker validation. The integration of these endpoints promises to enhance patient stratification, treatment monitoring, and outcome assessment in clinical trials, ultimately accelerating the development of novel therapeutics for conditions affecting cognitive control.

The Head Motion Challenge: From Artifact to Insight

In-scanner head motion has historically been treated as a technical confound in functional magnetic resonance imaging (fMRI) data acquisition, with researchers employing various retrospective and prospective correction methods to mitigate its contaminating effects on the blood oxygen level-dependent (BOLD) signal [3]. Conventional processing pipelines routinely identify and exclude "invalid scans" flagged as motion outliers, often discarding data from participants exceeding predefined movement thresholds (e.g., framewise displacement >0.5mm) [3] [88].

However, emerging evidence suggests that head motion may reflect inherent neurobiological traits rather than mere technical artifact. Studies demonstrate that the propensity for head motion remains stable within individuals across separate MRI sessions, indicating it may constitute a reliable trait characteristic [3]. Furthermore, motion severity correlates with age and clinical status, potentially carrying diagnostic information relevant to neurological and psychiatric populations [3].

The Executive Function Connection

The relationship between head motion and executive functions (EFs)—particularly inhibition and cognitive flexibility—provides a compelling biological rationale for treating motion as a biomarker rather than artifact. Research with healthy older adults reveals that:

• Greater head motion is significantly associated with poorer performance on standardized tasks of inhibition and set-shifting [3].
• This relationship is domain-specific, as head motion shows no significant correlation with working memory, verbal memory, or processing speed performance [3].
• The association persists after controlling for age, suggesting motion captures variance in cognitive control beyond general age-related decline [3].

These findings raise critical concerns about systematic exclusion biases in neuroimaging studies, as current motion control practices may disproportionately eliminate data from older adults with lower executive functioning, potentially skewing sample representations and limiting generalizability of findings [3].

Table 1: Cognitive Correlates of In-Scanner Head Motion in Older Adults

Cognitive Domain	Relationship with Head Motion	Statistical Significance
Inhibition	Negative correlation	Significant (p<0.05)
Cognitive Flexibility	Negative correlation	Significant (p<0.05)
Processing Speed	No significant correlation	Not Significant
Verbal Memory	No significant correlation	Not Significant
Working Memory	No significant correlation	Not Significant

Methodological Framework for Motion Biomarker Validation

Quantification and Standardization

Establishing head motion as a validated biomarker requires standardized quantification approaches. The following parameters represent essential metrics for characterization:

• Framewise Displacement (FD): Quantifies volume-to-volume changes in head position, typically calculated as the root mean square of translational and rotational displacements [3]. Studies commonly employ FD thresholds of 0.2-0.5mm for censoring [88].
• Invalid Scan Count: The number of volumes flagged as motion outliers based on FD thresholds [3].
• Motion Stability Metrics: Intra-individual consistency in motion parameters across multiple scanning sessions [3].

Standardization across sites and scanners is essential for multi-center clinical trials. The Alzheimer's Disease Neuroimaging Initiative (ADNI) provides a exemplary framework for implementing standardized imaging protocols across multiple research sites [89] [90] [91].

Integrated Neuroimaging Biomarkers

Beyond motion metrics alone, integrative approaches combining multiple neuroimaging parameters show promise for creating robust biomarkers. The i-ECO (integrated-Explainability through Color Coding) methodology demonstrates how combining functional connectivity, network analysis, and spectral features enhances discriminatory power for psychiatric conditions [88].

Key imaging parameters for integrated biomarkers include:

• Regional Homogeneity (ReHo): Measures local connectivity by assessing the similarity of the time series of a given voxel to its nearest neighbors [88].
• Eigenvector Centrality (ECM): A graph-based measure of network influence, quantifying a node's connection to other highly connected nodes [88].
• Fractional Amplitude of Low-Frequency Fluctuations (fALFF): Reflects the power of spontaneous low-frequency oscillations in the BOLD signal [88].

Table 2: Multimodal Neuroimaging Parameters for Biomarker Development

Parameter	Biological Interpretation	Analysis Method	Clinical Relevance
Head Motion	Executive function capacity, cognitive control	Framewise displacement, outlier count	Patient stratification, treatment response
Regional Homogeneity (ReHo)	Local neural synchronization	Kendall's Coefficient of Concordance	Local network integrity
Eigenvector Centrality (ECM)	Network influence and connectivity	Graph theory analysis	Information integration capacity
fALFF	Spontaneous neural activity	Spectral analysis	Neural baseline activity level

Technical Implementation and Experimental Protocols

Data Acquisition and Preprocessing Pipeline

Robust biomarker validation requires meticulous attention to data acquisition and preprocessing methodologies. The following protocol, adapted from established frameworks [88], ensures data quality and consistency:

• Acquisition Parameters:

  • Pulse sequence: Gradient-echo EPI for BOLD fMRI
  • Spatial resolution: 3-4mm isotropic
  • Temporal resolution (TR): 2-2.5s
  • Field strength: 3T preferred for optimal signal-to-noise ratio

• Preprocessing Steps:

  • Removal of initial volumes (4-5 volumes) to allow magnetization stabilization
  • Slice timing correction for interleaved acquisition
  • Rigid-body realignment for head motion correction
  • Spatial normalization to standard template (e.g., MNI152)
  • Spatial smoothing (FWHM 6-8mm)
  • Bandpass filtering (0.01-0.1 Hz) to reduce low-frequency drift and high-frequency noise

• Motion-Specific Processing:

  • Regression of 6 rigid-body motion parameters and their derivatives
  • Implementation of censoring (e.g., FD > 0.5mm) for contaminated volumes
  • ANATICOR method for white matter noise regression [88]

Biomarker Development Workflow

Executive Function Assessment Protocol

Linking motion biomarkers to executive function requires standardized cognitive assessment. The within-subject fMRI paradigm investigating unity and diversity of executive functions provides a validated framework [92]:

• Task Design:

  • Four EF tasks derived from the same basic paradigm using identical stimuli
  • Each task paired with a matched control condition to isolate specific EF components
  • Counterbalanced presentation across participants

• EF Task Specifications:

  • Updating: n-back task (1-back vs. 0-back control)
  • Inhibition: Stroop task (incongruent vs. congruent stimuli)
  • Switching: Task-switching paradigm (switch vs. repetition trials)
  • Dual-tasking: Psychological Refractory Period (PRP) paradigm (dual-task vs. single-task)

• Imaging Acquisition:

  • Blocked or event-related designs during task performance
  • High-resolution structural scan for anatomical reference
  • Resting-state fMRI for functional connectivity assessment

Executive Function and Motion Correlation

Analytical Approaches for Biomarker Validation

Statistical Framework

Robust statistical methods are essential for establishing motion as a validated biomarker:

• Correlational Analyses:

  • Spearman's Rank-Order correlations for motion-cognition relationships [3]
  • Partial correlations controlling for age, sex, and education
  • Multivariate regression modeling motion as dependent variable

• Neuroimaging Analyses:

  • Conjunction analysis to identify unity across EF tasks [92]
  • Pairwise comparisons to assess diversity between EFs
  • Network-based statistics for functional connectivity alterations

• Machine Learning Integration:

  • Recursive feature elimination for biomarker selection [93]
  • Cross-validated predictive modeling (e.g., logistic regression, random forest)
  • Ensemble methods combining multiple algorithms [93]

Machine Learning Applications

Advanced machine learning approaches significantly enhance biomarker discovery and validation:

• Feature Selection: Recursive feature elimination with cross-validation identifies optimal feature subsets, improving model performance from AUC 0.89 to 0.92 in predictive modeling [93].
• Algorithm Comparison: Multiple algorithms (logistic regression, support vector machines, random forest, XGBoost) should be evaluated for optimal performance [93].
• Shared Features: Identifying features consistently selected across multiple models enhances biomarker reliability [93].

Table 3: Machine Learning Algorithms for Biomarker Validation

Algorithm	Strengths	Limitations	Optimal Use Cases
Logistic Regression	Highly interpretable, good for linear relationships	Limited complex pattern detection	Initial biomarker screening
Random Forest	Handles nonlinear relationships, robust to outliers	Less interpretable, computationally intensive	High-dimensional data
XGBoost	High accuracy, handles missing data	Complex parameter tuning	Final predictive modeling
Support Vector Machines	Effective in high-dimensional spaces	Memory-intensive for large datasets	Small sample sizes

Clinical Trial Implementation

Endpoint Validation Framework

Implementing motion biomarkers in clinical trials requires rigorous validation:

• Analytical Validation:

  • Establish assay precision, accuracy, and sensitivity
  • Determine reference ranges and cutoff values
  • Verify reproducibility across sites and scanners

• Clinical Validation:

  • Demonstrate association with clinical endpoints
  • Establish sensitivity to treatment effects
  • Verify specificity to target pathophysiology

• Utilization Validation:

  • Demonstrate improved decision-making versus standard approaches
  • Establish cost-effectiveness and clinical utility
  • Validate in intended use population

Current Clinical Trial Applications

Several ongoing clinical trials exemplify the application of neuroimaging biomarkers:

• CAR T-Cell Therapy Neurotoxicity: Investigating neuroimaging and blood biomarkers for early identification of immune effector cell-associated neurotoxicity syndrome [89].
• Radiation Therapy Cognitive Effects: Prospective examination of cognitive changes associated with radiation dosage to non-targeted brain tissue [89].
• Alzheimer's Disease Neuroimaging Initiative (ADNI): Multi-site study validating biomarkers for Alzheimer's disease clinical trials [89] [90] [91].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 4: Essential Resources for Motion Biomarker Research

Resource Category	Specific Tools/Solutions	Application/Purpose
Data Processing	AFNI, FSL, SPM, CONN	fMRI preprocessing and analysis
Motion Quantification	Framewise displacement algorithms, ART, FSL_MOTION	Head motion parameter calculation
Quality Control	MRIQC, visual inspection protocols	Data quality assessment
Statistical Analysis	R, Python, SPSS, MATLAB	Statistical modeling and inference
Machine Learning	scikit-learn, TensorFlow, PyTorch	Predictive modeling and classification
Cognitive Testing	E-Prime, PsychoPy, Presentation	Executive function task administration
Data Sharing	NDA, COINS, LORIS	Multi-site data management

The reconceptualization of in-scanner head motion from artifact to biomarker represents a paradigm shift in neuroimaging research with profound implications for clinical trial design and implementation. By establishing standardized methodologies for quantifying and interpreting motion data in the context of executive function, researchers can leverage this readily available metric to enhance patient stratification, treatment monitoring, and outcome assessment in neurological and psychiatric clinical trials.

Future development should focus on:

• Standardization: Establishing consensus guidelines for motion metric calculation and reporting.
• Integration: Developing multimodal biomarkers combining motion with other neuroimaging and biological parameters.
• Validation: Conducting large-scale prospective studies to verify predictive utility across diverse populations.
• Implementation: Creating user-friendly tools for clinical trialists to incorporate motion biomarkers into study designs.

As evidence continues to accumulate linking head motion to executive function deficits across neurological and psychiatric conditions, the validation of this measure as a reliable biomarker promises to enhance the efficiency and success of clinical trials, ultimately accelerating the development of novel therapeutics for conditions affecting cognitive control.

Conclusion

The relationship between executive function and in-scanner head motion is not merely a technical confound but a fundamental consideration for any neuroimaging study targeting cognitive processes. Acknowledging this link is paramount for developing reliable neuroimaging biomarkers for drug development. The path forward requires a multi-faceted approach: integrating robust, state-of-the-art motion tracking and correction methodologies, adopting rigorous validation frameworks like SHAMAN to quantify motion's impact on specific research questions, and establishing universal reporting standards. Future research must focus on refining these techniques to enhance the translational power of neuroimaging, ensuring that discoveries in the scanner accurately reflect brain function and not just the ability to remain still. This will ultimately accelerate the development of safer and more effective therapeutics for cognitive disorders.

References

• 1. Executive Function: What It Is, How To Improve & Types [https://my.clevelandclinic.org/health/articles/executive-function]
• 2. Executive Functions - PMC - PubMed Central - NIH [https://pmc.ncbi.nlm.nih.gov/articles/PMC4084861/]
• 3. The association between head motion during functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10299743/]
• 4. The association between head motion during functional ... [https://experts.arizona.edu/en/publications/the-association-between-head-motion-during-functional-magnetic-re/]
• 5. Executive functions [https://en.wikipedia.org/wiki/Executive_functions]
• 6. Autistic traits are associated with lower perceived executive ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12486804/]
• 7. Impact of In-Scanner Head Motion on Multiple Measures of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3746318/]
• 8. Executive Function Deficits [https://www.asha.org/practice-portal/clinical-topics/executive-function-deficits/?srsltid=AfmBOopLuwqxM1RfHyz1EfOSe-sySfhUt6Z5H05raGzpIhPWzZ-Dh5ko]
• 9. Executive Function Processes Predict Mobility Outcomes in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3927159/]
• 10. Data-Driven Approaches to Executive Function Performance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9907731/]
• 11. Executive function in children with neurodevelopmental ... [https://www.nature.com/articles/s41562-024-02000-9]
• 12. Executive Functioning Profiles in Neurodevelopmental ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11352683/]
• 13. Association between the performance of executive function ... [https://www.sciencedirect.com/science/article/abs/pii/S0165032724011959]
• 14. Longitudinal relations between impaired executive function ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9790505/]
• 15. Predicting executive functioning from walking features in ... [https://www.nature.com/articles/s41598-024-80144-4]
• 16. Neural efficiency hypothesis [https://en.wikipedia.org/wiki/Neural_efficiency_hypothesis]
• 17. Neural efficiency as a function of task demands - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3907682/]
• 18. Neural Efficiency and Acquired Motor Skills: An fMRI Study ... [https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.02752/full]
• 19. Superior performance and neural efficiency: The impact of ... [https://www.sciencedirect.com/science/article/abs/pii/S0361923006000530]
• 20. Review Intelligence and neural efficiency [https://www.sciencedirect.com/science/article/abs/pii/S0149763409000591]
• 21. Mental workload and neural efficiency quantified in the ... [https://www.nature.com/articles/s41598-017-05378-x]
• 22. The Effect of Physical Activity on Executive Functions in the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12293948/]
• 23. fMRI insights into differential brain activation, executive ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12194046/]
• 24. The effects of a short exercise bout on executive functions ... [https://www.nature.com/articles/s41598-024-79685-5]
• 25. fMRI insights into differential brain activation, executive ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0327163]
• 26. Motion impact score for detecting spurious brain-behavior ... [https://www.nature.com/articles/s41467-025-63661-2]
• 27. Spurious but systematic correlations in functional connectivity ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3254728/]
• 28. Spurious correlations in simultaneous EEG-fMRI driven by ... [https://www.sciencedirect.com/science/article/abs/pii/S1053811916002366]
• 29. Executive function, limbic circuit dynamics and repetitive ... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1508077/full]
• 30. Power‐law dynamics in neuronal and behavioral data ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6869015/]
• 31. An evaluation of the efficacy, reliability, and sensitivity ... [https://www.sciencedirect.com/science/article/pii/S1053811917310972]
• 32. Task and resting state fMRI modelling of brain-behavior ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12593242/]
• 33. Typicality of functional connectivity robustly captures motion ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7670643/]
• 34. IQMs for functional images - MRIQC's - Read the Docs [https://mriqc.readthedocs.io/en/latest/iqms/bold.html]
• 35. Head Motion in Diffusion Magnetic Resonance Imaging [https://pmc.ncbi.nlm.nih.gov/articles/PMC11814480/]
• 36. Exploring biomarkers of processing speed and executive ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9460835/]
• 37. Eye movement as a biomarker of impaired organizational ... [https://www.nature.com/articles/s41598-021-97885-1]
• 38. Constructing Carbon Fiber Motion-Detection Loops for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4283719/]
• 39. Carbon-wire loop based artifact correction outperforms ... [https://www.sciencedirect.com/science/article/pii/S1053811915009787]
• 40. Using Carbon Wire Loops (CWLs) to Correct for Artifacts in ... [https://pressrelease.brainproducts.com/cwl-ux-maastricht/]
• 41. Artifact Reduction in Simultaneous EEG-fMRI: A Systematic ... [https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2021.622719/full]
• 42. NeuXus open-source tool for real-time artifact reduction in ... [https://www.sciencedirect.com/science/article/pii/S1053811923005049]
• 43. The Executive Functioning Paradox in Substance Use Disorders [https://pmc.ncbi.nlm.nih.gov/articles/PMC9687962/]
• 44. The impact of drugs of abuse on executive function [https://pmc.ncbi.nlm.nih.gov/articles/PMC6097763/]
• 45. 2.1. EEG-fMRI Artifact Reduction Techniques [https://bio-protocol.org/exchange/minidetail?id=9221834&type=30]
• 46. An optimized framework for simultaneous EEG-fMRI at 7T ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12587055/]
• 47. Using Temporal ICA to Selectively Remove Global Noise ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6237431/]
• 48. Using temporal ICA to selectively remove global noise ... [https://www.sciencedirect.com/science/article/abs/pii/S1053811918303963]
• 49. Comparison of PCA approaches for very large group ICA [https://pmc.ncbi.nlm.nih.gov/articles/PMC4554805/]
• 50. Comparison of PCA approaches for very large group ICA [https://www.sciencedirect.com/science/article/abs/pii/S1053811915004292]
• 51. How to clean fMRI data with FIX-ICA [https://www.caroline-nettekoven.com/post/ica-cleaning/]
• 52. An automated and flexible tool for comprehensive fMRI ... [https://pubmed.ncbi.nlm.nih.gov/40851911/]
• 53. Interactive Visual Analysis of Functional Brain Connectivity [https://pmc.ncbi.nlm.nih.gov/articles/PMC5585064/]
• 54. Correction of Physiological Artifacts in Multi‐Echo fMRI Data ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12183604/]
• 55. Prospective Motion Correction in Functional MRI - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC5427003/]
• 56. Reducing the effects of motion artifacts in fMRI: A structured ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8934405/]
• 57. Real-time fetal head motion tracking for prospective ... [https://archive.ismrm.org/2025/3611.html]
• 58. Accelerated Navigator for Rapid ∆B0 Field Mapping for Real ... [https://pubmed.ncbi.nlm.nih.gov/40905334/]
• 59. Real-time fMRI using multi-band echo-volumar imaging ... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1543206/full]
• 60. The association between head motion during functional ... [https://www.sciencedirect.com/science/article/pii/S2666956022000095]
• 61. Comparison of prospective and retrospective motion ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8635810/]
• 62. Comparison of prospective and retrospective motion ... [https://pubmed.ncbi.nlm.nih.gov/34490929/]
• 63. Test-retest of automated segmentation with different motion ... [https://www.sciencedirect.com/science/article/pii/S1053811919310857]
• 64. Comparison of MRI head motion indicators in ... [https://www.nature.com/articles/s41598-024-79827-9]
• 65. Less is more: balancing noise reduction and data retention ... [https://www.sciencedirect.com/science/article/pii/S1053811923001180]
• 66. Balancing Data Quality and Bias: Investigating Functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11717557/]
• 67. High Temporal Resolution Motion Estimation Using a Self ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6153080/]
• 68. The association between head motion during functional ... [https://pubmed.ncbi.nlm.nih.gov/37377763/]
• 69. Development and evaluation of image preprocessing ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12514937/]
• 70. Deep learning for sports motion recognition with a high ... [https://www.nature.com/articles/s41598-025-22701-z]
• 71. Brief mock-scan training reduces head motion during real ... [https://www.sciencedirect.com/science/article/pii/S187892932300049X]
• 72. Real-time feedback reduces participant motion during task ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9882243/]
• 73. Behavioral interventions for reducing head motion during MRI ... [https://pubmed.ncbi.nlm.nih.gov/29337280/]
• 74. The common factor of executive functions measures nothing ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11143038/]
• 75. Assessment of the Relationship Between Executive ... [https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.742184/full]
• 76. Overestimating or underestimating risk? [https://quant.stackexchange.com/questions/27868/overestimating-or-underestimating-risk]
• 77. Overestimating Outcome Rates: Statistical Estimation ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC1955272/]
• 78. Motion impact score for detecting spurious brain-behavior ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12479937/]
• 79. A multi-measure approach for assessing the performance ... [https://www.sciencedirect.com/science/article/abs/pii/S0730725X21001958#!]
• 80. Multi‐Metric Approach for the Comparison of Denoising ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12044599/]
• 81. Systematic evaluation of fMRI data-processing pipelines for ... [https://www.nature.com/articles/s41467-024-48781-5]
• 82. DeepPrep: an accelerated, scalable and robust pipeline for ... [https://www.nature.com/articles/s41592-025-02599-1]
• 83. Transdiagnostic Symptom Domains Have Distinct Patterns of... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12163392/]
• 84. Head motion in the UK Biobank imaging subsample [https://pmc.ncbi.nlm.nih.gov/articles/PMC11249925/]
• 85. Opportunities for use of neuroimaging in de-risking drug ... [https://www.nature.com/articles/s41386-024-01970-8]
• 86. Review Neuroimaging Biomarkers for Drug Discovery and ... [https://www.sciencedirect.com/science/article/pii/S0006322324000362]
• 87. Use of functional imaging across clinical phases in CNS drug ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3731782/]
• 88. The colors of our brain: an integrated approach for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9107439/]
• 89. UCSD Neuroimaging Clinical Trials for 2025 — San Diego [https://clinicaltrials.ucsd.edu/neuroimaging]
• 90. UC Irvine Neuroimaging Clinical Trials — Orange County, CA [https://clinicaltrials.icts.uci.edu/neuroimaging]
• 91. UCLA Neuroimaging Clinical Trials for 2025 — Los Angeles [https://ucla.clinicaltrials.researcherprofiles.org/neuroimaging]
• 92. Characterising the unity and diversity of executive functions ... [https://www.nature.com/articles/s41598-022-11433-z]
• 93. Machine learning approaches for biomarker discovery to ... [https://www.nature.com/articles/s41598-023-42338-0]