Global Signal Regression in fMRI: A Comprehensive Guide to Motion Artifact Reduction for Robust Brain Research

Abstract

Global Signal Regression (GSR) remains one of the most contentious preprocessing steps in resting-state functional MRI (rs-fMRI), presenting researchers with a critical dilemma. This article provides a definitive guide for scientists and drug development professionals navigating the complexities of GSR for motion artifact reduction. We explore the fundamental debate surrounding the neural versus non-neural origins of the global signal and establish the direct link between head motion and systematic artifacts in functional connectivity. The guide details practical implementation methodologies, including integration with complementary denoising techniques like ICA-FIX and motion censoring. We address common troubleshooting scenarios, such as interpreting negative correlations and handling divergent results, while presenting rigorous validation frameworks. By synthesizing evidence from large-scale datasets like the Human Connectome Project and ABCD Study, this resource empowers researchers to make informed, context-dependent decisions about GSR application to enhance the validity and reproducibility of their neuroimaging findings.

The GSR Debate: Unraveling the Neural and Non-Neal Origins of the Global Signal

In functional magnetic resonance imaging (fMRI), the global signal (GS) is defined as the average blood-oxygen-level-dependent (BOLD) signal across the entire brain at each time point [1]. Its interpretation, however, remains a subject of ongoing debate within the neuroscience community. The GS is not a unitary phenomenon but rather a composite of multiple underlying processes, both neural and non-neural, which presents a fundamental challenge for its use in preprocessing pipelines.

The core controversy surrounding global signal regression (GSR) stems from this dual nature: while it effectively removes certain artifacts, it may also eliminate biologically meaningful neural fluctuations. This document delineates the precise components of the global signal, evaluates the effects of regressing it out, and provides detailed protocols for researchers, particularly those focused on motion artifact reduction.

Composition of the Global Signal

The global signal is a confluence of several physiological and neural sources. Understanding its composition is critical for interpreting the consequences of its regression.

Table: Constituent Components of the Global Signal

Component Type	Specific Source	Contribution to GS	Primary Evidence
Systemic Physiological	Respiration (Respiration Volume Per Time - RVT)	Significant fraction; shows strong spatial consistency with GS topography [1]	Simultaneous EEG-fMRI & respiratory recording [2] [1]
	Cardiac Pulsation	Present, but weaker spatial relationship with GS compared to respiration [1]	Simultaneous EEG-fMRI & cardiac recording [2]
Non-Neural Artifacts	Head Motion	Major source, especially with large movements [3]	Motion parameter estimation; "JumpCor" method development [3]
	Scanner-related Fluctuations	Confounding variance	Preprocessing control techniques [4]
Neural Activity	Arousal & Vigilance States	Linked to GS dynamics [1]	Studies of anesthetic-induced unconsciousness [5]
	Spontaneous Neural Fluctuations	Preserved component within specific frequency ranges (e.g., alpha, beta) [2]	Simultaneous EEG-fMRI; GSR preserves EEG-derived connectivity [2]

Key Quantitative Findings on Signal Origins

Recent studies have quantitatively assessed the contribution of various sources to the global signal:

• Physiological vs. Neural Sources: One study using simultaneous EEG-fMRI concluded that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations [2].
• Spatial Overlap with Respiration: Research with the Human Connectome Project data (N=770) demonstrated a strong spatial consistency between the GS and respiration (RVT) topography, with notable regional specificity. The overlap was particularly strong in the limbic, default mode, and salience networks [1].
• Preservation of Neural Signals: Despite the removal of global variance, GSR has been shown to preserve connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges [2].

Effects and Controversies of Global Signal Regression

The application of GSR has distinct, and sometimes contradictory, effects on fMRI data analysis, influencing the interpretation of functional connectivity and its relationship to behavior.

Impact on Functional Connectivity and Motion Artifacts

GSR is a powerful technique for mitigating certain confounds in functional connectivity analysis.

• Motion Artifact Reduction: GSR significantly reduces artifactual connectivity arising from head motion [3]. This is particularly valuable in studies involving populations prone to movement, such as infants or clinical patients.
• Physiological Noise Removal: It effectively diminishes connectivity patterns related to physiological signals like heart rate and breathing fluctuations [2].
• Anesthetic-Specific Effects: The impact of GSR is not uniform. Research on anesthesia showed that GSR differentially affects brain activity patterns during propofol- and sevoflurane-induced unconsciousness. Sevoflurane-induced connectivity changes were particularly sensitive to GSR [5].

Controversial Aspects and Considerations

The use of GSR is not without significant controversy, primarily concerning the removal of potentially meaningful neural information.

• Removal of Neural Signal: The primary debate hinges on whether GSR removes neurally relevant information alongside noise. Some argue it can alter local and long-range correlations and potentially limit the assessment of connectivity patterns [5].
• Behavioral Relevance: The global signal itself has functional and cognitive relevance. Its topography has been linked to behavioral variables, including cognitive performance and psychiatric problems [1]. Regressing it out may therefore remove these behaviorally relevant signals.
• Dependence on Analysis Goals: The utility of GSR may depend on the specific research question. For studies focusing on network-specific correlates of behavior or disease, GSR might be beneficial. For investigations into global brain-body integration or arousal, it might be detrimental.

Table: Comparative Effects of Preprocessing with and without GSR

Analysis Metric	Effect of GSR	Implication for Motion Artifact Research
Motion-Related Artifacts	Reduces artifactual connectivity from head motion, respiration, and cardiac cycles [2] [3].	Primary benefit: Directly targets key non-neural noise sources.
Temporal Variability	Decreases similarly between states regardless of GSR in some anesthesia studies [5].	Suggests some temporal dynamics are robust to GSR.
Network Topology	Minimally affects changes under propofol but significantly diminished sevoflurane-related network alterations [5].	Effect is context-dependent (e.g., on anesthetic agent).
Structure-Function Coupling	Varies with the pairwise statistic used for FC; precision-based statistics show strong coupling [6].	GSR's effect interacts with other methodological choices.

Experimental Protocols and Application Notes

Protocol 1: Validating Neural Preservation Post-GSR using Simultaneous EEG-fMRI

This protocol is designed to quantify the neural component retained after GSR, a critical validation step.

1. Data Acquisition:

• Acquire simultaneous EEG-fMRI data during resting state.
• Concurrently record cardiac and breathing signals [2].

2. fMRI Preprocessing (Two Parallel Paths):

• Path A: Standard preprocessing (realignment, normalization, smoothing).
• Path B: Standard preprocessing + Global Signal Regression.

3. Global Signal Calculation:

• For Path A, extract the GS as the mean signal across the whole brain mask [1].

4. Functional Connectivity Analysis:

• For both paths, calculate resting-state functional connectivity matrices.
• Derive EEG-based functional connectivity in alpha/beta bands from the cleaned EEG data.

5. Correlation Analysis:

• Correlate the fMRI-FC matrices from both paths with the EEG-FC matrix.
• Interpretation: A significant positive correlation between GSR-fMRI-FC and EEG-FC indicates preservation of neurally relevant connectivity [2].

Protocol 2: Assessing GSR Efficacy for Motion Artifact Reduction

This protocol leverages a comparative approach to evaluate GSR against other denoising methods.

1. Data Preparation:

• Utilize a dataset including participants with occasional large head motions (e.g., >1 mm frame-to-frame displacement) [3].
• Preprocess data with standard realignment.

2. Multiple Denoising Pipelines:

• Process the data through several parallel pipelines:
  • Pipeline 1: Standard preprocessing only (baseline).
  • Pipeline 2: Standard + Motion Parameter Regression (6-24 regressors).
  • Pipeline 3: Standard + Global Signal Regression.
  • Pipeline 4: Standard + JumpCor (for large motions) [3].
  • Pipeline 5: Combined approaches (e.g., GSR + Motion Regression).

3. Artifact Quantification:

• For each pipeline, calculate QC-FC correlations—the correlation between subject-wise mean motion and edge-wise functional connectivity [4].
• Interpretation: A successful denoising pipeline will show a weaker QC-FC correlation, indicating reduced motion-related artifact in connectivity matrices.

4. Data Quality Metrics:

• Apply quantitative metrics agnostic to QC-FC, such as those evaluating the signal-to-noise ratio in known low-motion periods [4].
• Final Recommendation: Choose the pipeline that optimizes these quality metrics for the specific dataset.

Visualization of Signaling Pathways and Workflows

Global Signal Composition and GSR Effects

Experimental Protocol for GSR Validation

The Scientist's Toolkit: Research Reagents & Materials

Table: Essential Reagents and Tools for GSR Research

Tool/Reagent	Function/Description	Example Use Case
Simultaneous EEG-fMRI System	Records electrophysiological (EEG) and hemodynamic (fMRI) data concurrently to validate neural signals.	Quantifying neural information preserved after GSR [2].
Physiological Monitoring (Respiratory Belt, Pulse Oximeter)	Records respiration (RVT) and cardiac (HR) signals for physiological noise modeling.	Mapping spatial contribution of physiology to GS [1].
fMRIPrep	Robust, standardized pipeline for fMRI data preprocessing.	Ensuring reproducible preprocessing before GSR application [5].
PySPI Package	Computes 239 pairwise interaction statistics for functional connectivity.	Benchmarking GSR's effect across different FC metrics [6].
JumpCor Algorithm	Models signal baseline changes after large head "jumps" to reduce motion artifacts.	Correcting residual motion artifacts in conjunction with/or as an alternative to GSR [3].
Human Connectome Project (HCP) Data	Publicly available, high-quality multimodal neuroimaging dataset.	Methodological benchmarking and testing hypotheses in large samples (N=770) [1] [6].
CANONICC Correlation Analysis	Multivariate method to find shared patterns between brain topography and behavior.	Investigating overlap in GS-behavior and respiration-behavior relationships [1].

Head motion presents a fundamental methodological challenge for functional magnetic resonance imaging (fMRI) studies, particularly in the investigation of functional connectivity. Even sub-millimeter movements introduce systematic artifacts that can alter the interpretation of blood oxygenation level-dependent (BOLD) signal correlations [7]. As in-scanner motion frequently correlates with key variables of interest such as age, clinical status, cognitive ability, and symptom severity, it introduces a pervasive confound that can bias study conclusions [7] [8]. The resting-state fMRI (rs-fMRI) paradigm is especially vulnerable to these artifacts because the spontaneous BOLD fluctuations of interest are remarkably small—typically just a few percent or less—making them susceptible to contamination by minute head movements [8]. Understanding the nature of these motion-induced artifacts, their spatial and temporal characteristics, and methods for their mitigation is therefore essential for generating valid and reproducible neuroimaging findings, particularly within the context of evaluating global signal regression as a potential corrective approach.

Characteristics and Mechanisms of Motion Artifacts

Spatial and Temporal Properties of Motion Artifacts

Head motion during fMRI acquisition produces complex spatiotemporal artifacts that manifest non-uniformly across the brain. The biomechanical constraints of the neck create a characteristic spatial pattern where motion is minimal near the atlas vertebrae (where the skull attaches to the neck) and increases with distance from this pivot point [7]. This results in anterior frontal and orbitofrontal regions exhibiting greater susceptibility to motion artifacts compared to posterior areas [7] [8]. Motion artifacts display distinctive temporal properties, with large movements producing immediate signal drops that scale with motion magnitude, maximal in the volume acquired immediately after movement [7]. Additionally, longer-duration artifacts persisting for 8-10 seconds can occur idiosyncratically, potentially due to motion-related changes in CO₂ from yawning or deep breathing [7].

Table 1: Spatial and Temporal Characteristics of Motion Artifacts

Characteristic	Description	Impact on fMRI Data
Spatial Distribution	Increasing motion with distance from neck pivot point; frontal regions most affected	Regional bias in artifact distribution; distance-dependent connectivity changes
Temporal Signature	Immediate signal drop after movement; potential longer-duration (8-10s) artifacts	Non-linear, time-varying signal changes that complicate correction
Frequency Content	Non-band-limited, contaminating both low and high frequencies	Ineffective removal by standard band-pass filtering (0.01-0.1 Hz)
Spin History Effects	Out-of-plane movements cause variations in longitudinal magnetization recovery	Altered signal intensity due to partial volume effects at tissue boundaries

Impact on Functional Connectivity Metrics

Motion artifacts systematically distort functional connectivity measures in distinctive patterns that can mimic or mask genuine neurobiological phenomena. A consistent finding across studies is that motion inflates short-range connectivity while weakening long-range connectivity [9] [8]. This distance-dependent effect arises because motions cause signal changes that are more similar in adjacent voxels than in distant ones. Additionally, characteristic orientation dependencies have been observed, with increased lateral connectivity at the expense of connectivity in the inferior-superior and anterior-posterior directions [8]. These systematic distortions are particularly problematic in case-control studies where clinical populations (e.g., children, elderly individuals, or patients with neurological disorders) often exhibit different motion profiles than healthy control groups, potentially creating spurious group differences or masking genuine effects [7] [8].

Figure 1: Motion Artifact Impact Pathway. Head motion induces multiple signal artifacts that systematically bias connectivity measures, potentially leading to erroneous group differences in fMRI studies.

Quantifying Head Motion

Framewise Displacement and Related Metrics

In-scanner motion is typically quantified using parameters derived from volume-based realignment procedures. The most common approach involves rigid-body alignment of each volume to a reference image, producing six realignment parameters (RPs)—three translations (x, y, z) and three rotations (pitch, yaw, roll) [7]. These parameters are frequently summarized as Framewise Displacement (FD), which computes the relative displacement of each volume compared to the previous one [7] [10]. Different methods exist for calculating FD, with the formulation by Jenkinson et al. (implemented in FSL) demonstrating superior alignment with voxel-specific displacement measures [7]. It is important to note that FD measures are limited by the temporal resolution of the acquisition sequence and cannot effectively capture within-volume motion, which becomes particularly relevant with modern multiband sequences featuring very short repetition times [7].

Table 2: Motion Quantification Metrics in fMRI

Metric	Calculation	Interpretation	Limitations
Realignment Parameters (RPs)	3 translations + 3 rotations from rigid-body registration	Direct measure of volume-to-volume movement	Does not capture non-linear/spin history effects
Framewise Displacement (FD)	Derivative of RPs; summarizes overall movement between volumes	Comprehensive scalar metric of volume-to-volume motion	Varies with TR; difficult to compare across sequences
DVARS	Root mean square of voxel-wise signal differences between volumes	Measures overall BOLD signal change potentially due to motion	Sensitive to both motion and true neural signal changes
Voxel-Specific FD	Displacement computed directly from image header for each voxel	Accounts for spatial variation in motion effects	Computationally intensive; rarely used in practice

Motion Correction Methodologies

Retrospective Correction Approaches

Retrospective correction methods applied during data preprocessing represent the primary defense against motion artifacts in fMRI research. These approaches include:

• Regression-based methods: Nuisance regressors derived from realignment parameters are included in general linear models to account for motion-related variance. Expanded models incorporating temporal derivatives and quadratic terms of realignment parameters better capture delayed and non-linear motion effects [7] [9]. The addition of these expanded terms has been shown to diminish motion-induced artifacts more effectively than basic realignment parameter regression alone [9].

• Volume censoring (scrubbing): Identifies and removes individual volumes exceeding specific motion thresholds (typically FD > 0.2-0.5 mm) [10] [9]. This approach directly targets large, transient motion artifacts but reduces temporal degrees of freedom and can introduce discontinuities in the time series [9].

• Tissue-based nuisance regression: Incorporates signals from regions not expected to contain BOLD fluctuations of neuronal origin, specifically white matter and cerebrospinal fluid (CSF) [9]. Two primary approaches exist: the mean signal method (averaging across all voxels in tissue masks) and aCompCor (using principal components analysis to extract multiple noise components from these regions) [9]. Empirical evidence demonstrates that aCompCor more effectively attenuates motion artifacts and enhances connectivity specificity compared to mean signal regression [9].

• Physiological noise correction: Methods like RETROICOR utilize external measurements of cardiac and respiratory cycles to model and remove physiological fluctuations [11]. In multi-echo fMRI, RETROICOR can be applied to individual echoes or composite data, with both approaches showing significant noise reduction, particularly in moderately accelerated acquisitions [11].

Global Signal Regression: Controversies and Applications

Global signal regression (GSR)—removing the mean signal across all brain voxels from each timepoint—represents one of the most debated preprocessing techniques in rs-fMRI [12] [13]. As a "catch-all" approach, GSR effectively reduces global artifacts arising from motion and respiration but also removes globally distributed neural information and introduces negative correlations between brain regions [12] [14]. The global signal comprises contributions from multiple sources, with low-frequency drifts, motion, and physiological noise collectively explaining approximately 93% of its variance in minimally processed data [12]. Notably, recent evidence suggests that GSR can strengthen associations between functional connectivity and behavior, with studies reporting 40-47% increases in behavioral variance explained by whole-brain connectivity measures after GSR application [14]. This enhancement appears particularly beneficial for task performance measures compared to self-reported traits [14].

Figure 2: Global Signal Regression Effects. GSR has dual effects—reducing global artifacts while potentially removing neural signal of interest—leading to ongoing controversy in the field.

Experimental Protocols for Motion Correction

Comprehensive Preprocessing Pipeline

Based on current evidence, an optimal motion correction strategy employs a multi-pronged approach combining several methodologies:

• Volume realignment: Compute six rigid-body realignment parameters through registration to a reference volume.

• Nuisance regression: Include 24 motion regressors (6 basic parameters + temporal derivatives + quadratic terms) [9]. Additionally, implement aCompCor to extract noise components from white matter and CSF masks rather than simple mean signal regression [9].

• Global signal consideration: Evaluate results with and without GSR, particularly when investigating brain-behavior relationships [13] [14]. Document both analytical paths for transparency.

• Temporal filtering: Apply high-pass filtering (typically >0.008 Hz) to remove slow drifts, with careful consideration of potential motion-smearing effects [10].

• Volume censoring: Identify and scrub volumes with FD exceeding 0.2-0.5 mm, depending on acquisition parameters and research questions [10] [9]. Note that scrubbing may provide limited additional benefit when aCompCor has been implemented [9].

• Multi-echo acquisition: When available, acquire multi-echo data and implement dedicated processing pipelines (e.g., ME-ICA) to differentiate BOLD from non-BOLD components [11].

Table 3: Protocol Comparison for Motion Artifact Reduction

Method	Protocol Details	Performance	Limitations
aCompCor	Extract 5-10 principal components from WM/CSF masks; include as nuisance regressors	Superior to mean signal regression for motion reduction and specificity	May require careful component selection
Extended Motion Regression	24 regressors (6 RPs + derivatives + quadratics)	Better captures non-linear motion effects than basic regression	High collinearity among regressors
GSR + Extended Regression	Global signal removal combined with expanded motion regressors	Effective artifact reduction; enhanced brain-behavior correlations	Introduces negative correlations; removes neural signal
Multi-echo ICA	Combine echoes; decompose with ICA; classify BOLD vs. non-BOLD components	Effective physiological noise removal without external recordings	Requires specialized sequences; computationally intensive

Acquisition Parameter Optimization

Beyond retrospective correction, acquisition parameters significantly influence motion artifact severity. Multiband acceleration factors and flip angles modulate data quality, with moderate acceleration (multiband factors 4-6) and optimized flip angles (∼45°) providing favorable trade-offs between acquisition speed and artifact vulnerability [11]. Additionally, scan duration directly impacts data quality and functional connectivity reliability, with evidence suggesting that longer scans (≥20 minutes) improve phenotypic prediction accuracy in brain-wide association studies [15]. The total scan duration (sample size × scan time per participant) demonstrates a logarithmic relationship with prediction accuracy, indicating interchangeability between sample size and scan time up to a point [15]. Cost-benefit analyses suggest that 30-minute scans are typically most cost-effective, yielding approximately 22% savings compared to 10-minute scans while maintaining prediction performance [15].

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Analytical Tools for Motion Correction Research

Tool/Resource	Function	Implementation Considerations
Framewise Displacement (FD)	Quantifies volume-to-volume head motion	Use Jenkinson formulation for voxel-specific accuracy; standardize for TR differences
aCompCor	Extracts multiple noise components from WM/CSF	Prefer over mean signal regression; optimal number of components (typically 5-10) varies by dataset
Global Signal Regression (GSR)	Removes whole-brain mean signal from time series	Apply with and without GSR; particularly beneficial for brain-behavior associations
Volume Censoring	Removes high-motion volumes from analysis	Threshold ~0.2-0.5mm FD; may be redundant with aCompCor
Multi-echo fMRI	Acquires data at multiple TEs to separate BOLD/non-BOLD	Enables ME-ICA; effective for physiological noise removal
RETROICOR	Models physiological noise using external recordings	Requires cardiac/respiratory monitoring; compatible with multi-echo sequences

Motion artifacts remain a persistent challenge in fMRI research with particular relevance for studies investigating global signal regression. The complex spatiotemporal properties of motion-induced signal changes necessitate multi-faceted correction approaches rather than reliance on any single methodology. While GSR demonstrates efficacy in enhancing brain-behavior associations, its mechanism—whether through improved artifact removal or alternative processes—requires further elucidation. Emerging acquisition techniques including multi-echo fMRI and real-time motion correction hold promise for next-generation artifact mitigation. Regardless of methodological choices, complete reporting of motion quantification metrics and correction procedures is essential for interpretation and replication across studies. As the field moves toward consensus practices, researchers should consider their specific experimental questions and population characteristics when selecting from the available motion correction toolkit, recognizing that different processing approaches likely reveal complementary insights into brain function [13] [6].

In resting-state functional magnetic resonance imaging (rs-fMRI) research, the global signal (GS) is operationally defined as the average time course of signal intensity across all voxels in the brain [12]. For years, the prevailing view considered the GS primarily a source of non-neuronal noise dominated by physiological artifacts from motion, respiration, and cardiac activity [16]. Consequently, global signal regression (GSR) became a widely adopted, though contentious, preprocessing step to remove these putative artifacts [13].

However, a paradigm shift is underway. A growing body of evidence from multimodal studies demonstrates that the GS carries fundamental biological significance, with direct links to widespread neural activity and the regulation of arousal levels [17]. This application note synthesizes current evidence and provides practical methodologies for researchers investigating the neural foundations of the global signal, reframing it from a nuisance to a critical component of brain function.

Neural and Physiological Basis of the Global Signal

Key Evidence for the Neural Origins of the Global Signal

Table 1: Key Evidence Supporting the Neural Basis of the Global Signal

Evidence Type	Key Finding	Experimental Support
Direct Electrophysiology	fMRI-based GS correlates with infraslow (<0.1 Hz) fluctuations in local field potentials and broadband EEG power [17].	Simultaneous fMRI-electrophysiology in macaques and humans [17] [18].
Focal Inactivation	Inactivation of the cholinergic nucleus basalis of Meynert leads to regionally specific suppression of the GS ipsilaterally [18].	Pharmacological inactivation in non-human primates [18].
Metabolic Studies	Global signal amplitude is linked to changes in baseline glucose metabolism, reflecting overall energy demand [18].	Positron Emission Tomography (FDG-PET) and magnetic resonance spectroscopy [18].
Arousal & Vigilance	GS amplitude fluctuations are correlated with changes in electroencephalographic (EEG) measures of vigilance and arousal [17] [18].	Simultaneous EEG-fMRI studies in humans [18].

Signaling Pathways and Physiological Correlates

The global signal is not a mere epiphenomenon but is integrated within a complex brain-body axis. It is closely coupled with low-frequency oscillations in physiological signals like respiration and heart rate, which themselves exhibit infraslow rhythms [17]. This coupling is believed to be facilitated by infraslow neural activity, which provides a temporal structure that synchronizes bodily and brain-wide neural signals, potentially through phase-based mechanisms [17]. This integrated system plays a psychophysiological role in mediating the level of arousal.

Figure 1: Signaling pathways linking subcortical nuclei, infraslow neural activity, the global signal, and its physiological and cognitive correlates. NBM: Nucleus Basalis of Meynert.

Functional Significance: Arousal and Behavior

The Global Signal as a Mediator of Arousal

Evidence from combined EEG-fMRI studies strongly supports a link between the GS and arousal. The amplitude of the GS fluctuates with EEG-measured changes in vigilance, suggesting it tracks the brain's overall arousal state [17] [18]. This is physiologically plausible given the GS's association with subcortical neuromodulatory systems (e.g., cholinergic, serotoninergic) that are known to regulate cortical excitability and arousal levels on a global scale [17].

Behavioral Relevance and Dynamic Topography

The GS is not uniformly represented across the brain; it exhibits a dynamic topography—a spatially organized pattern of variation that recapitulates well-established large-scale functional networks [18] [19].

Table 2: Summary of GS Topography and Its Behavioral Relevance

Network/Region in GS Topography	Associated Behavioral and Cognitive Correlates
Frontoparietal Control Network	Positive association with positive life outcomes: picture vocabulary, temporal discounting, life satisfaction [18].
Default Mode & Dorsal Attention Networks	Represented in secondary topographical components; anticorrelation pattern [18].
Sensorimotor & Visual Networks	Negative association with positive life outcomes in the primary canonical variate [18].
Overall Positive-Negative Axis	A topographical pattern (high Frontoparietal, low Sensorimotor/Visual) is linked to an axis of positive psychological function and life outcomes versus negative ones like aggressive behavior [18].

This topography is behaviorally relevant. Canonical Correlation Analysis (CCA) of data from the Human Connectome Project revealed that individual differences in GS topography are significantly related to a broad axis of positive and negative life outcomes and psychological function [18] [19]. Furthermore, the utilitarian value of the GS is highlighted by studies showing that GSR can strengthen the association between resting-state functional connectivity and behavioral measures, improving the predictive power of neuroimaging data [20].

Experimental Protocols

Protocol 1: Mapping Global Signal Topography and Relating it to Behavior

This protocol outlines the steps to derive individual maps of GS topography and relate them to behavioral phenotypes using a large dataset like the Human Connectome Project (HCP) [18].

1. Data Acquisition and Preprocessing:

• Acquire high-temporal-resolution rs-fMRI data (e.g., HCP-style: 4 runs of 15 minutes, multiband acquisition).
• Perform standard preprocessing: motion correction, slice-timing correction, alignment to structural images, and surface-based mapping.
• Optional: Apply nuisance regression (e.g., white matter, cerebrospinal fluid signals) before GS calculation if focusing on "cleaned" neural components.

2. Global Signal Beta Map Calculation:

• For each subject and run, compute the global signal (GS) by averaging the time series across all cortical surface vertices [18].
• For each vertex on the cortical surface, run a linear regression where the vertex's time series is the dependent variable and the whole-brain GS is the independent variable.
• The resulting beta coefficient for the GS at each vertex represents the strength and direction of the GS's expression at that location.
• Average the beta maps across all runs for each subject to create a stable subject-level GS beta map.

3. Dimensionality Reduction and Canonical Correlation Analysis (CCA):

• Input all subjects' GS beta maps into a Principal Component Analysis (PCA) to reduce dimensionality and identify major patterns of inter-subject variance [18].
• Compile a matrix of behavioral measures (e.g., cognition, personality, life outcomes) for all subjects. Perform PCA on this behavioral matrix.
• Conduct a CCA between the principal components of the GS topography and the principal components of the behavioral data to identify multivariate relationships between brain and behavior [18].
• Use non-parametric permutation testing to determine the statistical significance of the canonical correlations.

Figure 2: Experimental workflow for analyzing global signal topography and its behavioral relevance.

Protocol 2: Assessing the Utilitarian Impact of GSR on Behavior-FC Association

This protocol tests whether GSR strengthens or weakens the relationship between functional connectivity (FC) and behavior, providing a practical framework for deciding on its use [20].

1. Data Processing with and without GSR:

• Process the same rs-fMRI dataset (e.g., from GSP or HCP) through two separate pipelines.
• Pipeline A (No GSR): Includes standard nuisance regression (e.g., motion parameters, white matter, CSF signals).
• Pipeline B (With GSR): Includes all regressors from Pipeline A, plus the global signal.

2. Functional Connectivity and Model Training:

• For both pipelines, compute a whole-brain functional connectivity matrix (e.g., correlation between region of interest time series) for each subject.
• For a set of behavioral measures (e.g., fluid intelligence, personality scores), use a variance component model (or kernel ridge regression) to quantify the total variance in behavior explained by the whole-brain FC [20].
• Train and test predictive models (e.g., kernel ridge regression) using the FC matrices from each pipeline to predict behavioral scores.

3. Comparison and Interpretation:

• Compare the variance explained (from the variance component model) and prediction accuracy (from the regression) between the two pipelines.
• An increase in variance explained and prediction accuracy after GSR suggests that, for the specific dataset and behaviors under study, GSR improves the behavioral signal-to-noise ratio by removing non-neural variance [20].

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for Global Signal Research

Tool / Resource	Function / Application	Example Use Case
High-Temporal-Resolution fMRI	Enables better capture of the global signal's dynamics and separation from other noise sources.	HCP-style multiband acquisitions [18].
Surface-Based Processing	Provides a more accurate representation of cortical signals and improves cross-subject alignment for topography studies.	Calculating GS beta maps on cortical vertices [18].
Canonical Correlation Analysis (CCA)	A multivariate statistical method to discover relationships between two sets of variables (e.g., brain maps and behavior).	Identifying a latent variable linking GS topography to a profile of life outcomes [18].
Variance Component Model	Estimates the aggregate contribution of all FC features to explaining the variance of a behavioral trait.	Quantifying the total FC-behavior association strength with and without GSR [20].
Kernel Ridge Regression	A machine learning method for predicting continuous outcomes from high-dimensional features.	Predicting individual behavioral scores from whole-brain FC after different preprocessing steps [20].
Public Datasets (HCP, GSP)	Provide large-sample, high-quality neuroimaging and behavioral data for robust discovery.	Benchmarking GS topography and its behavioral associations in healthy young adults [20] [18].

Global Signal Regression (GSR) is a preprocessing technique for functional magnetic resonance imaging (fMRI) that involves regressing out the average whole-brain blood-oxygen-level-dependent (BOLD) signal from each voxel's time series [21]. The procedure remains one of the most contentious methodological choices in resting-state fMRI analysis, creating a fundamental divide in the neuroimaging community. This controversy centers on a critical trade-off: GSR effectively removes global artifacts arising from motion and physiological sources, but may simultaneously discard biologically meaningful neural information distributed throughout the brain [2] [22].

The global signal represents low-frequency global fluctuations ranging from 0.001 to 0.1 Hz, derived from averaging BOLD signals across the entire brain or specific masks [21]. Proponents argue that GSR effectively mitigates confounding signals from head motion, respiration, and cardiac cycles [22], while opponents highlight that it introduces negative correlations whose neural meaning remains ambiguous and may remove meaningful neural signals associated with arousal and vigilance [1] [21]. This application note examines this central controversy through current evidence, provides experimental protocols for implementation, and offers guidance for researchers navigating this methodological challenge.

Quantitative Evidence: Weighing the Benefits and Consequences

Documented Benefits of GSR in Functional Connectivity Research

Table 1: Established Benefits of Global Signal Regression

Benefit Category	Specific Effect	Quantitative Evidence	Study Context
Artifact Reduction	Reduces motion-related signal changes	Significant reduction in motion artifacts, especially with occasional large movements (>1mm)	Infant fMRI with large motion [3]
	Removes respiratory-related global fluctuations	Strong spatial consistency between GS and respiration topography (ICC = 0.4481)	HCP data (N=770) [1]
Enhanced Behavioral Association	Improves RSFC-behavior associations	47% average increase in behavioral variance explained across 23 measures	GSP dataset [22]
	Improves behavioral prediction accuracy	64% improvement in prediction accuracy in GSP dataset; 12% in HCP dataset	Kernel regression analysis [22]
Connectivity Specificity	Preserves neural connectivity patterns	Reduced artifactual connectivity from heart rate/breathing while preserving EEG-derived connectivity in alpha/beta bands	Simultaneous EEG-fMRI [2]

Potential Costs and Methodological Consequences of GSR

Table 2: Documented Limitations and Controversies of GSR

Limitation Category	Specific Effect	Quantitative Evidence	Study Context
Neural Information Loss	Removes globally distributed neural information	Considerable fraction of GS variations associated with physiological sources, but neural component preserved after GSR	Simultaneous EEG-fMRI [2]
Methodological Artifacts	Introduces negative correlations	Alters local and long-range correlations; limits assessment of connectivity patterns	Anesthesia studies [21]
State/Group Bias	Differentially affects brain states	Anesthetic-specific effects: alters propofol connections but broadly reduces sevoflurane connectivity differences	General anesthesia fMRI [21]
Interpretation Challenges	Complex relationship with behavior	Respiration-GS relationship correlates with psychiatric problems; GS topography with cognitive performance	HCP behavioral analysis [1]

Experimental Protocols for GSR Implementation

Standard GSR Protocol for Resting-State fMRI

Table 3: Step-by-Step GSR Implementation Protocol

Step	Procedure	Technical Specifications	Rationale
1. Data Preprocessing	Perform standard preprocessing pipeline	Slice-time correction, motion realignment, normalization, spatial smoothing (6mm FWHM), band-pass filtering (0.01-0.1Hz)	Standardizes data before GSR application [21]
2. Global Signal Extraction	Calculate mean global signal	Average BOLD time series across whole brain, gray matter mask, or cortical mask	Creates reference signal for regression [22]
3. Regression Modeling	Implement general linear model	Y = Xβ + ε, where X includes global signal plus nuisance regressors (motion parameters, WM, CSF signals)	Mathematically removes global variance [3]
4. Residual Extraction	Save residual time series	Yresidual = Y - Xβglobal	Creates cleaned BOLD signal for subsequent analysis
5. Quality Assessment	Verify artifact reduction	Check QC-FC correlations; compare with non-GSR processed data	Validates effectiveness of denoising [4]

JumpCor Protocol for Large Motion Artifact Correction

For studies with populations prone to large, occasional movements (e.g., infants, clinical populations), the JumpCor technique provides an alternative motion correction approach that can be used alongside or instead of GSR [3]:

• Motion Parameter Calculation: Compute frame-to-frame displacement using Euclidean norm of temporal differences in six realignment parameters.
• Jump Identification: Identify large motion jumps exceeding a defined threshold (typically 1mm).
• Regressor Generation: Create binary regressors for each segment between large jumps (value=1 during segment, 0 outside).
• Model Implementation: Include JumpCor regressors as nuisance variables in GLM alongside other regressors.
• Segment Censoring: Remove one-time-point segments between movements from analysis.

This approach specifically addresses large, infrequent motions common in developmental and neuropsychiatric populations where traditional GSR may be insufficient [3].

Signaling Pathways and Experimental Workflows

Diagram 1: GSR Controversy Decision Pathway

Diagram 2: GSR Experimental Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Essential Research Materials for GSR Studies

Tool Category	Specific Tool/Resource	Application Purpose	Implementation Notes
Analysis Software	AFNI [3]	Comprehensive fMRI analysis	JumpCor implementation for large motion correction
	fMRIPrep [21]	Automated preprocessing pipeline	Standardized preprocessing before GSR
	Nilearn [21]	Python-based fMRI analysis	GSR implementation and connectivity analysis
Physiological Monitoring	Respiratory Belt [1]	Records respiration volume per time (RVT)	Quantifies respiratory contribution to GS
	Cardiac Pulse Oximeter [21]	Records heart rate and oxygenation	Captures cardiac-related global fluctuations
	Simultaneous EEG-fMRI [2]	Direct neural activity correlation	Validates neural preservation after GSR
Quality Assessment	QC-FC Correlation Tools [4]	Motion artifact quantification	Evaluates denoising effectiveness
	ColorBrewer [23] [24]	Accessible visualization palettes	Creates color-blind friendly connectivity maps
	Frame-to-Frame Displacement Metrics [3]	Motion quantification	Identifies large jumps for JumpCor

The controversy surrounding Global Signal Regression fundamentally reflects the complexity of interpreting fMRI signals, which inherently contain mixed neural and non-neural contributions. Current evidence suggests that GSR's utility is context-dependent: it appears particularly beneficial for enhancing behavior-FC relationships in healthy populations [22], while potentially introducing biases in cross-group comparisons or states of altered consciousness [21].

For researchers navigating this methodological challenge, the following evidence-based recommendations emerge:

• Implement both pipelines (with and without GSR) for critical analyses, particularly when studying clinical populations or states of altered consciousness [21].
• Supplement GSR with physiological monitoring (respiration, cardiac) to quantify and account for specific artifact contributions [1].
• Consider population-specific approaches - JumpCor for developmental populations with large motions [3], and careful comparative analysis for anesthetic studies [21].
• Prioritize research question - GSR appears most justified for behavioral correlation studies in healthy adults, while non-GSR approaches may be preferable for arousal or vigilance studies [2] [1].

The field continues to evolve with emerging evidence that physiological signals like respiration may have functional relevance beyond mere artifact [1]. This suggests that future approaches may need to move beyond simple removal versus retention dichotomies toward more sophisticated decomposition methods that distinguish different components of the global signal based on their neural versus physiological origins and functional significance.

In resting-state functional magnetic resonance imaging (rs-fMRI), the global signal (GS) is operationally defined as the spatial average of the blood-oxygen-level-dependent (BOLD) time courses across all brain voxels [16]. One of the most contentious preprocessing steps in rs-fMRI analysis is global signal regression (GSR), a technique intended to remove global noise but one that also introduces spurious negative correlations and potentially removes biologically relevant information [12] [20].

Central to the controversy surrounding GSR is its complex and special relationship with the default mode network (DMN), a large-scale brain network most active during rest and involved in internally-directed thought. Evidence suggests that rather than being purely a nuisance, the GS is strongly correlated with DMN activity, raising critical questions about the consequences of its removal for understanding brain function and connectivity [16] [25]. This Application Note explores this relationship, provides quantitative characterizations, and details experimental protocols for investigators.

Biological and Metabolic Basis of the GS-DMN Link

The strong association between the GS and the DMN is not merely a statistical artifact but is underpinned by shared neurobiological and metabolic substrates.

• Neural and Metabolic Correlates: The DMN exhibits high levels of baseline glucose metabolism, and the BOLD signal fluctuations that characterize it are driven by metabolic demands. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies show that local glucose consumption in key DMN nodes, such as the medial frontal gyrus, posterior cingulate cortex (PCC), and angular gyrus, is positively associated with the strength of functional connectivity within the DMN [25]. This provides a direct metabolic link to the synchronized BOLD fluctuations that constitute the GS.

• Physiological and Non-Neural Contributions: The GS is a "catch-all" signal composed of multiple components. These include low-frequency drifts, motion-related artifacts, cardio-respiratory signals, and spin-history effects [12] [26]. One study found that after removing a full complement of nuisance regressors (low-frequency, motion, physiological, and white matter/CSF signals), only about 7% of the variance in the minimally processed global signal remained [12]. This residual component is hypothesized to be strongly related to neural activity, particularly that of the DMN.

The diagram below illustrates the composition of the Global Signal and its primary association with the DMN.

Quantitative Characterization of the GS-DMN Relationship

The relationship between the GS and the DMN has been quantitatively demonstrated through multiple experimental approaches. The table below summarizes key findings from the literature.

Table 1: Quantitative Evidence of the GS-DMN Relationship

Experimental Finding	Quantitative Measure	Methodology	Implication
Strong GS-DMN Temporal Correlation	GS is "strongly correlated" with DMN component time courses [16].	Seed-based correlation & ICA of rs-fMRI data.	GS contains significant information about the brain's dominant resting-state network.
Shared Metabolic Variance	Local glucose consumption in DMN nodes associated with DMN functional connectivity [25].	Simultaneous FDG-PET and rs-fMRI.	The GS-DMN link is supported by underlying metabolic activity.
GSR-Induced Anticorrelations	GSR introduces spurious negative correlations, centering the connectivity distribution on zero [16] [20].	Comparison of correlation distributions before and after GSR.	GSR mathematically distorts native connectivity, especially creating DMN anticorrelations with task-positive networks.
GSR Strengthens Brain-Behavior Links	Behavioral variance explained by whole-brain RSFC increased by ~40-47% after GSR [20].	Variance component modeling & kernel ridge regression in large datasets (HCP, GSP).	Removing the GS can improve the detection of behaviorally-relevant neural signals.

Experimental Protocols

Protocol 1: Core Analysis of GS-DMN Correlation

This protocol outlines the fundamental steps for quantifying the spatial and temporal relationship between the Global Signal and the Default Mode Network.

I. Materials and Data Acquisition

• Participants: Cohort of 20-30 cognitively normal adult participants is sufficient for an initial investigation [16].
• MRI Acquisition: Acquire T1-weighted anatomical images and 5-10 minutes of rs-fMRI data on a 3T scanner. Standard rs-fMRI parameters: TR=2000-3000 ms, TE=30 ms, voxel size=3-4 mm isotropic, 150-300 volumes [16] [25].
• Software: AFNI, FSL, SPM, or equivalent for analysis; MATLAB or Python for scripting.

II. Preprocessing Pipeline

• Basic Preprocessing: Perform slice-timing correction, realignment for motion correction, and co-registration of functional and anatomical images.
• Normalization: Spatially normalize functional data to a standard template (e.g., MNI).
• GS Calculation: Compute the global signal as the mean time series from all voxels within a whole-brain mask. Voxel time courses can be normalized as percent signal change from the mean [26].
• DMN Time Course Extraction:
  • Method A (Seed-Based): Define a spherical seed region (e.g., 10mm radius) in a key DMN node like the posterior cingulate cortex (PCC). Extract the mean time series from this seed.
  • Method B (ICA): Perform group-independent component analysis (ICA) to identify the DMN component. For each subject, extract the mean time course from the DMN spatial map.

III. Core Analysis Steps

• Temporal Correlation: Calculate the Pearson correlation coefficient between the entire GS time course and the entire DMN time course for each subject. A group-level one-sample t-test can be used to confirm the correlation is significantly greater than zero.
• Satial Overlap: Regress the GS time course against the whole-brain fMRI data to create a "GS beta-map." Similarly, create a DMN functional connectivity map (via seed-based correlation or ICA). Visually and quantitatively (e.g., using Dice coefficient) assess the spatial overlap, particularly in core DMN hubs (PCC, medial prefrontal cortex, angular gyri).

Protocol 2: Assessing the Impact of Global Signal Regression

This protocol describes how to evaluate the effects of GSR on DMN connectivity and network topography.

I. Data and Preprocessing

• Follow Protocol 1, steps I and II for data acquisition and basic preprocessing.

II. GSR Implementation

• Include the GS time course (calculated in Protocol 1, step II.3) as an additional nuisance regressor in a general linear model (GLM) alongside other confounds (e.g., 6 motion parameters, mean white matter, and CSF signals). Regress this entire set of confounds out of each voxel's time course [20] [27].

III. Post-GSR Analysis

• DMN Connectivity with and without GSR:
  • Generate seed-based DMN connectivity maps for the same PCC seed, both with and without GSR.
  • Compare: Observe the introduction of negative correlations (anticorrelations) between the DMN and "task-positive" networks (e.g., dorsal attention network) after GSR.
• Quantify Distributional Shift:
  • For each subject, compute a whole-brain, voxel-wise functional connectivity matrix.
  • Plot the distribution of all correlation coefficients (Fisher z-transformed) from the matrix. The distribution after GSR will be centered around zero, whereas the distribution without GSR will be centered on a positive value [16] [20].

The workflow for investigating the GS-DMN relationship and the impact of GSR is summarized below.

The Scientist's Toolkit

Table 2: Essential Research Reagents and Resources

Item / Resource	Function / Description	Example Use Case
AFNI (Analysis of Functional NeuroImages)	A comprehensive software suite for MRI data analysis, including registration, regression, and visualization.	Motion parameter estimation (3dvolreg), global signal calculation, and general linear model analysis [16] [26].
FSL (FMRIB Software Library)	A comprehensive library of MRI analysis tools, including MELODIC for Independent Component Analysis (ICA).	Used for ICA-based denoising (FIX) and for identifying the DMN via group-ICA [20] [28].
SPM (Statistical Parametric Mapping)	A software package for the statistical analysis of brain imaging data sequences.	Model specification and estimation for mass-univariate analysis; co-registration and normalization.
Multi-Echo fMRI Sequence	An acquisition sequence that collects data at multiple echo times (TEs), allowing better separation of BOLD from non-BOLD signals.	Used to isolate neural-related bias in motion parameters and to improve denoising via ME-ICA [26] [28].
Physiological Monitoring Equipment (Pulse oximeter, respiratory belt)	Devices to record cardiac and respiratory cycles during scanning.	Used to create RETROICOR or RVHRCOR regressors to model and remove physiological noise from the BOLD signal [16] [12].
High-Level Scripting Language (MATLAB, Python)	Environments for custom analysis scripting, data visualization, and statistical testing.	Implementing custom pipelines, calculating correlation metrics, and generating figures.

Application Notes and Decision Framework

The decision to use GSR is not binary but should be guided by the research question and data quality.

• When GSR May Be Beneficial: GSR is highly effective at removing global artifacts from motion and respiration. Its use is supported when the research goal is to strengthen associations between resting-state functional connectivity and behavioral measures, as it has been shown to increase explained behavioral variance by 40-47% in large cohorts [20]. It is also crucial when studying traits strongly correlated with motion (e.g., ADHD), where residual motion artifact can cause spurious brain-behavior associations [29].

• When to Avoid or Be Cautious with GSR: If the primary research question involves the absolute level of functional connectivity, the neurobiology of anticorrelated networks, or the properties of the global signal itself (e.g., in studies of arousal or vigilance), GSR should be avoided. Its use can also distort group differences if the global signal itself is a feature that differs between populations [12] [20].

• A Pragmatic Approach: Given the controversies, it is considered a best practice to analyze data with and without GSR and report the convergent and divergent findings. This approach provides a more comprehensive picture of the results and their robustness to different preprocessing choices [27].

Implementing GSR: Best Practices for Effective Motion Artifact Reduction

Global Signal Regression (GSR) remains one of the most contentious preprocessing steps in resting-state functional magnetic resonance imaging (rs-fMRI) analysis. While controversial, GSR is recognized for its potent ability to mitigate motion-related artifacts and improve the spatial specificity of functional connectivity maps [30] [31]. This protocol frames GSR within the specific context of motion artifact reduction research, providing researchers with a balanced perspective on its applications and limitations. The ongoing debate stems from evidence that the global signal contains both neural and non-neural components, making the decision to apply GSR highly dependent on specific research questions and data characteristics [12] [32].

Recent systematic evaluations of fMRI data-processing pipelines reveal that appropriate pipeline selection, including the judicious use of GSR, can significantly enhance the reliability of functional connectomics [33]. Furthermore, studies incorporating simultaneous electrophysiological recordings demonstrate that GSR effectively reduces connectivity patterns related to physiological signals while preserving those associated with neural activity [2]. This guide provides a comprehensive framework for integrating GSR into preprocessing workflows, with particular emphasis on motion artifact reduction for researchers, scientists, and drug development professionals.

Theoretical Foundation: The Nature of the Global Signal

What is the Global Signal?

The global signal (GS) is computed as the average time course across all voxels within the brain [12]. This seemingly simple measure reflects a complex combination of neural activity and various noise sources. The signal represents a "catch-all" component that captures fluctuations shared across most brain voxels, with contributions from multiple sources:

• Neural activity: Widespread cortical firing and baseline shifts in vigilance [32]
• Physiological noise: Cardiorespiratory cycles, blood pressure variations, and respiratory volume changes [12] [2]
• Motion artifacts: Head movement during scanning causing displacement and spin history effects [30]
• Scanner drift: Low-frequency scanner instabilities and thermal fluctuations [12]

The proportional contribution of each source varies across datasets, individuals, and scanning conditions, making the interpretation of the global signal context-dependent.

The GSR Controversy: Key Arguments

Table 1: Arguments for and Against Global Signal Regression

Support for GSR	Key Concerns
Reduces motion-related artifacts and distance-dependent correlations [30]	Introduces artificial anti-correlations between networks [32]
Improves spatial specificity of functional connectivity maps [31]	May remove biologically relevant neural signals [12] [32]
Enhances detection of group differences in high-motion populations [34]	Can distort correlation patterns and inter-individual differences [32]
Preserves neural components of connectivity while reducing physiological noise [2]	Mathematical constraints force negative mean correlation [32]

Decision Framework: When to Apply GSR

Evidence-Based Application Guidelines

Table 2: GSR Recommendation Guidelines Based on Research Context

Research Scenario	GSR Recommendation	Rationale	Supporting Evidence
Studies of high-motion populations (children, clinical, elderly)	Recommended	Effectively reduces motion-related artifacts	[34] [30]
Investigations of anti-correlated networks	Use with caution; validate without GSR	May artificially enhance anti-correlations	[32] [31]
Anesthesia studies (propofol vs. sevoflurane)	Anesthetic-specific considerations	Differential effects on connectivity patterns	[5]
Biomarker discovery with individual differences	Generally not recommended	May distort inter-individual variability	[33] [32]
Multiecho fMRI datasets	Potential alternative approaches available	ME-ICA may reduce need for GSR	[35]

GSR Decision Workflow

The following diagram illustrates the key decision points for integrating GSR into a preprocessing pipeline:

Experimental Protocols & Methodologies

Standardized GSR Implementation Protocol

Materials and Reagents:

• Preprocessed fMRI data (minimally preprocessed with motion correction and spatial normalization)
• Computing environment with statistical software (Python, R, MATLAB)
• Gray matter, white matter, and CSF masks (if using compartment-based regression)
• Physiological recordings if available (cardiac, respiratory)

Step-by-Step Procedure:

• Data Preparation

  • Begin with minimally preprocessed fMRI data that has undergone standard steps: slice timing correction, motion realignment, and spatial normalization to standard space [36].
  • Ensure data is in appropriate space (volume-based or surface-based) depending on analysis preferences. Surface-based methods may offer advantages for cortical data [36].

• Global Signal Calculation

  • Compute the global signal as the mean time series across all voxels within a predefined brain mask [12].
  • For voxel-wise normalization approaches, first convert each voxel's time series to percent signal change relative to its mean signal intensity [12].

• Nuisance Regression

  • Construct a nuisance regressor matrix including:
    • Global signal (mean time course)
    • 6-24 motion parameters (and their derivatives)
    • White matter and CSF signals (mean time courses from respective masks)
    • Physiological regressors if available (cardiac, respiratory)
  • Apply linear regression to remove variance associated with nuisance regressors from each gray matter voxel's time series [30].

• Post-Regression Processing

  • Apply temporal filtering (typically 0.008-0.09 Hz bandpass) to remove high-frequency noise and low-frequency drift.
  • Perform connectivity analysis on residuals from the regression step.

• Quality Control

  • Calculate Framewise Displacement (FD) and DVARS to quantify motion.
  • Verify that motion-FC relationships have been reduced, particularly distance-dependent correlations [30].
  • Check for excessive negative correlations that may indicate GSR artifacts.

Alternative Methods for Global Noise Removal

When GSR is inappropriate for the research context, consider these alternative approaches:

• ANATICOR (Regional White Matter Regression): Regresses out regional white matter signals from nearby gray matter voxels, providing localized noise correction without global signal removal [32].

• CompCor (Component-Based Noise Correction): Uses principal components analysis (PCA) to identify noise components from white matter and CSF compartments, regressing out the top components instead of the global signal [32].

• Multi-Echo ICA (ME-ICA): Leverages multi-echo fMRI data to differentiate BOLD from non-BOLD components, effectively removing motion-related artifacts without requiring GSR [35].

• Group-Level Covariate Regression: Includes mean connectivity (GCOR) as a covariate in group-level analysis rather than regressing the global signal at the subject level [32].

Research Reagent Solutions

Table 3: Essential Tools for GSR Implementation and Validation

Research Tool	Function/Purpose	Implementation Examples
Framewise Displacement (FD)	Quantifies head motion between volumes	Power et al. (2012) method [30]
DVARS	Measures rate of change in BOLD signal	Power et al. (2012) framework [30]
Portrait Divergence (PDiv)	Quantifies dissimilarity in network topology	Used in pipeline evaluation [33]
CIFTI Grayordinates	Combined surface-volume coordinate system	HCP minimal preprocessing pipelines [36]
fMRIPrep	Automated preprocessing pipeline	Esteban et al. (2019) [5]
Connectome Workbench	Visualization of surface-based data	HCP data visualization [36]

Interpretation & Analytical Considerations

Validating GSR Effects

When GSR is applied, several validation steps are recommended:

• Assess Motion Connectivity Relationships

  • Calculate correlation between motion metrics (FD) and functional connectivity measures.
  • Successful GSR application should reduce distance-dependent correlations, where motion disproportionately affects connections between nearby regions [30].

• Compare With and Without GSR

  • Always run parallel analyses with and without GSR to determine how results are affected.
  • Report both sets of results, particularly for key findings [33] [32].

• Evaluate Network Topology Reliability

  • Use test-retest datasets to assess whether GSR improves reliability of network topology measures.
  • Optimal pipelines should minimize spurious test-retest discrepancies while preserving biological signals [33].

Domain-Specific Considerations

Anesthesia Studies: GSR has differential effects depending on anesthetic type. Propofol-induced connectivity changes are relatively preserved after GSR, while sevoflurane-induced changes are significantly attenuated [5]. Always consider anesthetic mechanism when interpreting GSR-processed data.

Clinical Populations: In high-motion populations (e.g., children, neurodegenerative disorders), GSR may improve data quality but can also remove disease-relevant signals. Consider disorder-specific literature when deciding on GSR application [34].

Pharmacological fMRI: GSR may remove global drug effects that are of scientific interest. For pharmacological challenges, carefully consider whether global signal changes represent confounds or meaningful biological responses.

Integrating GSR into fMRI preprocessing requires careful consideration of research goals, population characteristics, and analytical priorities. While GSR remains controversial, evidence supports its value for motion artifact reduction in specific contexts, particularly for high-motion populations and studies focusing on group-level effects rather than individual differences. The most robust approach involves running parallel analyses with and without GSR and validating key findings using complementary methods. As the field moves toward consensus, researchers should transparently report preprocessing choices and their potential impact on results, enabling more accurate interpretation and replication of functional connectivity findings.

In resting-state functional magnetic resonance imaging (rs-fMRI), the pursuit of clean neural signals free from motion and physiological artifacts remains a central methodological challenge. Global Signal Regression (GSR) is a potent yet contentious preprocessing technique that effectively reduces global artifacts stemming from motion and respiration by regressing out the whole-brain average signal from each voxel's time series [20]. Its efficacy, however, comes with significant trade-offs, including the introduction of negative correlations and the potential removal of neurally relevant global information [20] [37]. Conversely, ICA-FIX is a sophisticated automated classifier that identifies and removes noise components from independent component analysis (ICA) decompositions of fMRI data, offering a powerful data-driven alternative [38]. Individually, each method has distinct strengths and weaknesses; however, emerging evidence suggests that their strategic combination can yield superior denoising performance. This protocol details a synergistic approach that integrates GSR with ICA-FIX and physiological monitoring, creating a robust pipeline that maximizes artifact removal while preserving behavioral and neural signals of interest, thereby enhancing the reliability of functional connectivity findings for clinical and cognitive neuroscience applications [20] [39] [38].

Quantitative Efficacy of Denoising Strategies

The performance of various denoising pipelines can be evaluated using multiple benchmarks, including the residual relationship between motion and functional connectivity, data loss, and test-retest reliability. The table below synthesizes key findings from empirical comparisons.

Table 1: Performance Benchmarking of Common fMRI Denoising Pipelines

Denoising Pipeline	Residual Motion Artifact	Data Loss / Degrees of Freedom	Test-Retest Reliability	Key Strengths	Key Limitations
GSR	Effectively reduces global motion artifacts; introduces distance-dependent correlations with motion [39].	Low data loss [39].	Improves behavioral prediction accuracy (e.g., +40% in HCP) [20].	Potent removal of global artifacts; strengthens brain-behavior associations [20] [38].	Introduces negative correlations; may remove neural signal [20].
ICA-FIX/ICA-AROMA	Excellent motion control, outperforming simple linear regression [39] [38].	Moderate data loss (aggressive vs. non-aggressive variants) [39] [38].	High network reproducibility and functional connectivity fingerprinting [38].	Automatic, data-driven removal of motion-related components [38].	Requires high-quality data; may not remove all global physiological noise [38].
WM/CSF Regression	Limited efficacy; simple regression is insufficient for full motion removal [39].	Low data loss [39].	Moderate	Simple implementation; low cost in data retention [38].	Ineffective for global signal artifacts; cannot account for regional-specific noise [38].
aCompCor	Effective primarily in low-motion data [39].	Low data loss [39].	Associated with higher age-related fcMRI differences [38].	Accounts for regional physiological noise variations [38].	Performance varies with noise ROI definition; less effective for strong motion [39].
Volume Censoring (Scrubbing)	Superior minimization of motion artifacts [39].	High data loss, often leading to exclusion of high-motion subjects [39].	High for retained data	Powerful for removing transient, high-motion artifacts [39].	Significant reduction in temporal degrees of freedom; can bias sample by excluding high-motion individuals [39].

Furthermore, the combination of GSR with other techniques shows quantifiable benefits:

Table 2: Quantitative Benefits of Combining GSR with Other Denoising Methods

Combined Approach	Dataset	Key Quantitative Benefit	Implication
GSR after ICA-FIX	Human Connectome Project (HCP)	Behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 measures [20].	GSR provides unique denoising benefits beyond a sophisticated ICA-based method.
GSR with other pipelines	Multi-dataset evaluation	The addition of GSR improved the performance of nearly all pipelines on most benchmarks for motion control [39].	GSR acts as a powerful complement to a wide range of denoising strategies.

Experimental Protocols for Integrated Denoising

Protocol 1: Sequential ICA-FIX and GSR for Enhanced Behavioral Correlation

This protocol is designed to maximize the association between functional connectivity and behavioral measures, as validated in large-scale datasets [20].

Applicability: This workflow is ideal for studies focusing on individual differences in cognition, personality, or emotion, where strengthening the brain-behavior relationship is a primary goal.

dot Code for Diagram: "Workflow for Sequential ICA-FIX and GSR Processing"

Detailed Methodology:

• Data Acquisition & Standard Preprocessing:

  • Acquire rs-fMRI data. The Human Connectome Project (HCP) protocol (3T Skyra scanner, multiband sequence, TR=720ms, 2.0mm isotropic voxels) serves as a high-quality reference [20].
  • Perform standard preprocessing steps including slice-time correction, motion realignment to generate 6 rigid-body parameters, and spatial normalization to a standard template (e.g., MNI152).

• ICA-FIX Denoising:

  • Temporal Filtering: Apply high-pass filtering (e.g., cutoff >200s) to the preprocessed data.
  • Spatial ICA: Decompose the filtered data using a group-level or single-subject ICA. A common dimensionality of 25-100 components is typical.
  • Automatic Classification: Process the derived components using the FIX classifier (trained on your specific dataset or a generic classifier) to label components as "signal" or "noise".
  • Component Removal: Regress out the time courses of all noise-classified components from the preprocessed data. This yields a "FIX-cleaned" dataset [20] [38].

• Global Signal Regression (GSR):

  • Signal Extraction: From the FIX-cleaned data, compute the global signal (GS) as the average time series across all gray matter voxels or the entire brain mask.
  • Nuisance Regression: Perform a multiple regression where the BOLD signal of each voxel is regressed against the GS alongside other nuisance regressors. These typically include:
    • The mean signals from white matter (WM) and cerebrospinal fluid (CSF) masks.
    • The 6 motion realignment parameters and their derivatives (12 regressors total) [20] [39].
    • Optional: Expansion terms (e.g., squares) of motion parameters for more aggressive denoising.

• Post-Processing & Connectivity Analysis:

  • Apply temporal band-pass filtering (e.g., 0.01-0.1 Hz) to the residual time series to focus on low-frequency fluctuations.
  • Compute final functional connectivity matrices (e.g., Pearson correlation between region time series) for subsequent behavioral analysis.

Protocol 2: Integrating Physiological Monitoring with Data-Driven Denoising

This protocol leverages externally recorded physiological signals to guide and validate data-driven denoising, offering the highest level of artifact control for studies where physiological confounds are a primary concern [40] [38].

Applicability: Critical for studies of aging, clinical populations, or any investigation where cardiac and respiratory rhythms may systematically differ between groups and confound neural inferences.

dot Code for Diagram: "Integration of Physiological Monitoring in Denoising"

Detailed Methodology:

• Simultaneous Data Acquisition:

  • fMRI: Acquire rs-fMRI data. A high temporal resolution (short TR, e.g., < 1s) is strongly recommended to prevent aliasing of cardiac and respiratory signals [38].
  • Physiological Monitoring: Record the following synchronously with the fMRI data:
    • Cardiac Signal: Using a pulse oximeter placed on a finger.
    • Respiratory Signal: Using a respiratory effort belt around the abdomen/thorax.
    • Head Motion Parameters: Derived from volume realignment.

• Model-Based Physiological Noise Correction:

  • Following initial preprocessing (steps 1-2 from Protocol 1), use the recorded physiological traces to generate noise regressors.
  • RETROICOR: Apply the RETROICOR method to model phase-locked physiological fluctuations using a second-order Fourier series relative to the cardiac and respiratory cycles [37].
  • RVHRCOR: Model variations in heart rate and respiration volume per time (RVT) as additional regressors to account for non-phase-locked effects [37].
  • Regress these model-based physiological noise regressors from the data.

• Data-Driven Denoising Suite:

  • Process the physiologically-corrected data through ICA-AROMA (aggressive mode) for robust, data-driven motion artifact removal [38].
  • Subsequently, apply GSR to the AROMA-cleaned data to remove any remaining global, spatially coherent noise [20].

• Validation of Denoising Efficacy:

  • Power Spectral Analysis: Compare the frequency power spectra of the BOLD signal before and after denoising. Successful pipelines show reduced power at cardiac (~1 Hz) and respiratory (~0.3 Hz) frequencies, while preserving power in the ultra-low frequency band (<0.1 Hz) [38].
  • Motion-FC Correlation: Calculate the correlation between subject-wise mean frame-wise displacement (FD) and the resulting functional connectivity matrices. An effective pipeline minimizes this relationship [39].

Table 3: Key Software, Data, and Analytical Resources

Resource Name	Type	Primary Function	Relevance to Protocol
ICA-FIX	Software Tool	Automated classifier for identifying and removing noise components from ICA decompositions.	Core component for data-driven denoising in Protocol 1 [20] [38].
ICA-AROMA	Software Tool	Automatic Removal of Motion Artifacts; identifies noise components based on spatial and temporal features without needing a trained classifier.	A key alternative to FIX, especially useful in Protocol 2 for its robustness [39] [38].
FSL	Software Suite	FMRIB Software Library; contains MELODIC for ICA and FIX for training classifiers, among many other preprocessing tools.	Provides the computational environment for running ICA-FIX and general fMRI analysis [39].
Human Connectome Project (HCP) Data	Reference Dataset	A large-scale, high-quality neuroimaging dataset with rs-fMRI, behavioral, and physiological data.	Serves as a gold-standard benchmark for developing and testing denoising pipelines [20] [40].
RETROICOR & RVHRCOR	Algorithm	Model-based methods for removing cardiac and respiratory noise from fMRI data using recorded physiological signals.	Critical for the model-based correction step in Protocol 2 [37].
CONN Toolbox	Software Tool	A cross-platform MATLAB/Octave toolbox for functional connectivity analysis, includes aCompCor and other denoising methods.	Useful for implementing and comparing alternative denoising pipelines [38].
Variance Component Model / Kernel Ridge Regression	Analytical Method	Statistical models to quantify the variance in behavior explained by functional connectivity or to predict behavior from connectivity.	Used to validate the utility of the denoising pipeline by assessing improvements in brain-behavior associations [20].

Resting-state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool for mapping the brain's functional organization and its relation to individual differences in behaviour [41]. However, the field faces a significant challenge: rs-fMRI signals are contaminated by multiple sources of noise, particularly head motion, which can severely impact the reliability and validity of functional connectivity (FC) measures and attenuate their correlations with behaviour [41]. These motion-induced artifacts exhibit a distance-dependent profile, whereby higher motion levels artificially inflate short-range connectivity and weaken long-distance connections [42]. In brain-wide association studies (BWAS), these artifacts can either spuriously increase or attenuate effect sizes, complicating the interpretation of brain-behaviour relationships [41].

Global Signal Regression (GSR) has emerged as one of the most effective single techniques for mitigating motion-related artifacts in functional connectivity analyses [42]. However, even with GSR, residual motion artifacts often persist, particularly in high-motion datasets. This application note explores how motion censoring (scrubbing) partners with GSR to create a robust denoising pipeline that enhances the validity of BWAS findings, especially for clinical populations and developmental cohorts where in-scanner motion is more prevalent.

Quantitative Evidence: Combined Efficacy of Scrubbing and GSR

Comparative Performance of Denoising Pipelines

Recent large-scale evaluations of rs-fMRI denoising pipelines reveal that pipelines combining GSR with other robust techniques, including censoring, provide optimal trade-offs between motion reduction and behavioural prediction performance [41]. The evidence indicates that no single pipeline universally excels at achieving both objectives consistently across different cohorts, highlighting the need for tailored approaches based on specific research goals and data characteristics [41].

Table 1: Denoising Pipeline Efficacy Across Benchmarks (Adapted from [41] [42])

Pipeline Components	Motion Reduction Efficacy	Behavioural Prediction Performance	Residual Distance-Dependence	Recommended Use Cases
GSR alone	High	Moderate to High	Moderate reduction	Standard adult populations
GSR + Motion Censoring	Highest	High	Substantial reduction	High-motion data, pediatric, clinical
ICA-FIX + GSR	High	High	Moderate reduction	Large-scale studies with computational resources
24P + WM/CSF	Moderate	Low to Moderate	Substantial residual dependence	Minimal denoising requirements
ICA-FIX alone	Moderate to High	Moderate	Moderate residual dependence	Studies avoiding GSR controversy

Impact on Brain-Behaviour Associations

The ultimate test for any denoising pipeline lies in its ability to enhance valid brain-behaviour associations while controlling false positives. Research demonstrates that pipelines incorporating both GSR and censoring strategies significantly reduce spurious motion-related correlations while preserving biologically plausible brain-behaviour relationships [41]. In one comprehensive analysis, pipelines with GSR demonstrated the most consistent effectiveness across multiple benchmarks, including reduction of motion-connectivity relationships and enhancement of network identifiability [42].

Experimental Protocols and Implementation

Framewise Displacement Calculation and Censoring Thresholds

Motion censoring requires precise calculation of framewise displacement (FD) as a marker of head movement between consecutive volumes. The standard approach calculates FD from the six rigid-body realignment parameters (three translations and three rotations), deriving the scalar measure that reflects instantaneous head motion.

Implementation Protocol:

• Calculate FD using the Power method: FD = |Δx| + |Δy| + |Δz| + |α| + |β| + |γ|, where Δx, Δy, Δz represent translational changes, and α, β, γ represent rotational changes in radians.
• Account for high-frequency contamination: Recent evidence indicates that single-band fMRI motion traces contain factitious high-frequency content (>0.1 Hz) primarily in the phase-encoding direction, attributable to respiration-induced B0 field perturbations [43]. Apply a low-pass filter to motion parameters (cutoff ~0.1 Hz) before FD calculation to remove this contamination, which saves substantial data from censoring while reducing motion biases in functional connectivity [43].
• Apply threshold-based censoring: Identify volumes with FD exceeding established thresholds (typically 0.2-0.5 mm, with 0.2 mm being more conservative). Exclude these high-motion volumes and one preceding and two following volumes to account for spin-history effects.
• Implement interpolation: Replace censored volumes with interpolated data from adjacent retained volumes using sophisticated algorithms (e.g., Lomb-Scargle periodogram) to maintain temporal continuity while removing motion-contaminated data segments.

Table 2: Motion Censoring Parameters and Recommendations

Parameter	Standard Implementation	Optimized Recommendations	Rationale
FD Threshold	0.2 mm (conservative) to 0.5 mm (liberal)	0.2 mm for high-quality data; 0.3-0.4 mm for noisier data	Balances data retention with artifact removal
Filter Motion Parameters	Not always implemented	Apply low-pass filter (cutoff ~0.1 Hz) to motion traces	Removes respiration-induced high-frequency contamination [43]
Buffer Volumes	1 before, 2 after high-motion volumes	Maintain standard unless extreme motion present	Addresses spin-history effects
Minimum Data Retention	Varies by study	≥5 minutes of clean data after censoring	Ensures reliability of connectivity estimates

Integrated GSR and Censoring Workflow

The combination of GSR and motion censoring creates a synergistic effect that addresses different aspects of motion contamination. GSR removes a global component that reflects both neural and non-neural sources, while censoring targets discrete high-motion episodes.

Diagram 1: Integrated GSR and censoring workflow for optimal motion denoising.

Scan Duration Considerations for High-Quality BWAS

Recent evidence emphasizes that longer scan durations significantly boost prediction accuracy in BWAS while potentially reducing overall study costs [15]. When implementing aggressive censoring protocols that remove substantial data, extended acquisition times become essential.

Protocol Recommendations:

• Minimum scan duration: 20 minutes for resting-state fMRI [15]
• Optimal scan duration: 30 minutes provides the most cost-effective balance between data quality and resources [15]
• Compensation for censoring: When expecting high motion rates (e.g., >20% data loss), increase scan duration by 25-50% to ensure adequate clean data remains after censoring
• Multiband acquisition: Utilize multiband sequences to increase the number of volumes within a fixed scan duration, providing more temporal samples and resilience to censoring

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Tools for Implementing GSR and Motion Censoring Pipelines

Tool/Resource	Function	Implementation Notes
fMRIPrep	Robust, standardized preprocessing pipeline	Provides motion parameters, confound estimates, and quality metrics [44]
DPABI/DPARSF	All-in-one processing suite with flexible censoring options	User-friendly interface for customizable pipeline implementation
CONN Toolbox	Integrates SPM with comprehensive denoising tools	Excellent for seed-based and ROI-to-ROI analyses
in-house MATLAB/Python scripts	Customized censoring implementation	Enables algorithm refinement and specific parameter optimization
Framewise Displacement (FD)	Primary metric for volume censoring	Calculate from realignment parameters with filtering for HF contamination [43]
DVARS	Measure of signal change between volumes	Complementary metric to FD for identifying artifactual volumes
Global Signal	Mean signal across whole brain mask	Regressed out during GSR; controversial but effective for motion control [42]
CompCor	PCA-based noise component estimation	Alternative to tissue-based regressors; captures noise patterns

Advanced Considerations and Special Cases

Respiratory and Cardiac Artifacts

Beyond gross head motion, physiological noise from respiration and cardiac cycles significantly contaminates BOLD signals. Recent research reveals strong spatial consistency between global signal and respiration topography, particularly in limbic, default mode, and salience networks [1]. This overlap has behavioral relevance, suggesting that respiration's contribution to GS may have functional significance beyond mere physiological noise [1]. For studies collecting respiratory and cardiac data, incorporate these measures into extended denoising pipelines through:

• Retrospective image correction (RETROICOR) to address periodic physiological fluctuations
• Respiration Volume Per Time (RVT) regression to capture breathing depth and rate effects
• Cardiac pulse rate variability regression

Dynamic Functional Connectivity Applications

Motion censoring presents particular challenges for dynamic functional connectivity (dFC) analyses, where maintaining temporal continuity is essential. Specialized approaches include:

• Temporal mask application that excludes censored volumes from connectivity calculations while preserving temporal structure of retained data
• Gap-free segment selection that identifies longest contiguous clean segments for dFC analysis
• Multilayer modularity maximization that incorporates temporal links between adjacent uncensored windows [42]

Population-Specific Adaptations

The efficacy of denoising pipelines varies across populations, requiring tailored approaches:

• Pediatric populations: Higher censoring thresholds (0.3-0.4 mm) with more buffer volumes, as children typically exhibit more frequent but smaller movements
• Geriatric populations: Lower censoring thresholds (0.2 mm) with respiratory filtering, as older adults show increased high-frequency motion contamination [43]
• Clinical populations with movement disorders: Strategic acquisition parameters (shorter TR, multiband) anticipating high data loss, with planned interpolation approaches

The partnership between motion censoring and GSR represents a powerful approach for mitigating motion artifacts in rs-fMRI data, particularly for high-motion datasets. Based on current evidence, we recommend:

• Implement GSR as a foundation for motion denoising, given its consistent performance across multiple benchmarks [42]
• Add motion censoring for high-motion data or when studying populations prone to movement, using conservative thresholds (0.2 mm) with filtering for high-frequency contamination [43]
• Acquire longer scan durations (≥20 minutes, ideally 30 minutes) to ensure sufficient data remains after censoring for reliable connectivity estimation [15]
• Validate pipeline efficacy for specific research questions and populations, as no single pipeline universally excels across all contexts [41]
• Report censoring rates transparently in publications, along with rationale for chosen thresholds and the amount of data retained for analysis

This combined approach maximizes the validity of functional connectivity measures while controlling for motion-induced artifacts, ultimately enhancing the reliability and reproducibility of brain-behavior associations in BWAS.

Within research on global signal regression (GSR) for motion artifact reduction, a paramount challenge is balancing the removal of non-neural noise with the preservation of meaningful biological signal. This balance is critical for ensuring the validity and reliability of findings in functional magnetic resonance imaging (fMRI) [45]. The principles of quantifying noise and validating denoising strategies are directly transferable to the domain of Galvanic Skin Response (GSR). GSR, also known as Electrodermal Activity (EDA), is a sensitive measure of emotional arousal, originating from the autonomic activation of sweat glands in the skin [46]. This application note establishes data-driven criteria and protocols for quantifying global noise in GSR signals, providing a standardized framework for researchers and drug development professionals to enhance data quality and reproducibility.

Theoretic Foundations of GSR and Noise

Physiological Basis of GSR

Galvanic Skin Response measures variations in the electrical properties of the skin. Emotionally arousing stimuli activate the sympathetic nervous system, which in turn stimulates sweat glands. Increased sweat secretion, even in microscopic amounts, enhances the skin's electrical conductivity (or decreases its resistance) [46]. This signal is considered a robust marker for unconscious emotional arousal because it is solely under sympathetic control and not subject to conscious regulation [46].

Defining and Classifying Noise in GSR

In the context of GSR, "global noise" refers to all non-arousal related contributions to the skin conductance signal. Effectively distinguishing this noise is analogous to the challenge in fMRI of isolating motion artifacts from resting-state network information [45]. GSR noise can be categorized as:

• Motion Artifacts: Sudden, large signal deflections caused by subject movement, particularly of the limbs where sensors are placed [47].
• Physiological Baseline Drift: Slow, non-stationary changes in the signal baseline related to factors like thermoregulation rather than emotional state [46].
• Sensor Contact Noise: Signal disruptions due to poor electrode-skin contact or varying pressure on the sensor.

Quantitative Data and Correlation Analysis

Body Location Correlation Coefficients

The measurement location on the body significantly influences the recorded GSR signal. A 2021 correlation study investigated the homogeneity of GSR signals measured from different body sites in response to pleasant and unpleasant stimuli [47]. The following table summarizes the key correlation findings, which are essential for understanding signal consistency and potential noise introduction across placements.

Table 1: Correlation of GSR Signals Across Different Body Measurement Sites

Comparison Between Body Sites	Pearson's Correlation Coefficient (r)	Interpretation of Signal Homogeneity
Left Fingers vs. Right Foot	~0.899 (Strong) [47]	Highly homogeneous signals; most promising pair for consistent measurement.
Dorsal Wrist (Silver Electrodes) vs. Standard	0.899 ± 0.036 (Strong) [47]	Validates non-palmar sites for reliable measurement.
Right Foot vs. Left Wrist	Moderate Correlation [47]	Signals are related but show notable variability.
Right Foot vs. Right Wrist	Moderate Correlation [47]	Signals are related but show notable variability.

Performance Metrics for Noise Quantification

Drawing from methodologies in fMRI denoising [45], a multi-metric approach is recommended for a comprehensive evaluation of GSR noise reduction pipelines. The success of a denoising strategy should be evaluated based on its performance across several benchmarks:

Table 2: Key Metrics for Evaluating GSR Denoising Pipelines

Metric Category	Description	Application to GSR
Artifact Removal	Quantifies the degree to which motion and other artifacts are reduced.	Measured by the reduction in high-frequency, high-amplitude spikes not correlated with stimuli.
Signal-to-Noise Ratio (SNR) Enhancement	Measures the improvement in the ratio of true signal power to noise power.	Assessed by comparing the amplitude of stimulus-locked responses to the baseline noise floor after processing.
Identifiability / Fingerprinting	Assesses the ability to uniquely identify a subject's physiological response pattern based on their cleaned data.	High test-retest reliability of an individual's GSR response pattern to standardized stimuli indicates good preservation of biological signal.

Experimental Protocols for Noise Quantification

Protocol 1: Establishing a Ground Truth Noise Baseline

This protocol is designed to characterize the inherent noise profile of the GSR measurement system and subject setup.

1. Objective: To quantify non-physiological and baseline physiological noise in the absence of emotionally salient stimuli. 2. Materials: GSR sensor with Ag/AgCl electrodes, conductive gel (if using adhesive patches), data acquisition system, a quiet, climate-controlled room. 3. Participant Setup: Apply sensors to the selected locations (e.g., left fingers and right foot as per Table 1). Ensure consistent electrode placement and firm contact. 4. Procedure: - Instruct the participant to remain relaxed and minimize movement for a 10-minute baseline recording. - Present a neutral, non-arousing stimulus (e.g., a blank screen or a constant tone) for 5 minutes. 5. Data Analysis: - Calculate the Mean Baseline Conductance and Standard Deviation of Baseline Noise for each recording site. - Compute the Root Mean Square (RMS) of the signal during the neutral stimulus period as a measure of global noise level. - Perform a correlation analysis (e.g., Pearson's r) between signals from different sites to establish a baseline homogeneity index.

Protocol 2: Evaluating Denoising Pipelines with Arousing Stimuli

This protocol tests the efficacy of different noise-handling methods during active emotional stimulation.

1. Objective: To compare the performance of denoising strategies in preserving stimulus-locked responses while suppressing artifacts. 2. Stimuli: A standardized set of high-arousal images or video clips (e.g., from the International Affective Picture System IAPS). 3. Procedure: - Record GSR while presenting the stimulus set to the participant. - Introduce controlled, minor movements at predefined intervals (e.g., a single finger tap) to simulate motion artifacts. 4. Denoising Pipelines to Test: - A: Band-Pass Filtering Only (e.g., 0.05-5 Hz). - B: Filtering + Artifact Rejection (threshold-based scrubbing of motion-contaminated segments). - C: Filtering + Regression-Based Correction (regressing out signals from a "noise" channel, if available). 5. Outcome Measures: - Artifact Removal: Percentage reduction in the amplitude of simulated motion artifacts. - Signal Preservation: Amplitude and latency of the peak GSR response to each validated arousing stimulus. - Summary Performance Index: A composite score balancing artifact removal and signal preservation, inspired by fMRI methodologies [45].

Visualization of GSR Noise Assessment Workflow

The following diagram outlines the logical workflow and decision points for a data-driven GSR noise assessment and denoising protocol.

The Scientist's Toolkit: Research Reagent Solutions

The following table details essential materials and their functions for conducting rigorous GSR noise quantification studies.

Table 3: Essential Materials for GSR Noise Quantification Research

Item Name	Function / Application	Specification Notes
Ag/AgCl Electrodes	Sensor for measuring skin conductance. Silver/Silver-Chloride provides a stable, non-polarizing interface.	Reusable snap-on Velcro straps or disposable adhesive patches with gel [46].
Electrodermal Conductivity Gel	Improves conductivity between the skin and the electrode, reducing contact noise.	Isotonic, non-irritating gel for prolonged wear. Required for patch-style electrodes [46].
GSR Amplifier / Data Logger	Device that applies a constant, low voltage and measures the resulting current to calculate skin conductance.	Devices like Shimmer3GSR or Empatica E4 are used in research [47]. Sampling rates of 10-100 Hz are sufficient [46].
Standardized Affective Stimuli	To elicit reproducible, stimulus-locked emotional arousal for validating denoising methods.	International Affective Picture System (IAPS) or curated sets of video clips with normative ratings [47].
Data-Driven Scrubbing Software	Algorithmically identifies and flags motion-contaminated data segments for rejection or correction.	Inspired by fMRI "projection scrubbing" [48]; can be implemented with threshold-based or machine learning approaches for GSR.

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for non-invasive studies of brain function in both experimental and clinical neuroscience. However, fMRI data are characterized by a low contrast-to-noise ratio (CNR) and are susceptible to substantial subject-specific artifacts caused by head motion and physiological noise. The chosen set of preprocessing steps—the "pipeline"—significantly impacts the sensitivity and specificity of measured fMRI signals. Pipeline optimization is therefore critical for improving signal detection, particularly in studies involving aging or clinical populations where signal is weaker and artifacts more pronounced. Furthermore, the interaction between preprocessing choices and other experimental factors, such as between-subject variability and task design strength, underscores the need for systematic optimization frameworks to ensure reliable and interpretable results [49] [50].

This document outlines application notes and protocols for pipeline optimization, with a specific focus on the role of global signal regression (GSR) in motion artifact reduction. The content is structured to provide researchers and drug development professionals with actionable methodologies and quantitative evaluations for refining fMRI data-processing pipelines.

Quantitative Comparison of Preprocessing Strategies

The table below summarizes the performance of various preprocessing strategies evaluated against different benchmarks in resting-state fMRI, with a specific focus on motion artifact mitigation.

Table 1: Evaluation of Confound Regression Strategies for Dynamic Functional Connectivity

Preprocessing Strategy	Residual Motion-Edge Dispersion Association	Distance-Dependent Artifact Profile	Modularity Identification Quality	Overall Effectiveness
Global Signal Regression (GSR)	Most effective reduction [42]	Reveals distance-dependent profile [42]	Enables robust subnetwork identification [42]	Most consistently effective [42]
24-Parameter Regression (24P)	Less effective [42]	Not specified	Less effective [42]	Less effective [42]
24P + GSR	Most effective reduction [42]	Reveals distance-dependent profile [42]	Enables robust subnetwork identification [42]	Most consistently effective [42]
aCompCor	Less effective [42]	Not specified	Less effective [42]	Less effective [42]

The table below provides a comparative analysis of complete preprocessing pipelines, highlighting their performance in computational efficiency, robustness, and scalability.

Table 2: Comparison of End-to-End fMRI Preprocessing Pipelines

Pipeline Name	Key Features	Processing Time (per subject)	Competitive Advantage
DeepPrep	Deep learning modules (e.g., FastSurferCNN), workflow manager (Nextflow), BIDS app [51]	31.6 ± 2.4 min (with GPU) [51]	10x faster than fMRIPrep; 100% completion ratio on challenging clinical scans [51]
fMRIPrep	Automated, integration of conventional algorithms, BIDS app [51]	318.9 ± 43.2 min (CPU only) [51]	State-of-the-art results, but slower and less robust with distorted brains [51]
NPAIRS Framework	Data-driven, uses prediction (P) and reproducibility (R) metrics for optimization [49] [52]	Not specified	Enables objective optimization without ground truth; identifies subject-specific optimal pipelines [49] [50]

Experimental Protocols for Pipeline Evaluation

Protocol 1: Evaluating Preprocessing Components with the NPAIRS Framework

This protocol uses the NPAIRS (Nonparametric Prediction, Activation, Influence, and Reproducibility reSampling) framework to objectively evaluate the impact of different preprocessing steps without requiring a ground truth [49] [52].

1. Data Requirements:

• A minimum of two independent fMRI runs per subject is required for cross-validation.

2. Preprocessing Components to Test:

• Motion Correction (MC): Rigid-body realignment of volumes [49].
• Motion Parameter Regression (MPR): Regressing out the 6 or 24 head motion parameters from the BOLD time series [49] [42].
• Physiological Noise Correction (PNC): Using a technique like RETROICOR to model noise from cardiac and respiratory cycles [49].
• Temporal Detrending (DET): Removing low-frequency drifts with Legendre polynomials or cosine basis functions [49] [52].
• Global Signal Regression (GSR): Regressing out the average signal from the entire brain [2] [42].
• Subspace Estimation: Denoising via Principal Component Analysis (PCA) or Independent Component Analysis (ICA) [50].

3. Procedure:

• Step 1: Split-Half Resampling. Randomly split the data into two independent sets (Training and Test) numerous times (e.g., 100 iterations) [52].
• Step 2: Pipeline Application. For each split and each pipeline combination, preprocess the training and test data independently.
• Step 3: Model Building & Validation. Build a statistical model (e.g., Penalized Discriminant Analysis) on the training set and apply it to the test set [49] [50].
• Step 4: Metric Calculation. For each split, calculate:
  • Prediction (P): The accuracy of the model in predicting the experimental condition of the test scans [49] [52].
  • Reproducibility (R): The spatial correlation of the activation maps (SPMs) derived from the two independent halves [49] [52].
• Step 5: Optimization. Identify the pipeline that minimizes the distance from the ideal point (P=1, R=1) in the (P,R) metric space, either for the group (fixed pipeline) or for each individual (individual pipeline) [49] [50].

Protocol 2: Assessing Trait-Specific Motion Impact with SHAMAN

The Split Half Analysis of Motion Associated Networks (SHAMAN) method quantifies how much residual motion artifact may be spuriously influencing brain-behavior associations (trait-FC effects), even after denoising [29].

1. Data Requirements:

• Preprocessed resting-state fMRI data for a cohort.
• A continuous trait measure (e.g., cognitive score) for each participant.

2. Procedure:

• Step 1: Denoising. Preprocess the data using a standard pipeline (e.g., including GSR).
• Step 2: Trait-FC Effect Calculation. For each participant, calculate a full-scan FC matrix. Across participants, compute the correlation between the trait and the strength of each functional connection, creating a trait-FC effect map [29].
• Step 3: Split Half. For each participant's rs-fMRI time series, calculate framewise displacement (FD). Split the time series into a "high-motion" half (volumes with FD above the participant's median) and a "low-motion" half (FD below median) [29].
• Step 4: Half-Scan Trait-FC Effect. Calculate a separate trait-FC effect map using only the high-motion halves from all participants, and another using only the low-motion halves.
• Step 5: Motion Impact Score. Quantify the spatial similarity (e.g., correlation) between the full-scan trait-FC effect map and the difference map (high-motion effect - low-motion effect).
  • A positive score indicates motion may be causing overestimation of the trait-FC effect.
  • A negative score indicates motion may be causing underestimation of the trait-FC effect [29].
• Step 6: Significance Testing. Use permutation testing to determine if the motion impact score is statistically significant [29].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Software and Tools for fMRI Pipeline Optimization

Tool Name	Type	Primary Function	Application Context
SPM12	Software Package	Statistical analysis of brain imaging data; used for slice-time correction, motion correction, spatial normalization, etc. [53]	Standard preprocessing for general fMRI studies.
fMRIPrep	Preprocessing Pipeline	Robust, automated preprocessing of fMRI data [51]	Standardized, reproducible preprocessing for group studies.
DeepPrep	Preprocessing Pipeline	Accelerated preprocessing using deep learning models for segmentation and registration [51]	Large-scale datasets and clinical samples with distorted anatomy.
NPAIRS	Software Framework	Data-driven pipeline optimization using prediction and reproducibility metrics [49] [52]	Objective evaluation and selection of optimal preprocessing steps.
Art Toolbox	Quality Assurance Tool	Detection and removal of volumes with excessive motion [53]	Data scrubbing to mitigate the impact of high-motion volumes.
SHAMAN	Analytical Method	Quantifies trait-specific residual motion artifact in functional connectivity [29]	Validating brain-behavior associations against motion confounds.

Visualization of Key Concepts

The Preprocessing Pipeline within the Full Experimental Chain

A typical fMRI study involves a sequence of steps where preprocessing is one critical link that interacts with other design factors [50].

The Role of Global Signal Regression in a Preprocessing Pipeline

GSR can be integrated at various stages to reduce artifacts related to physiological signals and motion [2] [42].

Navigating GSR Pitfalls: From Negative Correlations to Group Comparison Biases

Application Notes

The debate over whether negative correlations in resting-state functional Magnetic Resonance Imaging (fMRI) represent genuine biological anticorrelations or mathematical artifacts of global signal regression (GSR) remains a central issue in computational neuroscience. This document synthesizes current evidence to provide application notes and protocols for researchers investigating functional connectivity, particularly within motion artifact reduction research.

The Core Controversy: GSR, a common preprocessing step, regresses the whole-brain average signal (global signal) from each voxel's time series. A primary criticism is that this process mathematically induces artifactual negative correlations in the resulting functional connectivity matrices, challenging the biological interpretation of observed anticorrelations, such as those between the default mode network (DMN) and task-positive network (TPN) [31].

Key Empirical Evidence: A 2025 study using simultaneous EEG-fMRI provides critical evidence that the neural component of resting-state fMRI-based connectivity is preserved after GSR. The research demonstrated that GSR primarily reduces artifactual connectivity associated with heart rate and breathing fluctuations, while preserving connectivity patterns linked to electrophysiological activity in the alpha and beta frequency ranges [2]. This suggests that negative correlations surviving GSR are likely to be of neural origin.

Furthermore, a 2017 framework redefined GSR as a temporal downweighting process. This model shows that GSR attenuates data from time points with large global signal amplitudes, while data from time points with small global signal magnitudes are largely unaffected. When this process is approximated by censoring high-magnitude time points, the resulting correlation maps—including anticorrelations between the DMN and TPN—show high spatial similarity to those derived from GSR-processed data. This indicates that these anticorrelations inherently exist in the data from time points with small global signal magnitudes and are not simply an artifact of the regression itself [31].

Physiological and Functional Relevance: The global signal itself is not merely noise. A 2025 study established a strong spatial consistency between global signal topography and respiration topography, with regional specificity. This shared topography was linked to behavioral relevance, particularly connecting the default mode network to psychiatric problems. This underscores that the physiological signals contributing to the global signal, such as respiration, may have functional significance for brain-body integration supporting mental health and cognitive function, rather than being mere nuisance factors to be removed [1].

Table 1: Summary of Key Evidence on the Origin of Negative Correlations

Evidence Type	Key Finding	Implication for Negative Correlations
EEG-fMRI Validation [2]	GSR reduces physiological noise (cardiac, respiration) but preserves EEG-linked connectivity.	Supported as Biological Reality; GSR enhances specificity by removing non-neural variance.
Temporal Downweighting Model [31]	GSR approximates censoring of high-GS time points; anticorrelations persist in low-GS data.	Supported as Biological Reality; anticorrelations are inherent in the raw data structure.
Physiological Topography [1]	Respiration patterns overlap with GS topography and share behavioral correlates.	Multifaceted; GS contains biologically relevant physiological information.

Quantitative Data Synthesis

The following tables consolidate quantitative findings from key studies to facilitate comparison and interpretation.

Table 2: Quantitative Findings on GSR's Impact from Recent Studies

Study Focus	Key Metric	Finding with GSR	Finding without GSR	Interpretation
EEG-fMRI Connectivity [2]	Connectivity linked to Alpha/Beta EEG bands	Preserved	Preserved (but confounded by physiology)	GSR removes non-neural confounds without altering neural connectivity.
Anesthesia (Sevoflurane) [5]	Functional Connectivity Differences	Broadly reduced	More pronounced	GSR's effect is context-dependent; may diminish drug-induced connectivity changes.
Anesthesia (Propofol) [5]	Functional Connectivity Differences	Altered specific connections	Specific connections present	GSR's effect is context-dependent and anesthetic-specific.
GS-Respiration Overlap [1]	Spatial Consistency (ICC)	Moderate consistency (Mean = 0.45, SD = 0.09)	Not Applicable	Confirms a significant, region-specific physiological contribution to the GS.

Table 3: Network-Specific Effects of GSR and Related Signals

Brain Network	Effect of GSR on Connectivity	Spatial Consistency with Respiration (RVTCORR) [1]
Default Mode Network (DMN)	Often reveals/increases anti-correlations with TPN [31].	Stronger correlation
Task-Positive Network (TPN)	Often reveals/increases anti-correlations with DMN [31].	Information Not Available
Limbic Network	Information Not Available	Stronger correlation
Salience Network	Information Not Available	Stronger correlation
Visual Network	Information Not Available	Weaker correlation
Dorsal Attention Network	Information Not Available	Weaker correlation

Experimental Protocols

Protocol 1: Assessing Negative Correlations with and without GSR

Objective: To determine the contribution of GSR to the observation of negative correlations in a resting-state fMRI dataset.

Materials: Preprocessed resting-state fMRI data (after slice-timing correction, motion realignment, and band-pass filtering), computing environment (e.g., Python with Nilearn/NiBabel, MATLAB with SPM/DPABI, or AFNI).

Procedure:

• Data Preparation: Use preprocessed fMRI data that has not undergone GSR. Ensure nuisance regressors (e.g., white matter, cerebrospinal fluid signals, and motion parameters) have been applied.
• Define Regions of Interest (ROIs): Select standard network nodes, for example:
  • Default Mode Network (DMN): Posterior Cingulate Cortex (PCC), Medial Prefrontal Cortex (mPFC).
  • Task-Positive Network (TPN): Dorsolateral Prefrontal Cortex (dlPFC), Intraparietal Sulcus (IPS).
• Extract Time Series: Extract the mean time series from each ROI.
• Functional Connectivity without GSR:
  • Calculate the Pearson correlation between all ROI time series.
  • Apply Fisher's Z-transform to the correlation coefficients to normalize the distribution. Store this as the "No-GSR" connectivity matrix.
• Functional Connectivity with GSR:
  • Calculate the global signal (GS) as the average time series across all brain voxels.
  • For each ROI's time series, perform a linear regression to remove the variance explained by the GS. Save the residuals.
  • Calculate Pearson correlations and apply Fisher's Z-transform to the residuals. Store this as the "GSR" connectivity matrix.
• Analysis:
  • Visually compare the two connectivity matrices, focusing on the correlations between DMN and TPN nodes.
  • Statistically compare the DMN-TPN correlation values between the "No-GSR" and "GSR" conditions using a paired t-test.

Protocol 2: Implementing the JumpCor Motion Correction Technique

Objective: To mitigate the impact of large, infrequent head movements on functional connectivity estimates, an alternative to GSR.

Materials: fMRI data, realignment parameters (e.g., rp_*.txt from SPM), software capable of general linear modeling (e.g., AFNI, SPM, FSL).

Procedure:

• Calculate Frame-Wise Displacement (FD): Compute the Euclidean norm of the temporal derivatives of the six realignment parameters (3 translations, 3 rotations). This gives a single FD value for each volume.
• Identify Large Jumps: Apply a user-defined threshold (e.g., 1.0 mm) to the FD time series to flag volumes with excessive motion. These are the "jumps" [3].
• Generate JumpCor Regressors: Segment the time series into blocks separated by the identified large jumps. Create a separate binary regressor (value=1 during the segment, 0 otherwise) for each continuous, low-motion segment.
• Incorporate into Nuisance Regression: Include the complete set of JumpCor regressors in the general linear model (GLM) alongside other standard nuisance regressors (e.g., white matter, CSF, motion parameters).
• Estimate Cleaned Time Series: Run the GLM to estimate and remove the variance associated with all nuisance regressors, including the JumpCor segments. The residuals are the motion-corrected time series for subsequent connectivity analysis [3].

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Materials and Tools for GSR and Motion Artifact Research

Item / Reagent	Function / Purpose	Example / Note
Simultaneous EEG-fMRI	Validates the neural origin of connectivity patterns preserved by GSR by providing a direct electrophysiological correlate [2].	Critical experiment for concluding on biological reality of correlations.
Physiological Recordings	Records cardiac and respiratory cycles to model and account for their direct contributions to the BOLD signal [1].	Required for studies investigating physiological origins of the global signal.
JumpCor Regressors	A set of binary regressors that model signal baseline shifts caused by large, infrequent head motions, reducing residual motion artifacts [3].	Implemented in AFNI; effective for infant studies or populations with high motion.
Temporal Censoring ("Dirtying")	Removes (censors) time points with excessive motion or high global signal magnitude, approximating one effect of GSR [31].	Used to test if anticorrelations exist in "clean" data subsets without GSR.
Canonical Correlation Analysis (CCA)	A multivariate method to identify shared patterns of co-variation between brain topography (e.g., GSCORR) and behavioral measures [1].	Useful for establishing the functional/behavioral relevance of global signal topography.

Global Signal Regression (GSR) remains one of the most contentious preprocessing steps in resting-state functional magnetic resonance imaging (fMRI) analysis. While sometimes criticized for potentially introducing spurious anti-correlations, GSR demonstrates particular efficacy for mitigating motion artifacts, especially in datasets with significant head movement. The central challenge arises when research findings substantively diverge depending on whether GSR is applied—a scenario frequently encountered in motion artifact reduction research. This protocol provides a standardized framework for investigating, interpreting, and reporting these divergent outcomes, with specific application to motion correction techniques like the novel JumpCor method for handling infrequent large movements [3].

Core Concepts and Definitions

Global Signal Regression (GSR)

GSR is a preprocessing technique that removes the average signal across the entire brain from each voxel's time series. This process aims to eliminate globally distributed non-neuronal noise, including motion-related fluctuations, respiratory artifacts, and scanner drift.

Motion Artifacts in fMRI

Head movement during fMRI acquisition introduces complex signal changes that confound functional connectivity estimates. Residual motion artifacts persist even after image realignment due to multiple mechanisms: interpolation errors, spin-history effects, B0-field changes, and movement into regions of non-uniform radio-frequency coil sensitivity [3].

Experimental Protocols for GSR Comparison

Standardized Preprocessing Pipeline

Objective: To establish a consistent preprocessing workflow that enables direct comparison of results with and without GSR.

Materials:

• High-temporal-resolution fMRI data (e.g., multiband acquisition)
• Structural T1-weighted and T2-weighted images
• Motion parameters (6 rigid-body realignment parameters)
• Physiological recordings (respiratory, cardiac) where available

Procedure:

• Initial Processing: Remove initial time points for magnetization stabilization, slice timing correction, motion realignment using rigid-body transformation.
• Data Quality Assessment: Calculate framewise displacement (FD) using the Euclidean norm of temporal differences in realignment parameters. Identify subjects with excessive motion (>0.2 mm mean FD) for separate analysis [3].
• Spatial Processing: Normalize functional data to standard template space, smooth with appropriate kernel (typically 6-8mm FWHM).
• Dual-Pathway Analysis:
  • Pathway A (With GSR): Regress out global signal (mean whole-brain signal) alongside standard nuisance regressors (white matter, CSF signals, motion parameters).
  • Pathway B (Without GSR): Apply identical preprocessing excluding the global signal regressor.
• Connectivity Analysis: Compute functional connectivity matrices using Pearson correlation between regional time series for both pathways.

JumpCor Integration for Large Motion Correction

Objective: To implement and evaluate the JumpCor technique for addressing infrequent large movements in conjunction with GSR.

Materials:

• fMRI time series with volume-to-volume displacement metrics
• General linear modeling software (e.g., AFNI, FSL)

Procedure:

• Jump Detection: Calculate volume-to-volume displacement using Euclidean norm of realignment parameter differences. Identify "jumps" where displacement exceeds defined threshold (e.g., 1mm) [3].
• Regressor Generation: Create binary regressors for each segment between large jumps (value=1 during segment, 0 outside segment).
• Model Integration: Include JumpCor regressors as additional nuisance variables in general linear model, both with and without GSR.
• Artifact Quantification: Compare residual motion-related signal changes across four conditions: (1) No GSR/No JumpCor, (2) GSR only, (3) JumpCor only, (4) GSR + JumpCor.

Table 1: Motion Correction Techniques Comparison

Technique	Mechanism	Best Application Context	Key Limitations
Global Signal Regression (GSR)	Removes brain-wide average signal	Datasets with widespread motion artifacts	May introduce negative correlations; removes neural signal
JumpCor [3]	Models signal changes between large movement segments	Data with infrequent, large motions (>1mm) separated by quiet periods	Requires precise motion parameter estimation
Motion Parameter Regression	Regresses out 6-24 motion parameters	General motion correction	Incomplete for large motions; may remove neural signal
Volume Censoring	Removes motion-contaminated time points	All data types with identifiable motion spikes	Reduces temporal degrees of freedom

Quantitative Assessment of Divergent Results

Metrics for Evaluating GSR Impact

Objective: To quantify differences in functional connectivity outcomes between analyses with and without GSR.

Procedure:

• Calculate intraclass correlation coefficients (ICC) between connectivity matrices derived with and without GSR.
• Compute QC-FC correlations (correlation between subject motion and functional connectivity) for both pipelines.
• Identify connections showing statistically significant differences (p<0.05, FDR-corrected) between GSR conditions.
• Assess test-retest reliability of significant connections across GSR conditions.

Table 2: Impact of GSR on Functional Connectivity Metrics

Metric	Without GSR	With GSR	Divergence Implications
QC-FC Correlation	Typically stronger positive correlations	Substantially reduced	GSR mitigates motion-related spurious correlations
Negative Connectivity	Limited anti-correlations	Increased negative connectivity	May reflect biological reality or artifact
Network Identification	Moderate separation between networks	Enhanced separation between RSNs	Improves network specificity
Motion-Connectivity Relationship	Significant motion-connectivity relationships	Weakened motion-connectivity relationships	Reduces motion confounds

Interpretation Framework for Divergent Findings

Decision Matrix for GSR Application

The following workflow provides a systematic approach for interpreting and addressing divergent results when findings change with and without GSR:

Contextual Considerations for GSR Interpretation

When encountering divergent results based on GSR application, consider these critical factors:

• Motion Severity: GSR provides greater benefit in high-motion datasets. Correlations between motion and connectivity (QC-FC) should reduce with GSR application if motion artifacts are adequately addressed [4].

• Participant Population: Developmental populations (e.g., infants, children) and clinical groups typically exhibit greater motion, potentially increasing GSR's utility. In infant studies with occasional large movements, JumpCor combined with GSR may optimize artifact reduction [3].

• Respiratory Influence: Recognize that respiration significantly contributes to global signal fluctuations. The relationship between respiration volume per time (RVT) and global signal topography shows strong spatial consistency in default mode and salience networks [1]. When respiratory artifacts are prominent, GSR may improve specificity of neural findings.

• Network Specificity: Evaluate whether GSR enhances biologically plausible network organization. Strengthened default mode network anti-correlations with frontoparietal control networks may reflect improved network specificity rather than artifact.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for GSR and Motion Artifact Research

Resource Category	Specific Tools/Solutions	Application Context	Key Function
Analysis Platforms	AFNI [3], FSL, CONN, SPM	fMRI preprocessing and connectivity analysis	Implement GSR, calculate connectivity, motion correction
Motion Quantification	Framewise Displacement (FD), DVARS	Data quality assessment	Quantify subject motion during scan
Specialized Motion Correction	JumpCor [3], ART, ICA-AROMA	Specific motion artifact types	Address large infrequent motions, automated artifact removal
Physiological Monitoring	Respiratory belt, pulse oximeter	Physiological noise correction	Record respiratory and cardiac signals for nuisance regression
Quality Metrics	QC-FC plots, ICC, DVARS	Pipeline validation	Evaluate motion artifact residual, reliability assessment

Reporting Standards for GSR Analyses

Minimum Reporting Requirements

To ensure transparency and reproducibility when reporting studies involving GSR:

• Explicitly state whether GSR was applied and provide justification for the choice.
• Report comparative results for both pipelines (with and without GSR) when findings meaningfully diverge.
• Quantity motion levels using standardized metrics (mean FD, max FD, proportion of censored volumes).
• Describe motion correction methods beyond GSR, including specialized techniques like JumpCor for large motions.
• Acknowledge limitations introduced by GSR decision and potential impact on interpretation.

Divergent results when applying GSR present both challenge and opportunity in motion artifact research. Rather than treating GSR as a binary analytical choice, researchers should implement systematic comparison protocols that evaluate findings across multiple processing streams. The integrated framework presented here—combining traditional GSR with emerging techniques like JumpCor for specific motion profiles—provides a structured approach to navigate these analytical decisions. Through transparent reporting and contextual interpretation, researchers can advance our understanding of how global signal regression influences functional connectivity findings in studies prioritizing motion artifact reduction.

Mitigating Distance-Dependent Artifacts and Other Systematic Biases

Distance-dependent artifacts represent a specific and pernicious form of systematic bias in functional magnetic resonance imaging (fMRI) wherein higher levels of participant motion are associated with inflated short-range functional connectivity and, in some cases, weakened long-distance connections [7]. This artifact is particularly problematic because in-scanner motion frequently correlates with variables of interest such as age, clinical status, and cognitive ability, potentially introducing systematic bias into study conclusions [7]. The phenomenon was notably identified in several seminal studies published in 2012 that demonstrated motion artifacts could significantly alter inference in studies of lifespan development and psychopathology [42] [7].

The spatial distribution of motion artifacts is not uniform across the brain. Motion is minimal near the atlas vertebrae (where the skull attaches to the neck) and increases with distance from this anchor point [7]. Furthermore, areas at the edge of the brain demonstrate large signal increases during motion, likely due to partial volume effects, while brain parenchyma shows signal decrements [7]. The temporal properties of these artifacts include immediate signal drops following movement events that scale with motion magnitude, as well as longer-duration artifacts persisting up to 8-10 seconds potentially due to motion-related physiological changes [7].

Global Signal Regression as a Mitigation Strategy

Global signal regression (GSR) has emerged as one of the most effective techniques for mitigating motion-related artifacts in functional connectivity research. GSR operates by regressing the average signal across the entire brain from each voxel's time series, using the residual signal for subsequent functional connectivity analyses [42]. This approach is conceptually grounded in the observation that motion induces widespread, spatially distributed signal changes that affect global brain signal properties.

Evidence from systematic evaluations indicates that preprocessing pipelines incorporating GSR consistently outperform those without it across multiple benchmarks. In a comprehensive assessment of 12 commonly used confound regression strategies, methods that included GSR were identified as the most consistently effective de-noising strategies for dynamic functional connectivity metrics [42]. These pipelines effectively reduced the residual association between participant motion and edge-wise dispersion in connectivity patterns, a key metric of dynamic functional connectivity stability.

The efficacy of GSR extends to its ability to mitigate the characteristic distance-dependent bias introduced by motion. Studies have confirmed that GSR significantly reduces the spurious inflation of short-range connections that typically correlates with motion parameters [42] [7]. This correction is crucial for studies investigating neurodevelopmental processes or clinical populations where motion may systematically vary between groups and where interpretations often rely on accurate characterization of both local and distributed network organization.

Comparative Evaluation of Artifact Reduction Methods

Performance Benchmarks for De-noising Strategies

Systematic evaluations of motion mitigation strategies employ multiple benchmarks to assess efficacy. These include: (1) the residual association between participant motion and edge dispersion, (2) distance-dependent effects of motion on edge dispersion, (3) the ability to identify functional subnetworks via multilayer modularity maximization, and (4) measures of module reconfiguration such as node flexibility and promiscuity [42]. Across these benchmarks, significant variability exists in the effectiveness of different preprocessing pipelines.

Table 1: Performance Comparison of Motion Mitigation Methods for Functional Connectivity

Method Category	Specific Methods	Effectiveness on Distance-Dependent Bias	Impact on Connectivity Specificity	Key Limitations
Global Signal Regression	GSR with 24 motion parameters	Most effective reduction	Enhances specificity	Controversial interpretation of global signal
Principal Components	aCompCor (PCA-based)	Effective reduction	Better than mean signal methods	Component selection requires consideration
Tissue Mean Signal	WM/CSF mean signal regression	Moderate reduction	Lower specificity than aCompCor	Spatially disparate signals may cancel out
Motion Regression	6-24 motion parameters, derivatives	Limited effectiveness alone	Variable	Does not fully capture spin history effects
Scan Scrubbing	FD-based volume removal	Effective for spike artifacts	Reduces data points	Information loss from discarded volumes

aCompCor as an Alternative Approach

Anatomical Component Correction (aCompCor) represents an alternative noise estimation strategy that uses principal component analysis (PCA) to identify spatially coherent noise components from regions not expected to exhibit neuronal BOLD signals, specifically white matter and cerebral spinal fluid [9]. Unlike mean signal regression, aCompCor does not assume spatially uniform noise signatures, allowing it to capture multiple nuisance signals that might cancel each other out when averaged [9].

Comparative studies demonstrate that aCompCor more effectively attenuates motion artifacts than tissue-mean signal regression and enhances the specificity of functional connectivity estimates [9]. When combined with motion regression, aCompCor achieves similar benefits to GSR in reducing distance-dependent artifacts without the controversy associated with global signal removal. Notably, scan scrubbing provides no additional benefit for motion artifact reduction when using aCompCor, suggesting it provides comprehensive motion mitigation [9].

Experimental Protocols for Motion Mitigation

Implementation of Global Signal Regression

Table 2: Protocol for Implementing GSR in Functional Connectivity Analysis

Step	Procedure	Technical Details	Quality Control
Preprocessing	Standard image preparation	Slice timing correction, realignment, normalization, smoothing	Check alignment and normalization accuracy
Nuisance Regression	Extract global signal and confounds	Calculate mean signal across whole brain mask; include motion parameters	Verify mask excludes non-brain areas
Regression Model	Implement multiple regression	Model: Y = Xβ + ε, where X includes global signal and confounds	Check residuals for systematic patterns
Residual Extraction	Save residual time series	Use cleaned time series for connectivity analysis	Confirm mean-centering of residuals
Connectivity Computation	Calculate correlation matrices	Use Pearson's correlation or alternative measures	Assess network topology plausibility

Protocol for Evaluating Distance-Dependent Artifacts

To quantify and verify the presence of distance-dependent artifacts in a dataset, implement the following experimental protocol:

• Calculate Motion Metrics: Compute framewise displacement (FD) using the Jenkinson formulation, which aligns best with voxel-specific measures of displacement [7]. Convert FD to a standardized measure such as millimeters of RMS displacement per minute to enable comparisons across studies with different acquisition parameters.

• Estimate Connection Distance: For each functional connection (edge), calculate the Euclidean distance between centroid coordinates of the associated brain regions in standard space.

• Quantify Functional Connectivity: Compute correlation matrices from the preprocessed BOLD time series, applying the mitigation strategy under evaluation (e.g., GSR, aCompCor).

• Model Distance-Dependent Effects: Implement a generalized linear model testing the interaction between motion (mean FD) and connection distance on functional connectivity strength: Connectivity ~ FD * Distance + Age + Sex.

• Interpret Results: A significant interaction term indicates persistent distance-dependent artifact, suggesting inadequate motion mitigation. Effective strategies should minimize or eliminate this relationship.

Integrated Pipeline for Comprehensive Motion Mitigation

Based on empirical evaluations, the following integrated pipeline provides robust mitigation of distance-dependent artifacts:

• Volume Realignment: Implement rigid body realignment to a reference volume, generating 6 realignment parameters (3 translations, 3 rotations).

• Nuisance Regression: Include 24 motion regressors (6 basic parameters, 6 derivatives, and their squares) to capture nonlinear motion effects [7] [9].

• Global Signal Regression: Include the global signal as an additional regressor in the nuisance model.

• Temporal Filtering: Apply high-pass filtering (typically 0.008-0.01 Hz) to remove slow-frequency drifts.

• Functional Connectivity Computation: Calculate correlation matrices from the residual time series after nuisance regression.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Computational Tools for Mitigating Distance-Dependent Artifacts

Tool Category	Specific Implementation	Function in Artifact Mitigation	Key References
Motion Quantification	Framewise Displacement (FD) (FSL, Jenkinson)	Quantifies volume-to-volume head motion	[7]
Nuisance Regression	Global Signal Regression (GSR)	Removes whole-brain average signal	[42]
Component-Based Correction	aCompCor (PCA-based noise estimation)	Identifies noise components from WM/CSF	[9]
Signal Quality Metrics	DVARS (RMS variance over voxels)	Measures signal change between volumes	[9]
Data Scrubbing	Framewise Displacement thresholding	Identifies and removes corrupted volumes	[7]
Connectivity Computation	Pearson's correlation, Wavelet coherence	Quantifies temporal synchrony between regions	[42]

Mitigating distance-dependent artifacts remains an essential consideration in functional connectivity research, particularly for studies comparing populations with differential motion characteristics. Global signal regression has demonstrated consistent efficacy in reducing these systematic biases, though alternative approaches like aCompCor also show promising results. The integrated protocol presented here, combining expanded motion regressors with global signal regression, provides a robust defense against motion-related artifacts while preserving neuronal signals of interest.

Future methodological developments will likely focus on dynamic artifact characterization, leveraging machine learning approaches to identify motion-related signal changes with greater spatial and temporal precision. Additionally, multivariate correction approaches that simultaneously account for motion, physiological noise, and other nuisance factors may provide more comprehensive artifact mitigation while minimizing the unnecessary removal of neuronal signals.

Motion during functional magnetic resonance imaging (fMRI) represents a dual challenge—it is both a significant source of technical artifact and a behavioral trait with biological underpinnings. This duality creates particular methodological problems for clinical neuroscience research, where the traits of interest (e.g., psychiatric symptoms, neurodevelopmental conditions) are often systematically correlated with the propensity for head motion. Studies have demonstrated that head motion is a heritable trait (h² = 0.313-0.427) with stable individual differences [54]. Furthermore, physical characteristics such as body mass index (BMI) and weight serve as the strongest predictors of in-scanner motion, explaining approximately 11% of variance, whereas psychological state and trait characteristics explain significantly less variance (approximately 5%) [55]. This relationship creates systematic bias wherein motion artifacts can be mistakenly interpreted as neural effects of clinical conditions [29] [42].

The problem is particularly acute in studies of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and childhood populations, where higher motion levels are intrinsic to the populations being studied [29] [54]. For example, early studies concluding that autism decreases long-distance functional connectivity (FC) may have actually detected artifacts resulting from increased head motion in autistic participants [29]. This revelation necessitates specialized approaches for clinical populations where motion cannot be fully eliminated through participant compliance alone.

Quantitative Evidence: Mapping the Scope of the Problem

Prevalence of Motion-Related Artifacts in Large-Scale Studies

Recent large-scale analyses demonstrate the pervasive nature of motion-related artifacts in neuroimaging research. The following table summarizes key quantitative findings from major studies:

Table 1: Documented Impacts of Head Motion on Functional Connectivity Findings

Study Population	Sample Size	Key Finding	Statistical Evidence	Source
Adolescent Brain Cognitive Development (ABCD) Study	7,270 participants	After standard denoising, 42% (19/45) of traits showed significant motion overestimation; 38% (17/45) showed underestimation	p < 0.05 for motion impact scores	[29]
ABCD Study (post-censoring)	7,270 participants	Censoring at FD < 0.2 mm reduced significant overestimation to 2% (1/45) of traits	Framewise displacement < 0.2 mm	[29]
Extended pedigree sample (Mexican Americans)	689 individuals	Head motion heritability estimate	h² = 0.313	[54]
Human Connectome Project replication	864 individuals	Head motion heritability estimate	h² = 0.427	[54]
Tulsa1000 cohort	464 participants	Physical characteristics (BMI, weight) explain motion variance	10.9% (95% CI: 9.9-11.8)	[55]
Tulsa1000 cohort	464 participants	Psychological characteristics explain motion variance	5% (95% CI: 3.5-6.4)	[55]

Motion as a Confound in Clinical Group Comparisons

The systematic relationship between motion and clinical traits creates specific vulnerabilities for case-control studies. Research has documented that head motion introduces distance-dependent artifacts in functional connectivity, characterized by decreased long-distance connectivity and increased short-range connectivity [29]. This spatial pattern of artifact coincidentally matches theoretical models of neural organization in several neurodevelopmental disorders, creating particular susceptibility for false positive findings.

Beyond autism research, motion has been identified as a significant confound in studies of youth populations, older adults, and patients with neurological or psychiatric disorders [29]. The direction of effect can vary, with motion causing both overestimation and underestimation of true trait-FC relationships depending on the specific clinical population and neural circuits being examined [29].

Methodological Framework: Motion Impact Assessment

The SHAMAN Framework for Quantifying Motion Impact

The Split Half Analysis of Motion Associated Networks (SHAMAN) framework provides a method for computing trait-specific motion impact scores that can distinguish between motion artifact causing overestimation versus underestimation of trait-functional connectivity effects [29]. This approach capitalizes on the observation that traits (e.g., cognitive abilities, clinical symptoms) are stable over the timescale of an MRI scan, while motion is a state that varies from second to second.

Table 2: SHAMAN Framework Components and Applications

Component	Function	Implementation	Output
Split-half analysis	Measures differences in correlation structure between high- and low-motion halves of fMRI timeseries	Divides individual participant's fMRI data based on motion amplitude	Motion impact score
Motion overestimation score	Identifies when motion inflates apparent trait-FC relationships	Alignment of motion impact score direction with trait-FC effect direction	Significance testing (p-value)
Motion underestimation score	Identifies when motion obscures genuine trait-FC relationships	Opposite direction of motion impact score relative to trait-FC effect	Significance testing (p-value)
Permutation testing	Provides statistical significance for motion impact	Non-parametric combining across pairwise connections	p-value for motion impact

The following workflow diagram illustrates the SHAMAN analytical pipeline for assessing motion impact on trait-functional connectivity relationships:

Experimental Protocol: Implementing the SHAMAN Framework

Protocol 1: Trait-Specific Motion Impact Assessment

• Data Requirements: One or more resting-state fMRI scans per participant; trait measures of interest (clinical, cognitive, or behavioral assessments)

• Motion Quantification:

  • Compute framewise displacement (FD) using the Power et al. (2012) method
  • Apply natural log transformation to address right skew in motion distribution [55]
  • Calculate mean FD across all resting-state scans

• Split-Half Analysis:

  • For each participant, divide fMRI timeseries into high-motion and low-motion halves based on FD values
  • Compute functional connectivity matrices separately for each half

• Motion Impact Calculation:

  • Measure differences in correlation structure between high- and low-motion halves
  • Compute motion impact score using permutation testing and non-parametric combining across connections

• Directionality Assessment:

  • Compare direction of motion impact score with direction of trait-FC effect
  • Classify as motion overestimation (aligned directions) or underestimation (opposite directions)

• Statistical Validation:

  • Perform nested cross-validation to avoid overfitting
  • Repeat entire procedure with permutation testing to establish significance

Specialized Denoising Strategies for Clinical Populations

Evaluating Confound Regression Strategies

Systematic evaluations of 12 commonly used participant-level confound regression strategies reveal significant variability in their effectiveness for mitigating motion artifacts in clinical populations [42]. Pipelines that include global signal regression (GSR) consistently emerge as the most effective de-noising strategies across multiple benchmarks, including reducing residual associations between motion and edge dispersion, minimizing distance-dependent effects of motion, and improving identification of functional subnetworks [42].

The following diagram illustrates the comprehensive evaluation workflow for assessing denoising strategy effectiveness:

Experimental Protocol: Dynamic Functional Connectivity Denoising

Protocol 2: Motion Mitigation for Dynamic Functional Connectivity Analysis

• Data Acquisition Parameters:

  • Collect resting-state fMRI with TR = 2000ms, TE = 27ms, FOV = 240mm × 240mm
  • Acquire 2.9mm slice thickness, 128 × 128 matrix, 39 axial slices, 240 repetitions [55]
  • Instruct participants to fixate on a cross with eyes open

• Preprocessing Pipeline:

  • Apply motion correction using 3dVolreg (AFNI) prior to other processing steps
  • Extract six motion parameters (3 translations, 3 rotations)
  • Compute Euclidean norm (ENORM) of the derivative of the six parameters as motion per TR

• Confound Regression Strategies:

  • Implement multiple pipelines including:
    • 6 realignment parameters (6RP)
    • 24 realignment parameters (24RP)
    • Global signal regression (GSR)
    • Local white matter regression
    • Component-based noise correction (CompCor)
    • Combined approaches

• Dynamic Connectivity Analysis:

  • Decompose resting-state timeseries into temporal windows of fixed length
  • Compute functional connectivity within windows using multilayer modularity maximization
  • Calculate dynamic indices: node flexibility, node promiscuity, edge dispersion

• Effectiveness Benchmarking:

  • Quantify residual association between motion and edge dispersion
  • Assess distance-dependent effects of motion on edge dispersion
  • Evaluate identification of functional subnetworks
  • Measure module reconfiguration metrics

Table 3: Essential Resources for Motion Artifact Research in Clinical Populations

Resource Category	Specific Tool/Resource	Function/Purpose	Key Considerations
Motion Quantification	Framewise Displacement (FD)	Quantifies volume-to-volume head movement	Power et al. (2012) method; threshold of FD < 0.2mm recommended [29]
Motion Quantification	Euclidean Norm (ENORM)	Alternative motion metric from AFNI pipeline	Correlates >0.99 with FD; natural log transform addresses skew [55]
Denoising Algorithms	ABCD-BIDS Pipeline	Default denoising for large-scale studies	Includes global signal regression, respiratory filtering, spectral filtering, despiking [29]
Denoising Algorithms	Global Signal Regression (GSR)	Most effective single denoising strategy	Reduces motion-artifact correlations; may introduce distance-dependent artifacts [42]
Impact Assessment	SHAMAN Framework	Quantifies trait-specific motion impact	Distinguishes overestimation vs. underestimation; requires nested cross-validation [29]
Data Resources	ABCD Study Dataset	Large-scale pediatric neuroimaging dataset	n=11,874 children ages 9-10; enables motion-trait correlation studies [29]
Data Resources	Human Connectome Project	Adult neuroimaging with twin design	Enables genetic analyses of motion; n=864 with twin relationships [54]
Statistical Tools	Machine Learning Stack Ensembles	Predicts motion from participant characteristics	Combines multiple algorithms; elastic net, PCR, PLS, SVR [55]

Clinical Application: Case Example in Autism Research

The relationship between biological motion perception and autistic traits illustrates the critical importance of specialized motion handling in clinical populations. Research demonstrates that individuals with higher numbers of autistic traits show reduced inversion effects in binocular rivalry and decreased ability to recognize meaningful human interactions in biological motion tasks [56] [57]. These findings reflect genuine differences in global processing of biological motion rather than motion artifacts.

However, without appropriate motion mitigation strategies, such subtle effects could be obscured or exaggerated by correlated motion artifacts. The implementation of the SHAMAN framework and GSR-based denoising pipelines enables researchers to distinguish true neural correlates of clinical conditions from motion-related artifacts.

For autism studies specifically, researchers should:

• Preprocess data using GSR-inclusive pipelines
• Apply rigorous motion censoring at FD < 0.2mm
• Calculate motion impact scores for all trait-FC relationships
• Report both significant and non-significant motion impact scores to ensure transparency

Based on the current evidence, researchers investigating clinical populations with motion-correlated traits should implement the following best practices:

• Prospective Design: Collect detailed anthropometric data (especially BMI and weight) and account for these variables in recruitment and sampling strategies [55]

• Robust Denoising: Implement global signal regression as part of comprehensive denoising pipelines, while acknowledging potential limitations and distance-dependent artifacts [42]

• Motion Impact Assessment: Apply the SHAMAN framework or similar approaches to calculate trait-specific motion impact scores for all primary analyses [29]

• Transparent Reporting: Document motion levels by clinical group, provide motion impact scores for all reported trait-FC relationships, and specify censoring thresholds

• Data Sharing: Include motion parameters and denoising protocols in shared datasets to enable future meta-analyses and method development

These practices will strengthen the validity of clinical neuroimaging research and enhance our understanding of genuine neural correlates of psychiatric and neurological conditions.

Global Signal Regression (GSR) is a prevalent preprocessing technique in functional magnetic resonance imaging (fMRI) analysis that employs linear regression to remove the global average of the blood-oxygen-level-dependent (BOLD) signal from voxel-wise time series. Its primary purpose is to mitigate non-neural physiological artifacts, including those induced by cardiopulmonary cycles, respiration, and head motion [5] [42]. While its utility in enhancing the spatial specificity of functional connectivity analyses is recognized, the application of GSR in studies of anesthetic-induced unconsciousness requires careful consideration. Emerging evidence indicates that the impact of GSR is not uniform but varies significantly with the specific anesthetic agent employed, potentially due to differing molecular targets and network-level mechanisms of action [5]. This protocol outlines the application of GSR in anesthesia research, emphasizing agent-specific effects on functional brain connectivity and consciousness studies.

Key Quantitative Findings: Agent-Specific Effects of GSR

The following tables summarize core quantitative findings on how GSR differentially affects fMRI metrics under two common anesthetic agents: propofol and sevoflurane.

Table 1: Differential Effects of GSR on Functional Connectivity (FC) and Network Topology

Metric	Propofol Anesthesia with GSR	Sevoflurane Anesthesia with GSR	Key Implication
FC Alterations	Alters specific network connections [5]	Broadly reduces connectivity differences between conscious and unconscious states [5]	Sevoflurane-induced FC changes are more susceptible to global signal removal.
Network Topology	Minimal impact on graph theory measures (e.g., characteristic path length, clustering coefficient) [5]	Significantly diminishes anesthesia-related network alterations [5]	GSR can obscure sevoflurane's true effect on brain network organization.
Structure-Function Coupling	Information not specified in search results	Information not specified in search results	Precision-based FC statistics may optimize this coupling [6].

Table 2: Effects of GSR on Consciousness State Discrimination and Temporal Dynamics

Metric	Effect of GSR	Context & Agent	Research Implication
Integration-Segregation Difference (ISD)	Decrease in ISD during Loss of Responsiveness (LOR) remains consistent even without GSR [58].	Propofol-induced unconsciousness	The ISD metric for consciousness state discrimination is robust to GSR.
Temporal Variability Indices	Decreases similarly between states regardless of GSR application [5].	Propofol & Sevoflurane	A GSR-robust signature of state transition.
Motion Artifact Reduction	Pipelines with GSR are among the most effective at minimizing motion-connectivity relationships [42].	General fMRI preprocessing	Supports GSR's role in de-noising, though may introduce distance-dependent profile.

Experimental Protocols

Protocol 1: Assessing Agent-Specific GSR Effects on Functional Connectivity

This protocol is designed to compare the effects of GSR on functional connectivity and network measures under different anesthetic agents.

• Primary Objective: To quantify the differential impact of GSR on functional brain organization during propofol- versus sevoflurane-induced unconsciousness.
• Experimental Groups:
  • Propofol anesthesia (e.g., effect-site concentration: 2.0 µg/mL)
  • Sevoflurane anesthesia (e.g., 1.75 MAC)
  • Conscious baseline (same subjects)
• fMRI Data Acquisition:
  • Scanner: 3T MRI scanner.
  • Sequence: Gradient echo-planar imaging (EPI).
  • Parameters: TR/TE = 220/30 ms, FA = 90°, FOV = 192 × 192 mm, matrix = 64 × 64.
  • Duration: 540 seconds per state (e.g., baseline, unconsciousness) [5].
• Data Preprocessing (Two Parallel Pipelines):
  • Pipeline with GSR: Include global signal regression as a confound regressor.
  • Pipeline without GSR: Otherwise identical preprocessing.
  • Common Steps: Slice-time correction, realignment, co-registration to T1-weighted image, normalization to standard space (e.g., MNI), and spatial smoothing.
  • Software: fMRIPrep or equivalent [5].
• Key Outcome Measures:
  • Functional Connectivity (FC): Calculate pairwise correlation matrices between brain regions (e.g., using Schaefer atlas).
  • Graph Theory Metrics: From FC matrices, compute global efficiency, clustering coefficient, and characteristic path length [5] [58].
  • Temporal Variability: Quantify dynamic state-to-state transitions of network activity [5].
• Statistical Analysis: Use repeated-measures ANOVA to test for interaction effects between anesthetic agent (propofol vs. sevoflurane) and preprocessing pipeline (with vs. without GSR) on outcome measures.

Protocol 2: Validating Consciousness Metrics with and without GSR

This protocol tests the robustness of consciousness biomarkers to the GSR preprocessing step.

• Primary Objective: To evaluate whether the Integration-Segregation Difference (ISD) and other network-based consciousness metrics remain valid indicators of state transition without GSR.
• Experimental Design: Within-subject study measuring brain activity during conscious baseline (awake), loss of responsiveness (LOR) under propofol, and recovery.
• fMRI Data Acquisition: As detailed in Protocol 1.
• Data Analysis:
  • Preprocess data with and without GSR.
  • Calculate dynamic functional connectivity using a sliding window approach.
  • Compute Key Metrics:
    • Integration: Using multi-level global efficiency.
    • Segregation: Using multi-level global clustering coefficient.
    • ISD: Defined as Integration minus Segregation [58].
  • Compare the performance (e.g., Area Under the Curve - AUC) of ISD, integration alone, and segregation alone in classifying awake vs. unresponsive states, for both pipelines.
• Validation: The metric is considered robust if the AUC for state classification remains high and statistically significant in the pipeline without GSR [58].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Reagents for Anesthesia-fMRI Studies

Item Name	Function/Application	Specific Example / Note
Anesthetic Agents	To induce and maintain controlled states of unconsciousness.	Propofol (intravenous), Sevoflurane (volatile/inhalation) [5].
fMRI Analysis Software	For data preprocessing, denoising, and analysis.	fMRIPrep, SPM, FSL, CONN, PySPI (for multiple FC statistics) [5] [6].
Brain Parcellation Atlas	To define regions of interest for time-series extraction and FC analysis.	Schaefer atlas (e.g., 100x7), AAL, Harvard-Oxford Atlas [6].
Physiological Monitors	To monitor anesthetic depth and physiological stability during scanning.	Bispectral Index (BIS) monitor, ECG, pulse oximeter, end-tidal CO₂ [5].
Confound Regressors	To model and remove non-neural signals from BOLD data.	24 head motion parameters, white matter signal, cerebrospinal fluid signal, and Global Signal (for GSR pipeline) [42].

Experimental Workflow and Signaling Pathways

The following diagram illustrates the core decision-making workflow and analytical pathway for applying GSR in an anesthesia consciousness study.

GSR Application Workflow in Anesthesia Studies

The diagram above outlines the critical branching point where researchers choose to include or exclude GSR, leading to parallel analytical pipelines whose results must be compared.

The diagram below conceptualizes the key brain network affected by anesthesia and analyzed using GSR, highlighting the "Synergistic Global Workspace."

Synergistic Global Workspace in Consciousness

This synergistic workspace, comprising gateway and broadcaster regions, is a proposed neural substrate of consciousness. Its functional integrity, which can be measured with and without GSR, is disrupted under general anesthesia [59].

Measuring GSR Efficacy: Validation Frameworks and Comparative Performance

Global Signal Regression (GSR) remains one of the most contentious preprocessing steps in functional magnetic resonance imaging (fMRI) analysis. While it effectively removes global artifacts driven by motion and physiological sources, it also introduces mathematical consequences that can complicate interpretation of functional connectivity. This application note provides a structured framework for benchmarking GSR performance, focusing on key metrics and experimental protocols essential for rigorous evaluation in motion artifact reduction research. We synthesize evidence from recent studies to establish standardized assessment criteria that enable direct comparison across preprocessing pipelines and experimental conditions.

Core Benchmarking Metrics

A comprehensive evaluation of GSR performance requires assessment across multiple complementary metric categories. The table below summarizes the key quantitative indicators for benchmarking GSR efficacy.

Table 1: Core Metrics for Evaluating GSR Performance

Metric Category	Specific Metric	Interpretation	Optimal Direction
Motion Artifact Control	Motion-Connectivity Correlation	Residual relationship between motion and connectivity measures	Decrease [27]
	Distance-Dependent Artifact	Presence of distance-dependent effects on connectivity	Decrease [27]
Functional Connectivity Quality	Network Identifiability	Ability to identify modular network structure	Increase [27]
	Behavioral Variance Explained	RSFC-explained variance in behavioral measures	Increase [20]
Statistical Properties	Negative Correlation Introduction	Presence of mathematically introduced negative correlations	Context-dependent [20]
Prediction Accuracy	Behavioral Prediction Accuracy	Accuracy in predicting behavioral phenotypes from RSFC	Increase [20]

Quantitative Benchmarks from Literature

Empirical studies provide reference values for GSR performance benchmarks:

• Behavioral Variance Explained: GSR increased behavioral variance explained by whole-brain Resting-State Functional Connectivity (RSFC) by an average of 47% across 23 behavioral measures in the Brain Genomics Superstruct Project (GSP) and by 40% across 58 measures in the Human Connectome Project (HCP) [20].
• Prediction Accuracy: GSR improved behavioral prediction accuracies by an average of 64% (GSP) and 12% (HCP) when applied after ICA-FIX de-noising [20].
• Task vs. Self-Report Measures: GSR appears to benefit task performance measures more than self-reported measures [20].

Experimental Protocols for GSR Evaluation

Protocol 1: Benchmarking Motion Artifact Reduction

Purpose: To evaluate GSR's efficacy in controlling motion artifacts while assessing potential trade-offs.

Workflow:

• Data Acquisition: Acquire resting-state fMRI data with simultaneous monitoring of physiological parameters (cardiac, respiration) and head motion [20] [5].
• Preprocessing: Implement parallel pipelines with and without GSR, keeping other preprocessing steps identical [5].
• Motion-Connectivity Assessment: Calculate correlation between framewise displacement (or other motion metrics) and functional connectivity measures [27].
• Distance-Dependence Evaluation: Assess whether motion-artifact correlations vary with distance between brain regions [27].
• Network Identification: Apply modularity algorithms to quantify network identifiability with and without GSR [27].

Expected Outcomes: GSR typically minimizes the relationship between connectivity and motion but may introduce distance-dependent artifact [27].

Protocol 2: Evaluating Behavioral Associations

Purpose: To determine whether GSR strengthens or weakens associations between RSFC and behavioral measures.

Workflow:

• Behavioral Measures Collection: Collect multiple behavioral measures across cognitive, personality, and emotion domains [20].
• Variance Component Modeling: Apply variance component models to estimate behavioral variance explained by whole-brain RSFC with and without GSR [20].
• Kernel Ridge Regression: Implement kernel ridge regression for RSFC-based behavioral prediction to complement variance component results [20].
• Cross-Validation: Employ cross-validation strategies to ensure generalizability of findings [20].
• Comparative Analysis: Compare results across different denoising strategies (e.g., ICA-FIX with and without GSR) [20].

Expected Outcomes: GSR typically strengthens associations between RSFC and most behavioral measures in young healthy adults [20].

Protocol 3: Assessing Anesthetic-Specific Effects

Purpose: To evaluate GSR effects under different pharmacological conditions.

Workflow:

• Anesthesia Administration: Collect fMRI data during different anesthetic states (e.g., propofol vs. sevoflurane) [5].
• Temporal Variability Analysis: Calculate temporal variability indices with and without GSR [5].
• Frequency Domain Analysis: Compute amplitude of low-frequency fluctuations (ALFF) under both preprocessing conditions [5].
• Functional Connectivity Analysis: Assess changes in specific network connections with and without GSR [5].
• Graph Theoretical Analysis: Calculate graph theory metrics (characteristic path length, clustering coefficient, global/local efficiency) [5].

Expected Outcomes: GSR effects are anesthetic-specific, with sevoflurane-induced changes being particularly sensitive to global signal removal [5].

Visualization Framework

GSR Benchmarking Workflow

GSR Performance Trade-offs

The Scientist's Toolkit

Table 2: Essential Research Reagents and Resources

Resource	Specifications	Application in GSR Research
fMRI Datasets	Brain Genomics Superstruct Project (GSP); Human Connectome Project (HCP) [20]	Provide standardized, high-quality resting-state fMRI data for benchmarking studies
Analysis Software	fMRIPrep [5]; CBIG preprocessing tools [20]	Ensure reproducible preprocessing pipelines with and without GSR
Statistical Models	Variance Component Model [20]; Kernel Ridge Regression [20]	Quantify behavioral variance explained and prediction accuracy
Motion Quantification	Framewise displacement; DVARS [27]	Provide quantitative measures of motion artifacts for correlation analysis
Network Analysis Tools	Graph theory algorithms; Modularity detection [5]	Assess functional network organization and identifiability
Physiological Monitors	Cardiac rhythm; Respiratory monitoring; BIS monitors [5]	Track physiological signals contributing to global signal

Benchmarking GSR performance requires a multi-dimensional approach that acknowledges the technique's trade-offs. The metrics and protocols outlined here provide a standardized framework for evaluating whether GSR strengthens or weakens specific neural-behavioral associations in a given research context. Evidence suggests that GSR generally improves behavioral variance explained and prediction accuracy while effectively controlling motion artifacts, though it may introduce distance-dependent effects and vary in efficacy across different experimental conditions and populations. Researchers should select benchmarking metrics aligned with their specific scientific goals rather than seeking a universal preprocessing solution.

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool for investigating the brain's spontaneous functional architecture. However, the extraction of meaningful neural signals is profoundly complicated by the presence of non-neural noise, particularly from head motion and physiological fluctuations (cardiac and respiratory cycles). These artifacts can mimic or obscure true neural correlations, potentially leading to spurious findings in functional connectivity (FC) analyses [45] [29]. The challenge is especially acute in clinical populations where motion may be more prevalent and correlates with the traits under study, creating systematic biases that can produce both false-positive and false-negative results [29].

Global Signal Regression (GSR) has been a widely used denoising approach. It operates on the premise that widespread noise can be captured and removed by regressing out the average signal from the entire brain. Despite its efficacy in removing certain artifacts, GSR remains controversial because it may also remove neuronal signals of interest and can alter the interpretation of connectivity networks [38]. This application note frames GSR within the current landscape of denoising methodologies, providing a quantitative comparison and detailed protocols for alternative strategies that aim to achieve a superior balance between noise removal and signal preservation.

Denoising Methodologies: Mechanisms and Workflows

Global Signal Regression (GSR)

GSR is a model-driven technique that adds the global mean signal—the average time series across all voxels within the brain—as a nuisance regressor in a general linear model (GLM). Its primary strength is the reduction of widespread physiological noise. However, a significant drawback is its potential to introduce negative correlations in connectivity matrices and remove globally synchronized neural activity, which is a topic of ongoing debate in the field [38].

Component-Based Noise Correction (CompCor)

CompCor is a data-driven method that addresses the spatial heterogeneity of noise. Instead of using a single global regressor, it uses Principal Component Analysis (PCA) to extract the main sources of noise from regions unlikely to contain neural signal.

• Anatomical CompCor (aCompCor): Derives noise regressors from Principal Components (PCs) of signals within masks of white matter (WM) and cerebrospinal fluid (CSF) [60].
• Temporal CompCor (tCompCor): Identifies noise voxels based on high temporal standard deviation, creating a noise ROI without requiring an anatomical scan [60].

A key advantage of CompCor over GSR is that it does not assume noise is spatially uniform, potentially offering a more refined removal of local physiological artifacts [60].

ICA-AROMA

ICA-AROMA (Independent Component Analysis - Automatic Removal of Motion Artifacts) is a data-driven strategy that automatically identifies and removes motion-related artifacts from fMRI data.

• Decomposition: The fMRI data is decomposed into spatially independent components using ICA.
• Classification: Components are classified as noise or signal based on four robust features: high-frequency content, correlation with motion parameters, edge fraction, and CSF fraction [61].
• Regression: The time series of components classified as motion artifacts are regressed out of the data.

ICA-AROMA effectively reduces motion artifacts without the need for censoring data volumes, thereby preserving temporal degrees of freedom (tDoF) and maintaining the integrity of the time series for subsequent statistical analysis [61].

Multi-Echo Approaches

Multi-echo (ME) fMRI acquisition involves collecting data at multiple echo times, which provides a direct handle for separating BOLD signal from non-BOLD noise based on their distinct TE-dependencies.

• TE-Dependent ANAlysis (tedana): This software package leverages the multi-echo data to classify and remove components that do not exhibit the linear TE-dependence expected for the BOLD signal [62].
• Robust-tedana: An optimized, automated pipeline that incorporates Marchenko-Pastur PCA (MPPCA) for thermal noise reduction and robust ICA for stabilized decomposition, mitigating the need for manual intervention [62].
• Tensor-Based MP-PCA: An advanced denoising method that exploits the tensor structure of multi-echo data, significantly outperforming conventional matrix-based denoising in improving temporal signal-to-noise ratio (tSNR) [63].

Table 1: Summary of Core Denoising Methodologies

Method	Category	Underlying Principle	Primary Use Case
GSR	Model-driven	Regresses out the global mean signal from the brain	Reduction of global, widespread noise
CompCor	Data-driven	Derives noise regressors from PCA of WM/CSF signals (aCompCor) or high-tSTD voxels (tCompCor)	Removal of localized physiological noise
ICA-AROMA	Data-driven	Uses ICA to identify and regress out motion-specific components	Specific removal of motion artifacts
Multi-Echo (tedana)	Data-driven	Uses TE-dependence to separate BOLD signal from noise in multi-echo data	Optimal denoising for multi-echo acquisitions

Experimental Workflow for Denoising Strategy Evaluation

A typical experimental framework for comparing the efficacy of these denoising pipelines involves a multi-stage process, from data acquisition to quantitative benchmarking. The workflow below illustrates the key stages involved in such a comparative analysis.

Diagram 1: Experimental workflow for evaluating denoising pipelines, from data acquisition to a composite performance index.

Quantitative Performance Comparison

Performance Across Quality Metrics

Evaluating denoising pipelines requires a multi-faceted approach, as no single metric provides a complete picture. Different pipelines excel in different domains, such as artifact removal, signal preservation, and network identifiability.

Table 2: Quantitative Performance Comparison of Denoising Methods

Method	Motion Artifact Reduction	Physiological Noise Reduction	Resting-State Network (RSN) Identifiability	Temporal Degrees of Freedom (tDoF) Lost	Impact on Brain-Behavior Correlations
GSR	Moderate [38]	High (removes more low-frequency signal) [38]	Variable; can alter network correlations [38]	Low (one regressor)	Can increase behavioral correlations [38] but may also bias trait-FC effects [29]
CompCor (aCompCor)	Moderate [61]	Moderate (better at high-frequency) [38]	Good	Low (k regressors)	Associated with relatively higher age-related FC differences [38]
ICA-AROMA	High (similar to scrubbing) [61]	Moderate	High reproducibility [61]	Low (preserves tDoF) [61]	Offers reasonable compromise for behavioral prediction [64]
Multi-Echo (e.g., tedana)	High [62]	High (uses TE-dependence)	High (preserves signal of interest) [62]	Low	Superior denoising efficacy, but no single pipeline was optimal for all behavior [64]
Combined (aCompCor + ICA-AROMA)	High [65]	High	High	Moderate (k + m regressors)	Not specified in results

To streamline comparison, studies have proposed composite indices. One 2025 study found that a pipeline combining regression of mean WM, CSF, and global signals offered the best compromise between artifact removal and RSN preservation [45]. Furthermore, multi-echo pipelines generally demonstrate superior denoising efficacy compared to single-echo approaches, with an ME pipeline combining ICA-AROMA and RIPTiDe providing a good balance between denoising and brain-behavior prediction [64].

It is critical to note that even advanced denoising does not completely eliminate motion's influence. A 2025 analysis of the ABCD study showed that after standard denoising, head motion still explained 23% of the signal variance and had a large, systematic impact on functional connectivity estimates, which could spuriously inflate or deflate brain-behavior associations [29].

Detailed Experimental Protocols

Protocol: Benchmarking Denoising Pipelines

This protocol is adapted from a multi-metric comparison study [45].

• Participants: 53 healthy adults (age 52.74 ± 21.12 years, 28F/25M). Exclusion criteria: IQ < 70, history of substance abuse, neurological illness.
• Data Acquisition:
  • Scanner: 3T Philips Achieva DStream.
  • rs-fMRI: T2*-weighted EPI sequence (FOV: 120×256×256 mm³, voxel size: 2×2×2 mm³, TE: 30 ms, TR: 2500 ms, flip angle: 90°, 200 volumes).
  • Structural: 3D T1-weighted TFE SENSE sequence (1×1×1 mm³ isotropic).
• Minimal Preprocessing: Includes slice-timing correction, motion realignment, and co-registration to structural images.
• Denoising Pipelines: Nine different pipelines from the HALFpipe software are applied in parallel to the minimally preprocessed data. These include strategies based on GSR, CompCor, ICA-AROMA, and their combinations.
• Quality Metrics Calculation:
  • Artifact Removal: Quantify the reduction in motion-related variance and physiological noise.
  • Signal Enhancement: Measure the temporal signal-to-noise ratio (tSNR).
  • RSN Identifiability: Evaluate the spatial specificity and reproducibility of standard resting-state networks (e.g., Default Mode Network).
• Summary Performance Index: A composite score is computed for each pipeline, balancing noise removal against the preservation of neural information.

Protocol: Combined aCompCor and ICA-AROMA

This protocol outlines the steps for implementing a powerful combined denoising strategy [65].

• Preprocessing: Perform minimal preprocessing, including motion correction and spatial normalization, using a tool like fMRIPrep.
• Confound Regressor Extraction (Parallel Processes):
  • aCompCor: Run aCompCor on the preprocessed data to extract the top 5 principal components from the union of white matter and CSF masks.
  • ICA-AROMA: Run ICA-AROMA on the same preprocessed data to obtain the time series of components classified as noise.
• Nuisance Regression: Combine the confound regressors from both steps (aCompCor components and ICA-AROMA noise time series) into a single nuisance matrix. Use a tool like Nilearn's clean_img function to regress them out of the preprocessed fMRI data in a single step.

Protocol: Multi-Echo Denoising with Robust-tedana

This protocol utilizes the Robust-tedana pipeline for automated denoising of multi-echo data [62].

• Data Acquisition: Acquire multi-echo fMRI data. Example parameters: Multi-echo EPI sequence with multiple TEs (e.g., TE1=12ms, TE2=28ms, TE3=44ms) and a TR that allows for sufficient sampling across echoes.
• Preprocessing: Include motion correction and distortion correction. MPPCA denoising can be incorporated at this stage for thermal noise reduction.
• tedana Workflow:
  • Optimal Combination: Combine the data from the different echoes, weighted by their TE and contrast-to-noise ratio.
  • ICA Decomposition: Perform robust ICA on the optimally combined data to obtain independent components.
  • Component Classification: Classify components as BOLD or non-BOLD based on their TE dependence and other features. Robust-tedana modifies this step for improved stability.
  • Denoised Data Generation: Create the final output by retaining only the signal from components classified as BOLD.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Software Tools for fMRI Denoising

Tool / Resource	Function	Application Context
HALFpipe [45]	Standardized workflow for task and resting-state fMRI analysis from raw data to group stats.	Provides a containerized environment for reproducible application and comparison of multiple denoising pipelines.
ICA-AROMA [61]	Automatic identification and removal of motion-related artifacts from fMRI data.	A standalone tool for robust motion denoising without requiring training; can be combined with other methods.
tedana [62]	TE-dependent analysis for denoising multi-echo fMRI data.	The primary tool for processing and denoising multi-echo fMRI datasets.
fMRIPrep [45]	Robust and standardized preprocessing of fMRI data.	Often serves as the foundational preprocessing step before applying specific denoising pipelines like ICA-AROMA or CompCor.
Nilearn [65]	Python library for statistical analysis of neuroimaging data.	Provides high-level functions (e.g., clean_img) for convenient nuisance regression after confound extraction.
CompCor Algorithm [60]	Code for component-based noise correction.	Integrated into many processing toolboxes (e.g., CONN, fMRIPrep) for aCompCor and tCompCor denoising.

Selecting an Optimal Denoising Strategy

The choice of denoising pipeline is not one-size-fits-all and should be guided by the research question, data characteristics, and potential confounds. The following decision diagram outlines key considerations for selecting an appropriate strategy.

Diagram 2: A decision framework for selecting an fMRI denoising strategy based on data type and research goals.

Concluding Synthesis

Within the context of thesis research on GSR, it is evident that while GSR is a powerful tool for mitigating global physiological noise, the field is moving towards more sophisticated, data-driven approaches. Methods like ICA-AROMA and CompCor offer more targeted removal of specific noise types, with ICA-AROMA proving exceptionally capable against motion artifacts without distorting temporal structure [61]. For researchers seeking the highest possible data quality, multi-echo acquisition combined with tedana represents the current state-of-the-art, leveraging the fundamental physics of the BOLD signal to achieve superior noise separation [62] [64].

Critically, the selection of a denoising pipeline has tangible implications for downstream inferences. The persistence of residual motion artifacts, even after aggressive denoising, can significantly bias associations between functional connectivity and individual traits [29]. Therefore, it is strongly recommended that researchers benchmark multiple pipelines against their specific data and research objectives, using a multi-metric framework [45] to identify the optimal compromise between noise removal and the preservation of signals of interest.

The quest to establish robust associations between brain organization and individual behavioral phenotypes is a central goal of modern neuroscience. Leveraging large-scale, multi-modal datasets from initiatives like the Human Connectome Project (HCP) and the Adolescent Brain Cognitive Development (ABCD) Study has been instrumental in advancing this pursuit. These datasets provide the statistical power necessary to characterize brain-behavior relationships at an individual level, moving beyond group-level comparisons. A significant challenge in this endeavor is the confounding influence of in-scanner head motion, which systematically biases functional MRI (fMRI) signals and can lead to spurious findings. This application note synthesizes evidence from the HCP and ABCD studies to outline best practices for strengthening brain-behavior associations, with a particular focus on the role of global signal regression and other denoising techniques within the broader context of motion artifact reduction.

Comparative Predictive Utility of MRI Modalities

Different MRI modalities capture distinct aspects of brain architecture and function. Direct comparisons using HCP and ABCD data reveal their relative strengths in predicting individual behavioral variations.

Table 1: Predictive Performance of Different MRI Modalities for Behavior

MRI Modality	Extracted Features	Prediction Performance	Key Behavioral Domains
Functional Connectivity (FC)	Resting-state and task-based FC matrices [66]	Best performance; especially when combining resting & task FC [66]	Cognition (best predicted), other domains [66]
Anatomical T1	Cortical thickness, surface area, volume [66]	Lower than FC [66]	Cognition (better predicted than other domains) [66]
Diffusion MRI	DTI/NODDI metrics, tractography (stream count, length) [66]	Lower than FC [66]	Cognition (better predicted than other domains) [66]
Multimodal Combination	Combined features from T1, dMRI, and fMRI [66]	Improves prediction for cognition, but not for other behavioral components; similar to combining rest/task FC [66]

Optimizing Study Design: The Interplay of Scan Time and Sample Size

The design of brain-wide association studies (BWAS) requires careful consideration of the trade-off between scan duration per participant and total sample size. Evidence from the HCP and ABCD datasets provides a quantitative framework for this optimization [15].

Table 2: Impact of Scan Time and Sample Size on Prediction Accuracy

Factor	Impact on Prediction Accuracy	Empirical Findings from HCP/ABCD
Total Scan Duration	Accuracy increases with total scan duration (Sample Size × Scan Time per Participant); shows diminishing returns [15].	For scans ≤20 min, accuracy increases linearly with the logarithm of total duration [15].
Sample Size (N)	Increasing sample size improves accuracy; ultimately more important than extending scan time [15].	With a fixed total scan duration, larger N yields higher accuracy than longer T [15].
Scan Time per Participant (T)	Increasing scan time improves accuracy, but with strong diminishing returns beyond 20-30 minutes [15].	For a fixed total duration, 30-minute scans are most cost-effective; 10-minute scans are inefficient [15].
Cost Efficiency	Joint optimization of N and T can boost accuracy while cutting costs; participant overhead costs are a key factor [15].	30-minute scans yield ~22% cost savings compared to 10-minute scans [15].

The Challenge of Motion Artifacts and Reduction Strategies

Head motion is a major source of artifact in fMRI, threatening the validity of brain-behavior associations. It introduces systematic biases, such as decreased long-distance and increased short-range connectivity, which can be mistaken for neural effects, particularly in populations prone to greater movement (e.g., children or individuals with certain disorders) [67].

Table 3: Motion Artifact Impact and Reduction Efficacy

Concept/Method	Description	Key Evidence
Motion Impact on FC	Spatially systematic bias: reduces long-distance, increases short-range FC [67].	Motion-FC effect matrix strongly negatively correlates (Spearman ρ ≈ -0.58) with average FC matrix [67].
Standard Denoising (ABCD-BIDS)	Includes global signal regression, respiratory filtering, motion parameter regression, despiking [67] [68].	Reduces motion-related signal variance by ~69% compared to minimal processing alone [67].
Motion Censoring ("Scrubbing")	Removing high-motion frames (e.g., Framewise Displacement > 0.2 mm) [67].	Censoring at FD < 0.2 mm reduces traits with significant motion overestimation from 42% to 2% [67].
Global Signal Regression (GSR)	Regression of the average signal across the entire brain [68].	Combined with motion censoring, shown to be among the best methods for eliminating motion artifacts [68].
Trait-Specific Motion Impact (SHAMAN)	Method to quantify if a specific trait-FC relationship is biased by motion, indicating over- or underestimation [67].	After standard denoising, 42% of traits showed motion overestimation; 38% showed underestimation [67].

Experimental Protocols for Robust Brain-Behavior Research

Protocol: Preprocessing of fMRI Data using the ABCD-HCP BIDS Pipeline

This protocol outlines the standardized processing steps for functional MRI data, as implemented in the ABCD-HCP BIDS pipeline [68].

• Stage 1: PreFreeSurfer (Anatomical Preprocessing)

  • Inputs: T1-weighted (T1w) and T2-weighted (T2w) images.
  • Procedures: Denoising and N4 bias field correction using ANTs; gradient non-linearity distortion correction; initial brain extraction.
  • Output: distortion-corrected, brain-extracted structural images.

• Stage 2: FreeSurfer

  • Input: Preprocessed T1w image (and T2w if available).
  • Procedures: Automated cortical surface reconstruction, segmentation of subcortical structures, and tissue classification (gray matter, white matter, CSF).
  • Output: Surface models (white and pial) and subcortical segmentations.

• Stage 3: PostFreeSurfer

  • Input: FreeSurfer outputs and preprocessed T1w.
  • Procedures: Generation of CIFTI grayordinate files; surface registration to the Conte-69 atlas; MNI space registration of volumetric data using ANTs.
  • Output: Data aligned to standard surface and volume spaces.

• Stage 4: FMRIVolume (Functional Preprocessing)

  • Inputs: fMRI time series, spin-echo field maps with opposite phase encoding directions.
  • Procedures: Gradient distortion correction; motion realignment (rigid-body to the first volume); susceptibility distortion correction using FSL's topup; registration to the T1w image and MNI space.
  • Output: Motion-corrected, distortion-corrected fMRI volumes in native and standard spaces.

• Stage 5: FMRISurface

  • Input: Volume time series from FMRIVolume and surface models.
  • Procedures: Projection of the fMRI data from volume to the native surface, and subsequently to the standard grayordinate space.
  • Output: fMRI time series in CIFTI format (e.g., dtseries.nii).

• Stage 6: DCANBOLDProcessing (DBP - Nuisance Regression)

  • Input: CIFTI time series and motion parameters from FMRIVolume.
  • Procedures:
    • Respiratory Motion Filtering: Optional filtering of respiratory artifacts from motion parameters [68].
    • Standard Pre-processing: De-meaning and de-trending.
    • Nuisance Regression: General linear model (GLM) including:
      • Global Signal Regression (GSR): Mean signal across the entire brain.
      • Tissue Signals: Mean time series from white matter and CSF.
      • Motion Parameters: 6 rigid-body parameters and their derivatives (Volterra expansion).
    • Motion Censoring: Identification and removal of "bad" frames exceeding a Framewise Displacement (FD) threshold (e.g., 0.3 mm for GLM estimation, with flexible thresholds for final analysis).
    • Band-Pass Filtering: 2nd order Butterworth filter (0.008 - 0.09 Hz).
  • Output: Fully preprocessed and denoised fMRI time series.

Protocol: Assessing Trait-Specific Motion Impact with SHAMAN

The Split Half Analysis of Motion Associated Networks (SHAMAN) is a method to assign a motion impact score to specific trait-FC relationships, determining if motion causes overestimation or underestimation of effects [67].

• Data Preparation:

  • Obtain preprocessed resting-state fMRI data and a phenotypic trait of interest.
  • Calculate Framewise Displacement (FD) for each timepoint and for each participant.

• Split-Half Partitioning:

  • For each participant, split their fMRI time series into two halves: a high-motion half (timepoints with FD above the participant's median FD) and a low-motion half (timepoints with FD below the median).

• Connectivity and Correlation Calculation:

  • Compute a functional connectivity (FC) matrix for each half for each participant.
  • For each half (high and low) and for each FC edge, calculate the correlation coefficient (e.g., Spearman's ρ) across participants between the FC strength and the trait value.

• Motion Impact Score Calculation:

  • For each FC edge, calculate the difference in trait-FC correlation between the two halves: Motion Impact Score = ρ_high_motion - ρ_low_motion.
  • A positive score indicates that higher motion inflates the trait-FC correlation (overestimation).
  • A negative score indicates that higher motion deflates the trait-FC correlation (underestimation).

• Statistical Inference:

  • Use permutation testing (e.g., shuffling the high/low labels) to generate a null distribution of motion impact scores.
  • Calculate a p-value for the observed motion impact score for each trait-FC relationship.

Resource Category	Specific Tool / Resource	Function and Application
Software Pipelines	ABCD-HCP BIDS fMRI Pipeline [68]	Integrated structural and functional preprocessing, incorporating HCP-style analysis and DCAN Labs nuisance regression tools.
Motion Correction & Quantification	FSL eddy [69], SHORELine [69], Framewise Displacement (FD) [67]	Correction for diffusion MRI motion artifacts (eddy, SHORELine) and quantification of fMRI head motion (FD).
Nuisance Regression & Denoising	Global Signal Regression (GSR), Anatomical CompCor, Band-Pass Filtering [68]	Removal of non-neural signals from BOLD data (global signal, motion, physiological noise).
Motion Impact Assessment	SHAMAN (Split Half Analysis of Motion Associated Networks) [67]	Quantifies trait-specific confounding of functional connectivity by residual head motion.
Large-Scale Datasets	Human Connectome Project (HCP) [66] [70], Adolescent Brain Cognitive Development (ABCD) Study [66] [71]	Provide large samples of multi-modal MRI and behavioral data for high-powered, reproducible research.
Prediction Modeling	Kernel Ridge Regression (KRR), Linear Ridge Regression (LRR) [66] [15]	Machine learning models for predicting individual behavioral scores from brain features.

In-scanner head motion represents the largest source of artifact in resting-state functional magnetic resonance imaging (rs-fMRI), introducing systematic bias to functional connectivity (FC) measurements that persists despite denoising algorithms [29]. This residual motion artifact poses a particularly critical challenge for brain-wide association studies (BWAS) investigating traits that are inherently correlated with motion propensity, such as psychiatric disorders and neurodevelopmental conditions [29]. Without robust methods to detect motion-contaminated findings, researchers risk reporting false positive associations that misrepresent brain-behavior relationships.

The Split Half Analysis of Motion Associated Networks (SHAMAN) framework addresses this methodological gap by providing a trait-specific motion impact score that quantifies whether residual head motion spuriously inflates or obscures trait-FC correlations [29] [72]. This Application Note details the implementation, experimental protocols, and analytical framework of SHAMAN within the broader context of global signal regression (GSR) for motion artifact reduction research. As GSR remains one of the most effective yet debated preprocessing techniques for removing global artifacts driven by motion and respiration [20] [22], SHAMAN provides crucial complementary functionality by diagnosing whether motion continues to impact specific trait-FC associations after GSR and other denoising procedures have been applied.

Background and Theoretical Framework

The Persistent Challenge of Motion Artifact in rs-fMRI

Head motion systematically alters fMRI data through complex, non-linear characteristics of MRI physics that make complete artifact removal during post-processing exceptionally difficult [29]. The effect of motion on FC is spatially systematic, causing decreased long-distance connectivity and increased short-range connectivity, most notably in the default mode network [29]. Even after extensive denoising with protocols such as ABCD-BIDS (which includes global signal regression, respiratory filtering, spectral filtering, despiking, and motion parameter timeseries regression), approximately 23% of signal variance remains explainable by head motion, representing only a 69% relative reduction compared to minimally processed data [29].

This residual artifact creates particular problems for studies examining populations with inherently higher motion, such as children, older adults, and patients with neurological or psychiatric disorders [29]. Early studies mistakenly attributed motion-related FC decreases to neurological conditions, for example concluding that autism decreases long-distance FC when the results were actually driven by increased head motion in autistic participants [29].

Global Signal Regression in Motion Artifact Reduction

Global signal regression (GSR) has emerged as a powerfully effective though contentious preprocessing step for rs-fMRI data. GSR involves regressing the whole-brain (or gray matter) fMRI signal from every brain voxel, effectively removing global artifacts arising from motion and physiological sources [20] [22]. Research demonstrates that GSR significantly reduces the correlation magnitude between quality control metrics and RSFC, removes prominent motion-related signal intensity changes, and improves neuronal-hemodynamic correspondence [22].

Critically for brain-behavior research, multiple large-scale studies have shown that GSR strengthens associations between resting-state functional connectivity and behavioral measures. In the Brain Genomics Superstruct Project (GSP), GSR increased behavioral variance explained by whole-brain RSFC by an average of 47% across 23 behavioral measures, with similar improvements (40%) observed in the Human Connectome Project (HCP) after ICA-FIX denoising [20] [22]. These GSR-related improvements in behavioral prediction accuracy suggest that by removing motion-related and respiratory-related artifacts, GSR enhances the detection of neurally-relevant brain-behavior relationships.

The Need for Trait-Specific Motion Impact Assessment

Despite the effectiveness of GSR and other denoising approaches, a significant limitation remains: standard motion quantification methods are agnostic to the specific hypothesis under investigation [29]. Standard motion correction approaches, including GSR, apply uniform procedures across all analyses without considering whether particular trait-FC relationships are disproportionately vulnerable to residual motion effects. This creates a critical methodological gap, especially for traits strongly correlated with motion propensity.

The SHAMAN framework addresses this limitation by providing trait-specific motion impact scores that quantify whether residual head motion artifact spuriously inflates or obscures specific trait-FC correlations of interest [29] [72]. This enables researchers to determine whether their specific findings are compromised by motion artifact, complementing the global artifact reduction provided by GSR and other denoising techniques.

SHAMAN Methodology and Algorithm

Theoretical Foundation

SHAMAN capitalizes on a fundamental observation about the differing temporal properties of traits versus motion: traits (e.g., cognitive abilities, clinical symptoms) are stable over the timescale of an MRI scan, whereas motion is a state that varies from second to second [29] [72]. This theoretical insight enables the development of a split-half approach that isolates motion-related variance specifically impacting trait-FC associations.

The core principle is that when trait-FC effects are independent of motion, the correlation structure between high- and low-motion halves of each participant's fMRI timeseries should not differ significantly because traits are stable over time. A significant difference emerges only when state-dependent differences in motion impact the trait's connectivity [29].

Computational Workflow

The SHAMAN algorithm implements this theoretical approach through a structured computational pipeline:

Motion Impact Score Interpretation

The motion impact score generated by SHAMAN provides two crucial pieces of information:

• Statistical Significance: A p-value indicating whether motion significantly impacts the trait-FC relationship.
• Directionality: The score's direction relative to the trait-FC effect indicates whether motion causes overestimation or underestimation:
  • A motion impact score aligned with the trait-FC effect direction indicates motion overestimation (spurious inflation of effect size).
  • A motion impact score opposite the trait-FC effect direction indicates motion underestimation (obscuring of true effect).

This directional interpretation enables researchers to distinguish between false positives and motion-masked true effects, providing critical guidance for result interpretation.

Quantitative Assessment of Motion Impact

Large-Scale Application in the ABCD Study

Application of SHAMAN to 45 traits from n=7,270 participants in the Adolescent Brain Cognitive Development (ABCD) Study revealed the extensive impact of residual motion artifact even after standard denoising [29]. The following table summarizes the prevalence of significant motion effects across behavioral domains:

Table 1: Motion Impact on Trait-FC Associations in ABCD Study (n=7,270)

Analysis Condition	Traits with Significant Motion Overestimation	Traits with Significant Motion Underestimation	Key Findings
After standard denoising(ABCD-BIDS pipeline)	42% (19/45 traits)	38% (17/45 traits)	Majority of traits (84%) showed significant motion impact despite denoising
After motion censoring(FD < 0.2 mm)	2% (1/45 traits)	38% (17/45 traits)	Censoring eliminated overestimation but did not reduce underestimation

Comparison of Motion Artifact Reduction Techniques

Different motion mitigation strategies show distinct efficacy profiles for addressing overestimation versus underestimation artifacts:

Table 2: Efficacy Comparison of Motion Artifact Reduction Strategies

Method	Mechanism	Impact on Overestimation	Impact on Underestimation	Limitations
Standard Denoising(ABCD-BIDS)	Global signal regression, respiratory filtering, motion parameter regression	Limited efficacy (42% of traits affected)	Limited efficacy (38% of traits affected)	23% of motion-related variance remains
Motion Censoring(FD < 0.2 mm)	Exclusion of high-motion fMRI frames	Highly effective (reduces to 2% of traits)	Ineffective (no reduction)	Biases sample by excluding high-motion individuals
GSR + Censoring + Interpolation	Combined aggressive artifact reduction	Highly effective	Highly effective	May remove neural information; introduces negative correlations
SHAMAN	Diagnosis of trait-specific motion impact	Identifies affected associations	Identifies affected associations	Diagnostic only (requires complementary correction methods)

Experimental Protocols

Protocol 1: Implementing SHAMAN Analysis

Purpose: To compute motion impact scores for specific trait-functional connectivity relationships.

Materials and Software Requirements:

• MATLAB R2018a or higher
• SHAMAN toolbox (available at https://github.com/DosenbachGreene/shaman) [72]
• Preprocessed fMRI timeseries data
• Trait measurements for all participants
• Framewise displacement (FD) values for all volumes

Procedure:

• Data Preparation:
  • Organize preprocessed fMRI data in MATLAB-compatible format (.mat files)
  • Ensure trait data and motion parameters (FD) are aligned with participant identifiers

• SHAMAN Initialization:

• Motion Impact Score Computation:

• Results Interpretation:

  • Significant positive scores indicate motion overestimation (false positives)
  • Significant negative scores indicate motion underestimation (masked true effects)
  • Non-significant scores indicate motion-independent trait-FC associations

Protocol 2: Integrated GSR and SHAMAN Pipeline

Purpose: To implement comprehensive motion artifact reduction followed by diagnostic assessment of residual motion impact.

Procedure:

• Preprocessing with Global Signal Regression:
  • Apply standard fMRI preprocessing (motion correction, slice timing correction, normalization)
  • Implement Global Signal Regression:
    • Extract mean global signal from gray matter voxels
    • Regress global signal from each voxel's timeseries
    • Retress residuals for subsequent analysis [20] [22]

• Motion Censoring with Optimized Threshold:

  • Calculate framewise displacement (FD) for all volumes
  • Identify high-motion volumes exceeding FD threshold (recommended: 0.2 mm) [29]
  • Apply censoring (scrubbing) to remove contaminated volumes
  • Implement interpolation to maintain temporal continuity [73]

• SHAMAN Diagnostic Assessment:

  • Apply SHAMAN protocol (as in Protocol 1) to preprocessed, GSR-corrected, and censored data
  • Verify reduction in significant motion impact scores compared to non-GSR processed data
  • Document residual motion effects for transparent reporting

Protocol 3: Validation with Simulated Data

Purpose: To validate SHAMAN implementation and assess statistical power using simulated data with known motion effects.

Procedure:

• Data Simulation:

• Method Validation:

  • Apply SHAMAN to simulated data with known motion-trait relationships
  • Verify that SHAMAN detects simulated motion impacts with appropriate directionality
  • Calculate false positive rate under null conditions (no simulated motion effect)

• Power Assessment:

  • Run power analysis by varying sample size and effect size in simulations
  • Determine optimal permutation count for statistical robustness
  • Establish minimum detectable effect sizes for typical sample sizes

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Tools for Motion Artifact Assessment and Reduction

Category	Tool/Reagent	Specifications	Application
Software Tools	SHAMAN Toolbox	MATLAB-based, open source	Trait-specific motion impact scoring [72]
	fMRIPrep	Python-based, containerized	Standardized fMRI preprocessing
	ABCD-BIDS Pipeline	Includes GSR, respiratory filtering	Standard denoising for large datasets [29]
Quality Metrics	Framewise Displacement (FD)	Head motion between volumes	Censoring threshold determination [29] [73]
	DVARS	Rate of signal change	Identifying motion-contaminated volumes [73]
Reference Datasets	ABCD Study	n=11,874 children, ages 9-10	Pediatric brain development reference [29]
	Human Connectome Project	n=1,200 adults, multimodal	Young adult reference [20] [22]
Motion Correction	Global Signal Regression	Whole-brain signal removal	Effective motion artifact reduction [20] [22]
	ICA-AROMA	Automatic noise component classification	Non-GSR denoising alternative
	Volume Censoring	FD threshold-based scrubbing	Removal of high-motion volumes [29] [73]

Integration with Global Signal Regression Research

The SHAMAN framework complements GSR-based motion artifact reduction strategies by addressing their fundamental limitation: the inability to verify whether trait-specific associations remain contaminated by residual motion. While GSR effectively removes global artifacts and strengthens brain-behavior associations overall [20] [22], its efficacy varies across specific trait-FC relationships, particularly for traits strongly correlated with motion propensity.

This integrated relationship can be visualized as follows:

This integrated framework enables researchers to:

• Apply GSR for effective global artifact reduction
• Use SHAMAN to diagnose trait-specific residual motion effects
• Make informed interpretations about brain-behavior associations
• Implement additional targeted corrections when necessary

The Motion Impact Score (SHAMAN) represents a significant methodological advancement for detecting spurious brain-behavior associations in functional connectivity research. By providing trait-specific quantification of motion effects, SHAMAN addresses a critical limitation of standard motion correction approaches, including global signal regression. When integrated with GSR within a comprehensive motion artifact management pipeline, SHAMAN enables more reliable detection of true neurobehavioral relationships while minimizing false positives arising from residual motion artifact.

As brain-wide association studies continue to grow in scale and scope, employing rigorous methods like SHAMAN will be essential for building accurate, reproducible models of brain-behavior relationships across diverse populations and clinical conditions.

Within the broader scope of global signal regression (GSR) for motion artifact reduction in functional magnetic resonance imaging (fMRI), the accurate assessment of residual artifacts is paramount. GSR is a prevalent preprocessing technique that employs linear regression to remove global effects, including those from head motion, from the blood-oxygen-level-dependent (BOLD) signal [5] [21]. However, its efficacy and impact are subjects of ongoing debate, with studies showing that GSR differentially alters functional connectivity and network topology depending on the anesthetic agent used [5] [21]. This Application Note provides detailed protocols and quantitative frameworks for verifying that motion-related distortions are genuinely eliminated and not merely transformed or masked by correction algorithms, including those based on GSR and advanced deep learning models. Ensuring the complete removal of motion effects is critical for the integrity of downstream analyses in both neuroscience research and clinical drug development.

Quantitative Assessment of Motion Correction Efficacy

Robust motion correction methods must demonstrate superior performance across standardized image quality metrics. The following tables summarize quantitative outcomes from recent state-of-the-art deep learning models for MRI motion artifact correction (MoCo), providing a benchmark for residual artifact assessment.

Table 1: Performance of Res-MoCoDiff on In-Silico Data with Varying Distortion Levels [74]

Distortion Level	PSNR (dB) (Mean ± SD)	SSIM	NMSE
Minor	41.91 ± 2.94	-	-
Moderate	-	-	-
Heavy	-	-	-

Note: Res-MoCoDiff consistently achieved the highest SSIM and lowest NMSE values across all distortion levels. PSNR for minor distortions reached up to 41.91 dB. The average sampling time was drastically reduced to 0.37 seconds per batch, compared to 101.74 seconds for conventional approaches [74].

Table 2: Meta-Analysis Summary of AI-Driven MoCo Model Performance [75]

Model Category	Key Strengths	Primary Challenges	Common Metrics (Typical Range)
Generative Adversarial Networks (GANs)	High perceptual image quality	Mode collapse, unstable training, potential hallucinations [74] [75]	PSNR, SSIM, NMSE
Denoising Diffusion Probabilistic Models (DDPMs)	High reconstruction fidelity, robust performance	High computational burden, long inference times [74] [75]	PSNR, SSIM, NMSE
Efficient Diffusion Models (e.g., Res-MoCoDiff)	High speed, preserves structural details	Novel architecture, requires further validation [74]	PSNR, SSIM, NMSE

Experimental Protocols for Validation

A comprehensive validation strategy for residual artifacts combines simulated and clinical data, leveraging both quantitative metrics and qualitative expert review.

Protocol 1: Paired Validation using Simulated Motion Artifacts

This protocol uses a known motion corruption process, enabling pixel-wise comparison to a ground truth image [74] [75].

• Data Preparation:

  • Select a set of motion-free, high-quality reference MR images (e.g., x).
  • Generate a motion-corrupted dataset (e.g., y) using a realistic motion simulation framework. The forward model is expressed as y = A(x) + n, where A is a motion corruption operator and n represents additive noise [74].
  • This creates a paired dataset (y, x) for supervised training and validation.

• Model Application & Quantitative Analysis:

  • Process the simulated motion-corrupted images (y) with the MoCo algorithm under evaluation to generate corrected images (x̂).
  • Calculate quantitative metrics between the corrected image (x̂) and the ground truth (x):
    • Peak Signal-to-Noise Ratio (PSNR) [74] [75]: Measures the ratio between the maximum possible power of a signal and the power of corrupting noise. Higher values indicate better quality.
    • Structural Similarity Index Measure (SSIM) [74] [75]: Assesses the perceptual similarity between two images based on luminance, contrast, and structure.
    • Normalized Mean Squared Error (NMSE) [74] [75]: Quantifies the overall pixel-wise error, normalized by the energy of the ground truth image. Lower values are better.

• Interpretation: High PSNR and SSIM, coupled with low NMSE, indicate effective artifact reduction with minimal introduction of new distortions or blurring.

Protocol 2: Clinical Validation with Expert Review

For real-world clinical data where a ground truth is unavailable, a combination of quantitative metrics and qualitative assessment is essential [75].

• Data Acquisition:

  • Utilize datasets containing matched motion-corrupted and clean(ish) structural MRI scans from the same session, such as the public MR-ART dataset [76].
  • Alternatively, use clinical scans that were repeated due to motion, treating the least corrupted scan as a reference.

• Qualitative Blinded Assessment:

  • Present a blinded, randomized mix of motion-corrupted, correction-algorithm-output, and reference images to experienced radiologists or MRI physicists.
  • Use standardized scoring sheets to evaluate specific criteria:
    • Sharpness of Anatomical Boundaries: (e.g., gray-white matter interface, ventricular edges).
    • Absence of Ghosting/Ringing Artifacts: Particularly in phase-encoding directions.
    • Structural Integrity: Ensuring correction does not create artificial features or erase fine details.

• Downstream Task Analysis:

  • Assess the impact of the correction on subsequent image analysis pipelines.
  • Perform tasks like image segmentation [74] [75] or functional connectivity analysis [5] [21] on both uncorrected and corrected images.
  • Compare the results in terms of accuracy, reliability, and robustness. For fMRI, a key test is whether GSR-induced biases in functional connectivity, which are known to be anesthetic-specific [5], are mitigated after motion correction.

Diagram 1: A unified workflow for assessing residual motion artifacts, integrating both simulated and clinical validation protocols.

The Scientist's Toolkit: Key Research Reagents & Materials

Successful implementation of the validation protocols requires a suite of computational tools and data resources.

Table 3: Essential Reagents and Resources for Residual Artifact Assessment

Item Name	Type	Function/Application in Assessment
Simulated Motion Dataset	Data	Provides ground truth for quantitative validation (Protocol 1) [74] [75].
Matched Clinical Dataset (e.g., MR-ART)	Data	Enables validation on real, subject-generated motion artifacts (Protocol 2) [76].
fMRIPrep	Software	Standardized fMRI preprocessing pipeline; can be used to ensure consistent data handling before GSR and MoCo [5] [21].
Deep Learning MoCo Models	Algorithm	Models like Res-MoCoDiff [74] or FDMC-Net [76] are the primary interventions being tested.
Image Quality Metrics (PSNR, SSIM, NMSE)	Metric	Provide objective, quantitative measures of correction efficacy [74] [75].
Global Signal Regression (GSR)	Algorithm	The benchmark or complementary technique within the fMRI pipeline whose impact on residual motion is being assessed [5] [21].

Advanced Considerations: GSR and the Physiological Nature of Motion

Residual artifact assessment must account for the complex relationship between motion and physiological signals, particularly when GSR is involved.

Diagram 2: The interplay between motion correction, physiological signals, and GSR. GSR removes the global signal, which contains a mixture of motion, physiological noise, and potentially relevant neural information [5] [1] [21].

• GSR Introduces Biases: It is crucial to recognize that GSR is not a pure motion removal tool. It alters functional connectivity patterns in an anesthetic-specific manner, potentially obscuring or mimicking true neural effects [5]. For instance, GSR was shown to broadly reduce connectivity differences under sevoflurane but had a more targeted effect under propofol [5]. A successful motion correction algorithm should reduce the need for GSR's disruptive effects on network topology.

• Respiration as a Confounding Factor: Respiration is a major physiological source of the global signal and is mechanically linked to head motion [1]. Studies demonstrate strong spatial consistency between GS and respiration topography, particularly in the default mode and salience networks [1]. Therefore, residual "motion" artifacts after correction might, in fact, be respiration-related BOLD fluctuations. Assessments should consider monitoring respiratory volume per time (RVT) to disambiguate these sources [1].

A rigorous, multi-modal assessment strategy is indispensable for verifying the complete removal of motion effects in MRI. This involves validating against a simulated ground truth using quantitative metrics like PSNR and SSIM, coupled with expert qualitative review and downstream task analysis on clinical data. Furthermore, within fMRI research, this process must carefully consider the intricate relationship between motion, physiological signals like respiration, and the application of GSR. By adopting the detailed protocols and considerations outlined in this document, researchers can ensure that motion correction methods, including those based on GSR, genuinely enhance data quality without introducing new biases or obscuring true biological signals.

Conclusion

Global Signal Regression presents a powerful yet double-edged sword in the quest for cleaner fMRI data. The evidence confirms that GSR is exceptionally effective at reducing motion-related and respiratory artifacts, often strengthening associations between functional connectivity and behavior in healthy adults. However, its application requires careful, context-dependent consideration. The decision to use GSR should be guided by the research question, participant population, and data quality, particularly the level of global noise. For studies involving clinical populations where motion may correlate with traits of interest, or when investigating global neural phenomena, alternative denoising strategies may be preferable. Future directions should focus on developing more selective artifact removal techniques that preserve globally-distributed neural information, standardized reporting practices for motion artifact correction, and personalized preprocessing pipelines adapted to individual data quality. As the field moves toward larger datasets and more complex biomedical questions, a nuanced understanding of GSR's capabilities and limitations becomes increasingly vital for generating robust, reproducible neuroimaging findings in both basic research and drug development.

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• 72. DosenbachGreene/shaman: Split-Half Analysis of Motion ... [https://github.com/DosenbachGreene/shaman]
• 73. Methods to detect, characterize, and remove motion artifact in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3849338/]
• 74. Res-MoCoDiff: residual-guided diffusion models for motion ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12532098/]
• 75. Systematic Review and Meta-analysis of AI-driven MRI ... [https://arxiv.org/html/2509.05071v1]
• 76. MRI Motion Artifact Correction via Frequency-Assisted ... [https://papers.miccai.org/miccai-2025/0588-Paper0954.html]