Assessing Residual Motion Artifact After Denoising Pipelines: A Comprehensive Guide for Biomedical Researchers

Abstract

Residual motion artifacts persist as a critical challenge in neuroimaging, potentially confounding study results and undermining the validity of functional connectivity and behavioral correlations. This article provides a systematic assessment of motion artifact correction, exploring the fundamental origins of residual motion, evaluating the efficacy of current denoising pipelines across multiple imaging modalities (including fMRI, MRI, and EEG), and presenting advanced strategies for troubleshooting and optimization. By synthesizing evidence from recent methodological advances and comparative validation studies, we offer a framework for researchers and drug development professionals to select, optimize, and validate denoising approaches that minimize residual artifacts while preserving biological signals of interest.

The Persistent Challenge: Understanding Residual Motion Artifacts and Their Impact on Data Integrity

Residual motion artifacts represent a critical and often overlooked challenge in medical imaging, particularly in magnetic resonance imaging (MRI). These artifacts persist after the application of initial motion correction or denoising techniques, continuing to compromise image quality, quantitative analysis, and subsequent scientific conclusions. In the context of a broader thesis on assessing residual motion artifacts after denoising pipelines, it is essential to recognize that even state-of-the-art correction methods cannot fully eliminate motion-related distortions. This persistence creates a significant bottleneck in research reliability, especially in domains where precise image-based quantification is paramount, such as in pharmaceutical development and clinical neuroscience.

The fundamental issue stems from the complex nature of motion itself—both rigid body movements and non-rigid physiological motions (e.g., breathing, cardiac pulsation) create artifacts that conventional pipelines struggle to fully resolve [1]. Moreover, the problem is particularly acute in functional MRI (fMRI), where residual motion artifacts can systematically bias functional connectivity estimates, potentially leading to spurious brain-behavior associations [2]. As we move toward larger-scale brain-wide association studies (BWAS), understanding and addressing these residual artifacts becomes not merely technical but fundamental to neuroscientific and drug development research.

Defining the Artifact: Characterization and Impact

What Are Residual Motion Artifacts?

Residual motion artifacts are the systematic distortions, blurring, or signal alterations that remain in medical images after applying standard motion correction or denoising algorithms. Unlike primary motion artifacts, which result directly from patient movement during scanning, residual artifacts are byproducts of incomplete correction and often manifest as more subtle, yet more insidious, image distortions.

In resting-state fMRI (rs-fMRI), for instance, residual head motion introduces systematic bias into functional connectivity (FC) measurements that persists despite denoising. These artifacts notably decrease long-distance connectivity while increasing short-range connectivity, with pronounced effects within the default mode network [2]. This specific spatial pattern can create the false appearance of neurological differences between study populations, particularly those with inherently higher motion levels (e.g., children, older adults, or patients with neurological disorders).

The Clinical and Research Impact

The consequences of residual motion artifacts extend beyond mere image quality concerns, potentially affecting diagnostic accuracy, research validity, and clinical outcomes:

• Compromised Quantitative Analysis: In hyperpolarized 129Xe MRI, residual noise and artifacts can bias the quantification of key pulmonary functional parameters, including ventilation defect percentage (VDP) and apparent diffusion coefficient (ADC) values, potentially affecting diagnostic interpretations in cardiopulmonary conditions [3].
• Spurious Brain-Behavior Associations: As demonstrated in large-scale studies like the Adolescent Brain Cognitive Development (ABCD) Study, residual motion artifacts can lead to both overestimation and underestimation of trait-functional connectivity relationships. After standard denoising without motion censoring, 42% of examined traits showed significant motion overestimation scores, while 38% showed significant underestimation scores [2].
• Reduced Statistical Power: The presence of residual artifacts increases unexplained variance in imaging data, thereby attenuating the effect sizes of true brain-behavior relationships and reducing the reproducibility of findings in brain-wide association studies [4].

Quantitative Comparison of Correction Performance

Performance Across Motion Severity Levels

Table 1: Performance of Res-MoCoDiff Across Motion Distortion Levels

Distortion Level	PSNR (dB)	SSIM	NMSE	Inference Time
Minor	41.91 ± 2.94	~0.98*	Lowest	0.37 s per batch
Moderate	High	High	Low	0.37 s per batch
Heavy	Superior	Highest	Lowest	0.37 s per batch

Note: SSIM values close to 1 indicate excellent structural preservation; exact SSIM values were not provided in the source for all distortion levels, though the method consistently achieved the highest SSIM across all levels [5].

The Res-MoCoDiff (Residual-guided Motion Correction Diffusion) model demonstrates particularly robust performance across varying degrees of motion severity, consistently achieving the highest structural similarity (SSIM) and lowest normalized mean squared error (NMSE) values compared to established methods like cycleGAN, Pix2pix, and vision transformer-based diffusion models [5]. Its exceptional computational efficiency, processing a batch of two image slices in just 0.37 seconds, represents a significant advancement for potential clinical integration.

Comparative Performance of Denoising Pipelines

Table 2: Multi-Metric Comparison of Denoising Pipeline Efficacy

Denoising Approach	Artifact Reduction	Signal Preservation	RSN Identifiability	Computational Demand
WM/CSF Regression + GSR	Moderate-High	Moderate	Good	Low
ICA-FIX + GSR	High	Good	Good	Medium
DiCER	Moderate	Good	Moderate	Medium
Motion Censoring (FD < 0.2 mm)	High	Variable*	Variable*	Low (but data loss)
Deep Learning (Res-MoCoDiff)	Highest	Excellent	N/A	Low (inference)

Note: Motion censoring effectively reduces artifacts but can introduce bias by systematically excluding high-motion participants and reducing statistical power; RSN = Resting-State Networks [6] [7] [2].

No single denoising pipeline universally excels across all performance metrics. Pipelines combining ICA-FIX and global signal regression (GSR) typically represent a reasonable trade-off between motion reduction and behavioral prediction performance [4]. However, deep learning approaches like Res-MoCoDiff demonstrate superior artifact reduction and structural preservation, though their effect on functional connectivity measures requires further validation.

Experimental Protocols for Residual Artifact Assessment

Res-MoCoDiff Methodology

The Res-MoCoDiff framework introduces a novel approach to residual motion correction through a residual-guided diffusion process:

• Residual Error Integration: The model explicitly incorporates the residual error (r = y - x) between motion-corrupted (y) and motion-free (x) images during the forward diffusion process, enabling a probability distribution that closely matches the corrupted data [5].
• Architectural Innovation: The U-net backbone incorporates Swin Transformer blocks instead of standard attention layers, enhancing robustness across resolutions [5].
• Efficient Reverse Diffusion: The refined forward process enables a dramatically shortened reverse diffusion process requiring only four steps instead of the hundreds or thousands typical of conventional denoising diffusion probabilistic models (DDPMs) [5].
• Combined Loss Function: Training utilizes a combined ℓ1+ℓ2 loss function that simultaneously promotes image sharpness while reducing pixel-level errors [5].

Evaluation was performed on both in-silico datasets (generated using realistic motion simulation frameworks) and in-vivo movement-related artifact datasets, with comparative analyses against established methods using quantitative metrics including PSNR, SSIM, and NMSE [5].

Multi-Metric Pipeline Evaluation Framework

A comprehensive framework for evaluating denoising pipelines for rs-fMRI data involves multiple assessment dimensions:

• Data Acquisition: Fifty-three participants underwent rs-fMRI sessions, with synthetic rs-fMRI data also generated for controlled comparisons [6] [7].
• Pipeline Application: Nine different denoising pipelines were applied in parallel to minimally preprocessed fMRI data, including strategies based on white matter/cerebrospinal fluid regression, global signal regression, ICA-based artifact removal, and volume censoring [6] [7].
• Multi-Metric Assessment: Evaluation incorporated previously proposed and novel metrics quantifying:
  • Degree of artifact removal
  • Signal enhancement
  • Resting-state network (RSN) identifiability [6] [7]
• Summary Performance Index: A composite index accounting for both noise removal and information preservation was proposed to enable direct pipeline comparisons [6] [7].

This systematic approach identified that denoising strategies incorporating regression of mean signals from white matter and cerebrospinal fluid areas plus global signal regression provided the optimal compromise between artifact removal and preservation of resting-state network information [6] [7].

Visualization of Methodologies and Workflows

Residual Motion Artifact Correction Workflow

Multi-Metric Evaluation Framework

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Tools for Residual Artifact Investigation

Tool/Resource	Function	Application Context
HALFpipe Software	Standardized workflow for rs-fMRI analysis from raw data to group-level statistics	Provides containerized, reproducible processing environment with multiple denoising options [6] [7]
Swin Transformer Blocks	Enhanced attention mechanism replacement for U-net architectures	Improves robustness across resolutions in deep learning models like Res-MoCoDiff [5]
Computer Vision Systems	Real-time motion tracking and extraction without physical markers	Enables prospective gating and residual motion characterization in behaving specimens [8] [9]
In-Silico Motion Simulation	Generation of realistic motion-corrupted datasets with known ground truth	Provides controlled framework for algorithm development and validation [5] [1]
Summary Performance Index	Composite metric combining artifact removal and information preservation	Enables direct comparison of denoising pipeline efficacy [6] [7]
Motion Impact Score (SHAMAN)	Quantifies trait-specific impact of residual motion on functional connectivity	Identifies spurious brain-behavior relationships in large datasets [2]

The systematic investigation of residual motion artifacts reveals a complex landscape where no single correction approach universally excels across all applications and performance metrics. The persistence of these artifacts after initial correction underscores the necessity for rigorous, multi-metric evaluation frameworks in denoising pipeline research. For drug development professionals and neuroscientists, the implications are substantial: residual artifacts can systematically bias functional connectivity measures and potentially lead to spurious brain-behavior associations that compromise research validity.

Future directions should prioritize the development of standardized evaluation protocols, expanded validation across diverse patient populations and imaging modalities, and enhanced integration of computer vision systems for real-time motion tracking. Deep learning approaches, particularly those incorporating residual guidance like Res-MoCoDiff, show exceptional promise for balancing correction efficacy with computational efficiency. However, their validation in preserving biologically relevant signals, particularly in functional connectivity applications, requires further investigation. As medical imaging continues to play an expanding role in both basic research and clinical trials, addressing the challenge of residual motion artifacts will remain essential for ensuring the validity and reproducibility of scientific findings.

Physical and Technical Origins of Residual Signals in fMRI and MRI

Subject motion during magnetic resonance imaging (MRI) and functional MRI (fMRI) has been problematic since its introduction as a clinical imaging modality, representing one of the most frequent sources of artefacts [10]. While sensitivity to particle motion or blood flow can provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications [10]. Residual head motion artifact in motion-corrected resting-state (rs-) fMRI and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these studies [11]. Despite advanced motion correction techniques, these residual signals persist due to the complex interplay between physical motion and the MR image acquisition process.

The prolonged time required for most MR imaging sequences to collect sufficient data to form an image makes MRI particularly sensitive to subject motion [10]. This timeframe far exceeds the timescale of most physiological motions, including involuntary movements, cardiac and respiratory motion, gastrointestinal peristalsis, vessel pulsation, and blood and CSF flow [10]. Recent technological improvements have paradoxically both improved and exacerbated the situation; while hardware advances have enabled faster imaging, they have also improved achievable resolution and signal-to-noise ratio (SNR), consequently increasing sensitivity to motion [10].

Physical Principles of Motion Artifacts

K-Space and the Image Acquisition Process

Spatial encoding in MRI is an intrinsically slow and sequential process that occurs not directly in image space but in frequency or Fourier space, commonly termed 'k-space' [10]. Understanding motion artefacts requires appreciating that each sample in k-space describes the contribution of a spatial frequency wave to the entire image [10]. A change in a single sample in k-space theoretically affects the entire image, and similarly, a change in the intensity of a single pixel generally affects all k-space samples [10].

The most common and clinically relevant approach collects data on a rectilinear grid in k-space (Cartesian sampling), allowing computationally efficient image reconstruction using the fast Fourier transform (FFT) [10]. Simple reconstruction using an inverse FFT (iFFT) assumes the object has remained stationary during the time the k-space data were sampled, and violation of this assumption results in artefacts [10].

Manifestations of Motion Artefacts

Typical motion-induced deterioration effects observed in MR images consist of a combination of several basic effects [10]:

• Blurring of sharp contrast or object edges (intuitively similar to photography)
• Ghosting (both coherent and incoherent) originating from moving structures
• Signal loss due to spin dephasing or undesired magnetization evolution
• Appearance of undesired strong signals

The first two points are related to the signal readout process, whereas the latter two are related to the signal generation and contrast preparation within the pulse sequence [10]. Ghosting appears as a partial or complete replication of the object or structure along the phase-encoding dimension, or along multiple phase-encoding dimensions for 3D imaging [10].

Figure 1: Relationship between motion during k-space acquisition and resulting image artifacts.

Residual head motion artifact remains even after perfect motion correction, primarily due to the partial volume (PV) effect of surrounding voxels caused by resampling of the target image aligned to the reference [11]. Additional sources include:

• Altered spin excitation history effect: Head motion causes protons to shift between slices, altering the time between RF excitations and permuting the steady state of magnetization of each slice [11].
• B0 field fluctuations: Breathing patterns induce phase encoding direction image motion in 2D EPI acquisitions, with different scales of PE direction image shift reflected in each slice [11].
• Sensitivity alterations: Motion during acquisition leads to alterations in the sensitivity of the radiofrequency (RF) transmitter/receiver [11].

Denoising Pipelines and Methodologies

Default Denoising Pipeline in CONN

CONN's default denoising pipeline combines two general steps: linear regression of potential confounding effects in the BOLD signal, and temporal band-pass filtering [12]. The linear regression step uses Ordinary Least Squares (OLS) regression to project each BOLD signal timeseries to the sub-space orthogonal to all potential confounding effects, which include [12]:

• aCompCor components: Five noise components each from cerebral white matter and cerebrospinal areas
• Motion parameters: 12 potential noise components from estimated subject-motion parameters (3 translation + 3 rotation parameters + their derivatives)
• Scrubbing regressors: One component for each identified outlier scan
• Session and task effects: Constant and linear session effects, and constant task effects if applicable

Temporal band-pass filtering removes frequencies below 0.008 Hz or above 0.09 Hz to focus on slow-frequency fluctuations while minimizing physiological, head-motion and other noise sources [12].

Intravolume Motion Correction (SLOMOCO)

The slice-oriented motion correction method (SLOMOCO) represents an advanced approach that addresses intravolume motion by measuring in-plane and out-of-plane motion separately in each slice [11]. This method has been validated in cadaver studies using the simulated prospective acquisition correction (SIMPACE) sequence, which synthesizes motion-corrupted MR data by altering the imaging plane before each slice and volume acquisition [11].

The modified SLOMOCO (mSLOMOCO) pipeline incorporates 6 volume-wise rigid intervolume motion parameters (Vol-mopa), 6 slice-wise rigid intravolume motion parameters (Sli-mopa), and a proposed PV motion nuisance regressor [11]. This approach has demonstrated superior performance compared to traditional intervolume motion-correction methods (VOLMOCO) and the original SLOMOCO (oSLOMOCO) [11].

Alternative Denoising Approaches

Several alternative denoising approaches exist beyond the standard pipelines:

• ICA denoising: A data-driven approach where Independent Component Analyses identify potential noise-related temporal components manually or semi-automatically [12].
• Retroicor: Uses cardiac and respiratory state information recorded during scanning to build predicted sine and cosine components of respiratory and cardiac effects [12].
• Simultaneous regression and filtering: An alternative implementation where both regression and filtering are implemented simultaneously as a single regression step [12].
• FIX and AROMA: Blind-source denoising strategies that can eliminate signal as well as noise, with effects depending on algorithm and design [13].

Comparative Performance of Denoising Pipelines

Quantitative Comparison of Pipeline Effectiveness

Table 1: Performance comparison of denoising pipelines on SIMPACE motion-corrupted data

Pipeline	Motion Parameters	Residual Motion Regressors	Average SD in GM (1× Motion)	Average SD in GM (2× Motion)	Performance Notes
VOLMOCO	6 Vol-mopa	PV	Baseline	Baseline	Standard intervolume approach
mSLOMOCO	6 Vol-mopa + 6 Sli-mopa	PV	29% smaller than VOLMOCO	45% smaller than VOLMOCO	Superior intravolume correction
oSLOMOCO	14 voxel-wise	14 voxel-wise	-28% vs mSLOMOCO	-31% vs mSLOMOCO	Less effective than modified approach

Data derived from Shin et al. (2024) using SIMPACE motion-corrupted data [11]

Quality Control Metrics for Denoising Effectiveness

Three primary metrics are used to evaluate denoising effectiveness [12]:

• Data Validity (DV): Characterizes potential presence of global biases in functional connectivity estimates by exploring properties of empirical FC distributions. DV scores range from 0% to 100%, with values above 95% representing distributions with peak displacements below 3.8% of distribution interquartile range [12].

• Data Quality (DQ): Summarizes potential influence of subject-motion and other forms of outliers on functional connectivity estimates. DQ is defined as the minimum of overlap coefficients between observed QC-FC distribution and its permutation-derived null distribution for quality control measures [12].

• Data Sensitivity (DS): Represents expected power to detect small effect-size in simple fixed-effect analysis at p<0.05 false positive control level [12].

In exemplary data, DV improved from 13.2% before denoising to 97.2% after denoising, while DQ improved from 38.2% to 98.7% after denoising [12].

Figure 2: Experimental workflow for evaluating denoising pipeline effectiveness using standardized metrics.

Task-Based fMRI Denoising Comparisons

For task-based fMRI designs, denoising approaches show variable effectiveness depending on the experimental design [13]. Comparative studies across four sets of event-related fMRI and block-design datasets collected with multiband 32-channel (TR = 460 ms) or older 12-channel (TR = 2,000 ms) head coils revealed that [13]:

• Blind-source denoising strategies (FIX and AROMA) eliminated signal as well as noise relative to motion parameter regression
• Undesired signal effects depended on both algorithm (FIX > AROMA) and design (block-design > event-related)
• Motion parameter regression (MP12/24) showed minimal differences compared to MP0 pipelines in both event-related and block-designs
• MP12/24 pipelines were detrimental for tasks with longer block length (30 ± 5 s) and higher correlations between head motion parameters and design matrix

These findings suggest there does not appear to be a single denoising approach appropriate for all fMRI designs [13].

Experimental Protocols for Residual Signal Analysis

SIMPACE Sequence for Motion Corruption Simulation

The SIMPACE (simulated prospective acquisition correction) sequence generates motion-corrupted MR data by altering the imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom [11]. This approach enables:

• Controlled motion injection: Precisely defined intervolume and/or intravolume motion patterns
• Gold standard comparison: Known ground truth for evaluating correction efficacy
• Realistic artifact simulation: Emulation of motion-induced alterations without confounding physiological variables

It should be noted that SIMPACE synthesizes motion-corrupted MR data by altering the imaging plane, resulting in emulation of intervolume/intravolume motion, but does not model additional motion artifacts from altered B0 and B1 inhomogeneity effects due to motion [11].

Quality Control Assessment Protocol

A standardized quality control protocol after denoising includes [12]:

• Distribution analysis: Estimating functional connectivity values between randomly-selected pairs of points within the brain before and after denoising
• Data Validity calculation: Computing DV scores based on mode and interquartile range of empirical FC distributions
• QC-FC correlations: Evaluating correlations between connectivity values and quality control measures across subjects
• Data Quality computation: Calculating DQ scores as minimum overlap coefficients for multiple QC measures
• Data Sensitivity estimation: Approximating effective degrees of freedom and expected power for detection

Comparative Testing Framework

A robust testing framework for residual motion artifact assessment should incorporate [11] [13]:

• Multiple motion patterns: Testing with various intervolume motion patterns, including amplified intravolume motion
• Different acquisition parameters: Evaluating performance across varying temporal resolutions and coil designs
• Gray matter focus: Quantifying residual signal standard deviation specifically in gray matter regions
• Statistical validation: Comparing observed QC-FC distributions to permutation-derived null distributions

Research Reagent Solutions and Essential Materials

Table 2: Essential research materials for residual signal analysis in fMRI/MRI

Item	Function/Application	Technical Specifications	Research Context
Ex Vivo Brain Phantom	Motion artifact simulation without physiological confounds	Formalin-fixed, Fomblin-soaked, bubble-free [11]	Gold standard validation of correction methods
SIMPACE Sequence	Injection of controlled intervolume/intravolume motion	Alters imaging plane before slice/volume acquisition [11]	Realistic motion corruption for method validation
Respiratory Gating Equipment	Reduction of respiratory motion artifacts	Sensor, belt, tubing for respiratory waveform detection [14]	Physiological motion management during acquisition
Cryogenic RF Coils	Signal-to-noise ratio enhancement	Liquid nitrogen or cryogenic helium cooling [15]	Preclinical fMRI with improved tSNR
High-Performance Gradients	Enable high spatial/temporal resolution fMRI	400-1000 mT/m strength, 1000-9000 T/m/s slew rates [15]	Advanced EPI sequences for motion reduction
Multi-Channel Array Coils	Parallel imaging acceleration	2-32 channel configurations, stretchable designs available [15]	Reduced scan time through acceleration
Optical Motion Tracking	Prospective motion correction	External camera systems with reflective markers [10]	Real-time motion detection and correction
Immobilization Equipment	Motion restriction during scanning	Wedges, cushions, straps, sandbags [14]	Patient motion minimization

The investigation into physical and technical origins of residual signals in fMRI and MRI reveals a complex landscape where no single solution effectively addresses all motion artifacts. The multifaceted nature of motion artifacts—ranging from bulk subject movement to physiological processes and altered spin excitation history—necessitates a toolbox approach rather than a universal solution [10]. Current evidence suggests that advanced intravolume motion correction methods like mSLOMOCO with integrated partial volume regressors outperform traditional intervolume approaches, particularly for challenging motion scenarios [11].

For researchers and drug development professionals, these findings highlight the critical importance of selecting denoising pipelines appropriate for specific experimental designs and motion characteristics. The availability of standardized quality control metrics (DV, DQ, DS) provides an objective framework for pipeline optimization and validation [12]. Future developments in hardware, particularly ultrahigh field systems with enhanced gradient performance and cryogenic coils, promise improved functional contrast-to-noise ratio, though these advances may introduce new challenges in residual signal management [15].

The continued refinement of experimental protocols using gold-standard approaches like SIMPACE validation will be essential for advancing our understanding of residual motion artifacts and developing increasingly effective correction strategies. As fMRI continues to play a crucial role in neuroscience research and drug development, comprehensive assessment and mitigation of residual signals remains paramount for generating reliable, interpretable results.

Functional magnetic resonance imaging (fMRI) has become a cornerstone technique for investigating the brain's functional organization. Analyses of resting-state fMRI (rs-fMRI) data, particularly functional connectivity (FC), are widely used to identify large-scale brain networks and explore their relationship to behavior and cognition. However, rs-fMRI signals are notoriously contaminated by multiple noise sources, including head motion, cardiac activity, and respiratory variations. These artifacts can severely compromise the reliability and validity of derivative functional connectivity phenotypes, ultimately attenuating or distorting correlations with behavioral measures. The choice of preprocessing strategy to mitigate these artifacts is therefore not merely a technical detail but a fundamental decision that directly impacts the quality and interpretability of downstream analyses, from basic network mapping to sophisticated brain-behavior prediction models. This guide objectively compares the performance of various denoising pipelines, focusing on their efficacy in reducing residual motion artifacts and enhancing the prediction of behavioral and cognitive traits.

Comparing Denoising Pipeline Performance

Quantitative Metrics for Pipeline Evaluation

The performance of denoising pipelines is typically benchmarked using multiple quality control (QC) metrics that reflect a pipeline's capacity for artifact removal and signal preservation. A multi-measure approach is essential, as no single metric provides a complete picture of pipeline efficacy.

Table 1: Key Quality Control Metrics for fMRI Denoising Evaluation

Metric Category	Specific Metrics	What It Measures	Desired Outcome
Motion Artifact Reduction	Framewise Displacement (FD) correlation, Distance-Dependent bias	Reduction of motion-induced biases, especially in short-distance connections	Lower scores indicate better motion mitigation
Signal-to-Noise Ratio (SNR)	Temporal Signal-to-Noise Ratio (tSNR)	Ratio of signal strength to noise level in the time series	Higher scores indicate cleaner data
Resting-State Network (RSN) Identifiability	Contrast-to-Noise Ratio (CNR) of RSNs	How clearly known functional networks (e.g., Default Mode) can be distinguished	Higher scores indicate better preservation of biological signal

Performance of Common Pipeline Strategies

Different denoising strategies offer varying balances between noise removal and signal preservation. Recent benchmarking studies have evaluated their performance against the metrics in Table 1.

Table 2: Performance Comparison of Common Denoising Pipelines

Denoising Pipeline	Motion Reduction	RSN Identifiability	Impact on Degrees of Freedom	Overall Compromise
Global Signal Regression (GSR)	High	High	High	Excellent artifact reduction but may remove neural signal
aCompCor	Medium	Medium-High	Medium	Good balance, depends on number of components removed
ICA-AROMA + FIX	Medium-High	High	Medium	Effective for automated noise removal
GSR + aCompCor	High	High	High	Often a top performer for a balance of metrics
Low-Pass Filtering (<0.20 Hz)	Low	Medium	Low	Mild improvement when combined with other methods

A 2025 benchmarking study concluded that a pipeline combining the regression of the global signal (GS) and about 17% of principal components from white matter (a variant of aCompCor) yielded the most significant improvement across multiple QC metrics. The addition of low-pass filtering at 0.20 Hz provided a small further improvement, whereas "scrubbing" (removing motion-contaminated volumes) showed minimal benefit [7] [16].

Another 2025 study proposed a summary performance index that synthesizes multiple QC metrics. This index favored a denoising strategy that included the regression of mean signals from white matter and cerebrospinal fluid areas, plus global signal regression. This pipeline represented the best compromise between artifact removal and preservation of information on resting-state networks [7].

Impact on Behavioral Prediction

Linking Functional Connectivity to Real-World Outcomes

The ultimate test of a denoising pipeline is its ability to enhance the validity of fMRI measures in predicting real-world outcomes. Significant advances have been made in using functional connectivity to predict cognitive performance on ecologically valid tasks.

A pivotal 2025 study demonstrated that resting-state functional connectivity could significantly predict real-world performance on the Psychometric Entrance Test, a standardized exam used for university admissions in Israel. The study predicted not only the global test score but also specific cognitive domains: quantitative reasoning, verbal reasoning, and English proficiency. Predictions were robust across four different prediction approaches [17].

Crucially, the study found that different cognitive abilities were primarily predicted by unique connectivity patterns. However, predictive features were more similar for scores that were more strongly correlated at the behavioral level, suggesting both unique and shared neural mechanisms. Using a transfer learning approach, where predicted domain-specific scores were used to forecast the global score, further improved prediction accuracy compared to a direct prediction from functional connectivity [17].

Pipeline Performance in Brain-Wide Association Studies (BWAS)

The efficacy of pipelines in supporting behavioral prediction does not always align with their performance on standard QC metrics.

A 2025 investigation evaluated 14 different denoising pipelines on their ability to both mitigate motion artifacts and augment brain-behavior associations across three independent datasets (CNP, GSP, HCP). The study used kernel ridge regression to predict 81 different behavioral variables [4].

Key Finding: No single pipeline universally excelled at achieving both objectives consistently across different cohorts. Pipelines that combined ICA-FIX and Global Signal Regression (GSR) demonstrated a reasonable trade-off between motion reduction and behavioral prediction performance. However, inter-pipeline variations in predictive performance were generally modest, indicating that pipeline choice, while important, is not the sole determinant of successful brain-behavior prediction [4].

Experimental Protocols for Benchmarking

Protocol 1: Evaluating Denoising Efficacy

Objective: To quantitatively compare the performance of multiple denoising pipelines in reducing artifacts and preserving resting-state network information [7] [16].

Workflow Description: The experimental workflow for this protocol involves a structured process from data preparation to multi-metric evaluation. Raw resting-state fMRI data first undergoes minimal preprocessing, which includes steps like slice-timing correction, head motion realignment, and spatial normalization. The preprocessed data is then fed into multiple, parallel denoising pipelines. Each pipeline applies a different combination of noise correction techniques, such as nuisance regression (e.g., WM/CSF signals, global signal), ICA-based cleaning, and temporal filtering. The output from each pipeline is then evaluated using a set of quantitative quality control metrics. These metrics collectively measure motion artifact reduction, temporal signal quality, and the identifiability of canonical resting-state networks. Finally, a summary performance index is computed to rank the pipelines based on their overall compromise between noise removal and signal preservation.

Protocol 2: Validating Behavioral Prediction Accuracy

Objective: To assess how different denoising pipelines influence the accuracy of predicting behavioral and cognitive traits from functional connectivity data [17] [4].

Workflow Description: This validation protocol tests the practical downstream impact of preprocessing. It begins with preprocessed fMRI data that has been cleaned using different denoising pipelines, creating multiple versions of the dataset. For each version, a functional connectivity matrix is computed for every subject, often using Pearson's correlation or other pairwise statistics. These matrices, which represent the brain features, are then used in a predictive model alongside behavioral data (e.g., cognitive test scores). A machine learning model, such as kernel ridge regression, is typically employed. To obtain a robust estimate of prediction accuracy, nested cross-validation is used, which involves an inner loop for hyperparameter tuning and an outer loop for testing the model on held-out data. The final predictive accuracy (e.g., measured as correlation between predicted and actual scores) is then compared across the different denoising pipelines to determine which one best supports brain-behavior association studies.

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 3: Key Software Tools and Analytical Resources

Tool/Resource	Primary Function	Role in Analysis	Key Reference
fMRIPrep	Automated, robust fMRI preprocessing	Standardizes initial preprocessing steps, ensuring reproducibility and data quality.	[7]
HALFpipe (ENIGMA)	Harmonized analysis pipeline	Provides a standardized workflow from raw data to group-level stats, containerized for reproducibility.	[7]
ICA-AROMA / FIX	ICA-based noise removal	Automates identification and removal of noise components from fMRI data.	[4]
PySPI	Library of pairwise interaction statistics	Enables benchmarking of 200+ FC estimation methods beyond Pearson's correlation.	[18]
Schaefer / Gordon Atlases	Brain parcellation	Provides predefined regions of interest for consistent network definition and FC calculation.	[18] [16]

In resting-state functional magnetic resonance imaging (rs-fMRI) research, in-scanner head motion represents a paramount confounding factor, systematically introducing spurious signal fluctuations that can profoundly bias measures of functional connectivity (FC) [19] [20]. The challenge is particularly acute in studies involving populations prone to greater movement, such as children, older adults, or individuals with certain neurological or psychiatric conditions, where motion artifacts can create false positives or mask genuine effects [19] [2]. Consequently, the development and validation of robust metrics for identifying motion-contaminated data is a critical pursuit. Among the most established and investigated metrics are Framewise Displacement (FD) and DVARS, which serve as the frontline tools for quantifying head motion and its impact. Meanwhile, the analysis of spectral signatures offers a complementary approach for detecting anomalous signal patterns. This guide provides a detailed comparison of these key metrics, outlining their methodologies, applications, and performance in the context of assessing residual motion artifacts following the application of denoising pipelines.

Understanding Motion Artifacts and the Denoising Context

Before delving into the metrics, it is essential to understand the nature of the problem. Motion artifact impacts FC data in spatially systematic ways, primarily characterized by a distance-dependent profile [19] [20]. This manifests as:

• Inflated short-range connectivity: Signal correlations between nearby brain regions are artificially strengthened.
• Deflated long-range connectivity: Correlations between distant regions are weakened [2] [20].

Even with prospective and retrospective motion correction, residual motion artifact often persists, necessitating the use of denoising pipelines that may include confound regression, component-based methods, and censoring (or "scrubbing") of motion-contaminated volumes [19] [21]. The efficacy of these pipelines is not universal; they exhibit marked heterogeneity in performance, with differential success in mitigating motion's distance-dependent effects on connectivity [22]. Therefore, reliable metrics are required to identify contaminated time points and subjects, both before and after denoising, to ensure the validity of subsequent neuroscientific or clinical inferences.

A Comparative Analysis of Key Metrics

Framewise Displacement (FD)

Framewise Displacement is a summary measure of the volume-to-volume displacement of the head, derived from the rigid-body realignment parameters generated during image preprocessing [19] [20]. It quantifies the absolute head movement between consecutive frames.

• Experimental Protocol & Calculation: FD is computed by summing the absolute values of the translational displacements (in mm) and the rotational displacements (converted to mm by assuming a default brain radius, often 50 mm or 80 mm) across the six realignment parameters [19]. Different implementations exist (e.g., FDJenkinson via FSL's mcflirt or FDPower via scripts like fd.R in XCP Engine) which may use slightly different formulas for combining these parameters [19].
• Primary Application: FD is predominantly used for motion censoring ("scrubbing"). A threshold is applied (e.g., FD < 0.2 mm) to flag and remove individual volumes deemed excessively contaminated by motion [22] [2]. It is also used as a covariate in group-level analyses to control for between-subject differences in motion.

DVARS

DVARS (D referring to the temporal derivative of the timecourses, VAR referring to variance, and S referring to root mean square) is a measure of the rate of change of the BOLD signal across the entire brain at each frame [19]. It indexes the total frame-to-frame signal fluctuation.

• Experimental Protocol & Calculation: For each time point t, DVARS is calculated as the root mean square of the temporal derivative of the voxel-wise time series over the brain. The standardized DVARS (as implemented in tools like XCP's dvars) represents the intensity of change normalized to the whole time series, making it more comparable across subjects [19].
• Primary Application: Like FD, DVARS is used to identify outlier volumes for censoring. A sharp peak in the DVARS time series indicates a large, global signal change, often coinciding with a head movement. It provides a direct measure of signal corruption, whereas FD is an indirect measure based on estimated head position.

Spectral Signatures

The term "spectral signatures" refers to deviations from the expected power distribution of the BOLD signal across temporal frequencies. While the canonical rs-fMRI signal is dominated by low-frequency fluctuations (<0.1 Hz), motion artifacts can introduce distinctive high-frequency components or alter the overall spectral profile.

• Experimental Protocol & Calculation: This involves performing a Fourier transform on the preprocessed BOLD time series to decompose it into its constituent frequencies. The power spectrum is then examined for anomalies. Another data-driven approach is to use Independent Component Analysis (ICA) to isolate components with spectral signatures atypical of neural signals (e.g., high power in high frequencies), which are then classified as noise [23].
• Primary Application: Spectral analysis is integral to data-driven denoising methods, such as ICA-based automatic classification of noise components (e.g., ICA-AROMA) [22]. It is also used in quality control to identify subjects with abnormal global spectral properties, which may indicate poor data quality even in the absence of extreme FD or DVARS values.

The following table provides a consolidated comparison of these three metrics.

Table 1: Comparative Overview of Key Motion Identification Metrics

Metric	What It Measures	Data Source	Primary Use	Key Strengths	Key Limitations
Framewise Displacement (FD)	Volume-to-volume head displacement	Image realignment parameters	Censoring, covariate in group analysis	Intuitive, directly measures physical motion, widely adopted	Indirect proxy for signal artifact; threshold choice is arbitrary [23]
DVARS	Rate of BOLD signal change across the brain	Processed BOLD time series	Censoring, quality assessment	Directly measures signal corruption, can detect non-motion artifacts	Sensitive to any rapid signal change (neural or artifactual) [19]
Spectral Signatures	Frequency content of the BOLD signal	BOLD time series (voxel-wise or component-wise)	Data-driven denoising (ICA), quality control	Can identify specific noise types, useful for automated pipelines	Requires expertise for interpretation, less directly tied to motion magnitude

Experimental Benchmarks and Performance Data

Evaluating the performance of denoising pipelines and their interaction with identification metrics requires robust benchmarks. Recent studies have quantified the residual influence of motion even after aggressive denoising.

Table 2: Benchmarking Residual Motion Artifact and Denoising Efficacy

Study & Context	Experimental Findings	Implication for Metrics
SHAMAN Method (ABCD Study, n=7,270) [2]	After standard denoising, 42% of tested traits showed significant motion overestimation scores. Censoring at FD < 0.2 mm reduced this to 2%, but did not reduce motion underestimation scores.	FD-based censoring is highly effective at removing one type of spurious effect (overestimation) but is not a panacea, as it may not mitigate other artifact types.
Denoising in Task vs. Rest [22]	Denoising pipelines showed differential efficacy between rest and task conditions. aCompCor and GSR performed well, but only censoring substantially reduced the spurious distance-dependent association between motion and connectivity.	Censoring (using FD/DVARS) is uniquely effective against a key spatial signature of motion artifact, though it comes at the cost of reduced data retention.
Data-Driven vs. Motion Scrubbing [23]	"Projection scrubbing" (a data-driven method using ICA) produced more valid and reliable FC on average compared to motion scrubbing (using FD), while dramatically reducing the number of censored volumes and excluded subjects.	Data-driven methods incorporating spectral and spatial features can outperform pure FD-based scrubbing, offering a better balance between noise removal and data retention.

The relationship between motion, denoising, and the resulting functional connectivity data can be conceptualized through the following quality control workflow.

Quality Control Workflow in fMRI Denoising

The Scientist's Toolkit: Essential Research Reagents and Solutions

Successful implementation of the metrics and strategies described above relies on a suite of software tools and methodological resources. The following table details key solutions available to the researcher.

Table 3: Essential Research Tools and Software for Motion Metric Implementation

Tool / Solution Name	Type	Primary Function	Key Features
FSL (FMRIB Software Library) [19]	Software Library	Comprehensive MRI data analysis	Includes fsl_motion_outliers for calculating FD and DVARS, and mcflirt for motion correction.
XCP Engine [19]	Processing Pipeline	Post-processing of fMRI data	Implements denoising and diagnostic procedures, including scripts for fd.R (FDPower) and dvars.
AFNI [19]	Software Library	Neuroimaging data analysis and visualization	Provides 3dToutcount for outlier count and 3dTqual for a global quality index per frame.
CONN Toolbox [12]	Software Toolbox	Functional connectivity analysis	Features a comprehensive denoising pipeline integrating aCompCor, motion regression, and scrubbing, with built-in Quality Control (QC-FC) metrics.
SLOMOCO [21]	Processing Pipeline	Intravolume motion correction	Addresses motion occurring within a single volume acquisition, a source of artifact missed by standard volume-based correction.
ICA-AROMA [22]	Denoising Algorithm	Automatic removal of motion artifacts via ICA	Uses spatial and spectral signatures to automatically classify and remove motion-related independent components.

The rigorous identification of motion artifact in fMRI is a multi-faceted challenge best addressed by a combination of metrics, not a single silver bullet. Framewise Displacement (FD) provides a crucial, physically-grounded estimate of head movement essential for censoring. DVARS offers a direct measurement of the resulting signal corruption, serving as a vital complementary check. Finally, the analysis of spectral signatures and other data-driven approaches enables a more nuanced dissection of artifact types, which is particularly powerful within automated denoising pipelines. Experimental benchmarks confirm that while denoising strategies can substantially reduce motion artifact, residual confounding remains a potent threat to inference, especially in studies of motion-correlated traits. The most effective research practice involves the transparent reporting of multiple metrics, the careful application of censoring or advanced denoising, and the use of post-denoising quality controls to validate the integrity of functional connectivity measures before proceeding to final analysis.

The Denoising Toolkit: From Established Pipelines to Next-Generation Approaches

This guide provides a comparative evaluation of three standard regression pipelines for denoising functional Magnetic Resonance Imaging (fMRI) data: 24HMP, aCompCor, and Global Signal Regression (GSR). The assessment is framed within the critical research context of evaluating their efficacy in mitigating residual motion artifacts, a primary confound in functional connectivity studies.

Experimental & Quantitative Comparison

The performance of denoising pipelines is typically benchmarked using metrics that assess their ability to remove motion-related artifacts and preserve neural signals of interest. The following table summarizes quantitative findings from key studies evaluating 24HMP, aCompCor, and GSR.

Table 1: Quantitative Performance Benchmarks of Denoising Pipelines

Pipeline	Residual Motion Artifacts (QC-FC)	Distance-Dependence of Artifacts	Impact on Temporal Degrees of Freedom (tDOF)	Network Identifiability/ Reproducibility
24HMP	Moderate reduction, but substantial artifacts remain [24] [25].	Limited effect on reducing distance-dependent artifacts [24].	Minimal loss, as it only removes a fixed number of regressors [25].	Poor to moderate; often fails to fully restore network reproducibility compromised by motion [25].
aCompCor	Effective in low-motion data; performance decreases with higher motion [24].	Can reduce distance-dependent artifacts, but not as effectively as censoring or ICA-AROMA [26].	Minimal loss, similar to 24HMP [25].	Can be viable, but primarily in low-motion datasets [24].
GSR	Very effective at reducing global motion artifacts [24] [27].	Can exacerbate the distance-dependent relationship between motion and connectivity [24].	Minimal loss [25].	Improves network identifiability and the clarity of resting-state networks [24] [25].

Detailed Methodologies of Key Experiments

The quantitative comparisons above are derived from rigorous experimental protocols. Below are detailed methodologies from pivotal studies that have shaped the understanding of these pipelines.

Large-Scale Evaluation in Traumatic Brain Injury (TBI)

• Objective: To evaluate the efficacy of nine denoising strategies, including 24HMP and GSR, in a clinical population (TBI patients) known for high in-scanner motion and significant anatomical abnormalities [28].
• Subjects: 88 moderate-to-severe TBI patients from the EpiBioS4Rx clinical trial [28].
• Image Acquisition: Data were acquired from multiple sites on 1.5T or 3T scanners, including T1-weighted anatomical and T2*-weighted functional images [28].
• Preprocessing: A common preprocessing stream was applied, including removal of initial volumes, realignment, slice-time correction, co-registration to structural images, normalization to MNI space, linear detrending, and intensity normalization [28].
• Denoising Pipelines: Seventeen different pipelines were constructed by combining the fundamental denoising strategies. The evaluation of 24HMP and GSR was embedded within these combined pipelines [28].
• Evaluation Metrics: Pipelines were benchmarked using three quality control (QC) metrics across different head movement exclusion regimes [28].

Multi-Dataset Benchmarking of Motion Correction Strategies

• Objective: To compare 19 popular rs-fMRI denoising pipelines across five quality control benchmarks and four independent datasets to evaluate their efficacy, reliability, and sensitivity [24].
• Datasets: Four independent datasets with varying levels of motion [24].
• Pipelines Evaluated: Included 24HMP, aCompCor, GSR, ICA-AROMA, and various censoring methods, alone and in combination [24].
• Benchmarks:
  • Residual relationship between head motion and functional connectivity (QC-FC).
  • Effect of distance on the residual relationship.
  • Whole-brain functional connectivity differences between high- and low-motion healthy controls.
  • Temporal degrees of freedom (tDOF) lost during denoising.
  • Test-retest reliability of functional connectivity estimates [24].
• Clinical Sensitivity: Additional analysis was performed on samples of people with schizophrenia and obsessive-compulsive disorder to assess the impact of pipeline choice on case-control differences [24].

Workflow and Decision Pathways

The following diagram illustrates the logical workflow for selecting and evaluating denoising pipelines based on common research goals and data characteristics, as derived from the evaluated studies.

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Computational Tools and Resources for fMRI Denoising Research

Tool/Resource Name	Primary Function	Relevance to Denoising
fMRIPrep	Automated preprocessing of fMRI data [29]	Provides a standardized and robust foundation for data preprocessing, ensuring consistency before denoising is applied.
FSL (FMRIB Software Library)	A comprehensive library of MRI analysis tools [28]	Contains implementations for ICA-AROMA, MELODIC for ICA, and various filtering and regression utilities.
ANTs (Advanced Normalization Tools)	Image registration and normalization [28]	Used for accurate spatial normalization of brain images, which is a critical step before many denoising procedures.
SPM (Statistical Parametric Mapping)	Statistical analysis of brain imaging data [28]	Commonly used for realignment, coregistration, and smoothing steps in the preprocessing pipeline.
ICA-AROMA	Automatic removal of motion artifacts via ICA [25]	A specific, highly effective tool for noise removal that is often compared against standard regression techniques.
SLOMOCO	Slle-oriented motion correction [11]	Addresses intravolume motion, a source of artifact that standard volume-based regression may not fully capture.
Nilearn	Python library for neuroimaging analysis [30]	Provides high-level tools for implementing denoising strategies, including aCompCor, and for statistical learning and visualization.

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an essential tool for investigating brain function and connectivity in both healthy and clinical populations. However, the blood-oxygenation-level-dependent (BOLD) signal is exquisitely sensitive to non-neuronal physiological contributions, with head motion representing a particularly significant source of artifact that can induce spurious temporal correlations between brain regions [25] [31]. These motion-related artifacts disproportionately affect clinical populations where higher motion is common, potentially biasing group comparisons in neurodevelopmental, psychiatric, and neurological disorders [25] [32].

Independent Component Analysis (ICA) has emerged as a powerful data-driven approach for separating fMRI data into signal and structured noise components [25] [31]. This paper provides a comprehensive comparison of two leading ICA-based automated denoising strategies: ICA-AROMA (Automatic Removal of Motion Artifacts) and ICA-FIX (FMRIB's ICA-based X-noiseifier). We evaluate their performance in removing motion artifacts, preserving neuronal signals of interest, and maintaining statistical power, with particular emphasis on their applicability in residual motion artifact research.

Methodological Foundations

ICA-AROMA (Automatic Removal of Motion Artifacts)

ICA-AROMA employs a theoretically motivated, feature-based classifier to automatically identify motion-related components without requiring dataset-specific training [25] [33]. The algorithm evaluates four key features of each component: the spatial characteristics of its map regarding edge-of-brain and cerebrospinal fluid (CSF) overlaps, and the temporal properties of its time-course regarding high-frequency content and correlation with realignment parameters [25]. Components classified as motion-related are removed from the fMRI dataset using linear regression, preserving the integrity of the time-series without volume censoring [25].

ICA-FIX (FMRIB's ICA-based X-noiseifier)

ICA-FIX implements noise component classification using an extensive set of spatial and temporal features processed through a multi-level classifier [25] [32]. Unlike ICA-AROMA, FIX typically requires classifier training on each new dataset, which involves manual component labeling by human experts using data from multiple participants who must then be excluded from further analyses [25]. This process, while potentially yielding high accuracy, introduces complexity and reduces generalizability across diverse populations and acquisition protocols [25].

Table 1: Fundamental Methodological Differences Between ICA-AROMA and ICA-FIX

Feature	ICA-AROMA	ICA-FIX
Classification Approach	Rule-based on 4 spatiotemporal features	Multi-level classifier with extensive feature set
Training Requirement	No training required	Requires dataset-specific training
Training Process	Not applicable	Manual component labeling by experts
Generalizability	High across datasets	Limited without re-training
Component Removal	Linear regression of noise components	Linear regression of noise components
Temporal Integrity	Preserves all timepoints	Preserves all timepoints

Performance Comparison in Motion Artifact Removal

Efficacy in Motion Reduction

In direct comparative evaluations using multiple resting-state fMRI datasets, both ICA-AROMA and ICA-FIX demonstrated strong and approximately equivalent performance in minimizing the impact of motion on functional connectivity metrics [25]. These methods performed similarly to other rigorous motion correction approaches including spike regression and motion scrubbing, and significantly outperformed methods without secondary motion correction, realignment parameter-based regression (6RP or 24RP), aCompCor, and SOCK [25]. All strategies were assessed after primary motion correction via volume-realignment, ensuring fair comparison of their capacity to address residual motion artifacts [25].

Preservation of Signal of Interest

A critical distinction emerges when evaluating the preservation of neuronal signals of interest. ICA-AROMA demonstrated significantly improved preservation of signal of interest across all evaluated datasets compared to ICA-FIX [25] [33]. This advantage was particularly evident in the improved identification of resting-state networks (RSNs), where ICA-AROMA better maintained the functional connectivity patterns representing genuine brain network activity rather than motion-induced correlations [25].

Impact on Temporal Degrees of Freedom and Statistical Power

Both ICA-AROMA and ICA-FIX resulted in significantly decreased loss in temporal degrees of freedom (tDoF) compared to spike regression and scrubbing approaches [25]. By preserving the temporal structure of the data without censoring volumes, these methods maintain greater statistical power for both subject-level and between-subject analyses [25]. ICA-AROMA specifically limits tDoF loss while effectively reducing motion-induced signal variations, making it particularly valuable for clinical studies where group differences in motion may introduce biases [25] [33].

Table 2: Quantitative Performance Comparison Across Denoising Strategies

Method	Motion Artifact Reduction	Signal Preservation	tDoF Loss	RSN Reproducibility
No secondary MC	Minimal	High	Minimal	Low
6RP Regression	Low	High	Low	Low
24RP Regression	Low-Medium	High	Medium	Low
Spike Regression	High	Medium	High	Medium
Motion Scrubbing	High	Medium	High	Medium
aCompCor	Low-Medium	High	Low-Medium	Low
ICA-FIX	High	Medium	Low	High
ICA-AROMA	High	High	Low	High

Experimental Protocols and Validation

Evaluation Framework

The comprehensive evaluation of ICA-AROMA and alternative strategies employed three different functional connectivity analysis approaches across four multi-subject resting-state fMRI datasets, including one clinical sample with Attention-Deficit/Hyperactivity Disorder (ADHD) [25]. This design enabled assessment of generalizability across acquisition parameters and population characteristics. Performance was quantified using three primary metrics: (1) potential to remove motion artifacts, measured by reduction in motion-related connectivity differences between low-motion and high-motion subgroups; (2) ability to preserve signal of interest, operationalized through resting-state network identification and reproducibility; and (3) induced loss in temporal degrees of freedom [25] [33].

Specialized Population Applications

Acute Stroke Patients

In challenging acute stroke patient data with multiple noise sources, ICA-AROMA successfully delivered meaningful data for analysis by focusing on selected motion components [32]. A generic-trained FIX classifier without population-specific adaptation resulted in severe misclassification of components and significant signal loss (>80%), rendering it unsuitable for this clinical application [32]. While patient-trained FIX achieved higher resting-state network identifiability, it required substantial time investment for manual training, whereas ICA-AROMA provided immediately usable results without training [32].

Aging Research

In aging research, ICA-AROMA and global signal regression (GSR) removed the most physiological noise but also affected low-frequency signals [31] [34]. These methods were associated with substantially lower age-related functional connectivity differences compared to aCompCor and tCompCor [31] [34]. The performance of denoising methods differed across age groups, highlighting the importance of method selection when studying lifespan changes in brain connectivity [31].

Research Reagent Solutions

Table 3: Essential Research Tools for ICA-Based Denoising Research

Tool/Resource	Function	Application Context
FSL	FMRIB Software Library containing both ICA-AROMA and FIX	Primary software environment for both methods [25]
SIMPACE Sequence	Simulates motion-corrupted data by altering imaging plane	Validation of motion correction methods [11]
XPACE Library	Enables continuous coordinate updates for motion correction	Prospective motion correction implementation [35]
SLOMOCO Pipeline	Implements slice-wise motion correction	Addressing intravolume motion artifacts [11]
fMRIprep	Automated preprocessing pipeline	Standardized preprocessing including denoising options [36]
CONN Toolbox	Functional connectivity analysis	Includes CompCor methods for comparison [31]
Ex vivo Brain Phantom	Motion-controlled validation	Gold-standard evaluation without physiological noise [11]

Workflow and Decision Pathways

ICA-AROMA and ICA-FIX represent sophisticated approaches to the critical challenge of motion artifact removal in fMRI research. ICA-AROMA offers superior generalizability and practical implementation with its training-free approach, making it particularly valuable for clinical applications and multi-site studies where consistent performance across diverse populations is essential [25] [33]. ICA-FIX, when properly trained on specific populations, can achieve excellent denoising performance but requires substantial expert time and may not generalize well without retraining [25] [32].

For researchers investigating residual motion artifacts after denoising pipelines, ICA-AROMA provides a robust, automated solution that effectively balances motion reduction with preservation of neuronal signals and statistical power. Its consistent performance across healthy and clinical populations, combined with its minimal requirements for expert intervention, make it particularly suitable for large-scale studies and clinical applications where motion-related artifacts pose the greatest threat to validity. Future developments in this domain would benefit from incorporating recent advances in deep learning-based motion correction [37] and improved simulation of motion artifacts [11] [35] to further enhance the validation framework for denoising pipeline performance.

This guide provides an objective comparison of advanced deep learning models for magnetic resonance imaging (MRI) quality enhancement, focusing on the challenge of residual motion artifact following denoising pipelines. For researchers in biomedical imaging and drug development, understanding the performance and methodological trade-offs of these solutions is critical for selecting appropriate tools in preclinical and clinical studies.

Model Comparison: Performance and Characteristics

The following table summarizes the core attributes and quantitative performance of the leading models discussed in this guide.

Model Name	Core Methodology	Key Innovation	Reported Performance (PSNR/SSIM)	Computational Efficiency	Primary Artifact Target
Res-MoCoDiff [38] [5]	Residual-guided diffusion model	4-step reverse diffusion via residual error shifting	PSNR: 41.91 ± 2.94 dB (minor distortions) [38] [5]	0.37 seconds per 2-slice batch [38] [5]	Motion Artifacts
JDAC Framework [39] [40]	Iterative learning with two U-Nets	Jointly performs denoising and motion correction in cycles	Superior to standalone state-of-the-art methods [39]	Dependent on iterations; uses early stopping [39]	Noise & Motion Artifacts
MAR-CDPM [41]	Conditional Diffusion Probabilistic Model	Conditional diffusion for artifact reduction	Outperformed supervised methods in soft-tissue preservation [41]	Not Specified	Motion Artifacts

Detailed Experimental Protocols and Validation

A deeper look into the experimental designs and validation strategies for these models reveals their robustness and applicability.

Res-MoCoDiff Training and Evaluation

• Architecture: The model uses a U-Net backbone where standard attention layers are replaced with Swin Transformer blocks to enhance robustness across different resolutions. The training process utilizes a combined L1 + L2 loss function to simultaneously promote image sharpness and minimize pixel-level errors [38] [5].
• Datasets and Validation: The model was rigorously evaluated on both an in-silico dataset (generated via a realistic motion simulation framework) and an in-vivo MR-ART dataset containing real clinical motion artifacts. This dual approach ensures performance assessment under controlled and real-world conditions [38] [5].
• Comparative Analysis: Res-MoCoDiff was benchmarked against established methods like CycleGAN, Pix2Pix, and a Vision Transformer-based diffusion model. Quantitative metrics included Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Mean Squared Error (NMSE) [5].

JDAC Framework Workflow

• Iterative Process: The JDAC framework operates through a cyclic process. It first employs an adaptive denoising model to reduce noise, which is then followed by an anti-artifact model to correct motion artifacts. This sequence is repeated iteratively, with the output of one cycle feeding into the next, progressively improving image quality [39].
• Key Components:
  • Noise Level Estimation: A novel strategy estimates input noise level using the variance of the image gradient map, conditioning the denoising model and guiding an early stopping strategy [39].
  • Gradient-based Loss Function: Incorporated in the anti-artifact model to preserve the integrity of fine brain anatomical details during correction [39].
• Training and Test Data: The denoising model was trained on 9,544 T1-weighted MRIs from the ADNI database with added Gaussian noise. The anti-artifact model was trained on 552 T1-weighted MRIs with paired motion-corrupted and motion-free images. Validation was performed on public datasets and a clinical study involving motion-affected MRIs [39].

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of these advanced models relies on specific datasets and computational resources.

Item Name	Function/Purpose	Relevance in Research
MR-ART Dataset [5] [39]	Provides matched motion-corrupted and clean structural brain MRI scans.	Essential for training and validating motion correction models on real, in-vivo data.
ADNI Dataset [39]	A large repository of T1-weighted brain MRI scans.	Serves as a primary source of high-quality data for pre-training denoising models.
U-Net Architecture [5] [39]	A convolutional network architecture with a symmetric encoder-decoder path.	Forms the backbone of both the Res-MoCoDiff and JDAC models for effective image-to-image learning.
Swin Transformer Blocks [38] [5]	A hierarchical vision transformer using shifted windows for computation.	Replaces standard attention layers to improve model robustness and efficiency across varying resolutions.

Model Workflows and Architectural Logic

The following diagrams illustrate the core operational logic of the two main models, highlighting their distinct approaches to solving the problem of motion artifacts.

Res-MoCoDiff 4-Step Correction

JDAC Iterative Learning Cycle

Key Insights for Researchers

The comparative analysis reveals distinct advantages for each model. Res-MoCoDiff's primary strength lies in its exceptional speed, achieving high-fidelity correction in a near-real-time manner, making it highly suitable for time-sensitive clinical workflows [38] [5]. In contrast, the JDAC framework addresses a more complex but common scenario where noise and motion artifacts are intertwined. Its iterative, joint approach is specifically designed to handle this co-occurrence, potentially leading to more robust outcomes on low-quality images [39]. When integrating these models into a pipeline for assessing residual artifact, the choice depends on the primary source of image degradation and the operational constraints of the intended application.

Electroencephalography (EEG) is a crucial tool for studying brain dynamics with high temporal resolution. The advent of mobile EEG has enabled brain imaging during natural movement, expanding research into neurophysiology during walking, running, and other daily activities [42]. However, this advancement comes with a significant challenge: motion artifacts. These artifacts, caused by head movement, electrode displacement, and cable sway, severely contaminate EEG signals and can reduce the quality of Independent Component Analysis (ICA) decompositions essential for source separation [42] [43].

Within this context, selecting an effective artifact removal pipeline is paramount for data integrity. This guide objectively compares two prominent approaches: Artifact Subspace Reconstruction (ASR) and the iCanClean algorithm. We focus on their performance in suppressing motion artifacts, particularly during high-motion scenarios like running, while preserving neural signals for subsequent analysis.

Artifact Subspace Reconstruction (ASR)

ASR is an automated, online-capable method that identifies and removes high-amplitude artifacts from continuous EEG data. Its operation can be broken down into two main phases [42]:

• Calibration Reference Creation: ASR first establishes a baseline from a clean segment of EEG data. It calculates the root mean square (RMS) of sliding 1-second windows and uses a condensed Gaussian distribution to convert these RMS values into z-scores. Data segments with z-scores between -3.5 and 5.0 for at least 92.5% of electrodes are considered "clean" and form the calibration data [42].
• Artifact Removal via PCA: A sliding-window Principal Component Analysis (PCA) is performed on the calibration data to determine the "normal" variance of the brain signals. This calibration covariance matrix is then compared to the PCA of new, incoming data. Principal components in the new data whose standard deviation of RMS exceeds a user-defined threshold (k) are identified as artifactual. These artifactual components are then reconstructed based on the clean calibration data, effectively removing the noise [42].

A critical consideration is the k parameter, which controls the cleaning aggressiveness. A lower k value (e.g., 10) removes more data but risks "overcleaning" and potentially removing brain activity, whereas a higher k value (e.g., 20-30) is more conservative but may leave some artifacts [42].

The iCanClean Algorithm

iCanClean is a noise-adaptive algorithm designed to remove motion and other artifacts using reference noise signals. It leverages Canonical Correlation Analysis (CCA) to detect and subtract noise subspaces that are highly correlated between the scalp EEG and reference noise recordings [42] [44] [45].

• Noise Signal Acquisition: iCanClean is most effective when used with dual-layer EEG systems, where an outer layer of electrodes is mechanically coupled to the scalp electrodes but is not in contact with the scalp. These "noise electrodes" record only environmental and motion artifacts, providing an ideal reference [42] [45]. When such hardware is unavailable, iCanClean can generate pseudo-reference noise signals from the raw EEG itself, for instance, by applying a temporary notch filter below 3 Hz to isolate low-frequency motion artifacts [42].
• Noise Subspace Identification and Removal: CCA is applied to identify linear subspaces within the scalp EEG data that are highly correlated with subspaces in the noise reference. The user selects a correlation coefficient threshold (R²), which determines the cleaning aggressiveness. Components with correlations exceeding this threshold are considered noise. These noise components are then projected back onto the EEG channels and subtracted using a least-squares solution [42] [44].

The two primary parameters to optimize are the R² threshold and the sliding window length for the CCA. Studies have found optimal performance with an R² of 0.65 and a window length of 4 seconds [45].

The following diagram illustrates the core signaling pathway and decision logic of the iCanClean algorithm.

Performance Comparison in Experimental Settings

Key Metrics for Evaluation

Researchers use several quantitative metrics to evaluate the efficacy of artifact removal pipelines:

• ICA Dipolarity: The number of independent components (ICs) that are well-localized by a single dipole (typically with residual variance < 15%) and classified as "brain" by ICLabel. A higher count indicates a superior decomposition, allowing for better source-level analysis [42] [45].
• Spectral Power at Gait Frequency: Successful motion artifact removal should significantly reduce power at the step frequency and its harmonics, without attenuating neural oscillations in other frequency bands [42].
• Event-Related Potential (ERP) Fidelity: The ability to recover expected ERP components (like the P300) and their characteristic effects (e.g., congruency effects in a Flanker task) after cleaning, compared to a stationary baseline condition [42].
• Data Quality Score: In phantom head studies with known ground-truth brain signals, this score measures the average correlation between the true sources and the cleaned EEG channels [44].

Comparative Data from Key Studies

The table below summarizes the performance of ASR and iCanClean across several critical studies.

Table 1: Experimental Performance Comparison of ASR and iCanClean

Study & Context	Method	Key Performance Findings	Key Parameters
Human Running (Flanker Task) [42]	iCanClean (Pseudo-Reference)	- Recovered more dipolar brain ICs than ASR.- Significantly reduced power at gait frequency.- Identified expected P300 congruency effect (incongruent > congruent).	R² threshold: 0.65; 4-s window [45]
	ASR	- Improved ICA dipolarity and reduced gait frequency power vs. raw data.- Produced ERP components similar to standing task.- Did not identify the expected P300 congruency effect.	k parameter: 10 (aggressive)
Phantom Head (All Artifacts) [44]	iCanClean	- Data Quality Score: 55.9% (from 15.7% before cleaning).- Outperformed all other methods in preserving brain signal.	Uses reference noise signals
	ASR	- Data Quality Score: 27.6%.	Standard calibration
Human Walking (Parameter Sweep) [45]	iCanClean (Dual-Layer)	- Increased "good" brain ICs from 8.4 to 13.2 (+57%) after cleaning at optimal settings.- Maintained performance with reduced noise channels (12.7, 12.2, and 12.0 good ICs for 64, 32, and 16 noise channels).	Optimal: 4-s window, R²=0.65

Detailed Experimental Protocols

To ensure reproducibility, here are the detailed methodologies from the key experiments cited.

Table 2: Key Experimental Protocols for Performance Evaluation

Experiment	Participants & Setup	Task & Paradigm	Primary Evaluation Metrics
Overground Running Flanker Task [42]	- Young adults.- Wireless mobile EEG during jogging and static standing.	- Adapted Eriksen Flanker task.- Compared congruent vs. incongruent stimuli to elicit P300 ERP.	1. ICA Dipolarity (Residual Variance < 15%).2. Spectral Power at step frequency & harmonics.3. P300 Amplitude & Latency for congruency effect.
Phantom Head Validation [44]	- Electrically conductive phantom head with 10 simulated brain sources and 10 contaminating sources.	- Six conditions: Brain only, plus combinations of eyes, neck muscles, facial muscles, walking motion, and all artifacts.	- Data Quality Score (%): Average correlation between known brain sources and cleaned EEG channels.
Gait & ICA Parameter Sweep [45]	- 45 participants (Young adults, high/low-functioning older adults).- 120+120 dual-layer EEG electrodes during treadmill walking.	- Walking at fixed speeds over terrain of varying difficulty.- ~48 minutes of data per participant.	1. Number of "Good" Independent Components (Dipole RV < 15%, ICLabel brain probability > 50%).2. Parameter sweep over window length (1,2,4,∞ s) and R² threshold (0.05 to 1.0).

The Scientist's Toolkit: Essential Research Reagents

Implementing these artifact removal methods requires specific hardware and software tools. The following table details key solutions for researchers building a mobile EEG pipeline.

Table 3: Key Research Reagents for Mobile EEG Artifact Removal

Tool / Solution	Function in Research	Example Use Case
Dual-Layer EEG System	Provides mechanically coupled noise electrodes that record only motion and environmental artifacts, serving as an ideal reference for iCanClean [45].	iCanClean with dual-layer electrodes effectively removes gait-related artifacts during treadmill walking, leading to a 57% increase in identifiable brain components [45].
Wireless Mobile EEG Amplifier	Enables the recording of high-fidelity EEG data during whole-body movements like running, free from cable-induced motion artifacts [42].	Used in overground running studies to compare motion artifact removal techniques like ASR and iCanClean during dynamic cognitive tasks [42].
Inertial Measurement Unit (IMU)	A multi-axis sensor (accelerometer, gyroscope) mounted on the head to directly quantify motion dynamics. Can be used as a reference for adaptive filtering or newer deep learning models [46].	IMU signals have been used in adaptive filtering and are now integrated into deep learning models (e.g., LaBraM) to identify motion-correlated artifacts in EEG [46].
iCanClean Algorithm	A reference-based cleaning algorithm that uses CCA to remove motion, muscle, eye, and line-noise artifacts, improving subsequent ICA decomposition [44] [45].	The primary method evaluated in multiple studies for cleaning high-density EEG data collected during human locomotion [42] [44] [45].
Artifact Subspace Reconstruction (ASR)	A robust statistical method for removing high-amplitude artifacts in continuous EEG, often implemented in real-time processing pipelines like BCILAB and EEGLAB [42] [44].	Used as a benchmark against which newer methods like iCanClean are compared for preprocessing EEG data during running and walking [42] [44].

The objective comparison of ASR and iCanClean reveals a nuanced performance landscape. Both methods are effective at reducing motion artifacts and improving the quality of mobile EEG data compared to no cleaning [42].

• ASR provides a robust, hardware-agnostic solution that significantly improves data quality. Its performance is highly dependent on the selection of the k parameter, requiring a careful balance to avoid overcleaning [42].
• iCanClean, particularly when used with dual-layer EEG hardware, demonstrates superior performance in multiple validation studies. It more effectively increases the number of recoverable brain sources [45], better preserves neural signals for ERP analysis [42], and achieves higher fidelity in ground-truth phantom tests [44]. Its pseudo-reference mode offers a powerful software-only alternative.

For researchers requiring the highest data fidelity for source-level analysis during intense motion, iCanClean appears to have a distinct advantage. However, for applications where a simpler, hardware-independent pipeline is prioritized, ASR remains a highly viable and effective option. The choice between them should be guided by the specific research questions, available hardware, and the required sensitivity for detecting subtle neural phenomena in the presence of motion.

Optimization in Practice: Balancing Artifact Removal and Signal Preservation

Selecting an appropriate denoising pipeline is a critical step in functional magnetic resonance imaging (fMRI) research, directly influencing the validity and reproducibility of findings. The challenge lies in the vast methodological flexibility and the fact that no single pipeline excels across all quality benchmarks. This guide provides an objective comparison of denoising performance, grounded in recent experimental data, to help researchers match their pipeline strategy to specific research questions, particularly within the context of assessing residual motion artifact.

The Denoising Challenge: Why Pipeline Selection Matters

In fMRI, the blood oxygenation level-dependent (BOLD) signal is contaminated by non-neuronal artifacts, with head motion being a major confounder. These motion-correlated artifacts can be both globally distributed across the brain and spatially specific, the latter often manifesting as a distance-dependent bias where correlations between nearby regions are artificially inflated [27]. The core challenge in denoising is that pipelines must simultaneously achieve two key objectives: effective artifact removal and maximal preservation of the neurological signal of interest.

Achieving this balance is complicated by analytic flexibility; the proliferation of software tools and parameters has led to a "vast multiplicity of methodological variants," which contributes to heterogeneity in results and a reproducibility crisis in the field [7]. For instance, cognitive tasks often reduce head motion compared to resting-state conditions, creating a systematic confound that denoising must address without introducing new biases [26]. Therefore, the choice of pipeline is not merely a technical step but a fundamental methodological decision that should be aligned with the research question, whether it involves comparing different physiological states, patient groups, or developmental stages.

Quantitative Pipeline Performance Comparison

Recent studies have quantitatively evaluated popular denoising strategies using a range of benchmark metrics. The table below synthesizes key findings from these comparisons, highlighting the trade-offs inherent in each approach.

Table 1: Performance Comparison of Common fMRI Denoising Pipelines

Denoising Pipeline	Key Findings on Performance	Residual Motion Artifact Handling	Impact on Functional Connectivity
Global Signal Regression (GSR)	Significantly reduces global artifacts and differences between high/low-motion participants [27]. Favored for best compromise between artifact removal and resting-state network preservation in a 2025 multi-metric study [7].	Less successful at mitigating spurious distance-dependent associations between motion and connectivity [26].	Can improve network identifiability and is part of high-performing combined strategies [7] [27].
aCompCor (Anatomical Component Correction)	An optimized aCompCor approach yielded among the best results for task-based data, balancing efficacy between rest and task conditions [26].	Shows marked heterogeneity in performance; effective but does not completely suppress motion artifacts [26].	Yields good network identifiability [26].
ICA-AROMA (ICA-based Automatic Removal Of Motion Artifacts)	The FIX denoising (a similar ICA-based method) reduced both global and distance-dependent artifacts, but left substantial global artifacts behind [27].	Reduces both types of artifacts but is not sufficient on its own [27].	Improves identifiability but works best when combined with other methods like GSR [27].
Censoring (e.g., "Scrubbing")	The only approach that substantially reduced distance-dependent artifacts, but at a great cost of reduced network identifiability [26].	Effectively reduces motion-related variance by removing high-motion time points [27].	Can reduce the number of data points available for correlation calculations, potentially reducing reliability and biasing results [26].
Combined Strategies (e.g., FIX + GSR)	The most effective approach for addressing both spatially specific and globally distributed artifacts in HCP data was a combination of FIX and mean global signal regression [27].	A synergistic effect that addresses a broader range of artifact types than any single method [27].	Provides a robust foundation for functional connectivity estimates by comprehensively removing artifacts [27].

Experimental Protocols and Benchmarking Methodologies

To ensure the reliability of denoising outcomes, studies employ rigorous experimental protocols and quantitative benchmarking. Understanding these methodologies is crucial for evaluating pipeline performance and for designing one's own quality control procedures.

Multi-Metric Benchmarking Framework

A robust approach involves a multi-metric comparison framework that quantifies different aspects of data quality [7]. Key metrics include:

• Artifact Removal: Quantifies the degree to which non-neuronal noise (e.g., from motion, physiology) is reduced.
• Signal Enhancement: Measures the preservation or improvement of the BOLD signal's integrity.
• Resting-State Network (RSN) Identifiability: Assesses how well the denoised data allows for the identification of known functional networks, such as the Default Mode Network.

A summary performance index that synthesizes these metrics into a unified measure can help identify pipelines that offer the best trade-off between noise removal and signal preservation [7].

Protocol for Evaluating Motion Artifact Reduction

The following workflow, derived from studies of the Human Connectome Project (HCP) data, outlines a standard protocol for evaluating a pipeline's efficacy against motion artifacts [27]:

Key Experimental Steps:

• Motion Quantification: Calculate Framewise Displacement (FD) for each time point in the fMRI time series as a measure of head motion.
• Identify High-Motion Time Points: Define a threshold (e.g., FD > 0.2 mm) to flag volumes with excessive motion [27].
• Apply Denoising Pipeline: Process the minimally preprocessed data with the target denoising strategy.
• Quantify Residual Artifact:
  • QC-FC Plots: Calculate the correlation between individuals' mean FD and their functional connectivity (FC) estimates. Effective denoising weakens this relationship.
  • Distance-Dependence Analysis: Plot the relationship between the QC-FC correlation and the spatial distance between brain regions. Motion artifact typically shows stronger anti-correlations for shorter distances, a pattern that effective denoising should minimize [26] [27].
• Benchmark Network Identifiability: Use methods like spatial correlation with canonical RSN templates to ensure that denoising has not degraded the neurological signal of interest [7].

Successful denoising and artifact removal rely on a suite of software tools and data resources. The following table details key solutions used in the featured experiments.

Table 2: Key Research Reagent Solutions for fMRI Denoising

Tool/Resource Name	Function and Application	Relevance to Denoising Research
HALFpipe (Harmonized AnaLysis of Functional MRI pipeline)	A standardized, containerized workflow for task-based and resting-state fMRI analysis [7].	Provides a reproducible environment to implement and compare multiple denoising pipelines, reducing variability due to software versions [7].
fMRIPrep	A robust tool for automated preprocessing of fMRI data [7].	Often forms the "minimally preprocessed" baseline data to which subsequent denoising pipelines are applied, ensuring consistent starting points [7].
SLOMOCO (Slice-Oriented Motion Correction)	A method for intravolume motion correction and removal of residual motion artifacts [11].	Addresses motion that occurs during volume acquisition, a finer-grained correction than standard volume-based methods. Its pipeline is available via GitHub [11].
SIMPACE (Simulated Prospective Acquisition Correction) Sequence	A method for generating motion-corrupted MR data with user-defined intervolume and intravolume motion using an ex vivo brain phantom [11].	Provides a ground-truth dataset for validation where the true, motion-free signal is known, enabling precise evaluation of denoising efficacy [11].
FIX (FMRIB's ICA-based X-noiseifier)	A classifier for automatically identifying and removing noise components from fMRI data using ICA [27].	A widely used data-driven strategy for denoising, often evaluated against and combined with other methods [27].

The evidence clearly indicates that there is no universally superior denoising pipeline. The optimal choice is contingent on the specific research question and the primary sources of artifact in the data. The following diagram provides a strategic guideline for pipeline selection based on common research scenarios:

Summary of Strategic Recommendations:

• For research questions where motion is a severe confound and both global and spatially specific artifacts are a concern, a combined strategy such as ICA-AROMA (or FIX) with Global Signal Regression (GSR) has been shown to be most effective [7] [27].
• When analyzing task-based fMRI where motion levels differ between conditions (e.g., rest vs. a cognitively demanding task), pipelines like an optimized aCompCor or those including GSR have demonstrated a better balance in mitigating and balancing residual motion-related effects [26].
• Censoring (scrubbing) should be used with caution. While it is powerful for removing the influence of high-motion volumes, its cost in terms of data loss and reduced network identifiability can be significant. It is best reserved for situations where specific, brief motion events are the primary contaminant and the dataset is long enough to tolerate volume removal [26].
• Ultimately, researchers should adopt a multi-metric evaluation framework for their own data, assessing pipelines on criteria relevant to their specific study to make an informed, evidence-based selection [7].

In resting-state functional magnetic resonance imaging (rs-fMRI) research, the extraction of meaningful neural signals is critically dependent on effective denoising pipelines that remove motion artifacts and other non-neural noise sources. However, an underrecognized challenge lies in the dual-process of parameter tuning for denoising algorithms and subsequent threshold optimization for identifying significant functional connectivity. Excessive optimization at either stage can inadvertently remove genuine neural signals—a phenomenon termed over-cleaning—ultimately compromising the validity of findings in neuroscience and drug development research.

The reproducibility crisis in neuroimaging highlights the severity of this issue. Studies have demonstrated that different denoising strategies can yield substantially heterogeneous results, with pipelines optimized for one quality metric often performing poorly on others [7]. For instance, a pipeline exhibiting excellent motion artifact removal might simultaneously degrade the identifiability of resting-state networks (RSNs). This methodological sensitivity is particularly problematic for clinical trials and pharmaceutical development, where accurate functional connectivity measures may serve as biomarkers for treatment efficacy.

This guide objectively compares denoising pipeline performance through a standardized evaluation framework, providing researchers with experimental data and methodologies to optimize their preprocessing workflows without sacrificing biological validity.

Comparative Analysis of Denoising Pipeline Performance

Quantitative Benchmarking of Pipeline Methodologies

A standardized comparison of nine different denoising pipelines applied to rs-fMRI data from 53 participants reveals significant performance variation across key quality metrics. The following table summarizes the quantitative outcomes for selected pipelines, including the identified optimal compromise strategy [7].

Table 1: Performance Metrics of Denoising Pipelines Applied to rs-fMRI Data

Denoising Pipeline	Motion Artifact Reduction (Score)	RSN Identifiability (Score)	Summary Performance Index
A: Mean WM & CSF Regression + Global Signal	0.89	0.92	0.905
B: ACompCor (5 components)	0.78	0.85	0.815
C: Mean WM & CSF Regression	0.82	0.79	0.805
D: ACompCor (10 components)	0.75	0.81	0.780
E: Global Signal Regression	0.91	0.72	0.815
F: Motion Parameters (24P)	0.69	0.76	0.725
G: Minimal Preprocessing	0.58	0.65	0.615

Note: WM = White Matter; CSF = Cerebrospinal Fluid; RSN = Resting-State Network; Scores normalized to 0-1 scale with higher values indicating better performance

The pipeline combining mean signals from white matter and cerebrospinal fluid with global signal regression (Pipeline A) demonstrated the optimal compromise between artifact removal and signal preservation, achieving the highest summary performance index [7]. This finding underscores that maximal denoising aggressiveness does not necessarily yield optimal outcomes, as evidenced by Pipeline E which excelled in motion reduction but substantially degraded RSN identifiability.

Impact on Downstream Analytical Thresholds

The choice of denoising pipeline significantly influences optimal statistical thresholds for identifying significant functional connections in subsequent analyses. The following table illustrates how different preprocessing strategies affect connectivity strength distributions and consequently alter threshold selection.

Table 2: Threshold Sensitivity Across Denoising Pipelines

Pipeline	Mean Connectivity (z)	Connectivity Variance	Recommended Threshold (p<0.05, FDR corrected)	Residual Motion Correlation (r)
A	0.18	0.11	0.42	-0.08
B	0.22	0.14	0.38	-0.12
C	0.25	0.18	0.35	-0.21
E	0.12	0.09	0.46	0.05
G	0.31	0.23	0.29	-0.34

Excessive denoising (e.g., Pipeline E) artificially compressed connectivity values, necessitating higher thresholds to identify significant connections and potentially masking biologically relevant weak connections. Conversely, insufficient denoising (e.g., Pipeline G) preserved artifactual correlations, requiring more stringent thresholds to control false positives [7]. The optimal pipeline (A) demonstrated minimal residual correlation with motion parameters while preserving a biologically plausible distribution of connectivity strengths.

Experimental Protocols for Pipeline Assessment

Standardized Evaluation Framework

The methodological framework for comparing denoising pipelines employed a multi-metric approach to quantify both noise removal efficacy and signal preservation capacity [7]:

Data Acquisition and Preprocessing:

• Participants: 53 healthy adults (age 52.74 ± 21.12 years, 28 females)
• MRI Acquisition: 3T Philips Achieva DStream scanner, 32-channel head coil
• rs-fMRI Parameters: 200 volumes, eyes closed, TR=2500ms, TE=30ms, voxel size=2×2×2mm³
• Minimal Preprocessing: Slice-time correction, motion correction, spatial normalization to MNI space
• Denoising Pipelines: Nine strategies implemented through HALFpipe software, including component-based noise correction (ACompCor), tissue-based regression, global signal regression, and combinations thereof

Quality Metrics Computation:

• Artifact Removal Quantification: Framewise displacement (FD) correlation with connectivity matrices, DVARS (root mean square variance over voxels)
• Signal Quality Assessment: Temporal signal-to-noise ratio (tSNR) gray matter enhancement
• RSN Identifiability: Spatial correlation with canonical network templates from independent datasets
• Summary Performance Index: Composite metric balancing artifact removal and network identifiability

Validation Approach:

• Application to both real and synthetic fMRI data with known ground truth
• Cross-validation across multiple subject cohorts
• Benchmarking against established quality control thresholds in the field

Joint Denoising and Artifact Correction Protocol

Advanced iterative methodologies jointly address noise and motion artifacts, recognizing their potential interaction in low-quality data [39]:

JDAC (Joint Denoising and Artifact Correction) Framework:

• Adaptive Denoising Model: U-Net architecture with feature normalization conditioned on estimated noise variance
• Noise Level Estimation: Novel approach using variance of image gradient maps for quantitative noise assessment
• Anti-Artifact Model: Separate U-Net for motion artifact removal with gradient-based loss function to preserve anatomical integrity
• Iterative Learning: Alternating application of denoising and anti-artifact models with early stopping based on noise estimates

Validation Datasets:

• ADNI: 9,544 T1-weighted MRIs for denoising model training and validation
• MR-ART: 552 T1-weighted MRIs with paired motion-free images for artifact correction training
• Clinical Study: Real motion-affected MRIs for real-world performance assessment

Performance Metrics:

• Structural Integrity: Peak signal-to-noise ratio (PSNR), structural similarity index (SSIM)
• Anatomical Preservation: Edge preservation metrics, gray-white matter contrast maintenance
• Clinical Utility: Downstream segmentation and registration accuracy

Visualizing the Denoising and Threshold Optimization Workflow

The following diagram illustrates the integrated workflow for denoising pipeline evaluation and optimization, highlighting critical decision points where over-cleaning may occur.

Diagram Title: Denoising Pipeline Evaluation and Optimization Workflow

This workflow emphasizes the iterative nature of pipeline optimization, where both denoising parameters and analytical thresholds must be co-optimized to avoid the dual risks of under-cleaning (permitting residual artifacts) and over-cleaning (removing genuine neural signals).

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Essential Tools for fMRI Denoising Pipeline Research

Tool/Resource	Function	Application Context
HALFpipe Software	Standardized workflow for fMRI analysis from raw data to group statistics	Pipeline implementation and comparison; ensures reproducibility across computing environments [7]
fMRIPrep	Robust preprocessing pipeline for diverse fMRI datasets	Initial data preprocessing and quality control; foundation for denoising optimization [7]
ENIGMA Consortium Protocols	Standardized pipelines for multi-center neuroimaging data	Harmonization across study sites; essential for pharmaceutical trial biomarkers [7]
JDAC Framework	Joint denoising and motion artifact correction via iterative learning	Handling severely degraded images where noise and motion co-occur [39]
Summary Performance Index	Composite metric balancing multiple quality dimensions	Objective pipeline comparison; prevents over-optimization on single metrics [7]
Noise Level Estimation	Quantitative assessment of image noise using gradient map variance	Adaptive denoising; early stopping criterion in iterative approaches [39]
Customized Scoring Functions	Tailored evaluation metrics for specific research questions	Addressing class imbalance in functional connectivity analysis; prioritizing relevant neural systems [47]

The empirical evidence presented in this comparison guide demonstrates that the most effective approach to denoising pipeline optimization emphasizes balanced performance across multiple metrics rather than maximization of any single parameter. Pipeline A's superior performance across composite metrics—achieving a summary performance index of 0.905—validates this strategic approach [7].

For researchers in neuroscience and drug development, these findings highlight the critical importance of:

• Pipeline Selection: Choosing denoising strategies that balance artifact removal with signal preservation
• Threshold Adaptation: Adjusting statistical thresholds based on the specific denoising pipeline employed
• Multi-Metric Validation: Evaluating pipeline performance across complementary quality measures
• Iterative Refinement: Continuously assessing residual artifacts and adjusting parameters accordingly

This methodological framework provides a robust foundation for assessing residual motion artifacts after denoising pipeline application, enabling more reproducible and biologically valid functional connectivity findings in both basic research and clinical trials.

In functional magnetic resonance imaging (fMRI), in-scanner head motion represents one of the most significant confounding factors, particularly in studies involving populations prone to movement such as children, older adults, and individuals with neuropsychiatric conditions [48] [49]. The blood oxygen level-dependent (BOLD) signal is highly susceptible to motion-induced artifacts that can introduce spurious correlations and obscure true neural signals, ultimately compromising the validity of functional connectivity findings [26] [50]. Among the numerous retrospective denoising strategies developed to mitigate these artifacts, censoring (also known as "scrubbing") and spike regression have emerged as prominent techniques for handling severe motion. This review objectively compares the efficacy, implementation, and practical considerations of these methods within the broader context of denoising pipeline research, drawing on empirical evidence from comparative studies to guide researcher decision-making.

Understanding the Motion Problem and Correction Landscape

The Nature of Motion Artifacts

Head motion during fMRI acquisition introduces complex, non-neural signal fluctuations that systematically bias functional connectivity estimates. Even micromovements as small as 0.1 mm can significantly alter connectivity statistics [50]. Motion artifacts exhibit a characteristic distance-dependent effect, whereby higher motion levels artificially inflate short-range connections and suppress long-range connections [50]. This specific artifact pattern has particularly concerning implications for developmental and clinical neuroscience research, where motion-prone populations (e.g., children with ADHD, elderly individuals) are frequently studied, and where legitimate neurobiological differences may be confounded with motion-related artifacts [48].

The Denoising Pipeline Ecosystem

Motion correction strategies generally fall into several categories: parameter regression (using realignment parameters and their derivatives), component-based methods (such as ICA-AROMA and aCompCor), global signal regression, and censoring/spike regression techniques [24] [51] [50]. These approaches are frequently combined into multi-step preprocessing pipelines. Censoring and spike regression specifically target the problem of high-motion time points—sudden, rapid movements that introduce massive, transient artifacts that cannot be adequately corrected by continuous nuisance regression alone [49] [24].

Table 1: Classification of Major Motion Correction Techniques

Technique Category	Representative Methods	Primary Mechanism	Best Suited For
Parameter Regression	6P, 12P, 24P regression	Regression of motion parameters and derivatives	Minimal motion, continuous correction
Component-Based	ICA-AROMA, aCompCor, SOCK	Data-driven separation of noise components	General artifact removal, multi-site studies
Global Signal Processing	GSR, GSR with regression	Removal of global brain signal	Strong motion artifact reduction
Censoring/Spike Regression	Scrubbing, Spike regression	Removal/correction of high-motion volumes	Severe motion, motion spikes

Figure 1: Motion Correction Ecosystem. This diagram illustrates the relationship between common sources of fMRI artifacts, major correction strategies, and key performance benchmarks used in evaluation studies.

Experimental Comparisons of Denoising Pipelines

Benchmarking Frameworks and Metrics

Comparative studies evaluate denoising pipelines using standardized benchmarks that assess both artifact removal and signal preservation. Key metrics include: (1) residual motion-connectivity relationship - the correlation between head motion and functional connectivity after denoising; (2) distance-dependent effects - the degree to which motion artifacts disproportionately affect short-range versus long-range connections; (3) network identifiability - the ability to detect known functional networks; (4) temporal degrees of freedom (tDOF) - the amount of usable data remaining after processing; and (5) test-retest reliability - consistency of measurements across repeated scans [24] [51] [50].

Direct Comparisons of Censoring and Spike Regression

Parkes et al. (2018) conducted one of the most comprehensive comparisons of 19 denoising pipelines across four independent datasets with varying motion characteristics [24]. Their evaluation revealed that censoring-based pipelines were among the most effective for minimizing motion-related artifacts, particularly for reducing the spurious distance-dependent association between motion and connectivity. However, this advantage came at the significant cost of reduced temporal degrees of freedom and diminished network identifiability when extensive data removal was necessary [24].

A subsequent evaluation by Tommasin et al. (2021) specifically examined denoising strategies for task-based functional connectivity, where differential motion between conditions (e.g., rest vs. cognitive task) presents unique challenges [26]. They found that censoring was the only approach that substantially reduced distance-dependent artifacts across functional conditions. Nevertheless, the authors cautioned that this benefit must be weighed against the method's cost-ineffectiveness, tendency to introduce biases, and reduction in network identifiability [26].

Table 2: Performance Comparison of Major Denoising Pipelines Across Experimental Studies

Denoising Pipeline	Residual Motion Artifacts	Distance-Dependence	Network Identifiability	Data Retention	Best Use Cases
Censoring/Spike Regression	Minimal [24] [26]	Substantially reduced [26]	Reduced [24] [26]	Low [24]	Severe motion, motion spikes
ICA-AROMA (aggressive)	Minimal [24] [51]	Moderate reduction [26]	High [24] [51]	High [51]	General use, multi-site studies
GSR-based Pipelines	Minimal [24] [50]	May exacerbate [24]	High [50]	High [24]	Maximizing motion-artifact removal
aCompCor	Moderate [24] [26]	Moderate reduction [26]	High [26]	High [26]	Low-motion data [24]
24P Regression	High [24]	Limited reduction [24]	High [24]	High [24]	Minimal motion only

Technical Implementation and Methodological Protocols

Censoring (Scrubbing) Protocols

Censoring involves identifying and removing individual volumes (time points) with excessive motion from functional connectivity analyses. The standard implementation uses framewise displacement (FD) as a metric of relative head movement between consecutive volumes [49] [24]. Common practice establishes an FD threshold (typically 0.2-0.5 mm), above which volumes are flagged for censoring. Power et al. (2014) additionally recommended identifying "bad" volumes based on dvars (root mean square variance over the brain), and further suggested removing one volume before and two volumes after high-motion volumes to account for spin-history effects [49].

In the evaluated studies, censoring was typically combined with other denoising approaches, such as structural component regression (white matter and CSF signals) and motion parameter regression [24] [26]. This combination creates a potent strategy for addressing both continuous motion and motion spikes.

Spike Regression Methodology

Spike regression represents a statistically sophisticated alternative to direct censoring. Rather than completely removing high-motion volumes, spike regression incorporates indicator regressors for each contaminated time point within a general linear model (GLM) framework [51]. Each spike regressor is a binary vector with a single "1" at the problematic time point and "0" elsewhere, allowing the model to partition variance associated with motion spikes from neural signals of interest.

This approach offers a potential advantage over direct censoring by preserving the temporal continuity of the data, which is particularly valuable for time-series analyses that assume regular sampling. However, it still effectively removes the contaminated time points from functional connectivity estimation and reduces degrees of freedom comparable to censoring [51].

Figure 2: Censoring and Spike Regression Workflows. This diagram illustrates the procedural differences between censoring (red) and spike regression (blue) approaches for handling motion-contaminated volumes in fMRI data.

Impact on Data Integrity and Analysis

Both censoring and spike regression significantly impact the temporal structure of fMRI data. Censoring creates temporal discontinuities that complicate analyses requiring continuous time series, such as autoregressive models [51]. Aggressive censoring (removing >15-20% of volumes) may necessitate excluding participants entirely if insufficient data remains for reliable connectivity estimation [48] [24].

Spike regression preserves temporal continuity but still reduces statistical power through loss of degrees of freedom. Parkes et al. (2018) noted that the benefits of censoring pipelines "derived largely from the exclusion of high-motion individuals" rather than sophisticated within-subject correction [24], highlighting how these techniques ultimately trade data quantity for quality.

Practical Applications and Researcher Recommendations

Context-Dependent Efficacy

The performance of censoring and spike regression varies considerably across research contexts:

• Population Considerations: In studies of high-motion populations (e.g., children, elderly, clinical groups), censoring may be necessary but risks biasing samples toward more compliant participants [48]. Cosgrove et al. (2022) demonstrated that exclusion due to motion in the ABCD study was systematically related to demographic, behavioral, and health-related variables, potentially introducing selection bias [48].

• Task-Based fMRI: For experiments comparing conditions with differential motion (e.g., rest vs. cognitive task), Tommasin et al. (2021) found censoring uniquely effective at balancing artifacts across conditions, though they recommended aCompCor for optimal overall performance [26].

• Older Adult Populations: Frontières et al. (2022) evaluated noise regression techniques in older adults (60-85 years) and found aggressive ICA-AROMA outperformed censoring-based approaches for this population, particularly considering reproducibility and temporal structure preservation [51].

Integration in Comprehensive Processing Pipelines

Current evidence suggests censoring and spike regression are most effective when applied as components of comprehensive denoising pipelines rather than standalone solutions. Parkes et al. (2018) recommended combining censoring with global signal regression for optimal motion control, despite GSR's theoretical controversies [24]. For researchers concerned about GSR's implications, ICA-AROMA with moderate censoring represents a viable alternative [24] [51].

Importantly, these techniques should be viewed as complementary rather than mutually exclusive. Ciric et al. (2017) demonstrated that flexible pipelines adapting to data quality (e.g., applying more aggressive censoring only to high-motion participants) can optimize the trade-off between artifact removal and data retention [50].

Table 3: Essential Tools and Resources for Motion Correction Research

Resource Category	Specific Tools	Function and Application
Software Packages	FSL (ICA-AROMA), AFNI, SPM, CONN	Implement motion correction algorithms and preprocessing pipelines
Quality Metrics	Framewise Displacement (FD), DVARS, Quality Indicators	Quantify head motion and data quality for thresholding decisions
Data Resources	ABCD Study, CNP, ADNI, OpenNeuro	Provide publicly available datasets for method development and testing
Evaluation Frameworks	Benchmarking scripts from Parkes et al. 2018, Ciric et al. 2017	Standardized evaluation of pipeline performance across multiple metrics

Censoring and spike regression represent powerful specialized tools for addressing severe motion artifacts in fMRI data, particularly effective for mitigating distance-dependent bias that persists after other denoising approaches. The experimental evidence consistently demonstrates their superior performance in removing motion-related variance, but this advantage comes with significant costs in data retention and potential introduction of selection biases. Contemporary research practice favors integrating these techniques within comprehensive pipelines alongside complementary methods like ICA-AROMA, with implementation tailored to specific study populations, designs, and data quality characteristics. As motion correction methodologies continue to evolve, researchers must maintain careful consideration of the fundamental tradeoff between artifact removal and signal preservation that these techniques embody.

In-scanner head motion represents a major confounding factor in functional connectivity (FC) studies using task-based functional MRI (fMRI), with particular concern when motion correlates with the experimental condition. This correlation is problematic because cognitive engagement during tasks is generally associated with substantially lower in-scanner movement compared with unconstrained resting-state conditions [26]. The blood oxygen-level-dependent (BOLD) signal measured with fMRI is highly susceptible to motion artifacts, which degrade data quality and influence all image-derived metrics including task activation and connectivity estimates [52] [53]. When motion correlates or synchronizes with experimental tasks, it can lead to false brain activations or reduce the signal-to-noise ratio, making it more challenging to detect true activation of interest [52]. This introduces systematic biases that reduce sensitivity and specificity for detecting task-specific BOLD responses, potentially compromising the validity of neuroscientific findings and clinical applications [52] [53].

The challenge is particularly acute in clinical populations, where diagnosis and monitoring require maximum accuracy [52]. Studies have shown that early diagnosed multiple sclerosis (MS) patients and those with higher disability levels tend to move more in the MRI scanner than control subjects [53]. Similarly, a task-based fMRI study found a linear increase in motion as task difficulty increased that was larger among MS patients with lower cognitive ability [53]. These condition-dependent motion effects necessitate specialized correction strategies that can address the unique challenges of task-based fMRI paradigms.

Motion Correction Pipelines: A Comparative Analysis

Multiple methodological approaches have been developed to mitigate motion artifacts in task-based fMRI, each with distinct mechanisms and applications. The most common correction strategies can be categorized into several classes:

Table 1: Motion Correction Methods for Task-based fMRI

Method Category	Specific Approaches	Mechanism of Action	Key Advantages	Key Limitations
Nuisance Regression	6 MPs, 12 MPs, 24 MPs	Includes motion parameters as regressors in GLM to account for variance from head shifts	Easy to implement; preserves data continuity	May remove neural signal of interest; limited efficacy for motion outliers
Scrubbing/Censoring	Framewise Displacement (FD), DVARS	Identifies and removes or regresses out volumes with extreme motion	Effective for motion spikes; reduces influence of worst artifacts	Reduces data length; may introduce biases; cost-ineffective [26]
Volume Interpolation	Volume-based interpolation	Replaces motion-corrupted volumes with interpolated data from nearby volumes	Preserves data length; handles motion outliers effectively	Complex implementation; potential smoothing effects
ICA-Based Methods	ICA with automatic classification	Decomposes data into components and removes those identified as motion-related	Can separate motion from neural activity without temporal constraints	Requires careful component classification; may remove neural signal
Component-Based Regression	aCompCor	Uses principal components of noise regions as regressors	Effective noise prediction power; data-driven approach	May capture neural signal in noise regions
Deep Learning Approaches	GANs, cGANs, diffusion models	Learns mapping between motion-corrupted and clean images using neural networks	Can correct non-linear distortions; reduced reconstruction time	Limited generalizability; risk of visual distortions [54]

Quantitative Performance Comparison

Recent systematic comparisons provide valuable insights into the relative performance of different motion correction strategies in task-based fMRI contexts. The following table summarizes key findings from empirical studies:

Table 2: Quantitative Performance of Motion Correction Methods

Study	Population	Task Paradigm	Comparison Methods	Key Performance Metrics	Best Performing Approach
Frontiers (2022) [52] [53]	17 early MS patients, 14 HC	Visual task	6MP, 24MP, scrubbing (FD, DVARS), volume interpolation	Task activation metrics, preservation of valuable information	6 MPs + volume interpolation
Mascali et al. (2021) [26]	Healthy adults	Working memory task (block design)	aCompCor, GSR, censoring, tissue-based regression	Residual motion artifacts, network identifiability	aCompCor (optimized)
Shin et al. (2024) [11]	Ex vivo brain phantom	SIMPACE sequence with injected motion	VOLMOCO, oSLOMOCO, mSLOMOCO	Standard deviation of residual time series in gray matter	mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors
PMC (2021) [26]	Healthy adults	Rest vs. working memory task	Multiple denoising pipelines	Balancing motion artifacts between conditions, network identifiability	aCompCor, GSR (but poor on distance-dependent artifacts)

The comparative analysis reveals a complex performance landscape where no single method universally outperforms others across all metrics. Parsimonious models with 6 motion parameters (MPs) combined with volume interpolation have shown particular promise in task-based fMRI studies with clinical populations [52]. This combination effectively corrected motion in both MS patients and healthy controls, surpassing the performance of scrubbing methods that use Framewise Displacement or DVARS for outlier detection [52] [53].

Component-based methods such as aCompCor (component-based noise correction method) demonstrate excellent performance in minimizing and balancing residual motion-related artifacts between resting-state and task conditions [26]. However, censoring remains the only approach that substantially reduces distance-dependent artifacts, though this comes at the cost of reduced network identifiability [26].

Experimental Protocols and Methodologies

Systematic Comparison Framework

A 2022 study provides a comprehensive experimental protocol for comparing motion correction approaches in task-based fMRI [52] [53]. The researchers acquired fMRI data from 17 early multiple sclerosis patients and 14 matched healthy controls during performance of a visual task. They characterized motion in both groups and quantitatively compared the most frequently used motion correction methods, including:

• Models containing 6 or 24 motion parameters (MPs) as nuisance regressors
• Models containing nuisance regressors for 6 or 24 MPs and motion outliers detected with Framewise Displacement (FD) or Derivative of root mean square variance over voxels (DVARS)
• Models with 6 or 24 MPs and motion outliers corrected through volume interpolation

The experimental design allowed for direct comparison between scrubbing methods and volume interpolation, the latter of which had not been systematically investigated in task-fMRI clinical studies in MS [52]. The evaluation metrics focused on task-activation maps and the preservation of biologically plausible signal, with the optimal approach determined by its ability to maximize the detection of task-related activations while minimizing residual motion artifacts.

Advanced Motion Correction Pipeline

Recent methodological advances have introduced more sophisticated motion correction pipelines. The modified SLOMOCO (mSLOMOCO) pipeline represents a significant technical innovation that addresses both intervolume and intravolume motion [11]. The experimental protocol for this approach involves:

• Data Acquisition: Using the SIMPACE sequence to generate motion-corrupted MR data by altering imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom.
• Motion Parameter Estimation: Calculating 6 volume-wise rigid intervolume motion parameters and 6 slice-wise rigid intravolume motion parameters.
• Partial Volume Regressor: Implementing a novel voxel-wise motion nuisance regressor to address partial volume effects.
• Residual Artifact Removal: Applying the mSLOMOCO pipeline with 12 volume/slice-wise motion parameters and partial volume regressors.

Validation studies demonstrated that this comprehensive pipeline reduced the average standard deviation of residual time series signals in gray matter by 29-45% compared to conventional volume-based motion correction [11].

Figure 1: Workflow for task-based fMRI motion correction strategies integrating multiple complementary approaches.

Table 3: Essential Research Tools for Task-fMRI Motion Correction Studies

Tool Category	Specific Tools/Software	Function	Application Context
fMRI Analysis Packages	FSL, AFNI, SPM, BrainSuite	Volume realignment, motion parameter estimation, scrubbing implementation	General motion correction preprocessing
Specialized Motion Correction Tools	SLOMOCO (GitHub)	Intravolume motion correction, slice-wise motion parameter estimation	Advanced motion correction addressing spin history effects
Motion Detection Metrics	Framewise Displacement (FD), DVARS	Quantifying head motion, identifying motion outlier volumes	Quality assessment, scrubbing implementation
Component-Based Correction	ICA-AROMA, aCompCor	Automatic removal of motion-related components via ICA or PCA	Data-driven denoising without requiring motion parameters
Deep Learning Frameworks	TensorFlow, PyTorch	Implementing GANs, cGANs, diffusion models for motion correction	AI-based artifact reduction and image reconstruction
Motion Simulation	SIMPACE sequence	Generating motion-corrupted data with known ground truth	Validation and comparison of correction methods
Quality Assessment Tools	MRIQC	Automated quality control metrics for fMRI data	Standardized evaluation of motion correction efficacy

The selection of appropriate tools depends on specific research requirements. For standard task-fMRI studies, established packages like FSL, AFNI, and SPM provide robust implementations of basic motion correction approaches including realignment, parameter regression, and scrubbing [52] [11]. For more advanced applications, specialized tools like SLOMOCO address intravolume motion and spin history effects that conventional methods may miss [11]. Emerging deep learning approaches, particularly generative adversarial networks (GANs) and conditional GANs, show significant potential for reducing motion artifacts and improving image quality, though challenges remain regarding generalizability and potential visual distortions [54].

The systematic comparison of motion correction strategies for task-based fMRI reveals a complex landscape where method selection must be guided by specific research contexts and constraints. Based on current evidence, parsimonious models with 6 motion parameters combined with volume interpolation offer an optimal balance for many task-fMRI applications, particularly in clinical populations where motion may be condition-dependent [52]. However, different pipelines show marked heterogeneity in performance, with many approaches demonstrating differential efficacy between rest and task conditions [26].

Future research directions should focus on standardizing evaluation metrics and validation approaches to enable more direct comparison across studies. The emergence of AI-driven methods, particularly deep learning generative models, shows significant potential for advancing motion correction in task-based fMRI [54]. These approaches can learn direct mappings between corrupted and clean images, often yielding improved perceptual quality and reduced reconstruction time compared to conventional iterative algorithms. However, critical challenges including limited generalizability, reliance on paired training data, and risks of introducing visual distortions must be addressed through comprehensive public datasets, standardized reporting protocols, and more advanced, adaptable deep learning techniques [54].

For researchers addressing condition-dependent motion in task-based fMRI, we recommend a hierarchical approach: begin with established methods (6 MPs + volume interpolation) for robust correction, then explore component-based approaches (aCompCor) for optimized denoising, and consider specialized tools (SLOMOCO) or AI-based methods when standard approaches prove insufficient for addressing specific motion patterns or artifact types.

Benchmarks and Validation: Quantifying Pipeline Efficacy for Robust Science

The pursuit of robust and reproducible findings in resting-state functional magnetic resonance imaging (rs-fMRI) is fundamentally linked to effective data denoising. Insufficient data quality and a lack of consensus on optimal denoising methods continue to hamper progress in the field [6]. This challenge is particularly acute when studying clinical populations, who may exhibit higher levels of in-scanner head movement, introducing substantial noise that can systematically bias results and lead to false inferences [55] [56]. The problem is further compounded by the diversity of available denoising pipelines and the absence of a standardized framework for their evaluation. Consequently, comparing the performance of these pipelines using a comprehensive set of Quality Control (QC) measures is a critical step in the research process. This guide provides an objective comparison of denoising pipeline performance, detailing experimental protocols and quantitative outcomes to inform researchers, scientists, and drug development professionals in their analytical choices.

Experimental Protocols for Benchmarking Denoising Pipelines

The quantitative data presented in this guide are derived from published comparative studies that have implemented rigorous benchmarking experiments. The core methodologies are summarized below.

Multi-Metric Comparison Framework

A 2025 study by Goffi et al. established a robust framework for comparing denoising techniques using both real and synthetic data [6]. Fifty-three participants underwent an rs-fMRI session, and synthetic data were also generated for one subject. Nine different denoising pipelines were applied in parallel to minimally preprocessed fMRI data. The comparison was conducted by computing a suite of metrics quantifying the degree of artifact removal, signal enhancement, and resting-state network (RSN) identifiability. A key feature of this study was the proposal of a summary performance index that accounts for both noise removal and the preservation of neurological information [6].

SIMPACE Validation with Ex Vivo Phantom

To rigorously test residual motion artifact removal, a 2024 study by Shin et al. employed a gold-standard simulation approach [11]. They used an ex vivo brain phantom and a custom SIMPACE (Simulated Prospective Acquisition Correction) sequence to generate motion-corrupted data with high fidelity. This sequence alters the imaging plane coordinates before each volume and slice acquisition, emulating realistic intervolume and intravolume motion. The study then investigated the mechanism of residual motion signals and proposed a novel voxel-wise partial volume (PV) nuisance regressor. Several pipelines, including a modified SLOMOCO (mSLOMOCO), VOLMOCO, and the original SLOMOCO (oSLOMOCO), were compared using the standard deviation (SD) of the residual time series signals in the gray matter as a primary metric [11].

Clinical Cohort Validation

A 2025 study by Wunderlich et al. extended the comparison to clinical populations, analyzing data from four cohorts: healthy subjects, patients with brain lesions (glioma, meningioma), and patients with a non-lesional encephalopathic condition [56]. This design allowed for the evaluation of various denoising strategies using QC metrics tailored to different disease types, acknowledging that the effectiveness of a pipeline can depend on the underlying pathophysiology and data quality [56].

Quantitative Performance Comparison

The following tables summarize the key quantitative findings from the cited experiments, providing a direct comparison of pipeline performance across different QC measures.

Table 1: Performance of Denoising Pipelines on Real and Synthetic rs-fMRI Data [6]

Denoising Pipeline	Key Components	Performance on Artifact Removal	Performance on RSN Identifiability	Summary Performance Index
Global Signal Regression (GSR)	Regression of mean WM, CSF, and global signal	High	High (Best Compromise)	Favored
ICA-AROMA	Independent Component Analysis-based Automatic Removal Of Motion Artifacts	High	Moderate	High
ANATICOR	Local non-gray matter signal regression	Moderate	Moderate	Moderate
CompCor	Component-Based Noise Correction Method	Moderate	Moderate	Moderate

Table 2: Residual Motion Reduction in SIMPACE Phantom Data (Gray Matter Standard Deviation) [11]

Motion Correction Pipeline	Key Nuisance Regressors	Residual SD (1x Intravolume Motion)	Residual SD (2x Intravolume Motion)
mSLOMOCO (Modified SLOMOCO)	12 Vol-/Sli-mopa + PV Regressors	-29% vs. VOLMOCO, -28% vs. oSLOMOCO	-45% vs. VOLMOCO, -31% vs. oSLOMOCO
VOLMOCO	6 Vol-mopa + PV Regressors	Baseline (0%)	Baseline (0%)
oSLOMOCO (Original SLOMOCO)	14 Voxel-wise Regressors	+1% vs. VOLMOCO	+14% vs. VOLMOCO

Table 3: Optimal Pipeline by Clinical Cohort and Data Quality [56]

Clinical Cohort	Data Quality / Motion Level	Recommended Denoising Strategy
Non-lesional Encephalopathic Condition	Comparable head motion	Combinations involving ICA-AROMA
Lesional Conditions (Glioma, Meningioma)	Comparable head motion	Combinations involving Anatomical Component Correction (CC)
Healthy Subjects	Low head motion	Multiple pipelines effective (e.g., GSR, CompCor)

Signaling Pathways and Workflow Diagrams

The following diagrams illustrate the logical workflows for the multi-metric comparison framework and the mechanism of residual motion artifact.

Multi-Metric Pipeline Evaluation Workflow

Residual Motion Artifact Formation and Removal

This section details essential software, data, and methodological resources for conducting performance comparisons of denoising pipelines.

Table 4: Essential Research Reagents and Resources

Resource Name	Type	Primary Function in Pipeline Comparison	Source / Reference
HALFpipe Software	Software Tool	Enables the application and comparison of multiple denoising pipelines in a standardized framework.	Goffi et al. 2025 [6]
SIMPACE Sequence	Pulse Sequence	Generates gold-standard, motion-corrupted fMRI data with known ground truth for rigorous pipeline validation.	Shin et al. 2024 [11]
Ex Vivo Brain Phantom	Biological Sample	Provides a motion-free, physiologically stable control for developing and testing motion correction algorithms.	Shin et al. 2024 [11]
SLOMOCO Pipeline	Software Tool	A slice-oriented motion correction method that addresses intravolume motion, available via GitHub.	Shin et al. 2024 [11]
ICA-AROMA	Algorithm	A data-driven method for the automatic removal of motion artifacts via independent component analysis.	Wunderlich et al. 2025 [56]
Frame Displacement (FD)	QC Metric	A concise index of volume-to-volume motion, used to quantify and control for head motion in fMRI data.	Satterthwaite et al. 2017 [55]
Summary Performance Index	Composite Metric	A proposed metric that balances artifact removal with the preservation of neurological network information.	Goffi et al. 2025 [6]
U-Net Deep CNN	Algorithm	A deep learning technique used to compensate for residual motion artifacts after initial correction.	Chenakkara et al. 2025 [8]

The empirical data presented in this guide demonstrate that the performance of denoising pipelines is heterogeneous and context-dependent. No single pipeline is universally superior; the optimal choice is influenced by the specific noise profile of the data, the presence and type of clinical pathology, and the analytical goals of the study. For general-purpose rs-fMRI analysis, a pipeline incorporating global signal regression (GSR) may offer the best compromise between artifact removal and signal preservation [6]. In scenarios with significant intravolume motion, slice-wise correction methods like mSLOMOCO with a partial volume regressor show marked superiority [11]. Finally, for clinical applications, the choice should be tailored to the patient population, with ICA-AROMA potentially better suited for non-lesional conditions and anatomical component correction for lesional brains [56]. This evidence underscores the necessity of a multi-metric, hypothesis-driven approach to selecting a denoising pipeline, which is fundamental for ensuring the validity and reproducibility of functional connectivity research.

In the field of magnetic resonance imaging (MRI), motion artifacts represent a significant challenge that can compromise image quality and subsequent analysis. For researchers investigating the performance of denoising pipelines, quantifying residual motion artifact remains a critical validation step. Simulation-based validation using phantoms provides a controlled, reproducible framework for this assessment, enabling precise evaluation of imaging technologies without the variability inherent in human studies [57] [58]. These models simulate human tissues or anatomical structures and serve essential roles in technology validation, performance benchmarking, protocol optimization, and artificial intelligence development [58].

Phantom studies are particularly valuable in motion artifact research because they allow for systematic investigation under conditions where "ground truth" is known [58] [59]. This controlled environment enables researchers to isolate the effects of motion from other confounding factors, providing clearer insight into the efficacy of denoising pipelines. Well-designed phantom studies establish essential methodological foundations for assessing how effectively various algorithms correct motion artifacts while preserving anatomical integrity [57].

Phantom Classifications and Research Applications

Categorizing Phantoms for Imaging Research

Phantoms can be broadly classified into physical and computational models, with physical phantoms further divided into subcategories based on their composition and structural complexity [58]. The selection of an appropriate phantom type should align with the specific research objectives, balancing anatomical realism against reproducibility and cost considerations.

Table: Classification of Phantoms for Medical Imaging Research

Phantom Type	Composition	Key Advantages	Research Applications
Standard Synthetic	Simple, well-characterized materials (PMMA, solid water, gels)	High reproducibility, cost-effective, durable	System calibration, basic parameter evaluation (resolution, noise)
Anthropomorphic Synthetic	Tissue-equivalent polymers, silicones, composite materials, 3D-printed materials	Anatomical realism, heterogeneous tissue properties	Protocol optimization, clinical scenario simulation, AI algorithm validation
Mixed Phantoms	Biological tissues embedded within synthetic structures	Combines structural realism with biological texture	Validation requiring realistic microstructure or contrast kinetics
Biophantoms	Excised animal tissues, plant-based materials	Close approximation of human tissue properties	Proof-of-concept studies, interventional applications
Computational Phantoms	Digital models based on mathematical algorithms	No physical limitations, easily modified	Simulation studies, method development, testing impractical physical setups

Research Reagent Solutions for Motion Artifact Studies

The materials and tools used in phantom construction and validation represent essential research reagents with specific functions in experimental workflows:

Table: Essential Research Reagents for Phantom-Based Motion Artifact Studies

Reagent Category	Specific Examples	Function in Research
Structural Phantom Materials	High Temp resin (3D printing), ballistics gelatin, agar-gelatin mixtures, polyvinyl chloride (PVC) compounds	Creates anatomical structures with tissue-equivalent properties for MRI [60] [61]
Dielectric Property Modifiers	Propylene glycol, sodium chloride (NaCl), graphite powder, carbon black, kerosene/oil emulsions	Adjusts electrical properties to match human tissues (critical for microwave imaging) [61]
Quality Assurance Test Objects	Contrast-detail test objects (CDRAD), low-contrast test tools, resolution patterns	Provides standardized targets for quantitative image quality assessment [62]
Motion Simulation Systems	Programmable actuators, robotic platforms, hydraulic systems	Introduces controlled, reproducible motion for artifact generation [63]
Computational Model Observers	Channelized Hotelling observer, non-prewhitening matched filter	Provides objective, human-like image assessment for detectability studies [63]

Experimental Protocols for Phantom-Based Validation

JDAC Framework for Joint Denoising and Motion Correction

The Joint image Denoising and Motion Artifact Correction (JDAC) framework represents an innovative approach that addresses both noise and motion artifacts simultaneously through an iterative learning strategy [64] [65]. This methodology is particularly relevant for assessing residual artifacts because it explicitly models the interaction between these two degradation sources.

The experimental protocol involves two principal models working in sequence [64]:

• Adaptive Denoising Model: Incorporates a novel noise level estimation strategy using the variance of image gradient maps, followed by conditional denoising through a U-Net architecture normalized by the estimated noise variance.
• Anti-Artifact Model: Utilizes a separate U-Net architecture with a gradient-based loss function specifically designed to maintain brain anatomical integrity during motion correction.

The iterative framework applies these models sequentially, with an early stopping strategy based on noise level estimation to optimize processing time [64]. This approach was validated on 9,544 T1-weighted MRIs with manually added Gaussian noise and 552 T1-weighted MRIs with motion artifacts paired with motion-free images [65].

3D-Printed Phantom Validation Protocol

The OMERACT GCA phantom project demonstrates a rigorous protocol for validating ultrasonography findings using high-resolution 3D-printed phantoms of temporal and axillary arteries [60]. This methodology provides a template for motion artifact research validation:

Phantom Design and Fabrication:

• Phantoms were designed using computer-aided design software based on 60 ultrasound images of giant cell arteritis (GCA) cases
• Utilization of stereolithography 3D printing with High Temp resin, offering layer resolution up to 25μm
• Embedding in ballistic gelatin that mimics human muscle tissue ultrasound propagation properties

Validation Study Protocol:

• Twenty-eight experts from 12 countries conducted blinded evaluations of eight phantom sets
• Each set contained both normal and pathological vessels (acute/chronic changes)
• Standardized scanning protocol with recommended settings: B-mode frequency 18MHz, depth 1.5cm
• Quantitative assessment through intima-media thickness (IMT) measurements
• Qualitative classification as normal/abnormal based on established definitions

This protocol achieved high inter-rater reliability with Fleiss' kappa of 0.80 and intraclass correlation coefficient of 0.98 for IMT measurements [60].

Quantitative Comparison of Phantom Performance

Performance Metrics Across Phantom Types

Different phantom designs exhibit varying performance characteristics that influence their suitability for motion artifact validation. The table below summarizes key quantitative comparisons:

Table: Performance Comparison of Phantom Types in Validation Studies

Phantom Characteristic	Standard Synthetic	Anthropomorphic	3D-Printed Anatomical	Computational
Anatomical Accuracy	Low (simple geometries)	High (complex structures)	Very high (patient-specific)	Configurable (mathematically defined)
Reproducibility	Very high (CV < 5%)	Moderate to high	Moderate (batch variations)	Perfect (deterministic)
Dielectric Property Accuracy	High (0.5-8% error) [61]	Moderate to high	Moderate (material limitations)	Perfect (by definition)
Inter-rater Reliability	Not applicable	High (Fleiss' κ 0.74-0.80) [60]	High (Fleiss' κ 0.74-0.80) [60]	Not applicable
Quantitative Measurement ICC	High (0.95-0.99)	Very high (ICC 0.98) [60]	Very high (ICC 0.98) [60]	Perfect (1.0)
Cost Efficiency	High	Moderate	Moderate to high	Very high (after development)

Validation Outcomes for Denoising and Artifact Correction Methods

The JDAC framework's performance highlights the potential of iterative approaches for addressing residual motion artifacts:

Table: Performance Metrics of JDAC Framework for MRI Denoising and Motion Correction

Evaluation Metric	JDAC Performance	Comparative Methods	Significance
Noise Reduction Efficiency	Superior with noise level estimation	Suboptimal without explicit noise estimation	Adaptive denoising crucial for variable noise conditions [64]
Anatomical Integrity	Enhanced through gradient-based loss	Conventional losses may distort anatomy	Preservation of structural details critical for diagnostic utility [65]
3D Consistency	Maintained through volumetric processing	2D slice-by-slice processing causes discontinuities	Essential for multi-planar reconstruction and analysis [64]
Computational Efficiency	Accelerated via early stopping	Full iteration cycles without convergence checking	Enables practical clinical application [64]
Task-based Performance	Improved detection of pathological features	Traditional methods may preserve artifacts	Direct impact on diagnostic accuracy [64]

Integrated Validation Framework for Residual Motion Artifact Assessment

A comprehensive approach to assessing residual motion artifact after denoising pipelines requires integrating multiple validation strategies:

This integrated framework emphasizes several critical aspects for comprehensive validation:

Multi-modal Assessment Strategy:

• Quantitative metrics including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and task-based detectability indexes provide objective performance measures [58] [59]
• Qualitative evaluation through blinded reader studies with appropriate statistical analysis of inter-rater reliability [60] [59]
• Spatial resolution assessment via modulation transfer function (MTF) and noise characteristics through noise power spectrum (NPS) analysis [62]

Clinical Correlation Imperative: While phantom studies provide essential controlled validation, researchers must maintain perspective on clinical relevance [57] [58]. Phantom validation should ideally be followed by clinical studies to establish diagnostic efficacy, as improved technical metrics alone do not guarantee enhanced diagnostic performance [59].

Simulation-based validation using phantoms represents a methodological cornerstone for assessing residual motion artifact in denoising pipeline research. The structured approach outlined in this guide—incorporating appropriate phantom selection, rigorous experimental protocols, and multi-modal assessment strategies—provides a comprehensive framework for generating scientifically valid, reproducible results. As the field progresses toward increasingly sophisticated computational methods like the JDAC framework [64] [65], the role of robust validation methodologies becomes ever more critical. By adhering to these principles, researchers can advance the development of denoising techniques that genuinely enhance diagnostic capability while maintaining anatomical fidelity, ultimately bridging the gap between technical innovation and clinical utility.

The fidelity of functional magnetic resonance imaging (fMRI) data serves as the foundation for understanding the neural correlates of behavior. Motion artifacts, a pervasive challenge in neuroimaging, introduce signal distortions that can profoundly impact the reliability of brain-behavior associations. Within the context of assessing residual motion artifact after denoising pipelines, it becomes imperative to evaluate how different correction methodologies perform not merely in artifact reduction but in preserving biologically meaningful signals that predict real-world behaviors. Resting-state fMRI (rs-fMRI) is a pivotal tool for mapping the brain's functional organization and its relation to individual differences in behavior, but its signals are notoriously contaminated by multiple noise sources, including head motion, cardiac cycle, and respiratory variations [4]. These artifacts reduce the reliability and validity of functional connectivity (FC) estimates and can attenuate brain-wide association study (BWAS) effect sizes—or in the case of head motion, spuriously increase them [4]. This comparison guide objectively evaluates the performance of leading denoising pipelines, focusing on their dual capacity to mitigate motion artifacts while augmenting the predictive power of brain-behavior models.

Comparative Performance of Denoising Pipelines

Efficacy in Motion Artifact Reduction

Table 1: Denoising Pipeline Performance Metrics Across Methodologies

Pipeline/Method	Primary Approach	Key Performance Metrics	Notable Strengths	Identified Limitations
Res-MoCoDiff [66] [5]	Residual-guided diffusion model	PSNR: 41.91±2.94 dB; SSIM: Highest; NMSE: Lowest; Sampling time: 0.37s per batch	Superior artifact removal across distortion levels; computational efficiency; preserves structural details	Requires further validation in diverse clinical populations
ICA-FIX + GSR [4]	Independent component analysis with global signal regression	Moderate motion reduction with reasonable trade-off for behavioral prediction	Balanced approach for both motion mitigation and behavioral correlation preservation	Modest inter-pipeline variations in predictive performance
MP Regressions (12/24) [49]	Motion parameter nuisance regression	Variable performance across task designs; detrimental for long block designs	Simple implementation; widely accessible	Can remove meaningful signal in task-based fMRI; design-dependent efficacy
Conventional DDPMs [66]	Standard denoising diffusion probabilistic model	High computational overhead (101.74s sampling time)	Strong theoretical foundation for image generation	Slow inference time; may encourage unrealistic reconstructions
IMC-Denoise [67]	Content-aware denoising pipeline	87% noise reduction; 5.6x higher contrast-to-noise ratio	Effective for mass cytometry imaging; automated processing	Specialized for IMC rather than fMRI applications

The comparative analysis reveals substantial methodological diversity in addressing motion artifacts. Res-MoCoDiff demonstrates exceptional performance in quantitative image quality metrics, achieving a peak signal-to-noise ratio (PSNR) of up to 41.91±2.94 dB for minor distortions while significantly reducing computational overhead compared to conventional approaches [66]. This residual-guided diffusion model employs a novel noise scheduler and Swin Transformer blocks to enhance robustness across resolutions, enabling a dramatically shortened reverse diffusion process of only four steps compared to hundreds or thousands in traditional denoising diffusion probabilistic models (DDPMs) [5].

For resting-state fMRI applications, integrated approaches like ICA-FIX combined with global signal regression (GSR) demonstrate a reasonable trade-off between motion reduction and behavioral prediction performance [4]. However, current evidence suggests no single pipeline universally excels at achieving both objectives consistently across different cohorts, highlighting the context-dependent nature of denoising efficacy.

Impact on Behavioral Prediction Accuracy

Table 2: Pipeline Effects on Brain-Behavior Association Studies

Denoising Pipeline	Effect on Behavioral Prediction	Optimal Use Context	Datasets Validated
ICA-FIX + GSR [4]	Modest enhancement of brain-behavior correlations	Resting-state fMRI with diverse behavioral measures	CNP, GSP, HCP
MP Regressions (12/24) [49]	Variable effects; potential signal loss in task-based fMRI	Simple designs without motion-design correlation	Event-related and block-design fMRI
Blind-Source Denoising [49]	Eliminates both signal and noise; design-dependent effects	Scenarios with minimal motion-design correlation	Multiband and standard coil acquisitions
DiCER [4]	Investigated for motion mitigation in BWAS	Large-scale brain-wide association studies	Multiple independent cohorts
Global Signal Regression [4]	Can enhance behavioral prediction in some contexts	When motion artifacts strongly correlate with signal	HCP, GSP

The efficacy of denoising pipelines extends beyond mere artifact reduction to their impact on behavioral prediction accuracy—a crucial consideration for real-world applications. Research examining the relationship between denoising efficacy and brain-behavior associations has revealed that pipelines combining ICA-FIX and GSR demonstrate a reasonable trade-off between motion reduction and behavioral prediction performance across multiple datasets, including the Human Connectome Project (HCP) and Genomics Superstruct Project (GSP) [4]. However, inter-pipeline variations in predictive performance remain modest, suggesting that denoising approaches alone cannot fully overcome the fundamental challenge of small effect sizes in brain-behavior associations.

Notably, the impact of denoising varies significantly between resting-state and task-based fMRI. Blind-source denoising strategies eliminate both signal and noise relative to motion parameter regression, with undesired effects on signal depending both on algorithm (FIX > AROMA) and design (block-design > event-related fMRI) [49]. This highlights the critical importance of matching denoising approaches to specific experimental paradigms and research questions.

Experimental Protocols and Methodologies

Res-MoCoDiff: A Novel Framework for Motion Correction

The Res-MoCoDiff framework introduces significant innovations in motion artifact correction through a residual-guided diffusion process [66] [5]. The experimental protocol involves:

Architecture and Training: The model employs a U-net backbone with attention layers replaced by Swin Transformer blocks to enhance robustness across resolutions. The training process integrates a combined ℓ1+ℓ2 loss function, which promotes image sharpness while reducing pixel-level errors [5].

Residual Error Integration: A key innovation involves explicitly incorporating the residual error (r = y - x) between motion-corrupted (y) and motion-free (x) images into the forward diffusion process. This allows the model to simulate noise evolution with a probability distribution closely matching the corrupted data, enabling a reverse diffusion process requiring only four steps instead of the hundreds typical in conventional DDPMs [5].

Evaluation Framework: The model was rigorously evaluated on both an in-silico dataset generated using a realistic motion simulation framework and an in-vivo movement-related artifacts dataset. Comparative analyses were conducted against established methods including cycle generative adversarial network, Pix2pix, and a diffusion model with a vision transformer backbone, using quantitative metrics such as PSNR, SSIM, and NMSE [66].

Res-MoCoDiff Workflow Integrating Residual Guidance

Resting-State fMRI Denoising Evaluation Protocol

The assessment of denoising pipeline efficacy for behavioral prediction follows a rigorous methodological framework [4]:

Dataset Integration: Analysis employs multiple independent datasets including the Consortium for Neuropsychiatric Phenomics (CNP; N = 121), Genomics Superstruct Project (GSP; N = 1,570), and Human Connectome Project (HCP; N = 1,200) to ensure generalizability across acquisition parameters and participant populations.

Pipeline Configurations: Fourteen distinct denoising pipelines are constructed from combinations of five common approaches: white matter and cerebrospinal fluid regression, ICA-based artifact removal, volume censoring, global signal regression, and diffuse cluster estimation and regression.

Evaluation Metrics: Pipeline performance is assessed using three distinct quality control metrics to evaluate motion influence and kernel ridge regression for behavioral predictions of 81 different behavioral variables. This dual evaluation framework enables simultaneous assessment of motion mitigation and behavioral prediction enhancement.

rs-fMRI Denoising and Behavioral Prediction Evaluation Pipeline

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

Tool/Resource	Function	Application Context	Accessibility
Swin Transformer Blocks [5]	Replace attention layers in U-net; enhance multi-resolution robustness	Res-MoCoDiff architecture for motion artifact correction	Open-source implementation
ℓ1+ℓ2 Loss Function [5]	Combined loss promoting image sharpness and reducing pixel errors	Training phase of diffusion models for medical imaging	Standard DL frameworks
fMRIPrep [4]	Standardized preprocessing of fMRI data	Initial processing of resting-state and task-based fMRI	Open-source software
ICA-FIX Classifier [4]	Automated identification of noise components in fMRI data	Denoising of resting-state fMRI data	Publicly available
DIMR Algorithm [67]	Differential intensity map-based restoration for hot pixel removal	Imaging Mass Cytometry denoising	Open-source pipeline
DeepSNiF [67]	Self-supervised deep learning for shot noise filtering	Mass cytometry image enhancement	Available on GitHub
Kernel Density Estimation [67]	Statistical method for outlier detection in noise distribution	Hot pixel identification in IMC-Denoise	Standard statistical packages

The experimental workflows highlighted in this comparison rely on specialized computational tools and algorithms that form the essential toolkit for researchers in this field. Swin Transformer blocks have emerged as a particularly innovative component, enabling more robust attention mechanisms across resolutions in diffusion models [5]. For loss function optimization, the combined ℓ1+ℓ2 approach has demonstrated superior performance in balancing image sharpness and pixel-level accuracy during model training.

In fMRI research, standardized preprocessing tools like fMRIPrep have become indispensable for ensuring reproducible initial processing across diverse datasets [4]. Similarly, automated classifiers like ICA-FIX provide crucial infrastructure for scalable denoising of large-scale neuroimaging datasets. For mass cytometry applications, the IMC-Denoise pipeline offers specialized algorithms like DIMR and DeepSNiF that address the unique noise characteristics of this imaging modality [67].

The comprehensive evaluation of denoising pipelines reveals a complex landscape where methodological advances in artifact reduction must be carefully balanced against their impact on meaningful biological signals. Res-MoCoDiff represents a significant leap forward in computational efficiency and image quality enhancement for structural MRI, achieving clinical-grade processing times while maintaining superior artifact correction [66] [5]. However, in the realm of functional MRI and behavioral prediction, the absence of a universally superior pipeline underscores the context-dependent nature of denoising efficacy.

Future research directions should prioritize the development of task-specific denoising approaches that account for the unique statistical relationships between signal and noise sources in different experimental paradigms. Furthermore, standardized evaluation frameworks that simultaneously assess motion mitigation and behavioral prediction enhancement across multiple independent datasets will be crucial for advancing the field. As denoising methodologies continue to evolve, their real-world impact must be measured not merely by artifact reduction metrics but by their capacity to preserve and enhance the behavioral signals that form the foundation of meaningful brain-behavior relationships.

The pursuit of high-quality data in biomedical research necessitates a balanced approach to managing noise and preserving statistical integrity. This guide objectively compares various motion reduction techniques, highlighting a critical trade-off: overly aggressive denoising can artificially inflate data consistency, thereby increasing false positive rates and compromising statistical power. Conversely, insufficient cleaning leaves true effects obscured by noise, reducing statistical sensitivity. The following analysis, framed within research on residual motion artifacts, provides a quantitative and methodological comparison to inform researchers and drug development professionals.

Table 1: Quantitative Performance Comparison of Denoising and Analysis Techniques

Method Category	Specific Technique	Key Performance Metrics	Impact on Statistical Power & Key Trade-offs
Exposure-Response Analysis [68]	Logistic regression using drug exposure (AUC)	Enables sample size reduction while maintaining 80% power [68]	↑ Power via more precise dose-response characterization, informs better dose selection.
fMRI Denoising Pipelines [7]	WM/CSF Regression + Global Signal Regression	High summary performance index (artifact removal vs. signal preservation) [7]	↑ Power via improved resting-state network identifiability; trade-off with potential signal removal.
AI-Driven MRI Motion Correction [5] [1]	Res-MoCoDiff (Diffusion Model)	PSNR: ~41.91 dB; SSIM: Highest; NMSE: Lowest [5]	↑ Power by restoring image fidelity for segmentation/analysis; risk of hallucinated structures.
Self-Supervised Deep Learning [69]	SUPPORT (for voltage imaging)	Effective on Poisson-Gaussian noise; preserves fast dynamics [69]	↑ Power via accurate signal recovery without temporal bias, crucial for fast physiological signals.
Conventional Denoising Algorithms [70]	BM3D (for MRI/HRCT)	High PSNR/SSIM at low-moderate noise levels [70]	↑ Power by improving signal clarity; trade-off is potential over-smoothing and loss of fine detail.

Detailed Experimental Protocols

Protocol for Exposure-Response Power Analysis

This model-based drug development (MBDD) approach determines the power for dose-ranging studies more efficiently than conventional methods [68].

• Objective: To calculate the statistical power for detecting a significant exposure-response relationship in a clinical trial.
• Input Parameters:
  • Assumed probabilities of response at two dose levels (e.g., P1 and P2).
  • Pharmacokinetic (PK) data: Population mean and variance of drug clearance (CL/F) to calculate typical exposure (AUC) for each dose [68].
• Algorithm Workflow:
  • Calculate Model Parameters: Using the logit transformation, compute the intercept (β0) and slope (β1) of the logistic regression equation based on the assumed response probabilities and their corresponding AUC values [68].
  • Simulate Population: For a given sample size n at each of m doses, simulate individual drug exposures based on the population PK model (e.g., log-normal distribution for CL/F) [68].
  • Simulate Response: For each simulated exposure, calculate the probability of response using the logistic model and simulate a binary response (yes/no) [68].
  • Analyze and Replicate: Fit an exposure-response model to the simulated dataset and determine if the slope (β1) is statistically significant. Repeat this process for a large number of simulated study replicates (e.g., 1,000) [68].
  • Determine Power: The statistical power is the proportion of study replicates in which a significant exposure-response relationship is detected [68].

The following diagram illustrates this simulation-based workflow:

Protocol for Comparative Denoising Pipeline Evaluation

This methodology quantitatively benchmarks different denoising strategies, such as those for resting-state fMRI (rs-fMRI), to identify the optimal compromise between artifact removal and signal preservation [7].

• Objective: To define an appropriate denoising strategy by comparing the performance of multiple pipelines based on a multi-metric framework.
• Input Data: Rs-fMRI data from participants (e.g., 53 subjects) and/or synthetic rs-fMRI data generated for ground-truth comparison [7].
• Experimental Workflow:
  • Minimal Preprocessing: Apply consistent, minimal preprocessing to all raw fMRI data.
  • Parallel Denoising: Apply multiple denoising pipelines in parallel to the same preprocessed data. Example pipelines include:
    • A: Regression of mean signals from White Matter (WM) and Cerebrospinal Fluid (CSF).
    • B: Pipeline A + Global Signal Regression.
    • C: Other combinations of nuisance regressors [7].
  • Multi-Metric Calculation: Compute a set of quality metrics for each pipeline's output. These quantify:
    • Artifact Removal: The degree to which non-neural noise (e.g., from motion) is reduced.
    • Signal Preservation/Enhancement: The identifiability of resting-state networks (RSNs) and the retention of physiological signal [7].
  • Composite Index Scoring: Propose and calculate a summary performance index that synthesizes the multiple metrics into a unified measure, favoring pipelines that offer the best trade-off [7].

Research Reagent Solutions

Table 2: Essential Tools for Denoising and Statistical Analysis

Tool Name	Category	Primary Function	Relevance to Trade-off Analysis
HALFpipe [7]	Software Pipeline	Standardized workflow for rs-fMRI analysis, from raw data to group stats.	Provides a containerized environment to run and compare multiple denoising pipelines reproducibly.
Population PK Model [68]	Statistical Model	Describes the distribution of drug exposure (e.g., AUC) in the target population.	Critical input for the exposure-response powering methodology, quantifying a key source of variability.
Res-MoCoDiff [5]	AI Correction Model	An efficient diffusion model for correcting motion artifacts in MRI.	Demonstrates advanced artifact reduction; its 4-step reverse process highlights innovation in computational efficiency.
SUPPORT [69]	Self-Supervised DL	Removes Poisson-Gaussian noise in functional imaging data without temporal bias.	Excellently preserves fast underlying dynamics (e.g., neural spikes), preventing bias that would harm statistical power.
BM3D [70]	Denoising Algorithm	A high-performance algorithm for removing Gaussian noise from images.	A dependable benchmark for conventional methods, against which newer AI-based approaches are often compared.

Critical Considerations for Statistical Power

A fundamental challenge in this domain is the phenomenon of regression-to-the-mean, which is often mistaken for a placebo effect [71]. In clinical trials, participants often enroll at a low point in their health journey, leading to a natural improvement over time regardless of treatment. Misattributing this statistical phenomenon to a treatment effect can severely distort power calculations and lead to false conclusions about efficacy [71]. Hierarchical models (Bayesian or frequentist) that account for variability across patients, subgroups, and endpoints help mitigate this risk by providing more accurate estimates of treatment effects [71].

Furthermore, the choice of denoising strategy directly impacts the bias-variance trade-off inherent in all statistical estimation. Overly aggressive denoising that oversmooths data reduces statistical variance but introduces high bias by distorting the true underlying signal [69]. This bias can make effects look more consistent than they are, inflating false positive rates. Conversely, insufficient denoising leaves high variance, obscuring true effects and increasing false negatives. Therefore, the goal of any pipeline must be to minimize variance without introducing bias, thereby safeguarding statistical power.

Conclusion

The assessment of residual motion artifacts reveals that no single denoising pipeline universally excels across all contexts, necessitating a tailored approach based on specific research objectives, imaging modalities, and subject populations. Foundational understanding of artifact origins combined with methodological awareness of both standard and emerging deep learning approaches enables more informed pipeline selection. Critical evaluation through robust validation frameworks is essential, as even advanced pipelines may differentially impact signal preservation and behavioral prediction accuracy. Future directions should prioritize the development of integrated processing frameworks that jointly address multiple artifact sources, creation of standardized benchmarking datasets, and adoption of reproducible practices to enhance reliability in clinical and translational research applications, ultimately strengthening the foundation for drug development and biomarker discovery.

References

• 1. Systematic Review and Meta-analysis of AI-driven MRI ... [https://arxiv.org/html/2509.05071v1]
• 2. Motion impact score for detecting spurious brain-behavior ... [https://www.nature.com/articles/s41467-025-63661-2]
• 3. Comparative Evaluation of Supervised and Unsupervised ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12370284/]
• 4. The efficacy of resting-state fMRI denoising pipelines for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12336063/]
• 5. Res-MoCoDiff: residual-guided diffusion models for motion ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12532098/]
• 6. Multi-Metric Approach for the Comparison of Denoising ... [https://pubmed.ncbi.nlm.nih.gov/40309965/]
• 7. Multi‐Metric Approach for the Comparison of Denoising ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12044599/]
• 8. Residual motion artifact removal enables dynamic [https://www.sciencedirect.com/science/article/abs/pii/S1090780725001260]
• 9. Motion compensated magnetic resonance imaging of an ... [https://www.nature.com/articles/s41598-025-27800-5]
• 10. Motion Artefacts in MRI: a Complex Problem with Many Partial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4517972/]
• 11. Estimation and Removal of Residual Motion Artifact in ... [https://apertureneuro.org/article/123369-estimation-and-removal-of-residual-motion-artifact-in-retrospectively-motion-corrected-fmri-data-a-comparison-of-intervolume-and-intravolume-motion-u]
• 12. CONN toolbox - Denoising pipeline [https://web.conn-toolbox.org/fmri-methods/denoising-pipeline]
• 13. A comparison of denoising in high temporal resolution... pipelines [https://pubmed.ncbi.nlm.nih.gov/31119818/]
• 14. MRI Motion Artifacts (Ghosting Artifact) [https://mrimaster.com/mri-artifacts/]
• 15. Advanced preclinical functional magnetic resonance ... [https://www.nature.com/articles/s44303-025-00085-z]
• 16. A multi-measure approach for assessing the performance ... [https://www.sciencedirect.com/science/article/abs/pii/S0730725X21001958#!]
• 17. Predicting Real‐Life Cognitive Scores From Functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12358942/]
• 18. Benchmarking methods for mapping functional connectivity ... [https://www.nature.com/articles/s41592-025-02704-4]
• 19. Mitigating head motion artefact in functional connectivity MRI [https://pmc.ncbi.nlm.nih.gov/articles/PMC8161527/]
• 20. Recent progress and outstanding issues in motion ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4262543/]
• 21. Estimation and Removal of Residual in... Motion Artifact [https://apertureneuro.org/article/123369]
• 22. Evaluation of denoising strategies for task-based functional... [https://pubmed.ncbi.nlm.nih.gov/33528884/]
• 23. Less is more: balancing noise reduction and data retention ... [https://www.sciencedirect.com/science/article/pii/S1053811923001180]
• 24. An evaluation of the efficacy, reliability, and sensitivity ... [https://www.sciencedirect.com/science/article/pii/S1053811917310972]
• 25. Evaluation of ICA-AROMA and alternative strategies for ... [https://www.sciencedirect.com/science/article/abs/pii/S1053811915001809]
• 26. Evaluation of denoising strategies for task‐based ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7978116/]
• 27. Evaluation of Denoising Strategies to Address Motion- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5105353/]
• 28. Evaluating denoising strategies in resting‐state functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9491287/]
• 29. Distinct effects of global signal regression on brain activity ... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1576535/full]
• 30. Denoising for fMRI pattern analysis [https://neurostars.org/t/denoising-for-fmri-pattern-analysis/19005]
• 31. Comparing data-driven physiological denoising ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10876882/]
• 32. Impact of automated ICA-based denoising of fMRI data in ... [https://www.nitrc.org/forum/forum.php?forum_id=7639]
• 33. Evaluation of ICA-AROMA and alternative... : Neuroimage [https://www.ovid.com/journals/neuima/fulltext/10.1016/j.neuroimage.2015.02.063~evaluation-of-ica-aroma-and-alternative-strategies-for]
• 34. Comparing data-driven physiological denoising ... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1223230/full]
• 35. Reproduction of Motion Artifacts for Performance Analysis ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3674114/]
• 36. Testing different noise models output from fmriprep [https://neurostars.org/t/testing-different-noise-models-output-from-fmriprep-unclear-results-with-ica-aroma/2352/20]
• 37. Learning residual motion correction for fast and robust 3D ... [https://www.sciencedirect.com/science/article/pii/S1361841522000391]
• 38. Res-MoCoDiff: Residual-guided diffusion models for ... [https://arxiv.org/abs/2505.03498]
• 39. Iterative Learning for Joint Image Denoising and Motion ... [https://www.sciencedirect.com/science/article/pii/S1361841525004189?dgcid=rss_sd_all]
• 40. Iterative learning for joint image denoising and motion ... [https://pubmed.ncbi.nlm.nih.gov/41274083/]
• 41. MRI motion artifact reduction using a conditional diffusion ... [https://www.semanticscholar.org/paper/MRI-motion-artifact-reduction-using-a-conditional-Safari-Yang/1224fa128caaa9109405ac4aac7af3e47c4ee138]
• 42. A Comparison of Approaches for Motion Artifact Removal from ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12349273/]
• 43. Motion Artifact Removal from EEG Signals Using the ... [https://www.sciencedirect.com/science/article/abs/pii/S1746809425013886]
• 44. iCanClean Removes Motion, Muscle, Eye, and Line-Noise ... [https://www.mdpi.com/1424-8220/23/19/8214]
• 45. iCanClean Improves Independent Component Analysis of ... [https://www.mdpi.com/1424-8220/23/2/928]
• 46. IMU-Enhanced EEG Motion Artifact Removal with Fine ... [https://arxiv.org/html/2509.01073v1]
• 47. How to Customize Hyperparameter Optimization [https://medium.com/analytics-vidhya/how-to-customize-hyperparameter-optimization-5ab1a55db1a]
• 48. Balancing Data Quality and Bias: Investigating Functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11717557/]
• 49. A comparison of denoising pipelines in high temporal ... - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC6865567/]
• 50. Evaluation of confound regression strategies for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6370491/]
• 51. Evaluation of noise regression techniques in resting-state ... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1006056/full]
• 52. Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC9768441/]
• 53. Frontiers | Task-based functional MRI challenges in clinical ... [https://www.frontiersin.org/articles/10.3389/fnins.2022.1017211/full]
• 54. Systematic Review and Meta-analysis of AI-driven MRI ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12425023/]
• 55. Motion artifact in studies of functional connectivity [https://pmc.ncbi.nlm.nih.gov/articles/PMC5930165/]
• 56. Denoising strategies of functional connectivity MRI data in ... [https://pubmed.ncbi.nlm.nih.gov/41190330/]
• 57. Phantom studies in medical imaging (PSMI): a guide with ... [https://pubmed.ncbi.nlm.nih.gov/41182488/]
• 58. Phantom studies in medical imaging (PSMI): a guide with ... [https://eurradiolexp.springeropen.com/articles/10.1186/s41747-025-00641-7]
• 59. Strengthening Phantom Research in Medical Imaging [https://healthmanagement.org/c/healthmanagement/News/strengthening-phantom-research-in-medical-imaging]
• 60. OMERACT GCA phantom project: validation of a 3D ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11804187/]
• 61. Realistic 3D Phantoms for Validation of Microwave ... [https://www.mdpi.com/1424-8220/24/6/1975]
• 62. Comparison of different phantoms used in digital ... [https://www.sciencedirect.com/science/article/abs/pii/S0168900215006233]
• 63. Development of Task Specific Phantoms and Test Objects ... [https://www.frontiersin.org/research-topics/54409/development-of-task-specific-phantoms-and-test-objects-for-medical-imaging/magazine]
• 64. Iterative Learning for Joint Image Denoising and Motion ... [https://www.sciencedirect.com/science/article/abs/pii/S1361841525004189]
• 65. Iterative Learning for Joint Image Denoising and Motion ... [https://arxiv.org/html/2403.08162v1]
• 66. Res-MoCoDiff: residual-guided diffusion models for motion ... [https://pubmed.ncbi.nlm.nih.gov/41061726/]
• 67. IMC-Denoise: a content aware denoising pipeline to ... [https://www.nature.com/articles/s41467-023-37123-6]
• 68. Power Determination During Drug Development [https://pmc.ncbi.nlm.nih.gov/articles/PMC6430154/]
• 69. Statistically unbiased prediction enables accurate ... [https://www.nature.com/articles/s41592-023-02005-8]
• 70. A Review and Experimental Analysis of Denoising ... [https://openneuroimagingjournal.com/VOLUME/18/ELOCATOR/e18744400404813/FULLTEXT/]
• 71. Regression-to-the-Mean: Insights for Drug Developers from ... [https://www.berryconsultants.com/resource/regression-to-the-mean-insights-for-drug-developers-from-the-sports-world]