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Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in
Júlia F Soares1, Rodolfo Abreu1, Ana Cláudia Lima2
1Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal.
Introduction:
Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes.
Methods:
We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS.
Results:
No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation.
Discussion:
Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.
Insights
Volume interpolation effectively corrects head motion in functional MRI (fMRI) for multiple sclerosis (MS) patients. This method, using 6 motion parameters, offers a superior alternative to scrubbing for improving fMRI analysis accuracy in clinical populations.
Area of Science:
- Neuroimaging
- Clinical Neuroscience
- Biomedical Engineering
Background:
- Functional MRI (fMRI) is vital for studying brain organization and connectivity in neurological conditions like multiple sclerosis (MS).
- Head motion significantly degrades fMRI data quality, influencing image-derived metrics and complicating analysis.
- Existing motion correction strategies lack consensus, especially for clinical populations prone to motion, necessitating systematic comparisons.
Purpose of the Study:
- To systematically compare common motion correction strategies in task-based fMRI.
- To identify optimal correction models for fMRI data, particularly in early multiple sclerosis (MS) patients.
- To evaluate the efficacy of volume interpolation versus scrubbing for motion artifact removal in fMRI.
Main Methods:
- Acquired fMRI data during a visual task from 17 early MS patients and 14 healthy controls (HC).
- Compared fMRI metrics using models with 6 or 24 motion parameters (MPs), with or without scrubbing (Framewise Displacement, Derivative, or root mean square VARiance over voxelS), or volume interpolation.
- Quantitatively assessed the performance of each motion correction method.
Main Results:
- No significant differences in head motion were observed between MS patients and healthy controls.
- Models utilizing 6 motion parameters generally outperformed models with 24 MPs, indicating a better balance between motion correction and data preservation.
- The combination of 6 MPs with volume interpolation demonstrated superior motion correction performance compared to scrubbing methods in both groups.
Conclusions:
- Volume interpolation of motion outliers is a highly effective and easily implementable technique for fMRI data.
- This method offers a valuable alternative to scrubbing, potentially enhancing the accuracy of fMRI analyses in clinical studies, especially for neurological conditions like MS.
- The findings suggest that parsimonious models with 6 MPs and volume interpolation are optimal for motion correction in task-based fMRI studies involving clinical populations.
