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Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and
Nathan W Churchill1, Anita Oder, Hervé Abdi
1Rotman Research Institute, Baycrest, Toronto, Ontario, Canada. nchurchill@rotman-baycrest.on.ca
Human Brain Mapping
|April 2, 2011
Summary
Optimizing brain imaging preprocessing pipelines improves functional MRI (fMRI) results. Individually tailored steps enhance data reproducibility and reveal subtle brain activation patterns missed by fixed approaches.
Area of Science:
- Neuroimaging
- Functional Magnetic Resonance Imaging (fMRI)
- Data Science
Background:
- Subject-specific artifacts from head motion and physiological noise significantly confound BOLD fMRI analyses.
- Optimal data preprocessing strategies to mitigate these effects remain debated.
- Existing methods lack consensus on the best preprocessing pipeline for minimizing noise and maximizing signal quality.
Purpose of the Study:
- To evaluate the impact of various preprocessing strategies on fMRI data quality and analysis performance.
- To assess the relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending.
- To determine if subject-specific optimized preprocessing pipelines improve reproducibility over fixed pipelines.
Main Methods:
- Developed a framework combining nonparametric testing (NPAIRS) with intersubject comparison (DISTATIS) for evaluating fMRI preprocessing.
- Utilized an fMRI adaptation of the Trail-Making Test in young, healthy adults.
- Assessed analysis performance and activation map quality using Penalized Discriminant Analysis (PDA).
Main Results:
- Preprocessing choices significantly impact fMRI results, with effects being subject-dependent.
- Individually optimized preprocessing pipelines substantially improve the reproducibility of fMRI results compared to fixed pipelines.
- A significant interaction between motion parameter regression and physiological noise correction was detected, even with minimal head motion.
Conclusions:
- Subject-specific optimization of fMRI preprocessing pipelines is crucial for reliable results.
- Fixed preprocessing pipelines may obscure or miss important brain activation patterns.
- Tailoring preprocessing steps enhances the interpretation and validity of fMRI data, particularly in clinical settings.
