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Shi – Pipeline for Preprocessing and Denoising Structural and Functional MRI Data with fMRIPrep

 

Pipeline for Preprocessing and Denoising Structural and Functional MRI Data with fMRIPrep (2023)

Undergraduate: Zhuoyu Shi


Faculty Advisor: Aysenil Belger
Department: Department of Psychiatry, Psychiatry

Neuroimaging techniques such as MRI, fMRI, EEG, DTI, and PET are widely used in neuroscience and clinical research to understand the brain's connectivity, function, dynamics, anatomy, and pathology. However, it is necessary to preprocess and denoise the imaging data before analyzing it. In this study, we utilize the fMRIPrep pipeline to preprocess CogNIT structural MRI and resting state functional MRI with FSL, AFNI, FreeSurfer, and ANTs on UNC Longleaf. The fMRIPrep BOLD pipeline is then compared with the CONN toolbox's BOLD denoising pipeline concerning the detailed steps. In addition, we display one of our preprocessed subjects using the fMRIPrep pipeline and the functional connectivity values distribution before and after applying CONN toolbox's denoising pipeline. Our results highlight the importance of carefully designing the pipeline and preprocessing neuroimaging data to ensure the accuracy and reliability of further analyses.

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