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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases.

Yurui Gao1,2, Richard D Lawless1,3, Muwei Li1,4

  • 1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.

Proceedings of Spie--The International Society for Optical Engineering
|August 21, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new pipeline to analyze functional MRI (fMRI) signals in white matter (WM), revealing reliable patterns related to neural activity. This tool enhances the study of aging and brain degeneration using fMRI data.

Keywords:
Alzheimer’s diseaseXNATagingautomatic preprocessing pipelineresting state fMRIwhite matter function

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Area of Science:

  • Neuroimaging
  • White Matter Neuroscience
  • Aging Research

Background:

  • Functional MRI (fMRI) signals in white matter (WM) are increasingly recognized as relevant to neural activity.
  • Conventional fMRI analysis tools are optimized for gray matter and poorly suited for WM signal processing.
  • Changes in WM are well-documented in aging and neurodegenerative diseases.

Purpose of the Study:

  • To develop an automated, high-performance preprocessing pipeline for fMRI data, specifically designed to quantify BOLD signals in white matter.
  • To facilitate the investigation of functional alterations in white matter in an aging population.

Main Methods:

  • An automated pipeline integrating modified existing software modules for enhanced extraction of weak WM signals.
  • Implementation of a distributed computing strategy using XNAT and DAX for high-performance cluster processing.
  • Preprocessing of 5,034 fMRI/T1 scans, generating whole-brain time-courses, functional connectivity maps, and tissue masks.

Main Results:

  • The pipeline demonstrated high reliability, with intraclass correlation coefficients (ICC) ranging from 0.52 to 0.86 (N=1000) in test-retest experiments.
  • Achieved preprocessing quality comparable to established methods for gray matter fMRI.
  • Successfully processed a large dataset of 5,034 fMRI/T1 scans.

Conclusions:

  • The developed automated preprocessing pipeline is effective for analyzing white matter fMRI signals.
  • This pipeline significantly aids future research into functional changes in white matter associated with aging.
  • The tool offers substantial benefits to the broader research community studying white matter in fMRI.