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Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy

Amir Hosein Riazi1, Hossein Rabbani1, Rahele Kafieh1,2

  • 1Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran.

Diagnostics (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

This study introduces a novel spectral independent component analysis (ICA) framework to analyze brain connectivity in multiple sclerosis (MS). The new method reveals significant differences in brain network connectivity between MS patients and healthy controls, particularly in the anterior and posterior cingulate cortex.

Keywords:
ICAbrain connectivityfunctional MRImultiple sclerosis

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Multiple sclerosis (MS) is a neuroinflammatory disease causing structural and functional brain damage, altering functional brain connectivity.
  • Resting-state functional magnetic resonance imaging (fMRI) is crucial for assessing brain region interactions.
  • Current fMRI analysis often relies on graph theory and independent component analysis (ICA), with limited exploration of spectral approaches.

Purpose of the Study:

  • To develop and validate a novel spectral ICA framework for analyzing static and dynamic brain connectivity.
  • To apply this framework to fMRI data from MS patients and healthy controls (HCs).
  • To identify alterations in brain connectivity patterns associated with MS.

Main Methods:

  • A spectral ICA method was developed to extract brain graph nodes, improving reliability and processing time compared to traditional ICA.
  • Dynamic connectivity was assessed using mutual information and sliding time-window correlations on selected independent component (IC) time courses.
  • Static and dynamic connectivity were analyzed using correlations between spectral ICA components, mapped to an anatomical automatic labeling (AAL) atlas.

Main Results:

  • While standard AAL-based connectivity analysis showed no significant differences, the spectral ICA method revealed significantly decreased connectivity in the anterior cingulate cortex of MS patients.
  • In the posterior cingulate cortex, MS patients exhibited weaker core connectivity but stronger peripheral connectivity.
  • The spectral ICA approach demonstrated advantages over Infomax ICA in analyzing dynamic range and fractional amplitude of low-frequency fluctuations (fALFF).

Conclusions:

  • The developed spectral ICA framework offers a sensitive method for detecting subtle alterations in brain connectivity in MS.
  • Findings highlight specific changes in the anterior and posterior cingulate cortex connectivity in MS patients.
  • This spectral approach provides valuable insights into the neurobiological underpinnings of MS and its impact on brain networks.