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Related Experiment Video

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A spatially constrained independent component analysis jointly informed by structural and functional network

Mahshid Fouladivanda1,2, Armin Iraji1,2, Lei Wu1

  • 1Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

Network Neuroscience (Cambridge, Mass.)
|December 30, 2024
PubMed
Summary

This study introduces a new model combining structural and functional brain connectivity to better understand brain networks, especially in schizophrenia. The multimodal approach enhances network distinction and reveals significant group differences.

Keywords:
Diffusion MRIMultimodal independent component analysisMultiobjective modelResting-state fMRISchizophreniaSpatial constraint

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Psychiatric Disorders

Background:

  • Joint analysis of structural and functional brain connectivity offers complementary insights into brain organization.
  • Understanding brain disorders like schizophrenia benefits from multimodal connectivity approaches.
  • Existing methods may not fully leverage the synergy between different connectivity types.

Purpose of the Study:

  • To propose and validate a multimodal independent component analysis (ICA) model integrating structural and functional brain connectivity.
  • To estimate intrinsic connectivity networks (ICNs) using structural-functional connectivity and spatially constrained ICA (sfCICA).
  • To evaluate the model's performance in distinguishing brain network characteristics, particularly in schizophrenia.

Main Methods:

  • Developed a structural-functional connectivity and spatially constrained ICA (sfCICA) model using a multiobjective optimization framework.
  • Estimated structural connectivity via whole-brain tractography on diffusion-weighted MRI (dMRI).
  • Derived functional connectivity from resting-state functional MRI (rs-fMRI) data.
  • Validated the model on synthetic and real datasets, including data from schizophrenia patients and controls.

Main Results:

  • The sfCICA model revealed enhanced functional coupling between ICNs with higher structural connectivity.
  • Improved modularity and network distinction were observed, particularly in schizophrenia patients.
  • Statistical analysis showed more significant group differences compared to unimodal approaches.

Conclusions:

  • Jointly utilizing structural and functional connectivity in the sfCICA model offers significant advantages over unimodal methods.
  • The model effectively learns and enhances connectivity estimates by integrating multimodal information.
  • This approach holds promise for advancing our understanding of brain connectivity and disorders like schizophrenia.