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Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia.

Armin Iraji1, Ashkan Faghiri1, Zening Fu1

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

Network Neuroscience (Cambridge, Mass.)
|June 23, 2022
PubMed
Summary

Multiscale ICA reveals dynamic brain connectivity differences in schizophrenia, with distinct patterns observed between sexes across multiple spatial scales. These findings highlight potential biomarkers for the condition.

Keywords:
Multi-model-order independent component analysis (ICA)Multi-spatial-scale dynamic functional connectivityMulti-spatial-scale intrinsic connectivity networksMultiscale ICA (msICA)Resting-state fMRI

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Psychiatric Disorders

Background:

  • Functional connectivity studies in schizophrenia often use traditional methods that may not capture the complexity of brain interactions.
  • Understanding sex-specific differences in brain connectivity is crucial for personalized treatment approaches in psychiatric disorders.

Purpose of the Study:

  • To introduce and validate multiscale independent component analysis (ICA) for analyzing dynamic functional brain interactions across multiple spatial scales.
  • To investigate sex-specific and sex-common functional connectivity patterns in schizophrenia using the novel multiscale ICA approach.

Main Methods:

  • Development of multiscale ICA to estimate functional sources at various spatial scales without anatomical constraints or model-order selection.
  • Application of multiscale ICA to functional magnetic resonance imaging (fMRI) data from individuals with schizophrenia to identify dynamic connectivity patterns.
  • Correlation analysis between multiscale functional interactions and schizophrenia symptom severity.

Main Results:

  • Multiscale ICA successfully captured dynamic functional source interactions within and between multiple spatial scales.
  • Significant sex-specific differences in functional connectivity were identified, particularly in interactions between different brain domains (e.g., subcortical, somatomotor, temporal, visual).
  • Multiscale functional interactions showed significant correlations with schizophrenia symptom scores, suggesting their potential as biomarkers.

Conclusions:

  • Multiscale ICA is a powerful tool for uncovering complex, dynamic, and multi-spatial scale functional connectivity patterns in the brain.
  • Sex-specific connectivity differences in schizophrenia are prominent and involve interactions across various spatial scales.
  • Multiscale functional connectivity analysis holds promise for identifying novel biomarkers for schizophrenia and informing future research and clinical applications.