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Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network

Xing Meng¹, Armin Iraji¹, Zening Fu¹

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

Brain Connectivity

|September 20, 2021

Summary

This summary is machine-generated.

This study introduces a new method to analyze brain connectivity across different spatial scales using multi-model order independent component analysis (ICA). This approach enhances the prediction of schizophrenia (SZ) by revealing unique connectivity patterns missed by single-scale analyses.

Keywords:

functional network connectivity independent component analysis intrinsic connectivity networks machine learning multiple spatial scales resting fMRI

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Area of Science:

Neuroimaging
Computational Neuroscience
Psychiatric Disorders

Background:

Functional connectivity (FC) is crucial in neuroscience, but studies often overlook varying spatial scales and their interrelationships.
Understanding FC across multiple spatial resolutions is essential for a comprehensive view of brain function.

Purpose of the Study:

To develop and evaluate a novel independent component analysis (ICA)-based approach for analyzing functional network connectivity (FNC) at multiple spatial scales (model orders).
To investigate group differences in FNC within and between model orders in individuals with schizophrenia (SZ) and healthy controls (HC).
To assess the predictive power of multi-scale FNC for classifying SZ using machine learning.

Main Methods:

Applied a multi-model order ICA approach to resting-state functional magnetic resonance imaging (rsfMRI) data from SZ and HC groups.
Evaluated FNC within and between different model orders.
Utilized support vector machine (SVM) classification to determine the predictive accuracy of FNC patterns.

Main Results:

Multi-model order ICA identified unique predictive FNC information not found in single-model order analyses.
FNC between model orders 25 and 50 showed the highest predictive power for distinguishing between HC and SZ.
Somatomotor and visual network connectivity were significant predictors both within and between scales, with specific subcortical, temporal, and somatomotor interactions being highly weighted for SZ prediction.

Conclusions:

Multi-model order ICA offers a more comprehensive analysis of FNC, revealing insights missed by traditional single-scale methods.
This approach demonstrates significant potential for brain disorder classification, particularly for schizophrenia.
The study provides valuable spatial templates for the neuroscience community to use in future ICA-based research.