Group Information Guided Smooth Independent Component Analysis Method for Multi-Subject fMRI Data Analysis

Group independent component analysis (ICA) has been extensively used to extract brain functional networks (FNs) and associated neuroimaging measures from multi-subject functional magnetic resonance imaging (fMRI) data. However, the inherent noise in fMRI data can adversely affect the performance of...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 13
Main Authors: Du, Yuhui, Huang, Chen, Calhoun, Vince D.
Format: Journal Article
Language:English
Published: United States IEEE 18.07.2025
Subjects:
Accuracy
Bioinformatics
Biomarkers
Brain functional network
Data mining
Functional magnetic resonance imaging
Independent component analysis
Multi-objective optimization
Noise
Optimization
Reliability
Smoothness
Training
ISSN:2168-2194, 2168-2208, 2168-2208
Online Access:Get full text
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Summary:Group independent component analysis (ICA) has been extensively used to extract brain functional networks (FNs) and associated neuroimaging measures from multi-subject functional magnetic resonance imaging (fMRI) data. However, the inherent noise in fMRI data can adversely affect the performance of ICA, often leading to noisy FNs and hindering the identification of network-level biomarkers. To address this challenge, we propose a novel method called group information-guided smooth independent component analysis (GIG-sICA). Our method effectively generates smoother functional networks with reduced noise and enhanced functional coherence, while preserving intra-subject independence and inter-subject correspondence of FN. Importantly, GIG-sICA is capable of handling different types of noise either separately or in combination. To validate the efficacy of our approach, we conducted comprehensive experiments, comparing GIG-sICA with traditional group ICA methods on both simulated and real fMRI datasets. Experiments on five simulated datasets, generated by adding various types of noise, demonstrate that GIG-sICA produces smoother functional networks with enhanced spatial accuracy. Additionally, experiments on real fMRI data from 137 schizophrenia patients and 144 healthy controls demonstrate that GIG-sICA more effectively captures functionally meaningful brain networks and reveals clearer group differences. Overall, GIG-sICA produces smooth and precise network estimations, supporting the discovery of robust biomarkers at the network level for neuroscience research.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2025.3590641