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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks.

Talha Imtiaz Baig¹, Junlin Jing^1,2, Peng Hu¹

¹The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, China.

|February 27, 2026

Summary

This summary is machine-generated.

This study accurately classified Autism Spectrum Disorder (ASD) using functional brain network analysis from multi-site neuroimaging data. Network-based machine learning approaches show promise for identifying reliable biomarkers for ASD.

Keywords:

Autism Spectrum Disorder (ASD)brain connectivity classification feature extraction machine learning multi-site neuroimaging network analysis spatial constraint ICA support vector machine variability reduction

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

Neuroscience
Machine Learning
Medical Imaging

Background:

Autism Spectrum Disorder (ASD) affects ~1% of children globally, characterized by social communication differences and repetitive behaviors.
Functional magnetic resonance imaging (fMRI) reveals altered brain connectivity in ASD, but classification is challenging due to disorder heterogeneity and data variability.
Large-scale, multi-site resting-state fMRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I and II) were utilized.

Purpose of the Study:

To classify Autism Spectrum Disorder (ASD) from healthy controls using a network-based machine learning approach.
To identify reliable functional brain network features for ASD classification across diverse datasets.
To evaluate the effectiveness of Independent Component Analysis (ICA) and ComBat harmonization in multi-site neuroimaging studies.

Main Methods:

Spatial constraint reference ICA was applied to identify functional brain networks from rs-fMRI data.
ComBat harmonization was used to correct for site-specific variability across 11 independent datasets.
Support Vector Machines (SVMs) classified participants based on features from the Default Mode Network (DMN), Sensorimotor Network (SMN), and Visual Sensory Network (VSN).

Main Results:

High classification accuracy was achieved for ASD vs. controls.
The Visual Sensory Network (VSN) showed the highest accuracy (83.23%) and AUC (87.90%).
The Default Mode Network (DMN) and Sensorimotor Network (SMN) also demonstrated strong performance (81.43% and 80.52% accuracy, respectively).

Conclusions:

Integrating ICA-based feature extraction with ComBat harmonization significantly enhanced ASD classification accuracy.
Network-based approaches show potential for robust ASD classification using multi-site neuroimaging data.
Identifying reproducible network-level features is crucial for advancing ASD research and diagnosis.