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Updated: Jul 1, 2025

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

Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data.

Soham Bandyopadhyay¹, Santhoshkumar Peddi², Monalisa Sarma³

¹Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, India.

Journal of Neuroscience Methods

|March 2, 2024

Summary

This summary is machine-generated.

This study uses a sparsity approach on resting-state functional MRI (rs-fMRI) to identify brain connectivity biomarkers for Autism prediction, achieving over 88% accuracy.

Keywords:

ASD detection Brain functional connectivity Direct region based connectivity Hybrid approach Optimization of brain connectivity graph Sparse representation

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Last Updated: Jul 1, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Published on: September 12, 2011

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroimaging
Biomarker Discovery
Machine Learning

Background:

Accurate diagnosis of neuro-disorders like Autism requires objective, imaging-based biomarkers.
Resting-state functional MRI (rs-fMRI) offers a non-invasive method for brain activity assessment.

Purpose of the Study:

To develop and validate a novel sparsity-based approach for analyzing brain functional connectivity (FC) in rs-fMRI data.
To identify robust imaging biomarkers for predicting Autism Spectrum Disorder (ASD).

Main Methods:

Utilized three probabilistic brain atlases to define functionally homogeneous brain regions from rs-fMRI data.
Employed a hybrid Graphical Lasso and Akaike Information Criteria approach to optimize sparse inverse covariance matrices for FC estimation.
Applied autoencoder-based feature extraction and AI classifiers for Autism prediction.

Main Results:

An ensemble classifier achieved 84.7% ± 0.3% accuracy using the MSDL atlas.
A 1D-CNN model attained 88.6% ± 1.7% accuracy with the Smith 2009 atlas.
The proposed sparsity-based method significantly outperformed traditional correlation-based FC analysis (70-79% accuracy).

Conclusions:

Sparsity-based FC analysis of rs-fMRI data shows significant potential as a prognostic biomarker for Autism detection.
This methodology offers a more accurate and reliable approach to identifying neuroimaging markers for Autism.