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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jul 24, 2026

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 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 24, 2026

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

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.