Autism Spectrum Disorder Detection Using Resting-State fMRI Imaging with Convolutional Neural Networks (S31.006)

This study aims to develop an approach to automated autism spectrum disorder (ASD) detection using resting-state fMRI imaging with resting-state functional connectivity (RSFC) patterns learned with convolutional neural networks (CNN).

Authors

Gopikrishna Deshpande (ORCID: https://orcid.org/0000-0001-7471-5357)
Xiao Chang (ORCID: https://orcid.org/0000-0002-0230-0416)
Md. Mahmudul Hasan (ORCID: https://orcid.org/0000-0003-2747-4348)

Institutions

Tuskegee University (US)
Auburn University (US)

Publication Details

Journal: Neurology
Published: 2025-04-07
DOI: https://doi.org/10.1212/wnl.0000000000210838
Primary Topic: EEG and Brain-Computer Interfaces
Type: article
Field-Weighted Citation Impact: 0.00

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article

Autism Spectrum Disorder Detection Using Resting-State fMRI Imaging with Convolutional Neural Networks (S31.006)

Gopikrishna Deshpande, Xiao Chang, Md. Mahmudul Hasan

Apr 7, 2025

1 links

Neurology

EEG and Brain-Computer Interfaces

article

Autism Spectrum Disorder Detection Using Resting-State fMRI Imaging with Convolutional Neural Networks (S31.006)

Gopikrishna Deshpande, Xiao Chang, Md. Mahmudul Hasan

April 7, 2025article en

Abstract

This study aims to develop an approach to automated autism spectrum disorder (ASD) detection using resting-state fMRI imaging with resting-state functional connectivity (RSFC) patterns learned with convolutional neural networks (CNN).

NeurologyVol. 104(7_Supplement_1)

Tuskegee University (US), Auburn University (US)

Openalex Percentile: Top 93%

EEG and Brain-Computer Interfaces

DOI Source OpenAlex

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