Age-specific diagnostic classification of asd using deep learning approaches

Vaibhav Jain; Sandeep Sengar; Jac Fredo Agastinose Ronickom

doi:10.3233/SHTI230794

Age-specific diagnostic classification of asd using deep learning approaches

Vaibhav Jain, Sandeep Sengar, Jac Fredo Agastinose Ronickom

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetics, and brain functional connectivity (FC). This makes it devoid of any robust universal biomarker. This study aims to analyze the role of age and multivariate patterns in brain FC and their accountability in diagnosing ASD by deep learning algorithms. We utilized functional magnetic resonance imaging data of three age groups (6 to 11, 11 to 18, and 6 to 18 years), available with public databases ABIDE-I and ABIDE-II, to discriminate between ASD and typically developing. The blood-oxygen-level dependent time series were extracted using the Gordon’s, Harvard Oxford and Diedrichsen’s atlases, over 236 regions of interest, as 236x236 sized FC matrices for each participant, with Pearson correlations. The feature sets, in the form of FC heat maps were computed with

Original language	English
Title of host publication	Telehealth Ecosystems in Practice
Subtitle of host publication	Proceedings of the EFMI Special Topic Conference 2023
Publisher	IOS Press
Pages	267 - 271
Volume	309
ISBN (Electronic)	978-1-64368-451-2
ISBN (Print)	978-1-64368-450-5
DOIs	https://doi.org/10.3233/SHTI230794
Publication status	Published - 2023

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	309
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Access to Document

10.3233/SHTI230794Licence: CC BY-NC

Cite this

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title = "Age-specific diagnostic classification of asd using deep learning approaches",

abstract = "Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetics, and brain functional connectivity (FC). This makes it devoid of any robust universal biomarker. This study aims to analyze the role of age and multivariate patterns in brain FC and their accountability in diagnosing ASD by deep learning algorithms. We utilized functional magnetic resonance imaging data of three age groups (6 to 11, 11 to 18, and 6 to 18 years), available with public databases ABIDE-I and ABIDE-II, to discriminate between ASD and typically developing. The blood-oxygen-level dependent time series were extracted using the Gordon{\textquoteright}s, Harvard Oxford and Diedrichsen{\textquoteright}s atlases, over 236 regions of interest, as 236x236 sized FC matrices for each participant, with Pearson correlations. The feature sets, in the form of FC heat maps were computed with",

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Age-specific diagnostic classification of asd using deep learning approaches. / Jain, Vaibhav; Sengar, Sandeep; Ronickom, Jac Fredo Agastinose.
Telehealth Ecosystems in Practice : Proceedings of the EFMI Special Topic Conference 2023. Vol. 309 IOS Press, 2023. p. 267 - 271 (Studies in Health Technology and Informatics; Vol. 309).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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