Cargando…

A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection

Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous wor...

Descripción completa

Detalles Bibliográficos
Autores principales: Benabdallah, Fatima Zahra, Drissi El Maliani, Ahmed, Lotfi, Dounia, El Hassouni, Mohammed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299127/
https://www.ncbi.nlm.nih.gov/pubmed/37367458
http://dx.doi.org/10.3390/jimaging9060110
_version_ 1785064287064031232
author Benabdallah, Fatima Zahra
Drissi El Maliani, Ahmed
Lotfi, Dounia
El Hassouni, Mohammed
author_facet Benabdallah, Fatima Zahra
Drissi El Maliani, Ahmed
Lotfi, Dounia
El Hassouni, Mohammed
author_sort Benabdallah, Fatima Zahra
collection PubMed
description Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%.
format Online
Article
Text
id pubmed-10299127
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102991272023-06-28 A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection Benabdallah, Fatima Zahra Drissi El Maliani, Ahmed Lotfi, Dounia El Hassouni, Mohammed J Imaging Article Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%. MDPI 2023-05-31 /pmc/articles/PMC10299127/ /pubmed/37367458 http://dx.doi.org/10.3390/jimaging9060110 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Benabdallah, Fatima Zahra
Drissi El Maliani, Ahmed
Lotfi, Dounia
El Hassouni, Mohammed
A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
title A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
title_full A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
title_fullStr A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
title_full_unstemmed A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
title_short A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
title_sort convolutional neural network-based connectivity enhancement approach for autism spectrum disorder detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299127/
https://www.ncbi.nlm.nih.gov/pubmed/37367458
http://dx.doi.org/10.3390/jimaging9060110
work_keys_str_mv AT benabdallahfatimazahra aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT drissielmalianiahmed aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT lotfidounia aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT elhassounimohammed aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT benabdallahfatimazahra convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT drissielmalianiahmed convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT lotfidounia convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection
AT elhassounimohammed convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection