Dépouillements

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images / Emel KOC in Autism Research and Treatment, 2023 (2023)  

Public ISBD [article]  inAutism Research and Treatment > 2023 (2023) Titre : Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images Type de document : Texte imprimé et/ou numérique Auteurs : Emel KOC, Auteur ; Habil KALKAN, Auteur ; Semih BILGEN, Auteur Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies. En ligne : https://doi.org/10.1155/2023/4136087 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538 [article] Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images [Texte imprimé et/ou numérique] / Emel KOC, Auteur ; Habil KALKAN, Auteur ; Semih BILGEN, Auteur. Langues : Anglais (eng) in Autism Research and Treatment > 2023 (2023) Index. décimale : PER Périodiques Résumé : This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies. En ligne : https://doi.org/10.1155/2023/4136087 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538

"Don?t Promise Something You can?t Deliver:" Caregivers' Advice for Improving Services to Adolescents and Young Adults with Autism / Kristen A. BERG in Autism Research and Treatment, 2023 (2023)  

Public ISBD [article]  inAutism Research and Treatment > 2023 (2023) Titre : "Don?t Promise Something You can?t Deliver:" Caregivers' Advice for Improving Services to Adolescents and Young Adults with Autism Type de document : Texte imprimé et/ou numérique Auteurs : Kristen A. BERG, Auteur ; Karen J. ISHLER, Auteur ; Sarah LYTLE, Auteur ; Ronna KAPLAN, Auteur ; Fei WANG, Auteur ; Tugba OLGAC, Auteur ; Stacy MINER, Auteur ; Marjorie N. EDGUER, Auteur ; David E. BIEGEL, Auteur Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Approximately 50,000 youths with autism spectrum disorders (ASD) exit U.S. high schools yearly to enter adult systems of care, many of whom remain dependent on family for day-to-day care and service system navigation. As part of a larger study, 174 family caregivers for adolescents or young adults with ASD were asked what advice they would give service providers about how to improve services for youth with ASD. Reflexive thematic analysis identified a framework of five directives: (1) provide a roadmap to services; (2) improve service access; (3) fill gaps to address unmet needs; (4) educate themselves, their families, and society about autism; and (5) operate from a relationship-building paradigm with families. Education, health, and social service providers, as well as policymakers, can use these directives to better assist youth with ASD and their families in the transition to adulthood. En ligne : https://doi.org/10.1155/2023/6597554 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538 [article] "Don?t Promise Something You can?t Deliver:" Caregivers' Advice for Improving Services to Adolescents and Young Adults with Autism [Texte imprimé et/ou numérique] / Kristen A. BERG, Auteur ; Karen J. ISHLER, Auteur ; Sarah LYTLE, Auteur ; Ronna KAPLAN, Auteur ; Fei WANG, Auteur ; Tugba OLGAC, Auteur ; Stacy MINER, Auteur ; Marjorie N. EDGUER, Auteur ; David E. BIEGEL, Auteur. Langues : Anglais (eng) in Autism Research and Treatment > 2023 (2023) Index. décimale : PER Périodiques Résumé : Approximately 50,000 youths with autism spectrum disorders (ASD) exit U.S. high schools yearly to enter adult systems of care, many of whom remain dependent on family for day-to-day care and service system navigation. As part of a larger study, 174 family caregivers for adolescents or young adults with ASD were asked what advice they would give service providers about how to improve services for youth with ASD. Reflexive thematic analysis identified a framework of five directives: (1) provide a roadmap to services; (2) improve service access; (3) fill gaps to address unmet needs; (4) educate themselves, their families, and society about autism; and (5) operate from a relationship-building paradigm with families. Education, health, and social service providers, as well as policymakers, can use these directives to better assist youth with ASD and their families in the transition to adulthood. En ligne : https://doi.org/10.1155/2023/6597554 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538