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Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy
Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing AD...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543279/ https://www.ncbi.nlm.nih.gov/pubmed/37790426 http://dx.doi.org/10.21203/rs.3.rs-3272441/v1 |
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author | Feng, Aichen Feng, Yuan Zhi, Dongmei Jiang, Rongtao Fu, Zening Xu, Ming Zhao, Min Yu, Shan Stevens, Michael Sun, Li Calhoun, Vince Sui, Jing |
author_facet | Feng, Aichen Feng, Yuan Zhi, Dongmei Jiang, Rongtao Fu, Zening Xu, Ming Zhao, Min Yu, Shan Stevens, Michael Sun, Li Calhoun, Vince Sui, Jing |
author_sort | Feng, Aichen |
collection | PubMed |
description | Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment. Here we proposed graph convolutional network plus deep clustering for ADHD biotype detection using functional network connectivity (FNC), resulting in two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were well replicated on independent ADHD adolescents undergoing longitudinal medication treatment (n=130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 treated with methylphenidate demonstrated significantly better recovery than biotype 2 treated with atomoxetine (p<0.05, FDR corrected). This imaging-driven, biotype-guided approach holds promise for facilitating personalized treatment of ADHD, exploring possible boundaries through innovative deep learning algorithms aimed at improving medication treatment effectiveness. |
format | Online Article Text |
id | pubmed-10543279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-105432792023-10-03 Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy Feng, Aichen Feng, Yuan Zhi, Dongmei Jiang, Rongtao Fu, Zening Xu, Ming Zhao, Min Yu, Shan Stevens, Michael Sun, Li Calhoun, Vince Sui, Jing Res Sq Article Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment. Here we proposed graph convolutional network plus deep clustering for ADHD biotype detection using functional network connectivity (FNC), resulting in two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were well replicated on independent ADHD adolescents undergoing longitudinal medication treatment (n=130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 treated with methylphenidate demonstrated significantly better recovery than biotype 2 treated with atomoxetine (p<0.05, FDR corrected). This imaging-driven, biotype-guided approach holds promise for facilitating personalized treatment of ADHD, exploring possible boundaries through innovative deep learning algorithms aimed at improving medication treatment effectiveness. American Journal Experts 2023-09-14 /pmc/articles/PMC10543279/ /pubmed/37790426 http://dx.doi.org/10.21203/rs.3.rs-3272441/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Feng, Aichen Feng, Yuan Zhi, Dongmei Jiang, Rongtao Fu, Zening Xu, Ming Zhao, Min Yu, Shan Stevens, Michael Sun, Li Calhoun, Vince Sui, Jing Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy |
title | Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy |
title_full | Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy |
title_fullStr | Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy |
title_full_unstemmed | Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy |
title_short | Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy |
title_sort | functional imaging derived adhd biotypes based on deep clustering may guide personalized medication therapy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543279/ https://www.ncbi.nlm.nih.gov/pubmed/37790426 http://dx.doi.org/10.21203/rs.3.rs-3272441/v1 |
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