Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy

Aichen Feng; Yuan Feng; Dongmei Zhi; Rongtao Jiang; Zening Fu; Ming Xu; Min Zhao; Shan Yu; Michael Stevens; Li Sun; Vince Calhoun; Jing Sui

doi:10.21203/rs.3.rs-3272441/v1

Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy

Res Sq [Preprint]. 2023 Sep 14:rs.3.rs-3272441. doi: 10.21203/rs.3.rs-3272441/v1.

Authors

Aichen Feng^{1

2}, Yuan Feng³, Dongmei Zhi⁴, Rongtao Jiang⁵, Zening Fu⁶, Ming Xu^{1

2}, Min Zhao^{1

2}, Shan Yu^{1

2}, Michael Stevens^{7

8}, Li Sun³, Vince Calhoun⁶, Jing Sui^{4

6}

Affiliations

¹ Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190.
² School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049.
³ Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191.
⁴ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875.
⁵ Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA.
⁶ Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303.
⁷ Department of Psychiatry, Olin Neuropsychiatry Research Center, Institute of Living, Hartford Healthcare Corporation, Hartford, CT, United States.
⁸ Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.

Abstract

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.

Keywords: ABCD; Attention deficit hyperactivity disorder (ADHD); biological subtype detection; deep clustering; graph convolutional network (GCN).

Publication types

Preprint

Abstract

Publication types

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