Evidence map›Paper›PMID 37790426›Full record

ArticleResearch square2023

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 and 2 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 3 countries.

Aichen FengBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190.
Yuan FengPeking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191.
Dongmei ZhiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875.
Rongtao JiangDepartment of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA.ORCID 0000-0003-4657-0079
Zening FuTri-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.
Ming XuBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190.
Min ZhaoBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190.
Shan YuBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190.
Michael StevensDepartment of Psychiatry, Olin Neuropsychiatry Research Center, Institute of Living, Hartford Healthcare Corporation, Hartford, CT, United States.
Li SunPeking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191.
Vince CalhounTri-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.
Jing SuiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875.ORCID 0000-0001-6837-5966
Chinese Academy of Sciences · CNGeorgia Institute of Technology · USPeking University · CNBeijing Normal University · CNHartford Hospital · USYale University · US

Funding

Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood dR01MH118695 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2019 to 2023
$3.8M
Data-driven approaches to identify biomarkers from multimodal imaging big dataR01MH117107 · NIMH · GEORGIA STATE UNIVERSITY · PI CALHOUN, VINCE D · 2019 to 2022
$1.6M
NIMH NIH HHS R01 MH117107NIMH NIH HHS R01 MH118695
6 · The paper itself

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.

Indexed as

ABCDAttention deficit hyperactivity disorder (ADHD)biological subtype detectiondeep clusteringgraph convolutional network (GCN)

Identifiers

PMID37790426
PMCPMC10543279
OpenAlexW4386739589

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.