Machine learning-derived multimodal Neurobiological profiles of behavioral activation traits in adolescents.
Behavioral activation (BA) traits mediate responses to positive reinforcement, and then to promote reward-seeking actions. However, few studies have investigated the neurobiological profiles of BA traits in adolescents based on multimodal neuroimaging and machine learning techniques. In this study,...
| Publicado en: | European Child & Adolescent Psychiatry Vol. 34; no. 10; pp. 3059 - 3071 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189168591&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189168591 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10188827 EJ3 jtl: European Child & Adolescent Psychiatry issn: 10188827 maglogo: N pubinfo: dt: Oct2025 vid: 34 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189168591 184645986 189168591 189168591 10.1007/s00787-025-02714-9 189168591 ppf: 3059 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning-derived multimodal Neurobiological profiles of behavioral activation traits in adolescents. aug: au: Xu, Hui Li, Jiahao Xu, Jing Li, Dandong affil: https://ror.org/0156rhd17 Department of Neurosurgery, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, 325027, Wenzhou, China sug: subj: Personality In Adolescence Adolescent Behavior Personality In Infancy and Childhood Neurobiology Child Behavior Machine Learning Neuroradiography Human Male Female Child Adolescence Magnetic Resonance Imaging Questionnaires Functional Connectivity Univariate Statistics Post Hoc Analysis Cross Sectional Studies Pearson's Correlation Coefficient Secondary Analysis Data Analysis Software Descriptive Statistics Funding Source Child: 6-12 years Adolescent: 13-18 years Male Female ab: Behavioral activation (BA) traits mediate responses to positive reinforcement, and then to promote reward-seeking actions. However, few studies have investigated the neurobiological profiles of BA traits in adolescents based on multimodal neuroimaging and machine learning techniques. In this study, a total of 6626 adolescents with both valid multimodal magnetic resonance imaging (MRI) and questionnaire data were included in the Adolescent Brain Cognitive Development Study. Machine learning-based elastic net regression with 5-fold cross-validation (CV) was used to characterize the neurobiological profiles of BA traits using multimodal MRI data as predictors. Using 5-fold CV, the multi-region neurobiological profiles substantively predicted BA traits, and this finding was robust in an out-of-sample. Regarding specific regions, neurobiological profiles were enriched in the bilateral pallidum. Regarding functional networks, functional connectivity of the cingulo-opercular and the fronto-parietal networks with both the pallidum and nucleus accumbens, showed high beta weights. The relationships of the neurobiological profiles with BA traits were further supported by traditional univariate linear mixed effects models, in which many of the profiles identified as part of the neurobiological pattern showed significant univariate associations with BA traits, including the hub region pallidum. In summary, these findings revealed robust machine learning-derived neurobiological profiles of BA traits, those that comprised a key node the pallidum, which is involved in the motivational brain network. These findings suggested that the pallidum might play a vital role in developing BA traits in adolescents. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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