Publications

Detailed Information

Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging

DC Field Value Language
dc.contributor.authorAhn, Gun-
dc.contributor.authorKim, Bogyeom-
dc.contributor.authorKim, Ka-kyeong-
dc.contributor.authorKim, Hyeonjin-
dc.contributor.authorLee, Eunji-
dc.contributor.authorAhn, Woo-Young-
dc.contributor.authorKim, Jae-Won-
dc.contributor.authorCha, Jiook-
dc.date.accessioned2024-05-16T04:39:12Z-
dc.date.available2024-05-16T04:39:12Z-
dc.date.created2022-04-26-
dc.date.issued2022-01-
dc.identifier.citationStudies in Computational Intelligence, Vol.1013, pp.75-86-
dc.identifier.issn1860-949X-
dc.identifier.urihttps://hdl.handle.net/10371/203081-
dc.description.abstract© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Suicide is among the leading causes of death in youth worldwide. Early identification of children with high risk for suicide is a key to effective screening and intervention strategies. Yet, little is known about the neural pathways to the clinical outcomes of youth suicide. In this study, we tested brain functional substrates associated with the risk for youth suicidality. Based on the large, multi-site, multi-ethnic, representative, and prospective developmental population study in the US, we trained a state-of-the-art interpretable deep neural network on functional brain imaging, behavioral, and self-reported questionnaires. Our best model contains the functional estimates of key brain regions important for attention, emotion regulation, and motor coordination, such as the anterior cingulate cortex, temporal gyrus, and precentral gyrus. The interpretable neural network shows that these brain functional features interact with depression and impulsivity, the known risk factors of youth suicidality. This study demonstrates a novel application of the interpretable deep neural network to childhood suicidal research, uncovering the complex interactions between psychological and neural factors underlying youth suicidality.-
dc.language영어-
dc.publisherSpringer Verlag-
dc.titleIdentifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging-
dc.typeArticle-
dc.identifier.doi10.1007/978-3-030-93080-6_7-
dc.citation.journaltitleStudies in Computational Intelligence-
dc.identifier.scopusid2-s2.0-85127081994-
dc.citation.endpage86-
dc.citation.startpage75-
dc.citation.volume1013-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorAhn, Woo-Young-
dc.contributor.affiliatedAuthorCha, Jiook-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.subject.keywordAuthorDeep neural network-
dc.subject.keywordAuthorMultimodal brain imaging-
dc.subject.keywordAuthorPrepubertal children-
dc.subject.keywordAuthorSuicide-
Appears in Collections:
Files in This Item:
There are no files associated with this item.

Related Researcher

  • College of Social Sciences
  • Department of Psychology
Research Area Addiction, computational neuroscience, decision neuroscience, 계산 신경과학, 의사결정 신경과학, 중독

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share