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International Workshop on Health Intelligence

W3PHAI 2021: AI for Disease Surveillance and Pandemic Intelligence pp 75–86Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 1013))

Abstract

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.

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Acknowledgements

This work was supported by the New Faculty Startup Fund from Seoul National University (Cha); the BK21 FOUR Program (5199990314123) through the National Research Foundation of Korea (Cha and Ahn); National IT Promotion Agency GPU award (Cha); Intel PRTI award (Cha); and Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (Ahn).

Author information

Authors and Affiliations

  1. Department of Material Sciences, College of Engineering, Seoul National University, Seoul, South Korea

    Gun Ahn

  2. Department of Psychology, College of Social Sciences, Seoul National University, Seoul, South Korea

    Bogyeom Kim, Hyeonjin Kim, Eunji Lee, Woo-Young Ahn & Jiook Cha

  3. Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, South Korea

    Ka-kyeong Kim, Woo-Young Ahn & Jiook Cha

  4. Division of Child and Adolescent Psychiatry, Seoul National University Hospital, Seoul, South Korea

    Jae-Won Kim

  5. AI Institute, Seoul National University, Seoul, South Korea

    Woo-Young Ahn & Jiook Cha

Authors
  1. Gun Ahn
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  2. Bogyeom Kim
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  3. Ka-kyeong Kim
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  4. Hyeonjin Kim
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  5. Eunji Lee
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  6. Woo-Young Ahn
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  7. Jae-Won Kim
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  8. Jiook Cha
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Corresponding author

Correspondence to Jiook Cha .

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Editors and Affiliations

  1. Oak-Ridge National Lab (ORNL), Department of Pediatrics, Center for Biomedical Informatics, College of Medicine, The University of Tennessee Health Science Center (UTHSC), Memphis, TN, USA

    Arash Shaban-Nejad

  2. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  3. IBM Almaden Research Center, San Jos, CA, USA

    Simone Bianco

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Ahn, G. et al. (2022). Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Disease Surveillance and Pandemic Intelligence. W3PHAI 2021. Studies in Computational Intelligence, vol 1013. Springer, Cham. https://doi.org/10.1007/978-3-030-93080-6_7

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