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Background: <p>Early detection of youth depression is crucial, given its rising prevalence and long-term consequences. Although genetic factors contribute significantly to youth depression, their integration with neuroimaging remains limited. This study aims to bridge the gap by predicting youth depression through a combined genetic and neuroimaging framework.</p> Methods: <p>We present a deep learning framework using polygenic risk scores to pretrain a 3D convolutional neural network on track-weighted fractional anisotropy. This approach captures gene and brain associations from a multi-ethnic cohort of 4741 youth in the Adolescent Brain Cognitive Development Study (age range: 9-10 years old). We fine-tune the model on separate held-out datasets for cross-sectional and 2-year follow-up prediction, respectively.</p> Results: <p>Here we show that the model improves cross-sectional (266 participants) and two-year predictions of depression and suicidality, with area under the curve values of 0.61 to 0.66. It outperforms unimodal models, increasing accuracy over genetics-only and brain-only models. Explainable artificial intelligence identifies key white matter tracts, including the superior longitudinal fasciculus, cingulum, and corpus callosum, as primary predictive features. The model effectively generalizes to an independent Korean youth sample (age range: 9-17 years old), achieving an area under the curve of 0.67.</p> Conclusions: <p>This establishes the cross-ethnic scalability of integrating genetics with brain imaging. These findings highlight the promise of multimodal deep learning for precision psychiatry and early clinical intervention.</p>

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Polygenic risk-informed white matter integrity improves deep learning-based prediction of youth depression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Jungyoun Janice Min,
  • Heehwan Wang,
  • Eunji Lee,
  • Bo-Gyeom Kim,
  • Gakyung Kim,
  • Seung Yun Choi,
  • Kahyun Choi,
  • Sung Hee Hong,
  • Yumin Jang,
  • Yu Jin Rah,
  • Ji Yeon Kim,
  • Seunghui Han,
  • Kyung Hwa Lee,
  • Junhyung Kim,
  • Milenna T. van Dijk,
  • Jae-Won Kim,
  • Grace H. Chung,
  • Dongil Chung,
  • Sang Ah Lee,
  • Yoonjung Yoonie Joo,
  • Jiook Cha

摘要

Background:

Early detection of youth depression is crucial, given its rising prevalence and long-term consequences. Although genetic factors contribute significantly to youth depression, their integration with neuroimaging remains limited. This study aims to bridge the gap by predicting youth depression through a combined genetic and neuroimaging framework.

Methods:

We present a deep learning framework using polygenic risk scores to pretrain a 3D convolutional neural network on track-weighted fractional anisotropy. This approach captures gene and brain associations from a multi-ethnic cohort of 4741 youth in the Adolescent Brain Cognitive Development Study (age range: 9-10 years old). We fine-tune the model on separate held-out datasets for cross-sectional and 2-year follow-up prediction, respectively.

Results:

Here we show that the model improves cross-sectional (266 participants) and two-year predictions of depression and suicidality, with area under the curve values of 0.61 to 0.66. It outperforms unimodal models, increasing accuracy over genetics-only and brain-only models. Explainable artificial intelligence identifies key white matter tracts, including the superior longitudinal fasciculus, cingulum, and corpus callosum, as primary predictive features. The model effectively generalizes to an independent Korean youth sample (age range: 9-17 years old), achieving an area under the curve of 0.67.

Conclusions:

This establishes the cross-ethnic scalability of integrating genetics with brain imaging. These findings highlight the promise of multimodal deep learning for precision psychiatry and early clinical intervention.