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Neural predictors of late-life suicide risk

Nature Mental Health volume 1, pages 86–87 (2023)Cite this article

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Will connectome-based predictive modeling change how we care for people at risk of late-life suicide? A novel two-step modeling approach used by Gao et al. in their study sheds light on the road ahead.

Suicide is a leading global cause of death, especially for older adults1. Loneliness, dependence on others, feeling of abandonment, loss of loved ones and physical illness are risk factors for suicide in older adults2. Emerging data link certain dispositional characteristics (for example, perfectionism and overcontrol)3,4 to suicide later in life and may demarcate late and early onset suicidal behavior5. Importantly, traits that confer advantages early in life may become vulnerabilities later during aging3,4,5. In response to the rise in suicide deaths during the past two decades, major public health institutions have invested in and championed the use of predictive modeling and machine learning as avenues toward reducing suicide mortality. The promise is alluring: these models will help guide intervention resources toward those at the greatest risk of attempting suicide, thereby reducing future attempts and suicide deaths.

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Fig. 1: Predicting suicide risk and suicidal behavior history using machine learning models.

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Authors and Affiliations

  1. Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA

    Jennifer Barredo & Nauder Namaky

  2. VA Providence Health System, Providence, RI, USA

    Jennifer Barredo & Nauder Namaky

Authors
  1. Jennifer Barredo
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  2. Nauder Namaky
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Corresponding authors

Correspondence to Jennifer Barredo or Nauder Namaky.

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The authors declare no competing interests.

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Barredo, J., Namaky, N. Neural predictors of late-life suicide risk. Nat. Mental Health 1, 86–87 (2023). https://doi.org/10.1038/s44220-022-00006-8

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  • DOI: https://doi.org/10.1038/s44220-022-00006-8

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