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Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

Matters Arising to this article was published on 05 April 2021


The clinical assessment of suicidal risk would be substantially complemented by a biologically based measure that assesses alterations in the neural representations of concepts related to death and life in people who engage in suicidal ideation. This study used machine-learning algorithms (Gaussian Naive Bayes) to identify such individuals (17 suicidal ideators versus 17 controls) with high (91%) accuracy, based on their altered functional magnetic resonance imaging neural signatures of death-related and life-related concepts. The most discriminating concepts were ‘death’, ‘cruelty’, ‘trouble’, ‘carefree’, ‘good’ and ‘praise’. A similar classification accurately (94%) discriminated nine suicidal ideators who had made a suicide attempt from eight who had not. Moreover, a major facet of the concept alterations was the evoked emotion, whose neural signature served as an alternative basis for accurate (85%) group classification. This study establishes a biological, neurocognitive basis for altered concept representations in participants with suicidal ideation, which enables highly accurate group membership classification.

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Fig. 1: Clusters of stable voxels of the suicidal ideator group and the control group.
Fig. 2
Fig. 3: Group separation in the multidimensional scaling of the activation features of the participants used by the classifier.
Fig. 4: Group separation in the multidimensional scaling of the activation features of the nine ideators who have attempted suicide and the eight ideators without attempts used by the classifier.


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This research was partially supported by the National Institute of Mental Health Grant MH029617 and an Endowed Chair in Suicide Studies at the University of Pittsburgh School of Medicine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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The experiments were conceived and designed by M.A.J., L.P., V.L.C., D.L.M. and D.B. The experiments were performed by M.A.J., L.P. and D.B. The materials and analysis tools were contributed by M.A.J., V.L.C., C.C. and M.K.N. The data were analysed by V.L.C. The paper was written by M.A.J., V.L.C. and D.B.

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Correspondence to Marcel Adam Just.

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Supplementary Methods, Supplementary Table 1, Supplementary Figures 1–4, Supplementary Notes and Supplementary References

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Just, M., Pan, L., Cherkassky, V.L. et al. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat Hum Behav 1, 911–919 (2017).

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