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

Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Marcel Adam Just, Lisa Pan, Vladimir L. Cherkassky, Dana L. McMakin, Christine Cha, Matthew K. Nock & David Brent. Nature Human Behaviour (2017), doi:10.1038/s41562-017-0234-y

Abstract: 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.