Dataset Information

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

ABSTRACT: The clinical assessment of suicidal risk would be significantly 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 Naïve Bayes) to identify such individuals (17 suicidal ideators vs 17 controls) with high (91%) accuracy, based on their altered fMRI neural signatures of death and life-related concepts. The most discriminating concepts were death, cruelty, trouble, carefree, good, and praise. A similar classification accurately (94%) discriminated 9 suicidal ideators who had made a suicide attempt from 8 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. The study establishes a biological, neurocognitive basis for altered concept representations in participants with suicidal ideation, which enables highly accurate group membership classification.

SUBMITTER: Just MA

PROVIDER: S-EPMC5777614 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Just Marcel Adam MA Pan Lisa L Cherkassky Vladimir L VL McMakin Dana L DL Cha Christine C Nock Matthew K MK Brent David D

Nature human behaviour 20171030

The clinical assessment of suicidal risk would be significantly 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 Naïve Bayes) to identify such individuals (17 suicidal ideators vs 17 controls) with high (91%) accuracy, based on their altered fMRI neural signatures of death and life-related concepts. The most dis ...[more]

PMID: 29367952

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Project description:PurposeYouth suicide clusters may be exacerbated by suicide contagion-the spread of suicidal behaviors. Factors promoting suicide contagion are poorly understood, particularly in the advent of social media. Using cross-sectional data from an ongoing youth suicide cluster in Ohio, this study examines associations between suicide cluster-related social media and suicidal behaviors.MethodsWe surveyed 7th- to 12th-grade students in northeastern Ohio during a 2017-2018 suicide cluster to assess the prevalence of suicidal ideation (SI), suicide attempts (SAs), and associations with potential contagion-promoting factors such as suicide cluster-related social media, vigils, memorials, news articles, and watching the Netflix series 13 Reasons Why before or during the cluster. Generalized estimating equations examined associations between potential contagion-promoting factors and SI/SA, adjusting for nonmodifiable risk factors. Subgroup analyses examined whether associations between cluster-related factors and SI/SA during the cluster varied by previous history of SI/SA.ResultsAmong participating students, 9.0% (876/9,733) reported SI and 4.9% attempted suicide (481/9,733) during the suicide cluster. Among students who posted suicide cluster-related content to social media, 22.9% (267/1,167) reported SI and 15.0% (175/1,167) attempted suicide during the suicide cluster. Posting suicide cluster-related content was associated with both SI (adjusted odds ratio 1.7, 95% confidence interval 1.4-2.0) and SA during the cluster (adjusted odds ratio 1.7, 95% confidence interval 1.2-2.5). In subgroup analyses, seeing suicide cluster-related posts was uniquely associated with increased odds of SI and SA during the cluster among students with no previous history of SI/SA.ConclusionsExposure to suicide cluster-related social media is associated with both SI and SA during a suicide cluster. Suicide interventions could benefit from efforts to mitigate potential negative effects of social media and promote prevention messages.

Dataset Information

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

Publications

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

Similar Datasets

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