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Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging.

S N Gosnell1,2,3, J C Fowler1, R Salas1,2,3

  • 1Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA.

Acta Psychiatrica Scandinavica
|April 1, 2019
PubMed
Summary
This summary is machine-generated.

Neuroimaging biomarkers can identify suicidal behavior in psychiatric inpatients. This study found altered brain connectivity patterns accurately classify individuals at risk for suicide.

Keywords:
biostatisticsmagnetic resonance imagingneuroimagingself-harmsuicide

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Biomarker Discovery

Background:

  • Suicidal behavior often goes unreported by patients to mental health providers.
  • Identifying objective biomarkers for suicidal behavior is crucial for clinical risk assessment.

Purpose of the Study:

  • To identify neural biomarkers of suicidal behavior.
  • To classify suicidal psychiatric inpatients using neuroimaging data.

Main Methods:

  • Utilized structural and resting-state functional connectivity measures in 128 psychiatric inpatients.
  • Employed a random forest classification model trained on 80% of the sample.
  • Validated the model on an independent 20% of the sample.

Main Results:

  • The classification model achieved 79.4% sensitivity and 72.3% specificity in training, and 81.3% sensitivity and 75.0% specificity in testing.
  • Key predictive features included altered functional connectivity in frontal and middle temporal regions, amygdala, parahippocampus, putamen, and vermis.
  • These neuroimaging features generalized well to an independent patient cohort.

Conclusions:

  • Neuroimaging provides an unbiased biomarker for classifying suicidal behavior in psychiatric inpatients.
  • This approach can identify individuals at risk for suicide independent of clinical presentation or reported ideation.