Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging
Abstract
Objective: About 80% of patients who commit suicide do not report suicidal ideation the last time they speak to their mental health provider, highlighting the need to identify biomarkers of suicidal behavior. Our goal is to identify suicidal behavior neural biomarkers to classify suicidal psychiatric inpatients. Methods: Eighty percent of our sample [suicidal ( n = 63) and non‐suicidal psychiatric inpatients ( n = 65)] was used to determine significant differences in structural and resting‐state functional connectivity measures throughout the brain. These measures were used in a random forest classification model on 80% of the sample for training the model. Results: The model built on 80% of the patients had sensitivity = 79.4% and specificity = 72.3%. This model was tested on an independent sample (20%; n = 32) with sensitivity = 81.3% and specificity = 75.0% for confirming the generalizability of the model. Altered resting‐state functional connectivity features from frontal and middle temporal regions, as well as the amygdala, parahippocampus, putamen, and vermis were found to generalize best. Conclusion: This work demonstrates neuroimaging (an unbiased biomarker) can be used to classify suicidal behavior in psychiatric inpatients without observing any clinical features.
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In a broader psychiatric sample, machine learning applied to resting-state data demonstrated altered network-level connectivity in frontal and temporal regions as well as in the amygdala, parahippocampus, and putamen.
“In a broader psychiatric sample, machine learning applied to resting-state data demonstrated altered network-level connectivity in frontal and temporal regions as well as in the amygdala, parahippocampus, and putamen (77).”
From New methods for assessing rapid changes in suicide risk (2021) · cites this paper as [77]
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New methods for assessing rapid changes in suicide risk
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- Authors
- Gosnell, S. N., Fowler, J. C., & Salas, R.
- Affiliations
- Michael E. DeBakey VA Medical Center (US)
- Baylor College of Medicine (US)
- Corresponding author
- Salas, R.
- Venue
- Acta Psychiatrica Scandinavica
- Publisher
- Wiley
- Published
- 2019-04-20
- Volume
- 140
- Issue
- 1
- Pages
- 20-29
- ISSN
- 0001-690X (ISSN-L)
- 1600-0447
- OpenAlex
- W2933723289
- Cited by
- 49
- References
- 77
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 8 · 2023: 9 · 2022: 9 · 2021: 7 · 2020: 10 · 2019: 1
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Acta Psychiatrica Scandinavica (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Foundation for Suicide Prevention (SRG‐2‐125‐14)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- suicidal behavior, suicide risk prediction, resting-state functional connectivity, structural neuroimaging, neural markers, psychiatric inpatients, random forest classification, amygdala, parahippocampal gyrus, frontal cortex, middle temporal cortex, putamen, cerebellar vermis
- MeSH terms
- Machine Learning, Adult, Mental Disorders, Brain, Humans, Magnetic Resonance Imaging, Models, Theoretical, Sensitivity and Specificity, Suicide, Suicide, Attempted, Biomarkers, Suicidal Ideation, Neuroimaging, Connectome
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