Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions: A Preliminary Study
| Autor: | Keith Bush, G. Andrew James, Ricardo Cáceda, Clint D. Kilts, Zachary N. Stowe |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Bipolar Disorder Suicide Attempted Suicide prevention Article Suicidal Ideation 03 medical and health sciences Young Adult 0302 clinical medicine Functional neuroimaging medicine Humans Bipolar disorder Major depressive episode Suicidal ideation Default mode network Depressive Disorder Major business.industry Functional Neuroimaging Not Otherwise Specified Brain medicine.disease Magnetic Resonance Imaging 030227 psychiatry Psychiatry and Mental health Cross-Sectional Studies Case-Control Studies Major depressive disorder Female medicine.symptom business 030217 neurology & neurosurgery Clinical psychology |
| Zdroj: | J Clin Psychiatry |
| Popis: | Objective A major target in suicide prevention is interrupting the progression from suicidal thoughts to action. Use of complex algorithms in large samples has identified individuals at very high risk for suicide. We tested the ability of data-driven pattern classification analysis of brain functional connectivity to differentiate recent suicide attempters from patients with suicidal ideation. Methods We performed a cross-sectional study using resting-state functional magnetic resonance imaging in depressed inpatients and outpatients of both sexes recruited from a university hospital between March 2014 and June 2016: recent suicide Attempters within 3 days of an attempt (n = 10), Suicidal Ideators (n = 9), Depressed Non-Suicidal Controls (n = 17), and Healthy Controls (n = 18). All depressed patients fulfilled DSM-IV-TR criteria for major depressive episode and either major depressive disorder, bipolar disorder, or depression not otherwise specified. A subset of suicide attempters (n = 7) were rescanned within 7 days. We used a support vector machine data-driven neural pattern classification analysis of resting-state functional connectivity to characterize recent suicide attempters and then tested the classifier's specificity. Results A binary classifier trained to discriminate patterns of resting-state functional connectivity robustly differentiated Suicide Attempters from Suicidal Ideators (mean accuracy = 0.788, signed rank test: P = .002; null hypothesis: area under the curve = 0.5), with distinct functional connectivity between the default mode and the limbic, salience, and central executive networks. The classifier did not discriminate stable Suicide Attempters from Suicidal Ideators (mean accuracy = 0.58, P = .33) or presence from absence of lifetime suicidal behavior (mean accuracy = 0.543, P = .348) and was not improved by modeling clinical variables (mean accuracy = 0.736, P = .002). Conclusions Measures of intrinsic brain organization may have practical value as objective measures of suicide risk and its underlying mechanisms. Further incorporation of serum or cognitive markers and use of a prospective study design are needed to validate and refine the clinical relevance of this candidate biomarker of suicide risk. |
| Databáze: | OpenAIRE |
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