Classifying suicidal behavior with resting‐state functional connectivity and structural neuroimaging

• Published in: Acta psychiatrica Scandinavica 2019-07, Vol.140 (1), p.20-29
• Main Authors: Gosnell, S. N., Fowler, J. C., Salas, R.
• Format: Article
• Language: English
• Subjects: Amygdala; Biomarkers; biostatistics; magnetic resonance imaging; Medical imaging; Mental health; Neural networks; Neuroimaging; Parahippocampal gyrus; Putamen; Self destructive behavior; self‐harm; Structure-function relationships; Suicidal behavior; Suicide; Suicides & suicide attempts
• ISSN: 0001-690X; 1600-0447
• DOI: 10.1111/acps.13029

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.