RESUMO
Background: Brain ventricles have been reported to be enlarged in several neuropsychiatric disorders and in aging. Whether human cerebral ventricular volume can decrease over time with psychiatric treatment is not well-studied. The aim of this study was to examine whether inpatients taking serotonin reuptake inhibitors (SRI) exhibited reductions in cerebral ventricular volume. Methods: Psychiatric inpatients, diagnosed mainly with depression, substance use, anxiety, and personality disorders, underwent two imaging sessions (Time 1 and Time 2, approximately 4 weeks apart). FreeSurfer was used to quantify volumetric features of the brain, and ANOVA was used to analyze ventricular volume differences between Time 1 and Time 2. Inpatients' brain ventricle volumes were normalized by dividing by estimated total intracranial volume (eTIV). Clinical features such as depression and anxiety levels were collected at Time 1, Time 1.5 (approximately 2 weeks apart), and Time 2. Results: Inpatients consistently taking SRIs (SRI + , n = 44) showed statistically significant reductions of brain ventricular volumes particularly for their left and right lateral ventricular volumes. Reductions in their third ventricular volume were close to significance (p = .068). The inpatients that did not take SRIs (SRI-, n = 25) showed no statistically significant changes in brain ventricular volumes. The SRI + group also exhibited similar brain structural features to the healthy control group based on the 90% confidence interval comparsions on brain ventricular volume parameters, whereas the SRI- group still exhibited relatively enlarged brain ventricular volumes after treatment. Conclusions: SRI treatment was associated with decreased brain ventricle volume over treatment.
RESUMO
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.
