Systematic Review and Meta-analysis: Rates of Violence During First-Episode Psychosis (FEP).

BACKGROUND: Most people with psychotic disorders will never commit an act of violence. However, the risk of violence committed by people with schizophrenia is higher than the general population. Violence risk is also known to be highest during the first episode of psychosis compared to later stages of illness. Despite this, there have been no comprehensive reviews conducted in the past 10 years examining rates of violence during FEP. We aimed to provide an updated review of the rate of violence in people with FEP. STUDY DESIGN: Meta-analytical techniques were used to identify pooled proportions of violence according to severity (less serious, serious, severe) and timing of violence (before presentation, at first presentation, after presentation to services). STUDY RESULTS: Twenty-two studies were included. The pooled prevalence was 13.4% (95% CI [9.0%-19.5%]) for any violence, 16.3% (95% CI [9.1%-27.4%]) for less serious violence, 9.7% (95% CI [5.4%-17.0%]) for serious violence and 2.7% for severe violence, regardless of time point. The pooled prevalence of any violence was 11.6% (95% CI [6.8%-18.9%]) before presentation, 20.8% (95% CI [9.8%-38.7%]) at first presentation and 13.3% (95% CI [7.3%-23.0%]) after presentation to services. CONCLUSION: Overall, rates of violence appear to be lower in more recent years. However, due to the high between-study heterogeneity related to study design, the findings must be interpreted with consideration of sample characteristics and other contextual factors. The prevalence of violence remained high at all-time points, suggesting that more targeted, holistic, and early interventions are needed for clinical FEP groups.

Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability.

BACKGROUND: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS: Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). CONCLUSIONS: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.