Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis.

Context: Brain-derived neurotrophic factor (BDNF) modulates hippocampal plasticity, which is believed to be altered in patients with major depression.Objective: To examine the effect of the BDNF Val66Met polymorphism on hippocampal and amygdala volumes in patients with major depression and in healthy control subjects.Design: Cross-sectional comparison between patients and controls.Setting: Inpatients with major depression from the Department of Psychiatry and Psychotherapy and healthy controls from the community were recruited. Participants:The study population of 120 subjects included 60 patients with major depression and 60 healthy controls. Main Outcome Measures:Using a combined strategy, hippocampal and amygdala volumes were estimated on high-resolution magnetic resonance images, and genotyping was performed for the BDNF Val66Met polymorphism.Results: Patients had significantly smaller hippocampal volumes compared with controls (P = .02). Significantly smaller hippocampal volumes were observed for patients and for controls carrying the Met-BDNF allele compared with subjects homozygous for the Val-BDNF allele (P=.006). With respect to amygdala volumes, no significant differences between patients and controls and no significant main effects for the BDNF Val66Met polymorphism were observed.Conclusions: These genotype-related alterations suggest that Met-BDNF allele carriers might be at risk to develop smaller hippocampal volumes and may be susceptible to major depression. This study supports findings from animal studies that the hippocampus is involved in brain development and plasticity. Psychiatry. 2007;64:410-416 Arch Gen

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