[Hippocampal Development Deviation and Its Relationship With Cognition in Major Psychiatric Disorders].

Objective: To investigate hippocampal development deviation and its association with cognition in patients with major psychiatric disorders (MPDs), including schizophrenia, bipolar disorder and major depressive disorder. Methods: The T1-weighted MRI data of 174 first-episode drug-naïve schizophrenia (FES) atients, 169 bipolar disorder (BD) patients, 184 major depressive disorder (MDD) patients, and 321 healthy controls were collected and their hippocampal volume was extracted after preprocessing with Freesurfer 5.3. A normative neurodevelopment model was applied to calculate the hippocampal deviation scores. Data on cognitive functions, including visual memory, attention, spatial working memory, were collected. Comparison by different sexes was made to identify difference between the hippocampal development deviation scores of the control group and those of the disease groups. We also investigated the moderating effect of age on the deviation score and explored the association between the deviation score and cognitive function. Results: The hippocampal development deviation scores of patients with MPDs were significantly lower than those of the healthy controls (false discovery rate [FDR]-P<0.05). Analysis of the moderating effect of age revealed lower deviation scores in young patients (<[25.83-28.56] yr.) and higher deviation scores in old patients (>[35.87-54.35] yr.) in comparison with those of the healthy controls. The right hippocampal deviation scores in male FES patients were positively correlated with the number of errors for tasks concerning spatial working memory ( r=0.32, FDR-P=0.04). Conclusion: Our findings suggest abnormal hippocampal development in MPDs patients and its different distribution in MPDs patients of different age groups. The hippocampal development deviation score may provide a new perspective for further understanding of etiology in MPDs.

Multivariate classification based on large-scale brain networks during early abstinence predicted lapse among male detoxified alcohol-dependent patients.

Identifying biomarkers to predict lapse of alcohol-dependence (AD) is essential for treatment and prevention strategies, but remains remarkably challenging. With an aim to identify neuroimaging features for predicting AD lapse, 66 male AD patients during early-abstinence (baseline) after hospitalized detoxification underwent resting-state functional magnetic resonance imaging and were then followed-up for 6 months. The relevance-vector-machine (RVM) analysis on baseline large-scale brain networks yielded an elegant model for differentiating relapsing patients (n = 38) from abstainers, with the area under the curve of 0.912 and the accuracy by leave-one-out cross-validation of 0.833. This model captured key information about neuro-connectome biomarkers for predicting AD lapse.