Jonghee Hong, Minji Bang. Clin Psychopharmacol Neurosci 2020;18:10-24. https://doi.org/10.9758/cpn.2020.18.1.10

WonKyung Cho, Won-Suk Shin, Iseul An, Minji Bang, Doo-Yeoun Cho, Sang-Hyuk Lee. Clin Psychopharmacol Neurosci 2019;17:475-86. https://doi.org/10.9758/cpn.2019.17.4.475

Schizophrenia is a complex brain disorder of unknown etiology. Based on the notion of “cognitive dysmetria,” we aimed to investigate aberrations in structural white matter (WM) connectivity that links the cerebellum to cognitive dysfunction in patients with schizophrenia. A total of 112 participants (65 patients with schizophrenia and 47 healthy controls [HCs]) were enrolled and underwent diffusion tensor imaging. Between-group voxel-wise comparisons of cerebellar WM regions (superior/middle [MCP]/inferior cerebellar peduncle and pontine crossing fibers) were performed using Tract-Based Spatial Statistics. Cognitive function was assessed using the Trail Making Test Part A/B (TMT-A/B), Wisconsin Card Sorting Test (WCST), and Rey-Kim Memory Test in 46 participants with schizophrenia. WM connectivity, measured as fractional anisotropy (FA), was significantly lower in the MCP in participants with schizophrenia than in HCs. The mean FAs extracted from the significant MCP cluster were inversely correlated with poorer cognitive performance, particularly longer time to complete the TMB-B (r = 0.559, p < 0.001) and more total errors in the WCST (r = 0.442, p = 0.003). Our findings suggest that aberrant cerebro-cerebellar communication due to disrupted WM connectivity may contribute to cognitive impairments, a core characteristic of schizophrenia. Our results may expand our understanding of the neurobiology of schizophrenia based on the cerebro-cerebellar interconnectivity of the brain.

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81–0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.

Brain inflammation is one of hypotheses explaining complex pathomechanisms of depression. Angiotensin II (ANGII), which is associated with hypertension, also induces brain inflammation. However, there is no animal study showing the direct relationship between ANGII and depression. To address this issue, ANGII-containing osmotic pumps were implanted into adult male C57BL/6 mice subcutaneously for subacute (7 days) and chronic (at least 21 days) periods and behavioral and molecular analyses were conducted. Chronic infusion of ANGII into mice induced depressive-like behaviors, including the tail suspension test and forced swimming test, which were reversed by imipramine. Chronic infusion of ANGII also induced microglial activation in the hippocampus with increase of Il-1β mRNA and decrease of Arg1 mRNA. In addition, chronic ANGII infusion activated the hypothalamic–pituitary–adrenal axis (HPA axis) and resulted in decreased hippocampal glucocorticoid receptor level. However, subacute ANGII infusion did not induce significant molecular and behavioral changes in mice compared to that of control. The molecular and behavioral changes by chronic ANGII infusion were reversed by co-treatment of minocycline or telmisartan. In addition, ANGII treatment also induced the pro-inflammatory changes in BV-2 microglial cells. Our results indicate that ANGII can induce depressive-like behaviors via microglial activation in the hippocampus and HPA axis hyperactivation in mice. These might suggest possible mechanism on depressive symptom in chronic hypertensive state.