BackgroundAn overlap of clinical symptoms between major depressive disorder (MDD) and social anxiety disorder (SAD) suggests that the two disorders exhibit similar brain mechanisms. However, few studies have directly compared the brain structures of the two disorders. The aim of this study was to assess the gray matter volume (GMV) and cortical thickness alterations between non-comorbid medication-naive MDD patients and SAD patients.MethodsHigh-resolution T1-weighted images were acquired from 37 non-comorbid MDD patients, 24 non-comorbid SAD patients and 41 healthy controls (HCs). Voxel-based morphometry analysis of the GMV (corrected with a false discovery rate of p < 0.001) and vertex-based analysis of cortical thickness (corrected with a clusterwise probability of p < 0.001) were performed, and group differences were compared by ANOVA followed by post hoc tests.OutcomesRelative to the HCs, both the MDD patients and SAD patients showed the following results: GMV reductions in the bilateral orbital frontal cortex (OFC), putamen, and thalamus; cortical thickening in the bilateral medial prefrontal cortex, posterior dorsolateral prefrontal cortex, insular cortex, left temporal pole, and right superior parietal cortex; and cortical thinning in the left lateral OFC and bilateral rostral middle frontal cortex. In addition, MDD patients specifically showed a greater thickness in the left fusiform gyrus and right lateral occipital cortex and a thinner thickness in the bilateral lingual and left cuneus. SAD patients specifically showed a thinner cortical thickness in the right precentral cortex.InterpretationOur results indicate that MDD and SAD share common patterns of gray matter abnormalities in the orbitofrontal-striatal-thalamic circuit, salience network and dorsal attention network. These consistent structural differences in the two patient groups may contribute to the broad spectrum of emotional, cognitive and behavioral disturbances observed in MDD patients and SAD patients. In addition, we found disorder-specific involvement of the visual processing regions in MDD and the precentral cortex in SAD. These findings provide new evidence regarding the shared and specific neuropathological mechanisms that underlie MDD and SAD.

ObjectiveThe functions of both the central and peripheral autonomic nervous system, indexed by heart rate variability (HRV), are affected by psychology and physiology. This review summarizes the results of studies comparing the evaluation of HRV parameters between individuals with posttraumatic stress disorder (PTSD) and healthy controls. Methods Eligible studies (n=499) were identified through literature searches of the EMBASE, Medline, PubMed and Web of Science databases. Nineteen studies met our inclusion criteria. A random effects model was used, and standardized mean differences for highfrequency HRV(HF-HRV), low-frequency HRV(LF-HRV) and the root mean square of successive R-R interval differences (RMSSD) were calculated. Results Significant effects were found for HF-HRV [p<0.0001, Z=4.18; Hedges'g=-1.58, 95% confidence interval (CI) (-2.32, -0.84); k=14] and RMSSD [p<0.00001, Z=4.80;; k=9] relative to healthy controls. Considerable heterogeneity was revealed, but the main effects for HF-HRV and RMSSD were robust in subsequent meta-regression and subgroup analyses. Conclusion Given the relationships among low vagal state, inflammation, and alterations in brain structure and function, including executive function and emotion regulation, reduced HRV may be regarded as an endophenotype in PTSD research.

BackgroundAmygdala is considered as the core pathogenesis of generalized social anxiety disorder (GSAD). However, it is still unclear whether effective group cognitive behavioral therapy (CBT) could modulate the function of amygdala-related network. We aimed to examine the resting-state functional connectivity (rsFC) of the amygdala before and after group CBT.MethodsFifteen patients with GSAD were scanned on a 3T MR system before and after 8 weeks of group CBT. For comparison, nineteen healthy control participants also underwent baseline fMRI scanning. We used bilateral amygdala as seed regions and the rsFC maps of the right and left amygdala were created separately in a voxel-wise way. Clusters survived two-tailed Gaussian Random Field (GRF) correction at p <0.05 (voxel z value >2.3).ResultsCompared with baseline, patients with CBT showed significantly decreased connectivity of the left amygdala with the right putamen, the left dorsal medial prefrontal cortex (dmPFC) and the right dorsal anterior cingulate cortex (dACC). Especially, the changes of the connectivity between the left amygdala and the dACC positively correlated with changes of the anxiety symptom in patients. Furthermore, in relative to controls, patients showed higher connectivity of left amygdala with dmPFC and dACC at baseline, while normal after CBT.ConclusionsShort-term group CBT could down-regulate the abnormal higher connectivity of prefrontal-amygdala network, along with clinical improvement. This may provide a potential biomarker to monitor the treatment effect of CBT in GSAD patients.

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Recent studies involving connectome analysis including graph theory have yielded potential biomarkers for mental disorders. In this study, we aimed to investigate the differences of resting-state network between patients with social anxiety disorder (SAD) and healthy controls (HCs), as well as to distinguish between individual subjects using topological properties. In total, 42 SAD patients and the same number of HCs underwent resting functional MRI, and the topological organization of the whole-brain functional network was calculated using graph theory. Compared with the controls, the patients showed a decrease in 49 positive connections. In the topological analysis, the patients showed an increase in the area under the curve (AUC) of the global shortest path length of the network (Lp) and a decrease in the AUC of the global clustering coefficient of the network (Cp). Furthermore, the AUCs of Lp and Cp were used to effectively discriminate the individual SAD patients from the HCs with high accuracy. This study revealed that the neural networks of the SAD patients showed changes in topological characteristics, and these changes were prominent not only in both groups but also at the individual level. This study provides a new perspective for the identification of patients with SAD.

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Background: The chronic phase of post-traumatic stress disorder (PTSD) and the limited effectiveness of existing treatments creates the need for the development of potential biomarkers to predict response to antidepressant medication at an early stage. However, findings at present focus on acute therapeutic effect without following-up the long-term clinical outcome of PTSD. So far, studies predicting the long-term clinical outcome of short-term treatment based on both pre-treatment and post-treatment functional MRI in PTSD remains limited.Methods: Twenty-two PTSD patients were scanned using resting-state functional MRI (rs-fMRI) before and after 12 weeks of treatment with paroxetine. Twenty patients were followed up using the same psychopathological assessments 2 years after they underwent the second MRI scan. Based on clinical outcome, the follow-up patients were divided into those with remitted PTSD or persistent PTSD. Amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) derived from pre-treatment and post-treatment rs-fMRI were used as classification features in a support vector machine (SVM) classifier.Results: Prediction of long-term clinical outcome by combined ALFF and DC features derived from pre-treatment rs-fMRI yielded an accuracy rate of 72.5% (p < 0.005). The most informative voxels for outcome prediction were mainly located in the precuneus, superior temporal area, insula, dorsal medial prefrontal cortex, frontal orbital cortex, supplementary motor area, lingual gyrus, and cerebellum. Long-term outcome could not be successfully classified by post-treatment imaging features with accuracy rates <50%.Conclusions: Combined information from ALFF and DC from rs-fMRI data before treatment could predict the long-term clinical outcome of PTSD, which is critical for defining potential biomarkers to customize PTSD treatment and improve the prognosis.