Comprehensive elucidation of resting-state functional connectivity in anorexia nervosa by a multicenter cross-sectional study.

BACKGROUND: Previous research on the changes in resting-state functional connectivity (rsFC) in anorexia nervosa (AN) has been limited by an insufficient sample size, which reduced the reliability of the results and made it difficult to set the whole brain as regions of interest (ROIs). METHODS: We analyzed functional magnetic resonance imaging data from 114 female AN patients and 135 healthy controls (HC) and obtained self-reported psychological scales, including eating disorder examination questionnaire 6.0. One hundred sixty-four cortical, subcortical, cerebellar, and network parcellation regions were considered as ROIs. We calculated the ROI-to-ROI rsFCs and performed group comparisons. RESULTS: Compared to HC, AN patients showed 12 stronger rsFCs mainly in regions containing dorsolateral prefrontal cortex (DLPFC), and 33 weaker rsFCs primarily in regions containing cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between anterior cingulate cortex (ACC) and thalamus (p < 0.01, false discovery rate [FDR] correction). Comparisons between AN subtypes showed that there were stronger rsFCs between right lingual gyrus and right supracalcarine cortex and between left temporal occipital fusiform cortex and medial part of visual network in the restricting type compared to the binge/purging type (p < 0.01, FDR correction). CONCLUSION: Stronger rsFCs in regions containing mainly DLPFC, and weaker rsFCs in regions containing primarily cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between ACC and thalamus, may represent categorical diagnostic markers discriminating AN patients from HC.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Individual cognitive therapy reduces frontal-thalamic resting-state functional connectivity in social anxiety disorder.

Introduction: Previous neuroimaging studies in social anxiety disorders (SAD) have reported potential neural predictors of cognitive behavioral therapy (CBT)-related brain changes. However, several meta-analyses have demonstrated that cognitive therapy (CT) was superior to traditional exposure-based CBT for SAD. Objective: To explore resting-state functional connectivity (rsFC) to evaluate the response to individual CT for SAD patients. Methods: Twenty SAD patients who attended 16-week individual CT were scanned pre- and post-therapy along with twenty healthy controls (HCs). The severity of social anxiety was assessed with the Liebowitz Social Anxiety Scale (LSAS). Multi-voxel pattern analysis (MVPA) was performed on the pre-CT data to extract regions associated with a change in LSAS (∆LSAS). Group comparisons of the seed-based rsFC analysis were performed between the HCs and pre-CT patients and between the pre-and post-CT patients. Results: MVPA-based regression analysis revealed that rsFC between the left thalamus and the frontal pole/inferior frontal gyrus was significantly correlated with ∆LSAS (adjusted R2 = 0.65; p = 0.00002). Compared with HCs, the pre-CT patients had higher rsFCs between the thalamus and temporal pole and between the thalamus and superior/middle temporal gyrus/planum temporale (p < 0.05). The rsFC between the thalamus and the frontal pole decreased post-CT (p < 0.05). Conclusion: SAD patients had significant rsFC between the thalamus and temporal pole, superior/middle temporal gyrus, and planum temporale, which may be indicators of extreme anxiety in social situations. In addition, rsFC between the thalamus and the frontal pole may be a neuromarker for the effectiveness of individual CT.