Search

Magnetization transfer imaging (MTI) may provide more sensitivity and mechanistic understanding of neuropathological changes associated with schizophrenia than volumetric MRI. This study aims to identify brain magnetization transfer ratio (MTR) changes in antipsychotic-naïve first-episode schizophrenia (FES), and to correlate MTR findings with clinical symptom severity. A total of 143 individuals with antipsychotic-naïve FES and 147 healthy controls (HCs) were included and underwent 3.0 T brain MTI between August 2005 and July 2014. Voxelwise analysis was performed to test for MTR differences with family-wise error corrections. Relationships of these differences to symptom severity were assessed using partial correlations. Exploratory analyses using a support vector machine (SVM) classifier were conducted to discriminate FES from HCs using MTR maps. Model performance was examined using a 10-fold stratified cross-validation. Compared with HCs, individuals with FES exhibited higher MTR values in left thalamus, precuneus, cuneus, and paracentral lobule, that were positively correlated with schizophrenia symptom severity [precuneus (r = 0.34, P = 0.0004), cuneus (r = 0.33, P = 0.0006) and paracentral lobule (r = 0.37, P = 0.001)]. Whole-brain MTR maps identified individuals with FES with overall accuracy 75.5% (219 of 290 individuals) based on SVM approach. In antipsychotic-naïve FES, clinically relevant biophysical abnormalities detected by MTI mainly in the left parieto-occipital regions are informative about local brain pathology, and have potential as diagnostic markers., (© 2022. The Author(s).)

Although functional and structural abnormalities in brain regions involved in the neurobiology of fear and anxiety have been observed in patients with social anxiety disorder (SAD), the findings have been heterogeneous due to small sample sizes, demographic confounders, and methodological differences. Besides, multimodal neuroimaging studies on structural-functional deficits and couplings are rather scarce. Herein, we aimed to explore functional network anomalies in brain regions with structural deficits and the effects of structure-function couplings on the SAD diagnosis. High-resolution structural magnetic resonance imaging (MRI) and resting-state functional MRI images were obtained from 49 non-comorbid patients with SAD and 53 demography-matched healthy controls. Whole-brain voxel-based morphometry analysis was conducted to investigate structural alterations, which were subsequently used as seeds for the resting-state functional connectivity analysis. In addition, correlation and mediation analyses were performed to probe the potential roles of structural-functional deficits in SAD diagnosis. SAD patients had significant gray matter volume reductions in the bilateral putamen, right thalamus, and left parahippocampus. Besides, patients with SAD demonstrated widespread resting-state dysconnectivity in cortico-striato-thalamo-cerebellar circuitry. Moreover, dysconnectivity of the putamen with the cerebellum and the right thalamus with the middle temporal gyrus/supplementary motor area partially mediated the effects of putamen/thalamus atrophy on the SAD diagnosis. Our findings provide preliminary evidence for the involvement of structural and functional deficits in cortico-striato-thalamo-cerebellar circuitry in SAD, and may contribute to clarifying the underlying mechanisms of structure-function couplings for SAD. Therefore, they could offer insights into the neurobiological substrates of SAD., (© 2022. The Author(s).)

Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.