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Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainNet-GA CNN and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainNet-GA CNN showed an accuracy of 83.13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also significant in the functional connectivity analysis. Our findings suggest that the proposed BrainNet-GA CNN can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information. These results highlight the potential use of the BrainNet-GA CNN in the diagnosis of schizophrenia.

Background and Hypothesis The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). Study Design We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. Study Results The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. Conclusions Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.

There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.

Abstract Background Brain age is a popular brain-based biomarker that offers a powerful strategy for using neuroscience in clinical practice. We investigated the brain-predicted age difference (PAD) in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging data. The association between brain-PAD and clinical parameters was also assessed. Methods We developed brain age prediction models for the association between 77 average structural brain measures and age in a training sample of controls (HCs) using ridge regression, support vector regression, and relevance vector regression. The trained models in the controls were applied to the test samples of the controls and 3 patient groups to obtain brain-based age estimates. The correlations were tested between the brain PAD and clinical measures in the patient groups. Results Model performance indicated that, regardless of the type of regression metric, the best model was support vector regression and the worst model was relevance vector regression for the training HCs. Accelerated brain aging was identified in patients with SCZ, FE-SSDs, and TRS compared with the HCs. A significant difference in brain PAD was observed between FE-SSDs and TRS using the ridge regression algorithm. Symptom severity, the Social and Occupational Functioning Assessment Scale, chlorpromazine equivalents, and cognitive function were correlated with the brain PAD in the patient groups. Conclusions These findings suggest additional progressive neuronal changes in the brain after SCZ onset. Therefore, pharmacological or psychosocial interventions targeting brain health should be developed and provided during the early course of SCZ.