Further, PCCE-CNN pipeline gives better average test accuracy (70.31 %) and area under the curve (0.73) compared to other pipelines. We found that top-20 PCCE based FC features are from networks such as Dorsal Attention (DA), Cingulo-Opercular Task Control (COTC), somatosensory motor hand and subcortical. In addition, among top 20 PCCE features, many FC links are found between COTC and DA (4 connections) which helped to discriminate the ASD and TD.
 
  The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
 The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
  The processing of brain signals for Motor imagery (MI) classification to have better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional methods like Artificial neural network (ANN), Linear discernment analysis (LDA), K-Nearest Neighbor (KNN), Support vector machine (SVM), etc. have made significant progress in terms of classification accuracy, deep transfer learning-based systems have shown the potential to outperform them. BCI can play a vital role in enabling communication with the external world for persons with motor disabilities.
 
  Deep learning has been a success in many fields. However, for Electroencephalogram (EEG) signals, relatively minimal work has been carried out using deep learning. This paper proposes a combination of Continuous Wavelet Transform (CWT) along with deep learning-based transfer learning to solve the problem. CWT transforms one dimensional EEG signals into two-dimensional time-frequency-amplitude representation enabling us to exploit available deep networks through transfer learning.
 
  The effectiveness of the proposed approach is evaluated in this study using an openly available BCI competition data-set. The results of the approach have been compared to earlier works on the same dataset, and a promising validation accuracy of 95.71% is achieved in our investigation.
 
  Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.
 Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.It is thought that the hippocampal neurogenesis is an important mediator of the antidepressant effect of electroconvulsive therapy (ECT). However, most previous studies failed to demonstrate the relationship between the increase in the hippocampal volume and the antidepressant effect. We reinvestigated this relationship by looking at distinct hippocampal subregions and applying repeated measures correlation. Using a 3 Tesla MRI-scanner, we scanned 22 severely depressed in-patients at three time points before the ECT series, after the series, and at six-month follow-up. The depression severity was assessed by the 17-item Hamilton Rating Scale for Depression (HAMD-17). The hippocampus was segmented into subregions using Freesurfer software. The dentate gyrus (DG) was the primary region of interest (ROI), due to the role of this region in neurogenesis. The other major hippocampal subregions were the secondary ROIs (n = 20). The general linear mixed model and the repeated measures correlation were used for statistical analyses. Immediately after the ECT series, a significant volume increase was present in the right DG (Cohen's d = 1.7) and the left DG (Cohen's d = 1.5), as well as 15 out of 20 secondary ROIs. The clinical improvement, i.e., the decrease in HAMD-17 score, was correlated to the increase in the right DG volume (rrm = -0.77, df = 20, p less then .001), and the left DG volume (rrm = -0.75, df = 20, p less then .001). Similar correlations were observed in 14 out of 20 secondary ROIs. Thus, ECT induces an increase not only in the volume of the DG, but also in the volume of other major hippocampal subregions. The volumetric increases may reflect a neurobiological process that may be related to the ECT's antidepressant effect. Further investigation of the relationship between hippocampal subregions and the antidepressant effect is warranted. A statistical approach taking the repeated measurements into account should be preferred in the analyses.In December 2019, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) infection was reported. In only few weeks it has caused a global pandemic, with mortality reaching 3.4%, mostly due to a severe pneumonia. However, the impact of SARS-CoV-2 virus on the central nervous system (CNS) and mental health outcomes remains unclear. Previous studies have demonstrated the presence of other types of coronaviruses in the brain, especially in the brainstem. There is evidence that the novel coronavirus can penetrate CNS through the olfactory or circulatory route as well as it can have an indirect impact on the brain by causing cytokine storm. There are also first reports of neurological signs in patients infected by the SARS-Cov-2. They show that COVID-19 patients have neurologic manifestations like acute cerebrovascular disease, conscious disturbance, taste and olfactory disturbances. In addition, there are studies showing that certain psychopathological symptoms might appear in infected patients, including those related to mood and psychotic disorders as well as post-traumatic stress disorder. Accumulating evidence also indicates that the pandemic might have a great impact on mental health from the global perspective, with medical workers being particularly vulnerable. In this article, we provide a review of studies investigating the impact of the SARS-CoV-2 on the CNS and mental health outcomes. We describe neurobiology of the virus, highlighting the relevance to mental disorders. Furthermore, this article summarizes the impact of the SARS-CoV-2 from the public health perspective.  https://www.selleckchem.com/products/vorapaxar.html  Finally, we present a critical appraisal of evidence and indicate future directions for studies in this field.