Graph Attention Based Spatial Temporal Network for EEG Signal Representation

Graph attention networks (GATs) based architectures have proved to be powerful at implicitly learning relationships between adjacent nodes in a graph. For electroencephalogram (EEG) signals, however, it is also essential to highlight electrode locations or underlying brain regions which are active when a particular event related potential (ERP) is evoked. Moreover, it is often im-portant to identify corresponding EEG signal time segments within which the ERP is activated. We introduce a GAT Inspired Spatial Temporal (GIST) net-work that uses multilayer GAT as its base for three attention blocks: edge atten-tions, followed by node attention and temporal attention layers, which focus on relevant brain regions and time windows for better EEG signal classification performance, and interpretability. We assess the capability of the architecture by using publicly available Transcranial Electrical Stimulation (TES), neonatal pain (NP) and DREAMER EEG datasets. With these datasets, the model achieves competitive performance. Most importantly, the paper presents atten-tion visualisation and suggests ways of interpreting them for EEG signal under-standing

CD28null pro-atherogenic CD4 T-cells explain the link between CMV infection and an increased risk of Cardiovascular death

An increased risk of cardiovascular death in Cytomegalovirus (CMV)-infected individuals remains unexplained, although it might partly result from the fact that CMV infection is closely associated with the accumulation of CD28null T-cells, in particular CD28null CD4 T-cells. These cells can directly damage endothelium and precipitate cardiovascular events. However, the current paradigm holds that the accumulation of CD28null T-cells is a normal consequence of aging, whereas the link between these T-cell populations and CMV infection is explained by the increased prevalence of this infection in older people. Resolving whether CMV infection or aging triggers CD28null T-cell expansions is of critical importance because, unlike aging, CMV infection can be treated. Methods: We used multi-color flow-cytometry, antigen-specific activation assays, and HLA-typing to dissect the contributions of CMV infection and aging to the accumulation of CD28null CD4 and CD8 T-cells in CMV+ and CMV− individuals aged 19 to 94 years. Linear/logistic regression was used to test the effect of sex, age, CMV infection, and HLA-type on CD28null T-cell frequencies. Results: The median frequencies of CD28null CD4 T-cells and CD28null CD8 T-cells were >12-fold (p=0.000) but only approximately 2-fold higher (p=0.000), respectively, in CMV+ (n=136) compared with CMV− individuals (n=106). The effect of CMV infection on these T-cell subsets was confirmed by linear regression. Unexpectedly, aging contributed only marginally to an increase in CD28null T-cell frequencies, and only in CMV+ individuals. Interestingly, the presence of HLA-DRB1*0301 led to an approximately 9-fold reduction of the risk of having CD28null CD4 T-cell expansions (OR=0.108, p=0.003). Over 75% of CMV-reactive CD4 T-cells were CD28null. Conclusion: CMV infection and HLA type are major risk factors for CD28null CD4 T-cell-associated cardiovascular pathology. Increased numbers of CD28null CD8 T-cells are also associated with CMV infection, but to a lesser extent. Aging, however, makes only a negligible contribution to the expansion of these T-cell subsets, and only in the presence of CMV infection. Our results open up new avenues for risk assessment, prevention, and treatment