Automatic Detection of Conversion Blindness on Functional Brain Network Information Automatic detection of conversion blindness is significant for improving clinical management, and it is also pivotal for sentencing in judicial disputes. Due to the weak signal properties and complex interaction of brain functional regions, it is a challenging task to diagnose conversion blindness. This paper proposed a method which utilize graph theory to capture subtle brain network information, and complement with local coarse characteristics selected by algorithm of minimum redundancy maximum relevance. The performance of our proposed method was evaluated with an automatic detection model using least square support vector machines as classifier. The results showed that the detection model yielded high accuracy (total accuracy of 95.00%, sensitivity of 96.67%, and specificity of 93.33%) in identifying brain network of conversion blindness from controls. Experimental results demonstrated the potential of the proposed approach to discriminate brain network of conversion blindness with bilateral visual loss.

GSP Analysis of Brain Imaging Data from Athletes with History of Multiple Concussions Study of neurological disorders affecting the structurefunction relationships in the brain has been an ongoing challenge in neuroscience. Joint analysis of structure and function of the brain may disentangle a number of mechanisms and operations that can help interpret the interdependence between white matter degeneration and degradation of cognitive abilities. In this scenario, graph signal processing analysis of different signals generated within the physical structure of the brain may provide new insights and corroborate existing clinical findings. This paper illustrates the utility of graph signal processing tools in the joint analysis of diffusion and functional magnetic resonance imaging (i.e. dMRI and fMRI) data collected from a population of former athletes with a history of multiple concussions, and healthy subjects. Specifically, the distributions of the energy of lowgraphfrequency components of the functional networks (derived from fMRI) are observed to be significantly different for frontotemporal regions of the brain in athletes and healthy subjects. Furthermore, for the two groups of subjects, we observe significantly different associations between the ages of subjects and the energies of high graph frequency components in lingual region. While the effect on frontotemporal regions for former athletes is in line with the existing clinical studies on concussion, significantly different associations between age and features extracted using GSP for the two groups of subjects could inform future clinical applications and medical diagnosis.