Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this talk, I will discuss a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We further learned the joint eigen spectra of both the connectomes in order to predict the functional connectome of a subject from the structural counterpart. In the second part of the presentation, I will discuss a deep learning framework for classification of tinnitus disease, which is an auditory phantom perceptual disorder. Motivated by the evidence of subtle anatomical or functional morphological information in magnetic resonance images (MRI) of the brain, we developed a data-driven framework for tinnitus classification (tinnitus or healthy subject) and prediction of tinnitus severity. The proposed classification method could be used for early detection, monitoring clinical trials, and tracking the progression of the disease. Speaker(s): Dr. Sanjay Ghosh Room: N208, Bldg: Department of Electrical Engineering, Seminar Room, IIT Kharagpur, Kharagpur, West Bengal, India, 721302, Virtual: https://events.vtools.ieee.org/m/366638