BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230721T064057Z UID:C1025184-69A5-4B69-B44C-2F1EBB85C4F9 DTSTART;TZID=Asia/Kolkata:20230713T173000 DTEND;TZID=Asia/Kolkata:20230713T190000 DESCRIPTION:Understanding the connection between the brain’s structural c onnectivity and its functional connectivity is of immense interest in comp utational neuroscience. Some studies have suggested that whole brain funct ional connectivity is shaped by the underlying structure\, the rule by whi ch anatomy constraints brain dynamics remains an open question. In this ta lk\, I will discuss a computational framework that identifies a joint subs pace of eigenmodes for both functional and structural connectomes. We furt her learned the joint eigen spectra of both the connectomes in order to pr edict the functional connectome of a subject from the structural counterpa rt. In the second part of the presentation\, I will discuss a deep learnin g framework for classification of tinnitus disease\, which is an auditory phantom perceptual disorder. Motivated by the evidence of subtle anatomica l or functional morphological information in magnetic resonance images (MR I) of the brain\, we developed a data-driven framework for tinnitus classi fication (tinnitus or healthy subject) and prediction of tinnitus severity . The proposed classification method could be used for early detection\, m onitoring clinical trials\, and tracking the progression of the disease.\n \nSpeaker(s): Dr. Sanjay Ghosh\n\nRoom: N208\, Bldg: Department of Electri cal Engineering\, Seminar Room\, IIT Kharagpur\, Kharagpur\, West Bengal\, India\, 721302\, Virtual: https://events.vtools.ieee.org/m/366638 LOCATION:Room: N208\, Bldg: Department of Electrical Engineering\, Seminar Room\, IIT Kharagpur\, Kharagpur\, West Bengal\, India\, 721302\, Virtual: https://events.vtools.ieee.org/m/366638 ORGANIZER:ieee.sps.sb.iitkgp@gmail.com SEQUENCE:27 SUMMARY:Learning Based Methods for Brain Structure-Function Mapping and Dis order Classification URL;VALUE=URI:https://events.vtools.ieee.org/m/366638 X-ALT-DESC:Description: <br /><p><span style="font-weight: 400\;">Understan ding the connection between the brain&rsquo\;s structural connectivity and its functional connectivity is of immense interest in computational neuro science. Some studies have suggested that whole brain functional connectiv ity is shaped by the underlying structure\, the rule by which anatomy cons traints brain dynamics remains an open question. In this talk\, I will dis cuss a computational framework that identifies a joint subspace of eigenmo des for both functional and structural connectomes. We further learned the joint eigen spectra of both the connectomes in order to predict the funct ional connectome of a subject from the structural counterpart. In the seco nd part of the presentation\, I will discuss a deep learning framework for classification of tinnitus disease\, which is an auditory phantom percept ual 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 (tinni tus or healthy subject) and prediction of tinnitus severity. The proposed classification method could be used for early detection\, monitoring clini cal trials\, and tracking the progression of the disease.</span></p> END:VEVENT END:VCALENDAR