BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Mexico/General BEGIN:DAYLIGHT DTSTART:20210404T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211031T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210426T180609Z UID:4AF44995-4297-4EA2-9815-9CCD2F96E252 DTSTART;TZID=Mexico/General:20210426T090000 DTEND;TZID=Mexico/General:20210426T101500 DESCRIPTION:Abstract:\n\nState-of-the-art magnetic resonance imaging (MRI) provides unprecedented opportunities to study brain structure (anatomy) an d function (physiology). Based on such data\, graph representations can be built where nodes are associated to brain regions and edge weights to str engths of structural or functional connections. In particular\, structural graphs capture major neural pathways in white matter\, while functional g raphs map out statistical interdependencies between pairs of regional acti vity traces. Network analysis of these graphs has revealed emergent system -level properties of brain structure or function\, such as efficiency of c ommunication and modular organization.\n\nIn this talk\, graph signal proc essing (GSP) will be presented as a novel framework to integrate brain str ucture\, contained in the structural graph\, with brain function\, charact erized by activity traces that can be considered as time-dependent graph s ignals. Such a perspective allows to define novel meaningful graph-filteri ng operations of brain activity that take into account smoothness of signa ls on the anatomical backbone. This allows to define a new measure of “c oupling” between structure and function based on how activity is express ed on structural graph harmonics. To provide statistical inference\, we al so extend the well-known Fourier phase randomization method to generate su rrogate data to the graph setting. This new measure reveals a behaviorally relevant spatial gradient\, where sensory regions tend to be more coupled with structure\, and high-level cognitive ones less so. In addition\, we also make a case to introduce the graph modularity matrix at the core of G SP\, in order to incorporate knowledge about graph community structure whe n processing signals on the graph\, but without the need for community det ection. Finally\, recent work will highlight how the spatial resolution of this type of analyses can be increased to the voxel level\, representing a few hundredth thousands of nodes.\n\nSpeaker(s): Ph.D. Dimitri Van De Vi lle\, \n\nGuadalajara\, Jalisco\, Mexico\, Virtual: https://events.vtools. ieee.org/m/265252 LOCATION:Guadalajara\, Jalisco\, Mexico\, Virtual: https://events.vtools.ie ee.org/m/265252 ORGANIZER:r.calderonr@ieee.org SEQUENCE:8 SUMMARY:Graph Signal Processing for Computational Neuroimaging\, by Dr. Dim itri Van De Ville URL;VALUE=URI:https://events.vtools.ieee.org/m/265252 X-ALT-DESC:Description: <br /><p class="p1"><strong>Abstract:</strong></p>\ n<p class="p3">State-of-the-art magnetic resonance imaging (MRI) provides unprecedented opportunities to study brain structure (anatomy) and functio n (physiology). Based on&nbsp\;such data\, graph representations can be bu ilt where nodes are associated to brain regions and edge weights to streng ths of structural or functional connections. In&nbsp\;particular\, structu ral graphs capture major neural pathways in white matter\, while functiona l graphs map out statistical interdependencies between pairs of regional&n bsp\;activity traces. Network analysis of these graphs has revealed emerge nt system-level properties of brain structure or function\, such as effici ency of communication&nbsp\;and modular organization.</p>\n<p class="p4">& nbsp\;</p>\n<p class="p3">In this talk\, graph signal processing (GSP) wil l be presented as a novel framework to integrate brain structure\, contain ed in the structural graph\, with brain&nbsp\;function\, characterized by activity traces that can be considered as time-dependent graph signals. Su ch a perspective allows to define novel meaningful graph-filtering operati ons of brain activity that take into account smoothness of signals on the anatomical backbone. This allows to define a new measure of &ldquo\;coupli ng&rdquo\;&nbsp\;between structure and function based on how activity is e xpressed on structural graph harmonics. To provide statistical inference\, we also extend the well-known&nbsp\;Fourier phase randomization method to generate surrogate data to the graph setting. This new measure reveals a behaviorally relevant spatial gradient\, where&nbsp\;sensory regions tend to be more coupled with structure\, and high-level cognitive ones less so. In addition\, we also make a case to introduce the graph modularity&nbsp\ ;matrix at the core of GSP\, in order to incorporate knowledge about graph community structure when processing signals on the graph\, but without th e need for&nbsp\;community detection. Finally\, recent work will highlight how the spatial resolution of this type of analyses can be increased to t he voxel level\, representing a few&nbsp\;hundredth thousands of nodes.</p > END:VEVENT END:VCALENDAR