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SUMMARY:Neuro-X Seminar: Connectivity informed interpretation of brain act
 ivity with graph signal processing and spectral residual networks
DTSTART:20231009T110000
DTEND:20231009T120000
DTSTAMP:20260408T134453Z
UID:64a17c7aac6cf148954ffd3713719d630acd757ce318efcbd2d6458f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof Nicolas Farrugia\nNetwork neuroscience is the application
  of graph theory to model the complex structure and function of the brain.
  While network neuroscience enables many new insights on brain organisatio
 n\, still relatively few methods exploit brain connectivity in the analysi
 s of brain activity. Recent attempts in this direction have leveraged on t
 he one hand graph spectral analysis (to de compose brain connectivity into
  eigenmodes or gradients) and graph signal processing (to decompose brain 
 activity “coupled to” an underlying network in graph Fourier modes). I
 n this talk\, I will describe two ongoing works that attempt at integratin
 g knowledge from brain connectivity in order to decode and interpret brain
  activity. In the first contribution\, we use functional connectivity grap
 hs to define spectral convolution operators in a deep residual network tra
 ined on task decoding. We show how paramete r pruning can be used to selec
 t the most important connectivity gradients for the task. In the second st
 udy\, we analyze brain activity measured using high density EEG\, and perf
 orm an analysis using graph signal processing to estimate coupling and dec
 ouplin g of source localized electrophysiological activity on a structural
  connectivity graph. We discuss the similarity between structure function 
 coupling during resting state and video watching at the individual level. 
 The overarching goal of this line of work is to explore whether connectivi
 ty informed analysis of brain activity can contribute to a better understa
 nding of brain complexity as multimodal signals over networks.
LOCATION:H8-1-D https://plan.epfl.ch/?room==H8%201%20144.167 https://epfl.
 zoom.us/j/64162425067?pwd=V2xVNk1pZDc2MzZXSDJyVEFIUkYvdz09
STATUS:CONFIRMED
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