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SUMMARY:Signals from the Brain: A Tale of Dynamics and Networks
DTSTART:20230120T150000
DTEND:20230120T160000
DTSTAMP:20260427T200820Z
UID:b78d3cd0d97f95441b326dc3805151893fc751656a7b74d123794d98
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dimitri Van De Ville\nState-of-the-art neuroimaging such as ma
 gnetic resonance imaging (MRI) provides unprecedented opportunities to non
 -invasively measure human brain structure (anatomy) and function (physiol
 ogy). To fully exploit the rich spatiotemporal structure of these data and
  gain insights into brain function in health and disorder\, novel signal 
 processing and modeling approaches are needed\, instilled by domain knowl
 edge from neuroscience and instrumentation.\nI will highlight our main res
 earch axes to pursue this endeavor. First\, we propose a new framework tha
 t leverages sparsity-pursuing hemodynamic deconvolution of functional MRI
  (fMRI) time series to represent them in terms of transients. Reoccurring 
 spatial configurations of transients then identify a neurologically perti
 nent repertoire of large-scale distributed patterns. Their temporal dynami
 cs reveal the complex interplay of systems-level brain organization\, bot
 h during task and resting-state conditions\, with relevance for applicati
 ons in cognitive and clinical neuroscience. The versatility of our methods
  lets us explore other parts of the central nervous system. In particular
 \, we discovered that spontaneous activity recorded by spinal cord fMRI is
  highly restless\, but can be meaningfully represented by interacting ana
 tomical components. \nSecond\, the emerging framework of graph signal pro
 cessing is tailored to neuroimaging by integrating a brain graph (i.e.\, t
 he structural connectome determined by diffusion-weighted MRI and tractog
 raphy) and graph signals (i.e.\, the spatial activity patterns obtained by
  fMRI). The latter are decomposed onto a graph harmonic basis defined thr
 ough the eigendecomposition of the graph Laplacian. Spectral filtering op
 erations are then designed to separate brain activity into its structurall
 y aligned and liberal parts\, respectively\, which allows quantifying of 
 how strongly function is shaped by the underlying structure. The structure
 -function strength throughout the brain uncovers a behaviorally-relevant 
 spatial gradient from uni- to transmodal regions\, which is also informati
 ve about task conditions or identifying individuals.\nFinally\, I will in
 dicate how advances in instrumentation\, for instance\, ultrafast function
 al ultrasound\, will allow us to surpass current temporal and spatial lim
 itations of fMRI and thus advance analysis and modeling techniques\, furth
 er contributing to our understanding of the brain at work. 
LOCATION:BM 5202 https://plan.epfl.ch//?room==BM%205202 https://epfl.zoom.
 us/j/67988366127?pwd=K21VVkNSNlBxTURRa2NWNkRGcnhMQT09
STATUS:CONFIRMED
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