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SUMMARY:Computational Neuroimaging to Understand Dynamics of Human Brain A
 ctivity
DTSTART:20181009T161500
DTSTAMP:20260407T163545Z
UID:f7912757661755037c0e0ee1d45cdfc5ef3fc6096598fd7c3ab81669
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Dimitri Van De Ville\nAbstract: Over the past decade\, a
 pproaches from signal processing\, machine learning\, and network science\
 , have had a profound impact on the analysis and the interpretation of bra
 in activity measured by functional magnetic resonance imaging (fMRI). Func
 tional connectivity studies have given not only insights into how the brai
 n supports coordinated cognition\, learning\, or stability in a changing e
 nvironment\, but also to what extent networks are altered in neurological 
 disease and disorder. Recently\, the quest for better understanding of bra
 in dynamics has triggered new approaches to functional connectivity. In th
 is talk\, I will highlight two of our promising recent advances for fMRI a
 nalysis. First\, a new sparsity-driven temporal deconvolution scheme (« t
 otal activation ») allows to study interactions between brain regions in 
 terms of synchronized transient activity. This approach reveals a rich rep
 ertoire of functional networks and their temporal features (e.g.\, occurre
 nce\, duration\, overlap) that can be fitted with systems-level temporal m
 odels (i.e.\, sparsely-coupled hidden Markov models)\, thus unraveling int
 erdigitated and parallel organization. Second\, graph signal processing ha
 s provided a new framework to analyze activity traces on top of a graph mo
 del—in this case\, the brain’s structural backbone—and derive new re
 levant operations\, for instance\, filtering these signals according to be
 ing aligned versus liberal w.r.t. structure. Ultimately\, these developmen
 ts will contribute to build better\, more mechanistic\, models of brain fu
 nction and will find application to disease diagnosis and prognosis.
LOCATION:MA A3 30 https://plan.epfl.ch/?room=MAA330
STATUS:CONFIRMED
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