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SUMMARY:Robust brain functional connectivity network estimation - Dr. Han
 â Lbath\, Université Grenoble Alpes
DTSTART:20231207T150000Z
DTEND:20231207T160000Z
UID:TALK207859@talks.cam.ac.uk
CONTACT:Sofia Orellana
DESCRIPTION:Functional connectivity estimation between brain regions is a 
 challenging problem. Indeed\, both noise and the possible heterogeneity of
  the brain regions lead to either under- or overestimation of the inter-re
 gional correlation. While some existing methods handle either of these iss
 ues\, none tackle both at the same time. Popular methods include correlati
 ng regional averages\, which is not robust to low average intra-regional c
 orrelation\, and averaging pairwise inter-regional correlations\, which is
  not robust to high noise. Here I will discuss our novel clustering-based 
 non-parametric estimator of the inter-regional correlation\, which simulta
 neously offsets the impact of noise and arbitrary intra-regional correlati
 on through an intermediate aggregation. I will also describe both empirica
 l validation on synthetic data and illustrations on real-world rat and hum
 an rs-fMRI datasets that further demonstrate its effectiveness. By design\
 , our method provides as well an empirical distribution of the inter-corre
 lation. I will then present an ongoing work introducing the concept of dis
 tribution-weighted connectivity networks and applying it on real-world dat
 a.
LOCATION:Online
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