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CATEGORIES:Brain Mapping Unit Networks Meeting and the Cambri
 dge Connectome Consortium
SUMMARY:Mapping overlapping\, dynamic brain networks from 
 resting-state FMRI - Professor Stephen Smith\, Oxf
 ord Centre for Functional MRI of the Brain (FMRIB)
 \, University of Oxford
DTSTART;TZID=Europe/London:20120703T110000
DTEND;TZID=Europe/London:20120703T120000
UID:TALK38616AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/38616
DESCRIPTION:Resting-state functional magnetic resonance imagin
 g has become a powerful tool for the study of func
 tional networks in the brain. Even “at rest\,” the
  brain's different functional networks spontaneous
 ly fluctuate in their activity level\; each networ
 k's spatial extent can therefore be mapped by find
 ing temporal correlations between its different su
 bregions. The talk will start with a brief backgro
 und on the use of resting-state fluctuations to ma
 p functional networks\,\nincluding our previous wo
 rk showing correspondence between resting-state ne
 tworks and networks found from explicit task-activ
 ation studies. I will then go on to describe our m
 ost recent work\, where we reconsider how we shoul
 d separate distinct networks from each other. Curr
 ent correlation-based approaches measure the avera
 ge functional connectivity between regions\, but t
 his average is less meaningful for regions that ar
 e part of multiple networks\; one ideally wants a 
 network model that explicitly allows overlap\, for
  example\, allowing a region's activity pattern to
  reflect one network's activity some of the time\,
  and another network's activity at other times. Ho
 wever\, even those approaches that do allow overla
 p have often maximized mutual spatial independence
 \, which may be suboptimal if distinct networks ha
 ve significant overlap. In this work\, we identify
  functionally distinct networks by virtue of their
  temporal independence\, taking advantage of the a
 dditional temporal richness available via improvem
 ents in functional magnetic resonance imaging samp
 ling rate. We identify multiple “temporal function
 al modes\,” including several that subdivide the d
 efault-mode network (and the regions anticorrelate
 d with it) into several functionally distinct\, sp
 atially overlapping\, networks\, each with its own
  pattern of correlations and anticorrelations. The
 se functionally distinct modes of spontaneous brai
 n activity are\, in general\, quite different from
  resting-state networks previously reported\, and 
 may have greater biological interpretability.
LOCATION:MRC CBU\, Chaucer Road
CONTACT:Mikail Rubinov
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