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SUMMARY:Dynamic causal modelling the 'resting' Parkinsonian brain\, and ne
 twork discovery - Joshua Kahan\, UCL Institute of Neurology
DTSTART:20131029T110000Z
DTEND:20131029T120000Z
UID:TALK48634@talks.cam.ac.uk
CONTACT:Mikail Rubinov
DESCRIPTION:Starved of dopamine\, the dynamics of the Parkinsonian brain i
 mpact on both ‘action’ and ‘resting’ motor behaviour. Deep brain s
 timulation (DBS) has become an established means of managing these symptom
 s\, although its mechanisms of action remain unclear. Non-invasive charact
 erisations of induced brain responses\, and the effective connectivity und
 erlying them\, generally appeals to dynamic causal modelling of neuroimagi
 ng data. When the brain is at rest however\, this sort of characterisation
  has been limited to correlations (functional connectivity). Stochastic dy
 namic causal modelling for fMRI is a recent extension capable of modelling
  BOLD data collected during task-free periods. \nIn this talk I will intro
 duce dynamic causal modelling and its stochastic variant\, and discuss how
  this can be used to model dynamics of the resting state. I will present s
 ome recent work where we model the effective connectivity underlying low f
 requency blood oxygen level dependent (BOLD) fluctuations in the resting P
 arkinsonian motor network – disclosing the distributed effects of deep b
 rain stimulation on cortico-subcortical connections\, and how these relate
  to clinical severity.\nThe talk will also briefly touch upon recent devel
 opments in deterministic schemes for modelling the resting state\, as well
  as using model optimisation techniques to search large model spaces to id
 entify functional networks.	\n
LOCATION:MRC CBU lecture theatre\, 15 Chaucer Road
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