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SUMMARY:Small\, network models of effective connectivity in the human brai
 n: evidence from fMRI and MEG - Dr. Rik Henson (MRC Cognition and Brain Sc
 iences Unit\, Cambridge)
DTSTART:20081023T144000Z
DTEND:20081023T150500Z
UID:TALK13262@talks.cam.ac.uk
CONTACT:Duncan Simpson
DESCRIPTION:I will review recent methods for inferring changes in effectiv
 e connectivity within small network models (typically a handful of interco
 nnected regions) using human functional neuroimaging data from fMRI and EE
 G/MEG. These methods are based on Dynamic Causal Modelling (DCM)\, in whic
 h the parameters of a dynamic input-state-output system are identified wit
 hin a Bayesian framework\, given known deterministic inputs and observed o
 utputs. The outputs (data) are derived from the (hidden) neural variables 
 via modality-specific observer models (i.e\, the “balloon model” of ha
 emodynamics for fMRI\, or equivalent current dipole models for EEG/MEG). T
 he inputs represent experimental perturbations\, with bilinear parameters 
 in particular reflecting changes in connectivity induced by inputs. Infere
 nces based on the posterior density of the connectivity parameters therefo
 re reflect more than just functional connectivity. While those inferences 
 are model-dependent\, the Bayesian model evidence allows different models 
 to be compared (particularly useful for EEG/MEG\, where the data are fixed
 ). While fMRI data provide greater spatial resolution\, EEG/MEG data offer
  the exciting opportunity to explore regional dynamics and coupling at the
  millisecond level. 
LOCATION:Kaetsu Centre\, New Hall
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