Dynamic causal modelling of effective connectivity from fMRI: are results reproducible and sensitive to Parkinson's disease and its treatment?

ABSTRACT: Dynamic causal modelling (DCM) of functional magnetic resonance imaging (fMRI) data offers new insights into the pathophysiology of neurological disease and mechanisms of effective therapies. Current applications can be used both to identify the most likely functional brain network underlying observed data and estimate the networks' connectivity parameters. We examined the reproducibility of DCM in healthy subjects (young 18-48 years, n=27; old 50-80 years, n=15) in the context of action selection. We then examined the effects of Parkinson's disease (50-78 years, Hoehn and Yahr stage 1-2.5, n=16) and dopaminergic therapy. Forty-eight models were compared, for each of 90 sessions from 58 subjects. Model-evidences clustered according to sets of structurally similar models, with high correlat

SUBMITTER: Rowe JB 

PROVIDER: S-EPMC3021391 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature