Dystonia related disease pattern using ICA and resting state fMRI

The purpose of this study is using rsfMRI to determine affected brain networks in dystonia. rsfMRI were analyzed using spatial group independent component analysis. Four ICs representing independent contributions from cerebellar, thalamic and premotor/prefrontal regions achieved maximum between-group separation. Dystonia pattern was obtained by a linear combination of these four components using estimated parameters of nominal logistic model. Subject scores representing the mean expression of the dystonia-related pattern were abnormally elevated in the DYT1 and DYT6 patients and the sporadic patients as well. The topography of the rsfMRI-based network closely resembled that previously described in the resting state with FDG PET.

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