Objective: To identify disease-related brain networks in hereditary and sporadic primary dystonia using resting state fMRI (rs-fMRI).

Background: Abnormalities of the cerebello-thalamo-cortical (CbTC) pathways have been indicated in hereditary and sporadic dystonia in terms of structural connectivity in diffusion tensor MRI.[1] It remains unclear, however, whether functional connectivity in rs-fMRI is altered in these disorders and, if so, the alteration is associated with abnormal structural connectivity.

Methods: We studied 38 primary dystonia (male,17; DYT1, 7; DYT6, 17; sporadic, 14) and 26 healthy control (male,16) subjects. The subjects were divided into training and testing datasets. The fMRI was analyzed using spatial group independent component analysis (ICA). Fifty independent components (ICs) were obtained; subject spatial maps and temporal dynamics were estimated. We computed subject expression values for each IC using a voxel-based computational algorithm. We used logistic regression with bootstrap resampling to identify the subset of ICs that best distinguished disease from control subjects in the training dataset. A linear combination of these ICs resulted in a specific disease-related pattern. Expression values (subject scores) for this pattern were computed and correlated with fractional anisotropy (FA) values from a prespecified deep cerebellar white matter region in which structural connectivity has been found consistently to be abnormal in primary dystonia.[1]

Results: A dystonia-related pattern was constructed from three ICs involving contributions from the cerebellum, occipital and sensorimotor regions, and thalamus. Subject scores for this pattern separated dystonia from control subjects in the training dataset (p<0.005, permutation test, 5000 iterations). Prospectively computed subject scores were elevated in the patients in the testing dataset (p<0.05, Student’s t-test). A significant negative correlation (r=−0.54, p<0.05) was observed between subject scores for the dystonia-related pattern and cerebellar FA values measured in the sporadic dystonia subjects.

Conclusions: Using ICA and bootstrap resampling, we identified and validated a dystonia-related functional brain network in rs-fMRI scans from patients and control subjects. The expression of this pattern correlated with alterations in CbTC pathway connectivity in sporadic dystonia. References: 1. Cereb Cortex 2015;25:3086-94.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/brain-networks-revealed-by-resting-state-functional-mri-in-familial-and-sporadic-primary-dystonia/