Objective: To apply data-driven methods to characterize dystonic patterns across body regions in non-focal dystonia.

Background: Current categorization of body distributions affected by dystonia lacks well-defined anatomical delineations when considering focal versus non-focal subtypes.  Clinician subjectivity and consensus-based guidelines may contribute to biased categorization of focal and non-focal dystonia subtypes.

Method: We analyzed 1618 participants with isolated dystonia affecting more than one body region from the Dystonia Coalition (DC) database. We performed analysis of frequency, asymmetric binary distance, agglomerative hierarchical clustering, consensus hierarchical clustering, and independent component analysis (ICA) to describe associations and clusters for dystonia noted by movement disorder experts affecting any combination of 18 pre-defined body regions.

Results: We observed closest relationships between dominant and non-dominant upper leg (distance = 0.40), upper and lower face (distance = 0.45), dominant and non-dominant hand (distance = 0.53), and dominant and non-dominant foot (distance = 0.53). For consensus clustering at k=9, 3 major clusters were observed with 4 minor clusters and 2 single case clusters. Major clusters consisted primarily of a) cervical dystonia with nearby regions including larynx and shoulder, b) bilateral hand dystonia, and c) cranial dystonia. We observed similarity between major hierarchical clusters and independent components, with less similarity apparent for the smallest clusters.

Conclusion: Data-driven analysis reinforces some commonly described patterns in non-focal dystonia while challenging others. Notably, the association of facial regions with each other in cranial dystonia and cervical dystonia with other nearby body regions is consistent with previously observed patterns. Preferential association of bilateral limb regions rather than ipsilateral appendage and limb involvement may help to further refine consensus definitions and provide insight regarding pathophysiologic mechanisms.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/categorizing-non-focal-dystonia-an-un-biased-data-driven-approach/