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Anatomical categorization of isolated non-focal dystonia: novel and existing patterns using a data-driven approach

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Last modified
  • 06/25/2025
Type of Material
Authors
    J.R. Younce, University of North Carolina at Chapel HillR.H. Cascella, Washington University, St. LouisB.D. Berman, Virginia Commonwealth UniversityHyder A Jinnah, Emory UniversityS. Bellows, Baylor College of MedicineJ. Feuerstein, University of Colorado, AuroraA. Wagle Shukla, University of Florida, GainesvilleA. Mahajan, Rush UniversityF.C.F. Chang, University of SydneyK.R. Duque, University of CincinnatiS. Reich, University of Maryland, BaltimoreS. Pirio Richardson, University of New Mexico, AlbuquerqueA. Deik, University of PennsylvaniaN. Stover, University of Alabama at BirminghamJ.M. Luna, Washington University, St. LouisS.A. Norris, Washington University, St. Louis
Language
  • English
Date
  • 2023-06-08
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2023 Younce, Cascella, Berman, Jinnah, Bellows, Feuerstein, Wagle Shukla, Mahajan, Chang, Duque, Reich, Pirio Richardson, Deik, Stover, Luna and Norris.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 2
Start Page
  • 11305
Grant/Funding Information
  • The Dystonia Coalition (HJ: PI) has received the majority of its support through the NIH (grants NS116025, NS065701 from the National Institutes of Neurological Disorders and Stroke TR001456 from the Office of Rare Diseases Research at the National Center for Advancing Translational Sciences). The Dystonia Coalition has received additional material or administrative support from industry sponsors (Allergan Inc. and Merz Pharmaceuticals) as well as private foundations (The Benign Essential Blepharospasm Foundation, Cure Dystonia Now, The Dystonia Medical Research Foundation, and The National Spasmodic Dysphonia Association). Work on this study was also funded by NIH R01 NS124789 (SAN: PI) and NIH K23 NS121630 (JRY: PI).
Supplemental Material (URL)
Abstract
  • According to expert consensus, dystonia can be classified as focal, segmental, multifocal, and generalized, based on the affected body distribution. To provide an empirical and data-driven approach to categorizing these distributions, we used a data-driven clustering approach to compare frequency and co-occurrence rates of non-focal dystonia in pre-defined body regions using the Dystonia Coalition (DC) dataset. We analyzed 1,618 participants with isolated non-focal dystonia from the DC database. The analytic approach included construction of frequency tables, variable-wise analysis using hierarchical clustering and independent component analysis (ICA), and case-wise consensus hierarchical clustering to describe associations and clusters for dystonia affecting any combination of eighteen pre-defined body regions. Variable-wise hierarchical clustering demonstrated closest relationships between bilateral upper legs (distance = 0.40), upper and lower face (distance = 0.45), bilateral hands (distance = 0.53), and bilateral feet (distance = 0.53). ICA demonstrated clear grouping for the a) bilateral hands, b) neck, and c) upper and lower face. Case-wise consensus hierarchical clustering at k = 9 identified 3 major clusters. Major clusters consisted primarily of a) cervical dystonia with nearby regions, b) bilateral hand dystonia, and c) cranial dystonia. Our data-driven approach in a large dataset of isolated non-focal dystonia reinforces common segmental patterns in cranial and cervical regions. We observed unexpectedly strong associations between bilateral upper or lower limbs, which suggests that symmetric multifocal patterns may represent a previously underrecognized dystonia subtype.
Author Notes
Keywords
Research Categories
  • Biology, Biostatistics
  • Biology, Neuroscience

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