Volume 23, Issue 10 pp. 1517-1527
Original Article

Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia

G. Battistella

G. Battistella

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

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S. Fuertinger

S. Fuertinger

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

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L. Fleysher

L. Fleysher

Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

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L. J. Ozelius

L. J. Ozelius

Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA

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K. Simonyan

Corresponding Author

K. Simonyan

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Correspondence: K. Simonyan, Department of Neurology, One Gustave L. Levy Place, Box 1137, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (tel.: +1 212 241-0656; fax: +1 646 537-8628; e-mail: [email protected]).Search for more papers by this author
First published: 27 June 2016
Citations: 50

Abstract

Background and purpose

Spasmodic dysphonia (SD), or laryngeal dystonia, is a task-specific isolated focal dystonia of unknown causes and pathophysiology. Although functional and structural abnormalities have been described in this disorder, the influence of its different clinical phenotypes and genotypes remains scant, making it difficult to explain SD pathophysiology and to identify potential biomarkers.

Methods

We used a combination of independent component analysis and linear discriminant analysis of resting-state functional magnetic resonance imaging data to investigate brain organization in different SD phenotypes (abductor versus adductor type) and putative genotypes (familial versus sporadic cases) and to characterize neural markers for genotype/phenotype categorization.

Results

We found abnormal functional connectivity within sensorimotor and frontoparietal networks in patients with SD compared with healthy individuals as well as phenotype- and genotype-distinct alterations of these networks, involving primary somatosensory, premotor and parietal cortices. The linear discriminant analysis achieved 71% accuracy classifying SD and healthy individuals using connectivity measures in the left inferior parietal and sensorimotor cortices. When categorizing between different forms of SD, the combination of measures from the left inferior parietal, premotor and right sensorimotor cortices achieved 81% discriminatory power between familial and sporadic SD cases, whereas the combination of measures from the right superior parietal, primary somatosensory and premotor cortices led to 71% accuracy in the classification of adductor and abductor SD forms.

Conclusions

Our findings present the first effort to identify and categorize isolated focal dystonia based on its brain functional connectivity profile, which may have a potential impact on the future development of biomarkers for this rare disorder.

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