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

Abstract

Background 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 MRI data to investigate brain organization in different SD phenotypes (abductor vs. adductor type) and putative genotypes (familial vs. sporadic cases) and to characterize neural markers for genotype/phenotype categorization. Results We found abnormal functional connectivity within sensorimotor and frontoparietal networks in SD patients compared to 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 cortex. When categorizing between different forms of SD, the combination of measures from 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.

Functional and structural neural bases of task specificity in isolated focal dystonia

“…Preprocessing of resting‐state fMRI data was performed using FSL and AFNI software following a standard pipeline, as described earlier . Each time‐series was first truncated by removing the first four volumes to account for potential T 1 stabilization effects, slice‐time corrected, and high‐pass filtered at 0.01 Hz.…”

“…For example, patients with focal hand dystonia, writer's cramp (WC), were found to exhibit decreased connectivity of the hand region of primary sensorimotor cortex accompanied by decreased dorsal premotor and superior parietal connectivity and increased putaminal connectivity . In a separate study, patients with spasmodic dysphonia (SD) were characterized by decreased connectivity of the laryngeal region of primary sensorimotor cortex, again along with decreased premotor and putaminal connectivity but increased inferior parietal connectivity . Further analysis of large‐scale neural networks identified TSFD‐specific pathophysiological traits in global brain organization, including altered information transfer through a group of highly influential sensorimotor brain regions, hubs, that form an abnormal dystonic network kernel …”

Neuroimaging Applications in Dystonia

“…In cervical dystonia, alterations were found not only in sensorimotor but also in visual and executive control networks (Delnooz, Pasman, Beckmann, & van de Warrenburg, 2013), whereas changes in blepharospasm were related to the default-mode network (Yang et al, 2013). It was further shown that vulnerable connectivity of primary sensorimotor and inferior parietal cortices in laryngeal dystonia is tightly associated with the polygenic risk of dystonia, likely representing an endophenotypic imaging marker of this disorder; genes contributing to the polygenic score are involved in synaptic transmission and neuron development (Battistella, Fuertinger, Fleysher, Ozelius, & Simonyan, 2016; Putzel et al, 2018). Schematic knowledge of functional alterations in dystonia can be viewed in Fig.…”

“…Algorithmic classifiers using functional MRI voxel-wise time series have been successfully applied in several neurodegenerative disorders (Fornari, Maeder, Meuli, Ghika, & Knyazeva, 2012; Janousova, Schwarz, & Kasparek, 2015; Yourganov et al, 2014). The first study in dystonia used multivariate classification algorithm of linear discriminant analysis (LDA) based on the measures of abnormal functional resting-state connectivity in primary sensorimotor, premotor and inferior parietal regions, achieving 71% accuracy in classifying laryngeal dystonia and healthy controls (Battistella et al, 2016). It further improved its accuracy in classifying familial vs sporadic laryngeal patients at 81% and remained at the same 71% accuracy level when considering different (adductor and abductor) phenotypes.…”

Forebrain knock-out of torsinA reduces striatal free-water and impairs whole-brain functional connectivity in a symptomatic mouse model of DYT1 dystonia

“…Understanding how striatal pathology affects network-level FC in preclinical models is important for dissecting pathophysiology and providing readouts for disease modifying interventions. Further, understanding network-level connectivity in a translational model of generalized dystonia is opportune because motor impairment in human subjects with focal dystonia points to disturbances in FC (Battistella et al, 2015; 2016). …”