Neural endophenotypes and predictors of laryngeal dystonia penetrance and manifestation

Focal dystonias are the most common forms of isolated dystonia; however, the etiopathophysiological signatures of disorder penetrance and clinical manifestation remain unclear. Using an imaging genetics approach, we investigated functional and structural representations of neural endophenotypes underlying the penetrance and manifestation of laryngeal dystonia in families, including 21 probands and 21 unaffected relatives, compared to 32 unrelated healthy controls. We further used a supervised machine-learning algorithm to predict the risk for dystonia development in susceptible individuals based on neural features of identified endophenotypes. We found that abnormalities in prefrontal-parietal cortex, thalamus, and caudate nucleus were commonly shared between patients and their unaffected relatives, representing an intermediate endophenotype of laryngeal dystonia. Machine learning classified 95.2% of unaffected relatives as patients rather than healthy controls, substantiating that these neural alterations represent the endophenotypic marker of dystonia penetrance, independent of its symptomatology. Additional abnormalities in premotor-parietal-temporal cortical regions, caudate nucleus, and cerebellum were present only in patients but not their unaffected relatives, likely representing a secondary endophenotype of dystonia manifestation. Based on alterations in the parietal cortex and caudate nucleus, the machine learning categorized 28.6% of unaffected relative as patients, indicating their increased lifetime risk for developing clinical manifestation of dystonia. The identified endophenotypic neural markers may be implemented for screening of at-risk individuals for dystonia development, selection of families for genetic studies of novel variants based on their risk for disease penetrance, or stratification of patients who would respond differently to a particular treatment in clinical trials.

Automated summary

The study identified shared neural endophenotypes between patients with laryngeal dystonia and their unaffected relatives, representing the endophenotypic marker of dystonia penetrance. Additional abnormalities found only in patients likely represent a secondary endophenotype of dystonia manifestation. Machine learning was able to predict the risk for dystonia development in susceptible individuals based on neural features, showing potential for screening at-risk individuals and selecting families for genetic studies or clinical trials.