Isolated focal dystonias are a group of disorders with diverse symptomatology but unknown pathophysiology. Although recent neuroimaging studies demonstrated regional changes in brain connectivity, it remains unclear whether focal dystonia may be considered a disorder of abnormal networks. We examined topology as well as the global and local features of large-scale functional brain networks across different forms of isolated focal dystonia, including patients with task-specific (TSD) and nontask-specific (NTSD) dystonias. Compared with healthy participants, all patients showed altered network architecture characterized by abnormal expansion or shrinkage of neural communities, such as breakdown of basal ganglia-cerebellar community, loss of a pivotal region of information transfer (hub) in the premotor cortex, and pronounced connectivity reduction within the sensorimotor and frontoparietal regions. TSD were further characterized by significant connectivity changes in the primary sensorimotor and inferior parietal cortices and abnormal hub formation in insula and superior temporal cortex, whereas NTSD exhibited abnormal strength and number of regional connections. We suggest that isolated focal dystonias likely represent a disorder of large-scale functional networks, where abnormal regional interactions contribute to network-wide functional alterations and may underline the pathophysiology of isolated focal dystonia. Distinct symptomatology in TSD and NTSD may be linked to disorder-specific network aberrations.

Background Numerous brain imaging studies have demonstrated structural changes in the basal ganglia, thalamus, sensorimotor cortex and cerebellum across different forms of primary dystonia. However, our understanding of brain abnormalities contributing to the clinically well-described phenomenon of task-specificity in dystonia remained limited. Methods We used high-resolution MRI with voxel-based morphometry and diffusion tensor imaging with tract-based spatial statistics of fractional anisotropy to examine gray and white matter organization in two task-specific dystonia forms, writer’s cramp and laryngeal dystonia, and two non-task-specific dystonia forms, cervical dystonia and blepharospasm. Results A direct comparison between the both dystonia forms revealed that characteristic gray matter volumetric changes in task-specific dystonia involve the brain regions responsible for sensorimotor control during writing and speaking, such as primary somatosensory cortex, middle frontal gyrus, superior/inferior temporal gyrus, middle/posterior cingulate cortex, occipital cortex as well as the striatum and cerebellum (lobules VI-VIIa). These gray matter changes were accompanied by white matter abnormalities in the premotor cortex, middle/inferior frontal gyrus, genu of the corpus callosum, anterior limb/genu of the internal capsule, and putamen. Conversely, gray matter volumetric changes in non-task-specific group were limited to the left cerebellum (lobule VIIa) only, while white matter alterations were found to underlie the primary sensorimotor cortex, inferior parietal lobule and middle cingulate gyrus. Conclusion Distinct microstructural patterns in task-specific and non-task-specific dystonias may represent neuroimaging markers and provide evidence that these two dystonia subclasses likely follow divergent pathophysiological mechanisms precipitated by different triggers.

Health care providers seeing young patients with tendon xanthomas and relatively normal cholesterol levels, especially those with cataracts and learning problems, should consider the diagnosis of CTX so they can receive treatment. CDCA should receive regulatory approval to facilitate therapy for the prevention of the complications of the disease.

Aberrant sensory processing plays a fundamental role in the pathophysiology of dystonia; however, its underpinning neural mechanisms in relation to dystonia phenotype and genotype remain unclear. We examined temporal and spatial discrimination thresholds in patients with isolated laryngeal form of dystonia (LD), who exhibited different clinical phenotypes (adductor vs. abductor forms) and potentially different genotypes (sporadic vs. familial forms). We correlated our behavioral findings with the brain gray matter volume and functional activity during resting and symptomatic speech production. We found that temporal but not spatial discrimination was significantly altered across all forms of LD, with higher frequency of abnormalities seen in familial than sporadic patients. Common neural correlates of abnormal temporal discrimination across all forms were found with structural and functional changes in the middle frontal and primary somatosensory cortices. In addition, patients with familial LD had greater cerebellar involvement in processing of altered temporal discrimination, whereas sporadic LD patients had greater recruitment of the putamen and sensorimotor cortex. Based on the clinical phenotype, adductor form-specific correlations between abnormal discrimination and brain changes were found in the frontal cortex, whereas abductor form-specific correlations were observed in the cerebellum and putamen. Our behavioral and neuroimaging findings outline the relationship of abnormal sensory discrimination with the phenotype and genotype of isolated LD, suggesting the presence of potentially divergent pathophysiological pathways underlying different manifestations of this disorder.

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The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease.

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