Low-frequency oscillations in cortical level to help diagnose task-specific dystonia

NEUROBIOLOGY OF DISEASE

卷:157

Article

Chen, Chun-Chuan¹  Macerollo, Antonella²  Heng, Hoon-Ming³  Lu, Ming-Kuei^3,4  Tsai, Chon-Haw^3,4  Daniyal¹  Wang, Wei-Jen⁵  Chen, Jui-Cheng^3,4,6 

[1] Natl Cent Univ, Dept Biomed Sci & Engn, Taoyuan, Taiwan

[2] Walton Ctr NHS Fdn Trust, Lower Lane, Liverpool L9 7LJ, Merseyside, England

[3] China Med Univ Hosp, Dept Neurol, Neurosci Lab, 2 Yude Rd, Taichung 404332, Taiwan

[4] China Med Univ, Sch Med, Coll Med, Taichung 404, Taiwan

[5] Natl Cent Univ, Dept Comp Sci & Informat Engn, Taoyuan, Taiwan

[6] China Med Univ, Dept Neurol, Hsinchu Hosp, Hsinchu, Taiwan

关键词: Low frequency (LF);    Task-specific dystonia (TSD);    EEG;    Wrist extension;    Effective connectivity;    Machine learning;   

DOI  :  10.1016/j.nbd.2021.105444

来源: Elsevier

【 摘 要 】

Task-specific dystonia is a neurological movement disorder that abnormal contractions of muscles result in the twisting of fixed postures or muscle spasm during specific tasks. Due to the rareness and the pathophysiology of the disease, there is no test to confirm the diagnosis of task-specific dystonia, except comprehensive observations by the experts. Evidence from neural electrophysiological data suggests that enhanced low frequency (4-12 Hz) oscillations in the subcortical structure of the globus pallidus were associated with the pathological abnormalities concerning 13 and gamma rhythms in motor areas and motor cortical network in patients with task-specific dystonia. However, whether patients with task-specific dystonia have any low-frequency abnormalities in motor cortical areas remains unclear. In this study, we hypothesized that low-frequency abnormalities are present in core motor areas and motor cortical networks in patients with task-specific dystonia during performing the non-symptomatic movements and those low-frequency abnormalities can help the diagnosis of this disease. We tested this hypothesis by using EEG, effective connectivity analysis, and a machine learning method. Fifteen patients with task-specific dystonia and 15 healthy controls were recruited. The machine learning method identified 8 aberrant movement-related network connections concerning low frequency, 13 and gamma frequencies, which enabled the separation of the data of patients from those of controls with an accuracy of 90%. Importantly, 7 of the 8 aberrant connections engaged the premotor area contralateral to the affected hand, suggesting an important role of the premotor area in the pathological abnormities. The patients exhibited significantly lower low frequency activities during the movement preparation and significantly lower 13 rhythms during movements compared with healthy controls in the core motor areas. Our findings of low frequency- and 13-related abnormalities at the cortical level and aberrant motor network could help diagnose task-specific dystonia in the clinical setting, and the importance of the contralesional premotor area suggests its diagnostic potential for task-specific dystonia.