An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia

Author:

Luo Huichun12,Huang Xiaojun1,Li Ziyi1,Tian Wotu1,Fang Kan13,Liu Taotao1,Wang Shige1,Tang Beisha4,Hu Ji5,Yuan Ti‐Fei267,Cao Li18ORCID

Affiliation:

1. Department of Neurology Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai 200233 China

2. Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai 200030 China

3. Department of Neurology Shanghai General Hospital, Shanghai Jiao Tong University Shanghai China

4. Department of Neurology Xiangya Hospital, Central South University Hunan Province 410008 China

5. School of Life Science and Technology ShanghaiTech University Shanghai 201210 China

6. Co‐innovation Center of Neuroregeneration Nantong University Nantong Jiangsu 226019 China

7. Institute of Mental Health and drug discovery Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) Wenzhou Zhejiang 325000 China

8. Shanghai Neurological Rare Disease Biobank and Precision Diagnostic Technical Service Platform Shanghai China

Abstract

AbstractParoxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high‐density electroencephalogram (hd‐EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd‐EEGs are recorded from two independent datasets and then source‐localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high‐gamma FCs in non‐remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high‐gamma FCs are related to the motor symptoms. Finally, the authors established connectome‐based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd‐EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.

Funder

Shanghai Municipal Health Commission

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

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