以腦電圖研究肌張力不全病患的非線性神經網路連結;Nonlinear Oscillatory Connectivity in Patients with Dystonia : an Eeg Study

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Title:	以腦電圖研究肌張力不全病患的非線性神經網路連結;Nonlinear Oscillatory Connectivity in Patients with Dystonia : an Eeg Study
Authors:	陳純娟;陳睿正
Contributors:	國立中央大學生醫科學與工程學系
Keywords:	寫字型手部痙攣症;腦電波;大腦神經網路異常;神經標記;Writer’s cramp;EEG;network alternation;neuromarkers
Date:	2018-12-19
Issue Date:	2018-12-20 11:29:24 (UTC+8)
Publisher:	科技部
Abstract:	寫字型手部痙攣症(Writer’s Cramp)為一種自發性的肌張力不全症，患者在寫字時其手部會產生不正常的扭曲或顫動，臨床上，因為罕見且症狀跟其他運動相關疾病如柏金森氏症相似，主要致病機轉尚未完全被了解，導致確定診斷要花平均約1年的時間與醫療資源，非常依賴醫師經驗，並且可能延遲病人接受肉毒桿菌治療的時機。因此，臨床神經醫學界認為關於肌張力不全症的研究，首要之務即為發展出一客觀之診斷程序與標準，過去運用穿顱磁刺激與神經影像科學研究結果的研究顯示，此類病人的基底核蒼白球的theta律動在病人有顯著的增加，並進一步導致神經皮質網路例如輔助運動區、運動前區、初級運動區的連結異常，主要反應在皮質beta 律動的不正常上，綜合以上研究結果，寫字型手部痙攣症除了皮質-皮質下迴路異常外，可能與不正常的theta-beta 皮質網路連結亦有關，然而，受限於過去使用線性的研究方法，未曾有文獻探討過是否有異常的theta-beta cortical coupling。所以，本計畫第一年預計應用腦電圖與動態因果模型方法研究寫字型手部痙攣症病人於執行非症狀運動時的大腦神經網路theta 與beta頻率連結的異常，以了解其神經網路機轉，並且以人工智慧方法客觀地萃取出當成神經標記(neuromarkers)，以輔助臨床加速寫字型手部痙攣症的診斷，在第二年，我們預計以巴金森氏症病人執行相同動作時的神經網路參數，檢驗dystonia neuromarkers偵測之專一性與信效度，同時，我們將再收集寫字型手部痙攣症病人的資料以進行再現性測試，最後並探討其可能之轉譯醫學價值。 ;Writer’s cramp 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 nonspecific symptoms ( in some circumstances, it is associated with parkinsonian disorders), there is no test to confirm diagnosis of writer's cramp, except the comprehensive observations by experts. It was estimated that the average time between the appearance of the first symptoms and a clear diagnosis of focal dystonia was about 1 year. Diagnostic delay in dystonia may result in the delay of a proper treatment and it was proposed that one of the future research priorities in task-specific dystonias is the development of diagnostic criteria for limb dystonia. Evidences from previous rTMS studies as well as neuroimaging data have accumulated that focal hand dystonia has the neurological origin. Specifically, the hyper activities of theta oscillations in globus pallidus and beta rhythms in motor cortex were seen in patients with focal dystonia when performing motor tasks. In addition to the alternation of the subcortical-cortical loop, dystonia can be considered a cortical network disorder as the remote healthy nodes of the brain, such as supplementary motor area / anterior cingulate cortex (SMA/ACC), lateral premotor cortex (PMC), and primary motor cortex (M1) may react and rearrange themselves in response to the primary abnormality of globus pallidus by means of network reorganization, possibly in theta-beta interactions. However, because of the nonlinearity nature of cross frequency theta-beta coupling, there has no study that investigates this cross frequency coupling in the neuronal network. In this proposal, we aim to test whether there are abnormal theta-beta couplings between cortical areas in patients with dystonia when they perform nonsymptomic motor tasks by using EEG and dynamic causal modelling. Importantly, we will apply artificial intelligence (AI) methods to extract the neuromarkers that can significantly differentiate the motor network of dystonia patients from that of normal subjects. Having established the neuromarkers for dystonia, in the second year, we will use data from patients with Parkinson's disease performing the identical task to test whether these neuromarkers are specific to patients with dystonia. In addition, we will also acquire some more data from dystonia patients to perform the construct validity of these neuromarkers. Finally, we will discuss the translational potential of these neuromarkers .Finally, we will discuss the translational potential of these neuromarkers.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Department of Biomedical Sciences and Engineering ] Research Project

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