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|Title: ||基於虛擬實境復健之中風後運動網路功能性重組研究;Cerebral re-organization of motor networks in response to VR based rehabilitation after stroke|
|Keywords: ||動態因果模型;中風;復健機制;虛擬實境系統;Dynamic Casual Modelling;stroke;rehabilitation mechanisms;Virtual-Reality system|
|Issue Date: ||2014-04-02 15:19:04 (UTC+8)|
本研究共收錄了21受試者，11位study組的病患(平均年齡: 54.73±10.93歲)和10位control組的病患(64.4±8.72歲)。每位受試者經歷了24小時的復健訓練，一週復健四天，一天復健一小時，其中復健時study組玩一小時類似傳統復健的虛擬實境遊戲，而control組則是執行一小時的傳統復健。在治療前和治療後(完成復健訓練)每位受試者會接受實驗記錄30頻道腦電波，取樣頻率為2000Hz。在實驗中，我們要求所有受試者執行80次患側上肢的動作，例如肩膀或手肘作曲屈/伸展的動作(動作是由治療師判斷受試者狀況而決定的)。腦電波訊號前處理先抑制60Hz的電線干擾的影響，再將訊號以治療師的口令(受試者每次開始執行上肢動作都是聽從治療師的口令)為中心裁切出 -800到 +800毫秒的資訊，然後再濾波使用4Hz和48Hz的帶通濾波器，接著將腦電波資訊以動態因果模型之誘發響應分析估測大腦網路模型之結構(本研究使用的網路模型有六種，其中都是由雙側的初級運動皮質區和前運動區以及輔助運動區所構成)，估測方法是由貝氏分類決策理論選擇出最能代表個人和群體的大腦網路結構模型，
最後，本研究實驗中不只使用動態因果模型估測大腦執行運動任務時的網路結構，還進一步探討大腦網路結構內彼此連結相關之差異，這與先前的EEG研究和fMRI研究相比，我們更能觀察到區域彼此間連結的改變，呈現一個更完整更合理的分析結果，我們也的確觀察到VR復健和傳統復健對於大腦運動網路的影響；在未來，希望可以將這些資訊加以證明VR復健之成效優於傳統復健以及應用在中風後評估患者之臨床研究上的參考。; In this study, we aimed to investigate the cerebral re-organization of motor networks in response to rehabilitation after stroke using Dynamic Causal Modelling for induced responses (DCM_IR) as measured with electroencephalography (EEG). Specifically, the difference in changes due to Virtual-Reality game based rehabilitation (VR) and conventional rehabilitation (CR), including the architecture of the motor network and the coupling parameters govern it resulting from DCM, was examined and compared.
21 stroke patients were recruited in this study and divided randomly into two groups: 11 for VR group (average age:54.73±10.93) and 10 for CR group (average age:64.4 ±8.72). All subjects underwent a totall 24 hours training program with the frequency of 1 hour a day, four days a week. The daily treatment for the VR group consisted of playing 1 hour of home-made VR game which was specially designed to match the conventional CR protocol while the CR group underwent 1 hour route CR treatment. 30 channel EEG were acquired with 2000 Hz sampling rate before (pre-treatment) and after (post-treatment) completed the rehabilitation training. During the EEG acquisition, all subjects were asked to perform about 80 trials of simple upper-limb movement, like shoulder or elbow flexion-extension (subjective to the subject' ability and decided by the therapist), using their affected hand. The EEG data were filtered with 60Hz stop-pass filter to remove power line noise and epoched form -800 to +800 ms where the time zero indicated the inhibition of movement as commanded by the therapist. The epoched data were further filtered with 4~48 Hz band-pass filter and the entered DCM_IR for network identification. Six plausible models, comprising bilateral primary motor cortex (M1), premotor cortex(PM)and (supplementary motor area)SMA, were tested and Bayesian model selection (BMS) was used to selected the model that can explain the data best at both the single subject and group level. Statistic test on the estimates of the best model were performed using analysis of variance (ANOVA), paired t-test, two sample t-test. Clinical measures included FM-UE, TEMPA, WMFT were also conducted by the therapist pre- and post-treatment.
The statistic test on FM, TEMPA, WOLF shows there has a significant difference between pre- and post-treatment but no significant difference between VR and CR groups, indicating that VR based rehabilitation has the similar treatment effect as the convention CR does. The BMS identifies the model with contralesional M1 dominating the network structure for both groups and for pre- and post-rehab, suggesting that the strategy used by the brain for functional restoration is identical: to be as normal as before, irrelavent to the means of intervention. We compared the changes in coupling parameters and found that CR group is better than VR group with four connections and VR group is better than CR group with two connections only.
In conclution, we investigated the architecture of the motor network during task by DCM and the difference of connection between architecture of the brain. We found a more complete result by focus on connection changed from cortical area compared with previous studies and we observed that cerebral motor networks indeed affected by occupational therapy and VR based rehabilitation. We believe the outcome of this study could be an evidence that VR based rehabilitation is better for stroke patient and benefit the study of motor recovery during rehabilitation n after stroke in the future.
|Appears in Collections:||[生物醫學工程研究所 ] 博碩士論文|
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