博碩士論文 993211004 詳細資訊




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姓名 朱易明(YI-MING ZHU)  查詢紙本館藏   畢業系所 生物醫學工程研究所
論文名稱 基於動態因果模型之老化相關的運動網路研究
(Aging related changes in motor network:A dynamic causal modelling study)
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摘要(中) 本論文使用動態因果模型之誘發響應研究老化對於運動網路結構上的影響以及頻率上的動態改變。此外我們使用15%和45%最大自主握力兩種力量級的任務研究老化是否會改變握力調變對於初級運動皮質(M1)、前運動皮質(PM)、輔助運動區(SMA)等區域運動網路的影響。
本研究收錄了14位健康的右撇子受試者,其中8位為年輕組另外6位為年長組,平均年齡分別為23.25±2.05、71.17±9.79歲。在實驗前我們會先量測受試者自身的最大握力值(Maximum Voluntary Contraction ; MVC),以決定受試者對應的15%和45%握力輸出。在訓練階段要求受試者完成2種不同握力量級(15%、45%MVC)的訓練各50次,並且提供即時的視覺回饋讓受試者知道握力資訊,在此階段受試者必須習慣握力的控制並記住適當的施力大小。在正式實驗中我們要求受試者執行2種不同握力量級的任務各100次,兩種力量出現的順序為隨機,並且不提供即時的視覺回饋同時要求受試者握力的誤差必須落在10%MVC之內,每次任務間隔有3秒鐘的休息時間使肌肉放鬆與2±0.5秒隨機的準備時間避免預期心理,每次試驗開始前以圖形提示受試者準備。實驗過程中記錄16頻道腦電波,取樣頻率為2000Hz,並同時以壓力感測裝置收集握力資訊,取樣頻率為40Hz。腦電波訊號前處理將訊號以5%MVC的時刻為中心裁切出-1500到+1000毫秒的資訊,接著使用改良後的獨立成份析法與經驗模態分解法以及0.1~35Hz的帶通濾波器去除眼動訊號,最後將去除眼動訊號的腦電波資訊以動態因果模型之誘發響應分析。
實驗結果顯示在運動表現方面年長組的反應時間與持續時間皆比年輕組長(P<0.01),在大腦運動網路的表現年長組的增益性與抑制性連結強度皆比年輕組強且較為複雜,此外年長組區域間的溝通傾向使用更廣泛的頻帶,同時當年長組執行握力任務時左右腦活性提升較為平均然而年輕組則傾向活化左腦。另外力量調變的影響只在年長組輔助運動區往初級運動皮質的連結造成顯著差異,隨著肌肉力量上升輔助運動區與初級運動皮質間的溝通會漸漸由Beta往Beta + low Gamma頻帶移動。
本研究發展了一個新的方法結合獨立成分析法與經驗模態分解法並加上時窗的選擇改良了眼動訊號的處理方法,比起舊有方法在移除眼動訊號時更加準確。另外,我們觀察出老化與力量調變對大腦運動網路的影響,在未來,希望可以將這些資訊應用在中風後運動功能復健之臨床研究的參考。
摘要(英) In this study, we aim to examine age-related network changes in terms of the motor network architecture and the frequency content of the ensuring dynamics using Dynamic Causal Modelling for induced responses. In particular, we wanted to test whether there are any differences with aging in force modulation in the motor network, comprising bilateral primary motor areas (M1), premotor areas (PM), and supplemental motor area (SMA) when performing 15% and 45% of Maximum Voluntary Contraction (MVC) of hand grips.
14 healthy right-handed subjects, 8 for the younger group and 6 for the older group, with the average age of 23.25±2.05 and 71.17±9.79 years old, respectively, were recruited in this study. Before the experiment, subjects were asked to generate their own MVC for the determination of subject-specific 15% and 45% force levels. 50 times practice for each force level was performed with the visual feedback of grip level for them to memorize the force level. During the experiment the subjects were asked to perform 100 times grips both two levels of force without visual feedback which was paced by visual cue of randomized order of force level. In this session, only 10% force error was permitted. The inter-movement interval is 2±0.5 seconds to avoid the anticipating effect and for each trial there has 3 seconds break to avoid muscle fatigue. 12 channels electroencephalogram (EEG) with 2000 Hz sampling rate and right-handed grip force with 40 Hz sampling rate were recorded during the task. The EEG data were epoched form -1500 to +1000 ms where the time zero indicated the grip force level at 5% MVC. The epoched data were processes by Independent Component Analysis (ICA) method and Empirical Mode Decomposition (EMD) and filtered with 0.1~35 Hz band-pass filter to remove the EOG artifact. The EOG-free data then entered into DCM of induced responses analysis.
Behavior result showed older subjects had longer reaction and duration time (P<0.01). Older subjects have increase coupling strength and more complex coupling than younger subjects in the motor network. The Inter-regional communications of older brain tend to use more frequency band. When executing grip task the motor cortex activity became more symmetric in elders while the youngers tend to have more activation in the left brain. Force modulation has significant effect in the coupling between supplemental motor area and primary motor cortex. Specifically, the communication from supplemental motor area to primary motor cortex engaged more Beta to low Gamma band when force level increases.
In conclusion, we first engineered a new method that combines ICA and EMD to remove artifact more accurately. In addition, we observed the impact of aging on force modulation of motor network. We believe that the outcome of this study could benefit the studies of motor recovery during rehabilitation after stroke in the future.
關鍵字(中) ★ 經驗模態分解法
★ 獨立成份分析法
★ 動態因果模型
★ 老化
★ 運動網路
★ 眼動
關鍵字(英) ★ EMD
★ ICA
★ EOG
★ motor network
★ DCM
★ aging
論文目次 第一章 文獻回顧與背景知識 1
1.1研究動機與目的 1
1.2 老化對運動相關皮質的影響 2
1.3腦電波圖 4
1.4運動皮質相關電位 6
1.5大腦連通性的研究方法 8
1.5.1功能性連結與有效性連結 8
1.5.2誘發響應(induced responses)的動態因果模型 9
1.5.3線性非線性效應 12
1.6論文架構 13
第二章 實驗設計與研究方法 14
2.1實驗設計 14
2.2實驗參數 17
2.3研究工具 18
2.3.1.1硬體架構 18
2.3.1.2儀錶放大器 19
2.3.1.3 Sallen-Key 低通濾波器 21
2.3.1.4電路架構 23
2.3.2軟體工具 24
2.4獨立成分分析法 25
2.4.1獨立成分分析法簡介 25
2.4.2獨立成分分析法的限制 26
2.4.3 獨立成分分析法的演算法架構流程 27
2.5經驗模態分解法 30
2.6結合獨立成份分析與經驗模態分解法的分析方式 32
2.7腦電波前處理流程與分析參數 35
2.7.1獨立成份分析法參數 35
2.7.2經驗模態分解法參數 36
2.7.3受眼動訊號影響的時窗選擇 36
2.7.4眼動訊號的移除方法 38
2.8模型設計 39
第三章 實驗結果 41
3.1受試者運動相關表現 41
3.2演算法濾除眼動訊號之結果 42
3.2.1傳統獨立成份分析法與本研究演算法比較 42
3.2.2使用本研究之演算法移除眼動訊號結果 43
3.3 模型闡述 47
3.3.1年齡對大腦有效性連結的影響 47
3.3.2力量調變對大腦有效性連結的影響 52
第四章 討論 53
4.1年齡相關的影響 53
4.2力量調變相關的影響 56
第五章 結論 58
第六章 未來展望 59
附錄 60
參考資料 65
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指導教授 陳純娟(Chun-Chuan Chen) 審核日期 2012-7-30
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