使用動態非線性腦波分析方法及握力測量探討動作控制的神經機制

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阮文鐘(Nguyen Van Trung) 查詢紙本館藏

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認知與神經科學研究所

論文名稱

使用動態非線性腦波分析方法及握力測量探討動作控制的神經機制
(Motor inhibitory control as a function of grip force and its electrophysiological dynamics were revealed with nonlinear and nonstationary of brain activity)

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至系統瀏覽論文 (2030-8-5以後開放)

摘要(中)

在實驗室環境中，經常使用停止信號典範進行反應抑制能力的研究。然而，目前的研究證據，仍無法清楚區分注意力攫取和動作抑制歷程的機制。因此，研究者在本研究中改良了傳統的停止信號作業，在原有的作業中增添「繼續運行」的指令條件，藉此更精準地探究抑制控制的歷程。此外，研究者也合併使用能提供精細估計力量反應的握力裝置，此裝置可區分部分抑制與完全抑制的動作反應，以及同時記錄腦電波訊號。研究者將力量反應和力量反應率等指標，用於衡量研究參與者在動作抑制期間的行為反應，另採用事件相關模式（Event-related mode, ERM）、希爾伯特-黃轉換（Hilbert–Huang transform, HHT）與全息-希爾伯特頻譜分析（Holo-Hilbert Spectrum Analysis, HHSA）等新穎的腦電波數據分析技術，解析腦波訊號中的非線性和非穩態訊號，以闡明動態抑制歷程的電生理相關性。
研究結果顯示，未成功抑制的力量反應和力量反應率，隨著停止信號出現的延遲而增加，為動態抑制歷程提供了新的客觀指標。另外，側化準備電位（lateralized readiness potential, LRP）的振幅則會在新刺激出現時增加，表明了新刺激對中樞運動處理的影響。再者，本研究也發現，除了過去研究中報告中的事件相關腦電位N2 成分波之外，前期 N1 成分波也可視為動作抑制的指標。在溯源分析結果中顯示，N2 的激發源於抑制控制相關區域，意即右側額下迴、前運動皮質區和初級運動皮質區。關於部分反應的結果，LRP 和錯誤相關負性（ERN）成分波則與錯誤校正過程相關，而 N2 成分波可能表明抑制和錯誤校正功能之間的重疊。
希爾伯特-黃轉換的頻譜分析結果揭示，相較於繼續進行條件（Cont_Go），成功停止的試驗（SST）中具有更高的beta（14-28 Hz）和low gamma（28-56 Hz）頻率活動，這可能作為抑制控制的電生理指標。此外，與「繼續進行」條件相比，在完全不成功停止的試驗（unsuccessful stop trials, USST）中觀察到更高的theta（3.5-7 Hz）和 alpha（7-14 Hz）活動，並且可能反應了錯誤處理歷程。最後，在動作反應開始前大約 100 毫秒，相較於完全不成功停止試驗，在部分不成功停止的試驗中觀察到更高的 theta 和 alpha 帶活動，這可能反應了錯誤的早期檢測和相應的校正過程。
全息-希爾伯特頻譜分析（HHSA）結果顯示，theta/beta、alpha/beta、theta/low gamma 和 alpha/low gamma的交叉頻率耦合可作為抑制控制的電生理指標。此外，delta （0.45-1.8 Hz）/delta（1.8-3.7 Hz；delta（0.45-3.7 Hz）/theta；delta、theta/alpha調控也被發現與錯誤檢測有關。最後，在反應開始前大約 100 毫秒，部分不成功停止比完全不成功停止試驗觀察到更高的delta（0.9-3.7 Hz）/theta 調控，這可能反應了錯誤的早期檢測和相應的校正過程。
總而言之，本研究透過引入力量反應、力量反應率和電生理數據，開發了可靠和客觀的動作抑制指標，幫助我們對動態動作抑制和錯誤校正的機制，取得更深入的認識。

摘要(英)

Response inhibition has been widely explored using the stop-signal paradigm in a laboratory setting. However, the mechanisms differentiating attentional capture and motor inhibition processes is still unclear. Thus, in the present study, a modified stop signal task with the newly-added ‘continue go’ condition was adopted to investigate inhibitory control processes. Additionally, a grip force device, which provided a gradient and fine estimate of the inhibitory process (e.g. partially versus fully inhibited motor responses), was innovatively used to measure behavior which was obtained in conjunction with electroencephalographic (EEG) recordings. Instead of conventional indices such as reaction time and accuracy, the measurement of partial responses, as well as additional indices such force and force rate, was used to gauge the participants’ behavioral responses during motor inhibition. EEG data analysis employed advanced techniques, namely: Event-related mode (ERM), Hilbert–Huang transform (HHT), Holo-Hilbert Spectrum Analysis (HHSA) which are designated for non-linear and non-stationary brain signal analysis, to elucidate the electrophysiological correlates of the dynamic inhibition processes
The results illustrate that the non-canceled force and force rate increased as a function of stop signal delay, offering new objective indices for gauging the dynamic inhibitory process. Motor response (time and force) was a function of delay in the presentation of novel/infrequent stimuli. A larger lateralized readiness potential (LRP) amplitude in go and novel stimuli indicated an influence of the novel stimuli on central motor processing. Moreover, an early N1 component reflects an index of motor inhibition in addition to the N2 component reported in previous studies. Source analysis revealed that the activation of N2 originated from inhibitory control associated areas: the right inferior frontal gyrus, pre-motor cortex and primary motor cortex. Regarding partial responses, LRP and error-related negativity (ERN) components were associated with error correction processes, whereas the N2 component may indicate the functional overlap between inhibition and error correction.
The HHT results demonstrated higher beta (14-28Hz) and low gamma (28-56Hz) activity bands in the successful stop trials (SST) relative to continue go trials (Cont_Go) and this might serve as an electrophysiological index of inhibitory control. Furthermore, higher theta (3.5-7Hz) and alpha (7-14Hz) bands of activity were observed in full unsuccessful stop trials (USST) compared to the Cont_Go trials and might mirror error processing. Finally, higher theta and alpha band activity were observed in partial USST over full USST stop trials about 100ms before the response onset and this may reflect the early detection of errors and a corresponding correction process.
The HHSA results showed that theta/beta, alpha/beta, theta/low gamma and alpha/low gamma modulation may serve as an electrophysiological index of inhibitory control in the level of cross frequency couplings. Moreover, delta (0.45-1.8 Hz)/ delta (1.8-3.7 Hz); delta (0.45-3.7 Hz)/theta; delta, theta/alpha modulations was also found to be associated with error detection. Finally, higher delta (0.9-3.7 Hz)/ theta modulation were observed in partial USST over full USST stop trials about 100ms before the response onset and this may reflect the early detection of errors and a corresponding correction process.
In sum, the present study has developed reliable and objective indices of motor inhibition by introducing force, force rate and electrophysiological measures, further elucidating our understandings of dynamic motor inhibition and error correction.

關鍵字(中)

★ 抑制控制
★ 錯誤校正
★ 錯誤監控
★ 力量
★ 部分反應
★ 事件相關模式（ERM）
★ 希爾伯特-黃轉換（HHT）
★ 全息-希爾伯特頻譜分析（HHSA）

關鍵字(英)

★ inhibitory control
★ error correction
★ error monitoring
★ force
★ partial response
★ ERM
★ LRP
★ Hilbert–Huang transform
★ Holo-Hilbert Spectrum Analysis

論文目次

中文摘要 i
Abstract iii
Acknowledgments v
Table of Content vi
Table of figures ix
List of Tables xi
List of Abbreviations xii
Chapter 1: General introduction 1
1.1 Inhibitory control 1
1.2 Measurement of inhibitory control 1
1.2.1 Inhibitory control tasks 1
1.2.2 Modeling response inhibition - Horse Race Model 3
1.3 Electroencephalography and neural oscillations 6
1.3.1 Event-related potential (ERP) – Event-related mode (ERM) 6
1.3.2 Neural oscillations 7
1.4 Purpose and hypothesis 12
Chapter 2: To go or not to go: degrees of dynamic inhibitory control revealed by the function of grip force and early electrophysiological indices 15
2.1 Introduction 15
2.2 Materials and Methods 19
2.2.1 Participants 19
2.2.2 Apparatus and stimulus 19
2.2.3 Electroencephalography recording 24
2.2.4 Data analysis 24
2.3 Results 30
2.3.1 Behavioral results 30
2.3.2 Event Related Mode (ERM) 38
2.3.3 Error-related negativity (ERN) results 43
2.3.4 Source localization of ERM 44
2.3.5 LRP results 45
2.4 Discussion 49
2.4.1 New behavioral measures of motor inhibitory control 50
2.4.2 Modulation of reaction time and force rate for the novel/infrequent stimuli 51
2.4.3 The electrophysiological characteristics of Stop and Continue Go 52
2.4.4 Effect of novel/infrequent stimuli on central processing 54
2.4.5 Differential characteristics of full and partial USST 54
2.4.6 The temporal processes of inhibitory control 58
Chapter 3: Dynamical EEG indices of progressive motor inhibition and error-monitoring 59
3.1 Introduction 59
3.2 Materials and Methods 61
3.2.1 Participants 61
3.2.2 Apparatus and stimulus 61
3.2.3 Electroencephalography recording 61
3.2.4 Data analysis 61
3.3 Results 63
3.3.1 Behavior results 63
3.3.2 HHT results 63
3.4 Discussion 70
3.4.1 Neural mechanisms of motor inhibitory control 71
3.4.2 Neural mechanisms of error detection and correction 74
Chapter 4: Nonlinear and nonstationary perspectives of motor inhibition and error-monitoring as revealed by Holo-Hilbert Spectrum Analysis 76
4.1 Introduction 76
4.2 Materials and Methods 77
4.2.1 Participants 77
4.2.2 Apparatus and stimulus 78
4.2.3 Electroencephalography recording 78
4.2.4 Data analysis 78
4.3 HHSA Results 79
4.3.1 Inhibitory control 79
4.2.2 Error detection and error correction 82
4.4 Discussion 89
4.4.1 Inhibitory control 89
4.4.2 Error detection and error correction 90
Chapter 5: General discussion 92
5.1 Results summary 92
5.2 Conclusion 93
References 95
Appendixes 108

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指導教授

阮啟弘(Chi-Hung Juan)

審核日期

2021-8-5

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