近年來，人類致力於探索人腦的奧秘和神經活動的研究如雨後春筍般出現，其中功能性核磁共振造影 (functional Magnetic Resonance Imaging，fMRI) 因為在空間上相較其他醫學影像，有著相當高的解析度，因此針對功能性核磁造影進行的相關研究分析越來越受到學者們看重。本篇論文欲利用此醫學影像達到三大目標：首先，偵測出進行握拳動作時大腦中的高強度反應區域；第二則是嘗試去分析這些反應區域之間是否有時間上的連結關係；最後，將分析出的區域間連結關係用更系統、精確且直觀的方式描述，期望這些方法得出的結果可以做為臨床上醫師判斷的驗證。
我們使用一套名為Statistical Parametric Mapping (SPM) 的腦部醫學軟體進行反應區域定位的分析:首先對fMRI進行一連串的前處理，再配合動作時間點的資訊偵測出結果並挑選出代表該區域的反應點。接著擷取握拳動作前後訊號並利用內插得到頭與頭之間的值，最後，即為本篇論文的核心方法，將這些訊號透過格蘭傑因果關係模型(Granger Causality Model, GCM)的計算，推導出在大腦中具有區域性、時間性、方向性的訊號因果關係。在本篇論文的實驗設計中，受測者會被刺激到的大腦區域有視覺區、前運動區與運動區，我們提出的假設認為這些區域之間必然存在一定程度的關連性，並且透過實驗設計可以彰顯出這樣的神經鏈結；所以我們也就將這些區域定為本研究的焦點區域 (Region of Interest，ROI)。
研究結果隨著各種刺激呈現出相對應的反應區域，接著利用數種不同方法分析藉由GCM計算出的因果關係之結果，並引入圖論概念呈現，可清楚且一目了然的看出整個握拳動作中因果關係傳達的變化。在後續的統計比較中，不論是區域間的連結方向與強度、時間上的傳遞順序以及系統穩定度方面，都得到了顯著且值得探討的結論。最後，藉由重建各ROI的fMRI反應曲線並統計峰值出現的時間，探討由視覺接收訊號到真正握拳的時間差，同時也驗證了先前的GCM分析結果。

The research of functional neural activity within human brain has been discussed widely in recent years, while functional Magnetic Resonance Imaging (fMRI) is one of the major equipments for examination. Due to the high spatial resolution of fMRI, the analysis of fMRI has become more and more important to medical researcher and expert. There were three goals we wished to achieve: first, to specify the active brain regions with respect to certain motions; second, to analyze the functional connectivity between these regions; finally, to describe the result of Granger causality model with a more systematic and accurate way.
We localized the active regions from five different regions of Interest (ROI) through a medical image analysis software called Statistical Parametric Mapping (SPM). First we do some preprocessing to fMRI data, combined with information of event onset time to estimate points of significant response and picked one point in each ROI. Then, we took signals nearby event onset time and used linear interpolate to calculate intensity between volumes. At last, we applied the Granger Causality Model (GCM) which was the kernel method of this research to the signals and analyzed the causal relation with respect to regions, time, and direction. The experiments were designed to activate the visual areas, premotor area, and motor areas. The hypothesis we proposed was that these regions must have a certain amount of connectivity, and through our experiment, this neural connectivity was observed evidently. Therefore, these areas were defined as the ROIs of our research.
In summary, the results showed different activated ROI according to different kinds of stimuli, and then utilized several methods to analyze the results of causal relation calculated by GCM. We could clearly and intuitively show the variation of GCM results in whole hand grasping motion by introducing the concept of graph theory. We gained some significant conclusions either in the transfer direction and causal level of regional connectivity or the transfer procedure of signals in time axis by statistical analysis, and the comparisons were worthy of discussions. Finally, by reconstructing the fMRI response curve of each ROI, we can obtain some statistical results of its peak time, which was useful to explore the time delay between receiving a visual guidance and actually grasping. These results also verified the previous analysis of GCM and showed a more comprehensive concept of how our brain dealt with a motive command and what the path of connectivity transferred.

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