功能性核磁共振造影 (Functional magnetic resonance imaging ; fMRI)已被廣泛應用在臨床檢測大腦血液流動變化狀況。借由這種fMRI的分析，我們可以了解大腦功能區域的運作。在本篇論文中主要有兩大目標:其一是偵測出與眨眼動作相關的大腦反應區域,其二是測量反應區域間是否存在著功能性連通區域。
藉由獨立成分分析(Independent component analysis; ICA)將功能性核磁共振造影影像分解出許多符合統計上獨立的訊號成分，以加權平均訊號的方式推算出每個獨立成份的代表訊號，再藉由代表訊號與理像刺激腦血流反應模型的關聯性對每個成分做評分動作，進而尋找出符合於刺激參考函式的最佳成分，該成分可能含有與刺激相關的訊號源。 然後將找出最佳成份對其數值標準化並轉換成Z值，再對Z值取閥值找出Z值極大極小的體素即為主要反應區域。此外，我們利用彈性對位的方是將每位受測者的腦部對位到標準腦，並做加權分析得到多人的共同反應區域結果。最後我們對這些反應區域以格蘭傑因果關係檢定(Granger causality theory)方式進行功能性連通區域分析，其中我們使用vector autoregressive model為預測模型來實現格蘭傑因果關係檢定。
本研究中我們成功找出的反應區域有運動區、Supplementary Motor Area (SMA)以及視覺區，除此之外我們成功的分析出SMA對運動區有功能性連通的現象，此現象與臨床理論的文獻一致。

Functional Magnetic Resonance Imaging (fMRI) data has been commonly used to measure the hemodynamic response of brain which is proven to be related with the neural activities in the brain. In this paper, we propose to measure the active region of blinking and to measure the functional connectivity between activated regions.
Independent component analysis (ICA) is a technique that attempts to separate sensory image data into spatial independent non-Gaussian components which are then used to determine the component with time course best matched with the time course of stimulation. We calculate the represented signal of every component from ICA and calculate the score of everyone based on the correlation between component signal and ideal hemodynamic response of stimulation. By ranking the correlation, we can find the best component whose values were then normalized into standard Z scores. The voxels with extreme Z scores which are higher than a given threshold in magnitude are then regarded as the active regions. The intra-subject result is obtained. By using the elastic registration, the intra-subject result can be mapped to the same standard brain and then the inter-subject result can be calculated. Thus, the Granger causality theory (GCT) is then used to estimate the functional connectivity between each pair of active regions. The vector autoregressive model is used as the prediction model of GCT.
We find the activated regions of blinking and measure the functional connectivity between each pair of regions. Three main active regions, which are visual area, supplementary motor area (SMA), and motor area, have been detected. In our experiments, the functional connectivity between SMA and motor area has been verified. This result is consistent with expectation from neural physiology.

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