靜息態大腦的功能性磁振造影近年來被廣泛討論著，由於它的實驗設計簡單，對於受試者無任何侵入性，也沒有複雜的任務指令，所以即使對於孩童、老人，或者是病人，也容易進行休息狀態時的掃瞄，因此，它常被用於大腦神經疾病方面的研究。在醫學相關研究中，利用休息狀態的資料以及經過分析後出現的功能性連結。以得到的連結，將正常人與病人進行對照，並比較其在特定連結上的差異，進而判斷是否可能患有疾病。其中“迴歸”被認為是分析休息狀態資料時重要且有效的步驟，用以降低生理雜訊對影像品質的干擾，進而得到較清晰的功能性連結。然而，迴歸對於資料影響如何，在影像結果的作用又為何，目前仍存有爭議。在本研究中，利用將種子點設定於大腦預設模式網路為分析方法，並藉由將生理雜訊變數分成五種不同的組合，以進行不同的分析步驟，來觀察其對於資料上的影響，以及影像上的變化，發現迴歸這一分析步驟雖可幫助我們方便觀察連結的成像，但卻會對原始資料產生很大的變化。除此之外，可能會產生類似於假影的負相關性連結，這是目前研究還無法明確解釋的現象。總結來說，經由本論文的研究，我們建議對靜息態大腦功能性造影進行資料分析時，分別得到使用全腦訊號迴歸與不使用迴歸的兩組資料，來比較其結果。當這兩種資料所得到的統計意義不同時，我們就必須小心解釋靜息態實驗所得到的資料。若不需要負相關性連結的資訊，我們則建議不使用全腦訊號迴歸的步驟，以免產生假性的連結資料。

Resting state functional magnetic resonance imaging (rsfMRI) has been widely investigated in recent years, due to its relatively simple and non-invasive experimental design. During the experiment, the operator requires neither complex instructions nor preparing the stimuli facilities. The rsfMRI reveals the brain functional connectivity which has been linked to brain diseases. In the pre-processing steps analyzing rsfMRI, the "regression" is considered an effective step to reduce the interference of the physiological noise on the signal time course. However, it remains a debate whether the regression method benefits the analysis of rsfMRI. In the present study, we used seed-based method to obtain the brain default mode network analysis methods using five combinations of the regression methods. The results reveal that the regression of global mean curve plays a major role of the preprocessing steps. Regressing out global mean signal or not may produce completely different rsfMRI results. This step not only increases the inter-subject variations but also produces anticorrelated brain areas. The anticorrelated brain areas produced by global signal removal are currently under debate. Care should be taken in interpreting datasets processed by the regression of Global-curve.

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