臺灣博碩士論文加值系統

在功能性磁振造影活化分析上，基於一般線性假設下的典型血流動力反應模型已被廣為使用。典型模型在使用上對於信號外形或時間參數，如延遲時間、峰值時間或持續時間等，在不同個體之間往往採用固定式的設定，然而不同人或不同腦區之間的信號表現已被證實是不盡相同的，因此統一的典型血流動力反應模型對於腦功能活化分析使用上並不夠彈性，以至於降低腦活化偵測靈敏度。本研究中，我們透過資料推論分析（data-driven）方式，建立出更能貼切表達出個人腦血流變化的個人化血流動力反應曲線，以提高腦功能活化偵測敏感度。
一開始，透過空間-空間群聚分析（spatial temporal clustering analysis，STCA）對時間序列分群，根據活化機率分佈挑選出包含活化資訊區域。再者，每個群聚經由空間相關性與肯氏和諧係數（Kendall’s coefficient of concordance）兩個純化過程排除干擾信號，提高信號一致性。最後計算最大活化機率群聚與其信號以建立活化區的個人化血動力反應曲線。
結果顯示出每個人之間的腦功能反應對於相同刺激，不論是在波形或是反應時間上都有差異。其中對於動作刺激的反應延遲時間（onset latency）而言，典型反應模型採用的固定延遲時間明顯長於個人化血流動力反應曲線所捕捉的真實時間長度約1~2 TR。另外，根據腦活化圖，對於活化位置分佈（spatial extent）或是 t 值範圍而言，與典型反應模型結果相較之下，本法所得結果有更顯著的統計活化表現。
本研究以有效的資料推論分析，建立出個人化血流動力反應曲線。不僅能更真實地表達出每個人腦功能信號反應上的差異，增加分析上的個人適應性，提高活化偵測靈敏度。

In the analysis of fMRI, the canonical hemodynamic response model (canonical HRM) based on the hypothesis of the general linear model (GLM) is widely used. However, the canonical HRM, which used the fixed response parameters of onset latency, time-to-peak and duration, limits the flexibility of statistical analysis and results in the reduction of sensitivity for the brain activity detection. In this work, we introduced a method for estimating the subject-specific hemodynamic response without a prior paradigm. First, according to the spatial temporal clustering analysis (STCA), the clusters of potential activated region were selected. Second, each cluster was purified iteratively to increase the concordance of the time courses. Finally, the specific estimated response was computed from the time courses of the cluster being activated. The results show that the response shape and the time parameters of specific response across subjects were different, and the onset latency of motor stimulation of canonical HRM was slower than the specific HRM one about 1 to 2 TR. In addition, the activation maps presented more significant statistical results for the activated voxels and the T value. We conclude that the specific HRM is adaptive to signal variety due to subjects even if the temporal information of stimulation is unknown, and it enhances the sensitivity of the brain activation detection in contrast to the canonical HRM.

中文摘要….………………………………………………………………………………I
英文摘要………………………………………………………………………………III
誌謝…………………………………………………………………………………………IV
目錄……………………………………………………………………………………………V
圖表目錄………………………………………………………………………………VII
第一章 緒論…………………………………………………………………………1
1.1 研究背景…………………………………………………………………1
1.2 研究動機與目的……………………………………………………3
1.3 論文架構…………………………………………………………………4
第二章 原理…………………………………………………………………………5
2.1 BOLD fMRI…………………………………………………………………5
2.1.1 血氧濃度對比………………………………………………………5
2.1.2 血液動力學反應…………………………………………………6
2.2 數據前處理……………………………………………………………………8
2.3 fMRI 資料分析………………………………………………10
2.3.1 假說推論分析………………………………………………………10
2.3.2 資料推論分析………………………………………………………13
2.3.2.1組成分解法………………………………………………………………14
2.3.2.2群聚分析…………………………………………………………………15
第三章 材料與方法……………………………………………………………19
3.1 活化信號的空間域分群…………………………………………………20
3.1.1 空間-時間群聚分析分群…………………………………………20
3.1.2 多次性空間-時間群聚分析………………………………………22
3.1.3 歧異點信號的過濾………………………………………………………23
3.2 群聚純化……………………………………………………………………………24
3.2.1 空間相關性純化…………………………………………………………24
3.2.2 肯氏和諧係數純化……………………………………………………25
3.3 最適個人化血流動力反應曲線的建立…………………………26
3.4 刺激任務設計與數據收集………………………………………………26
第四章 研究結果……………………………………………………………………29
4.1 多次性分群………………………………………………………………………30
4.2 群聚純化……………………………………………………………………………31
4.3 個人化血流動力反應曲線………………………………………………33
4.4 腦區活化圖…………………………………………………………………………37
4.5 ROI 測試…………………………………………………………………………41
第五章 討論………………………………………………………………………………45
5.1 分群正確性的影響……………………………………………………………46
5.2 群聚純化………………………………………………………………………………47
5.3 最適血流反應曲線建立……………………………………………………49
5.4 個人化血動力反應曲線與典型反應曲線比較………………50
5.5 腦活化圖的比較…………………………………………………………………51
5.6 ROI 測試…………………………………………………………………………52
第六章 結論………………………………………………………………………………53
參考文獻……………………………………………………………………………………………54

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