背景：在功能性磁振造影的研究中，休息狀態時的去活化區域被認為是個『預設模式網路』，這種去活化現象被認為是一種資源的重整，以便大腦能將資源分布給所需的工作利用。而在功能性連結的研究上，低頻率的自發性內部同步波動代表著功能性連結，而研究顯示有許多功能的網路連結存在於休息狀態的腦內，包含運動網路、視覺網路、工作記憶網路、預設網路等等。這些網路被視為是一個功能性連結的基態，會因為不同的工作而改變，因此我們將此改變視為功能性連結的資源重整。
目標與假設：我們希望能探討休息狀態和工作記憶作業狀態的功能性連結改變及資源重整的方式。我們假設休息狀態的基態網路中，當一個工作記憶執行時，工作記憶網路的連結會上升，其它功能的網路連結會下降。並用圖論參數來量化資源重分配的方式。
資料與方法：三十位受試者皆接受休息狀態及工作記憶狀態的功能性磁振造影掃描，利用時頻交互訊息法以及多對一連結等級計算，用配對t 檢定檢測每個腦區在兩個狀態下的改變。再將連結等級在工作記憶狀態下有顯著上升的腦區當做工作記憶子網路，而在工作記憶狀態下有顯著下降的腦區當做休息狀態子網路，用圖論參數檢定兩個子網路在兩種狀態下變化的情形。
結果：後扣帶腦皮質等二十區在休息狀態的連結等級顯著的大於工作記憶狀態，而另外有額中葉等二十一區則完全相反。在全腦的圖論參數比較顯示，工作記憶狀態和休息狀態的連結沒有差別。但在子網路的比較時發現，工作記憶子網路在工作記憶時連結數、連結強度、連結群聚度及連結效率都比在休息狀態時高，而休息狀態子網路在休息狀態時，連結數、連結強度，連結群聚度及連結效率都比在工作記憶時高。
結論：此結果證實了工作記憶網路在工作記憶時功能連結等級會上升，而運動、視覺等其它基態網路則因資源重分配導致功能連結等級比休息狀態時下降。我們可以用圖論參數了解，這樣的資源調整是使工作區域的網路連結數上升、連結強度增加、連結群聚度上升及連結效率增強以支援所需工作，而其他區域則以連結數下降、連結強度減弱、連結群聚度下降及效率下降來平衡腦內的資源利用。

Background: In the functional magnetic resonance imaging (fMRI) studies, ‘Default Mode Network’ deactivated because of reallocation of processing resource for accomplishing a goal-directed task. Many studies related to functional connectivity have demonstrated that the low-frequency spontaneous fluctuation coherence in the resting brain may exhibit many functional networks such as motor network, visual network, default mode network and working-memory network. The functional connectivity in the resting state can be considered as baseline connectivity, and it modulates during performing a task. This motivates us to consider the modulation of functional connectivity as reallocation of processing resource.
Hypothesis and Aim: We hypothesize that the brain requires reallocation of processing resources while the brain changes the state form resting to performing a task. In specific, we hypothesize the stronger functional connectivity strength, more functional connections, a more cliqueness network, or a more efficient network occurs so that more processing resources are recruited from the resting state networks and transferred to the task-related network.
Materials and Methods: Thirty subjects performed both the resting-state and n-back tasks during the fMRI scans. After the analysis of time-frequency cross-mutual-information and calculation of n-to-1 functional connectivity, we compared the functional connectivity between two states using the paired-t test. Regions with larger increase of functional connectivity during the working-memory state were referred to the working-memory subnet, and regions with larger increase of functional connectivity during the resting state were referred to the resting subnet. Topological properties in graph theory of both subnets were computed and compared in the resting and working-memory state.
Results: The bilateral posterior cingulate gyrus, bilateral calcarine fissure, bilateral cuneus, bilateral superior occipital gyrus, left inferior occipital gyrus, left middle occipital gyrus, bilateral paracentral lobule, bilateral precentral gyrus, bilateral postcentral gyrus, right thalamus, right rolandic operculum, right fusiform gyrus, and right insula manifested a significantly larger connectivity degree in the resting state than that in the working-memory state. On the contrary, The bilateral median cingulate cortex, left insula, left superior parietal gyrus, right superior temporal gyrus, left rolandic operculum, bilateral middle frontal gyrus, left inferior parietal gyrus, left inferior frontal gyrus_opercular pole, left fusiform gyrus, right supplementary motor area, left superior temporal gyrus_temporal pole, bilateral inferior frontal gyrus_triangular pole, bilateral amygdala, bilateral hippocampus, and bilateral superior frontal gyrus presented a significantly larger connectivity degree during the working-memory state. Although the topological properties of whole brain between two states had no significant differences, the degree, strength, clustering coefficient, and global efficiency were larger for the working-memory subnet in the working-memory state, and that were larger for the resting subnet in the resting state.
Conclusions: The results demonstrated that in the working-memory state, the working-memory network recruited processing resources from motor network, visual network, and default mode network by increasing the connection number, connection strength, and rearranging the connection pattern to increase the efficiency.

論文電子檔著作權授權書…………………………………………………………………Ⅰ
論文審定同意書……………………………………………………………………………Ⅱ
誌謝…………………………………………………………………………………………Ⅲ
中文摘要……………………………………………………………………………………Ⅳ
英文摘要……………………………………………………………………………………Ⅴ
目錄…………………………………………………………………………………………Ⅶ
圖目錄………………………………………………………………………………………XI
表目錄………………………………………………………………………………………VII
第一章 前言…………………………………………………………………………… -1-
第一節 功能性特化及整合………………………………………………………… -2-
第二節 休息狀態相關研究………………………………………………………… -3-
第三節 工作記憶相關研究………………………………………………………… -4-
第三節 假設及目標………………………………………………………………… -4-
第二章 材料與方法 …………………………………………………………………… -6-
第一節 　材料 …………………………………………………………………… -7-
第二節 　實驗設計 ……………………………………………………………… -7-
第三節 　取像方法 ……………………………………………………………… -8-
第四節 影像前處理 …………………………………………………………… -8-
第五節 解剖分區 ……………………………………………………………… -8-
第六節 時頻互訊息法 ………………………………………………………… -9-
第七節 全腦圖論分析 ………………………………………………………… -11-
第八節 動態連結網路選取 …………………………………………………… -13-
第九節 動態網路功能性連結強度分析 ……………………………………… -13-
第十節 子網路圖論分析 ……………………………………………………… -14-
第三章 結果…………………………………………………………………………… -15-
第一節 　全腦圖論參數比較 …………………………………………………… -16-
第二節 　選取的動態連結網路 ………………………………………………… -16-
第三節 　兩狀態功能性連結強度調節 ………………………………………… -16-
第四節 　子網路圖論參數比較 ………………………………………………… -17-
第四章 討論…………………………………………………………………………… -18-
第一節 　腦內網路調節 ………………………………………………………… -19-
第二節 　功能性連節強度排行 ………………………………………………… -21-
第三節 　資源重整 ……………………………………………………………… -21-
第四節 　時頻互訊息法的特性 ………………………………………………… -22-
第五章 結論…………………………………………………………………………… -23-
參考文獻………………………………………………………………………………… -25-

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