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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65090


    Title: 探討同步功能性磁振造影與腦電圖於夜間睡眠研究;Investigating Dynamic Brain Oscillation and Synchronization in Nocturnal Sleep using Simultaneous fMRI and EEG
    Authors: 柯煜庭;Ko,Yu-ting
    Contributors: 生物醫學工程研究所
    Keywords: 功能性磁振造影;腦電圖;睡眠;希爾伯特黃轉換
    Date: 2014-08-29
    Issue Date: 2014-10-15 14:40:06 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 睡眠對人類非常的重要,其功能除了恢復身體的疲憊外,睡眠中的大腦還進行了多種認知功能像是訊息的傳輸、記憶鞏固…等。這些功能皆源自於大腦本身的自發反應,但是自發活化反應的產生與機制仍然無法清楚解釋。以往在自發腦活化的研究中,大多探討在低頻振盪下的功能性連結,像是在預設模式網路(Default Mode Network)中產生與前額葉連結中斷,與睡眠中意識的消退有關聯。但是對於低頻振盪甚至是較高的頻帶其頻率的關聯性並沒有太深入的了解,因此我們利用了同步腦電圖與靜息態功能性磁振造影來進行研究,觀察夜間睡眠期間的大腦自發活化反應的情形。藉由腦電圖在睡眠當中具有分辨睡眠階段的能力,以及其不同頻帶的波形在生理和心理上的意義,探討靜息態功能性磁振造影訊號的頻率關係是否也具有相同的判別功效。本實驗從睡前經過睡眠到清醒之間這樣的差異性來進行觀察,並研究四個主要的關於睡眠調節和影響的大腦網路: 1. 主要運動區(Primary Motor Cortex) 2. 預設模式網路(Default Mode Network) 3. 海馬迴網路(Hippocampal Network) 和4. 丘腦網路(Thalamic Network),除觀察各大腦腦區活化和連結性變化外,也對照腦電圖上頻帶改變的相關性。我們的結果發現功能性連結在睡眠中對側大腦會有下降的情形產生,但是在DMN下則是前後腦的功能性連結下降,將ALFF與功能性連結相比發現兩者在睡眠不同階段下會有正好相反的趨勢產生。其中ALFF分頻結果顯示出睡眠中的大腦改變最劇烈的頻帶會落在低頻的部份(0.01-0.1Hz),而在頻率的對應關係上,我們發現在不同大腦區域下隨著睡眠階段不一樣,腦電圖與功能性磁振造影的頻率對應也會有所不同。;Sleep recovers our tired body and refreshes our brain. Multiple types of cognitive function occurs during sleep, including information processing, memory consolidation…etc. These functions are originated from the spontaneous activities in the brain and the mechanism remains to be solved in sleep. Previous study found the default mode network had loss of longitudinal connectivity, related with the drop of consciousness level in the deep sleep. However, unlike EEG waves, the low-frequency fMRI fluctuations are lack of its spectral meanings, and the connectivity changes only provide the empirical observations. Therefore, we used the simultaneous EEG-fMRI recordings during sleep to map the spectral associations of thelow-frequency activities between two modalities in this study. EEG can help us to identify the sleep stages. It also provided the different frequency bands which have particular meaning of the physiological and psychological. So, we can compare the frequency corresponding between EEG and fMRI in the different statements. Furthermore, by the EEG frequency bands, we can more understand the different frequency on the fMRI signal spontaneous fluctuation in resting-state.
    Appears in Collections:[生物醫學工程研究所 ] 博碩士論文

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