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系統識別號 U0026-2008201516262700
論文名稱(中文) 以結構方程模型下之動態因素分析模型探索靜息態功能性連結歷程
論文名稱(英文) Use of Dynamic Factor Analysis Models within SEM framework to Explore the Process of Resting-State Functional Connectivity
校院名稱 成功大學
系所名稱(中) 心理學系認知科學碩士班
系所名稱(英) MS in Cognitive Science
學年度 103
學期 2
出版年 104
研究生(中文) 陳佳楨
研究生(英文) Jia-Jen Chen
電子信箱 student3841701@gmail.com
學號 U76024046
學位類別 碩士
語文別 中文
論文頁數 67頁
口試委員 指導教授-鄭中平
口試委員-陳欣進
口試委員-李俊霆
中文關鍵字 動態因素分析  靜息態功能性連結  結構方程模型 
英文關鍵字 dynamic factor analysis  resting-state functional connectivity  structural equation modeling 
學科別分類
中文摘要 人類於靜息態中,大腦血流活動會有某種同步自發性的活動,也就是大腦有功能性連結的情形,稱之為靜息態功能性連結,此議題核心的關鍵在於分類不同大腦單位的能力,甚至是進一步對大腦功能的動態歷程變化作深入探究。然而,現行常用的分析方法如設定種子相關分析缺乏其適用性,其原因一為忽略時間向度,二則是未考量測量誤差存在的可能性。如果以心理計量的角度著眼,動態因素分析模型是一個合適的取向,不僅具備資料縮減的能力,對於時間序列資料的序列相依特性也予以考量。

本研究將分為兩個研究,第一個蒙地卡羅模擬研究檢驗動態因素分析模型嵌於結構方程模型中,對於時間序列資料恢復其真實參數的能力。此模擬研究結果顯示動態因素分析模型,能用於分析非常態之時序資料,且不論是適配度指標或參數回復指標都有良好表現,說明以動態因素分析模型分析時序資料是適宜的。接著,實徵研究包含兩個子研究:研究(a)測試應用到實徵的近紅外線腦血流分析儀研究時,動態因素分析模型的分類能力;研究(b)探索靜息態功能性連結於時間向度上的議題。研究(a)顯示動態因素分析模型也對於實徵資料具有良好的分類能力;而研究(b)發現認知功能的時間歷程存在個體差異,其影響不盡相同,但皆少於320毫秒。從實徵研究中發現大腦認知功能間會維持網絡特定性,且隨著時間推移,其動作皮質區與視覺皮質區也大致維持功能獨特的網絡。

本研究透過建構在結構方程模型框架下的動態因素分析模型去探索靜息態功能性連結議題的歷程,除了比現行方法更能理解時間的動態歷程變化外,也提供實務研究者一種新的分析取向。
英文摘要 The goal of a resting-state functional connectivity(RSFC) study is to find certain grouping units of brain regions to represent their interconnected cognitive functions. However, most dominant dimension reductions methodologies are unsuitable for time series data. There are two main causes for the lack of suitability. First, the time-lagged relationships are highly correlated. Second, measurement errors are not considered. Dynamic factor analysis(DFA) not only represents the psychological process of human brain as factors but also decomposes RSFC into observable regional and inter-regional networks. Therefore, we suggest to use DFA approach to conquer these shortcomings. There are two studies including (1) A simulation study is conducted to examine the self-generated program’s ability to recover true parameters, and (2) empirical near-infrared spectroscopy data is used in addition to the simulation study to further access the validity of the DFA method. The simulation demonstrates DFA approach has a good dimension reductions potential in exploring for RSFC issue. From empirical study, we verify correlations were network specific and functionally distinct, we also find that cortices would maintain their specific function over time (less than 320 ms). Through dynamic factor analysis models embedded in SEM framework to explore the RSFC issues not only gives dynamic understanding of cognitive activity over time but also provides researchers a new analysis orientation.
論文目次 第一章 緒論 1

第二章 文獻回顧 3
第一節 靜息態功能性連結 3
第二節 時間序列的因素分析 6
第三節 動態因素分析 8
第四節 小結 13

第三章 蒙地卡羅模擬研究 17
第一節 研究目的與假設 17
第二節 研究方法 17
第三節 研究結果 20
第四節 小結 21

第四章 實徵分析研究 28
第一節 研究目的與假設 28
第二節 研究方法 28
第三節 動態因素分析方法 30
第四節 研究結果 34
第五節 小結 42

第五章 綜合討論與結論 49
第一節 綜合討論 49
第二節 研究限制與未來方向 51

參考文獻 54
附錄一 60
附錄二 64
附錄三 65
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