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研究生:吳翊禎
研究生(外文):Yi-Zhen Wu
論文名稱:大腦網路中核心子網路之連結在老化歷程中的變化
論文名稱(外文):The Changes of the Rich-Club Organization in Brain White Matter Networks in Normal Aging
指導教授:林慶波林慶波引用關係
指導教授(外文):Ching-Po Lin
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:腦科學研究所
學門:醫藥衛生學門
學類:醫學學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:47
中文關鍵詞:擴散權重造影老化圖形理論核心子網路結構
外文關鍵詞:diffusion weighted MRIaginggraph theoryrich-club organization
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全球人口正逐漸邁入高齡社會,老化相關的議題越加受到重視。在老化歷程中時常伴隨生理功能、認知功能的變化。先前文獻觀察到大腦中白質結構會隨著年紀而逐漸退化,進一步影響到大腦的功能整合導致認知功能的衰退。利用擴散磁振張量造影和網路分析,過去研究發現大腦網路中有一群彼此緊密連接的中樞節點形成核心子網路結構(Rich-club organization),被認為在大腦功能訊息的傳遞跟整合上扮演骨架的角色。然而,核心子網路的特性可能因分區數目或分區形式(表面積式分區或是體積式分區)有所改變;而且,核心子網路結構在老化的過程中的改變也尚未有定論。本研究目標將利用大腦皮質表面結合隨機分區的方式得到高解析度的分區,讓建構的大腦網路含有較多連結的資訊並且包含大腦皮質結構的訊息,再以此觀察老化過程中核心子網路結構的變化。
本論文主要分成兩個部分,第一部分以區域成長之方式在大腦皮質曲面積上進行隨機分區,並評估大腦網路中的核心子網路的穩定度,首先收取10位健康年輕受測者(平均年齡: 25 ± 4 歲)的兩次掃描影像,以區域成長之方式對平均大腦皮質表面積做100次隨機分區將大腦分成480~500區,並透過限制球型反卷積和機率性神經纖維追蹤技術建立大腦白質網路,計算核心子網路係數曲線,同時定義出每個受測者的核心子網路區域。將每個人的核心子網路區域疊加並對回大腦皮質上得到機率圖譜,觀察兩次掃描間核心區域位置的相似度。為了定量,本研究計算每個隨機分區下核心子網路區域在兩次掃描間的組內相關係數值(Intraclass correlation coefficient, ICC)值,藉此比較兩次結果的再現性。在第二部分則藉由第一部分研究所建立的分區方式,探討核心子網路在老化過程中的變化。收集567位健康高齡人(平均年齡:51~88)之擴散權重影像及T1加權影像,利用隨機分區方式結合限制性球面反卷積(CSD)演算建立大腦網路,並個別計算受測者的網路係數及核心子網路係數曲線(Rich-club coefficient curve) ,並將核心子網路係數曲線下面積當作整體核心子網路結構連結情況的代表參數。為了觀察核心子網路結構外連結的情形,本研究將大腦網路中的連結分成三種形態,分別為:核心區域連結(Rich-club connection, RC)、輔核心區域連結(Feeder connection, FC)及非核心區域連結(Local connection, LC)三種形式,並計算個別型態連結的平均強度和其強度曲線個的曲線下面積 (Area under curve, AUC)來代表整體連結強度,再將這些評估參數與年齡、行為功能的評量參數進行皮爾森相關性分析。
第一部分之結果顯示,不同隨機分區情況下兩次掃描的核心子網路區域的 ICC 值為 0.75,且頻率腦圖上頻率較高的區域落點都互相一致,例如顳橫皮層(transverse temporal cortex)、島葉(insula)、楔前葉(precuneus) 等。第二部分結果顯示,大腦整體連結及強度與年紀有顯著負相關性,顯示大腦結構會老化過程中發生變化。同時,核心區域、輔核心區域及非核心區域連結都與年紀有顯著負相關但非核心區域間的連結強度隨著年紀下降的幅度較大,代表在老化過程中非核心區域間連結容易衰退。然而,核心子網路係數曲線下面積與年齡呈現正向關,可能代表隨著年紀核心子網路連結更加緊密。三種不同型態的連結強度與多數認知分數無顯著相關性,除了核心區域跟輔核心區域,其連結強度與台灣老年憂鬱量表(TGDS)分數間有顯著的正相關。而核心區域間的連結強度與多數生理參數有顯著的負相關,例如:體重、淨體重、腰圍等。代表高齡人重要腦區間的連結強度與生理狀態的改變有關的,可能在老化過程中這些重要腦區間白質纖維的衰退是調控生理狀態的影響因素之一。
總結而言,本篇研究利用隨機分區之方式建立高解析度大腦分區模板,並且在兩次掃描中其重要腦區間的連結在不同分區中相對穩定。並觀察到高齡族群大腦網路連結特性及連結強度會隨著年紀改變,但重要區域間的連結架構卻相對穩定,但是在非核心區域的連結強度隨著年紀下降得情況比其他區域來的明顯,可能表示老化過程中,結構的衰退不成比例的發生在大腦不同角色的區域上。除此之外,核心網路的連結架構跟強度都與認知功能無顯著相關,很可能代表腦部重要區域間連結的變化不會影響到高齡人的重要認知功能的變化,但卻會間接的影響到身體狀態,而當中的機轉還需要細部探討。本研究提供高齡人大腦中重要區域的架構跟連結的變化,並且這些連結反映出生理狀態的關聯,為晚期的生命週期間大腦變化與老化相關疾病的研究提供標準。
Population aging is growing worldwide, and the related studies become more and more important. The physiological and cognitive functions usually decline with normal aging. The white matter (WM) degeneration with aging may affect the functional integration of brain connectivity and lead to cognitive alteration. Studies using diffusion magnetic resonance (MR) imaging and network analysis have demonstrated the rich-club organization as a core-subnetwork for communication in brain WM network, characterized by densely connecting hubs. However, the topological properties of rich-club organization may depend on the choice of parcellation for gray matter including the resolution and different method (volume-base or surface-base) of parcellation. Moreover, the connectivity changes in the rich-club organization of WM network during aging remains largely unknown. In this thesis, we proposed a random parcellation to build a high resolution brain network which provide more detail information of fiber connections and cortical structure. We further applied this parcellation to study the alteration of rich-club organization in elderly.
The studies in this thesis is twofold. In the first part, we proposed a surface-based random parcellation for anatomical network re-construction to examine the rich-club structures and tested the robustness of the rich-club organization. The MR data were acquired from ten healthy adults (age: 25 ± 4 years) who underwent MR scan twice within two weeks. The cortical parcellation was performed by a region growing-based algorithm for the cortical surface 100 times randomly to subdivide a predefined atlas to 480~500 regions once a time. To assess the topology of the brain network, we re-constructed the whole brain WM network with the constrained spherical deconvolution (CSD) tractography and calculated the rich-club coefficient for individual subject to define the rich-club organization. We overlapped the rich-club regions for 100 networks to evaluate the regions involved in the rich-club organization across random-parcellations. For quantification, the averaged intraclass correlation coefficient (ICC) between two session was calculated to access the reproducibility. The second part is to explore the rich-club organization changes in elderly by using the random parcellation to reconstruct brain network. Diffusion and T1-weighted imaging data from five hundred sixty-seven healthy elderly (aged from 51 to 88) were recruited. We re-constructed the whole brain WM network with the constrained spherical deconvolution (CSD) tractography, and calculated individual rich-club coefficient and its area under curve (AUC) as a presentation of connectivity of rich-club organization. Moreover, to study the intra-/inter- connectivity of rich-club organization, we classified connections to three different categories including rich-club (RC), feeder (FC) and local (LC) connections. We calculated the mean strength in each type of connections for each threshold k and the area under curve of resulting strength curve as a quantification of connectivity strength. We performed the Pearson’s correlation to analyze the relationship between the connectivity and the strength of WM network with age and physiological scores.
The results of first part showed that the ICC between two session is 0.75, indicating mid to excellent reproducibility. The regions involved in rich-club organization are consistent between two session, including transverse temporal cortex, insula and precuneus. The results of second part showed that global metrics of strength and density of whole brain connections were decreased with age. The area under curve of the strength curve in three types of connection were significantly negative correlated with age, especially the local connection (LC). However, the AUC of rich-club coefficient correlated positively with age, indicating that the connectivity in rich-club organization stay stable during aging. The AUC of both normalized rich-club coefficient and strength curve showed no significant correlation with cognitive scores except Taiwan Geriatric Depression Scale (TGDS). There were significantly positive correlations between TGDS score and both the AUC of strength curve of rich-club connection and feeder connection. In addition, the AUC of both normalized rich-club coefficient and strength curve exhibited significantly negative correlation with physical indices including weight, height and muscle strength indices. These results suggest that the alteration of rich-club connection due to white matter loss may indirectly affect the body states in elderly.
In summary, the rich-club organization showed relatively high reproducibility using surface-base random parcellation. During normal aging, the rich-club organization may stay robust and unchanged, but the strength of connections declined at the local connections, indicating the structural degeneration may occur disproportionally in different location of brain. Moreover, the changes of connections in rich-club were not associated with cognition but body-related indices. The cognition of the elderly may not be affected due to the preserved architecture of the core regions, but the decreasing of connections may indirectly affect the physical states. However, the different factors and mechanism involving in the alteration of the body states in aging needs to be addressed. The mechanism of how strength between the rich-club regions affect body and behavioral states needs to be further studied. These results provide the alteration of the connections between important regions during aging as a reference to study the changes of structural network during late life-span or in the aged-related diseases.
致謝 .................................................................................................i
摘要 ................................................................................................ii
Abstract..........................................................................................iv
目錄 ................................................................................................vi
圖目錄.............................................................................................viii
表目錄..............................................................................................x
壹.緒論 ...............................................................................................1
壹.一. 前言 ......................................................................................1
壹.二.研究背景...................................................................................1
壹.三.研究目的....................................................................................2
壹.四.論文架構......................................................................................3
貳. 基礎理論 ......................................................................................4
貳.一. 水分的布朗運動 (Brownian motion) ..................4
貳.二.擴散權重影像 ....................................................................6
貳.三.神經追蹤技術 .....................................................................8
貳.三.一.限制性球面反卷積 ......................................................8
貳.四.大腦皮質分區 .....................................................................10
貳.四.一.區域成長 (Region growing) ...................................10
貳.五.圖形理論與網路析 ................................................................11
貳.五.一.度、群聚係數、路徑長 ............................................12
貳.五.二.小世界特性 .......................................................................14
貳.五.三.核心子網路係數 ..............................................................14
貳.五.四.標準化核心網路係數 ...................................................15
參.方法與材料......................................................................................17
參.一.利用隨機大腦分區的核心子網路之穩定度評估 ......17
參.一.一.受測者....................................................................................17
參.一.二.影像擷取 .........................................................................17
參.一.三.影像處理及分析..............................................................17
參.一.四.隨機大腦分區 ................................................................18
參.一.五.白質神經纖維建立 .......................................................18
參.一.六.網路分析 ..........................................................................18
參.一.七.統計分析 ............................................................................20
參.二.核心子網路在老化過程中的變化....................................20
參.二.一.受測者 ................................................................................20
參.二.二.影像擷取 ...........................................................................21
參.二.三.影像處理和重建神經纖維分佈 .................................21
參.二.四.大腦網路建立與分析 ..................................................22
肆.結果..................................................................................................24
肆.一. 利用隨機大腦分區的核心子網路之穩定度評估....24
肆.一.一.隨機大腦分區 ...................................................24
肆.一.二.核心子網路係數 (Rich-club coefficient)........24
肆.一.三.核心子網路頻率分佈圖 ...............................................25
肆.一.四.核心子網路之 (ICC) 組內相關係數係數..............25
肆.二. 老化過程中,核心子網路之連結變化............................26
肆.二.一.核心子網路係數評估圖 .................................................26
肆.二.二.核心子網路係數與年齡之相關性分析結果 ..........27
肆.二.三.核心子網路中連結與強度跟生理參數之相關性分析。 ...................................................................................................32
肆.二.四.核心子網路中連結與強度跟認知參數之相關性分析 .......................................................................................................35
伍.探討 ..............................................................................................38
陸.限制與未來展望 ........................................................................42
柒.結論 ................................................................................................43
捌. 參考文獻......................................................................................44

圖2-1 水分在空間中做布朗運動 ................4
圖2-2 水份在生物組織內擴散受到環境之限制 ................4
圖2-3 雙極脈衝梯度自旋迴旋時序................6
圖2-8 訊號強度與回應函數 R(θ) 跟神經纖維密度函數之關係示意圖................9
圖2-9 體素中水分走向示意圖................9
圖2-10 區域成長概念及應用示意圖................11
圖2-12 網路型態示意圖................12
圖2-13 網路節點群聚示意圖................13
圖2-14 網路型態................14
圖3-1 隨機分區利用區域成長概念流程圖................19
圖3-2 網路分析及製作核心區域頻率圖................19
圖3-3 第二部分之實驗流程圖................23
圖4-1 大腦皮質區面積隨機分區示意圖................24
圖4-2 核心子網路係數曲線圖................24
圖4-3 核心子網路區域頻率分佈圖。................25
圖4-4 二元網路及加權網路之NRC曲線及平均NRC曲線................26
圖4-5 二元網路下的核心子網路組成架構................27
圖4-6 大腦整體連結係數與年紀之相關性................28
圖4-7 核心子網路架構連結度與年齡之相關性................29
圖4-8 核心子網路整體連結架構與年齡之相關性................30
圖4-9 不同型態平均連結強度與年齡之相關性................31
圖4-10 核心子網路連結架構與生理參數之相關性................32
圖4-11 核心子網路連結架構與生理參數之相關性................33
圖4-12 不同型態平均連結強度與生理參數之相關性................35
圖4-13 不同型態平均連結強度與認知功能之相關性................37
表3-2 第二部分實驗之受測者基本資料................21
表3-2 第二部分實驗之造影參數................21
表4-1 小世界係數、平均連結密度與年紀之相關性................27
表4-2 核心網路係數峰值與年齡之相關性分析................28
表4-3 核心子網路係數之曲線下面積與年齡之相關性分析................29
表4-4 三種不同核心子網路曲線下面積與年紀之相關性................30
表4-5 二元網路之核心子網路結構區域數與年齡之相關性分析................32
表4-6 二元網路之曲線下面積與生理參數之相關性分析................32
表4-7 加權網路下核心子網路係數曲線面積與生理參數之相關性分析................33
表4-8 核心區域連結強度曲線下面積與生理參數之相關性分析................34
表4-9 輔核心區域連結強度曲線下面積與生理參數之相關性分析................35
表4-10 核心子網路連結之曲線下面積與認知分數之相關性分析................36
表4-11 不同種類連結強度之曲線下面積與認知分數之相關性分析................36
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