臺灣博碩士論文加值系統

我們大腦中的灰質與白質隨著年歲漸增而逐漸退化，應用神經造影技術來分析、歸類正常老化與病理性神經退化的特徵，能幫助我們定義正常老化並為腦健檢建立檢驗對照標準◦過去神經造影研究發現，健康老人與老年輕度認知障礙患者的颞葉邊緣神經束與胼胝體神經束皆會發生改變，本篇論文推論白質神經纖維束是一套互相協變的網絡系統，而非各自獨立運作的個體；我們假設正常老化過程的白質神經纖維系統的退化特徵與病理性神經退化過程的退化特徵不同。我們針對顳葉邊緣白質系統與胼胝體白質系統來探討這個假設，我們設計橫斷面研究來分析健康年輕人、健康老人與老年輕度認知障礙患者這三組受試者的神經退化特徵，分成三個子研究來比較：健康老化子研究(比較健康老人與健康年輕人的差異)，老年輕度認知障礙研究(比較健康老人與老年輕度認知障礙患者的差異)，與病理性老化研究(比較老年輕度認知障礙患者與健康年輕人的差異)。我們運用擴散磁振造影模板化自動分析影像技術評估運算六條顳葉邊緣白質系統神經束與十八條胼胝體白質系統的白質神經束廣義不等向性擴散指標的走向輪廓資料，我們應用三種統計分析方法：白質神經束廣義不等向性擴散指標的走向輪廓資料的平均值比較法、無閥值集群加權法與白質神經束協變性法來分析每個子研究中白質神經束退化特徵的組間差異。藉由這樣的研究設計，我們希望能辨別與分類正常老化過程與病理性老化過程白質神經束的細部變化特徵。我們發現這兩個白質系統在正常老化過程與病理性老化過程的改變具異質性，展現出各自的變化特色：顳葉邊緣白質系統的退化與正常老化緊密相關，而胼胝體白質系統的退化與病理性老化關係密切。更具體而言，顳葉邊緣白質系統隨著年歲增長退化，而胼胝體白質系統則在老年輕度認知障礙患者身上嚴重退化。胼胝體白質系統的白質神經束協變性隨著年歲增長而降低，而病理性老化過程更加明顯降低。我們的結果認為胼胝體白質系統的退化特徵具有做為病理性老化生理指標的潛力，本論文驗證兩個白質系統在老化過程展現不同特色，這項研究讓我們一窺神經退化疾病背後的複雜病理機制◦

Gray matter and white matter of the human brain degenerate with age. Using neuroimaging to characterize age-associated degenerative patterns in normal and pathological brain aging processes can help define normal aging and set up a reference for brain health exam. Previous neuroimaging studies on white matter fiber changes in healthy old people or patients with mild cognitive impairment (MCI) reported alteration of the tracts in the temporal-limbic regions and commissural fibers. The present thesis assumes that white matter tracts may be viewed as an inter-dependent network system, rather than isolated entities. We hypothesized that the degenerative patterns of white matter systems were different between normal aging and pathological aging. We investigated this hypothesis in the temporal-limbic fiber system and commissural fiber system. We conducted a cross-sectional study to investigate fiber degeneration patterns in three groups of participants, i.e. healthy young group, healthy old group and MCI group. The investigation entailed three separate studies including normal aging study (comparison between healthy young and healthy old groups), MCI study (comparison between healthy old and MCI groups) and abnormal aging study (comparison between healthy young and MCI groups). We used the template-based automatic analytical method (TBAA) to generate the profiles of generalized fractional anisotropy (GFA) index of the six tracts in the temporal-limbic fiber system and 18 tracts in the commissural fiber system. We used three statistical methods, i.e. mean GFA comparison, threshold-free cluster weighted (TFCW) method, and tract covariance analysis, to analyze fiber degeneration patterns in each of the studies. With this study design, we expect to identify the detailed patterns of fiber degeneration under healthy and pathological aging processes. We found heterogeneous aging effects and different patterns of age-associated changes in the two fiber systems. The degeneration pattern of the temporal-limbic fiber system adheres to the normal aging process, whereas that of the commissural fiber system is prone to pathological aging process. Specifically, the temporal-limbic fibers degenerated with age, whereas the callosal fibers degenerated more severely in MCI. The tract covariance of the commissural fiber system decreased with age and became more obvious in pathological aging. Our results suggest that the degeneration patterns of the commissural fiber system could serve as a promising biomarker of pathological aging. The present thesis demonstrates different age-associated patterns of fiber degeneration in two fiber systems. This knowledge may shed light on the complex pathological mechanisms of brain aging.

Contents

口試委員審定書 I
致謝 II
中文摘要 III
ABSTRACT V
CHAPTER 1. INTRODUCTION 1
1.1 BIOLOGY OF BRAIN AGING AND WHITE MATTER AGING 3
1.2 WHITE MATTER CHANGES IN MILD COGNITIVE IMPAIRMENT 4
1.3 WHITE MATTER INTEGRITY AS MARKERS OF NORMAL AGING AND PATHOLOGICAL AGING 5
1.4 HYPOTHESES OF THE TWO NETWORK SYSTEMS IN THREE STUDIES REGARDING TO NORMAL AGING AND PATHOLOGICAL AGING 6
CHAPTER 2. PARTICIPANTS AND METHODS 15
2.1 STUDY PARTICIPANTS 15
2.2 METHODS OF DIFFUSION SPECTRUM IMAGING AND TRACT-BASED AUTOMATIC ANALYSIS.. 17
2.3 STATISTICAL ANALYSIS 33
CHAPTER 3. RESULTS 37
3.1 MEAN OF GFA PROFILES 37
3.2 THRESHOLD FREE CLUSTER WEIGHTED METHOD 38
3.3 TRACT COVARIANCE MATRIX 44
CHAPTER 4. GENERAL DISCUSSION 51
4.1 AGING PATTERNS ARE HETEROGENEOUS IN THE TEMPORAL-LIMBIC FIBER SYSTEM AND COMMISSURAL FIBER SYSTEM 51
4.2 ANALYTICAL METHOD DETECTING EARLY PATHOLOGICAL CHANGES IN WHITE MATTER TRACT…53
CHAPTER 5. LIMITATIONS AND FUTURE DIRECTIONS 56
REFERENCE 58

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