思覺失調症是一種以社會行為異常以及認知混亂為特徵的精神疾病。過去的神經影像學研究發現，思覺失調症患者的大腦白質結構會發生異常改變，但是目前的研究對於白質結構的改變所發生的時機並不瞭解。因此本研究將探討思覺失調症的白質神經束病變和白質神經束發育與退化時機的相關性，以了解思覺失調症對於白質神經束改變的潛在致病機轉。首先，為了瞭解白質神經纖維束的微結構特徵在正常發育與退化過程中的模式，本研究使用全腦白質神經纖維自動化分析，獲得大腦主要76條白質神經束的完整度，以概化部分非等向性指標(GFA)和平均擴散係數(MD)加以量化。再者，利用524位其年齡涵蓋5至80歲的正常族群資料，依據性別分組，建構大腦白質神經束GFA和MD值隨著年齡變化的常模，並計算大腦白質神經束的常模特徵，例如白質成熟的巔峰年齡等。其後將158位思覺失調症患者與其同性別年齡的控制組，代入上述常模中計算標準偏差值(以z分數表示)，估計的標準偏差值將用來檢驗思覺失調症與常人間白質結構的差異、結構變化量與病程的關係以及結構變化量與神經束常模特徵之間的關係。研究結果發現:患者有12條神經束的GFA數值、49條神經束的MD數值和常人有顯著差異，其中有7條神經束的GFA值會隨著病程愈久而變得更低，而36條神經束的MD值在發病初期就和常人具有差異，但不會隨著病程而改變。除此之外，常模中GFA值到達成熟所需的年齡和患者z分數呈現正相關(r= 0.286，p值:0.012)，而常模中MD值的成熟年齡和z分數則呈現負相關(r= -0.586，p值:<0.0001)。本研究結果顯示思覺失調症與正常人間的白質神經束差異，並且其差異嚴重程度是與病程具有關聯性，最後藉由常模特徵與變化量之間的相關性，我們發現了越早發育的神經束受到思覺失調症的影響而損壞得較為嚴重。本研究發現了思覺失調症白質神經束的改變與神經束發育老化時機之間的關聯性，盼望此結果能作為思覺失調症致病機轉的相關學理基礎，並為臨床醫師提供診治時的依據。

Schizophrenia is a debilitating and multi-symptom mental disorder characterized by disrupted cognitive, social and behavior functions. Previous studies have investigated white matter impairments in schizophrenia via control-matched experiment design. However, the design of demographic-matched controls may encounter a problem of subject sampling bias that could lead to statistical results with potential preconception. Due to the arisen era of big data, the large amount of MRI data allows the researchers to create a normative database that covers nearly the whole lifespan to model the variables of interest in the scale of population. Therefore, in this study, we aimed to create a normative model using diffusion MRI data, then exploit it to investigate the white matter tract alteration in schizophrenia. To quantify the characteristics of white matter tracts, the whole brain tract-based automatic analysis (TBAA) was used to reconstruct and transform the image data into the diffusion index (generalized fractional anisotropy (GFA) and mean diffusivity (MD)) arrays called connectograms. The normative models were established based on 524 healthy subjects’ connectograms (292 males, age: 5-80 years). The connectograms of the 158 patients with schizophrenia were calculated into the standardized scores (z-score) individually via the normative models. The z-score of fiber tracts represented the degree of deviation from the normal population. In our results, there was significant difference of GFA z-scores in 12 tracts and MD z-score in 49 tracts. Also, the GFA z-scores of the 7 tracts were negatively correlated to the duration of illness. However, the MD z-scores of the 36 tracts revealed the difference against the normal population in the early stage of the disease onset instead of association in the duration of illness. In addition, the age of GFA peak in normal population was positively correlated (r = 0.286, p = 0.012) to the GFA z-scores in schizophrenia, and the age of MD trough was negatively correlated (r = -0.586, p < 0.001) to the MD z-scores. In summary, we found the white matter impairments in schizophrenia via the normative model approach. Also, the tracts developing earlier in normative trajectory were impaired more by the disease. Normative model approach allows us to investigate the structural changes in schizophrenia on an individual level. The derived z-score was a potential index as a reference for diagnosis. These results provided the scientific basis for the pathogenesis of schizophrenia.

中文摘要 i
Abstract iii
致謝 v
List of figures viii
List of tables ix
Chapter 1 introduction 1
1.1 Background 1
1.2 White matter changes in schizophrenia 2
1.3 Gender effect in white matter microstructure 4
1.4 Microstructural changes and atrophy in white matter tracts with aging 4
1.5 Statistical analysis method: ANCOVA 5
1.6 Motivation and purpose 6
Chapter 2 Materials and Methods 9
2.1 Imaging technique --- Diffusion spectrum imaging (DSI) 9
2.2 MRI data acquisition 9
2.3 Imaging analysis 11
2.3.1 Image quality assurance 11
2.3.2 DSI data reconstruction 12
2.4 Tract-based automatic analysis (TBAA) 13
2.5 Normative model of 76 tracts in healthy controls 18
2.5.1 Participants 18
2.5.2 Normative modeling 19
2.5.3 Model assessment 20
2.5.4 Age of peak/trough, percent change of maturation and senescence 20
2.6 Z-score of individual Schizophrenia patients 23
2.6.1 Schizophrenia demographics 23
2.6.2 Z-score and statistical analysis 24
Chapter 3 Results 26
3.1 Age-related changes within tracts 26
3.2 Z-score differences of 76 tracts in schizophrenia 31
3.3 Multiple linear regression analysis 34
3.4 Correlation between GFA/MD z-score and age of peak/trough in 76 white matter tracts 37
Chapter 4 Discussion 39
4.1 Normative model of white matter tract in whole brain 39
4.2 White matter microstructure changes in schizophrenia 43
4.3 Limitations and future work 44
Chapter 5 Conclusions 45
References 46
Appendix 50
Publication 58

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