臺灣博碩士論文加值系統

背景: 抗精神藥物主要用於治療思覺失調症病患，然而每位患者對於抗精神藥物的治療反應不盡相同。根據思覺失調症之實踐指南，至少需三至六周方可評估患者對於抗精神藥物之治療反應。許多研究指出，藥物治療效果的多樣性與分子基因間之異質性有著密不可分的關係，而這些差異可能表現於大腦白質纖維神經束之結構。因此，探討大腦白質纖維神經束結構與治療反應之間的關係是重要的議題。本實驗分為兩部分: 實驗一，探討緩解與非緩解兩組全腦白質纖維束之差異; 實驗二，建立預測獨立個案藥物治療效果之自動化機器學習模型，利用藥物介入前之擴散影像預測用藥後之治療反應。

實驗一: 實驗納入91位思覺失調症患者其中包含50位治療後症狀緩解與41位非緩解及50位健康受試者，運用擴散頻譜影像與全腦白質纖維神經束分析方式探討白質微觀結構與治療效果之相關性。三組比較結果顯示4條聯絡纖維(兩側穹窿及兩側鉤束)與2條連合纖維束 (顳葉端聯合神經束及海馬迴端聯合神經束)呈現顯著差異。組間比較結果顯示非緩解組在該6條纖維束之GFA值皆顯著低於健康受試組，而緩解組僅其中4條纖維束 (兩側穹窿、顳葉端聯合神經束及海馬迴端聯合神經束)之GFA值低於健康受試組，比較緩解與非緩解兩組，非緩解組在6條纖維束之GFA值皆低於緩解組。此結果表示白質纖維束改變之嚴重度在緩解組與非緩解組間有著相當程度的差異。根據此差異，大腦白質纖維束有潛力成為評斷治療效果之標誌。

實驗二: 實驗分為訓練組(123位思覺失調症個案，54位男性，年齡=31.9±8.7年)及測試組(25位未使用藥物之思覺失調症個案，12位男性，年齡=26.5±5.9年)。運用MAP-MRI 計算擴散指標，包括: GFA、AD、MD、RD、NG、NGO及NGP，利用以上7種擴散指標，逐步式分析擴散頻譜影像。應用一般線性回歸模式以年紀、性別及治療後藥物療效作為自變量，找出與藥物成效有相關性的區段並作為二次判別分析模型之預測因子。結果顯示該模型應用於未使用藥物之思覺失調症個案預測準確率為80%。此結果表示基於全腦白質纖維束之改變，個體化預測思覺失調症治療反應是可行的。白質纖維束特定區段的擴散指數可作為在抗精神病藥物介入之前，預測個案服用藥物後之治療反應之潛在影像生物標誌。

結論: 本研究最大突破是從組間比較邁向個體化預測，倘若結合更多臨床測量評估與其他神經影像資訊，此模型將可作為醫師擬定個案治療計畫之參考。

Background: Antipsychotic drugs are the standard treatment for schizophrenia; however, the treatment outcomes vary. According to the practice guidelines for schizophrenia, at least 3 to 6 weeks are required before treatment response can be determined. Previous studies revealed that different treatment outcomes may be attributed to the genetic and molecular heterogeneity of patients, which may be represented in the white matter structures of the brain. Therefore, a brain correlate that is characteristic of the treatment outcomes is desirable. This study was divided into two parts: Study 1, we aimed to demonstrate that the remission and non-remission groups had considerably distinct alterations in white matter tract integrity. Study 2, we proposed an approach to predict treatment response in each individual drug naïve patient.

Study 1: Ninety-one patients with schizophrenia (remitted, 50; non-remitted, 41) and 50 healthy controls in study, we performed diffusion spectrum imaging and the whole brain tract-based automatic analysis to investigate the relations between white matter microstructures and remission state in patients with schizophrenia. Results showed that 4 association fibers (bilateral fornices and bilateral uncinate fasciculi) and 2 commissure fibers (callosal fibers connecting the temporal poles, and hippocampi) had significantly different GFA values among the 3 groups. Post-hoc analysis showed that the non-remission group had lower GFA values in all 6 tracts than did the control; the remission group had lower GFA values than the control group only in 4 tracts (bilateral fornices and callosal fibers connecting the temporal poles, and hippocampi). Compared with the remission group, the non-remission group had lower GFA values in all 6 tracts. These results suggest that the remission and non-remission groups show considerably distinct severities of white matter tract alterations and it might be a potential prognostic marker for the symptomatic remission in patients with schizophrenia.

Study 2: The study involved a training group (123 patients with schizophrenia; male, 54; age, 31.9±8.7 years) and a testing group (25 drug-naïve patients with schizophrenia; male, 12; age, 26.5±5.9 years). Diffusion images were analyzed by MAP-MRI to produce 7 diffusion indices including GFA, axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), non-Gaussianality (NG), NG in orthogonal direction (NGO), and NG in parallel direction (NGP). The analysis was performed at each step of the tract-specific profiles and across all 7 diffusion indices. Each diffusion index was analyzed by a general linear model with age, sex and treatment response as independent variables and the steps that showed significant correlation with treatment response were selected as the predictors in a quadratic discriminant model. Results showed that the model was successful about 80% in correctly predicting the true response on individual drug-naïve patients with schizophrenia subject basis. The results imply that individualized prediction of treatment response in schizophrenia is feasible based on alterations of the whole brain white matter tracts. Diffusion indices in specific segments of the white matter tracts could serve as potential imaging biomarkers for predicting treatment response before antipsychotic treatment.

Conclusion: This thesis aims to find biomarkers of treatment response in schizophrenia. We approach the aim from group comparison to individualized prediction. In combination with clinical measures, the prediction model could be useful in treatment planning for each individual patient.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract v
Chapter 1 introduction 1
1.1 Outline 1
1.2 Background 1
1.2.1 Brain structure relationship in treatment outcomes of schizophrenia 2
1.2.2 White matter tract integrity as potential prognostic markers 2
1.3 White matter microstructure derived from diffusion MRI as predictor of schizophrenia on
an individual dataset. 5
1.4 Motivation and purpose 5
1.4.1 Limitation of prior research 5
1.4.2 Strategies for solving problems 6
Chapter 2 Methods of diffusion spectrum imaging and tract-based automatic analysis 7
2.1 Diffusion MRI and Diffusion spectrum imaging (DSI) 7
2.2 MRI data acquisition 7
2.3 Image Quality Assurance and DSI data reconstruction 8
2.4 Tract-based automatic analysis (TBAA) 9
2.4.1 A tract atlas comprising 76 major fiber tract bundles in a DSI template 9
2.4.2 A novel strategy for registering DSI datasets 14
Chapter 3 Discovery of white matter tract alterations of treatment outcomes in schizophrenia
using tract based automatic analysis 17
3.1 Introduction 17
3.1.1 Purpose 17
3.2 Methods 18
3.2.1 Participants 18
3.2.2 TBAA and Statistical analysis 19
3.3 Results 20
3.3.1 Demographic variables 20
3.3.2 White Matter Tract Integrity 20
3.3.3 Post-Hoc Between-Groups Analysis 21
3.3.4 Correlation between GFA in specific white matter tracts and PANSS items. 21
Chapter 4 Predicting treatment response in drug-naïve patients with schizophrenia using machine learning 25
4.1 Introduction 26
4.1.1 Purpose 26
4.2 Methods 26
4.2.1 Participants 26
4.2.2 MRI data acquisition and procedures 27
4.2.3 Image preprocessing and outline the procedure of prediction analysis 27
4.2.4 Feature extraction 28
4.2.5 Quadratic discriminant analysis 30
4.2.6 Receiver operating characteristic curve 31
4.3 Results 32
4.3.1 Demographic Variables of participants 32
4.3.2 Training group 33
4.3.3 Testing group 33
Chapter 5 Discussion and conclusion 35
5.1 Discovery of white matter tract alterations of treatment outcomes in schizophrenia using
tract based automatic analysis 35
5.1.1 Seven white matter tracts with significant integrity differences 35
5.1.2 Limitations 40
5.1.3 Brief summary 41
5.2 Predicting treatment response in drug-naïve patients with schizophrenia using machine
learning 42
5.2.1 White matter microstructure relationships with treatment response 42
5.2.2 Limitations 44
5.2.3 Brief summary 44
5.3 Conclusion 45
References 47
Appendix 56
Publications 57

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