近年來功能性核磁共振造影在理解人類行為的領域中成為一股潮流，神經科學方面的研究也顯示透過條件限制的腦活動可用來解釋人類的身心狀態或是行為的表現。同時隨著深度學習技術的發展，工程中開始利用磁振造影的特徵向量進行人類行為辨識以及疾病預測。然而，在學習特徵向量的過程中往往忽略了元資訊對磁振造影訊號所帶來的影響，換言之，神經科學所使用的限制條件(例如刺激物的設計或是行為量表結果)在磁振造影的訊號處理時被忽略了。為了解決這個問題，本研究提出條件對比嵌入網路以將這些額外卻重要的資訊融入在我們的架構中，一同學習更全面的特徵向量。透過此方法，我們在臉部識別能力辨識任務以及失智症辨識任務中都有較高的準確率，且透過視覺化、t-test、重要樞紐偵測等分析，我們發現對任務而言的重要腦區資訊也一同被學習在特徵向量中。

Functional Magnetic Resonance Imaging (fMRI) has recently become a trend for understanding human behavior. Researches from neuroscience have demonstrated that brain activation under certain condition shown in fMRI could explain the external behavior status and performance. With the advent of deep learning, computational works are conducted to fulfill the disease detection or behavior prediction using representation derived from fMRI. However, the previous works may ignore what meta information, i.e., stimuli manipulation and behavior performance, has brought into fMRI signal during the representation learning process. In this study, we propose a Condition-Contrastive Embedding Network (CCEN) to incorporate meta information into our architecture to learn a more comprehensive representation. We experiment this method on two tasks: Face Perception Ability Recognition and Dementia Classification Task, where we found better predicting performance using the representation learned from CCEN framework. Furthermore, through visualization, t-test and hub alteration detection analysis, we discover that the information of key brain regions is simultaneously embedded in our learned representation.

摘要 I
ABSTRACT II
誌謝 III
CONTENTS IV
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 FACE PERCEPTION ABILITY RECOGNITION 5
2.1 INTRODUCTION 5
2.2 DATABASE 8
2.2.1 fMRI Acquisition and Processing 9
2.2.2 Face Perception and Memory Assessments 11
2.2.2.1 Taiwanese Face and Memory Test (TFMT) 11
2.2.2.2 Component Task 11
2.3 RESEARCH METHODOLOGY 12
2.3.1 Graph Embedding Techniques 13
2.3.2 Event-Contrastive Connectome Network (E-cCN) 15
2.3.2.1 Graph Construction 15
2.3.2.2 Graph Embedding of Functional Connectivity 15
2.3.2.3 Contrastive Loss Embedded Network 16
2.4 EXPERIMENTS AND RESULTS 17
2.4.1 Experimental Settings 17
2.4.2 Network Configuration 18
2.4.3 Recognition Results 18
2.4.4 Brain Connectome Visualization 21
CHAPTER 3 DEMENTIA CLASSIFICATION TASK 23
3.1 INTRODUCTION 23
3.2 DATABASE 27
3.2.1 Resting-State fMRI Acquisition and Processing 28
3.2.2 DTI Acquisition and Processing 29
3.2.3 Neuropsychological Assessment 30
3.2.3.1 CDR 30
3.2.3.2 MMSE 30
3.2.3.3 MoCA 31
3.3 RESEARCH METHODOLOGY 31
3.3.1 Behavior Score-Embedded Encoder Network (BSEN) 31
3.3.1.1 The Convolutional Autoencoder (ModelAE) 32
3.3.1.2 The Contrastive Center Loss Model (ModelC) 32
3.3.1.3 Dementia Classification and Fusion Technique 33
3.4 EXPERIMENTS AND RESULTS 34
3.4.1 Experimental Settings 34
3.4.2 Network Configuration 35
3.4.3 Recognition Results of Rs-fMRI Database 36
3.4.4 Recognition Results of DTI Database 38
3.4.5 Statistical Analysis of Rs-fMRI Database 41
3.4.6 Nodal Efficiency Analysis of DTI Database 43
CHAPTER 4 CONCLUSION 47
REFERENCE 48

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