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研究生:李品萱
研究生(外文):Pin-Shiuan Lee
論文名稱:利用貝氏分析建立情感性精神疾患的腦磁波區辨分佈圖
論文名稱(外文):Discriminant map of neuromagnetic responses in patients with affective disorders using Bayesian analysis
指導教授:陳麗芬陳麗芬引用關係
指導教授(外文):Li-Fen Chen
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:2009
畢業學年度:97
語文別:英文
論文頁數:84
中文關鍵詞:腦磁波造影機器學習貝氏定理線性區別分析臉部情緒
外文關鍵詞:Neuroimagingpattern classificationBayesianlinear discriminant analysisfacial expression
相關次數:
  • 被引用被引用:0
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  • 下載下載:71
  • 收藏至我的研究室書目清單書目收藏:3
背景:憂鬱症和躁鬱症是很常見的情感性精神疾病,由於兩者有相似症狀,常造成診斷上偏誤。目標:我們利用貝氏分析及情緒誘發腦磁波之特徵,建立一個大腦活動區辨分佈圖藉此觀察情感性疾患和正常人之間對於情緒處理之差異。資料與方法:本研究包含憂鬱症、躁鬱症和正常人各二十人,記錄受試者看人臉表情圖片並作性別判斷時的腦磁波,我們利用最大對比光束構成法(MCB)估測大腦皮質活動,線性區辨函數作特徵擷取及貝氏分析來分類其群組間差異。結果:根據區域性的區辨分析,我們發現高敏感度的十二個區域:左緣上迴(supramarginal gyrus)、右賀希爾氏迴(Heschl's gyrus)、右海馬迴、左小腦、左中央前迴(precentral gyrus)、右前後扣帶迴、左右尾核、左枕葉楔葉(cuneus)、右嗅覺區、右頂下迴(inferior parietal gyrus),最後再將十二個區域作一次全域的區辨分析得到較好分類率的貝氏模型。結論:從分類器所得到的區辨區域和過去造影之文獻相符合,故情緒誘發腦磁波該特徵確實有助於特徵分群,提高診斷工具之敏感性。
Background: Patients with major depressive disorder (MDD) and bipolar disorder (BD) manifest similar clinical symptoms during their depressive episodes. This similarity often causes difficulties in diagnostic classification. In this study, we propose a novel method to construct discriminant maps for characterizing MDD patients, BD patients, and normal control (NC) groups.

Methods: Neuromagnetic responses to facial expressions of emotion were measured and analyzed by the maximum contrast beamformer. The estimated cortical activities were then processed by linear discriminant analysis for the extraction of essential features. Bayesian model was exploited to construct the sensitivity and specificity maps for each of the MDD, BD, and NC groups.

Results: Linear discriminant analysis identified twelve significant regions for differentiating the involved three groups. These regions include left supramarginal gyrus, right heschl gyrus, right hippocampus, left cerebellum, left precentral gyrus, right anterior and posterior cingulate gyrus, caudate, left cuneus, right olfactory, and right inferior parietal gyrus. Bayesian model derived from these discriminant regions benefit accurate classification among the MDD, BD, and NC groups.

Conclusions: The obtained discriminant patterns in this study were consistent with findings in previous studies. We suggest that emotion-related neuromagnetic response may be a reliable feature for classifying patients with affective disorders and potentially may benefit clinical diagnosis.
Contents



List of Figures iii
List of Tables vi
1 Introduction 1
1.1 Major depressive and Bipolar disorder 2
1.2 Alpha Oscillation 3
1.3 Pattern Classification 3
1.4 Thesis Scope and Organization 4
2 Feature Selection and Extraction 5
2.1 Introduction to Magnetoencephalography 6
2.2 Time-Frequency Map 9
2.3 Source Localization 10
2.3.1 Introduction to Maximum Contrast Beamformer (MCB) 10
2.3.2 Group analysis 12
2.4 Linear Discriminant Analysis 13
2.4.1 Introduction to LDA 13
2.4.2 Feature Selection 15
3 Classification 17
3.1 Framework of Discriminant Map 18
3.2 Bayes’ Theorem 18
3.3 Parzen Windows 19
3.4 Accuracy Evaluation 21
4 Experiment Results 23
4.1 Materials 24
4.1.1 Subjects 24
4.1.2 Paradigm 25
4.1.3 Data Acquisition and Preprocessing 25
4.2 Time-Frequency analysis 26
4.3 Source Localization Results 30
4.4 Conventional Analyses 34
4.4.1 ROI-Based Statistical analysis 34
4.4.2 Voxel-Based Statistical analysis 40
4.4.3 Correlations Between Psychopathology and Cortical Activity Within ROIs 45
4.5 Proposed Discriminant Analyses 47
4.5.1 Regional Discriminant Analysis 47
4.5.2 Global Discriminant Analysis 69
5 Discussion 72
5.1 Contribution of different ROIs for the classification accuracy 73
5.2 Discriminant ROIs compared with previous findings 74
5.3 Time components of discriminant regions for emotional processing 76
5.4 Feature extraction in Principal Components Analysis (PCA) 77
5.5 The parameter of parzen window 79
5.6 The two BD patients having diagnosed as MDD patients 80
6 Conclusions 82
Bibliography 85
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