決策行為與我們的生活息息相關，決策所涉及的範圍甚廣，不論是簡單的賭博，或是複雜的金融投資，都需要參與決策，在學術界中也是一項重要的研究領域。而學術上有許多運用社會網絡分析(Social Network Analysis)來研究大腦腦區彼此之間的關聯性，但是大部分研究的實驗設計並非涉及決策，且部分文獻僅探索大腦「結構性」的網絡，因而無法找出在特定的決策任務下「功能性」腦區之間的關聯程度。
為此，本研究的目的在建構一個大腦網絡分析的系統，收集彙整現有的大腦決策相關文獻，其中包括獎賞、選擇、情緒、風險等七項的決策事件，並應用社會網絡分析方法來呈現在某一特定的決策事件下，腦區彼此之間的連結與強度，以及活化腦區的網絡中心性。本研究匯入了150篇已發表期刊中決策事件和活化腦區的關係，並加以編碼鍵入資料庫，在系統網站建立分析指標演算法，並使用UCINET工具驗證此系統的正確性，最後以情緒事件來評估其效度。

Decision making is everywhere and it involves a broad range of tasks, from a simple gambling to a complex financial investment. Thus, decision making is very important in our life and also a key research area in academia. Since decisions are made by brains, decision making research has evolved from observing behavior to investigating the neural mechanisms involved in neural decision making. Many research findings regarding the functionality of various brain regions have been reported but conflicts also exist. We need to resolve the conflict through further analysis of prior research results.
Social Network Analysis is a powerful data mining tool for finding the correlation among different players in a community. It has been applied to analyzing neural networks in the brain but not many in the decision making area. Hence, the purpose of this study is to construct a network analysis system that can be used to consolidate findings in existing neural decision making literature for a more comprehensive and valid interpretation of decision networks in the brain.
A prototype system has been developed to apply the social network analysis method to present links, connection strengths and centrality of brain regions under a specific decision task, such as reward, emotion, and so on. To evaluate the designed system, over 150 published articles in neural decision making has been coded and the system was verified by comparing with UCINET for correctness. Emotion was selected as an event to assess its content validity. Both assessments show the value of this prototype system.

論文審定書 i
誌謝 ii
中文摘要 iii
英文摘要 iv
目錄 v
圖次 vii
表次 viii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的與問題 3
第四節 研究流程與論文結構 4
第二章 文獻探討 5
第一節 決策神經科學的研究問題 5
一、腦部功能概要 5
二、決策神經科學的研究 5
第二節 情緒決策 8
一、和情緒反應相關的腦區 8
二、情緒決策之彙總分析 9
第三節 社會網絡分析 11
一、社會網絡的源起、定義與發展 11
二、關係資料型態 13
三、社會網絡分析的衡量指標 14
四、加權後的中心性指標 20
第三章 研究方法 25
第一節 設計科學研究法 25
第二節 系統雛型之架構 26
第四章 系統分析設計與建置 27
第一節 系統需求分析 27
一、系統功能分析 27
二、系統開發環境分析 28
第二節 系統功能與介面設計 29
一、社會網絡分析方法 29
二、網站介面之功能 32
第五章 系統驗證 37
第一節 以UCINET分析中心性 37
第二節 情緒事件之分析 41
一、大腦第二層級之分析 41
二、大腦第三層級之分析 42
三、跨層級分析 44
第六章 結論與建議 45
第一節 研究結論 45
第二節 研究貢獻 46
第三節 研究限制與未來展望 47
參考文獻 48
附錄一 GID編碼 55
附錄二 編碼後的資料 57

中文文獻：

陳韋亭(民103),大腦決策機制之資料探勘研究,中山大學資訊管理學系學位碩士論文。

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參考書籍：
榮泰生（民 102）。UCINET在社會網絡分析(SNA)之應用。臺北市：五南。
陳世榮（譯）（民 102）。社會網絡分析方法：UCINET的應用（原作者：Robert A. Hanneman, Mark Riddle）。高雄市：巨流。（原著出版年： 2005）

參考網站：
http://neurosynth.org/
http://www.analytictech.com/networks/whatis.htm