動物行為與意識的形成機制一直是神經科學研究致力於解譯的問題，過去礙於光學系統與成像技術的限制，無法深入探究生物神經網路的運作情形，隨著螢光顯微術(Fluorescence Microscopy)與鈣離子成像技術(Calcium imaging)的發展，現今已可以透過螢光強度來量化鈣離子濃度，進而觀測特定腦區或神經細胞實時(real time)的訊號變化情形。
本研究旨在建立一套適用於鈣離子功能性螢光影像分析的系統，結合果蠅嗅覺刺激以及小鼠的震動感知等動物行為實驗，針對不同的數據條件與實驗需求進行調適，自動化的標註感興趣區域（Region of Interest, ROI），劃分因外在環境刺激而被激活的神經細胞，並且進一步對其分群，為動物行為實驗提供可參照的數據分析結果。
此系統架構可分成兩個部分，第一部分為數據前處理，針對鈣離子功能性影像拍攝過程中，因活體動物樣本晃動以及顯微術掃描時間差造成的影響進行動態校準與時間校準，並透過頻譜分析及空域平滑進行降維，最後重塑影像序列，以利後續的數據分析。第二部分為數據分析，有時域分析以及空域分析，對影像序列的時域資訊進行主成分分析、奇異值分解以及K-means集群分析；對影像序列的空域資訊則進行獨立成分分析、非負矩陣分解與點偵測法。最後結合時域與空域分析的結果，針對不同動物行為實驗所探討的變因可視化數據分析結果。

The formation mechanism of animal behavior and consciousness has always been a problem that neuroscience research is committed to interpreting. In the past, due to the limitations of optical systems and imaging technology, it was challenging to study the operation of biological neural networks. With the development of fluorescence microscopy and calcium imaging technology, it is possible to quantify calcium concentration through fluorescence intensity. Therefore, we can observe the real-time signal changes of specific brain regions or neurons.
This study presents a system architecture to analyze functional fluorescence images. Providing the data analysis result to animal behavior experiments, the developed system can mark the region of interest (ROI), delineate the area activated by external stimulus, and separate it into different clusters. The thesis can divide into two parts. The first part covers data preprocessing and image registration to eliminate the artifacts during data acquisition. Next, dimensionality reduction is applied to facilitate efficient data analysis. The second part focuses on data analysis, containing temporal and spatial analysis. The results of two different kind of analysis will be combined and visualized basing on animal behavior experiments’ influential factors.

摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 動物行為實驗 4
1.3.1 果蠅嗅覺刺激實驗 4
1.3.2 小鼠震動感知實驗 6
1.4 論文架構 8
第二章 影像校準與前處理 9
2.1 影像校準 9
2.1.1 時間校準 9
2.1.2 運動校準 10
2.2 影像前處理 11
2.2.1 快速傅立葉變換與功率頻譜分析 11
2.2.2 抗混疊濾波器 12
2.2.3 空域平滑 12
2.2.4 影像序列重塑 13
第三章 數據分析 14
3.1 時域分析 16
3.1.1 主成分分析 16
3.1.2 K-means集群分析 17
3.2 空域分析 18
3.2.1 獨立成分分析 19
3.2.2 非負矩陣分解 20
3.2.3 點偵測法 21
第四章 實驗結果 22
4.1 果蠅鈣離子功能性影像分析結果 22
4.1.1 主成分分析與K-means 集群分析結果 22
4.1.2 獨立成分分析與點偵測法分析結果 26
4.1.3 點偵測法K-means集群分析結果 30
4.2 小鼠鈣離子功能性影像分析結果 37
4.2.1 奇異值分解分析結果 37
4.2.2 非負矩陣分解分析結果 40
第五章 結果與討論 48
參考文獻 50

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