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研究生：	王鈺涵 Wang, Yu-Han
論文名稱：	即時神經回饋功能性磁振造影:開放平台分析工具之建置 Real Time Neurofeedback Functional Magnetic Resonance Imaging: An Open Platform Analysis Toolbox
指導教授：	吳明龍 Wu, Ming-Long
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	52
中文關鍵詞：	即時神經回饋功能性磁振造影、功能性磁振造影、神經回饋
外文關鍵詞：	real time fMRI neurofeedback, fMRI, neurofeedback
相關次數：	點閱：96 下載：0
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神經回饋是(Neurofeedback)一個很有希望的方法，以非侵入式調節人體大腦中的活動，應用於治療精神疾病或是強化大腦的功能。功能性磁振造影(Functional magnetic resonance imaging, fMRI)的進步目前已經能夠做到在下一個影像取得前完成所有資料的處理和分析並將分析後的結果回饋到掃瞄儀器中的受試者。我們利用即時功能性磁振造影的回饋機制(Real time fMRI neurofeedback)讓受試者能夠透過自我調控進的方式進而改變大腦的功能、認知或是行為。透過一段長時間的訓練，受試者能夠在感興趣的區域(Region of interest)上控制BOLD信號。
本研究將使用MATLAB程式製作出一套可供實驗上使用的開放式平台工具，工具中提供下列幾項執行Neurofeedback實驗所需的基本功能: DICOM影像轉檔、移動校正、即時的大腦活化圖、取得ROI上的訊號並估計回饋值(Neurofeedback signal)，希望以簡易的操作方式讓使用者能夠快速上手，除此之外，利用開放式平台能夠地輕易地擴充功能，依照使用者的需求整合並加入所需的功能到主程式中，為了確實達到工具的可行性，更搭配一個手指運動的實驗實際到掃瞄儀器上實作。

Neurofeedback is a promising approach for non-invasive modulation of human brain activity applied for the therapeutic treatment of mental disorders and enhancement of brain performance. Advances in fMRI data acquisition and processing have made it possible to analyze brain activity as fast as the images are acquired, allowing the information to be feedback to subjects in the scanner. We applied real-time fMRI neurofeedback to train self-regulation of neural activities to produce changes in brain function, cognition, or behavior. After a brief training period in the scanner, the participants were able to regulate their BOLD fMRI activation in target ROI. Here we present a generalized software toolbox for fMRI neurofeedback. The toolbox integrates tools for image pre-processing in real-time, ROI-based feedback and real time GLM. In addition, a user-defined neurofeedback module allows users to easily design and run fMRI neurofeedback experiments. In the presented open software platform, scientists can also use encapsulate codes to add additional functionalities from external libraries, enabling flexible customization or integration of graphical interfaces and data processing.

摘要	1
Abstract	2
誌謝	3
Table of Contents	4
List of Figures	6
List of Tables	8
Chapter 1 Introduction	1
1.1	Motivation	1
1.2	Background and Related Works	3
1.2.1	Functional Magnetic Resonance Imaging	3
1.2.2	Real time fMRI neurofeedback	5
Chapter 2 Materials and Methods	7
2.1	Toolbox Overview	7
2.2	The Graphical User Interface	10
2.3	Data Acquisition and Processing	12
2.4	Real-time data processing	14
2.4.1.	Motion Correction	15
2.4.2.	Offline General Linear Model	16
2.4.3.	Functional Localizers and Feature Selection	17
2.4.4.	Real time general linear model	18
2.4.5.	Neurofeedback procedure	21
2.5	Experimental procedures	23
2.5.1.	Task practice	23
2.5.2.	Localizer run (Figure 2-11)	24
2.5.3.	ROI localization	25
2.5.4.	Neurofeedback run (Figure 2-13)	26
2.5.5.	Transfer run	27
Chapter 3 Result	28
3.1	Offline analysis of the ROI activation	28
3.2	Retrospective ROI analysis	30
3.3	Whole-brain activation analysis	32
3.4	The signal change during real-time fMRI Sessions	37
3.5	Estimation of system routines delays	39
Chapter 4 Discussion	41
4.1	Efficiency of the real time neurofeedback training	42
4.2	Brain activation	43
4.3	Turbo Brain Voyager feedback values vs. our study	44
4.4	Post hoc analysis during real-time fMRI sessions	46
4.5	Activation in the brain ventricles	47
Conclusion and future work	48
Reference	50


Figure 2-1 The flowchart that shows step-by-step approach through our system for real-time neurofeedback.	7
Figure 2-2 “Data Input” frame	10
Figure 2-3 The “Training” frame.	10
Figure 2-4 The “Neurofeedback” frame	11
Figure 2-5 A typical hardware configuration required to run an fMRI experiment	12
Figure 2-6 Flow diagram of general linear model 	16
Figure 2-7 The flow of the algorithm to estimate the coefficients of general linear model in real time.	19
Figure 2-8 thermometer-like activation signal feedback	22
Figure 2-9 shows the schematic representation of the block design in the neurofeedback.	22
Figure 2-10 The procedure of the experiment	23
Figure 2-11 Design of the localizer run paradigms showing the alternating 30 s rest and 30 s active conditions.	24
Figure 2-12 The placement of square primary motor cortex ROI and whole brain axial slice for a reference ROI	25
Figure 2-13 real time fMRI neurofeedback task paradigms showing the alternating 30 s rest and active conditions with thermometer display.	26
Figure 3-1 The height of the thermometer changes in the learner and non-learner during up-regulation blocks.	29
Figure 3-2 The signal changes in the ROI during up-regulation blocks during localizer run and transfer run	31
Figure 3-3 Results of brain activation maps in finger tapping task for four runs	35
Figure 3-4 Paired T test in SPM	36
Figure 3-5 The BOLD signal change within the primary motor ROI during training, neurofeedback and transfer run for five subjects in two sessions.	38
Figure 3-6 Schematic presentation of system routines delays	40

Table 3-1 Brain regions in the subjects with significant clusters of activation and positive values during localizer, neurofeedback and transfer run	33
                                    

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校內：2019-02-03公開
校外：不公開電子論文尚未授權公開，紙本請查館藏目錄

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