本文關(guān)鍵詞： 功能磁共振成像 時空獨立成分分析 一般線性模型 激活模式 腦卒中康復(fù)　出處：《沈陽工業(yè)大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：“腦卒中”發(fā)病率的增加，嚴重的危害著人們的身體健康和生命安全，致殘率居高不下。功能磁共振成像（fMRI）能夠通過實時的檢測大腦血液中脫氧血紅蛋白含量的變化來間接反映參與活動任務(wù)的神經(jīng)元的激活狀況，它的出現(xiàn)為探索腦卒中后康復(fù)機制、評價和判斷預(yù)后方面提供了新的研究思路，因此，在康復(fù)醫(yī)學(xué)領(lǐng)域具有良好的應(yīng)用前景。然而，fMRI數(shù)據(jù)具有成分復(fù)雜（時空性）、數(shù)據(jù)量大、多噪聲且信號弱等特點，導(dǎo)致數(shù)據(jù)處理十分不易。獨立成分分析（ICA）是現(xiàn)有的方法中唯一一種基于四階統(tǒng)計量的方法，能夠?qū)MRI信號與噪聲的統(tǒng)計信息進行更深層次的挖掘，因此，在fMRI中被廣泛應(yīng)用并成為國際上的研究熱點。 本文深入分析了fMRI對比度機制、噪聲的產(chǎn)生和原有數(shù)據(jù)處理方法的不足，針對fMRI數(shù)據(jù)特點，通過提出新算法、優(yōu)化老算法或結(jié)合多種數(shù)據(jù)處理算法的方式，，來完善ICA在fMRI中的應(yīng)用，并以此為基礎(chǔ)，探索正常人下肢運動的大腦皮層激活模式和卒中后患者康復(fù)期內(nèi)運動功能皮層的重組規(guī)律，為卒中后的下肢運動功能臨床康復(fù)提供理論基礎(chǔ)。 （1）針對鄰域相關(guān)ICA算法嚴重依賴參考函數(shù)的問題，提出一種鄰域自相關(guān)ICA算法，在不需要參考函數(shù)的情況下，通過檢測體素點各周期的時間序列相關(guān)性，對fMRI數(shù)據(jù)進行激活區(qū)提取。將該算法分別應(yīng)用于對仿真數(shù)據(jù)和對12組真實fMRI數(shù)據(jù)的處理，并與前人方法進行了對比，分析了算法的準確性和穩(wěn)定性。 （2）針對傳統(tǒng)ICA對fMRI數(shù)據(jù)時空特性假設(shè)不合理的問題，提出了基于infomax判據(jù)的stICA優(yōu)化算法。該算法通過同時最大程度的優(yōu)化時間源和空間源的獨立性，來建立兩個領(lǐng)域的平衡，服從物理上更真實的假設(shè)。通過對仿真數(shù)據(jù)的處理討論了改進算法的準確性。將該算法應(yīng)用于踝關(guān)節(jié)主動運動與被動運動激活模式的對比，判斷被動運動是否可以作為無法進行主動運動時的替代刺激手段。 （3）討論了GLM和stICA兩種模型的共性和差別，針對stICA模型穩(wěn)定性差和GLM違反其基本假設(shè)的缺點，提出stICA-GLM聯(lián)合算法。通過對仿真數(shù)據(jù)的處理討論了聯(lián)合算法的準確性和穩(wěn)定性。通過同個體同條件下的不同被動fMRI實驗指出受試者在進行被動運動時，大腦思維不受控制，會產(chǎn)生大量神經(jīng)性噪聲。將stICA-GLM聯(lián)合算法應(yīng)用于對神經(jīng)性噪聲的消除，并將其結(jié)果與GLM結(jié)果進行了對比。 （4）針對fMRI數(shù)據(jù)量龐大，基于梯度算法的收斂方式很難滿足fMRI數(shù)據(jù)處理的速度要求的問題，提出了基于固定點的stICA聯(lián)合算法（Fast-stICA-GLM）。分析了Fast-stICA-GLM算法的收斂性能，提出在算法中添加步長因子來優(yōu)化其收斂性能。通過真實fMRI數(shù)據(jù)對Fast-stICA-GLM和Infomax-stICA-GLM兩種算法進行了對比，對比內(nèi)容包括準確性、穩(wěn)定性和運算速度。最后應(yīng)用Fast-stICA-GLM算法分析了不同個體之間被動運動的激活狀況。 （5）應(yīng)用Fast-stICA-GLM算法作為數(shù)據(jù)處理手段對卒中后患者進行為期6周（共4次）的跟蹤fMRI研究，記錄卒中患者在康復(fù)訓(xùn)練期間，大腦下肢運動功能皮層的重組情況，通過定量和定性指標給出功能皮層的重組規(guī)律。定量指標包括：偏側(cè)化指數(shù)LI、峰值點坐標、激活體積和峰值點體素信號強度；定性指標包括：下肢運動功能所在的解剖區(qū)域和激活體素所在的brodmann分區(qū)。通過這些指標，可以得知某個單獨關(guān)節(jié)的運動功能的恢復(fù)情況，從而對制定針對性的康復(fù)計劃起到指導(dǎo)作用。
[Abstract]:"The increase in stroke incidence, serious harm to people's health and life safety, the disability rate is still high. Functional magnetic resonance imaging (fMRI) can change real-time detection of brain blood deoxyhemoglobin content to indirectly reflect the activation of neurons participate in the activities of the task, it appears in order to explore the mechanism of rehabilitation after stroke, provided research ideas, new evaluation and prognosis so it has good application prospect in the field of rehabilitation medicine. However, fMRI data has a complex composition (time and space), the large amount of data, much noise and weak signal characteristics, resulting in data processing is not easy. Independent component analysis (ICA) is the existing methods only a method of four order statistics based on the statistical information of the fMRI to the signal and the noise of deeper mining, therefore, is widely used in fMRI and It is a hot spot of research in the world.
This paper analyzes the fMRI contrast mechanism, lack of noise and the original data processing, according to the characteristics of fMRI data, the proposed new algorithm, combined with a variety of data processing algorithms or the old algorithm, to improve the application of ICA in fMRI, and on this basis, to explore the rehabilitation of patients with lower limb reorganization law the motion of normal human brain cortex activation patterns and motor function after stroke within the cortex, provide a theoretical basis for clinical rehabilitation of lower limb motor function after stroke.
(1) the neighborhood ICA algorithm relies heavily on the reference function, proposes a neighborhood self correlation ICA algorithm, without reference function, through the detection of voxel time series correlation of each cycle, activation of the fMRI data extraction. The algorithm is respectively applied to the simulation data and treatment of 12 groups of real fMRI data, and compared with the previous methods, the algorithm accuracy and stability are analyzed.
(2) according to the traditional ICA on the spatial and temporal characteristics of fMRI data that is not reasonable, put forward the optimization algorithm of stICA Infomax based on the criterion of the algorithm. At the same time through the optimization of time and space independent source source to the maximum extent, to build two areas of physical balance, to a more realistic assumption. Accuracy of the improved algorithm the discussion by analyzing the simulation data. The algorithm is applied to active ankle movement and passive movement active mode of comparison, judgment whether passive motion can be used as an alternative to active movement of the stimulus.
(3) discuss the similarities and differences between GLM and stICA two models, stICA model for stability and GLM violation of the basic assumptions of the shortcomings, proposed stICA-GLM algorithm. The algorithm's accuracy and stability is discussed by analyzing the simulation data. Through different passive fMRI experiments with the same individual conditions are pointed out the subjects in the passive movement, the mind is not controlled, will produce a lot of noise. The neural stICA-GLM algorithm is applied to eliminate the noise of nerve, and the results were compared with the results of GLM.
(4) fMRI for the huge amount of data, based on the convergence of gradient algorithm is difficult to meet the requirements of the fMRI data processing speed, put forward stICA combined algorithm based on fixed point (Fast-stICA-GLM). The convergence performance of Fast-stICA-GLM algorithm is analyzed, it is suggested to add the step factor in the algorithm to optimize the convergence performance by real fMRI. The data of Fast-stICA-GLM and Infomax-stICA-GLM two kinds of algorithms are compared, including the comparison of accuracy, stability and speed of operation. Finally the application of Fast-stICA-GLM algorithm to analyze the passive motion activation between different individuals.
(5) the application of Fast-stICA-GLM algorithm as a data processing method for patients after stroke for 6 weeks (4 times) the fMRI tracking research, recording stroke patients in rehabilitation training during the reorganization of the brain cortex of lower extremity motor function, through the reorganization law of quantitative and qualitative indicators are functional cortex. Quantitative indicators include: laterality the LI index, the peak point coordinates, the activation volume and peak voxel signal strength; qualitative indicators include: Brodmann partition anatomical regions of lower extremity motor function and the activated voxel location. These indicators can be that a single joint movement function recovery, so as to develop guidance for rehabilitation plan the.

【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R743.3;R445.2

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