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  本文選題：靜息態(tài)功能磁共振成像　切入點：腦功能子網(wǎng)絡(luò)　出處：《南京理工大學(xué)》2014年碩士論文

【摘要】：靜息態(tài)功能磁共振成像(functional megnetic resonance imaging, fMRI)反映了大腦在靜息狀態(tài)下的活動情況,現(xiàn)已被廣泛用于研究人腦功能。本文主要采用靜息態(tài)fMRI數(shù)據(jù)進行了兩個方面的研究。首先,提出了一種基于稀疏表示構(gòu)建腦功能子網(wǎng)絡(luò)的方法。該方法將小波變換一致性定義為感興趣區(qū)域(ROI, Region Of Interest)之間功能交互模式的測度,然后通過稀疏表示提取每個ROI的特征,最后運用譜聚類的方法進行啟發(fā)式聚類。本文對10個健康被測試對象的靜息態(tài)fMRI數(shù)據(jù)進行了分析,構(gòu)建得到的腦功能網(wǎng)絡(luò)具有8個子網(wǎng)絡(luò)。結(jié)果表明,這8個子網(wǎng)絡(luò)分別對應(yīng)著大腦的視覺、運動感知等主要功能系統(tǒng),與當(dāng)前的神經(jīng)科學(xué)的研究相一致。同時本文還使用了兩種傳統(tǒng)方法來構(gòu)建功能交互子網(wǎng)絡(luò)進行比較,相比之下,本文方法構(gòu)建得到的子網(wǎng)絡(luò)效果更好。 本文研究的第二個內(nèi)容是,提出了一種基于稀疏表示推斷健康人群與輕度認知障礙(Mild Cognitive Impairment, MCI)患者的特征功能連接的方法。首先也是通過小波變換一致性計算兩個ROI之間功能連接的度量,然后對每個被試使用稀疏表示進行字典學(xué)習(xí)與稀疏編碼,最后通過統(tǒng)計的方法分別對健康被試對象和MCI患者提取共性的功能連接。為了使算法更加通用,本文對兩個不同的數(shù)據(jù)集共63個被試對象進行了研究。我們將得到的特征功能連接采用SVM進行分類,結(jié)果令人滿意。最后,為進一步驗證該方法的有效性與健壯性,本文生成了一系列人造數(shù)據(jù)并對其執(zhí)行本文的方法,結(jié)果顯示本文的方法是有效并且健壯的。
[Abstract]:Resting state functional magnetic resonance imaging (functional megnetic resonance imaging, fMRI) reflects the situation of the brain in the resting state activity, it has been widely used to study brain function. This paper mainly adopts the resting state fMRI was studied in two aspects. Firstly, propose a sparse representation based method to construct the brain functional network the method of wavelet transform. The consistency is defined as the region of interest (ROI Region, Of Interest) measure between functional interaction patterns, and then through the sparse representation of each ROI feature extraction, finally using the spectral clustering method of heuristic clustering. In this paper, 10 healthy test resting state fMRI data object is analyzed that building has 8 sub network of brain function network. The results show that the 8 sub network corresponding to the brain's visual perception, the main function of motion system, and the current It is consistent with the research of neuroscience. Meanwhile, two traditional methods are used to construct functional interaction sub networks for comparison. In contrast, the subnet constructed by this method has better effect.
The second content is proposed based on sparse representation that healthy people with mild cognitive impairment (Mild Cognitive, Impairment, MCI) method of feature function of patients. The first is connected by measuring function between two ROI connection calculation of wavelet transform consistency, then said the dictionary learning and sparse encoding for each is the use of sparse, finally through the statistical method of healthy subjects and patients with MCI extraction of common functions connected. In order to make the algorithm more general, based on two different data sets of a total of 63 subjects were studied. We will get the features connected by SVM classification, with satisfactory results finally, to further verify the effectiveness and robustness of the method, we create a series of synthetic data and the implementation of this method, results show that this party The law is effective and robust.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R445.2;TP391.41

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本文編號：1674066