本文選題：腦功能網(wǎng)絡(luò)　切入點：狀態(tài)觀測矩陣　出處：《昆明理工大學》2017年碩士論文

【摘要】：核磁共振成像技術(shù)為研究大腦的特性提供了有利的手段,其中基于血氧水平依賴的靜息態(tài)功能磁共振成像由于具有較高的時間和空間分辨率,為深入研究人腦功能的動態(tài)特性提供了一種重要方法,而基于它的腦網(wǎng)絡(luò)重構(gòu)技術(shù)也成為了研究人腦特性的有力工具之一。鑒于人腦網(wǎng)絡(luò)的復(fù)雜性,在提取人腦網(wǎng)絡(luò)的狀態(tài)特征時,由于所構(gòu)建的人腦網(wǎng)絡(luò)狀態(tài)觀測矩陣維數(shù)過高,所以很難識別它的主要特性,因此對其展開降維和聚類方法的研究是非常有必要的�；谏鲜霈F(xiàn)狀,本文以深度學習理論為基礎(chǔ),以腦網(wǎng)絡(luò)狀態(tài)觀測矩陣的降維和聚類方法為重點進行了以下研究:提出了一種基于深度自動編碼器的腦網(wǎng)絡(luò)狀態(tài)觀測矩陣降維方法,構(gòu)建并實現(xiàn)了一個基于5層受限玻爾茲曼機的深度自動編碼器系統(tǒng),可以將高維的腦網(wǎng)絡(luò)特征數(shù)據(jù)映射到低維空間內(nèi),從而為高維腦網(wǎng)絡(luò)狀態(tài)觀測矩陣的降維實現(xiàn)提供了一個新的解決思路。為了進一步驗證該方法的可靠性,采用自組織映射方法對降維后的低維空間腦網(wǎng)絡(luò)狀態(tài)觀測向量進行聚類,最后通過實驗和結(jié)果分析驗證了基于深度自動編碼器的降維方法的有效性。該方法為下一步深入研究人腦網(wǎng)絡(luò)動態(tài)特性提供了必要的基礎(chǔ)。
[Abstract]:Magnetic resonance imaging (MRI) provides a useful tool for the study of brain properties, in which resting functional magnetic resonance imaging based on the level of blood oxygen has higher temporal and spatial resolution. It provides an important way to study the dynamic characteristics of human brain function, and the brain network reconstruction technology based on it has become one of the powerful tools to study the characteristics of human brain, in view of the complexity of human brain network, When extracting the state characteristics of the human brain network, it is difficult to identify its main characteristics because the dimension of the state observation matrix of the human brain network is too high. Therefore, it is necessary to study the methods of reducing and clustering. Based on the above situation, this paper is based on the theory of depth learning. This paper focuses on the reduction and clustering of the state observation matrix of the brain network. A method of reducing the dimension of the state observation matrix of the brain network based on the depth automatic encoder is proposed. A depth automatic encoder system based on a 5-layer constrained Boltzmann machine is constructed and implemented. The feature data of high-dimensional brain network can be mapped to low-dimensional space. This provides a new solution for the dimensionality reduction of the state observation matrix of high-dimensional brain network, and further verifies the reliability of the method. The state observation vector of low-dimensional spatial brain network after dimensionality reduction is clustered by self-organizing mapping method. Finally, the effectiveness of the dimensionality reduction method based on the depth automatic encoder is verified by experiments and results analysis, which provides a necessary foundation for the further study of the dynamic characteristics of the human brain network.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R445.2;TP391.41

【參考文獻】

相關(guān)期刊論文 前8條

1 皇甫浩然;楊劍;楊陽;;基于fMRI動態(tài)功能連接的抑郁癥患者分類研究[J];計算機應(yīng)用研究;2017年03期

2 張欣;胡新韜;郭雷;;基于動態(tài)功能連接的運動任務(wù)大腦狀態(tài)表達[J];計算機應(yīng)用;2015年07期

3 楊劍;陳書q，

本文編號：1692009