本文選題：腦磁信號 + 精神分裂癥��； 參考：《南京郵電大學》2017年碩士論文

【摘要】：大腦有多個功能區(qū),具有復雜的處理機制,腦區(qū)之間相互作用,實現(xiàn)語言、行為、思維等功能。腦磁信號是大腦產(chǎn)生的一種磁場,具有高空間分辨率等腦電不具有的特點,包含了豐富的生理信息。靜息態(tài)和任務態(tài)下的腦磁信號分別表征了大腦的不同活動狀態(tài),是腦研究的兩個主要類別。精神分裂癥是一種常見的精神疾病,改變了病人大腦的作用機理,研究發(fā)現(xiàn),靜息態(tài)能夠反映大腦的本征狀態(tài),有利于發(fā)現(xiàn)患者的功能異常。對于患者來說,進行任務有時較為困難,故研究了正常人的任務態(tài),針對特定的任務——語義研究,分析不同語義條件下的腦磁信號,找出大腦處理特定任務具有顯著差異的主要區(qū)域。論文分別研究了靜息態(tài)精神分裂癥患者MEG和正常人不同語義條件下的MEG,基于復雜網(wǎng)絡理論,研究了兩種腦網(wǎng)絡構建方法。在精神分裂癥患者靜息態(tài)腦磁數(shù)據(jù)的基礎上,分別分析腦區(qū)域間的相關性及顳葉區(qū)內(nèi)通道間相關性,構建MEG相關網(wǎng)絡,并分析網(wǎng)絡特征參數(shù),探究患者與正常人網(wǎng)絡特征的差異;在語言語義數(shù)據(jù)上,研究能量在頭部分布特點以及變化過程,找出具有顯著差異的通道,分析大腦對不同語義做出的相應處理。主要工作如下:(1)根據(jù)大腦區(qū)域分布,將腦功能區(qū)作為節(jié)點,提出用可以度量非線性差異的斯皮爾曼秩次相關系數(shù)來計算區(qū)域節(jié)點間的相關性,構建MEG功能網(wǎng)絡,分析網(wǎng)絡特征參數(shù),精神分裂癥患者某些區(qū)域可能受到了損傷。(2)以往研究多是構建整個腦網(wǎng)絡,文章針對具有顯著差異的顳葉區(qū),計算格蘭杰因果關系,創(chuàng)新性地構建MEG有向二值網(wǎng)絡,計算腦網(wǎng)絡特征,找出網(wǎng)絡的關鍵節(jié)點,比較精神分裂癥患者和正常人的網(wǎng)絡差異。(3)研究不同語義條件下的能量分布,以及能量分布的變化過程,針對差異較大的初始不一致和初始相等條件,計算平面梯度,進行統(tǒng)計檢驗,找出具有顯著差異的通道。分析處理不同語義的句子大腦皮質的差異。
[Abstract]:The brain has multiple functional regions, with complex processing mechanisms, interaction between brain regions, the realization of language, behavior, thinking and other functions. Brain magnetic signal is a kind of magnetic field produced by the brain. It has the characteristics of high spatial resolution and other EEG signals, and contains abundant physiological information. The brain magnetic signals in resting state and task state represent different brain activity respectively, and are two main types of brain research. Schizophrenia is a common mental disease, which changes the mechanism of brain function of patients. It is found that resting state can reflect the intrinsic state of brain and is helpful to discover the abnormal function of patients. For patients, the task is sometimes difficult, so the task state of the normal person is studied. According to the specific task-semantic research, the brain magnetic signals under different semantic conditions are analyzed. Identify major areas in which the brain processes specific tasks with significant differences. In this paper, we studied the MEG of patients with resting schizophrenia and the normal subjects under different semantic conditions. Based on the complex network theory, we studied two kinds of brain network construction methods. Based on the resting magnetic data of schizophrenic patients, the correlation between brain regions and temporal lobe channels was analyzed, and the MEG correlation network was constructed, and the characteristic parameters of the network were analyzed. In terms of language and semantic data, the distribution of energy in the head and the process of change are studied to find out the channels with significant differences, and to analyze the corresponding processing of different semantics in the brain. The main work is as follows: (1) according to the distribution of the brain region, the functional area of the brain is regarded as the node, and the Spelman rank correlation coefficient, which can measure the nonlinear difference, is used to calculate the correlation between the regional nodes, and the functional network of MEG is constructed. According to the characteristic parameters of the network, some areas of schizophrenia may be injured. (2) previous studies were mostly about the construction of the whole brain network. Granger causality was calculated for the temporal lobe with significant differences. We creatively construct MEG directed binary network, calculate the characteristics of brain network, find out the key nodes of the network, compare the network difference between schizophrenic patients and normal people, and study the energy distribution under different semantic conditions. According to the initial inconsistency and the initial equality condition, the plane gradient is calculated, and the statistical test is carried out to find out the channel with significant difference. Analyze the differences in the cerebral cortex of sentences with different semantics.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R749.3;O157.5

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本文編號：1797031