本文關鍵詞： 腦網絡 珠心算 訓練 功能磁共振 認知能力　出處：《浙江大學》2017年博士論文　論文類型：學位論文

【摘要】：大腦是人體最復雜的器官,是調節(jié)人類各種功能的中樞。研究人員利用各種方法認識腦、保護腦、開發(fā)腦。近年來,人們發(fā)現腦區(qū)之間功能的整合與分化使腦存在特殊的網絡拓撲結構。而這種網絡拓撲結構與日常認知能力息息相關,并且會隨著發(fā)展發(fā)育、疾病、訓練而發(fā)生一定的改變。珠心算是一種基于視空間表征的計算方法。研究發(fā)現,珠心算訓練不僅會影響人的認知能力,并且會影響腦的功能和結構。然而,現有的研究缺乏從腦網絡角度研究珠心算訓練對腦神經機制的影響,從而無法從全腦整體角度和腦區(qū)之間的相互關系來了解珠心算的神經機制。本研究中,我們采用圖論理論,基于靜息態(tài)和任務態(tài)功能磁共振數據,探究珠心算訓練對腦網絡拓撲結構的影響。由于在兒童磁共振數據處理中,空間標準化步驟容易產生誤差,影響實驗結果。因此,在實驗中我們首先優(yōu)化磁共振處理步驟,希望能增強結果可靠性。在研究中:(1)我們構建了基于實驗樣本的兒童腦模板,檢驗兒童模板對于磁共振結果可靠性的影響。我們發(fā)現,即使實驗樣本量較小,構建的腦模板依然可以降低空間標準化帶來的誤差。同時,在統(tǒng)計分析中,將個體空間與模板空間的差異作為協(xié)變量進行相應的矯正,可以降低這種差異對統(tǒng)計檢驗的影響,達到增強統(tǒng)計檢驗的敏感性的目的。所以,作為整個研究的基礎,在之后的研究中我們均構建了符合被試信息的模板。之后,我們設計了三個子實驗研究珠心算訓練對腦功能網絡的影響,我們發(fā)現:(2)視空間策略的廣泛運用會增強相關腦區(qū)與其它腦區(qū)之間的聯(lián)系,增強這些腦區(qū)處理信息效率,并提升相應腦區(qū)在腦功能網絡中的重要性。這些腦區(qū)包括右側前扣帶回、右側頂下小葉和右側眶內額上回。(3)不同腦區(qū)之間聯(lián)系的緊密程度是不同的,并以不同的子網絡形式存在,不同認知能力相關的子網絡組成了全腦功能網絡。本研究發(fā)現,珠心算訓練會促進腦網絡中子網絡的功能分化,增加子網絡的內部連接,降低子網絡間的連接。同時,珠心算訓練對不同子網絡的拓撲結構也存在不同的影響,例如提升視覺網絡平均局部效率,而降低運動感知覺網絡的平均參與系數。我們推測這些網絡結構的變化與視空間策略在珠心算訓練中的廣泛運用密切相關,訓練促使這些網絡能更獨立、更有效地處理信息。(4)此前研究發(fā)現訓練與高級認知能力之間存在廣泛的遷移效應�；趫�(zhí)行功能任務,我們發(fā)現珠心算訓練會增強訓練者的行為績效。腦網絡分析發(fā)現,在執(zhí)行功能相關任務中,珠心算訓練者的額頂網絡的功能連接強度顯著的大于對照組。珠心算訓練不僅影響靜息態(tài)下腦網絡結構,同樣會影響特定任務態(tài)下的腦連接屬性。我們推測,在珠心算訓練中,視空間策略的廣泛運用會增強相應腦區(qū)之間的聯(lián)系,進而提高這些腦區(qū)信息處理的能力。而這些腦區(qū)信息處理效率的提升又會進一步影響與之相關的高級認知能力。因此,我們的研究從腦網絡的角度為珠心算訓練與認知能力之間的遷移效應提供了有力的理論支持,有助于我們進一步了解珠心算訓練與認知能力之間的關系。
[Abstract]:The brain is the most complex organ in the body, is the central regulation of various functions of human beings. The researchers use a variety of methods to recognize the brain, protect brain, brain development. In recent years, people found that the integration and differentiation of brain regions between the brain network topology. This special network topology structure and cognitive ability is closely related to the daily. And with the development of disease, development, training and change. Abacus is a calculating method based on visual spatial representation. The study found that mental arithmetic training will not only affect people's cognitive ability, and will affect the structure and function of the brain. However, existing studies lack of mental abacus training on of brain mechanisms from the brain network angle, which can not be from the relationship between whole brain and brain regions to the whole view of the neural mechanism of solution. In this study, bead mental arithmetic, we use graph theory, based on static State information and task state fMRI data, explore the impact of abacus training on brain network topology. Because of the children's MRI data processing, spatial normalization error prone, affect the experimental results. Therefore, in the experiment we first optimize the magnetic resonance processing steps, hoping to enhance the reliability of the results in the study.: (1) we constructed the experimental sample of children brain template based on template for magnetic resonance inspection of children influence the reliability. We found that even if the sample size is small, the brain template can still reduce the error caused by spatial normalization. At the same time, in the statistical analysis, the difference between individual space and space template as a covariate corrected, can reduce the impact of this difference on the statistical test, to enhance the sensitivity of the statistical test purpose. Therefore, as a basis for the whole research In the study, we have constructed with information subjects template. After that, we design, three sub experiments of abacus mental calculation training on brain functional network we found: (2) extensive use of visual space strategy can enhance the correlation between brain regions and other brain areas, enhance the efficiency of information treatment of brain regions, and enhance the importance of the corresponding brain regions in the brain functional network. These brain regions including the right anterior cingulate gyrus, right inferior parietal lobule and right orbital frontal gyrus. (3) the connection degree between different brain regions is different, and existed in the sub network of different forms and different cognitive abilities the sub network composed of whole brain functional networks. The study found that the functional differentiation will promote brain network trained network, increase internal network connection, reduce the connection among sub networks. At the same time, the calculation of different training The topological structure of sub networks have different effects, such as enhancing the visual network average local efficiency, and reduce the average participation coefficient of movement perception network is widely used. We speculate that these changes in network structure and spatial strategy in the closely related mental arithmetic training, training the network to be more independent and more efficient information processing. (4) previously found extensive migration effect between the training and cognitive ability. Based on the executive function tasks, we found that the training will enhance the trained performance behavior. Brain network analysis found that in executive function tasks, the trained frontoparietal network connectivity strength was significantly greater than that of the control the trained group. Not only affect the resting state brain network structure, will also affect the specific tasks under the state of brain connectivity. We speculate that in bead mental arithmetic training In practice, extensive use of visual space strategy will enhance the corresponding brain regions, thus improving the ability of information processing in these brain regions. And improve the efficiency of information processing in these brain regions and will further affect the related cognitive ability. Therefore, we provide a strong theoretical support of the research from the angle of brain network the network transfer effect between the trained and cognitive ability, help us to further understand the relationship between mental training and cognitive ability.

【學位授予單位】：浙江大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：R338

【參考文獻】

相關期刊論文 前1條

1 Jian HUANG;Feng-lei DU;Yuan YAO;Qun WAN;Xiao-song WANG;Fei-yan CHEN;;高數學能力珠心算兒童數量表征的腦電研究（英文）[J];Journal of Zhejiang University-Science B(Biomedicine & Biotechnology);2015年08期

，

本文編號：1493950