基于神經(jīng)影像學(xué)技術(shù)的工作記憶與珠心算訓(xùn)練效應(yīng)研究
發(fā)布時(shí)間：2018-10-29 12:33
【摘要】：本研究探索了工作記憶(WM)個(gè)體差異的神經(jīng)電生理指標(biāo),以及珠心算(AMC)對(duì)于WM的訓(xùn)練效應(yīng)及其神經(jīng)機(jī)制。研究采用多種實(shí)驗(yàn)范式,并結(jié)合事件相關(guān)電位(ERP)和事件相關(guān)同步化/去同步化(ERS/ERD)兩種腦電模式來檢測(cè)WM的個(gè)體差異。ERP可以反映認(rèn)知活動(dòng)的時(shí)程信息,其分析過程包括去除眼電偽跡、基線矯正、低通濾波、疊加平均等。而ERS/ERD能夠提供腦網(wǎng)絡(luò)的動(dòng)態(tài)變化信息,其分析主要是對(duì)分片信號(hào)作離散傅里葉變換后計(jì)算感興趣腦區(qū)的特定信號(hào)頻段的相對(duì)功率變化。另外,本研究采用縱向設(shè)計(jì),通過血氧水平依賴(BOLD)的功能磁共振成像(fMRI)技術(shù)為AMC訓(xùn)練對(duì)WM的影響提供大腦活動(dòng)模式上的依據(jù)。fMRI數(shù)據(jù)分析是在時(shí)間校正、頭動(dòng)校正、配準(zhǔn)、平滑等一系列預(yù)處理之后,采用廣義線性模型對(duì)數(shù)據(jù)作進(jìn)一步統(tǒng)計(jì)分析。在網(wǎng)絡(luò)分析上則使用圖論通過圖的多個(gè)拓?fù)湫再|(zhì)來刻畫腦功能網(wǎng)絡(luò)各方面的屬性。本論文中的研究一采用MODS和n-back范式,檢測(cè)了WM的個(gè)體差異在腦電上的反映。結(jié)果發(fā)現(xiàn)WM容量低的被試更易受到任務(wù)負(fù)荷的影響。此外,WM不同的被試在進(jìn)行認(rèn)知加工時(shí)在ERP和ERS/ERD響應(yīng)模式上都表現(xiàn)出顯著差異,WM容量較高的被試表現(xiàn)出更高的P300波幅、更強(qiáng)的額葉θ波ERS以及更弱的頂葉α波ERD。本研究結(jié)果表明WM不同的個(gè)體在執(zhí)行認(rèn)知任務(wù)時(shí)存在本質(zhì)差異,WM容量較高的個(gè)體能夠高效地整合較少的神經(jīng)資源將注意力集中于任務(wù)相關(guān)信息上。研究二采用記憶廣度測(cè)試和視覺空間n-back范式,通過任務(wù)態(tài)fMRI探索了 AMC訓(xùn)練是否有助于提高年輕成人的語音和視覺空間WM。結(jié)果顯示經(jīng)過訓(xùn)練后,AMC組被試對(duì)于語音信息的WM容量比對(duì)照組被試更高。同時(shí),AMC組被試在視覺空間WM任務(wù)中的表現(xiàn)得到明顯改善,且與右側(cè)額頂回路及左側(cè)枕顳聯(lián)合區(qū)的激活降低程度成正比。這意味著AMC訓(xùn)練效應(yīng)能夠遷移到個(gè)體的WM功能上,這種遷移效應(yīng)的產(chǎn)生可能與視覺空間加工相關(guān)的神經(jīng)資源在功能上的可塑性有關(guān)。研究三采用多種刺激類型的n-back范式,并結(jié)合靜息態(tài)fMRI以及圖論方法,探索了長(zhǎng)期AMC訓(xùn)練對(duì)于兒童的WM功能及自發(fā)活動(dòng)功能網(wǎng)絡(luò)的影響。結(jié)果表明AMC訓(xùn)練有助于促進(jìn)兒童WM的全面發(fā)展,同時(shí)加強(qiáng)WM神經(jīng)網(wǎng)絡(luò)的小世界屬性,提高網(wǎng)絡(luò)整體的信息傳輸效率。本論文研究揭示了WM不同的個(gè)體在認(rèn)知加工過程中的行為表現(xiàn)和神經(jīng)反應(yīng),填補(bǔ)了這一問題在腦電研究領(lǐng)域的空白。作為AMC訓(xùn)練成像研究領(lǐng)域的第一個(gè)縱向研究,本研究結(jié)果表明AMC對(duì)于計(jì)算能力的提高會(huì)全面地遷移到個(gè)體的WM功能上,為已有的AMC研究發(fā)現(xiàn)作了重要的延伸和補(bǔ)充。同時(shí),本研究結(jié)果也為WM網(wǎng)絡(luò)的功能可塑性提供了有力支持,對(duì)遠(yuǎn)遷移效應(yīng)的神經(jīng)機(jī)制研究具有一定的借鑒意義。
[Abstract]:The purpose of this study was to explore the neuroelectrophysiological parameters of individual differences in working memory (WM) and the training effect of (AMC) on WM and its neural mechanism. In this study, we used a variety of experimental paradigms, combined with event-related potentials (ERP) and event-related synchronization / desynchronization (ERS/ERD), to detect the individual differences in WM. ERP can reflect the time-history information of cognitive activity. The analysis process includes the removal of eye electrical artifacts, baseline correction, low pass filtering, superposition averaging and so on. ERS/ERD can provide the dynamic change information of the brain network, and its analysis is mainly to calculate the relative power change of the specific signal frequency band of the brain region of interest after the discrete Fourier transform (DFT) of the slice signal. In addition, the longitudinal design was used to provide the basis for the influence of AMC training on WM by using the functional magnetic resonance imaging (fMRI) technique of blood oxygen level dependent (BOLD). FMRI data analysis was performed in time correction, head moving correction. After a series of preprocessing, such as registration and smoothing, the generalized linear model is used to further analyze the data. In network analysis, graph theory is used to describe the attributes of brain functional network through several topological properties of graph. In this paper, we used MODS and n-back paradigm to examine the individual difference of WM in EEG. The results showed that the subjects with low WM capacity were more susceptible to the task load. In addition, different subjects with WM showed significant differences in ERP and ERS/ERD response patterns during cognitive processing. Those with higher WM volume showed higher P300 amplitude, stronger ERS in frontal lobe 胃 wave and weaker 偽 wave ERD. in parietal lobe. The results show that there are essential differences in cognitive tasks among different individuals in WM. Individuals with high WM capacity can efficiently integrate fewer neural resources to focus on task-related information. In the second study, using memory span test and visual space n-back paradigm, we explored whether AMC training can improve the speech and visual space WM. of young adults through task-based fMRI. The results showed that after training, the WM capacity of the AMC group was higher than that of the control group. At the same time, the performance of visual space WM task in AMC group was significantly improved, which was in direct proportion to the decrease of activation of the right frontal parietal circuit and the left occipito-temporal joint area. This means that the AMC training effect can be transferred to individual WM function, and this migration effect may be related to the functional plasticity of neural resources related to visual space processing. In the third study, the effects of long-term AMC training on children's WM function and spontaneous activity function network were explored by using multiple stimuli n-back paradigm, resting fMRI and graph theory. The results show that AMC training can promote the overall development of WM in children, strengthen the small-world attribute of WM neural network, and improve the overall information transmission efficiency of the network. The present study reveals the behavior and neural responses of different individuals in WM during cognitive processing, and fills up the gaps in the field of EEG research. As the first longitudinal study in the field of AMC training imaging, the results of this study show that the improvement of computational power of AMC can be transferred to individual WM function in a comprehensive way, which is an important extension and supplement to the existing findings of AMC research. At the same time, the results of this study also provide a strong support for the functional plasticity of WM networks, which is useful for the study of the neural mechanism of distant migration effect.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：B842.3
本文編號(hào)：2297707
[Abstract]:The purpose of this study was to explore the neuroelectrophysiological parameters of individual differences in working memory (WM) and the training effect of (AMC) on WM and its neural mechanism. In this study, we used a variety of experimental paradigms, combined with event-related potentials (ERP) and event-related synchronization / desynchronization (ERS/ERD), to detect the individual differences in WM. ERP can reflect the time-history information of cognitive activity. The analysis process includes the removal of eye electrical artifacts, baseline correction, low pass filtering, superposition averaging and so on. ERS/ERD can provide the dynamic change information of the brain network, and its analysis is mainly to calculate the relative power change of the specific signal frequency band of the brain region of interest after the discrete Fourier transform (DFT) of the slice signal. In addition, the longitudinal design was used to provide the basis for the influence of AMC training on WM by using the functional magnetic resonance imaging (fMRI) technique of blood oxygen level dependent (BOLD). FMRI data analysis was performed in time correction, head moving correction. After a series of preprocessing, such as registration and smoothing, the generalized linear model is used to further analyze the data. In network analysis, graph theory is used to describe the attributes of brain functional network through several topological properties of graph. In this paper, we used MODS and n-back paradigm to examine the individual difference of WM in EEG. The results showed that the subjects with low WM capacity were more susceptible to the task load. In addition, different subjects with WM showed significant differences in ERP and ERS/ERD response patterns during cognitive processing. Those with higher WM volume showed higher P300 amplitude, stronger ERS in frontal lobe 胃 wave and weaker 偽 wave ERD. in parietal lobe. The results show that there are essential differences in cognitive tasks among different individuals in WM. Individuals with high WM capacity can efficiently integrate fewer neural resources to focus on task-related information. In the second study, using memory span test and visual space n-back paradigm, we explored whether AMC training can improve the speech and visual space WM. of young adults through task-based fMRI. The results showed that after training, the WM capacity of the AMC group was higher than that of the control group. At the same time, the performance of visual space WM task in AMC group was significantly improved, which was in direct proportion to the decrease of activation of the right frontal parietal circuit and the left occipito-temporal joint area. This means that the AMC training effect can be transferred to individual WM function, and this migration effect may be related to the functional plasticity of neural resources related to visual space processing. In the third study, the effects of long-term AMC training on children's WM function and spontaneous activity function network were explored by using multiple stimuli n-back paradigm, resting fMRI and graph theory. The results show that AMC training can promote the overall development of WM in children, strengthen the small-world attribute of WM neural network, and improve the overall information transmission efficiency of the network. The present study reveals the behavior and neural responses of different individuals in WM during cognitive processing, and fills up the gaps in the field of EEG research. As the first longitudinal study in the field of AMC training imaging, the results of this study show that the improvement of computational power of AMC can be transferred to individual WM function in a comprehensive way, which is an important extension and supplement to the existing findings of AMC research. At the same time, the results of this study also provide a strong support for the functional plasticity of WM networks, which is useful for the study of the neural mechanism of distant migration effect.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：B842.3
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