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基于残差迭代分解(RIDE)的源定位揭示的记忆和基于线索的任务转换过程中的神经机制和功能神经解剖网络。

Neural mechanisms and functional neuroanatomical networks during memory and cue-based task switching as revealed by residue iteration decomposition (RIDE) based source localization.

机构信息

Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstrasse 74, 01307, Dresden, Germany.

Department of Neurology, Faculty of Medicine, MS Centre Dresden, Centre of Clinical Neuroscience, TU Dresden, Dresden, Germany.

出版信息

Brain Struct Funct. 2017 Nov;222(8):3819-3831. doi: 10.1007/s00429-017-1437-8. Epub 2017 May 3.

Abstract

Task switching processes reflect a faculty of cognitive flexibility. The underlying neural mechanisms and functional cortical networks have frequently been investigated using neurophysiological (EEG) or functional imaging methods. However, task switching processes are subject to strong intra-individual variability, especially when tested under varying levels of working memory demands. This intra-individual variability compromises the reliable estimation of neurophysiological processes and related functional neuroanatomical networks. In this study, we combine residue iteration decomposition (RIDE) of event-related potentials (ERPs) and source localization methods to circumvent this problem. Due to strong intra-individual variability, behavioral effects between memory-based and cue-based task switching were not reflected by classical ERPs, but were so after applying RIDE. Using RIDE, modulations paralleling the behavioral data were specifically reflected by processes related to the updating of internal representations for response selection (reflected by the C-cluster in the P3-component time range) rather than by stimulus and motor-related processes (reflected by the S-cluster and R-cluster). The C-cluster-processes were associated with activation differences in the inferior parietal cortex, including the temporo-parietal junction (TPJ, BA40) and likely reflect mechanisms related to the updating of internal representations and task sets for response selection. The results underline the necessity to use temporal decomposition methods to control the problem of intra-individual signal variability to decipher the neurophysiology and functional neuroanatomy of cognitive processes.

摘要

任务转换过程反映了认知灵活性的能力。神经生理学(EEG)或功能成像方法经常被用于研究其潜在的神经机制和功能皮质网络。然而,任务转换过程受到强烈的个体内变异性的影响,尤其是在不同工作记忆需求水平下进行测试时。这种个体内变异性会影响到对神经生理过程和相关功能神经解剖网络的可靠估计。在这项研究中,我们结合事件相关电位(ERP)的残差迭代分解(RIDE)和源定位方法来解决这个问题。由于强烈的个体内变异性,基于记忆和基于提示的任务转换之间的行为效应并没有通过经典 ERP 反映出来,但在应用 RIDE 后就反映出来了。使用 RIDE,与行为数据平行的调制过程,具体反映了与内部反应选择的更新有关的过程(在 P3 成分时间范围内反映为 C 簇),而不是与刺激和运动相关的过程(反映为 S 簇和 R 簇)。C 簇过程与顶下小叶皮层(包括颞顶联合(TPJ,BA40))的激活差异相关,可能反映了与内部反应选择的更新和任务集相关的机制。结果强调了使用时间分解方法来控制个体内信号变异性问题的必要性,以破译认知过程的神经生理学和功能神经解剖学。

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