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模式水平认知减法(MLCS)量化了大规模脑地形图中的时空重组。

Mode level cognitive subtraction (MLCS) quantifies spatiotemporal reorganization in large-scale brain topographies.

作者信息

Banerjee Arpan, Tognoli Emmanuelle, Assisi Collins G, Kelso J A Scott, Jirsa Viktor K

机构信息

Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida 33431, USA.

出版信息

Neuroimage. 2008 Aug 15;42(2):663-74. doi: 10.1016/j.neuroimage.2008.04.260. Epub 2008 May 11.

Abstract

Contemporary brain theories of cognitive function posit spatial, temporal and spatiotemporal reorganization as mechanisms for neural information processing. Corresponding brain imaging results underwrite this perspective of large-scale reorganization. As we show here, a suitable choice of experimental control tasks allows the disambiguation of the spatial and temporal components of reorganization to a quantifiable degree of certainty. When using electro- or magnetoencephalography (EEG or MEG), our approach relies on the identification of lower dimensional spaces obtained from the high dimensional data of suitably chosen control task conditions. Encephalographic data from task conditions are reconstructed within these control spaces. We show that the residual signal (part of the task signal not captured by the control spaces) allows the quantification of the degree of spatial reorganization, such as recruitment of additional brain networks.

摘要

当代关于认知功能的大脑理论认为,空间、时间和时空重组是神经信息处理的机制。相应的脑成像结果支持了这种大规模重组的观点。正如我们在此所示,合适地选择实验控制任务能够在可量化的确定程度上区分重组的空间和时间成分。当使用脑电图或脑磁图(EEG或MEG)时,我们的方法依赖于从适当选择的控制任务条件的高维数据中识别低维空间。任务条件下的脑电数据在这些控制空间内进行重构。我们表明,残余信号(未被控制空间捕获的任务信号部分)能够对空间重组程度进行量化,比如额外脑网络的募集情况。

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