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Neuropsychologia. 2012 Mar;50(4):479-86. doi: 10.1016/j.neuropsychologia.2011.07.007. Epub 2011 Jul 19.

Tracking cognitive fluctuations with multivoxel pattern time course (MVPTC) analysis.

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1
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA. chiu.yuchin@gmail.com

Abstract

The posterior parietal cortex, including the medial superior parietal lobule (mSPL), becomes transiently more active during acts of cognitive control in a wide range of domains, including shifts of spatial and nonspatial visual attention, shifts between working memory representations, and shifts between categorization rules. Furthermore, spatial patterns of activity within mSPL, identified using multivoxel pattern analysis (MVPA), reliably distinguish between different acts of control. Here we describe a novel multivoxel pattern-based analysis that uses fluctuations in cognitive state over time to reveal inter-regional functional connectivity. First, we used MVPA to model patterns of activity in mSPL associated with shifting or maintaining spatial attention. We then computed a multivoxel pattern time course (MVPTC) that reflects, moment-by-moment, the degree to which the pattern of activity in mSPL more closely matches an attention-shift pattern or a sustained-attention pattern. We then entered the MVPTC as a regressor in a univariate (i.e., voxelwise) general linear model (GLM) to identify voxels whose BOLD activity covaried with the MVPTC. This analysis revealed several regions, including the striatum of the basal ganglia and bilateral middle frontal gyrus, whose activity was significantly correlated with the MVPTC in mSPL. For comparison, we also conducted a conventional functional connectivity analysis, entering the mean BOLD time course in mSPL as a regressor in a univariate GLM. The latter analysis revealed correlations in extensive regions of the frontal lobes but not in any subcortical area. The MVPTC analysis provides greater sensitivity (e.g., revealing the striatal-mSPL connectivity) and greater specificity (i.e., revealing more-focal clusters) than a conventional functional connectivity analysis. We discuss the broad applicability of MVPTC analysis to a variety of neuroimaging contexts.

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