Time Lagged Multidimensional Pattern Connectivity (TL MDPC): An EEG/MEG Pattern Transformation Based Functional Connectivity Metric

Abstract

Functional and effective connectivity methods are essential to study the complex information flow in brain networks underlying human cognition. Only recently have connectivity methods begun to emerge that make use of the full multidimensional information contained in patterns of brain activation, rather than univariate summary measures of these patterns. To date, these methods have mostly been applied to fMRI data, and no method allows vertex-vertex transformation with the temporal specificity of EEG/MEG data. Here, we introduce time-lagged multidimensional pattern connectivity (TL-MDPC) as a novel bivariate functional connectivity metric for EEG/MEG research. TL-MDPC estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges. It determines how well patterns in ROI X at time point tx can linearly predict patterns of ROI Y at time point ty. In the present study, we use simulations to demonstrate TL-MDPCs increased sensitivity to multidimensional effects compared to a univariate approach across realistic choices of number of trials and signal-to-noise ratio. We applied TL-MDPC, as well as its univariate counterpart, to an existing dataset varying the depth of semantic processing of visually presented words by contrasting a semantic decision and a lexical decision task. TL-MDPC detected significant effects beginning very early on, and showed stronger task modulations than the univariate approach, suggesting that it is capable of capturing more information. With TL-MDPC only, we observed rich connectivity between core semantic representation (left and right anterior temporal lobes) and semantic control (inferior frontal gyrus and posterior temporal cortex) areas with greater semantic demands. TL-MDPC is a promising approach to identify multidimensional connectivity patterns, typically missed by univariate approaches. HighlightsO_LITL-MDPC is a multidimensional functional connectivity method for event-related EMEG C_LIO_LITL-MDPC captures both univariate and multidimensional connectivity C_LIO_LITL-MDPC yields both zero-lag and time-lagged dependencies C_LIO_LITL-MDPC produced richer connectivity than univariate approaches in a semantic task C_LIO_LITL-MDPC identified connectivity between the ATL hubs and semantic control regions C_LI