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 t_x can linearly predict patterns of ROI Y at time point t_y. In the present study, we use simulations to demonstrate TL-MDPC 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.

Identifying nonlinear Functional Connectivity with EEG/MEG using Nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC)

“…As in the linear method, the explained variance of the transformations will serve as the connectivity metric. between patterns using ridge regression (as in Rahimi et al, 2022b). c) Illustration of the novel nTL-MDPC method to detect nonlinear (and linear) transformations between patterns using an artificial neural network.…”

“…Figure 7 illustrates the interpretation of nTL-MDPC and TL-MDPC results, in the form of TTMs for one pair of ROIs, in this case PTC and IFG, two regions putatively involved in semantic control (Rahimi et al, 2022b). Figure 7a and 7b show results for TL-MDPC and nTL-MDPC, respectively, separately for the semantic decision task (SD) and lexical decision (LD) task, as well as their statistical comparison (using cluster-based permutation tests).…”

“…Consequently, source signals in different voxels within an ROI carry redundant information ("leakage"). To address this issue, as in TL-MDPC, we employed a k-means clustering approach as a "feature selection" method to select the most informative vertices within the ROIs (Rahimi et al, 2022b). Unlike "feature extraction" methods such as PCA, here we do not transform features to create new ones, instead we simply select the most informative features (Khalid et al, 2014).…”

“…While recent studies have introduced new functional connectivity methods that exploit multidimensional information, i.e., pattern-to-pattern relationships across regions, they have so far mostly been applied to fMRI data and therefore lack temporal information. We recently developed Time-Lagged Multidimensional Pattern Connectivity for EEG/MEG data, which detects linear dependencies between patterns for pairs of brain regions and latencies in event-related experimental designs (Rahimi et al, 2022b). Due to the linearity of this method, it may miss important nonlinear relationships between activity patterns.…”

An Eye on Semantics: a study on the influence of concreteness and predictability on early fixations durations

“…The coregistration of EEG/MEG will enable us not only to study brain processes that support natural reading while still maintaining a good amount of experimental control. Furthermore, it will make it possible to combine simultaneous behavioural responses (fixation durations) and brain activity (generate by each fixated word) to unravel the dynamics in semantic brain networks using novel connectivity and multivariate pattern-based methods (Farahibozorg et al, 2022;Huth et al, 2016;Rahimi et al, 2022) during natural language processing.…”