Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks.

Jorge I Padilla-Buritica,German A Castaño,Carlos D Acosta-Medina,Jose M Ferrandez-Vicente

doi:10.3389/fnins.2020.00446

Abstract

Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.

Highlights

Investigation into oddball tasks considers the detection and analysis of neural responses, mostly relying on event-related potentials (ERP), such as the well-known P300, which is associated with attentional orientation processes elicited by target stimulus identification (Harper et al, 2017)
Besides the fact that functional connectivity (FC) can be implemented at a reasonable cost on high-density electroencephalographic (EEG) recordings (Toppi et al, 2012), its advantages lie in the ability to map statistical patterns of dynamic coupling between distributed brain regions, i.e., the connectivity of brain areas at the channel level
With the purpose of following the relationship between the evoked responses and computed FC measures, the top row represents the ERP timecourses of each grand average that is calculated by averaging across all subject and trial sets, making clear the distinction in ERP amplitudes between either evoked condition and becoming more evident within a range of between 300 and 450 ms after the stimulus onset, which is marked by a red line

Summary

Introduction

Investigation into oddball tasks considers the detection and analysis of neural responses, mostly relying on event-related potentials (ERP), such as the well-known P300, which is associated with attentional orientation processes elicited by target stimulus identification (Harper et al, 2017). The differences between functional brain networks have been investigated to uncover the corresponding effect of a stimulus sequence, assuming that brain activities are predictably modulated within some spatio-temporal windows (Bridwell et al, 2018). This fact allows the use of neuroimaging measures to benefit from tracking the evoked time-variant responses in diverse brain structures. A major driving force for the rapid expansion of functional brain networks is the availability of relational data recording couplings and interactions among elements of neural systems (Sporns, 2018)

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