Abstract

The observation of highly variable sets of association neocortical areas across individuals, containing the estimated generators of Slow Potentials (SPs) and beta oscillations, lead to the persistence in individual analyses. This brought to notice an unexpected within individual topographic similarity between task conditions, despite our original interest in task-related differences. A recent related work explored the quantification of the similarity in beta topography between largely differing tasks. In this article, we used Independent Component Analysis (ICA) for the decomposition of beta activity from a visual attention task, and compared it with quiet resting, recorded by 128-channel EEG in 62 subjects. We statistically tested whether each ICA component obtained in one condition could be explained by a linear regression model based on the topographic patterns from the other condition, in each individual. Results were coherent with the previous report, showing a high topographic similarity between conditions. From an average of 12 beta component maps obtained for each task, over 80% were satisfactorily explained by the complementary task. Once more, the component maps including those considered unexplained, putatively “task-specific”, had their scalp distribution and estimated cortical sources highly variable across subjects. These findings are discussed along with other studies based on individual data and the present fMRI results, reinforcing the increasingly accepted view that individual variability in sets of active neocortical association areas is not noise, but intrinsic to cortical physiology. Actual ‘noise’, mainly stemming from group “brain averaging” and the emphasis on statistical differences as opposed to similarities, may explain the overall hardship in replication of the vast literature on supposed task-specific forms of activity, and the ever inconclusive status of a universal functional mapping of cortical association areas. A new hypothesis, that individuals may use the same idiosyncratic sets of areas, at least by their fraction of activity in the sub-delta and beta range, in various non-sensory-motor forms of conscious activities, is a corollary of the discussed variability.

Highlights

  • In the last decade, the constant observation of high inter-individual variability in the cortical distribution of various forms of non-sensory-motor electrical activity, has justified the preservation of individual data for group analysis and development of within individual forms of analysis [1,2,3,4,5,6,7]

  • All subjects reported that performance was relatively easy during the attention task, provided that they were strongly attending during the critical time of S2 presentation

  • The first encounter with the high inter-individual variability in the distribution of activity across neocortical association areas occurred as the frustration of our original purpose to functionally varying cortical areas, frontal and posterior, highly idiosyncratic to individuals

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Summary

Introduction

The constant observation of high inter-individual variability in the cortical distribution of various forms of non-sensory-motor electrical activity, has justified the preservation of individual data for group analysis and development of within individual forms of analysis [1,2,3,4,5,6,7]. Individually idiosyncratic topographies were observed, both of averaged SPs or beta, and non-averaged beta ICA patterns, as well as their modeled current density generators This robust individual variability, in the set of cortical areas presenting various forms of activity, valid for the non-sensory-motor domain, is an increasingly accepted fact, corroborated by other methods such as fMRI and PET, whenever individual data are presented [2,8,9,10,11,12,13,14,15]. The fact that most groups, both in EEG/MEG and fMRI research, maintain the use of spatial grand averaging (collapsing sensor montages across subjects or mapping results into an “average brain”, respectively), is the main reason for no widespread recognition of this variability as possibly inherent to neocortical physiology

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