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bioRxiv · 10.1101/2023.09.01.555874

Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis

Abstract

Central to our understanding of how visual evoked potentials (VEPs) contribute to visual processing is the question of where their anatomical sources are. Three well-established measures of low-level visual cortical activity are widely used: the first component ("C1") of the transient and multifocal VEP, and the steady-state VEP (SSVEP). Although primary visual cortex (V1) activity has often been implicated in the generation of all three signals, their dominant sources remain uncertain due to the limited resolution and methodological heterogeneity of source modelling. Here, we provide the first characterisation of all three signals in one analytic framework centred on the cruciform model, which describes how scalp topographies of V1 activity vary with stimulus location due to the retinotopy and unique folding pattern of V1. We measured the transient C1, multifocal C1, and SSVEPs driven by an 18.75Hz and 7.5Hz flicker, and regressed them against forward-models of areas V1, V2 and V3 generated from the Benson-2014 retinotopy atlas. The topographic variations of all four VEP signals across the visual field were better captured by V1 models, explaining between 2-6 times more variance than V2/V3. Models with all three visual areas improved fit further, but complementary analyses of temporal dynamics across all three signals indicated that the bulk of extrastriate contributions occur considerably later than V1. Overall, our data support the use of peak C1 amplitude and SSVEPs to probe V1 activity, although the SSVEP contains stronger extrastriate contributions. Moreover, we provide elaborated heuristics to distinguish visual areas in VEP data based on signal lateralization as well as polarity inversion.

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BibTeXRIS

Mohr, K. S., Geuzebroek, A. C., Kelly, S. P.. 2023-09-05. Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis. https://doi.org/10.1101/2023.09.01.555874

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