ArticleImaging neuroscience (Cambridge, Mass.)2024
Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Non-invasive mapping of the temporal processing hierarchy in the human visual cortex.PLoS computational biology · 2026Article
- Readout and delayed transmission of initial afferent V1 activity in decisions about stimulus contrast.eLife · 2026Article
- Alpha-band echoes evoked by contrast and luminance changes emerge in and travel out from early visual cortex.Journal of vision · 2026Article
- Pattern-pulses and pattern-reversals evoke different cascades of cortical sources in the multifocal visual evoked potential.Journal of vision · 2025Article
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3 authors.
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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 18.75 Hz and 7.5 Hz 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 and 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 lateralisation as well as polarity inversion.
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