eLife· 2026Q1
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG–fMRI
- 2citations
- Q1SCImago
- 2026year
Short summary
Interleaved EEG-fMRI reveals that gamma-band activity correlates with feature-specific visual signals in superficial cortical layers, while alpha-band power correlates negatively with both feature-specific and unspecific BOLD signals, with distinct frequency ranges linked to different layers and signal types.
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Key points
- Gamma-band activity correlates with feature-specific visual signals in superficial cortical layers.
- Alpha-band power negatively correlates with both feature-specific and unspecific BOLD signals.
- Lower alpha frequencies relate to feature unspecific BOLD in superficial layers.
- Upper alpha frequencies relate to feature-specific BOLD in superficial and deep layers.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract The role of cortical oscillations in brain function has been extensively debated, resulting in a variety of theoretical frameworks. Using inter-leaved simultaneous EEG-fMRI, we examined the layer-specific relationship between oscillatory activity and visual processing. We could demonstrate that γ-band activity positively correlates with feature specific signals in superficial layers, but we were able to report a deep layer contribution as well. In addition, we could demonstrate that α-band power not only correlates negatively with the feature unspecific BOLD signal, but related to feature specific BOLD as well. Lower frequency α was pre-dominantly related to feature unspecific superficial layer BOLD, while upper frequency α was found to be related to feature specific BOLD in superficial and deep layers. We conclude that the role of α-band oscillations extends beyond widespread inhibition and might be involved in active stimulus processing to the level of visual features.
The authors' abstract, as published at the source. eLife, 2026 · DOI ↗
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Field: Cognitive Neuroscience
Cognitive NeuroscienceNeuroscience