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Low and high frequency intracranial neural signals match in the human associative cortex
- Source :
- eLife, Vol 11 (2022)
- Publication Year :
- 2022
- Publisher :
- eLife Sciences Publications Ltd, 2022.
-
Abstract
- In vivo intracranial recordings of neural activity offer a unique opportunity to understand human brain function. Intracranial electrophysiological (iEEG) activity related to sensory, cognitive or motor events manifests mostly in two types of signals: event-related local field potentials in lower frequency bands (30 Hz, High frequency, HF). While most current studies rely exclusively on HF, thought to be more focal and closely related to spiking activity, the relationship between HF and LF signals is unclear, especially in human associative cortex. Here, we provide a large-scale in-depth investigation of the spatial and functional relationship between these 2 signals based on intracranial recordings from 121 individual brains (8000 recording sites). We measure category-selective responses to complex ecologically salient visual stimuli – human faces – across a wide cortical territory in the ventral occipito-temporal cortex (VOTC), with a frequency-tagging method providing high signal-to-noise ratio (SNR) and the same objective quantification of signal and noise for the two frequency ranges. While LF face-selective activity has higher SNR across the VOTC, leading to a larger number of significant electrode contacts especially in the anterior temporal lobe, LF and HF display highly similar spatial, functional, and timing properties. Specifically, and contrary to a widespread assumption, our results point to nearly identical spatial distribution and local spatial extent of LF and HF activity at equal SNR. These observations go a long way towards clarifying the relationship between the two main iEEG signals and reestablish the informative value of LF iEEG to understand human brain function.
Details
- Language :
- English
- ISSN :
- 2050084X
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- eLife
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.6e083895f493684df6d4404cd16ab
- Document Type :
- article
- Full Text :
- https://doi.org/10.7554/eLife.76544