Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
- Spanish National Research Council, Spain
- Icahn School of Medicine at Mount Sinai, United States
- University of Pittsburgh, United States
- Northwestern University, United States
- Imperial College London, United Kingdom
Abstract
The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, the 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.
Data availability
All data used for this paper are posted on Dryad.
-
Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent mannerDryad Digital Repository, doi:10.5061/dryad.xd2547dkt.
Article and author information
Author details
Juan Álvaro Gallego
Funding
National Institute of Neurological Disorders and Stroke (F31-NS092356)
- Matthew G Perich
European Research Council (ERC-2020-StG-949660)
- Juan Álvaro Gallego
National Science Foundation (DGE-1324585)
- Raeed H Chowdhury
National Institute of Neurological Disorders and Stroke (T32-NS086749)
- Raeed H Chowdhury
National Institute of Neurological Disorders and Stroke (NS053603)
- Lee E Miller
National Institute of Neurological Disorders and Stroke (NS074044)
- Lee E Miller
National Institute of Neurological Disorders and Stroke (NS095251)
- Lee E Miller
Comunidad de Madrid (2017-T2/TIC-5263)
- Juan Álvaro Gallego
Ministerio de Ciencia e Innovación (PGC2018-095846-A-I00)
- Juan Álvaro Gallego
Engineering and Physical Sciences Research Council (EP/T020970/1)
- Juan Álvaro Gallego
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All surgical and experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwestern University under protocol #IS00000367.
Copyright
© 2022, Gallego-Carracedo et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,727
- views
-
- 1,240
- downloads
-
- 36
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
eLife 11:e73155.
https://doi.org/10.7554/eLife.73155
Further reading
-
- Biochemistry and Chemical Biology
- Neuroscience
The buildup of knot-like RNA structures in brain cells may be the key to understanding how uncontrolled protein aggregation drives Alzheimer’s disease.
-
- Neuroscience
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.
