Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
Creators
- 1. Imperial College London
- 2. Icahn School of Medicine at Mount Sinai
- 3. University of Pittsburgh
- 4. Northwestern University
Description
The spiking activity of populations of cortical neurons is well described by 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 across behaviour: in each of the 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 recording.
Notes
This data set includes behavioral recordings and extracellular neural recordings from the motor cortex, premotor cortex, and area 2 of the primary somatosensory cortex of Rhesus macaques during an instructed-delayed reaching task. Matthew Perich and Raeed Chowdhury collected and processed the data in the laboratory of Lee Miller for use in Gallego-Carracedo et al. 2022, which characterised the relationship between neural population activity and local field potentials across these sensorimotor cortical regions. Results and methodology from these experiments are described in Gallego-Carracedo et al. 2022.
In these experiments, monkeys controlled a cursor on a screen using a two-link, planar manipulandum. Monkeys performed a simple center-out task to one of the eight possible targets, after a variable delayed period. During this reaching task, we tracked the endpoint position of the hand using sensors on the manipulandum. In addition to the behavioral data, we collected neural data from one or two of these areas using Blackrock Utah multielectrode arrays, yielding ~100 to ~200 channels of extracellular recordings per monkey. Recordings from these channels were thresholded online to detect spikes, which were sorted offline into putative single units.
Analysis code used to produce figures for Gallego-Carracedo et al. 2022 provides useful examples for how to work with this dataset. See https://github.com/BeNeuroLab/Relationship_between_latent_dynamics_and_LFP.git for code and README.
If you publish any work using the data, please cite the publication above (Gallego-Carracedo et al. 2022) and also cite this data set.
Funding provided by: National Institute of Neurological Disorders and Stroke
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000065
Award Number: R01-NS074044
Funding provided by: National Institute of Neurological Disorders and Stroke
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000065
Award Number: R01-NS095251
Funding provided by: National Institute of Neurological Disorders and Stroke
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000065
Award Number: F31-NS092356
Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: DGE-1324585
Files
README.txt
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Additional details
Related works
- Is cited by
- 10.7554/eLife.73155 (DOI)
- Is derived from
- https://github.com/BeNeuroLab/Relationship_between_latent_dynamics_and_LFP.git (URL)