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Long-term stability of cortical ensembles

Citation

Pérez-Ortega, Jesús; Alejandre-García, Tzitzitlini; Yuste, Rafael (2021), Long-term stability of cortical ensembles, Dryad, Dataset, https://doi.org/10.5061/dryad.cfxpnvx5m

Abstract

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons were commonly active across any two imaging sessions. These “common neurons” formed stable ensembles lasting weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved ~68 % of their neurons up to 46 days, our longest imaged period, and these “core” cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.

Methods

We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses.

A static blue screen was used to record spontaneous activity for 5 min. The visual stimulation protocol constituted of 50 times of a 2 s single-orientation drifting gratings with a mean static screen between each of them during 1-5 s randomly to record the evoked activity for 5 min.

Maximum intensity projection was obtained from three recorded planes. Detection of ROIs was done based on Suite2P. Then, we got the Ca signals with a peak signal-to-noise ratio (PSNR) > 18 dB. We perform the inference of spikes using the foopsi algorithm, and finally we thresholded the inference of spikes for each neuron to build a binary raster.

Usage Notes

Each MATLAB file contains a structure variable "Data" with the data processed.

Data.Movie (FilePath, DataName, Width, Height, Depth, Frames, FPS, ImageMaximum, ImageAverage, ImageSTD, ImagePSNR, Summary, ImageMask)

Data.ROIs (CellRadius, AuraRadius, SearchOptions, CellMasksImage, CellWeightedMasksImage, AuraMasksImage, NeuropilMask)

Data.VoltageRecording (Stimuli, Locomotion, File, SampleRate)

Data.XY.All

Data.Neurons (pixels, weight_pixels, x_pixels, y_pixels, num_pixels,x_median,y_median, overlap, overlap_fraction, Eccentricity, Circularity, Perimeter, PSNR)

Data.Transients (Raw, Filtered, Smoothed, F0, Field, Cells, PSNR, Inference, Model, InferenceMethod, ThresholdPSNR, Raster, InferenceTh, SameThreshold, Threshold

Data.InactiveNeurons (same as Data.Neurons, this neurons were discarded).

Funding

HHS | NIH | National Eye Institute (NEI), Award: R01EY011787

HHS | NIH | National Institute of Mental Health (NIMH), Award: R01MH115900

Consejo Nacional de Ciencia y Tecnología (CONACYT), Award: CVU365863