Dataset (MATLAB format) from Chen Kang et al (2021) Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.

Summary

These experiments simultaneously measure neuronal responses from the two hemispheres of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. We inactivate activity of one hemisphere (left or right) in some trials and both hemispheres in others. Data from 135 recording sessions are included in this release.

This dataset contains data from 39 mice (age > P60).  9 VGAT-ChR2-EYFP mice (Jackson laboratory, JAX Stock#014548) and 30 PV-IRES-Cre (JAX Stock#008069) crossed to Rosa26-LSL-ReaChR, red-shifted channelrhodopsin reporter mice (JAX 24846), were used for simultaneous electrophysiology and photoinhibition. The experiments (including experiment methods) are described in the following publication:

Chen, G., Kang, B., Lindsey, J., Druckmann, S., Li, N. (2021). Modularity and robustness of frontal cortical networks. Cell 184, 1-14. doi: 10.1016/j.cell.2021.05.026

How to cite the data

If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:

Chen G, Li N (2021); Data and simulations related to: Modularity and robustness of frontal cortical networks. Chen et al (2021) Cell, 184(14):3717-3730.

http://dx.doi.org/10.5281/zenodo.6713616

How to get started

Once downloaded

1) unzip “analysis_scripts”

2) unzip and combine all "data_structure_*.mat" and "meta_data_*.mat" files into a single folder “datafiles”.

      • data_structure_*.mat - contains raw spike data for one session.

      • meta_data_*.mat - contains the meta data information for one session.

           There is one "meta_data_*.mat" file for each "data_structure_*.mat"

3) Run scripts within "analysis_scripts". Follow the instruction in the "code instruction".

Data analysis

The extracellular recording traces were band-pass filtered (300-6 kHz).  Events that exceeded an amplitude threshold (4 standard deviations of the background) were subjected to spike sorting to extract single units. Spike sorting was either manual (Guo et al., 2014b) or using Kilosort2 (https://www.github.com/MouseLand/Kilosort2) (Pachitariu et al., 2016) followed by manually curated with the Phy 2.0 beta 1 GUI (https://github.com/cortex-lab/phy) (Rossant et al., 2016) and manual inspection (Guo et al., 2014a). Spike widths were computed as the trough-to-peak interval in the mean spike waveform.  Units with spike width < 0.35 ms were defined as fast-spiking (FS) neurons and units with spike widths > 0.45 ms as putative pyramidal neurons. Units with intermediate values (0.35 - 0.45 ms) were excluded.  We concentrated our analyses on the putative pyramidal neurons.