Outline

Each file is designed to be directly loaded into a Jupyter notebook and to be sufficient for running all analyses and generating a rough draft of figures. All corresponding notebooks and code are available @: https://github.com/donaldsonlab/NAcInhib

Notes: 'Stranger' and 'Novel' are interchangeable terms throughout, as are 'Assembly' and 'Ensemble'.

Questions can be sent to the corresponding author: zoe.donaldson@colorado.edu or to the primary author who produced this dataset: mostafa.el-kalliny@colorado.edu mostafa.elkalliny@gmail.com

preprocessedData.zip

Contains:

gria_sequencing.csv

Contains Gria subunit sequencing results. Each row is an individual cell, containing identified cluster (cluster), log-normalized expression levels of Gria1-4 subunits (Gria1-4), and the ratio of GluA2 to GluA1+3+4 subunits (gria_ratio). Data obtained from Brusman et al, as cited.

Pharmacological data

final_IEM_data.csv

Results of pharmacology-only experiment examining the effect of bilateral accumbal ACSF versus IEM-1460 administration on bond formation final_IEM_data.csv contains, for each animal, their group (control or expt), partner preference score (PP_day2), total movement in cm (Movement), mating latency in minutes (Mating latency), number of mating bouts (Mating Bouts), male or female (M_or_F), and huddle times in seconds for partner (huddle_time_partner), novel (huddle_time_novel), total (huddle_time_total), percent partner huddle (percent_phuddle), and time spent in seconds in partner chamber (chamber_time_partner), novel chamber (chamber_time_novel), and center chamber (chambter_time_center).

NASPM.zip contains: partner_novel_huddle_data.csv (containing huddle times in seconds for Vehicle and IEM-1460 groups), naspm_final_data.csv (containing huddle times in seconds for NASPM group), and mating_bouts_with_new_data.csv (containing # of mating bouts for each group).

Data for neuronal encoding

cell_encoding_total.pkl

Variety of data consolidated for analysis of behavior during imaging, neuronal encoding via auROC, encoding across days by individual neurons, and spatial clustering of individual neurons. All variables are loaded up front, then used by individual functions accordingly.

Data for ensemble and population encoding

population_encoding_total.pkl

Variety of data consolidated for neuronal encoding via ensembles and population analyses using PCA, decoding, cosine similarity.

stranger_assemblies_d14.pkl, assemblies_corrs.pkl, ensemble_behavior_data.pkl, and difference_scores.npy

Small set of additional data for revision analyses.

Electrophysiological data

ephys contains a #_day2_final.pkl with different # for each animal, containing all preprocessed electrophysiological data.

It is loaded by load_session_data and used as the basis for the remainder of the notebook.

ephys_snippet.pkl contains the snippet of data used by the load_and_plot_snippet function.

Patch-clamp data

patchedCells.zip contains a folder for each cell, containing individual .abf files and a metadata.csv describing which .abf files correspond to which protocol (e.g. current injections, baseline eEPSCs, drug eEPSCs).

Fi_cell_summary.csv contains resting membrane potential in mV (RestingVm_mean), maximum firing rate in Hz (MaxFR_Hz), and action potential half-width in ms (AP_HalfWidth_ms) for each neuron.

Fi_sweep_data.csv contains, for each neuron and sweep, current injection in pA (Current_pA), spike count (SpikeCount), firing rate in Hz (FiringRate_Hz), action potential half-width in ms (AP_HalfWidth_ms), the file name, cell ID, and resting membrane potential in mV (RestingVm_mV).

General code

general_utils.py

Several core functions, including auROC calculations.

assemblies.py

Core code for PCA/ICA methods described in Detecting cell assemblies in large neuronal populations, Lopes-dos-Santos et al (2013). https://doi.org/10.1016/j.jneumeth.2013.04.010

Implementation originally written by Vitor Lopez dos Santos, and modified with permission.