ARC project scripts

Exhaustive codebase to run the pleasantness related analyses on the NEMO dataset

1. System Requirements

Operating System: Windows

MATLAB Version: R2023B

Dependencies:

1. SPM12
2. GLMSingle
3. LibSVM

For control analyses involving sniffs, breathmetrics toolbox was used but is not essential for reproducing key results.

The code was tested on a machine with the following recommended specifications: RAM: 64GB; processor: AMD Ryzen Threadripper PRO 4.00 GHz or higher; No non-standard hardware required.

2. Installation Guide

Instructions

To install:

git clone https://github.com/viveksgr/ARC.git

Install MATLAB Toolboxes directly from the source links provided.

Typical Install Time

The installation should take approximately a few minutes on a standard desktop computer.

3. Demo

Demonstration to run representational similarity analyses are provided in the examples folder. The output should be similar to corresponding analyses in Sagar et. al., 2024 "Separable and integrated pleasantness coding for appetitive and aversive odors across olfactory and ventral prefrontal cortices" (In preparation) Runtime for the demo scripts varies by the analyses but most RSA analyses should be executable within an hour. NOTE: Demo is only for illustration purposes only.

Instructions to Run on Demo Data

1. Clone the repository and add all dependencies to filepath
2. Launch MATLAB and add <common_functions> to path
3. For each demo example, make sure file path for variable is set as that of cloned repository on local machine.
4. Currently, demo files are provided to recreate crucial behavioral results from representational similarity analyses.

4. Instructions for Use and Reproduction:

To repeat the complete analyses using these scripts, we have provided the single-trial files and other datasets in the supporting_files. The following scripts should be executed to reproduce figures from the manuscript. For more information, please refer to examples

1. ARC_RSA_analyses: Perform representational similarity analysis for valence, salience.
2. ARC_decoding_analyses: Decoding analyses based on support vector machine to predict odor valence, salience, appetitive or aversive pleasantness as well as cross-decoding analyses.

These analyses are based on the raw dataset. Please use ARC_createsingletrials to verify estimation of single trials responses using the GLM single package.

5. Additional Information:

Terms of use: This content is licensed under MIT License. If you use these data or analysis scripts, please cite the associated manuscript and acknowledge this repository as the source of the dataset and analysis code. Please contact sgr.vivek@gmail.com for any questions.