/foveal-feedback-2023

Primary LanguagePythonMIT LicenseMIT

🌟 Overview:

This repository hosts the code for the statistical analyses of functional magnetic resonance imaging (fMRI) data in the manuscript [PLACEHOLDER].

📁 Directory Structure:

More information about the scripts can be found in the docstrings.

├── data/
│ └── BIDS : Copy here the BIDS folder from https://osf.io/h95a2/
├── scripts/
│ ├── 1_GLM.m : Perform GLM on preprocessed data.
│ ├── 2_run_mvpa.py : Runs MVPA analysis and show plot.
│ ├── 3_polynomial_regression.py : Runs plynomial regression on MVPA accuracy.
│ ├── 4_perform_PPI.py : Performs PPI analysis.
│ └── 5_inspect_results.R : Inspect results.

📚 Setup Instructions:

To set up the dataset for this repository, follow the instructions below:

  1. Clone the repository:

    git clone costantinoai/foveal-feedback-2023
cd foveal-feedback-2023

  2. Download the dataset from this link.

  3. Extract the BIDS dataset:

    • For Linux and macOS:

      1. Open a terminal.
      2. Navigate to the directory where you downloaded the dataset.
      3. First, combine the parts into a single ZIP file using the cat command: 
        cat BIDS.zip.* > BIDS_combined.zip
      4. Next, extract the combined ZIP file: 
        unzip BIDS_combined.zip -d ./data
      5. This will extract the BIDS folder into ./data.

    • For Windows:

      1. Navigate to the folder where you downloaded the dataset using File Explorer.
      2. Select all the parts of the dataset (BIDS.zip.001, BIDS.zip.002, etc.).
      3. Right-click and choose Extract All.
      4. Choose the .\data folder within your cloned repository as the destination for extraction.

    Ensure that all parts of the dataset are in the same directory before extraction.

  4. Ensure that the BIDS folder is in the ./data/ directory.

    After extraction, check that the BIDS folder is located within ./data/ in your cloned repository. If it's not, move it there.

  5. Run the analysis.

    You can now run the scripts in ./scripts in order. The dataset includes the data before and after the GLM, so the GLM estimation can potentially be skipped.