Think like an expert paper (Neural alignment predicts learning outcomes, Nature Comms., 2021)

This repository contains code associated with the paper "Neural alignment predicts learning outcomes in students taking an introduction to computer science course" by Meir Meshulam, Liat Hasenfratz, Hanna Hillman, Yun-Fei Liu, Mai Nguyen, Kenneth A. Norman and Uri Hasson.

Imaging and behavioral data associated with this project is available on openNeuro.org.

The repository is organized as follows:

root
└── notebooks : jupyter notebooks
└── py : python code
└── masks : anatomical ROI and brain masks

Instructions

After downloading the data folder from openNeuro, set the variable 'bids_path' in the code to point to the data folder.

Use notebooks for pre-processing of raw data (requires FSL; dependencies in py folder), behavioral analysis and ROI analysis. Analysis notebooks contain the expected outputs. Run times for a single analysis on a single region of interest (ROI) are <1h on a single CPU core.

Use similarity_searchlight.py for whole-brain analysis (requires BrainIAK searchlight).

The code was tested under GNU/Linux (x86_64 architecture) with Jupyter Notebook and BrainIAK (version information below). No special installation is required.

Python v. 3.7.4

Jupyter Notebook v. 6.0.2

BrainIAK v. 0.9.1

FSL v. 6.0.1