This project develops an encoding model to analyze fMRI data from LeBel et al. (2023), involving eight subjects listening to narrative stories. The primary task is to process and analyze a selected subset of this data from three subjects, aiming to implement an effective encoding analysis. This goal is to understand the relationship between auditory stimuli and brain activity.
It is implemented as part of CMU 10-733 Course on Representation and Generation in Neuroscience and AI.
This repository contains the following files and directories:
notebook.ipynb: A comprehensive Jupyter notebook with all code.chartsfolder: All Pycortex-generated charts.py_files_huth: Support code files.rsa_values: Saved files with calculated RSA values.word_data: Saved files with transcript statistics.README.md: This file provides an overview of the project, its objectives, and the contents of the repository.
- Data: https://openneuro.org/datasets/ds003020/versions/2.0.0
- Kriegeskorte, N., Mur, M., and Bandettini, P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in systems neuroscience, page 4.
- LeBel, A., Wagner, L., Jain, S., Adhikari-Desai, A., Gupta, B., Morgenthal, A., Tang, J., Xu, L., and Huth, A. G. (2023). A natural language fmri dataset for voxelwise encoding models. Scientific Data, 10(1):555.
- FMRI Tutorial 1: https://github.com/HuthLab/speechmodeltutorial
- FMRI Tutorial 2: https://www.cs.cmu.edu/~lwehbe/files/Copy_of_workshop.html
Note: All data used in this project is sourced ethically, and the analysis adheres to the highest standards of research integrity and ethical guidelines.