fMRI-Quickstart

This repository provides a streamlined starting point for fast fMRI analyses and preprocessing workflows. Initially designed for rapid GLM-based analyses, this project will progressively integrate more advanced modeling and preprocessing strategies.

🔍 Overview

This project includes:

• A minimal working setup for first-level GLM analysis on fMRI data
• Step-by-step guide to create a valid BIDS dataset structure
• Integration with DeepPrep for automated preprocessing
• Modular scripts that can be expanded or replaced as the project scales

📁 Project Structure

🧠 Goals

• Enable reproducible and scalable fMRI workflows
• Provide a clear entry point for researchers or developers starting with BIDS + GLM
• Serve as a base to build more complex pipelines over time (e.g., MVPA, RSA, connectivity)

🚀 Getting Started

1. Prepare your dataset in BIDS format (see /preprocessing/create_bids_dataset.py)
2. Run the preprocessing pipeline using DeepPrep (/preprocessing/run_deepprep.py)
3. Launch your GLM analysis (/glm_analysis/run_glm.py)

📦 Requirements

• Python 3.8+
• nibabel, nilearn, pandas, deepprep, bids-validator, etc. (See requirements.txt for the full list)

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This repo is under active development and will evolve over time. Contributions and feedback are welcome!