DecodeWM is a predictive model for classifying working memory (WM) load types using task-based fMRI data. The project focuses on analyzing how different memory loads impact brain activity and leverages machine learning models to classify WM task conditions based on neural responses. By utilizing HCP (Human Connectome Project) fMRI data, the study applies logistic regression and random forest classifiers to predict memory load conditions (e.g., 0-back vs. 2-back tasks). The classification results not only validate the effectiveness of machine learning in cognitive neuroscience but also provide insights into the functional characteristics of brain regions involved in WM processing. Through feature importance analysis, the study identifies key brain networks, such as the Default Mode Network (DMN), Frontoparietal Network (FPN), Cingulo-Opercular Network (CON), and Dorsal Attention Network (DAN), which are most relevant to working memory tasks. These findings contribute to future research on memory load representation and neural mechanisms in cognitive neuroscience.
- 🧠 WM Load Classification: Uses task-based fMRI data to analyze how different memory loads affect brain activity.
- 📊 Machine Learning Models: Implements logistic regression and random forest classifiers to predict 0-back vs. 2-back task conditions.
- 🔥 High Classification Accuracy: Achieves 88% accuracy in distinguishing memory load types based on ROI (Region of Interest) activity levels.
- 🔍 Feature Importance Analysis: Identifies key brain networks (DMN, FPN, CON, DAN) that play crucial roles in working memory processing.
- 🛠️ Neuroscientific Relevance: Provides insights into WM-related brain networks, supporting further cognitive neuroscience research.
Whether you're a cognitive neuroscience enthusiast or a machine learning researcher, DecodeWM is the perfect project for exploring the intersection of working memory analysis and AI-driven neuroimaging classification. 🌟 You’ll gain hands-on experience with task-based fMRI data, learn how machine learning models can classify cognitive load, and uncover key insights into brain network dynamics—providing a solid foundation for further research in computational neuroscience and cognitive modeling.
- Python 🐍 – Core programming language for data processing and model implementation.
- scikit-learn 📊 – Used for logistic regression and random forest classification.
- Nilearn 🧠 – Library for neuroimaging analysis and visualization of fMRI data.
- HCP (Human Connectome Project) API 🌍 – Provides access to task-based fMRI datasets, enabling large-scale analysis of working memory-related brain activity.
- Nilearn 🧩 – A neuroimaging library for decoding fMRI signals, performing statistical analyses, and visualizing brain activation maps.
To get a local copy of DecodeWM up and running, follow these steps.
- Python (v3.8 or higher) and pip (or conda for package management).
- Clone the repository:
git clone https://github.com/mqqq333/DecodeWM.git cd DecodeWM - Open
./src/n_back_fmri.ipynbin your jupyter notebook
We welcome contributions to DecodeWM! If you'd like to contribute, please follow the steps below:
- Fork the repository.
- Create a new branch (
git checkout -b feature/your-feature-name). - Make your changes and commit them (
git commit -m 'Add some feature'). - Push to the branch (
git push origin feature/your-feature-name). - Open a pull request.
Please make sure to update tests as appropriate.
If you encounter any issues while using or setting up the project, please check the Issues section to see if it has already been reported. If not, feel free to open a new issue detailing the problem.
When reporting an issue, please include:
- A clear and descriptive title.
- A detailed description of the problem.
- Steps to reproduce the issue.
- Any relevant logs or screenshots.
- The environment in which the issue occurs (OS, browser, Python version, etc.).
Distributed under the MIT License. See License for more information.