Neuroimaging-Dashboard

Neuroimaging Data Pipeline & Interactive Dashboard

Overview

A comprehensive platform for processing, analyzing, and visualizing neuroimaging data. This project implements an end-to-end pipeline that processes raw neuroimaging data (fMRI/MRI) from open repositories and provides interactive visualizations through a modern web interface.

Features

Data Processing

Automated data ingestion from OpenNeuro and other open repositories
Advanced preprocessing pipeline with parallel processing
Quality control and artifact detection
Support for multiple imaging modalities (T1, fMRI, DTI)

Analysis Capabilities

Volumetric analysis (gray matter, white matter, CSF)
Statistical analysis with multiple comparison corrections
Machine learning classification
Longitudinal analysis
Region-based analysis using standard atlases

Visualization

Interactive 3D brain visualizations
Statistical plots and heatmaps
Real-time data exploration
Customizable dashboards

Technical Features

Progressive Web App (PWA) support
Offline functionality
Real-time updates via WebSocket
Background processing for long-running tasks
Automated quality control reports

Tech Stack

Backend

Framework: Python Flask
Database: PostgreSQL
Data Processing:
- NiBabel for neuroimaging data
- NumPy/SciPy for numerical computations
- Scikit-learn for machine learning
- Nilearn for advanced neuroimaging tools

Frontend

Framework: React.js
UI Components: Material-UI
Visualization:
- Plotly.js for statistical plots
- D3.js for custom visualizations
- Three.js for 3D rendering

Infrastructure

Docker containerization
Nginx web server
Redis for caching
Celery for task queue

Project Structure

neuroimaging-dashboard/
├── backend/                 # Python backend server
│   ├── api/                # REST API endpoints
│   │   ├── routes.py       # API route definitions
│   │   └── analysis.py     # Analysis services
│   ├── data_pipeline/      # Data processing pipeline
│   │   ├── pipeline.py     # Main pipeline implementation
│   │   └── processors/     # Individual processing steps
│   └── database/           # Database models and migrations
├── frontend/               # React frontend application
│   ├── public/            # Static assets
│   └── src/               # Source code
│       ├── components/    # React components
│       ├── services/      # API services
│       └── utils/         # Utility functions
├── config/                # Configuration files
├── tests/                # Test suites
├── docs/                 # Documentation
└── docker/              # Docker configuration

Installation

Prerequisites

Python 3.8+
Node.js 14+
PostgreSQL 13+
Docker and Docker Compose (optional)
CUDA-capable GPU (recommended for processing)

Development Setup

Clone the repository:

git clone https://github.com/yourusername/neuroimaging-dashboard.git
cd neuroimaging-dashboard

Set up the backend:

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configurations

# Initialize database
flask db upgrade

Set up the frontend:
```
cd frontend
npm install
```

Docker Setup

Build and start services:
```
docker-compose up --build
```

Initialize the database:

docker-compose exec backend flask db upgrade

Usage

Starting the Application

Development mode:

# Terminal 1 - Backend
cd backend
flask run

# Terminal 2 - Frontend
cd frontend
npm start

Production mode:
```
docker-compose up
```

Accessing the Dashboard

Development: http://localhost:3000
Production: http://your-domain.com

Processing Data

Upload neuroimaging data:
- Supported formats: NIfTI (.nii, .nii.gz), DICOM
- Batch upload available through web interface
- API endpoints for programmatic access
Configure processing pipeline:
- Select preprocessing steps
- Set analysis parameters
- Choose output formats
Monitor progress:
- Real-time status updates
- Quality control metrics
- Error notifications

Analyzing Results

Access analysis tools:
- Volume analysis
- Statistical comparisons
- Machine learning models
- Longitudinal tracking
Customize visualizations:
- Interactive plots
- 3D brain renderings
- Custom ROI analysis

API Documentation

Detailed API documentation is available at /docs/api.md

Configuration

Environment Variables

Variable	Description	Default
FLASK_ENV	Environment (development/production)	development
DATABASE_URL	PostgreSQL connection URL	postgresql://localhost/neuroimaging_dashboard
SECRET_KEY	Flask secret key	your-secret-key-here
OPENNEURO_API_KEY	OpenNeuro API key	None

Application Settings

Configuration files are located in config/:

config.py: Main configuration
logging.conf: Logging configuration
nginx.conf: Nginx configuration

Testing

Run backend tests:
```
pytest tests/
```
Run frontend tests:
```
cd frontend
npm test
```

Performance Optimization

Backend Optimization

Parallel processing using ThreadPoolExecutor
Caching with Redis
Database query optimization
Efficient file handling

Frontend Optimization

Code splitting
Lazy loading
Service Worker caching
WebGL acceleration

Security

HTTPS enforcement
CORS configuration
JWT authentication
Input validation
SQL injection prevention
XSS protection

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

Troubleshooting

Common issues and solutions are documented in TROUBLESHOOTING.md

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

OpenNeuro for providing open access to neuroimaging datasets
The neuroimaging research community
Contributors and maintainers

Support

Issue Tracker: GitHub Issues
Email: support@example.com
Slack: Join our community

Roadmap

See ROADMAP.md for planned features and improvements.

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