🧠 Neuro-Connectome Analysis: Creativity & Efficiency Pipeline

Functional Brain Connectivity & Graph Theory Analysis

📌 Project Overview

This project implements a computational pipeline to extract Functional Brain Connectivity Maps (Connectomes) from fMRI data and analyzes brain network efficiency using Graph Theory.

By utilizing the Schaefer 2018 Atlas, the brain is parcellated into 100 distinct functional regions. The pipeline computes Global Efficiency, Clustering Coefficients, and identifies critical Hub Regions that govern information flow during cognitive tasks.

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🔬 Methodology

1. Data Acquisition: Raw fMRI signals are fetched and preprocessed using Nilearn.
2. Signal Extraction: Time-series data is extracted from 100 brain regions (ROIs) based on the Schaefer Atlas using zscore_sample standardization.
3. Network Construction: A 100x100 Functional Connectivity Matrix is built using Pearson Correlation coefficients.
4. Graph Theory Analysis: Topological properties of the brain network (e.g., Hubs, Efficiency) are calculated using NetworkX.

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📊 Latest Analysis Results (Updated)

1. Quantitative Network Analysis (Creativity & Efficiency Metrics)

The latest run on the subject's connectome revealed an exceptionally integrated neural network:

Metric	Value	Scientific Interpretation
Global Efficiency	0.9019	Extremely high integration; represents rapid information transfer between distant brain regions. Correlates with high creativity and Openness to Experience.
Clustering Coefficient	0.8982	Indicates high local specialization and efficient processing within specific functional modules (e.g., visual, motor).
Network Topology	Small-World	The synergy of high efficiency and high clustering confirms an optimized "Small-World" architecture for complex cognitive tasks.

2. Identified High-Traffic Hubs (Top Centrality Nodes)

The analysis identified the following networks as the most active "Hubs" (Highway Junctions) for information flow:

• Salience/Ventral Attention Network (Region #28 & #76): Crucial for detecting and filtering significant environmental stimuli.
• Dorsal Attention Network (Region #68): Governs voluntary, top-down visual focus and selective attention.
• Default Mode Network (Region #92): The core of internal thought, imagination, and creative synthesis.

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🎨 Visualizations

Functional Connectivity Matrix	Functional Brain Connectome (3D)
Correlation heatmap reflecting 0.90 efficiency.	Visualization of the brain's 2% strongest "Highway" structure.

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🛠️ Installation & Usage

This project is fully Dockerized to ensure reproducibility and ease of setup.

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🐳 Option 1: Run with Docker (Recommended)

# 1. Clone the repository
git clone https://github.com/senaayy/Neuro-Connectome-Analysis.git

# 2. Navigate to the project directory
cd Neuro-Connectome-Analysis

# 3. Build and run the project
docker-compose up --build
Docker will handle all dependencies and environment setup automatically.

🐍 Option 2: Standard Local Installation (Without Docker)
If you prefer to run the analysis locally:

1. Create a Virtual Environment
python -m venv venv
Activate the environment:

Windows

.\venv\Scripts\activate
Linux / macOS

source venv/bin/activate
2. Install Dependencies
pip install -r requirements.txt
3. Run the Analysis
Open Brain_Analysis.ipynb and run the cells step by step.

👨‍💻 Contact Sena Ay - Software Engineering Student @ Fırat University

Focusing on Computational Neuroscience & Bioinformatics.