Computational DBS Optimization in the SCP

A comprehensive, research‑grade toolkit for simulating, analyzing, and optimizing deep brain stimulation (DBS) protocols within the Subthalamic Cerebellar Pathway (SCP).
Designed for academic, clinical, and commercial teams who need reproducible, high‑performance computational pipelines to evaluate DBS efficacy, latency, and persistence metrics.

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Table of Contents

1. Project Overview
2. Background & Motivation
3. Key Features
4. Repository Structure
5. Installation & Setup
6. Quick Start Guide
7. Data & File Formats
8. Core Scripts & Notebooks
9. Results & Visualisations
10. Contributing
11. License
12. Contact & Acknowledgements

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Project Overview

Deep Brain Stimulation (DBS) is a clinically proven therapy for movement disorders, psychiatric conditions, and emerging neuromodulation applications. This repository provides a fully‑documented, end‑to‑end computational framework that:

• Generates synthetic neuronal responses to a range of stimulation currents.
• Calculates clinically relevant metrics: peak membrane potential, persistence time, and latency.
• Produces publication‑ready visualisations (response plots & metric tables).
• Enables rapid prototyping of alternative stimulation waveforms or electrode configurations.

All code is written in Python 3.10+, leverages NumPy, Matplotlib, and optional NEURON simulation back‑end for biophysically realistic modeling.

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Background & Motivation

• Clinical Need: Optimising DBS parameters (amplitude, pulse width, frequency) is time‑consuming and often relies on trial‑and‑error during surgery.
• Computational Advantage: Simulations can predict neuronal outcomes in silico, reducing patient risk and shortening procedure time.
• SCP Focus: The Subthalamic Cerebellar Pathway (SCP) is a key target for treating tremor and dystonia. Precise modelling of this pathway can improve therapeutic windows.

This project translates the above motivations into a reproducible, modular codebase that can be extended to other brain regions or stimulation strategies.

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Key Features

• Parameter Sweep Engine: Easily explore a grid of current amplitudes (0.1–1.0 mA) and time points (0–20 ms).
• Metric Extraction: Automated calculation of:
  • Peak voltage (mV)
  • Persistence time – duration the response stays within ±1 % of the peak.
  • Latency – first crossing of a user‑defined threshold (default ‑64 mV).
• Dual‑Mode Plotting: Side‑by‑side line‑plot of all responses and a formatted metric table.
• Extensible Architecture: Plug‑in custom neuronal models (e.g., NEURON, Brian2) by implementing the simulate_* interface.
• Ready‑to‑Deploy Notebook: Jupyter notebooks (eeg brain.3.ipynb, eeg brain.4.ipynb) demonstrate step‑by‑step usage.
• Data Export: CSV/Excel outputs for downstream statistical analysis.

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Repository Structure

├── .gitignore                     # Standard Python ignores
├── Neuro Project                  # Core research assets
│   ├── CODE-3_FINAL.txt           # Baseline simulation script (simple model)
│   ├── CODE-4_FINAL.txt           # Advanced script with realistic response functions
│   ├── NEURO_CSV_ASUTOSHA.xlsx    # Sample dataset (downloaded from raw URL)
│   ├── eeg brain.3.ipynb          # Notebook: basic simulation workflow
│   └── eeg brain.4.ipynb          # Notebook: advanced analysis & visualisation
└── README.md                      # This file

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Installation & Setup

Prerequisites: Python 3.10+, Git, and optional NEURON for biophysical simulations.

# Clone the repository
git clone https://github.com/AsutoshaNanda/Computational-DBS-Optimization-in-the-SCP.git
cd Computational-DBS-Optimization-in-the-SCP

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

# Install core dependencies
pip install -U pip
pip install numpy matplotlib pandas jupyter

# (Optional) Install NEURON if you want realistic biophysical models
# pip install neuron

All scripts have been tested on Windows 10, macOS 13, and Ubuntu 22.04.

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Quick Start Guide

1. Launch the notebook

   jupyter notebook Neuro\ Project/eeg\ brain.3.ipynb

2. Run the cells – they will:
   • Generate a set of stimulation currents.
   • Simulate membrane potential responses.
   • Compute metrics and render a side‑by‑side figure.
3. Export results – the notebook includes a cell to save the metric table as results.csv or results.xlsx.

For the advanced pipeline (realistic neuronal models), open eeg brain.4.ipynb and follow the same pattern.

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Data & File Formats

• NEURO_CSV_ASUTOSHA.xlsx – Example CSV‑converted dataset containing raw electrophysiological recordings.
  URL: https://raw.githubusercontent.com/AsutoshaNanda/Computational-DBS-Optimization-in-the-SCP/e0ce33cbafe044fff507b890fb83bf64e6b8a49b/Neuro%20Project/NEURO_CSV_ASUTOSHA.xlsx
• Results files – Exported as CSV/Excel for compatibility with statistical packages (R, SPSS, MATLAB).

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eeg brain.4.ipynb CODE-4_FINAL.txt

Core Scripts & Notebooks

File	Purpose	Highlights
eeg brain.3.ipynb	Simple linear response model	Demonstrates core metric functions (simulate_response, calculate_persistence_time, calculate_latency).
eeg brain.4.ipynb	Advanced model with realistic neuronal dynamics	Uses placeholder functions simulate_realistic_response, calculate_peak_and_persistence. Extendable with NEURON.
CODE-3_FINAL.txt	Same as eeg brain.3.ipynb but in .txt format	Same as eeg brain.3.ipynb but in .txt format
CODE-4_FINAL.txt	Same as eeg brain.4.ipynb but in .txt format	Same as eeg brain.4.ipynb but in .txt format

All scripts are well‑commented and follow PEP‑8 style guidelines.

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Results & Visualisations

Running the notebooks produces a figure similar to:

• Left panel: Overlaid membrane potential traces for each current amplitude.
• Right panel: A concise table summarising Current (mA), Peak (mV), Persistence (ms), Latency (ms).

These outputs are ready for inclusion in conference posters, journal articles, or client presentations.

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Contributing

We welcome contributions from the neuroengineering community.

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/my‑new‑model).
3. Ensure PEP‑8 compliance (flake8) and unit tests (if added) pass.
4. Submit a Pull Request with a clear description of changes.

Please refer to the CONTRIBUTING.md template (to be added) for detailed guidelines.

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License

This project is released under the MIT License – you are free to use, modify, and distribute the code for commercial or academic purposes, provided the original license notice is included.

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Contact & Acknowledgements

• Lead Developer: Asutosha Nanda – GitHub Profile
• Institutional Support: [Your Institution / Lab Name]
• Funding: This work was supported by [Insert Grant/Funding Agency] under award number XXXXXX.

For questions, bug reports, or collaboration inquiries, please open an Issue or email [email protected].

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Empower your DBS research with reproducible, scalable, and clinically relevant simulations – all in one open‑source package.