electroMICA

Integrate electrophysiological and brain imaging data.

electroMICA is developed by MICA Lab at McGill University for use at the Neuro, McConnell Brain Imaging Center (BIC).

The main goal of electroMICA is to provide a robust framework to integrate electrophysiological data (scalp EEG, intracranial EEG) with information derived from multimodal MR images processed by micapipe <https://micapipe.readthedocs.io>`_and `hippunfold <https://hippunfold.readthedocs.io>. It contains two pipelines:

electroMICA_iEEG:

Projects intracranial EEG features (e.g. event rates) onto cortical and hipocampal surfaces.

electroMICA_scalp

Computes an Electric Source Imaging solution for scalp EEG data on the cortical and hippocampal surfaces.

Contents:

• Installation
  • Requirements
  • Prerequisites
  • Installation Steps
  • Troubleshooting
  • Development Installation
• Usage
  • Overview
  • Basic Workflow
  • Intracranial EEG (iEEG)
  • Scalp EEG
  • Feature File Format
  • Detailed Inputs
  • Output Structure
• API Reference
  • Core Functions
  • Dependencies
  • Examples
  • See Also
• Algorithms and Mathematical Background
  • Overview
  • Intracranial EEG (iEEG) Method
  • Scalp EEG Method
  • See Also
• Frequently Asked Questions
  • General
  • Installation & Setup
  • Data Preparation
  • Running electroMICA
  • Troubleshooting
  • Contributing & Support
• Contributing to electroMICA
  • Code of Conduct
  • Getting Started
  • Coding Guidelines
  • Types of Contributions
  • Workflow & Pull Requests
  • Documentation
  • Reporting Issues
  • Development Environment
  • Acknowledgments
  • Contact

Highlights

• Multimodal integration: Seamlessly integrates electrophysiology with micapipe structural and functional outputs.

• Detailed individualized surfaces: Projects features onto cortical and (optionally) hippocampal surfaces.

• BIDS-compliant: Follows Brain Imaging Data Structure (BIDS) conventions.

Quick Start

Install from requirements.txt:

pip install -r requirements.txt

Then edit an example script with your BIDS paths and run:

python example_electroMICA_iEEG.py
# or
python example_electroMICA_scalp.py

See Installation and Usage for detailed instructions.

Key Features

intracerebral or stereo EEG

Depth electrodes are modeled as line segments in a single-layer homogeneous conductor. Unique distributed dipolar sheets generator model with no mathematical singularity. Contact sensitivity profiles are computed via BEM with analytic element integration. Features are mapped to cortical/hippocampal vertices using contact sensitivity weighting.

Scalp EEG

A three-layer head model (scalp, skull, brain) is constructed from T1w MRI. Forward problem solved using BEM to compute leadfields from sources to electrodes. Inverse problem solved with modified eLORETA with spatial correlation weighting. Multiple SNR-dependent feature maps are generated.

Hippocampal Surfaces

When hippunfold outputs are available, hippocampal surfaces are included in the source space.

Reference

If you use electroMICA in your research, please cite:

(Citation details to be added)

Additionally, when using electroMICA, you should cite:

• micapipe — Cruces, R. R., et al. (2022). Micapipe: a pipeline for multimodal neuroimaging and connectome analysis. NeuroImage, 119612.

If you use HippUnfold, please cite

• hippunfold — DeKraker, J., et al. (2022), Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. elife 11: e77945.

See Algorithms and Mathematical Background for detailed algorithmic references.

License

This project is licensed under the GPL v3 license. See LICENSE for details.

Indices and tables

• Index

• Module Index

• Search Page