NeuroPycon

NeuroPycon provides a Python-based framework for reproducible processing and connectivity analysis of MEG, EEG, functional MRI (fMRI), and anatomical MRI data.

Key Features:

• Multi-Modal Data Processing: Handles MEG, EEG, fMRI, and anatomical MRI data within unified workflows.
• Reproducibility and Replication: Uses shareable parameter files that document analysis steps to enable replication.
• Integration with NiPype Framework: Wraps commonly used neuroimaging software via NiPype, enabling incorporation of Python-, Matlab-, and other-language tools.
• Parallel Processing Capabilities: Leverages NiPype's multi-threaded processing capabilities for parallel execution of pipeline nodes.
• Flexible Pipeline Configuration: Configures pipelines as connected nodes including Python-wrapped modules, user-defined functions, and tools such as MNE-Python and Radatools.
• Ephypype: Provides electrophysiology-focused functionality including data import, pre-processing, automatic removal of ocular and cardiac artifacts, and connectivity analyses at sensor and source levels.
• Graphpype: Provides functional connectivity analysis using graph-theoretical metrics, including modular partition analysis.
• Multi-Modal Data Fusion (planned): Plans integration for multi-modal data fusion such as MEG–fMRI and intracranial EEG–fMRI.

Scientific Applications:

• Connectivity Analysis: Performs connectivity analyses for MEG, EEG, and fMRI data at sensor and source levels.
• Electrophysiology Preprocessing and Artifact Correction: Supports pre-processing and automatic removal of ocular and cardiac artifacts in electrophysiology data.
• Source- and Sensor-Level Analysis: Enables sensor-level and source-level signal analyses for MEG and EEG datasets.
• Graph-Theoretical Network Analysis: Applies graph-theoretical metrics and modular partitioning to functional connectivity networks.
• Multi-Modal Fusion Studies (planned): Targets studies combining MEG and fMRI or intracranial EEG and fMRI for integrated analyses.

Methodology:

Implements NiPype-based workflows that wrap tools such as MNE-Python and Radatools, uses shareable parameter files to document analysis steps, executes pipeline nodes in parallel via NiPype, performs automatic ocular and cardiac artifact removal, computes connectivity measures, and derives graph-theoretical metrics including modular partitions.