Example: Connectivity Analysis (MNE-Python)¶

This page explains the connectivity_analysis_pipeline_mne.signalJourney.json example file, which documents a functional connectivity analysis workflow. This pipeline calculates spectral coherence between EEG sensors using the MNE-Python and SciPy libraries.

Pipeline Overview¶

This MNE-Python pipeline demonstrates connectivity analysis: - Load cleaned data from ICA decomposition pipeline - Extract epochs for connectivity analysis - Compute power spectral density for individual channels - Calculate coherence matrix between channel pairs - Generate connectivity report with visualization

Pipeline Flowchart¶

flowchart TD
    A[Load Cleaned Data
mne.io.read_raw_fif] --> B[Extract Epochs
mne.Epochs]
    B --> C[Compute PSD
epochs.compute_psd]
    C --> D[Calculate Coherence
mne.connectivity.spectral_connectivity_epochs]
    D --> E[Generate Report
connectivity_report]

    %% Input file
    F["📁 sub-01_task-rest_desc-cleaned_eeg.fif
From: ICA Decomposition Pipeline"] --> A

    %% Inline data
    B --> V1["📊 Event Windows
[-0.5, 1.5] s"]
    C --> V2["📊 Frequency Bands
Alpha: 8-12 Hz"]
    D --> V3["📊 Connectivity Method
Coherence (magnitude)"]

    %% Final outputs
    E --> G["💾 sub-01_task-rest_desc-connectivity_eeg.h5
Connectivity matrix"]
    E --> H["💾 sub-01_task-rest_desc-connectivity_plot.png
Connectivity visualization"]

    %% Quality metrics
    C --> Q1["📈 Frequency resolution: 0.5 Hz
Spectral range: 1-40 Hz"]
    D --> Q2["📈 Significant connections: 245/2016
Mean coherence: 0.32"]

    %% Styling
    classDef processStep fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef inputFile fill:#fff3e0,stroke:#e65100,stroke-width:2px
    classDef outputFile fill:#e8f5e8,stroke:#1b5e20,stroke-width:2px
    classDef inlineData fill:#f3e5f5,stroke:#4a148c,stroke-width:1px
    classDef qualityMetric fill:#f9f9f9,stroke:#666,stroke-width:1px

    class A,B,C,D,E processStep
    class F inputFile
    class G,H outputFile
    class V1,V2,V3 inlineData
    class Q1,Q2 qualityMetric

Key MNE-Python Features Demonstrated¶

Connectivity Functions¶

• mne.Epochs: Extract task-related epochs for connectivity analysis
• epochs.compute_psd: Calculate power spectral density using multitaper method
• mne.connectivity.spectral_connectivity_epochs: Compute spectral connectivity measures
• Connectivity metrics: Coherence, PLI, PLV, and other measures

Advanced Parameters¶

• Frequency bands: Specific frequency ranges for connectivity analysis
• Connectivity methods: Multiple algorithms for different connectivity aspects
• Statistical testing: Permutation tests for connectivity significance
• Visualization: Network plots and connectivity matrices

Example JSON Structure¶

The connectivity computation demonstrates advanced parameter documentation:

{
  "stepId": "4",
  "name": "Calculate Spectral Coherence",
  "description": "Compute coherence between all channel pairs in alpha band.",
  "software": {
    "name": "MNE-Python",
    "version": "1.6.1",
    "functionCall": "mne.connectivity.spectral_connectivity_epochs(epochs, method='coh', fmin=8, fmax=12)"
  },
  "parameters": {
    "method": "coh",
    "fmin": 8,
    "fmax": 12,
    "sfreq": 1000,
    "n_jobs": 1,
    "verbose": false
  }
}

Multi-Output Connectivity Analysis¶

Connectivity analysis typically produces multiple related outputs:

"outputTargets": [
  {
    "targetType": "file",
    "location": "./derivatives/signaljourney/sub-01/eeg/sub-01_task-rest_desc-connectivity_eeg.h5",
    "format": "HDF5",
    "description": "Connectivity matrix with channel pairs and frequency bins."
  },
  {
    "targetType": "inlineData",
    "name": "connectivity_matrix",
    "data": "{{coherence_matrix_alpha}}",
    "description": "Alpha band coherence matrix (64x64)."
  }
]

Connectivity Analysis Features¶

Frequency-Specific Analysis¶

• Band-specific connectivity: Focus on particular frequency ranges (alpha, beta, gamma)
• Broadband analysis: Connectivity across multiple frequency bands
• Time-frequency connectivity: Dynamic connectivity over time
• Cross-frequency coupling: Interactions between different frequency bands

Connectivity Metrics¶

• Coherence: Linear relationship in frequency domain
• Phase-Locking Value (PLV): Phase synchronization between signals
• Phase Lag Index (PLI): Phase relationship corrected for volume conduction
• Granger Causality: Directed connectivity measures

Statistical Assessment¶

• Permutation testing: Significance testing for connectivity values
• Multiple comparison correction: Control for multiple channel pairs
• Confidence intervals: Bootstrap confidence bounds for connectivity
• Network measures: Graph theory metrics on connectivity networks

MNE-Python vs EEGLAB Comparison¶

Usage Notes¶

This example demonstrates: - Connectivity analysis workflows with frequency-specific measures - Statistical testing integration for significance assessment - Multi-output documentation for matrices and visualizations - Quality metrics for connectivity analysis validation - Pipeline integration building on previous processing steps

The pipeline showcases MNE-Python's comprehensive connectivity analysis capabilities while maintaining full parameter transparency for reproducible network analysis.