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Updated: Sep 23, 2025
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
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Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic
Dylan Mann-Krzisnik1, Georgios D Mitsis2
1Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Quebec, Canada.
Human Brain Mapping
|May 14, 2022
Summary
This study introduces a new framework to analyze brain activity by linking electrophysiology (EEG) and BOLD-fMRI signals. The method reveals spatial patterns and individual differences in how brain signals relate to blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) responses.
Area of Science:
- Neuroscience
- Biophysics
- Signal Processing
Background:
- Understanding the relationship between electrophysiology and BOLD-fMRI is crucial for brain research.
- Current methods often use a standard hemodynamic response function (HRF), limiting the analysis of neurovascular coupling.
Purpose of the Study:
- To develop a novel framework for analyzing the spatial distribution of time-frequency features from electrophysiology.
- To estimate flexible, region-specific hemodynamic response functions (HRFs) for improved BOLD-fMRI modeling.
- To investigate inter-subject variability in EEG-to-BOLD neurovascular coupling.
Main Methods:
- Utilized Canonical Polyadic Decomposition of impulse response functions to extract spatial features.
- Convolved electrophysiological features with estimated region-specific HRFs to model BOLD time-series.
- Validated the framework on simulated data and publicly available EEG-fMRI datasets (task-based and resting-state).
Main Results:
- The proposed method successfully extracts spatial distributions of electrophysiological features and estimates flexible HRFs.
- Demonstrated the framework's robustness against simulated noise and physiological confounds.
- Revealed significant inter-subject variability in EEG-derived BOLD signal modeling.
Conclusions:
- The framework provides deeper insights into the electrophysiology-BOLD-fMRI relationship and neurovascular coupling.
- The method allows for the investigation of individual differences in brain signal processing.
- This approach enhances the modeling of BOLD signals using electrophysiological data.
