Modeling the Hemodynamic Response Function Using EEG-fMRI Data During Eyes-Open Resting-State Conditions and Motor...

Area of Science:

• Neuroimaging
• Electrophysiology
• Functional Magnetic Resonance Imaging (fMRI)

Background:

• Accurate quantification of the hemodynamic response function (HRF) is crucial for understanding neurovascular coupling and improving functional connectivity analyses in blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI).
• Estimating HRF from BOLD-fMRI data, especially during resting-state, is difficult due to the lack of direct information on neuronal dynamics.
• Simultaneous electroencephalography (EEG) and fMRI offer a promising solution, as EEG provides a more direct measure of neural activity.

Purpose of the Study:

• To investigate the regional characteristics of the HRF during resting conditions using simultaneous EEG-fMRI.
• To propose and validate a novel methodological approach for HRF estimation combining EEG source space reconstruction and block-structured models.
• To explore the contribution of different EEG frequency bands to the BOLD signal and their regional specificity.

Main Methods:

• Simultaneous EEG-fMRI acquisition during resting-state conditions.
• Distributed EEG source space reconstruction to enhance spatial resolution of HRF estimation.
• Application of block-structured linear and nonlinear models to simultaneously estimate HRF and EEG frequency band contributions.
• Validation of the method using simultaneous EEG-fMRI data acquired during a unimanual hand-grip task.

Main Results:

• The resting-state BOLD signal dynamics can be adequately described by linear models.
• The contribution of different EEG frequency bands to the HRF is region-specific.
• Sensory-motor cortices showed positive HRF shapes, while the lateral occipital cortex and parietal areas exhibited negative HRF shapes.
• Significant associations were found between BOLD signal variations and EEG power fluctuations in the ipsilateral primary motor cortex during a motor task, particularly in the beta band.

Conclusions:

• The proposed method effectively characterizes regional HRF dynamics using simultaneous EEG-fMRI.
• Linear models are sufficient for describing resting-state BOLD signal dynamics.
• Region-specific HRF shapes and EEG frequency band contributions highlight the complexity of neurovascular coupling.
• The findings align with previous research and validate the utility of combined EEG-fMRI for studying brain function.