Nonlinear local electrovascular coupling. II: From data to neuronal masses

Area of Science:

• Neuroscience
• Biophysics
• Computational Biology

Background:

• The companion article introduced a local electrovascular coupling (LEVC) model for cortical unit dynamics.
• Electrical and vascular states yield mesoscopic reflections reconstructible from EEG and fMRI data.

Purpose of the Study:

• To develop a recursive optimization algorithm for statistical inference on the LEVC model using hybrid EEG/fMRI data.
• To estimate unobserved states and unknown parameters of the LEVC model.
• To investigate the dynamics of electrical and vascular states and their interrelationships in the human striate cortex.

Main Methods:

• A recursive optimization algorithm based on the local linearization (LL) filter and an innovation method.
• Bayesian interpretation of the LL filter for hybrid data sampled at different rates.
• Estimation of exogenous synaptic input dynamics using Gaussian radial basis functions.
• Application to concurrent EEG and fMRI recordings during visual stimulation.

Main Results:

• The algorithm successfully estimated electrical/vascular states and LEVC model parameters in V1 for a 4.0 Hz reversion frequency using data from one subject.
• EEG data from a second subject revealed changes in electrical state dynamics across varying reversion frequencies (0.5-4.0 Hz).
• Estimated electrical states were used to predict vascular effects resulting from frequency variations.

Conclusions:

• The developed algorithm provides a robust method for statistical inference on the LEVC model using multi-modal neuroimaging data.
• The study successfully characterized the dynamic interplay between electrical and vascular states in the human visual cortex.
• The findings offer insights into how variations in neural activity impact cerebrovascular dynamics.