Parameter Sensitivity and Experimental Validation for Fractional-Order Dynamical Modeling of Neurovascular Coupling

Area of Science:

• Neuroscience
• Biophysics
• Mathematical modeling

Background:

• Neurovascular coupling is crucial for brain function but complex to model.
• Fractional-order modeling offers a novel approach due to its nonlocal properties, suitable for delayed and power-law phenomena.

Purpose of the Study:

• To analyze and validate a fractional-order model for neurovascular coupling.
• To demonstrate the added value of fractional-order parameters compared to integer-order models.

Main Methods:

• Developed and analyzed a fractional-order model of neurovascular coupling.
• Performed parameter sensitivity analysis comparing fractional and integer models.
• Validated the model using electrophysiology and laser Doppler flowmetry data from neural activity-cerebral blood flow experiments.

Main Results:

• The fractional-order model effectively fits diverse cerebral blood flow (CBF) response shapes with low complexity.
• Fractional parameters capture key hemodynamic response determinants, including post-stimulus undershoot, outperforming integer models.
• The model demonstrates flexibility and adaptability in characterizing CBF responses.

Conclusions:

• The proposed fractional-order framework provides a powerful and flexible tool for characterizing neurovascular coupling.
• This approach enhances the understanding of brain function by accurately modeling complex hemodynamic responses.