Cavitational capacitive drive: a computationally efficient model for ultrasonic neuromodulation

Area of Science:

• Computational Neuroscience
• Biophysics
• Neuroimaging

Background:

• Ultrasonic neuromodulation offers non-invasive brain stimulation.
• The Neuronal Intramembrane Cavitation Excitation (NICE) model explains ultrasound-membrane interactions but is computationally intensive.
• Simulations of large-scale neuronal networks are hindered by the NICE model's complexity.

Purpose of the Study:

• To develop a computationally efficient approximation of the NICE model, named the Cavitational Capacitive Drive (CCD) model.
• To enable the simulation of ultrasonic neuromodulation in complex, multicompartment neuronal models.
• To validate the accuracy and speed of the CCD model against the NICE framework.

Main Methods:

• Developed an analytical formulation for the CCD model based on ultrasound frequency and intensity.
• Calibrated the CCD model against NICE-generated capacitance waveforms.
• Implemented the CCD model in the NEURON simulation environment and compared neuronal responses.

Main Results:

• The CCD model accurately reproduced NICE-derived membrane capacitance oscillations (R^2 > 0.999).
• CCD simulations showed excellent agreement with NICE for passive and active neuronal responses.
• Achieved over 8,500-fold computational speed-up compared to the NICE model.

Conclusions:

• The CCD model provides a computationally efficient and accurate method for simulating ultrasonic neuromodulation.
• This framework facilitates the integration of intramembrane-cavitation-based neuromodulation into complex neuronal network models.
• The CCD model significantly reduces simulation costs, enabling broader research into ultrasound effects on neural activity.