Description
Transcranial Ultrasound Stimulation (TUS) provides a non-invasive method for modulating brain activity with spatial precision. It has application in treatment of neurological and psychological disease, for example depression, epilepsy and Parkinson’s, and for basic neuroscience research. Biophysical effects of TUS have not been fully quantified and physical mechanisms linking pressure and shear waves to neural circuit activity are not fully understood. Only a limited range of TUS protocols have been tried experimentally. Ultrasound is hypothesized to modulate neural voltage and firing rate via the stretching of neural membranes allowing flow of ions in and out of the cell. However, different neural cells (e.g. cortical excitatory, thalamic reticular, etc) respond differently, particularly to sound intensity. We have used established neural field theory to model the effect of TUS on brain activity (the ‘electroencephalogram’, EEG) and coupling between populations of neurons (‘synaptic plasticity’). The models permit investigation of the interaction between TUS parameters such as pulse repetition frequency, duration and intensity, and neural response as measured through EEG. We have (a) simulated TUS using the NFTsim software [1] and (b) analyzed plasticity response using a linearized approximation to the neural field equations. Simulated spatiotemporal EEG changes have been interpreted using a neural phonon method [2]. We demonstrate that appropriate protocol choice allows neural activity to be moved towards a specific, desired state, and that plasticity can be manipulated to increase or decrease synaptic strength. Protocols can be optimized for different parameters, representing different individuals. Results have been validated with experimental measurements of EEG before and after TUS. In conclusion, biophysical numerical modelling supports development and understanding of effective TUS neuromodulation strategies.
[1] Sanz-Leon, P., et al. (2018). PLoS Computational Biology, 14(8), e1006387.
[2] Batterton, C., et al. (2026). Journal of Computational Neuroscience, 54, 177–190.
| I am the presenting author | Yes |
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