Objective: In temporal interference (TI) deep brain stimulation (DBS), the neurons of mice react to two interfering sinusoids with a slightly different frequency. This is called a temporal interference (TI) signal. It was previously seen that for the same input intensity, the neurons do not react to a purely sinusoidal signal. This study aims to get a better understanding into the mechanism for this, which is largely unknown. Methods: This study makes use of single compartment models to computationally simulate the response of neurons to the sinusoidal and TI-waveform. This study also compares different neuron models to get insight in which models are able to model the experimental behavior. Results: It was found that integrate-and-fire models do not reflect the experimental behavior while the Hodgkin-Huxley and Frankenhaeuer-Huxley model do reflect this behavior. It was seen that changing the characteristics of the ion gates in the Frankenhaeuser-Huxley model alters the response to both sinusoidal and TI signal. It can even make sure that the firing threshold of the sinusoidal input becomes lower than that of the TI input. Conclusion: The results show that the ion-gates have a big influence on the behavior and their characteristics can define the way the neuron reacts to sinusoidal and TI inputs. Significance: This paper makes advances both in terms of biophysical insight of the neuron as well as the insight in computational modelling of TI-stimulation.

Temporal interference (TI) stimulation is a technique in which two high frequency sinusoidal electric fields, oscillating at a slightly different frequency are sent into the brain. The goal is to achieve stimulation at the place where both fields interfere. This study uses a simplified version of the Hodgkin-Huxley model to analyse the different parameters of the TI-waveform and how the neuron reacts to this waveform. In this manner, the underlying mechanism of the reaction of the neuron to a TI-signal is investigated.Clinical relevance-This study shows the importance of the parameter choice of the temporal interference waveform and provides insights into the underlying mechanism of the neuronal response to a beating sine for the application of temporal interference stimulation.