Evoked resonant neural activity long-term dynamics can be reproduced by a computational model with vesicle depletion

• Published in: Neurobiology of disease Vol. 199; p. 106565
• Main Authors: Sermon, James J., Wiest, Christoph, Tan, Huiling, Denison, Timothy, Duchet, Benoit
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2024; Academic Press; Elsevier
• Subjects: Computational modelling; Computer Simulation; Deep brain stimulation; Deep Brain Stimulation - methods; Evoked Potentials - physiology; Evoked resonant neural activity; Humans; Male; Models, Neurological; Neurons - physiology; Parkinson Disease - physiopathology; Parkinson Disease - therapy; parkinson's disease; Subthalamic Nucleus; Synaptic vesicle depletion; Subthalamic nucleus; Synaptic vesicle depletion; Evoked resonant neural activity; Deep brain stimulation; Computational modelling; parkinson's disease
• ISSN: 0969-9961; 1095-953X; 1095-953X
• DOI: 10.1016/j.nbd.2024.106565

Subthalamic deep brain stimulation (DBS) robustly generates high-frequency oscillations known as evoked resonant neural activity (ERNA). Recently the importance of ERNA has been demonstrated through its ability to predict the optimal DBS contact in the subthalamic nucleus in patients with Parkinson's disease. However, the underlying mechanisms of ERNA are not well understood, and previous modelling efforts have not managed to reproduce the wealth of published data describing the dynamics of ERNA. Here, we aim to present a minimal model capable of reproducing the characteristics of the slow ERNA dynamics published to date. We make biophysically-motivated modifications to the Kuramoto model and fit its parameters to the slow dynamics of ERNA obtained from data. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA (over hundreds of seconds) with a single neuronal population, and, crucially, with vesicle depletion as one of the key mechanisms behind the ERNA frequency decay in our model. We further validate the proposed model against experimental data from Parkinson's disease patients, where it captures the variations in ERNA frequency and amplitude in response to variable stimulation frequency, amplitude, and to stimulation pulse bursting. We provide a series of predictions from the model that could be the subject of future studies for further validation. •Long-term ERNA dynamics can be modelled using a single neural structure.•The proposed model captures the long-term characteristics of ERNA with variable neurostimulation paradigms.•Synaptic vesicle depletion is one the key features contributing to long-term ERNA dynamics in the model.