Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation.
OBJECTIVE
Clinical deep brain stimulation (DBS) systems can be programmed with thousands of different stimulation parameter combinations (e.g. electrode contact(s), voltage, pulse width, frequency). Our goal was to develop novel computational tools to characterize the effects of stimulation parameter adjustment for DBS.


METHODS
The volume of tissue activated (VTA) represents a metric used to estimate the spatial extent of DBS for a given parameter setting. Traditional methods for calculating the VTA rely on activation function (AF)-based approaches and tend to overestimate the neural response when stimulation is applied through multiple electrode contacts. Therefore, we created a new method for VTA calculation that relied on artificial neural networks (ANNs).


RESULTS
The ANN-based predictor provides more accurate descriptions of the spatial spread of activation compared to AF-based approaches for monopolar stimulation. In addition, the ANN was able to accurately estimate the VTA in response to multi-contact electrode configurations.


CONCLUSIONS
The ANN-based approach may represent a useful method for fast computation of the VTA in situations with limited computational resources, such as a clinical DBS programming application on a tablet computer.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, OH, USA.
Ashutosh
A
Chaturvedi
J Luis
JL
Luján
Cameron C
CC
McIntyre
D001369:Axons / Q000502:physiology; D001921:Brain / Q000502:physiology*; D046690:Deep Brain Stimulation / Q000379:methods*; D004558:Electric Stimulation; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results