Optimization of Patient Specific Stimulus for Deep Brain Stimulation Using Spatially Distributed Neural Sources

Abstract

Deep brain stimulation (DBS) becomes the therapy of choice in the later stages of Parkinson's disease (PD) due to the medication's side effects. For effective DBS treatment, it is important to have a controlled dosage of DBS. DBS dosage is administered using the tuning of electrical parameters of the stimulus signal. Since this tuning process is tedious, time-consuming and patient-specific, there is a need to study the properties of DBS stimulation signal for proper dose administration. We propose a simulation framework to define an optimized DBS stimulus using Electroencephalogram (EEG) signals. The objective is to provide a simulation environment inspired by a realistic brain. The framework uses spiking neurons in a reservoir modelled after real brain anatomy and is trained using a biologically inspired spike-time-dependent-plasticity learning algorithm. This reservoir is initially set to OFF-medication state and forced to drift to the ON-medication state by optimizing the synaptic changes. In later testing, the generalization of this framework is verified with EEG-inverse solutions like sLORETA which utilize time-domain EEG signals to estimate neural activations. The stimulus signal is generated by accumulating the variations in synaptic weights in the neural reservoir in the target brain region. We analyze this signal and show that the application of this signal as stimulus results in decreased <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> -band power in subthalamic-nucleus local-field-potential compared to OFF-medication local-field-potential without stimulation. Using SIM4LIFE simulation software, we show that the simulation increases chaos in the local-field-potential of subthalamic-nucleus neurons and shows that neuron weight variations follow specific trajectories in reconstructed state-space. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Impact Statement:</i> The primary contribution of this paper is to present a framework that utilizes the association between target signals of any modality (EEG, MEG, fMRI) and their respective post-synaptic activations to optimize the deep brain stimulation (DBS) stimulus signal. This framework combines coordinates of neural sources with patients' EEG data to optimize the stimulus of DBS. While defining this framework, we initially proposed the usage of spiking neurons in a biological neural reservoir to estimate the internal activations for given EEG data. Later, we extended our testing to incorporate inverse EEG algorithms that estimate neural activations directly from EEG data without any conversion. This is a novel framework, and there are not many examples available in the literature for similar work. This framework is in its early stages, yet it provides an adequate starting point for an under-explored area of study. This framework can potentially evolve into a useful tool for doctors in assisting the DBS tuning process before the surgical procedure. This framework's accuracy depends on the underlying technique to estimate the neural activations from the target signals (like EEG etc.).