Prediction of the Timing and the Rhythm of the Parkinsonian Subthalamic Nucleus Neural Spikes Using the Local Field Potentials

In this paper, we discuss the use of a nonlinear cascade model to predict the subthalamic nucleus spike activity from the local field potentials recorded in the motor area of the nucleus of Parkinson´s disease patients undergoing deep brain stimulation. We use a segment of appropriately selected and processed data recorded from five nuclei to acquire the information of the spike timing and rhythm of a single neuron and estimate the model parameters. We then use the rest of each recording to assess the model´s accuracy in predicting spike timing, rhythm, and interspike intervals. We show that the cumulative distribution function (CDF) of the predicted spikes remains inside the 95% confidence interval of the CDF of the recorded spikes. By training the model appropriately, we prove its ability to provide quite accurate predictions for multiple-neuron recordings as well, and we establish its validity as a simple yet biologically plausible model of the intranuclear spike activity recorded from Parkinson´s disease patients.