Analysis of microelectrographic neuronal background in deep brain nuclei in parkinson disease

This paper proposes that spectral characteristics of background neuronal potentials can be effective parameters to classifying and identifying neural activities from subthalamic nucleus (STN) and subtantia nigra (SNr). The spike-free background signals were obtained from inter-spike microelectrode recording signals. The averaged periodogram was then used to compute the power spectral density of the background signals. Three spectral parameters were extracted and used as discriminant features for artificial neural networks. The commonly used neuronal firing patterns were also extracted from the detected neuronal spikes and used as discriminant features. Our results showed that the classification performance based on background parameters was similar or better than using neuronal firing patterns. This implied that neuronal background can be useful as an aid in targeting STN as well as neuronal firing patterns, saving from spike identification as single- or multi-neuron discharges.