Principal Component Latent Variate Factorial Analysis of MER Signals of STN-DBS in Parkinson’s Disease (Electrode Implantation)

Although clinical benefits of deep brain stimulation (DBS) in subthalamic-nuclei (STN) neurons have been established, albeit, how its mechanisms improve the motor features of PD have not been fully established. DBS is effective in decreasing tremor and increasing motor-function of Parkinson’s disease (PD). However, objective methods for quantifying its efficacy are lacking. Therefore, we present a principal component analysis (PCA) method to extract-features from microelectrode-recording(MER) signals of STN-DBS and to predict improvement of unified Parkinson’s disease rating scale(UPDRS) following DBS (applied on 12 PD patients). Hypothesis of this study is that the developed-method is capable of quantifying the effects-of-DBS “on state” in PD-patients. We hypothesize that a data informed combination of features extracted from MER can predict the motor improvement of PD-patients undergoing-DBS-surgery. This shows the high-frequency-stimulation in diseased-brain did not damage subthalamic-nuclei (STN) neurons but protect. Further, it is safe to stimulate STN much earlier than it was accepted so far. At the experimental level, high-frequency-stimulation of the STN could protect neurons in the subsstantia-nigra (SN, an important element of the brain). Therefore, to test this hypothesis in humans, we need to perform STN stimulation at the very beginning of the disease so that we can predict the disease at an early-stage. The latent-variate-factorial is a statistical-mathematical technique PCA based tracking method for computing the effects of DBS in PD. Ten parameters capturing PD characteristic signal-features were extracted from MER-signals of STN. Using PCA, the original parameters were transformed into a smaller number of PCs. Finally, the effects-of-DBS were quantified by examining the PCs in a lower-dimensional-feature-space. This study showed that motor-symptoms of PD were effectively reduced with DBS.