Objective: To render the significance of microelectrode recording and elucidate its prognostic role in response to STNDBS.

Background: Studies[1]-[9] have correlated local field potential(LFP) and multi unit activity(MUA) features with DBS enhancement. Yet, quantification and prediction of the subject specific movement had not done.

Methods: The followed methods and techniques are used in this study: Data acquisition, data processing and feature selection, classification and regression.

Results: STN-DBS response with prediction We applied backward elimination feature selection scheme [10],[11] and found that four features attained a maximum MCC value of 0.9045, with a confusion matrix C of the form (TABLE I), where, N = TN + FP is the total number of actual “poor” responders (11 in our case) and P = FN + TP is the total number of actual “good” responders (9 in our case). In other words, only one “poor” responder was classified falsely in the “good” STN-DBS response group. The most significant features were found to be PKLFPHG, PowerBUAT, maxPLB, and maxPLHG (TABLE II) with FIs 0.1495, 0.9142, 0.3899, and 0.5982. The effect of each feature on model response is shown(Fig 1a). There is a negative correlation between the values of the above mentioned features and the probability of “good” response, e.g., when PKLFPHG, maxPLB, and maxPLHG attain their median values, an increase in the value of PowerBUAT leads to a decrease of “good” response probability. In order to extract MER features that can quantitatively predict the improvement in the “off”-state UPDRS scale pre and postoperatively, backward elimination scheme used. Five features achieved a maximum correlation co efficient of 0.9178, corresponding to an NMSE of 3.38%; PowerBUAT, maxPLB, maxPLHG, PAFCDT, and PAFCTG (TABLE II) with FIs 0.2058, 0.0561, 0.1506, 0.0764, and 0.1669, respectively. Clinically assessed and OOB predicted UP-DRS improvement (%) for all patients is shown in Fig2. The effect of each feature on the model response can be seen in Fig 1 (b). PowerBUAT, maxPLB, and maxPLHG were negatively correlated with the UPDRS improvement, while PAFCDT and PAFCTG were positively correlated with the latter.

Conclusions: Proposed approach can employ a small number of signal features of STN-neurons to forecast, separately for each patient, the behavioral outcome of STN DBS justifying further investigation and possibly clinical applications.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/deep-brain-stimulation-and-its-efficacy-using-microelectrode-recording/