Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease

PLoS One. 2021 Jan 26;16(1):e0244133. doi: 10.1371/journal.pone.0244133. eCollection 2021.

Abstract

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes.

Objective: We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients.

Methods: In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently.

Results: When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve.

Conclusion: Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Anesthesia, General
  • Area Under Curve
  • Deep Brain Stimulation*
  • Deep Learning*
  • Female
  • Humans
  • Male
  • Microelectrodes
  • Middle Aged
  • Parkinson Disease / pathology
  • Parkinson Disease / therapy*
  • ROC Curve
  • Severity of Illness Index
  • Subthalamic Nucleus / physiopathology*
  • Treatment Outcome
  • Wavelet Analysis

Grants and funding

This study was supported by the Korea Healthcare Technology R&D Project (grant No. HI11C21100200), funded by the Ministry of Health & Welfare, Republic of Korea; the Industrial Strategic Technology Development Program (grant No. 10050154, Business Model Development for Personalized Medicine Based on Integrated Genome and Clinical Information) funded by the Ministry of Trade, Industry & Energy (MI, Korea); the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant No. 2015M3C7A1028926); the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant No. 2017M3C7A1047392); and by Soonchunhyang University research funds.