Dataset Information

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

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.

SUBMITTER: Park KH

PROVIDER: S-EPMC7837468 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

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

Park Kwang Hyon KH Sun Sukkyu S Lim Yong Hoon YH Park Hye Ran HR Lee Jae Meen JM Park Kawngwoo K Jeon Beomseok B Park Hee-Pyoung HP Kim Hee Chan HC Paek Sun Ha SH

PloS one 20210126 1

<h4>Background</h4>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.<h4>Objective</h4>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 adva ...[more]

PMID: 33497391

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Project description:ObjectiveTo investigate hemispheric effects of directional versus ring subthalamic nucleus (STN) deep brain stimulation (DBS) surgery on cognitive function in patients with advanced Parkinson's disease (PD).MethodsWe examined 31 PD patients (Left STN n = 17; Right STN n = 14) who underwent unilateral subthalamic nucleus (STN) DBS as part of a NIH-sponsored randomized, cross-over, double-blind (ring vs directional) clinical trial. Outcome measures were tests of verbal fluency, auditory-verbal memory, and response inhibition. First, all participants were pooled together to study the effects of directional versus ring stimulation. Then, we stratified the groups by surgery hemisphere and studied the longitudinal changes in cognition post-unilateral STN DBS.ResultsRelative to pre-DBS cognitive baseline performances, there were no group changes in cognition following unilateral DBS for either directional or ring stimulation. However, assessment of unilateral DBS by hemisphere revealed a different pattern. The left STN DBS group had lower verbal fluency than the right STN group (t(20.66 = -2.50, p = 0.02). Over a period of eight months post-DBS, verbal fluency declined in the left STN DBS group (p = 0.013) and improved in the right STN DBS group over time (p < .001). Similarly, response inhibition improved following right STN DBS (p = 0.031). Immediate recall did not significantly differ over time, nor was it affected by implant hemisphere, but delayed recall equivalently declined over time for both left and right STN DBS groups (left STN DBS p = 0.001, right STN DBS differ from left STN DBS p = 0.794).ConclusionsDirectional and ring DBS did not differentially or adversely affect cognition over time. Regarding hemisphere effects, verbal fluency decline was observed in those who received left STN DBS, along with the left and right STN DBS declines in delayed memory. The left STN DBS verbal fluency decrement is consistent with prior bilateral DBS research, likely reflecting disruption of the basal-ganglia-thalamocortical network connecting STN and inferior frontal gyrus. Interestingly, we found an improvement in verbal fluency and response inhibition following right STN DBS. It is possible that unilateral STN DBS, particularly in the right hemisphere, may mitigate cognitive decline.

Project description:BackgroundSubthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.ObjectiveTo develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.MethodsSixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation.ResultsTweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.ConclusionsPreoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Dataset Information

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

Background

Objective

Methods

Results

Conclusion

Publications

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

Similar Datasets

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