Dataset Information

Subthalamic deep brain stimulation at individualized frequencies for Parkinson disease.

ABSTRACT:

Objectives

The oscillation model of Parkinson disease (PD) states that, in the subthalamic nucleus (STN), increased θ (4-10 Hz) and β (11-30 Hz) frequencies were associated with worsening whereas γ frequencies (31-100 Hz) were associated with improvement of motor symptoms. However, the peak STN frequency in each band varied widely from subject to subject. We hypothesized that STN deep brain stimulation (DBS) at individualized γ frequencies would improve whereas θ or β frequencies would worsen PD motor signs.

Methods

We prospectively studied 13 patients with PD. STN local field potential (LFP) was recorded after electrode implantations, in the OFF and then in ON dopaminergic medication states while patients performed wrist movements. Six individual peak frequencies of the STN LFP power spectra were obtained: the greatest decrease in θ and β and greatest increase in γ frequencies in the ON state (MED) and during movements (MOVE). Eight DBS frequencies were applied including 6 MED and MOVE frequencies, high frequency (HF) used for chronic stimulation, and no stimulation. The patients were assessed using the motor Unified Parkinson's Disease Rating Scale (mUPDRS).

Results

STN DBS at γ frequencies (MED and MOVE) and HF significantly improved mUPDRS scores compared to no stimulation and both γ frequencies were not different from HF. DBS at θ and β frequencies did not worsen mUPDRS scores compared to no stimulation.

Conclusion

Short-term administration of STN DBS at peak dopamine-dependent or movement-related γ frequencies were as effective as HF for reducing parkinsonian motor signs but DBS at θ and β frequencies did not worsen PD motor signs.

Classification of evidence

This study provides Class III evidence that STN DBS at patient-specific γ frequencies and at usual high frequencies both improved mUPDRS scores compared to no stimulation and did not differ in effect.

SUBMITTER: Tsang EW

PROVIDER: S-EPMC3369506 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDeep 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.ObjectiveWe 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.MethodsIn 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.ResultsWhen 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.ConclusionClinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.

Project description:Highlights • Neuroanatomical variations among patients are obscured in atlas-based VTA modeling.• N-of-1 neuroanatomical and VTA modeling enables patient-level precision.• Mean optimal stimulation is dorsomedial to the STN, near its posterior half.• Individual VTAs deviate from optimal stimulation sites to varying degrees.• Optimal stimulation sites for rigidity, bradykinesia, and tremor partially overlap. Background Motor outcomes after subthalamic deep brain stimulation (STN DBS) for Parkinson disease (PD) vary considerably among patients and strongly depend on stimulation location. The objective of this retrospective study was to map the regions of optimal STN DBS for PD using an atlas-independent, fully individualized (N-of-1) tissue activation modeling approach and to assess the relationship between patient-level therapeutic volumes of tissue activation (VTAs) and motor improvement. Methods The stimulation-induced electric field for 40 PD patients treated with bilateral STN DBS was modeled using finite element analysis. Neurostimulation models were generated for each patient, incorporating their individual STN anatomy, DBS lead position and orientation, anisotropic tissue conductivity, and clinical stimulation settings. A voxel-based analysis of the VTAs was then used to map the optimal location of stimulation. The amount of stimulation in specific regions relative to the STN was measured and compared between STNs with more and less optimal stimulation, as determined by their motor improvement scores and VTA. The relationship between VTA location and motor outcome was then assessed using correlation analysis. Patient variability in terms of STN anatomy, active contact position, and VTA location were also evaluated. Results from the N-of-1 model were compared to those from a simplified VTA model. Results Tissue activation modeling mapped the optimal location of stimulation to regions medial, posterior, and dorsal to the STN centroid. These regions extended beyond the STN boundary towards the caudal zona incerta (cZI). The location of the VTA and active contact position differed significantly between STNs with more and less optimal stimulation in the dorsal-ventral and anterior-posterior directions. Therapeutic stimulation spread noticeably more in the dorsal and posterior directions, providing additional evidence for cZI as an important DBS target. There were significant linear relationships between the amount of dorsal and posterior stimulation, as measured by the VTA, and motor improvement. These relationships were more robust than those between active contact position and motor improvement. There was high variability in STN anatomy, active contact position, and VTA location among patients. Spherical VTA modeling was unable to reproduce these results and tended to overestimate the size of the VTA. Conclusion Accurate characterization of the spread of stimulation is needed to optimize STN DBS for PD. High variability in neuroanatomy, stimulation location, and motor improvement among patients highlights the need for individualized modeling techniques. The atlas-independent, N-of-1 tissue activation modeling approach presented in this study can be used to develop and evaluate stimulation strategies to improve clinical outcomes on an individual basis.