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Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation.

ABSTRACT: Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS. MER was performed simultaneously for 5 microelectrodes in a setting of Ben's-gun pattern in awake patients. Using spikes and background activity several different parameters and their spectral estimates in various frequency bands including low frequency (2-7 Hz), Alpha (8-12 Hz), Beta (sub-divided as Low_Beta (13-20 Hz) and High_Beta (21-30 Hz)) and Gamma (31 to 49 Hz) were computed. The optimal STN lead placement with the most optimal clinical effect/side-effect ratio accorded to the maximum spike rate in 85% of the implantation. Mean amplitude of background activity in the low beta frequency range was corresponding to right depth in 85% and right location in 94% of the implantation respectively. MER can be used for STN mapping and intraoperative decisions for the implantation of DBS electrode leads with a high accuracy. Spiking and background activity in the beta range are the most promising independent parameters for the delimitation of the proper anatomical site.

SUBMITTER: Koirala N

PROVIDER: S-EPMC7648837 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation.

Koirala Nabin N Serrano Lucas L Paschen Steffen S Falk Daniela D Anwar Abdul Rauf AR Kuravi Pradeep P Deuschl Günther G Groppa Sergiu S Muthuraman Muthuraman M

Scientific reports 20201106 1

Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS. MER was performed simultaneously for 5 microelectrodes in a setting of Ben's-gun pattern in awake patients. Using spikes and background activity several different parameters a ...[more]

PMID: 33159098

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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:Deep brain stimulation therapy is an effective symptomatic treatment for Parkinson's disease, yet the precise mechanisms responsible for its therapeutic effects remain unclear. Although the targets of deep brain stimulation are grey matter structures, axonal modulation is known to play an important role in deep brain stimulation's therapeutic mechanism. Several white matter structures in proximity to the subthalamic nucleus have been implicated in the clinical benefits of deep brain stimulation for Parkinson's disease. We assessed the connectivity patterns that characterize clinically beneficial electrodes in Parkinson's disease patients, after deep brain stimulation of the subthalamic nucleus. We evaluated 22 patients with Parkinson's disease (11 females, age 57 ± 9.1 years, disease duration 13.3 ± 6.3 years) who received bilateral deep brain stimulation of the subthalamic nucleus at the National Institutes of Health. During an initial electrode screening session, one month after deep brain stimulation implantation, the clinical benefits of each contact were determined. The electrode was localized by coregistering preoperative magnetic resonance imaging and postoperative computer tomography images and the volume of tissue activated was estimated from stimulation voltage and impedance. Brain connectivity for the volume of tissue activated of deep brain stimulation contacts was assessed using probabilistic tractography with diffusion-tensor data. Areas most frequently connected to clinically effective contacts included the thalamus, substantia nigra, brainstem and superior frontal gyrus. A series of discriminant analyses demonstrated that the strength of connectivity to the superior frontal gyrus and the thalamus were positively associated with clinical effectiveness. The connectivity patterns observed in our study suggest that the modulation of white matter tracts directed to the superior frontal gyrus and the thalamus is associated with favourable clinical outcomes and may contribute to the therapeutic effects of deep brain stimulation. Our method can be further developed to reliably identify effective deep brain stimulation contacts and aid in the programming process.

Project description:ObjectiveCurrent understanding of the neuromodulatory effects of deep brain stimulation (DBS) on large-scale brain networks remains elusive, largely due to the lack of techniques that can reveal DBS-induced activity at the whole-brain level. Using a novel 3T magnetic resonance imaging (MRI)-compatible stimulator, we investigated whole-brain effects of subthalamic nucleus (STN) stimulation in patients with Parkinson disease.MethodsFourteen patients received STN-DBS treatment and participated in a block-design functional MRI (fMRI) experiment, wherein stimulations were delivered during "ON" blocks interleaved with "OFF" blocks. fMRI responses to low-frequency (60Hz) and high-frequency(130Hz) STN-DBS were measured 1, 3, 6, and 12 months postsurgery. To ensure reliability, multiple runs (48 minutes) of fMRI data were acquired at each postsurgical visit. Presurgical resting-state fMRI (30 minutes) data were also acquired.ResultsTwo neurocircuits showed highly replicable, but distinct responses to STN-DBS. A circuit involving the globus pallidus internus (GPi), thalamus, and deep cerebellar nuclei was significantly activated, whereas another circuit involving the primary motor cortex (M1), putamen, and cerebellum showed DBS-induced deactivation. These 2 circuits were dissociable in terms of their DBS-induced responses and resting-state functional connectivity. The GPi circuit was frequency-dependent, selectively responding to high-frequency stimulation, whereas the M1 circuit was responsive in a time-dependent manner, showing enhanced deactivation over time. Finally, activation of the GPi circuit was associated with overall motor improvement, whereas M1 circuit deactivation was related to reduced bradykinesia.InterpretationConcurrent DBS-fMRI using 3T revealed 2 distinct circuits that responded differentially to STN-DBS and were related to divergent symptoms, a finding that may provide novel insights into the neural mechanisms underlying DBS. ANN NEUROL 2020;88:1178-1193.

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Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation.

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Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation.

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