Pilot Study to Investigate the Use of In-Clinic Sensing to Identify Optimal Stimulation Parameters for Deep Brain Stimulation Therapy in Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) programming is time intensive. Recent advances in sensing technology of local field potentials (LFPs) may enable improvements. Few studies have compared the use of this technology with standard of care. OBJECTIVE/HYPOTHESIS: Sensing technology of subthalamic nucleus (STN) DBS leads in Parkinson's disease (PD) is reliable and predicts the optimal contacts and settings as predicted by clinical assessment. MATERIALS AND METHODS: Five subjects with PD (n = 9 hemispheres) with bilateral STN DBS and sensing capable battery replacement were recruited. An LFP sensing review of all bipolar contact pairs was performed three times. Contact with the maximal beta peak power (MBP) was then clinically assessed in a double-blinded fashion, and five conditions were tested: 1) entry settings, 2) off stimulation, 3) MBP at 30 µs, 4) MBP at 60 µs, and 5) MBP at 90 µs. RESULTS: Contact and frequency of the MBP power in all hemispheres did not differ across sessions. The entry settings matched with the contact with the MBP power in 5 of 9 hemispheres. No clinical difference was evident in the stimulation conditions. The clinician and subject preferred settings determined by MBP power in 7 of 9 and 5 of 7 hemispheres, respectively. CONCLUSIONS: This study indicates that STN LFPs in PD recorded directly from contacts of the DBS lead provide consistent recordings across the frequency range and a reliably detected beta peak. Furthermore, programming based on the MBP power provides at least clinical equivalence to standard of care programming with STN DBS.

The Impact of Burr Hole Device and Lead Design on Deep Brain Stimulation Lead Stability in Benchtop and Ovine Models.

BACKGROUND AND OBJECTIVES: A market-released deep brain stimulation (DBS) lead and burr hole device (BHD) have been used for more than ten years to provide stable DBS therapy using leads with four equally distributed cylindrical electrodes along the distal lead length. Newer directional leads cluster segmented electrodes at the center of the electrode array. This work tests the hypothesis that improved chronic translational and rotational stability through enhanced BHD design may ensure that these newer directional electrodes remain in a stable orientation near the stimulation target to maintain therapy and maximize opportunities to adjust therapy, if needed. MATERIALS AND METHODS: A new DBS lead system (commercially available in the United States and termed "new" throughout the manuscript) has been developed, and a combination of bench testing (45 product samples tested) and chronic sheep studies (17 animals followed for 13.5 weeks on average) was conducted to test the hypothesis that design changes incorporated into the new DBS system further stabilize the position and orientation of a DBS lead tip compared with a legacy DBS system. RESULTS: The new DBS system demonstrated a 55% relative improvement in chronic lead tip stability compared with the legacy DBS system with over a decade of clinical use. In a bench test, the new system required 79% more applied torque and 203% more lead body revolutions to rotate the lead in the BHD than the legacy system that was not designed to offer rotational stability. CONCLUSIONS: These measurements quantitatively demonstrate that DBS system design can positively improve lead translational and rotational stability and show that system design is an important consideration for future product development.

Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease.

Oscillatory activity within the beta frequency range (13-30 Hz) serves as a Parkinson's disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.

Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson's Disease (ADAPT-PD) clinical trial.

Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson's Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8-30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype.

Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording.

Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads. Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S® (Medtronic) or Percept PC°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects. Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network. Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.

Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease.

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity.

Increased theta band EEG power in sickle cell disease patients.

OBJECTIVE: Pain is a major issue in the care of patients with sickle cell disease (SCD). The mechanisms behind pain and the best way to treat it are not well understood. We studied how electroencephalography (EEG) is altered in SCD patients. METHODS: We recruited 20 SCD patients and compared their resting state EEG to that of 14 healthy controls. EEG power was found across frequency bands using Welch's method. Electrophysiological source imaging was assessed for each frequency band using the eLORETA algorithm. RESULTS: SCD patients had increased theta power and decreased beta2 power compared to controls. Source localization revealed that areas of greater theta band activity were in areas related to pain processing. Imaging parameters were significantly correlated to emergency department visits, which indicate disease severity and chronic pain intensity. CONCLUSION: The present results support the pain mechanism referred to as thalamocortical dysrhythmia. This mechanism causes increased theta power in patients. SIGNIFICANCE: Our findings show that EEG can be used to quantitatively evaluate differences between controls and SCD patients. Our results show the potential of EEG to differentiate between different levels of pain in an unbiased setting, where specific frequency bands could be used as biomarkers for chronic pain.

Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models.

OBJECTIVE: Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes. METHODS: A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed. RESULTS: The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both intersubject and intrasubject classification accuracy. CONCLUSION: The robustness and generalizability of the classifier are demonstrated. SIGNIFICANCE: Our results demonstrate the potential of this tool to be used clinically to help us to improve chronic pain treatment and establish spectral biomarkers for future pain-related studies using EEG.

Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease.

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.