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.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

A Pilot Evaluation of mHealth App Accessibility for Three Top-Rated Weight Management Apps by People with Disabilities.

Growing evidence demonstrates that people with disabilities face more challenges in accessing healthcare and wellness resources, compared to non-disabled populations. As mobile applications focused on health and wellness (mHealth apps) become prevalent, it is important that people with disabilities can access and use mHealth apps. At present, there is no source of unified information about the accessibility and usability of mHealth apps for people with disabilities. We set out to create such a source, establishing a systematic approach for evaluating app accessibility. Our goal was to develop a simple, replicable app evaluation process to generate useful information for people with disabilities (to aid suitable app selection) and app developers (to improve app accessibility and usability). We collected data using two existing assessment instruments to test three top-rated weight management apps with nine users representing three disability groups: vision, dexterity, and cognitive impairment. Participants with visual impairments reported the lowest accessibility ratings, most challenges, and least tolerance for issues. Participants with dexterity impairments experienced significant accessibility-related difficulties. Participants with cognitive impairments experienced mild difficulties and higher tolerances for issues. Our pilot protocol will be applied to test mHealth apps and populate a "curation" website to assist consumers in selecting mHealth apps.