Who May Benefit From On-Demand Control of Deep Brain Stimulation? Noninvasive Evaluation of Parkinson Patients

A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes

Introduction

In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD.

Methods

This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days.

Results

The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants.

Conclusion

These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.

Nanomedicine and nanobiotechnology applications of magnetoelectric nanoparticles

Unlike any other nanoparticles known to date, magnetoelectric nanoparticles (MENPs) can generate relatively strong electric fields locally via the application of magnetic fields and, vice versa, have their magnetization change in response to an electric field from the microenvironment. Hence, MENPs can serve as a wireless two-way interface between man-made devices and physiological systems at the molecular level. With the recent development of room-temperature biocompatible MENPs, a number of novel potential medical applications have emerged. These applications include wireless brain stimulation and mapping/recording of neural activity in real-time, targeted delivery across the blood-brain barrier (BBB), tissue regeneration, high-specificity cancer cures, molecular-level rapid diagnostics, and others. Several independent in vivo studies, using mice and nonhuman primates models, demonstrated the capability to deliver MENPs in the brain across the BBB via intravenous injection or, alternatively, bypassing the BBB via intranasal inhalation of the nanoparticles. Wireless deep brain stimulation with MENPs was demonstrated both in vitro and in vivo in different rodents models by several independent groups. High-specificity cancer treatment methods as well as tissue regeneration approaches with MENPs were proposed and demonstrated in in vitro models. A number of in vitro and in vivo studies were dedicated to understand the underlying mechanisms of MENPs-based high-specificity targeted drug delivery via application of d.c. and a.c. magnetic fields. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Digital Health Integration With Neuromodulation Therapies: The Future of Patient-Centric Innovation in Neuromodulation

Digital health can drive patient-centric innovation in neuromodulation by leveraging current tools to identify response predictors and digital biomarkers. Iterative technological evolution has led us to an ideal point to integrate digital health with neuromodulation. Here, we provide an overview of the digital health building-blocks, the status of advanced neuromodulation technologies, and future applications for neuromodulation with digital health integration.

Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review

Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

An update on adaptive deep brain stimulation in Parkinson's disease

Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.