Customizable Intraoperative Neural Stimulator and Recording System for Deep Brain Stimulation Research and Surgery.

Intraoperative targeting systems provide neurosurgeons with raw electrophysiological data through microelectrodes used for determining location in the brain. There are significant deficits to the available targeting systems, limiting the use in both clinical and research applications. The work presented in this dissertation is of the development and validation of an intraoperative neural stimulator and recording system for use in deep brain stimulation (DBS) surgeries. This intraoperative data acquisition system (IODA) was validated in three applications to ensure efficacy and improvements in research and clinical studies. The first application investigated was a clinical study illustrating the improvement IODA had on the targeting accuracy of DBS leads in the subthalamic nucleus (STN) over current targeting methods. It was demonstrated that the novel navigation algorithm developed for use with IODA targeted microelectrode probe locations significantly closer to final DBS lead positions compared to preoperatively planned trajectory positions. The second study investigated a clinical science application. There are considerable differences in recently published studies for the optimal chronic stimulation site in the STN region. It was shown, using beta oscillations of local field potentials (LFP) recorded by IODA, that optimal stimulation sites were significantly correlated with locations of peak beta activity when DBS leads were medial to the STN midpoint. While DBS lead trajectories lateral of the STN midpoint were significantly correlated with the dorsal border of the STN. The third study explored a basic science application involving the role of the STN in movement inhibition. Through wideband recordings made with IODA, it was shown that the STN is significantly activated during movement and movement inhibition cues as seen in the theta, alpha, and beta bands and single unit activity. Overall the results indicate the utility and adaptability of this system for use within DBS surgeries. There are many applications of IODA for use in research for other neurodegenerative disease including Essential Tremor and Depression. The use of this system has enables neurosurgeons to reduce surgical time, risk, and error for DBS procedures and made entry for those less experienced in this procedure easier.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99771/1/dodani_1.pd

A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson s disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson s disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability

Advances in closed-loop deep brain stimulation devices

BACKGROUND: Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner. METHODS: This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research. RESULTS: Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state. CONCLUSIONS: The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

Speech function in persons with Parkinson\u27s disease: effects of environment, task and treatment

Parkinson’s Disease (PD) is a degenerative neurological disease affecting aspects of movement, including speech. Persons with PD are reported to have better speech functioning in the clinical setting than in the home setting, but this has not been quantified. New methodologies in ambulatory measures of speech are emerging that allow investigation of non-clinical settings. The following questions are addressed: Is speech different between environments in PD and in healthy controls? Can clinical tasks predict speech behaviors in the home? Is treatment proven effective by measures in the home? What can we glean from methods of measurement of speech function in the home? The experiment included 13 persons with PD and 12 healthy controls, studied in the clinical and home environments, and 7 of those 13 persons with PD participated in a treatment study. Major findings included: Spontaneous speech intelligibility, not intensity, was the differentiating factor between persons with PD and healthy controls. Intelligibility and intensity were not related. Both groups presented with higher sentence intensity in the home environment. Spontaneous speech intelligibility in the clinic was related to spontaneous speech intelligibility in the home. The Sentence Intelligibility Test emerged as the best predictor of spontaneous speech intelligibility in the home. Differences between pilot treatment groups measured in the home on intensity and intelligibility were not large enough to make a clinical trial feasible. Individual differences may account for many of these results, for example more severely impaired patients may have shown different data. Drawing conclusions regarding the home environment via measures outside the home should be carefully considered. Ambulatory measures of speech are a viable option for studying speech function in non-clinical settings, and technology is advancing. Further investigation is needed to develop methodologies and normative values for speech in the home

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI): Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments.Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns.Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings.Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD.Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders