Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Summary

The protocol shows a prototype of the at-home multi-modal data collection platform that supports research optimizing adaptive deep brain stimulation (aDBS) for people with neurological movement disorders. We also present key findings from deploying the platform for over a year to the home of an individual with Parkinson's disease.

Abstract

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses symptom-related biomarkers to adjust stimulation parameters in real-time to target symptoms more precisely. To enable these dynamic adjustments, parameters for an aDBS algorithm must be determined for each individual patient. This requires time-consuming manual tuning by clinical researchers, making it difficult to find an optimal configuration for a single patient or to scale to many patients. Furthermore, the long-term effectiveness of aDBS algorithms configured in-clinic while the patient is at home remains an open question. To implement this therapy at large scale, a methodology to automatically configure aDBS algorithm parameters while remotely monitoring therapy outcomes is needed. In this paper, we share a design for an at-home data collection platform to help the field address both issues. The platform is composed of an integrated hardware and software ecosystem that is open-source and allows for at-home collection of neural, inertial, and multi-camera video data. To ensure privacy for patient-identifiable data, the platform encrypts and transfers data through a virtual private network. The methods include time-aligning data streams and extracting pose estimates from video recordings. To demonstrate the use of this system, we deployed this platform to the home of an individual with PD and collected data during self-guided clinical tasks and periods of free behavior over the course of 1.5 years. Data were recorded at sub-therapeutic, therapeutic, and supra-therapeutic stimulation amplitudes to evaluate motor symptom severity under different therapeutic conditions. These time-aligned data show the platform is capable of synchronized at-home multi-modal data collection for therapeutic evaluation. This system architecture may be used to support automated aDBS research, to collect new datasets and to study the long-term effects of DBS therapy outside the clinic for those suffering from neurological disorders.

Introduction

Deep brain stimulation (DBS) treats neurological disorders such as Parkinson's disease (PD) by delivering electrical current directly to specific regions in the brain. There are an estimated 8.5 million cases of PD worldwide, and DBS has proved to be a critical therapy when medication is insufficient for managing symptoms^1,2. However, DBS effectiveness can be constrained by side-effects that sometimes occur from stimulation that is conventionally delivered at fixed amplitude, frequency, and pulse width³. This open-loop implementation is not responsive to fluctuations in symptom stat....

Protocol

Patients are enrolled through a larger IRB and IDE approved study into the aDBS at the University of California, San Francisco, protocol # G180097⁵. The patient enrolled in this study additionally provided informed consent specifically for this study.

1. At-home system components

1. Central server and VPN
   1. Acquire a personal computer (PC) running a Linux-based operating system (OS) dedicated to serving a VPN. House the machine in a s.......

Discussion

We share the design for an at-home prototype of a multi-modal data collection platform to support future research in neuromodulation research. The design is open-source and modular, such that any piece of hardware can be replaced, and any software component can be updated or changed without the overall platform collapsing. While the methods for collecting and deidentifying neural data are specific to the selected INS, the remaining methods and overall approach to behavioral data collection are agnostic to which implantab.......

Materials

Name	Company	Catalog Number	Comments
Analysis RCS Data Processing	OpenMind		https://github.com/openmind-consortium/Analysis-rcs-data, open-source
Apple Watches	Apple, Inc		Use 2 watches for each patient, one on each wrist
BRIO ULTRA HD PRO BUSINESS WEBCAM	Logitech	960-001105	Used 3 in our platform design
DaVinci Resolve video editing software	DaVinci Resolve		used to support camera calibration
Dell XPS PC	Dell		2T hard disk drive, 500GB SSD
Dropbox	Dropbox
ffmpeg	N/A		open-source, install to run the Video Recording App
Gooseneck mounts for webcams	N/A
GPU	Nvidia		A minimum of 8GB GPU memory is recommended to run OpenPose, 12GB is ideal
Java 11	Oracle		Install to run the Video Recording App
Microsoft Surface tablet	Microsoft
NoMachine	NoMachine		Ideal when using a Linux OS, open-source
OpenPose	N/A		open-source
Rclone file transfer program	Rclone		Encrypts data and copies or moves data to offsite storage, open-source
StrivePD app	RuneLabs		We installed the app on the Apple Watches to start recordings and upload data to an online portal.
Summit RC+S neuromodulation system	Medtronic		For investigational use only
touchscreen-compatible monitor	N/A
Video for Linux 2 API	The Linux Kernel		Install if using a Linux OS for video recording
Wasabi	Wasabi		Longterm cloud data storage
WireGuard VPN Protocol	WireGuard		open-source