This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program (DGE-2140004), the Weill Neurohub, and the National Institute of Health (UH3NS100544). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the Weill Neurohub, or the National Institute of Health. We thank Tianjiao Zhang for his expert consultations on platform design and the incorporation of video data. We especially thank the patient for their participation in this study and for the feedback and advice on network security and platform design.

Optimizing Deep Brain Stimulation for Patients with Neurological Disorders

<ol>
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Neural Eng. 11, (2018).</li><li>Little, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+deep+brain+stimulation+in+advanced+Parkinson+disease.">Adaptive deep brain stimulation in advanced Parkinson disease.</a> Annals of Neurology. 74 (3), 449-457 (2013).</li><li>Connolly, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-objective+data-driven+optimization+for+improving+deep+brain+stimulation+in+Parkinson's+disease.">Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease.</a> Journal of Neural Engineering. 18 (4), 046046 (2021).</li><li>Stanslaski, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Creating+neural+&amp;#34;co-processors&amp;#34;+to+explore+treatments+for+neurological+disorders.">Creating neural &amp;#34;co-processors&amp;#34; to explore treatments for neurological disorders.</a> 2018 IEEE International Solid - State Circuits Conference - (ISSCC). , 460-462 (2018).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rights+(OCR),+O.+for+C.+The+HIPAA+Privacy+Rule.">Rights (OCR), O. for C. The HIPAA Privacy Rule.</a> HHS.gov. , (2008).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rights+(OCR),+O.+for+C.+The+Security+Rule.">Rights (OCR), O. for C. The Security Rule.</a> HHS.gov. , (2009).</li><li>Perrone, R., Gilron, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SCBS Patient Facing App (Summit Continuous Bilateral Streaming). , (2021).</li><li>Strandquist, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> In-Home Video and IMU Kinematics of Self Guided Tasks Correlate with Clinical Bradykinesia Scores. , (2023).</li><li>Donenfeld, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WireGuard:+Next+Generation+Kernel+Network+Tunnel.">WireGuard: Next Generation Kernel Network Tunnel.</a> Proceedings 2017 Network and Distributed System Security Symposium. , (2017).</li><li>etzion. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Ubuntu 20.04 and TPM2 encrypted system disk - Running Systems. , (2021).</li><li>Craig-Wood, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Rclone. , (2023).</li><li>Chen, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Role+of+Large-Scale+Data+Infrastructure+in+Developing+Next-Generation+Deep+Brain+Stimulation+Therapies.">The Role of Large-Scale Data Infrastructure in Developing Next-Generation Deep Brain Stimulation Therapies.</a> Frontiers in Human Neuroscience. 15, 717401 (2021).</li><li>Hidalgo, G. i. n. &. #. 2. 3. 3. ;. s., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> CMU-Perceptual-Computing-Lab/openpose. , (2023).</li><li>Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenPose:+Realtime+Multi-Person+2D+Pose+Estimation+Using+Part+Affinity+Fields.">OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 43 (1), 172-186 (2021).</li><li>Cao, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> openpose/calibration_module.md at master &#183; CMU-Perceptual-Computing-Lab/openpose. , (2023).</li><li>Sellers, K. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis-rcs-data:+Open-Source+Toolbox+for+the+Ingestion,+Time-Alignment,+and+Visualization+of+Sense+and+Stimulation+Data+From+the+Medtronic+Summit+RC+S+System.">Analysis-rcs-data: Open-Source Toolbox for the Ingestion, Time-Alignment, and Visualization of Sense and Stimulation Data From the Medtronic Summit RC+S System.</a> Frontiers in Human Neuroscience. 15, 398 (2021).</li><li>Ranti, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Rune Labs Stream API. , (2023).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ffmpeg+development+community.">ffmpeg development community.</a> FFmpeg Formats Documentation. , (2023).</li><li>Goetz, C. 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The authors have no conflicts of interest to disclose.

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...

Deep brain stimulation (DBS) treats neurological disorders such as Parkinson&#39;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 symptoms1,2. 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 state, resulting in stimulation settings that are not appropriately matched to the changing needs of the patient. DBS is further hindered by the time-consuming process of tuning stimulation parameters, which is currently performed manually by clinicians for each individual patient.
Adaptive DBS (aDBS) is a closed-loop approach shown to be an effective next iteration of DBS by adjusting stimulation parameters in real time whenever symptom-related biomarkers are detected3,4,5. Studies have shown beta oscillations (10-30 Hz) in the subthalamic nucleus (STN) occur consistently during bradykinesia, a slowing of movement that is characteristic of PD6,7. Similarly, high-gamma oscillations (50-120 Hz) in the cortex are known to occur during periods of dyskinesia, an excessive and involuntary movement also commonly seen in PD8. Recent work has successfully administered aDBS outside the clinic for prolonged periods5, however the long-term effectiveness of aDBS algorithms that were configured in-clinic while a patient is home has not been established.
Remote systems are needed to capture the time-varying effectiveness of these dynamic algorithms in suppressing symptoms encountered during daily living. While the dynamic stimulation approach of aDBS potentially enables a more precise treatment with reduced side-effects3,9, aDBS still suffers from a high burden on clinicians to manually identify stimulation parameters for each patient. In addition to the already large set of parameters to program during conventional DBS, aDBS algorithms introduce many new parameters which must also be carefully adjusted. This combination of stimulation and algorithm parameters yields a vast parameter space with an unmanageable number of possible combinations, prohibiting aDBS from scaling to many patients10. Even in research settings, the additional time required to configure and assess aDBS systems make it difficult to adequately optimize algorithms solely in the clinic, and remote updating of parameters is needed. To make aDBS a treatment that can scale, stimulation and algorithm parameter tuning must be automated. In addition, outcomes from therapy must be analyzed across repeated trials to establish aDBS as a viable long-term treatment outside the clinic. There is a need for a platform that can collect data for remote evaluation of therapy effectiveness, and to remotely deploy updates to aDBS algorithm parameters.
The goal of this protocol is to provide a reusable design for a multi-modal at-home data collection platform to improve aDBS effectiveness outside the clinic, and to enable this treatment to scale to a greater number of individuals. To our knowledge, it is the first data collection platform design that remotely evaluates therapeutic outcomes using in-home video cameras, wearable sensors, chronic neural signal recording, and patient-driven feedback to evaluate aDBS systems during controlled tasks and naturalistic behavior.
The platform is an ecosystem of hardware and software components built upon previously developed systems5. It is maintainable entirely through remote access after an initial installation of minimal hardware to allow multi-modal data collection from a person in the comfort of their home. A key component is the implantable neurostimulation system (INS)11 which senses neural activity and delivers stimulation to the STN, and records acceleration from chest implants. For the implant used in the initial deployment, neural activity is recorded from bilateral leads implanted in the STN and from electrocorticography electrodes implanted over the motor cortex. A video recording system helps clinicians monitor symptom severity and therapy effectiveness, which includes a graphical user interface (GUI) to allow easy cancellation of ongoing recordings to protect patient privacy. Videos are processed to extract kinematic trajectories of position in two dimensional (2D) or three dimensional (3D), and smart watches are worn on both wrists to capture angular velocity and acceleration information. Importantly, all data is encrypted before being transferred to long-term cloud storage, and the computer with patient-identifiable videos can only be accessed through a virtual private network (VPN). The system includes two approaches for post-hoc time-aligning of all data streams, and data is used to remotely monitor the patient&#39;s quality of movement, and to identify symptom-related biomarkers for refining aDBS algorithms. The video portion of this work shows the data collection process and animations of kinematic trajectories extracted from collected videos.
A number of design considerations guided the development of the protocol: 
Ensuring data security and patient privacy: Collecting identifiable patient data requires utmost care in transmission and storage in order to be health insurance portability and accountability act (HIPAA)12, 13 compliant and to respect the patient&#39;s privacy in their own home. In this project, this was achieved by setting up a custom VPN to ensure privacy of all sensitive traffic between system computers. 
Stimulation parameter safety boundaries: It is critical to ensure that the patient remains safe while trying out aDBS algorithms that may have unintended effects. The patient&#39;s INS must be configured by a clinician to have safe boundaries for stimulation parameters that do not allow for unsafe effects from over-stimulation or under-stimulation. With the INS system11 used in this study, this feature is enabled by a clinician programmer. 
Ensuring the patient veto: Even within safe parameter limits, the daily variability of symptoms and stimulation responses may result in unpleasant situations for the patient where they dislike an algorithm under test and wish to return to normal clinical open-loop DBS. The selected INS system includes a patient telemetry module (PTM) that allows the patient to manually change their stimulation group and stimulation amplitude in mA. There is also an INS-connected research application that is used for remote configuration of the INS prior to data collection14, which also enables the patient to abort aDBS trials and control their therapy. 
Capturing complex and natural behavior: Video data was incorporated in the platform to enable clinicians to remotely monitor therapy effectiveness, and to extract kinematic trajectories from pose estimates for use in research analyses15. While wearable sensors are less intrusive, it is difficult to capture the full dynamic range of motion of an entire body using wearable systems alone. Videos enable the simultaneous recording of the patient&#39;s full range of motion and their symptoms over time. 
System usability for patients: Collecting at-home multi-modal data requires multiple devices to be installed and utilized in a patient&#39;s home, which could become burdensome for patients to navigate. To make the system easy to use while ensuring patient control, only the devices that are implanted or physically attached to the patient (in this case it included the INS system and smart watches) must be manually turned ON prior to initiating a recording. For devices that are separate from the patient (in this case it includes data recorded from video cameras), recordings start and end automatically without requiring any patient interaction. Care was taken during GUI design to minimize the number of buttons and to avoid deep menu trees so that interactions were simple. After all devices are installed, a research coordinator showed the patient how to interact with all devices through patient-facing GUIs that are a part of each device, such as how to terminate recordings on any device and how to enter their medication history and symptom reports. 
Data collection transparency: Clearly indicating when cameras are turned ON is imperative so that people know when they are being recorded and can suspend recording if they need a moment of privacy. To achieve this, a camera-system application is used to control video recordings with a patient-facing GUI. The GUI automatically opens when the application is started and lists the time and date of the next scheduled recording. When a recording is ongoing, a message states when the recording is scheduled to end. In the center of the GUI, a large image of a red light is displayed. The image shows the light being brightly lit whenever a recording is ongoing, and changes to a non-lit image when recordings are OFF.
The protocol details methods for designing, building, and deploying an at-home data collection platform, for quality-checking the data collected for completeness and robustness, and for post-processing data for use in future research.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65305/65305fig01.jpg" /> 
Figure 1: Data flow. Data for each modality is collected independently from the patient&#39;s residence before being processed and aggregated into a single remote storage endpoint. The data for each modality is sent automatically to a remote storage endpoint. With the help of one of the team members, it can then be retrieved, checked for validity, time aligned across modalities, as well as subjected to more modality-specific pre-processing. The compiled dataset is uploaded then to a remote storage endpoint that can be securely accessed by all team members for continued analysis. All machines with data access, especially for sensitive data such as raw video, are enclosed within a VPN that ensures all data is transferred securely and stored data is always encrypted. <a href="https://www.jove.com/files/ftp_upload/65305/65305fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Analysis RCS Data Processing</td>
			<td>OpenMind</td>
			<td></td>
			<td>https://github.com/openmind-consortium/Analysis-rcs-data, open-source</td>
		</tr>
		<tr>
			<td>Apple Watches</td>
			<td>Apple, Inc</td>
			<td></td>
			<td>Use 2 watches for each patient, one on each wrist</td>
		</tr>
		<tr>
			<td>BRIO ULTRA HD PRO BUSINESS WEBCAM</td>
			<td>Logitech</td>
			<td>960-001105</td>
			<td>Used 3 in our platform design&#160;</td>
		</tr>
		<tr>
			<td>DaVinci Resolve video editing software&#160;</td>
			<td>DaVinci Resolve</td>
			<td></td>
			<td>used to support camera calibration</td>
		</tr>
		<tr>
			<td>Dell XPS PC&#160;</td>
			<td>Dell</td>
			<td></td>
			<td>2T hard disk drive, 500GB SSD</td>
		</tr>
		<tr>
			<td>Dropbox</td>
			<td>Dropbox</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ffmpeg&#160;</td>
			<td>N/A</td>
			<td></td>
			<td>open-source, install to run the Video Recording App</td>
		</tr>
		<tr>
			<td>Gooseneck mounts for webcams</td>
			<td>N/A</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>GPU</td>
			<td>Nvidia</td>
			<td></td>
			<td>A minimum of 8GB GPU memory is recommended to run OpenPose, 12GB is ideal</td>
		</tr>
		<tr>
			<td>Java 11</td>
			<td>Oracle</td>
			<td></td>
			<td>Install to run the Video Recording App</td>
		</tr>
		<tr>
			<td>Microsoft Surface tablet&#160;</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>NoMachine&#160;</td>
			<td>NoMachine</td>
			<td></td>
			<td>Ideal when using a Linux OS, open-source</td>
		</tr>
		<tr>
			<td>OpenPose&#160;</td>
			<td>N/A</td>
			<td></td>
			<td>open-source</td>
		</tr>
		<tr>
			<td>Rclone file transfer program</td>
			<td>Rclone</td>
			<td></td>
			<td>Encrypts data and copies or moves data to offsite storage, open-source</td>
		</tr>
		<tr>
			<td>StrivePD app</td>
			<td>RuneLabs</td>
			<td></td>
			<td>We installed the app on the Apple Watches to start recordings and upload data to an online portal.</td>
		</tr>
		<tr>
			<td>Summit RC+S neuromodulation system</td>
			<td>Medtronic</td>
			<td></td>
			<td>For investigational use only</td>
		</tr>
		<tr>
			<td>touchscreen-compatible monitor</td>
			<td>N/A</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Video for Linux 2 API</td>
			<td>The Linux Kernel</td>
			<td></td>
			<td>Install if using a Linux OS for video recording&#160;</td>
		</tr>
		<tr>
			<td>Wasabi&#160;</td>
			<td>Wasabi</td>
			<td></td>
			<td>Longterm cloud data storage</td>
		</tr>
		<tr>
			<td>WireGuard VPN Protocol</td>
			<td>WireGuard</td>
			<td></td>
			<td>open-source</td>
		</tr>
	</tbody>
</table>

bringing the clinic home: an at-home multi-modal data collection ecosystem to support adaptive deep brain stimulation

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.

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring 

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

Prototype platform design and deployment 
We designed a prototype platform and deployed it to the home of a single patient (Figure 1). After the first installation of hardware in the home, the platform can be maintained, and data collected entirely through remote access. The INS devices, smart watches, and cameras have patient-facing applications allowing patients to start and stop recordings. The video collection hardware enables automatic video recordings after an app...

Watch this Scientific Journal Video about Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring at JoVE.com

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.

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses ...

bringing-clinic-home-an-at-home-multi-modal-data-collection-ecosystem

Research

JoVE Journal

Neuroscience

1.0K Views.  University of Washington. 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.