Name: bringing the clinic home: an at-home multi-modal data collection ecosystem to support adaptive deep brain stimulation
Uploaded: 2023-07-14T13:30:00.000+00:00
Duration: 6 min 32 s
Description: 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

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

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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 implantable device is used. We deployed the platform to the home of an individual with PD and collected data during both experimental and naturalistic periods. During deployments, data collections and post-hoc data processing, several aspects were discovered that were particularly crucial to enabling successful research iterations.
A valuable member of our team was the research coordinator who traveled to the patient&#39;s home to install hardware, set up the VPN, perform camera calibration for 3D pose, and walk the patient through how to use each device&#39;s patient-facing GUI. Importantly, the research coordinator additionally served as the main point of contact between the patient and the research team. The patient preferred to use their email chat function to quickly send messages back and forth. Having a consistent and accessible point of contact was particularly helpful in two ways:
To establish a familiar communication channel for the patient to request changes to scheduled recordings and to communicate any difficulties in system use. This helped the research coordinator to identify convenient times for the patient to conduct recording experiments. The main difficulty in system use reported was the need to keep track of battery life for several devices.
To allow system troubleshooting to be minimally disruptive to the patient. Most troubleshooting stemmed from network connectivity problems which occurred on average once every couple of weeks. While rebooting devices typically resolved these issues, the watches frequently required multiple restarts, which the patient reported was burdensome.
It is essential to ensure robust remote access to the hardware placed in the patient&#39;s home. To accomplish this, having a stable internet connection is crucial. It is also necessary to configure a disk-encrypted machine to automatically unlock whenever a machine reboots. Unsurprisingly, an ethernet cable consistently yielded the fastest and most reliable network connections. Less expected was the need to configure a TPM chip, necessary due to choosing Linux as the OS. If a Windows OS is used, their Bitlocker program will take care of this automatically. Finally, configuring the deployed PC to automatically enable the VPN and re-mount the hard disk drive upon system reboots ensured continued remote access without needing to repeatedly re-visit the patient&#39;s home. Incorporating a VPN and a data encryption protocol in the platform design was pivotal for data security and integrity. The VPN allows a network of computers to be connected without needing custom port forwarding to be configured on a patient&#39;s private router. The open-source data encryption protocol Rclone program provided with an off-the-shelf data encryption and an easily automatable means of transferring data from patient devices to cloud storage18. The data encryption protocol makes back-up copies of raw data during its data transfer steps to ensure that losses are recoverable. These steps ensured that the patient&#39;s private data was kept secure and uncorrupted.
To be able to conduct meaningful data analysis, it is essential that the data collected from multiple devices be time aligned. The clocks on each device are likely not perfectly aligned to a common internet time, even if manufacturers suggest that they are. Additionally, some devices can experience drifting at unpredictable times, changing their offsets relative to the other devices. This creates difficulty in working towards fully automated, real-time adaptive algorithms, and future research will need to carefully consider solutions to this problem. Methods of automatic alignment were explored using normalized cross-correlation. This works reasonably well in many cases; however, time drifts must be minimal, and the data should contain clearly identifiable signals. Because both large drift and periods where data had too much noise or packet loss were encountered, this fully automated method cannot be fully relied upon. To minimize the burden of manually aligning data, we created a simple GUI to allow researchers to visually check data streams with relative ease and rapidity.
The inclusion of video data to the system enables clinicians to measure symptom severity through remote observation, and researchers can obtain event labels. In addition, pose estimates can be calculated from videos as a continuous metric of movement quality such as measuring the speed and smoothness of finger movements over time. However, collecting high resolution videos from multiple cameras requires extensive storage space. For example, collecting 8 hours of 4k videos in the MJPEG format from three cameras takes approximately 0.5&#160;TB of storage space. Recording and storing large quantities of data quickly becomes expensive, creating an economic bottleneck for deploying this system to many patients. In order to make such platforms scale to many patients, future system designers need to reduce the amount of data required for long-term storage. Future systems should consider including real-time pose processing so that videos can be promptly deleted after the pose is processed. Real-time pose could also provide feedback about fine motor skills in closed-loop algorithms, which is outside of the scope of this work. If preserving some video data is needed for clinician review or event labeling, these can be downsampled to a lower resolution before they are saved on the cloud storage.
Finally, to efficiently address the design flaws and implementation errors that invariably arise when building an integrated system, acquiring a replica of the hardware to be deployed for use as a test-rig is extremely valuable. This was true for testing the hardware and software that was selected for collecting videos and processing pose data. The entire process of acquiring videos and pose estimates in both 2D and 3D space was significantly more challenging than anticipated. A test rig allows for troubleshooting and stress-testing a number of important steps prior to deployment, including:
Properly calibrating cameras within the layout constraints of a given room. 
Identifying the appropriate video resolution and framerate to support high quality pose estimation. For small rooms or office-like environments, HD video recording is likely sufficient, as the size of individuals on the recorded video is large enough so the pose can be easily computed while requiring significantly less storage space than 4k video. 
Discovering bugs in recorded videos, such as freezing frames or time lags between sequentially written video files. 
Exposing unexpected software defaults such as re-setting the camera autofocus upon machine reboots, which occludes the benefit of camera calibration. 
Trial and error to find compatible versions of the software libraries that must be pre-installed to enable OpenPose to run on a medium-sized GPU.
A particular limitation of this work is deploying the platform in a single pilot study to the home of one individual, preventing us from discovering any cross-participant generalizations from being discovered. However, throughout the design and development process, the system was designed to be scalable and support multiple deployments to support remote studies, and the purpose of this pilot study was to establish the technological feasibility of a sophisticated at-home monitoring platform. Modifying this pilot design based upon some of the discussed crucial findings and deploying the platform to more homes will allow further refinement of the design to support future research in at-home aDBS. In addition, collecting data during additional time points when an individual is not performing pre-determined experiments will offer insights to improve analyses and overall therapy effectiveness. aDBS may provide a preferable method for treating neurological diseases including PD compared to conventional DBS that can have unacceptable side-effects. Bringing this important therapy to many individuals requires automating parameter tuning and analyzing therapy effectiveness outside the clinic across time. The platform provides a novel approach to collect in-home video camera, smart watch, neural recording, and patient-report data during experimental and natural activities from the comfort of the patient&#39;s own home. The system will further contribute to creating novel multi-modal datasets to support future discoveries in the treatment of neurological diseases15.

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.remotexs.ntu.edu.sg/files/ftp_upload/65305/65305fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><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&amp;nbsp;</td></tr><tr><td>DaVinci Resolve video editing software&amp;nbsp;</td><td>DaVinci Resolve</td><td></td><td>used to support camera calibration</td></tr><tr><td>Dell XPS PC&amp;nbsp;</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&amp;nbsp;</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&amp;nbsp;</td><td>Microsoft</td><td></td><td></td></tr><tr><td>NoMachine&amp;nbsp;</td><td>NoMachine</td><td></td><td>Ideal when using a Linux OS, open-source</td></tr><tr><td>OpenPose&amp;nbsp;</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&amp;nbsp;</td></tr><tr><td>Wasabi&amp;nbsp;</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.

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 approved schedule has been configured. Patients can easily cancel an ongoing recording by simply pressing a button on the video recording application GUI (Figure 2). All collected data was encrypted and transferred to a cloud storage site for researchers to process and analyze.
Data collection 
For the first deployments and data collection cycles, we asked the patient to conduct self-guided clinical tasks. The tasks were taken from the unified Parkinson&#39;s disease rating scale (UPDRS)26, namely resting tremor, thumb-to-index finger tapping, hand opening and closing, wrist pronation-supination, sit-to-stand movement and walking, and a typing task. All tasks were repeated three times for each recording day. For each repetition, a different stimulation amplitude was set to expose potential stimulation-related symptoms of PD. Figure 4 shows a schematized example of what a week of data collected with the system might look like.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65305/65305fig04.jpg" /> 
Figure 4: Data availability. A schematized demonstration of what a week of data collected with the system might look like. The top plot shows the stimulation level (blue) over the course of several day/night cycles. Stimulation changes for this patient are dependent on their sleep schedule and the times of medication intake (vertical red lines). At arbitrary times throughout the day, the data collection system can be enabled remotely to collect data for multiple modalities, shown as colored boxes. One example of all the parallel, time-aligned data-streams, just down-selected to the left side of the body, is shown in the bottom plot. During this recording, the patient was asked to perform a series of clinical assessments during low, therapeutic, and high amplitude stimulation conditions. All data shown here corresponds to real data collected but has been compressed across separate experiments for ease of visualization and to show variety. Abbreviations: LFP=local field potential, STN=Subthalamic nucleus, Accel=accelerometer, Gyro=gyroscope, 2D=two-dimensional. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65305/65305fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Manual alignment 
The manual alignment GUI provides an easy-to-use platform for aligning multiple streams of data. As shown in Figure 3, chest taps provide a clearly identifiable artifact in all data modalities (INS, smart watches, videos) that can be used in manual alignment. The GUI was a useful means of aligning the data, but this could be exchanged for any other alignment tool that researchers would like to use. In some instances, the data streams have a slight drift. A potential future solution to this problem would be to divide the session data into different trials, each with its own chest tap sequence. Each trial can then be individually aligned to minimize the impact of drift.
Zero-normalized cross correlation (ZNCC) time alignment 
The method for ZNCC works well in some cases but it has a few critical vulnerabilities. For example, for some movements, the two accelerometer signals can be phase shifted with respect to one another. If a phase aligned and phase shifted movement are both included in the analyzed epochs, then the ZNCC can have either multiple or even no clear peak. The normalization of ZNCC allows these alignments to be automatically identified and discarded as needed. This method works best if both signals are relatively noise free and windowed to an epoch with large, synchronized effects in both traces. The best results were achieved when the patient was asked to perform a series of strong taps with both hands against their chest. In practice however, manual verification of automated alignment was necessary for enough cases that the advantage of using the automated method was negligible.
Data quality 
Data loss during automated transfer was negligible since the data transfer protocol process backs up raw copies to ensure that any losses are recoverable. Data loss from connectivity issues occurred regularly, since Bluetooth and radio frequency sometimes have unexpected connection dropouts and are range limited. Short gaps of up to 2 seconds&#160;occurred approximately a few times per hour, and longer gaps of up to 2 minutes occurred approximately once every couple of hours. Beyond data loss, significant stimulation artifacts were observed in neural data, the severity of which depended on the recording and the stimulation groups chosen. The largest artifacts occur near the stimulation frequency, well outside ranges of interest. No artifacts were observed in data from smart watches. Videos were recorded at a constant frame rate; however duplicate frames were identified in videos. This yielded an actual frame rate to be a few frames less than the theoretical frame rate as stated by the webcam specifications. More noticeable than the duplicate frames however were freezing periods that were identified in videos at varying intervals depending on the recording day. Freeze periods of approximately 10 frames or less were regularly observed; however longer sections of approximately 2 to 30 seconds long were also observed at irregular periods.
Longitudinal data collection 
Table 1 shows the data that the platform prototype has periodically collected over the course of 1.5 years. In that time, hundreds of hours of data were collected, with a total of 293 hours of INS data across both sides of the body, 224 hours of smart watch data for both watches, and 2,037 hours of video data across three webcams. This demonstrates that the platform supports at-home data collection over extended periods of time while offering a rare opportunity to observe longitudinal changes in neural data and corresponding stimulation requirements.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Data Type</td>
			<td>Total Duration (hh:mm:ss)</td>
			<td>Total Days</td>
			<td>Storage Size</td>
		</tr>
		<tr>
			<td>Neural&#160;</td>
			<td>293:17:33</td>
			<td>90</td>
			<td>28.94 GB</td>
		</tr>
		<tr>
			<td>Watch</td>
			<td>224:06:05</td>
			<td>89</td>
			<td>35.67 GB</td>
		</tr>
		<tr>
			<td>Video</td>
			<td>2037:06:11</td>
			<td>228</td>
			<td>146,073.77 GB</td>
		</tr>
	</tbody>
</table>
Table 1: Longitudinal overview of collected data. The deployed platform collected data during several experiments over the course of 1.5 years. Approximately 90 days were recorded with neural, video, and smart watch data streams being collected.
2D and 3D pose estimates 
Several pose estimation software packages are now available. Pose estimation was tested using OpenPose, an open-source software package21. This was successfully installed following the documentation provided by the organization&#39;s GitHub, as well as many other unofficial tutorials found on the web. The processing time for OpenPose varies significantly based on how the OpenPose library and its extensive dependencies are installed, the size of the GPU used, and whether the optional hands and face key points are processed. 2D pose was relatively easy to implement, however 3D pose was notably more difficult and preliminary 3D results yielded inconsistent quality equal to that of 2D pose. The low-quality 3D pose estimation may have been impacted negatively by suboptimal camera calibration, periods where camera autofocus was erroneously turned on, or inherent in the OpenPose software itself. However, synchronized high-quality videos from multiple angles may provide rich inputs for a variety of available pose estimation software packages. It is recommended that a test setup be completed outside of the patient&#39;s home, with manual benchmarking of different available pose estimation software packages.
Supplementary Figure 1: Video frame lag analysis. Lags in timestamps generated from the video recording app were detected during system characterization. To investigate the cause of the lags, the frame number and timestamp generated from each camera was determined by recording a red LED light that blinked at random intervals, then the variations in timestamp lags across cameras was computed. (Top) LED intensities (in RGB units) measured on each of the three cameras, demonstrating the time offsets observed between the three cameras (denoted with red arrows). (Bottom) Three plots show the between-camera timestamp lags in number of frames for a series of LED blinks over the entire recording. Each recording was broken up into multiple segments and the frame lag was approximately constant over time. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65305/FigureS1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 1: Video frame and timestamp analysis method. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65305/Supplementary File 1_RE.docx" target="_blank">Please click here to download this File.</a>

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

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

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.

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 ...

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

Nanyang Technological University

Research

JoVE Journal

Neuroscience

1.2K Views.  University of Washington. Mijn onderzoek ondersteunt het automatiseren van adaptieve diepe hersenstimulatie of ADBS voor de ziekte van Parkinson in het comfort van iemands huis. Een vraag is of deze therapie veilig kan worden gemonitord voor een lange periode buiten de kliniek en tegelijkertijd de privacy van de patiënt kan waarborgen. Daarnaast onderzoeken we de mogelijkheid om ADBS-parameters automatisch aan te passen zonder dat de patiënt terug hoeft te keren naar de kliniek.ADBS-onderzoek heeft platforms ...

Video: Auteur in de schijnwerpers: geautomatiseerde diepe hersenstimulatie voor de ziekte van Parkinson - Onderzoek naar de mogelijkheden en uitdagingen van thuismonitoring 

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Development of an effective brain-machine-interface (BMI) system for restoration and rehabilitation of bipedal locomotion requires accurate decoding of user's intent. Here we present a novel experimental protocol and data collection technique for simultaneous non-invasive acquisition of neural activity, muscle activity, and whole-body kinematics during various locomotion tasks and conditions.

Recent  studies support the involvement of supraspinal networks in control of bipedal  human walking. Part of this evidence  encompasses studies, ...

Behavior

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3 It is thought the default mode network corresponds to self-referential or "internal mentation".2,3
It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6 and autism spectrum disorders 7 whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9
In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.

In this article, we examine the methodology and considerations relevant to the combination of TMS and fMRI to examine the effects of brain stimulation on the default network.

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful ...

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Adaptive deep brain stimulation (aDBS) has the potential to improve the treatment of Parkinson&#39;s disease by optimizing stimulation in real time according to fluctuating disease and medication state. In the present realization of adaptive DBS we record and stimulate from the DBS electrodes implanted in the subthalamic nucleus of patients with Parkinson&#39;s disease in the early post-operative period. Local field potentials are analogue filtered between 3 and 47 Hz before being passed to a data acquisition unit where they are digitally filtered again around the patient specific beta peak, rectified and smoothed to give an online reading of the beta amplitude. A threshold for beta amplitude is set heuristically, which, if crossed, passes a trigger signal to the stimulator. The stimulator then ramps up stimulation to a pre-determined clinically effective voltage over 250 msec and continues to stimulate until the beta amplitude again falls down below threshold. Stimulation continues in this manner with brief episodes of ramped DBS during periods of heightened beta power.
Clinical efficacy is assessed after a minimum period of stabilization (5 min) through the unblinded and blinded video assessment of motor function using a selection of scores from the Unified Parkinson&#39;s Rating Scale (UPDRS). Recent work has demonstrated a reduction in power consumption with aDBS as well as an improvement in clinical scores compared to conventional DBS. Chronic aDBS could now be trialed in Parkinsonism.

Adaptive deep brain stimulation (aDBS) is effective for Parkinson&#8217;s disease, improving symptoms and reducing power consumption compared to conventional deep brain stimulation (cDBS). In aDBS we track a local field potential biomarker (beta oscillatory amplitude) in real time and use this to control the timing of stimulation.

Adaptive deep brain stimulation (aDBS) has the potential to improve the treatment of Parkinson&#39;s disease by optimizing stimulation in real time ...

Medicine

Deep Brain Stimulation with Simultaneous fMRI in Rodents

In order to visualize the global and downstream neuronal responses to deep brain stimulation (DBS) at various targets, we have developed a protocol for using blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) to image rodents with simultaneous DBS. DBS fMRI presents a number of technical challenges, including accuracy of electrode implantation, MR artifacts created by the electrode, choice of anesthesia and paralytic to minimize any neuronal effects while simultaneously eliminating animal motion, and maintenance of physiological parameters, deviation from which can confound the BOLD signal.&#160;Our laboratory has developed a set of procedures that are capable of overcoming most of these possible issues. For electrical stimulation, a homemade tungsten bipolar microelectrode is used, inserted stereotactically at the stimulation site in the anesthetized subject. In preparation for imaging, rodents are fixed on a plastic headpiece and transferred to the magnet bore. For sedation and paralysis during scanning, a cocktail of dexmedetomidine and pancuronium is continuously infused, along with a minimal dose of isoflurane; this preparation minimizes the BOLD ceiling effect of volatile anesthetics. In this example experiment, stimulation of the subthalamic nucleus (STN) produces BOLD responses which are observed primarily in ipsilateral cortical regions, centered in motor cortex. Simultaneous DBS and fMRI allows the unambiguous modulation of neural circuits dependent on stimulation location and stimulation parameters, and permits observation of neuronal modulations free of regional bias. This technique may be used to explore the downstream effects of modulating neural circuitry at nearly any brain region, with implications for both experimental and clinical DBS.

This protocol describes a standard method for simultaneous functional magnetic resonance imaging and deep brain stimulation in the rodent. The combined use of these experimental tools allows for the exploration of global downstream activity in response to electrical stimulation at virtually any brain target.

In order to visualize the global and downstream neuronal responses to deep brain stimulation (DBS) at various targets, we have developed a protocol for ...

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for movement disorders and is being applied to a growing number of disorders. Computational modeling has been successfully used to predict the clinical effects of DBS; however, there is a need for novel modeling techniques to keep pace with the growing complexity of DBS devices. These models also need to generate predictions quickly and accurately. The goal of this project is to develop an image processing pipeline to incorporate structural magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) into an interactive, patient specific model to simulate the effects of DBS. A virtual DBS lead can be placed inside of the patient model, along with active contacts and stimulation settings, where changes in lead position or orientation generate a new finite element mesh and solution of the bioelectric field problem in near real-time, a timespan of approximately 10 seconds. This system also enables the simulation of multiple leads in close proximity to allow for current steering by varying anodes and cathodes on different leads. The techniques presented in this paper reduce the burden of generating and using computational models while providing meaningful feedback about the effects of electrode position, electrode design, and stimulation configurations to researchers or clinicians who may not be modeling experts.

The goal of this project is to develop an interactive, patient-specific modeling pipeline to simulate the effects of deep brain stimulation in near real-time and provide meaningful feedback as to how these devices influence neural activity in the brain.

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for ...

Bioengineering

Using Saccadometry with Deep Brain Stimulation to Study Normal and Pathological Brain Function

The oculomotor system involves a large number of brain areas including parts of the basal ganglia, and various neurodegenerative diseases including Parkinson's and Huntington's can disrupt it. People with Parkinson's disease, for example, tend to have increased saccadic latencies. Consequently, the quantitative measurement of saccadic eye movements has received considerable attention as a potential biomarker for neurodegenerative conditions. A lot more can be learned about the brain in both health and disease by observing what happens to eye movements when the function of specific brain areas is perturbed. Deep brain stimulation is a surgical intervention used for the management of a range of neurological conditions including Parkinson's disease, in which stimulating electrodes are placed in specific brain areas including several sites in the basal ganglia. Eye movement measurements can then be made with the stimulator systems both off and on and the results compared. With suitable experimental design, this approach can be used to study the pathophysiology of the disease being treated, the mechanism by which DBS exerts it beneficial effects, and even aspects of normal neurophysiology.

This paper describes the use of quantitative measurement of eye movements in conjunction with stimulation of focal areas of the deep brain in order to study physiology, pathophysiology, and the mechanisms of deep brain stimulation.

The oculomotor system involves a large number of brain areas including parts of the basal ganglia, and various neurodegenerative diseases including ...

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries unique challenges in children. Similarly, transcranial direct current stimulation (tDCS) can improve motor learning in adults but has only recently been applied to children. The use of tDCS and emerging techniques like high&#8211;definition tDCS (HD-tDCS) require special methodological considerations in the developing brain. Robotic TMS motor mapping may confer unique advantages for mapping, particularly in the developing brain. Here, we aim to provide a practical, standardized approach for two integrated methods capable of simultaneously exploring motor cortex modulation and motor maps in children. First, we describe a protocol for robotic TMS motor mapping. Individualized, MRI-navigated 12x12 grids centered on the motor cortex guide a robot to administer single-pulse TMS. Mean motor evoked potential (MEP) amplitudes per grid point are used to generate 3D motor maps of individual hand muscles with outcomes including map area, volume, and center of gravity. Tools to measure safety and tolerability of both methods are also included. Second, we describe the application of both tDCS and HD-tDCS to modulate the motor cortex and motor learning. An experimental training paradigm and sample results are described. These methods will advance the application of non-invasive brain stimulation in children.

We demonstrate protocols for the modulation (tDCS, HD-tDCS) and mapping (robotic TMS) of the motor cortex in children.

Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries ...

Deep Brain Stimulation for Treatment of Major Depression

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment 

Deep brain stimulation triggered by a patient-specific neural biomarker of a high-symptom state may better control symptoms of major depressive disorder compared to continuous, open-loop stimulation. This protocol provides a workflow for identifying a patient-specific neural biomarker and controlling the delivery of therapeutic stimulation based on the identified biomarker.

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major ...

Real-Life EEG and ECG Synchronization in Infant-Caregiver Interactions

Home-Based EEG Hyperscanning for Infant-Caregiver Social Interactions

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of the laboratory, thus limiting the generalizability of results to real-life settings. Here, a comprehensive protocol for capturing synchronized electroencephalography (EEG), electrocardiography (ECG), and behavioral recordings from infant-caregiver dyads during various interactive tasks at home is proposed. This protocol demonstrates how to synchronize the different data streams and report EEG data retention rates and quality checks. Additionally, critical issues and possible solutions with respect to the experimental setup, tasks, and data collection in home settings are discussed. The protocol is not limited to infant-caregiver dyads but can be applied to various dyadic constellations. Overall, we demonstrate the flexibility of EEG hyperscanning setups, which allow experiments to be conducted outside of the laboratory to capture participants' brain activities in more ecologically valid environmental settings. Yet, movement and other types of artifacts still constrain the experimental tasks that can be performed in the home setting.

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

This protocol describes how synchronized electroencephalography, electrocardiography, and behavioral recordings were captured from infant-caregiver dyads in a home setting.

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of ...

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

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