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The three stages of neuromodulation and digital health


Rune Labs recently attended the North American Neuromodulation Society (NANS) conference in Orlando, January 12-15, 2022 to discuss the three stages of neuromodulation and digital health. Brian Pepin, CEO and co-founder of Rune Labs spoke at the NANS i3: Ecosystem of Neuromodulation panel session along with Yelena Yesha and Nandan Lad. 

Below, you will find a summary of neuromodulation and digital health, and how Rune Labs plays a critical role in generating different sources of data to support patients, clinicians, and researchers for neurodegenerative diseases.  

What is digital health? 

The FDA defines digital health as a broad scope of categories including mobile health, health information technology, wearable devices, telehealth and telemedicine, and personalized medicine. Emerging digital tools provide care teams with a more holistic view of a patient’s health journey, while also allowing patients to be more in control over their health. 

What does digital health mean to Rune Labs? 

At Rune Labs, digital health means providing clinicians and patients with access to comprehensive data on patients’ symptoms, medication use, and brain activity. We compile data from three sources: App Data, Apple Watch, and Device Data. StrivePD, our patient-facing app, allows patients to track their symptoms and medication use in advance of clinical visits. The Apple Watch, a consumer friendly wearable used by millions of people, can objectively capture a range of Parkinson’s related symptoms, including tremor, dyskinesia, sleep, gait, and heart rate variability. Lastly, Medtronic’s Percept Deep Brain Stimulator continuously captures brain activity.

We combine and summarize these sources of data into an easy-to-use dashboard that helps clinicians tailor patient care. Through these dashboards, clinicians can surface patient patterns within and across patients that may help them better optimize medications, adjust stimulation settings, and account for the influence of external factors (meals, exercise etc.) on outcomes. 

Stage I: How can clinicians use readily available patient data to improve neuromodulation? 

Although the technology exists for collecting patient data (e.g. symptoms, medication use, and brain signals), clinicians lack the easy-to-use infrastructure needed to answer key questions: Are patients’ symptoms adequately controlled or should settings be adjusted? Which patients not currently on DBS might benefit from it?  

At Rune Labs, we use digital health tools to enhance existing treatments with data enriched programming. For example, patients with Parkinson’s enrolled in StrivePD wear their Apple Watch and answer questions on the app two weeks before and after a clinic visit. The clinician is then able to see a summarized view of the patient’s data before a clinic visit and the effects of a treatment after with additional longitudinal brain data from Medtronic’s Percept device. 

These data-enriched programming visits provide access to valuable brain data and a window into patients’ lifestyle, symptoms and activity. For example, clinicians can leverage the brain data and understand targeted patterns, local field potentials (LFP), correlations with medications, symptoms, stimulations, and different patient phenotypes for patients. Through our partnership with Medtronic, we are actively exploring the utility of brain data collected on the implanted Percept DBS device. The summarized DBS data alongside symptom information for clinicians enables our team to better understand the efficacy of the treatment for patients. 

At Rune Labs, we are also leveraging what we know about digital health and biomarkers in the Parkinson’s population to identify treatment candidates for movement disorder specialists. By identifying these individuals, patients can receive patient-centered care such as DBS. 

Stage II: How can clinicians leverage AI and data to deliver personalized neuromodulation therapies while minimizing clinician and patient burden? 

Emerging directional and adaptive neuromodulation therapies are complex. Adding more features means adding more options to choose from. At Rune Labs, we have the ability to target and personalize therapy for individuals which can be powerful for both the patient’s well-being, and research for other therapies. For example, by leveraging large multimodal datasets, we will be able to identify and automate parameter selections for aDBS. This enables care teams to improve patient outcomes and has the potential to de-risk the use of devices within larger patient populations. 

For directional DBS, it is imperative to strike the balance between the increasing progress of personalization and increasing burden for patients and clinicians to allow machines to solve complex issues. An application of artificial intelligence (AI) and machine learning (ML) can make adaptive therapy delivery more scalable and provide guided programming and administer optimal adaptive algorithms to patient phenotypes. In a recent publication featured in Frontiers Neuroscience, we explain our software’s architecture and how we bring rich data from neuromodulation devices, clinical history, patient symptoms, etc. and produce tools that reduce burden on clinicians and patients. 

Example scenario: Looking at a patient’s neurophysiological data, a clinician can appreciate the similarities across patients who have benefited from X adaptive therapy and suggest threshold parameters. 

Stage III: How can clinicians, researchers, and pharma leverage novel data access to develop entirely new treatment modalities? 

Currently, Parkinson’s disease, chronic pain and other adjacent indications to pain like Multiple Sclerosis (MS) are not fully understood in humans. By generating rich neuroscience data, we can pave the way towards new therapies. For example, by looking at data for individuals with MS with spinal cord stimulators, we can help quantify myelination. This quantification is important because pharma companies need generated data to run efficient clinical trials for new clinical therapies. This can positively impact:

  1. Patients’ wellbeing 
  2. Development of novel biomarkers for pharma in clinical trials 
  3. Quantifying disease modification
  4. Potential neuromodulation development in MS  

The Future:

This paradigm shift in the adoption of digital health and neuromodulation in the industry is the future of healthcare for neurodegenerative diseases. Understanding patients’ disease and management on an individual level using real world data and other clinical measures through wearables, implants and self-reported data can help not only clinicians provide better care, but pharma and med device companies create new therapies.


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