Abstract
Advances in neuromodulation technologies hold the promise of treating a patient’s unique brain network pathology using personalized stimulation patterns. In service of these goals, neuromodulation clinical trials using sensing-enabled devices are routinely generating large multi-modal datasets. However, with the expansion of data acquisition also comes an increasing difficulty to store, manage, and analyze the associated datasets, which integrate complex neural and wearable time-series data with dynamic assessments of patients’ symptomatic state. Here, we discuss a scalable cloud-based data platform that enables ingestion, aggregation, storage, query, and analysis of multi-modal neurotechnology datasets. This large-scale data infrastructure will accelerate translational neuromodulation research and enable the development and delivery of next-generation deep brain stimulation therapies.
Highlights
Precision medicine has changed the face of modern healthcare
Rune Labs has developed a data platform that is uniquely tailored to the needs of the neuromodulation community. We present this as an example of the type of infrastructure that can be used to develop and deliver dataintensive neuromodulation therapies
New data are immediately processed upon arrival with a high-availability upload application programming interface (API) that maintains at least 99.9% service uptime
Summary
Precision medicine has changed the face of modern healthcare. Historically, treatments have been developed assuming a one-size-fits-all approach. The resulting large-size, multi-modal datasets require significant data infrastructure that supports scalable data ingestion, time-syncing, storage, query, and analysis These systems are complex from a technical, reliability, and compliance standpoint and are beyond the capacity of most individual research groups. Testing aDBS over long time courses in patients’ homes requires efficient data transfer and availability Both researchers and clinicians need easy access to recorded data such that they can assess algorithm performance and iterate on tests. This involves transferring data from the patient’s implanted device to external computers and/or cloud based storage without requiring frequent clinic visits. Unlocking the full utility of these rich datasets requires a scalable and efficient data platform
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