Mobile sensor big data research has resulted in an exponential increase, over the past decade, in the amount of information collected about individuals in real-world settings. These increases create compounding problems for the researchers that need to develop, analyze, and report their findings based on these ever growing data sets. The Cerebral Cortex platform was built to address many of the challenges with these types of studies and data analyses through a high-performance real-time data collection and analysis platform to support in-the-field participants through interactive web dashboards.
As a big data cloud tool, Cerebral Cortex has the capability to:
- support population-scale data analysis, visualization, model development, and intervention design for mobile-sensor data;
- do machine learning model development on population scale datasets; and
- provide interoperable interfaces for aggregation of diverse data sources.
Additionally, Cerebral Cortex enables the bring-your-own-data model, allowing researchers to ingest any type of structured data into the system. The core of the platform is a library of research-grade biomarker algorithms that can be deployed at scale across a variety of data sources and are easily adapted to other types of data, enabling an accelerated development timeline for new algorithms and techniques.
Computing researchers can use the platform to conduct novel analytics, visualization, and discovery from large-scale mobile sensor data via a Jupyter notebook interface and discuss future direction, such as, real-time reinforcement learning for personalized interventions.
Details & Specifications
Publication: mCerebrum and Cerebral Cortex: A Real-time Collection, Analytic, and Intervention Platform for High-frequency Mobile Sensor Data
Publication: Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)
Webinar: Cerebral Cortex: A Real-Time Data Collection & Analytics Platform for High-Frequency Mobile Sensor Data
Website: Cerebral Cortex Data Visualization
Dr. Mani Srivastava (UCLA)
Dr. Timothy Hnat (Memphis)
Dr. Tyson Condie (UCLA)
Simone Carini (UCSF)
Dr. Syed Monowar Hossain (Memphis)
Dr. Nasir Ali (Memphis)
Nusrat Nasrin (Memphis)
Bo-Jhang Ho (UCLA)
Matteo Interlandi (UCLA)
Addison Mayberry (UMass)
- National Institutes of Health – Big Data to Knowledge Initiative
- Grants: R01MD010362, 1UG1DA04030901, 1U54EB020404, 1R01CA190329, 1R01DE02524, R00MD010468, 3UH2DA041713, 10555SC
- National Science Foundation
- Grants: 1640813, 1722646
- Intelligence Advanced Research Projects Activity
- Contract: 2017-17042800006
Cerebral Cortex Statistics
Person-Days of Data
Cerebral Cortex supports 10 concurrent studies combining 2,100+ participants.
Cerebral Cortex is capable of scaling thousands of concurrent mCerebrum instances.