Automating Workflows in Stanford’s Brain Stimulation Lab
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Flywheel
- Flywheel Gears
- Flywheel SDK
Tech Stack
- Data Management
- Workflow Automation
- Cloud Computing
Implementation Scale
- Departmental Deployment
Impact Metrics
- Productivity Improvements
- Innovation Output
Technology Category
- Platform as a Service (PaaS) - Data Management Platforms
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
Services
- Data Science Services
- Cloud Planning, Design & Implementation Services
About The Customer
Stanford Medicine’s Brain Stimulation Lab is a research facility dedicated to finding solutions for treatment-resistant depression. The lab is studying the use of Repetitive Transcranial Magnetic Stimulation (rTMS), a therapy that involves activating or inhibiting the brain directly with electromagnetic fields. The lab is led by Dr. Nolan Williams and includes researchers like Dr. Azeezat Azeez, a postdoctoral scholar with Stanford Psychiatry and Behavioral Sciences. The lab collaborates with the Stanford Center for Cognitive and Neurobiological Imaging (CNI) to obtain its scans. The lab's work has been published in high-impact journals and has attracted mainstream media attention from outlets like CBS and NPR.
The Challenge
Stanford Medicine’s Brain Stimulation Lab is working on solutions for treatment-resistant depression, a condition that affects 5% of adults worldwide. The lab is studying the use of Repetitive Transcranial Magnetic Stimulation (rTMS), a therapy that involves activating or inhibiting the brain directly with electromagnetic fields. The lab's work is growing, and so is their need for smart data management. The lab originally used the Flywheel platform to store raw and reconstructed data and applied its basic tools for reconstruction and quality control. However, when they wanted to perform analysis, researchers were still downloading data to a static lab PC. This process was time-consuming and made it difficult to track data provenance.
The Solution
To improve the lab’s workflow, Dr. Azeez began leveraging more of Flywheel’s capabilities. She dedicated time to building custom Flywheel Gears—containerized algorithms that are run and managed within the platform. Flywheel provides an ever-expanding library of ready-to-use Gears, for everything from routine data processing tasks like metadata extraction and classification, to more complicated processes like independent component analysis and structural segmentation. Users also have the ability to develop their own custom Gears. By better utilizing existing Flywheel Gears and developing her own, Dr. Azeez and her collaborators have been able to dramatically streamline their workflows. Flywheel also provides an SDK (software development kit) library, a powerful suite of tools and utilities that allows users to programmatically interface with the platform. Using these tools, Dr. Azeez has daisy-chained Gears together so that once a patient’s baseline scan is received, the platform automatically runs Gears in sequence for reconstruction, quality control, fMRIprep pre-processing, and the lab’s custom targeting algorithm.
Operational Impact
Quantitative Benefit
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