Consistently delivering validated results to Fair Lab: PrimeNeuro deploys fMRI image marking
The team at PrimeNeuro was honored to collaborate with the University of Minnesota’s Fair Lab to address critical challenges in fMRI image marking and pre-processing. The Damian Fair Lab, a leader in brain connectivity research, faced significant hurdles in achieving consistent and accurate image marking from fMRI datasets. These issues were exacerbated by high turnover and variability in talent among the graduate students and undergraduates performing manual marking. The stakes were high: inconsistency in data could compromise research integrity and delay groundbreaking findings.
The Fair Lab approached us to streamline their workflow and enhance the reliability of their processes. Their existing framework involved intensive manual effort to identify regions of interest (ROI) in brain images, which often led to inconsistencies. The absence of a quality control system further amplified these issues, making it difficult to achieve reproducible results.
We began by understanding the nuances of Fair Lab’s data processing needs. Our team developed a comprehensive Quality Control (QC) system to standardize image marking and analysis. This system was meticulously designed to ensure consistent and accurate measurements across all phases of the process, from realignment and normalization to smoothing and artifact removal.
By implementing advanced statistical mapping tools and leveraging our expertise in functional connectivity analysis, we were able to automate region of interest (ROI) identification and activity mapping.
To address the issue of workforce variability, we introduced a scalable solution: a validated workflow and parameter template adaptable to 12, 24, and 36-month scans. This approach not only ensured continuity but also facilitated training for new team members. Our pipeline enabled the lab to tweak parameters, such as thickness, with clarity and precision, ensuring every adjustment was informed and replicable.
The work was reliable and impactful. Fair Lab’s reliance on then-available human resources was replaced with a robust and scalable system capable of delivering consistent, high-quality results. This foundation allowed them to interrogate the data with confidence and in turn develop predictive algorithms with fidelity, paving the way for advanced research in brain function and connectivity.
Moreover, midline errors and related data challenges were addressed proactively, with actionable steps for resolution defined collaboratively.
This case exemplifies our commitment to empowering research teams with innovative solutions that bridge gaps in consistency and accuracy. If your organization is grappling with similar challenges, we invite you to connect with PrimeNeuro. Let us help transform your processes and deliver results that propel your mission forward.
