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Early Stopping in Experimentation With Real-Time Functional Magnetic Resonance Imaging Using a Modified Sequential
Sarah J A Carr1,2, Weicong Chen3, Jeremy Fondran4
1Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Frontiers in Neuroscience
|November 22, 2021
Summary
This study introduces dynamic real-time fMRI with early stopping, reducing scan times by up to 33% without compromising brain activity detection. This method lessens subject fatigue and improves data quality.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Medical Imaging Analysis
Background:
- Functional magnetic resonance imaging (fMRI) requires long scan durations, risking subject fatigue, motion artifacts, and reduced data quality.
- Detecting task-related brain activity efficiently is crucial for accurate fMRI studies.
Purpose of the Study:
- To introduce a novel statistically driven approach for dynamic real-time fMRI with automated early stopping.
- To assess the feasibility and efficiency of early stopping in fMRI experiments.
Main Methods:
- Implemented voxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLMs) for fMRI data.
- Utilized a two-stage estimation approach and numerical parallelization for real-time analysis.
- Applied the method to fMRI scans of a mathematical 1-back task in healthy and extremely preterm born (EPT) teenage subjects.
Main Results:
- Automated early stopping using SPRT demonstrated feasibility and efficiency, achieving comparable activation detection to full scan durations.
- Dynamic stopping reduced scan times by up to 33% in approximately half of the subjects.
- Group analysis showed similar activation patterns between early stopping and full scans in controls; EPT group exhibited more variability.
Conclusions:
- A systematic statistical approach for early stopping in real-time fMRI has been successfully implemented.
- This dynamic approach shows promise in reducing subject burden, minimizing fatigue effects, and enhancing overall data quality in fMRI studies.
