Restoring statistical validity in group analyses of motion-corrupted MRI data.
Antoine Lutti1, Nadège Corbin2,3, John Ashburner3
1Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Human Brain Mapping
|February 3, 2022
Summary
Motion during magnetic resonance imaging (MRI) acquisition degrades image quality. This study introduces a data-driven weighting method to improve analysis validity and optimize sample size, outperforming traditional exclusion criteria.
Area of Science:
- Medical Imaging
- Neuroimaging
- Biostatistics
Background:
- Motion during MRI acquisition significantly degrades image quality.
- Current quality control methods involve image exclusion, which can be subjective and reduce sample size.
- This compromises the ability to accurately characterize diseases in patient populations.
Purpose of the Study:
- To develop and validate a data-driven method for handling motion-affected MRI data.
- To provide an objective alternative to image exclusion in MRI analysis.
- To improve statistical validity and optimize the balance between image quality and sample size.
Main Methods:
- A novel method assigning weights to each MRI image based on an image quality index.
- Utilized restricted maximum likelihood (REML) for weight computation.
- Applied the method to quantitative MRI data analysis.
Main Results:
- The proposed weighting method restores the validity of statistical tests.
- The approach performs near-optimally across all brain regions, even with localized motion artifacts.
- Demonstrated effectiveness in quantitative MRI data analysis.
Conclusions:
- This data-driven weighting approach offers a superior alternative to image exclusion for motion-affected MRI data.
- The method is broadly applicable to various MRI data types and quality measures.
- Enhances the reliability and efficiency of neuroimaging research in clinical populations.
