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Empirical evaluation of human fetal fMRI preprocessing steps
Lanxin Ji1, Cassandra L Hendrix1, Moriah E Thomason1,2,3
1Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA.
Network Neuroscience (Cambridge, Mass.)
|October 7, 2022
Summary
Optimizing fetal functional MRI (fMRI) preprocessing is crucial. This study found that age-matched templates, stringent denoising, and larger smoothing kernels improve fetal brain image quality and functional connectivity analysis.
Area of Science:
- Neuroimaging
- Developmental Neuroscience
- Medical Imaging
Background:
- Fetal brain functional MRI (fMRI) is a rapidly growing field.
- Established fMRI preprocessing methods face challenges due to rapid fetal development and in-utero imaging constraints.
- Optimization of preprocessing pipelines is essential for accurate fetal fMRI analysis.
Purpose of the Study:
- To evaluate the impact of different preprocessing steps on fetal brain fMRI quality.
- To compare normalization strategies: group mean-age vs. age-matched templates.
- To assess the effectiveness of independent components analysis (ICA) denoising and spatial smoothing kernel sizes.
Main Methods:
- Utilized fMRI data from 121 fetuses (25-39 weeks gestation).
- Compared normalization to a group mean-age template versus an age-matched template.
- Evaluated ICA denoising at two thresholds and smoothing with three kernel sizes.
Main Results:
- Age-matched templates were more optimal for younger fetuses, while mean-age templates were better for older fetuses.
- More stringent ICA denoising yielded superior results compared to less stringent methods.
- Larger smoothing kernels enhanced cross-hemisphere functional connectivity.
Conclusions:
- Specific preprocessing choices significantly impact fetal fMRI image quality and connectivity metrics.
- Findings provide guidance for optimizing fetal fMRI preprocessing pipelines.
- Recommendations can inform the development of standardized best practices for fetal neuroimaging.
