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Published on: August 5, 2014
Reducing the Effects of Motion Artifacts in fMRI: A Structured Matrix Completion Approach
Abstract:
Functional MRI (fMRI) is widely used to study the functional organization of normal and pathological brains. However, the fMRI signal may be contaminated by subject motion artifacts that are only partially mitigated by motion correction strategies. These artifacts lead to distance-dependent biases in the inferred signal correlations. To mitigate these spurious effects, motion-corrupted volumes are censored from fMRI time series. Censoring can result in discontinuities in the fMRI signal, which may lead to substantial alterations in functional connectivity analysis. We propose a new approach to recover the missing entries from censoring based on structured low rank matrix completion. We formulated the artifact-reduction problem as the recovery of a super-resolved matrix from unprocessed fMRI measurements. We enforced a low rank prior on a large structured matrix, formed from the samples of the time series, to recover the missing entries. The recovered time series, in addition to being motion compensated, are also slice-time corrected at a fine temporal resolution. To achieve a fast and memory-efficient solution for our proposed optimization problem, we employed a variable splitting strategy. We validated the algorithm with simulations, data acquired under different motion conditions, and datasets from the ABCD study. Functional connectivity analysis showed that the proposed reconstruction resulted in connectivity matrices with lower errors in pair-wise correlation than non-censored and censored time series based on a standard processing pipeline. In addition, seed-based correlation analyses showed improved delineation of the default mode network. These demonstrate that the method can effectively reduce the adverse effects of motion in fMRI analysis.
Insights
This study introduces a novel method using structured low rank matrix completion to recover censored functional MRI (fMRI) data corrupted by motion artifacts. The approach improves functional connectivity analysis and brain network delineation.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Data Science
Background:
- Functional MRI (fMRI) is crucial for brain research but susceptible to motion artifacts.
- Existing motion correction methods are insufficient, leading to biased functional connectivity.
- Censoring corrupted data creates signal discontinuities, impacting analysis.
Purpose of the Study:
- To develop a novel method for artifact reduction in fMRI using structured low rank matrix completion.
- To recover censored fMRI data, mitigating motion-induced biases and signal discontinuities.
- To improve the accuracy of functional connectivity analysis.
Main Methods:
- Formulated artifact reduction as a super-resolved matrix recovery problem.
- Applied a low rank prior to a structured matrix derived from fMRI time series.
- Utilized a variable splitting strategy for efficient computation.
- Validated with simulations, diverse motion conditions, and ABCD study data.
Main Results:
- Reconstructed fMRI time series showed reduced errors in pairwise correlations compared to standard methods.
- Improved delineation of the default mode network in seed-based correlation analyses.
- Demonstrated effective reduction of motion-related adverse effects in fMRI.
Conclusions:
- The proposed structured low rank matrix completion method effectively mitigates motion artifacts in fMRI.
- This technique enhances the reliability and accuracy of functional connectivity and brain network analyses.
- The method offers a promising solution for improving fMRI data quality and interpretability.
