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Updated: Mar 6, 2026
Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat
Published on: August 28, 2021
Comparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions
Ottavia Dipasquale1,2,3, Arjun Sethi3, Maria Marcella Laganà2
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Abstract:
Artifact removal in resting state fMRI (rfMRI) data remains a serious challenge, with even subtle head motion undermining reliability and reproducibility. Here we compared some of the most popular single-echo de-noising methods-regression of Motion parameters, White matter and Cerebrospinal fluid signals (MWC method), FMRIB's ICA-based X-noiseifier (FIX) and ICA-based Automatic Removal Of Motion Artifacts (ICA-AROMA)-with a multi-echo approach (ME-ICA) that exploits the linear dependency of BOLD on the echo time. Data were acquired using a clinical scanner and included 30 young, healthy participants (minimal head motion) and 30 Attention Deficit Hyperactivity Disorder patients (greater head motion). De-noising effectiveness was assessed in terms of data quality after each cleanup procedure, ability to uncouple BOLD signal and motion and preservation of default mode network (DMN) functional connectivity. Most cleaning methods showed a positive impact on data quality. However, based on the investigated metrics, ME-ICA was the most robust. It minimized the impact of motion on FC even for high motion participants and preserved DMN functional connectivity structure. The high-quality results obtained using ME-ICA suggest that using a multi-echo EPI sequence, reliable rfMRI data can be obtained in a clinical setting.
Insights
Multi-echo ICA (ME-ICA) effectively removes artifacts in resting-state fMRI (rfMRI), outperforming other methods. This approach enhances data quality and preserves functional connectivity, even in participants with significant head motion.
Area of Science:
- Neuroimaging
- Functional Magnetic Resonance Imaging (fMRI)
- Signal Processing
Background:
- Head motion is a significant challenge in resting-state fMRI (rfMRI), compromising data reliability and reproducibility.
- Existing single-echo denoising methods like MWC, FIX, and ICA-AROMA have limitations in artifact removal.
Purpose of the Study:
- To compare the effectiveness of multi-echo ICA (ME-ICA) against popular single-echo denoising techniques for artifact removal in rfMRI.
- To assess the impact of denoising methods on data quality, motion artifact reduction, and default mode network (DMN) functional connectivity.
Main Methods:
- Comparison of ME-ICA with regression of motion parameters, white matter, and cerebrospinal fluid signals (MWC), FMRIB's ICA-based X-noiseifier (FIX), and ICA-based Automatic Removal Of Motion Artifacts (ICA-AROMA).
- Data acquired using a clinical scanner from healthy participants and ADHD patients with varying degrees of head motion.
- Evaluation metrics included post-cleaning data quality, BOLD signal-motion uncoupling, and preservation of DMN functional connectivity.
Main Results:
- Most tested cleaning methods improved data quality.
- ME-ICA demonstrated superior robustness, effectively minimizing motion's impact on functional connectivity, particularly in high-motion participants.
- ME-ICA preserved the functional connectivity structure of the DMN.
Conclusions:
- ME-ICA is a highly effective method for artifact removal in rfMRI, outperforming traditional single-echo techniques.
- The multi-echo approach enables reliable rfMRI data acquisition in clinical settings, even with significant head motion.
