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Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

View ORCID ProfileAdam Steel, Brenda D. Garcia, Edward H. Silson, Caroline E. Robertson
doi: https://doi.org/10.1101/2022.06.29.498113
Adam Steel
1Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, 03755
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  • For correspondence: adam.steel@dartmouth.edu
Brenda D. Garcia
1Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, 03755
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Edward H. Silson
2Psychology, School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, UK EH8 9JZ
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Caroline E. Robertson
1Department of Psychology and Brain Sciences, Dartmouth College, Hanover, NH, 03755
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Abstract

fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance on data acquired during a visual population receptive field (pRF) mapping paradigm (N=13 participants) after using one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyses and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including task fMRI studies, voxel-wise modeling, and naturalistic paradigms.

Competing Interest Statement

The authors have declared no competing interest.

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  • Conflict of interest statement: The authors declare no conflict of interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 06, 2022.
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Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI
Adam Steel, Brenda D. Garcia, Edward H. Silson, Caroline E. Robertson
bioRxiv 2022.06.29.498113; doi: https://doi.org/10.1101/2022.06.29.498113
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Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI
Adam Steel, Brenda D. Garcia, Edward H. Silson, Caroline E. Robertson
bioRxiv 2022.06.29.498113; doi: https://doi.org/10.1101/2022.06.29.498113

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