Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI

Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based...

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Vydáno v:	NeuroImage (Orlando, Fla.) Ročník 141; s. 452 - 468
Hlavní autoři:	Gonzalez-Castillo, Javier, Panwar, Puja, Buchanan, Laura C., Caballero-Gaudes, Cesar, Handwerker, Daniel A., Jangraw, David C., Zachariou, Valentinos, Inati, Souheil, Roopchansingh, Vinai, Derbyshire, John A., Bandettini, Peter A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.11.2016 Elsevier Limited
Témata:	Adult Algorithms Block design Brain - physiology Brain Mapping - methods Cardiac-Gated Imaging Techniques - methods Datasets Experiments Female Functional magnetic resonance imaging Heart Heart diseases Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Magnetic Resonance Imaging - methods Male ME-ICA Multi-echo fMRI Neural networks Noise Physiology Principal Component Analysis Principal components analysis Rapid event related Repetition Reproducibility of Results Sensitivity Sensitivity and Specificity Signal-To-Noise Ratio Task Performance and Analysis Time series Sensitivity Rapid event related Block design Multi-echo fMRI ME-ICA
ISSN:	1053-8119, 1095-9572
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Abstract	Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2⁎ weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data. •Sensitivity for task-based fMRI experiments significantly improved with ME-ICA.•Observed improvements generalized across tasks and experimental designs.•ME-ICA reliably and robustly removed T1 artifacts in cardiac-gated fMRI datasets.•Several enhancements to further improve ME-ICA's performance are proposed.
AbstractList	Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2⁎ weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data. Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2⁎ weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data. •Sensitivity for task-based fMRI experiments significantly improved with ME-ICA.•Observed improvements generalized across tasks and experimental designs.•ME-ICA reliably and robustly removed T1 artifacts in cardiac-gated fMRI datasets.•Several enhancements to further improve ME-ICA's performance are proposed.
Author	Buchanan, Laura C. Inati, Souheil Caballero-Gaudes, Cesar Roopchansingh, Vinai Jangraw, David C. Derbyshire, John A. Bandettini, Peter A. Gonzalez-Castillo, Javier Zachariou, Valentinos Panwar, Puja Handwerker, Daniel A.
AuthorAffiliation	b Basque Center on Cognition, Brain and Language, San Sebastian, Spain c Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States d Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States a Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
AuthorAffiliation_xml	– name: c Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – name: d Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – name: a Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – name: b Basque Center on Cognition, Brain and Language, San Sebastian, Spain
Author_xml	– sequence: 1 givenname: Javier orcidid: 0000-0002-6520-5125 surname: Gonzalez-Castillo fullname: Gonzalez-Castillo, Javier email: javier.gonzalez-castillo@nih.gov organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 2 givenname: Puja surname: Panwar fullname: Panwar, Puja organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 3 givenname: Laura C. surname: Buchanan fullname: Buchanan, Laura C. organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 4 givenname: Cesar surname: Caballero-Gaudes fullname: Caballero-Gaudes, Cesar organization: Basque Center on Cognition, Brain and Language, San Sebastian, Spain – sequence: 5 givenname: Daniel A. surname: Handwerker fullname: Handwerker, Daniel A. organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 6 givenname: David C. surname: Jangraw fullname: Jangraw, David C. organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 7 givenname: Valentinos surname: Zachariou fullname: Zachariou, Valentinos organization: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 8 givenname: Souheil surname: Inati fullname: Inati, Souheil organization: Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 9 givenname: Vinai surname: Roopchansingh fullname: Roopchansingh, Vinai organization: Functional MRI Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 10 givenname: John A. surname: Derbyshire fullname: Derbyshire, John A. organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States – sequence: 11 givenname: Peter A. surname: Bandettini fullname: Bandettini, Peter A. organization: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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SubjectTerms	Adult Algorithms Block design Brain - physiology Brain Mapping - methods Cardiac-Gated Imaging Techniques - methods Datasets Experiments Female Functional magnetic resonance imaging Heart Heart diseases Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Magnetic Resonance Imaging - methods Male ME-ICA Multi-echo fMRI Neural networks Noise Physiology Principal Component Analysis Principal components analysis Rapid event related Repetition Reproducibility of Results Sensitivity Sensitivity and Specificity Signal-To-Noise Ratio Task Performance and Analysis Time series
Title	Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI
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