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Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing


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Publication Date: 2016-07-11

Journal Title: NeuroImage

Publisher: Elsevier

Volume: 142

Number: 5

Pages: 55-66

Language: English

Type: Article

This Version: VoR

Metadata: Show full item record

Citation: Lombardo, M. V., Auyeung, B., Holt, R. J., Waldman, J., Ruigrok, A. N. V., Mooney, N., Bullmore, E. T., et al. (2016). Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing. NeuroImage, 142 (5), 55-66. https://doi.org/10.1016/j.neuroimage.2016.07.022

Description: This is the author accepted manuscript. It first appeared from Elseiver at http://dx.doi.org/10.1016/j.neuroimage.2016.07.022.

Abstract: Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40- 149%) were observed in non-canonical cerebellar areas. Effect size boosting occurs via reduction of non-BOLD noise at the subject-level and consequent reductions in betweensubject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICArelated effect size enhancements enable much higher-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could mitigate some issues regarding statistical power in fMRI studies and enable novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.

Keywords: multi-echo EPI, statistical power, denoising, task-fMRI, mentalizing, cerebellum

Sponsorship: This work was supported by a Wellcome Trust project grant to SB-C and ETB. MVL was supported by the Wellcome Trust and fellowships from Jesus College, Cambridge and the British Academy. PK was supported by the National Institutes of Health–Cambridge Scholars Program. ETB is employed half-time by the University of Cambridge and halftime by GlaxoSmithKline (GSK).

Identifiers:

External DOI: https://doi.org/10.1016/j.neuroimage.2016.07.022

This record's URL: https://www.repository.cam.ac.uk/handle/1810/257354



Rights: Attribution 4.0 International

Licence URL: http://creativecommons.org/licenses/by/4.0/





Autor: Lombardo, Michael V.Auyeung, BonnieHolt, Rosemary J.Waldman, JackRuigrok, Amber N. V.Mooney, NatashaBullmore, Edward T.Baron-Cohen

Fuente: https://www.repository.cam.ac.uk/handle/1810/257354



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