ArticleNeuroImage2016
Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
57 citing papers in PubMed.
- Intranasal oxytocin enhances intrinsic corticostriatal functional connectivity in women.Translational psychiatry · 2017Trial
- PsiConnect: Multimodal Neuroimaging of Context-Dependent Brain and Behaviour Dynamics under Psilocybin.Scientific data · 2026Article
- A new fMRI quality metric using multi-echo information: Theory, validation and implications.bioRxiv : the preprint server for biology · 2026Article
- PRISME: A MATLAB Toolbox For Large Data-Driven Multimodal Power Benchmarking.bioRxiv : the preprint server for biology · 2026Article
- Impact of multi-echo ICA modeling decisions on motor-task fMRI analysis.Aperture neuro · 2026Article
- Getting blood from a stone: Improving brain-behavior inferences without brain data.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Resting-State and Task-Based Functional Connectivity Reveal Distinct mPFC and Hippocampal Network Alterations in Major Depressive Disorder.Brain sciences · 2025Article
- Intermittent Fasting Enhances Motor Coordination Through Myelin Preservation in Aged Mice.Aging cell · 2025Article
- Comparing the effect of multi-gradient echo and multi-band fMRI during a semantic task.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Multi-echo versus single-echo EPI sequences for task-fMRI: A comparative study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Improving the Sensitivity of Task-Based Multi-Echo Functional Magnetic Resonance Imaging viaBrain sciences · 2024Article
- Precision mapping of the default network reveals common and distinct (inter) activity for autobiographical memory and theory of mind.Journal of neurophysiology · 2024Article
- Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.bioRxiv : the preprint server for biology · 2024Article
- Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.bioRxiv : the preprint server for biology · 2023Article
- Light modulates task-dependent thalamo-cortical connectivity during an auditory attentional task.Communications biology · 2023Article
- Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes.Biological psychiatry · 2023Review
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Corrections and comments
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Authors and funding
9 authors.
Funding
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 between-subject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICA-related 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.