Evidence map›Paper›PMID 29155843›Full record

ArticlePloS one2017

The relation between statistical power and inference in fMRI.

Henk R Cremers, Tor D Wager, Tal Yarkoni

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
206citing papers in PubMed, 14 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

206 citing papers in PubMed, 14 syntheses or guidelines pooled it.

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146 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Henk R CremersDepartment of Clinical Psychology, University of Amsterdam, Amsterdam, Netherlands.ORCID http://orcid.org/0000-0001-8555-8158
Tor D WagerDepartment of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, United States of America.
Tal YarkoniDepartment of Psychology, University of Texas at Austin, Austin, Texas, United States of America.

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Large-scale meta-analysis of functional MRI dataR01MH096906 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI Alejandro De La Vega · 2012 to 2026
$7.9M
fMRI-based Biomarkers for Multiple Components of PainR01DA035484 · NIDA · UNIVERSITY OF COLORADO · PI WAGER, TOR D. · 2013 to 2017
$3.1M
NeuroScout: A cloud-based platform for flexible re-analysis of naturalistic fMRI datasetsR01MH109682 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI DE LA VEGA, ALEJANDRO · 2016 to 2020
$3.0M
NIDA NIH HHS R01 DA035484NIMH NIH HHS R01 MH096906NIMH NIH HHS R01 MH109682NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

Statistically underpowered studies can result in experimental failure even when all other experimental considerations have been addressed impeccably. In fMRI the combination of a large number of dependent variables, a relatively small number of observations (subjects), and a need to correct for multiple comparisons can decrease statistical power dramatically. This problem has been clearly addressed yet remains controversial-especially in regards to the expected effect sizes in fMRI, and especially for between-subjects effects such as group comparisons and brain-behavior correlations. We aimed to clarify the power problem by considering and contrasting two simulated scenarios of such possible brain-behavior correlations: weak diffuse effects and strong localized effects. Sampling from these scenarios shows that, particularly in the weak diffuse scenario, common sample sizes (n = 20-30) display extremely low statistical power, poorly represent the actual effects in the full sample, and show large variation on subsequent replications. Empirical data from the Human Connectome Project resembles the weak diffuse scenario much more than the localized strong scenario, which underscores the extent of the power problem for many studies. Possible solutions to the power problem include increasing the sample size, using less stringent thresholds, or focusing on a region-of-interest. However, these approaches are not always feasible and some have major drawbacks. The most prominent solutions that may help address the power problem include model-based (multivariate) prediction methods and meta-analyses with related synthesis-oriented approaches.

Indexed as

BrainConnectomeHumansMagnetic Resonance ImagingNeuroimagingTheory of Mind

Identifiers

PMID29155843
PMCPMC5695788

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.