Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation
Open accessAbstract
When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80% power is not as simple as finding the smallest sample size capable of achieving 80% power, since smaller sample sizes require each subject to be scanned longer.
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From the sample size estimates, 20 participants per group (adolescent/adults) was recommended.
“From this 20 participants per group (adolescent/adults) was recommended.”
From Multimodal evidence for delayed threat extinction learning in adolescence and young adulthood (2019) · cites this paper as [56]
The sample size was determined using sample estimates for group fMRI based on 80% power.
“The sample size was determined using sample estimates for group fMRI based on 80% power56.”
From Multimodal evidence for delayed threat extinction learning in adolescence and young adulthood (2019) · cites this paper as [56]
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“To this end, a power analysis was performed using the fMRIpower software package (fMRIpower.org; Mumford & Nichols, 2008).”
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Self-citationRevealing the Neuroimaging Mechanism of Acupuncture for Poststroke Aphasia: A Systematic Review
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- Authors
- Mumford, J. A., & Nichols, T. E.
- Affiliations
- University of California, Los Angeles (US)
- Hammersmith Hospital (GB)
- Corresponding author
- Mumford, Jeanette A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 39
- Issue
- 1
- Pages
- 261-268
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2423281
- OpenAlex
- W1967252112
- Cited by
- 252
- References
- 22
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 8 · 2023: 12 · 2022: 18 · 2021: 17 · 2020: 29 · 2019: 21 · 2018: 20 · 2017: 19 · 2016: 11 · 2015: 10 · 2014: 7 · 2013: 17 · 2012: 10
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01 DA15410, R01 EB004346-01A1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- statistical power analysis, sample size determination, temporal autocorrelation, research design, neuroimaging
- MeSH terms
- Adult, Algorithms, Brain Mapping, Computer Simulation, Evoked Potentials, Auditory, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Sensitivity and Specificity, Speech Perception, Statistics as Topic, Reproducibility of Results
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