Valid population inference for information-based imaging: From the second-level t -test to prevalence inference
Open accessAbstract
In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a t-test vs chance level across subjects. We argue that while the random-effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes, rendering it effectively equivalent to fixed-effects analysis. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.
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The nonparametric permutation approach used for group-level hypothesis testing does not depend on assumptions about the distribution of classification accuracies.
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- Authors
- Allefeld, C., Görgen, K., & Haynes, J.-D.
- Affiliations
- Bernstein Center for Computational Neuroscience Berlin (DE)
- Humboldt-Universität zu Berlin (DE)
- Corresponding author
- Allefeld, Carsten
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 141
- Pages
- 378-392
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2469334922
- Cited by
- 157
- References
- 86
- Citations by year
- 2026: 11 · 2025: 7 · 2024: 12 · 2023: 7 · 2022: 16 · 2021: 24 · 2020: 22 · 2019: 24 · 2018: 17 · 2017: 12 · 2016: 4
- FWCI
- 12.09
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- German Research Foundation (GRK1589/1, FK:JA945/3-1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- population inference, multivariate pattern analysis, classification accuracy, minimum statistics
- MeSH terms
- Brain, Brain Mapping, Computer Simulation, Data Interpretation, Statistical, Humans, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Multivariate Analysis
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