The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
False discovery rate revisited: FDR and topological inference using Gaussian random fields
Abstract
In this note, we revisit earlier work on false discovery rate (FDR) and evaluate it in relation to topological inference in statistical parametric mapping. We note that controlling the false discovery rate of voxels is not equivalent to controlling the false discovery rate of activations. This is a problem that is unique to inference on images, in which the underlying signal is continuous (i.e., signal which does not have a compact support). In brief, inference based on conventional voxel-wise FDR procedures is not appropriate for inferences on the topological features of a statistical parametric map (SPM), such as peaks or regions of activation. We describe the nature of the problem, illustrate it with some examples and suggest a simple solution based on controlling the false discovery rate of connected excursion sets within an SPM, characterised by their volume.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Self-citation
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Müller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., Radua, J., Mataix-Cols, D., Tench, C. R., Yarkoni, T., Nichols, T. E., Turkeltaub, P. E., et al. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.012 · Cited by 805
Eickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., Bzdok, D., & Eickhoff, C. R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.072 · Cited by 683
“As previously pointed out, voxel-wise FDR correction, the type considered here and almost exclusively used in ALE analysis is not appropriate for inference on topological features such as regions of significant convergence of a smooth dataset (Chumbley and Friston, 2009).”
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Generative modeling of brain maps with spatial autocorrelation
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A., & Murray, J. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117038 · Cited by 528
Topological FDR for neuroimaging
Chumbley, J., Worsley, K., Flandin, G., & Friston, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.090 · Cited by 492
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Metadata
- Authors
- CHUMBLEY, J., & FRISTON, K.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-01-01
- Volume
- 44
- Issue
- 1
- Pages
- 62-70
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2087880221
- Cited by
- 464
- References
- 2
- FWCI
- 9.58
- Type
- journal article
- Language
- en
- Open access
- closed
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