False discovery rate revisited: FDR and topological inference using Gaussian random fields

By Justin R. Chumbley, Karl J. Friston. NeuroImage, 2009

485 citations recorded by the source corpus.

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.

Subfields: Biophysics, Computational Theory and Mathematics, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/85fb198635415207241fafc25fe90c011f18e1c3

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Citations in the corpus

Behavior, Sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation (2016, unknown): “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).”
Topological complexity of smooth random functions (2011, result): “We, of course, are partial to recent comparative studies [23, 24] which favour the random field approach described above.”
Topological FDR for neuroimaging (2010, unknown): “A recent communication (Chumbley and Friston, 2009) proposed a solution based upon conditional p-values associated with the spatial extent of excursion sets above an arbitrary threshold (conditional on such a set existing).”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “The FDR adjustment used in our example analysis is straight-forward, but a limitation is that it does not incorporate the relation between time points (Chumbley & Friston, 2009).”
An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling (2016, unknown): “However, the presence of spatial autocorrelation of the noise has been well described in the MRI literature, see for example (Chumbley and Friston, 2009) for fMRI or (Wang et al., 2003b) for ASL, and cannot be disregarded.”
of Serendipitous and Unexpected Results Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon : An Argument For Proper Multiple Comparisons Correction (2010, unknown): “In the future other methods such as topological FDR (Chumbley and Friston, 2009) have the potential to further improve false positive control while minimizing the impact on statistical power.”
Reward reduces conflict by enhancing attentional control and biasing visual cortical processing (2011, background): “As illustrated by Chumbley and Friston (2009), this approach reduces the likelihood that false discovery rate correction will reveal spurious activations.”
Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: an argument for multiple comparisons correction (2009, methodology): “In the future other methods such as topological FDR (Chumbley and Friston, 2009) have the potential to further improve false positive control while minimizing the impact on statistical power.”
Ten simple rules for neuroimaging meta-analysis (2017, background): “However, it has been argued that this correction approach is not adequate for topographic inference on smooth data (Chumbley and Friston, 2009), which also includes neuroimaging meta-analysis data.”
Quantifying and exploring camouflaging in men and women with autism (2016, methodology): “Multiple comparison correction was performed at the cluster level by controlling topological false discovery rate (FDR) calculated under Gaussian Random Field Theory (Chumbley and Friston, 2009), using a cluster-forming voxel-level height threshold of p < 0.”
Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control (2011, unknown): “…tests at proximate spatial locations are known to be interdependent, mainly due to two facts: first, the BOLD effects of interest are spatially widespread over several voxels (see Chumbley and Friston, 2009); second, local MVPA approaches introduce spatial correlations in the analyzing procedure.”
Activation Likelihood Estimation meta-analysis revisited (2011, background): “In this context, it is important to appreciate that cluster-level inference stand in stark contrast to FDR and voxel-level FDR correction as described above by operating on sets of voxels rather than individual voxels (cf. Chumbley and Friston, 2009).”
Generative modeling of brain maps with spatial autocorrelation (2020, unknown): “Physiological signals which smoothly vary across anatomical space will produce autocorrelated images: BOLD signals, for example, are driven by hemodynamic changes in oxygenated blood which diffuse rapidly throughout the brain (Chumbley and Friston, 2009).”