Topological FDR for neuroimaging

By Justin R. Chumbley, Keith J. Worsley, Guillaume Flandin, Karl J. Friston. NeuroImage, 2010

476 citations recorded by the source corpus.

In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.

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

View Paper: https://www.semanticscholar.org/paper/493fb60f449846b4b6a38fb951e989d75dc64593

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. École d'été de probabilités de Saint-Flour
2. Jonathan E. Taylor
3. Robert J. Adler

In Computational Theory and Mathematics

1. École d'été de probabilités de Saint-Flour
2. Jonathan E. Taylor
3. Robert J. Adler
4. Henry Lam

In Geometry and Topology

1. Jonathan E. Taylor
2. Robert J. Adler
3. École d'été de probabilités de Saint-Flour
4. Keith J. Worsley
5. Henry Lam
6. Thorsten Dickhaus
7. Mario Wschebor
8. Jean-Marc Azaïs

Citations in the corpus

• Counterfactual thinking: an fMRI study on changing the past for a better future. (2013, methodology): “The data were already preprocessed with SPM5 when we switched to SPM8 in order to apply peak-level correction instead of voxel-level correction (Chumbley et al., 2010).”
• 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.”