Notip: Non-parametric True Discovery Proportion control for brain imaging

TL;DR

Notip is a non-parametric method for brain imaging that provides statistically valid estimates of the proportion of activated voxels within clusters, improving detection power over existing approaches by leveraging randomization techniques.

Contribution

This paper introduces Notip, a novel non-parametric approach for true discovery proportion control in brain imaging, overcoming limitations of parametric methods with tighter, data-adaptive guarantees.

Findings

Notip achieves higher detection rates than state-of-the-art methods.

It provides valid estimates of activated voxel proportions within clusters.

Numerical experiments on 36 fMRI datasets demonstrate its effectiveness.

Abstract

Cluster-level inference procedures are widely used for brain mapping. These methods compare the size of clusters obtained by thresholding brain maps to an upper bound under the global null hypothesis, computed using Random Field Theory or permutations. However, the guarantees obtained by this type of inference - i.e. at least one voxel is truly activated in the cluster - are not informative with regards to the strength of the signal therein. There is thus a need for methods to assess the amount of signal within clusters; yet such methods have to take into account that clusters are defined based on the data, which creates circularity in the inference scheme. This has motivated the use of post hoc estimates that allow statistically valid estimation of the proportion of activated voxels in clusters. In the context of fMRI data, the All-Resolutions Inference framework introduced in [25] provides post hoc estimates of the proportion of activated voxels. However, this method relies on parametric threshold families, which results in conservative inference. In this paper, we leverage randomization methods to adapt to data characteristics and obtain tighter false discovery control. We obtain Notip, for Non-parametric True Discovery Proportion control: a powerful, non-parametric method that yields statistically valid guarantees on the proportion of activated voxels in data-derived clusters. Numerical experiments demonstrate substantial gains in number of detections compared with state-of-the-art methods on 36 fMRI datasets. The conditions under which the proposed method brings benefits are also discussed.