Towards a faster randomized parcellation based inference

Abstract

In neuroimaging, multi-subject statistical analysis is an essential step, as it makes it possible to draw conclusions for the population under study. However, the lack of power in neuroimaging studies combined with the lack of stability and sensitivity of voxel-based methods may lead to non-reproducible results. A method designed to tackle this problem is Randomized Parcellation-Based Inference (RPBI), which has shown good empirical performance. Nevertheless, the use of an agglomerative clustering algorithm proposed in the initial RPBI formulation to build the parcellations entails a large computation cost. In this paper, we explore two strategies to speedup RPBI: Firstly, we use a fast clustering algorithm called Recursive Nearest Agglomeration (ReNA), to find the parcellations. Secondly, we consider the aggregation of p-values over multiple parcellations to avoid a permutation test. We evaluate their the computation time, as well as their recovery performance. As a main conclusion, we advocate the use of (permuted) RPBI with ReNA, as it yields very fast models, while keeping the performance of slower methods.