Partial Conjunction Analysis in Neuroimaging: A Comparative Study

TL;DR

This study compares statistical methods for partial conjunction hypothesis testing in neuroimaging, focusing on their performance in identifying consistent brain activation patterns across subjects or tasks.

Contribution

It provides a comparative analysis of three PC hypothesis testing methods in neuroimaging, highlighting their strengths under different conditions.

Findings

BHY outperforms others at high gamma in simulated data.

CoFilter performs best at low gamma in simulated data.

CoFilter dominates at intermediate gamma in real data.

Abstract

Replicability is a cornerstone of science. The partial conjunction (PC) hypothesis testing framework objectively quantifies replicability across disciplines. Although several statistical methodologies for testing PC hypotheses exist, it is not clear which method performs well under which circumstances. In this paper, we consider the PC hypothesis testing problem from a neuroimaging perspective. Identifying the brain regions activated by a specific cognitive task constitutes a central challenge in neuroimaging. This problem becomes complex when the objective is to evaluate whether activation patterns are consistent across different cognitive tasks or subjects. In this paper, we cast this question as a PC hypothesis testing problem, assessing, for each location in the brain, whether it is activated in at least $\gamma$ subjects, for a pre-specified granularity $\gamma$. In our comparative study, we consider three methods, namely: adaFilter, CoFilter, and a method proposed by Benjamini, Heller, and Yekutieli (BHY). In equi-correlated simulated data, the BHY procedure tends to outperform the competing methods for high values of $\gamma$, while CoFilter performs well for low values of $\gamma$. In the real-data analysis, CoFilter dominates the other methods for intermediate values of $\gamma$.