Exact Combinatorial Inference for Brain Images

TL;DR

This paper introduces a novel combinatorial inference method that exhaustively enumerates all permutations without resampling, enabling exact statistical testing for brain imaging data.

Contribution

It presents a new combinatorial approach for exact permutation testing that overcomes computational limitations of traditional methods.

Findings

Validated against standard permutation tests in simulations

Applied to twin DTI data to assess genetic influence on brain connectivity

Achieved exact inference without resampling

Abstract

The permutation test is known as the exact test procedure in statistics. However, often it is not exact in practice and only an approximate method since only a small fraction of every possible permutation is generated. Even for a small sample size, it often requires to generate tens of thousands permutations, which can be a serious computational bottleneck. In this paper, we propose a novel combinatorial inference procedure that enumerates all possible permutations combinatorially without any resampling. The proposed method is validated against the standard permutation test in simulation studies with the ground truth. The method is further applied in twin DTI study in determining the genetic contribution of the minimum spanning tree of the structural brain connectivity.