Robust Group Comparison Using Non-Parametric Block-Based Statistics

TL;DR

This paper introduces a robust non-parametric block-based statistical method for group comparison in brain imaging, effectively handling registration imperfections and small sample sizes to improve detection of structural differences.

Contribution

The proposed block-based statistic (BBS) method combines block matching with permutation testing to enhance robustness in voxel-based analysis under imperfect registration and limited data.

Findings

BBS significantly improves statistical power in synthetic data.

BBS effectively detects differences in real diffusion MR data.

Method outperforms traditional voxel-wise analysis under challenging conditions.

Abstract

Voxel-based analysis methods localize brain structural differences by performing voxel-wise statistical comparisons on two groups of images aligned to a common space. This procedure requires highly accurate registration as well as a sufficiently large dataset. However, in practice, the registration algorithms are not perfect due to noise, artifacts, and complex structural variations. The sample size is also limited due to low disease prevalence, recruitment difficulties, and demographic matching issues. To address these issues, in this paper, we propose a method, called block-based statistic (BBS), for robust group comparison. BBS consists of two major components: Block matching and permutation test. Specifically, based on two group of images aligned to a common space, we first perform block matching so that structural misalignments can be corrected. Then, based on results given by block matching, we conduct robust non-parametric statistical inference based on permutation test. Extensive experiments were performed on synthetic data and the real diffusion MR data of mild cognitive impairment patients. The experimental results indicate that BBS significantly improves statistical power, notwithstanding the small sample size.