Robust-ComBat: Mitigating Outlier Effects in Diffusion MRI Data Harmonization

TL;DR

Robust-ComBat introduces a neural network-based method to effectively mitigate the impact of outliers caused by neurological disorders in diffusion MRI data harmonization, improving accuracy over traditional approaches.

Contribution

This paper presents Robust-ComBat, a novel outlier compensation technique using an MLP that enhances harmonization accuracy in the presence of pathological outliers.

Findings

Robust-ComBat outperforms traditional methods in harmonization error reduction.

The method maintains disease-related signals while removing site effects.

It is effective even with up to 80% of subjects having neurological disorders.

Abstract

Harmonization methods such as ComBat and its variants are widely used to mitigate diffusion MRI (dMRI) site-specific biases. However, ComBat assumes that subject distributions exhibit a Gaussian profile. In practice, patients with neurological disorders often present diffusion metrics that deviate markedly from those of healthy controls, introducing pathological outliers that distort site-effect estimation. This problem is particularly challenging in clinical practice as most patients undergoing brain imaging have an underlying and yet undiagnosed condition, making it difficult to exclude them from harmonization cohorts, as their scans were precisely prescribed to establish a diagnosis. In this paper, we show that harmonizing data to a normative reference population with ComBat while including pathological cases induces significant distortions. Across 7 neurological conditions, we evaluated 10 outlier rejection methods with 4 ComBat variants over a wide range of scenarios, revealing that many filtering strategies fail in the presence of pathology. In contrast, a simple MLP provides robust outlier compensation enabling reliable harmonization while preserving disease-related signal. Experiments on both control and real multi-site cohorts, comprising up to 80% of subjects with neurological disorders, demonstrate that Robust-ComBat consistently outperforms conventional statistical baselines with lower harmonization error across all ComBat variants.