BARTharm

BARTharm#

Paper

Prevot E, et al., (2025). BARTharm: MRI Harmonization Using Image Quality Metrics and Bayesian Non-parametric. bioRxiv. Published online 2025. doi:10.1101/2025.06.04.657792 https://www.biorxiv.org/content/10.1101/2025.06.04.657792v1

Source code

Overview#

BARTharm is a statistical harmonization framework for MRI-derived phenotypes (IDPs) that removes scanner-induced variability while preserving biological signal.

Unlike traditional methods (e.g., ComBat), BARTharm:

  • does not rely on discrete scanner/site labels

  • leverages Image Quality Metrics (IQMs) as continuous proxies of acquisition variability

  • uses Bayesian Additive Regression Trees (BART) to model complex, non-linear effects

This enables:

  • modeling continuous scanner variation

  • capturing within-scanner heterogeneity

  • harmonizing unseen or anonymized datasets

The method jointly models:

  • biological signal

  • scanner-related variation

within a unified Bayesian framework.

BARTharm is a fully data-driven harmonization framework with clear advantages in scenarios such as model misspecification or when scanner-related variables are correlated with biological covariates, where standard methods can lead to inflated false positive rates (FPR). By flexibly modeling scanner effects as non-linear functions of IQMs, BARTharm reduces residual acquisition-related variability that would otherwise bias downstream analyses, resulting in better-calibrated inference and more reliable detection of true biological effects.

Method Summary#

BARTharm decomposes each IDP as:

\[ y = \mu(\text{IQMs}) + \tau(\text{biological covariates}) + \epsilon \]

  • ฮผ(ยท) โ†’ scanner-related effects (learned from IQMs)

  • ฯ„(ยท) โ†’ biological signal

  • ฮต โ†’ noise

Both components are modeled using independent BART ensembles, allowing:

  • non-linear effects

  • high-order interactions

  • fully data-driven learning

Harmonized data is obtained by removing the estimated scanner component:

\[ \hat{y} = y - \hat{\mu} \]

This avoids the restrictive location-scale assumptions of classical approaches like ComBat.

Above is the homoskedastic version, which captures scanner effects in the mean structure through flexible, non-linear functions of IQMs, without requiring scanner or site labels. This allows the model to account for complex, continuous acquisition variability and within-scanner heterogeneity.

There is also a heteroskedastic version, which extends the model to account for scanner-specific differences in variance. In this setting, the residual variance is allowed to vary across scanners (when available), introducing a multiplicative scaling term that captures differences in noise levels and reliability across acquisition settings. The resulting harmonization removes both additive (mean) and multiplicative (variance) scanner effects, while preserving the estimated biological signal.

Key Advantages#

  • No reliance on scanner IDs

  • Works with missing or anonymized metadata

  • Handles non-linear scanner effects

  • Captures continuous acquisition variability

  • Naturally extends to unseen datasets

  • Provides uncertainty quantification via Bayesian inference

Compared to standard harmonization:

  • ComBat assumes linear additive + multiplicative effects

  • BARTharm learns flexible functions directly from data

Implementation#

  • To be implemented.