Editorial for the Special Issue on Harmonization Techniques for MRI

Clinical Snapshot

Key Findings

• P Value: Not applicable — no statistical testing performed

• Effect Size: Not applicable — no effect sizes reported

• Primary Outcome: No primary outcome reported — this is a narrative editorial with no original data analysis

• Nnt Or Sensitivity: Not applicable — no diagnostic, therapeutic, or prognostic metrics reported

• Confidence Interval: Not applicable — no confidence intervals reported

Clinical Application

The harmonization frameworks described (particularly ComBat-family methods) are already implemented in open-source software and are feasible for research groups with biostatistical support. Deep learning approaches require greater computational infrastructure and expertise. Implementation in routine clinical radiology workflows remains aspirational rather than established practice. Australian neuroimaging consortia — including those operating under the Australian Imaging Biomarkers and Lifestyle (AIBL) study, the Lifespan Human Connectome Project collaborations, and state-based dementia research networks — face precisely the multi-site harmonization challenges described. The TGA does not currently regulate harmonization software as a medical device in most research contexts, though clinical deployment of AI-based neuroimaging tools would require TGA conformity assessment. RACGP and RANZCR guidelines do not yet address harmonization standards for clinical neuroimaging. The National Imaging Facility (NIF) provides a relevant infrastructure context for Australian researchers seeking standardised acquisition protocols as an upstream alternative to post-hoc harmonization. Researchers and clinicians involved in multi-site neuroimaging studies, including those conducting large-scale brain morphometry, functional connectivity, diffusion tensor imaging, or connectome analyses across heterogeneous MRI platforms. Relevant to neurologists, psychiatrists, radiologists, and clinical neuroscientists aggregating data across institutions.

Abstract

This editorial introduces the special issue on neuroimaging harmonization and situates its contributions within the broader methodological landscape of multi-site MRI analysis. As large-scale neuroimaging studies continue to aggregate data across scanners, protocols, and institutions, harmonization has become essential for reducing non-biological variability while preserving meaningful biological signals. We review the major classes of harmonization approaches, including statistical methods based on the ComBat family and deep learning methods that operate at the voxel level. We also review domain generalization strategies designed for previously unseen sites, and network-aware harmonization techniques that go beyond the voxel for connectivity and connectome data. Across these developments, several cross-cutting challenges emerge, including modality-specific performance, preservation of biological information, validation using traveling-subjects and large observational datasets, and the need for scalable, standardized, and privacy-preserving frameworks. Collectively, the articles in this special issue illustrate the rapid progress of the field and highlight that robust harmonization will be critical for enabling reproducible and generalizable discoveries in multi-site neuroimaging.

References

1. 1.Zuo, L., Liu, Y., & Carass, A. (2026). Editorial for the Special Issue on Harmonization Techniques for MRI. NeuroImage. https://doi.org/10.1016/j.neuroimage.2026.121979