ArticleHuman brain mapping2022
Mitigating site effects in covariance for machine learning in neuroimaging data.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed.
- Macro-Micro structural integration for characterizing severity-related structural alterations in mild cognitive impairment.NeuroImage. Clinical · 2026Article
- Intrinsic timescale develops hierarchically from sensorimotor to association cortex in youth and stabilizes in adulthood.PLoS biology · 2026Article
- Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity.Nature human behaviour · 2026Article
- Personality Heterogeneity Shapes the Neural Organization of Conditioned Threat Learning.Research square · 2026Article
- Benchmarking fMRI Denoising Pipelines.Human brain mapping · 2026Article
- HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders.bioRxiv : the preprint server for biology · 2026Article
- RiTex: Harmonization of Radiomic Features Based on Riemannian Geometry.Journal of imaging · 2026Article
- Batch Effect Correction for Neuroimaging Data with Heterogeneous Spatial Correlations.bioRxiv : the preprint server for biology · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites.Human brain mapping · 2026Article
- White matter reflects the childhood exposome.bioRxiv : the preprint server for biology · 2026Article
- Normative Modeling of Static and Dynamic Functional Connectivity.bioRxiv : the preprint server for biology · 2026Article
- Residual Conditional Variational Autoencoder for Multi-Center PET/CT Radiomic Feature Harmonization with Integrated Modeling of Batch Effects and Clinical Covariates.Journal of imaging informatics in medicine · 2026Article
- Correspondence of large-scale functional brain network decline across aging mice and humans.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Unified Multi-Cohort Harmonisation and Normative Modelling of Neuroimaging Data via Hierarchical GAMLSS.bioRxiv : the preprint server for biology · 2026Article
- MV-ComBat and MV-CovBat: Multivariate Frameworks for Joint Harmonization of Multi-Metric Neuroimaging Data.bioRxiv : the preprint server for biology · 2026Article
- Two axes of white matter development.Nature communications · 2026Article
- Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction.Biostatistics (Oxford, England) · 2026Article
- Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain Data.medRxiv : the preprint server for health sciences · 2026Article
- Systematic fMRI signal differences across cohorts alter lifespan trajectories of functional brain networks.Imaging neuroscience (Cambridge, Mass.) · 2026Article
36 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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Abstract
To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.
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