ArticleHuman brain mapping2024
DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.
Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC.bioRxiv : the preprint server for biology · 2026Article
- Article
- Category-ComBat with distribution coefficient: a scale-adaptive harmonization algorithm for multicenter radiomic feature standardization.Frontiers in oncology · 2026Article
- Calibration of MRI-based reference intervals to new samples.bioRxiv : the preprint server for biology · 2025Article
- Challenges and best practices when using ComBAT to harmonize diffusion MRI data.Scientific reports · 2025Article
- Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.Neuroinformatics · 2025Article
- Article
- ComBatLS: A Location- and Scale-Preserving Method for Multi-Site Image Harmonization.Human brain mapping · 2025Article
- HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.GigaScience · 2025Article
- When no answer is better than a wrong answer: A causal perspective on batch effects.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Thinking points for effective batch correction on biomedical data.Briefings in bioinformatics · 2024Article
- DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.bioRxiv : the preprint server for biology · 2024Article
- DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.Human brain mapping · 2024Article
- DPABI harmonization: A toolbox for harmonizing multi-site brain imaging for big-data era.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies.PloS one · 2024Article
- Calibration of MRI-based reference intervals to new samples.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.