ArticleNeuroImage2022
Sample size requirement for achieving multisite harmonization using structural brain MRI features.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain structural abnormalities in treatment-resistant schizophrenia.Molecular psychiatry · 2026Pooled it
- Functional Connectome Predicts Cognition and Links White Matter Hyperintensity Burden to Cognitive Impairment Across the Vascular Cognitive Impairment Continuum.Brain sciences · 2026Article
- Data-centric strategies enable cross-center generalization of convolutional neural networks for CT brain extraction.Frontiers in neurology · 2026Article
- An explainable multimodal artificial intelligence model for classifying suicide attempters with borderline personality disorder: a pilot study.Scientific reports · 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
- Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning.EJNMMI physics · 2025Article
- Autism-associated brain differences can be observed in utero using MRI.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.Scientific data · 2024Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.
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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 OpenQuestion graph.