ArticleHuman brain mapping2019
Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Motor signs in late life depression relate to altered subcortical synaptic density and gray matter volume: anNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Identification of Regions of Interest in Neuroimaging Data With Irregular Boundary Based on Semiparametric Transformation Models and Interval-Censored Outcomes.Statistics in medicine · 2025Article
- Estimation of the Intracranial Volume Is Crucial in Multi-Site Studies: Reliability for Longitudinal Investigations and Traveling Subjects.Human brain mapping · 2025Article
- Longitudinal behavioral and neuropsychiatric changes and their MRI correlates in predementiaJournal of Alzheimer's disease : JAD · 2025Article
- A Latent-Class Model for Time-To-Event Outcomes and High-Dimensional Imaging Data.Statistics in medicine · 2025Article
- Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease.Statistics in medicine · 2025Article
- Better cardiovascular health is associated with slowed clinical progression in autosomal dominant frontotemporal lobar degeneration variant carriers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Vascular Disease Is Associated With Differences in Brain Structure and Lower Cognitive Functioning in Inflammatory Bowel Disease: A Cross-Sectional Study.Inflammatory bowel diseases · 2024Article
- Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.Frontiers in neuroscience · 2024Article
- Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies.PloS one · 2024Article
- A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.PeerJ · 2024Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- The cranial capacity of the Saudi population measured using 3D computed tomography scans.Neurosciences (Riyadh, Saudi Arabia) · 2023Article
- Inflammatory Markers and Brain Volume in Patients with Post-traumatic Stress Disorder.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2023Article
- White-matter abnormalities in presymptomaticBrain communications · 2023Article
- Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.Neurobiology of aging · 2023Article
- Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum.Journal of Alzheimer's disease : JAD · 2023Article
- A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age.Human brain mapping · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
When analyzing large multicenter databases, the effects of multiple confounding covariates increase the variability in the data and may reduce the ability to detect changes due to the actual effect of interest, for example, changes due to disease. Efficient ways to evaluate the effect of covariates toward the data harmonization are therefore important. In this article, we showcase techniques to assess the "goodness of harmonization" of covariates. We analyze 7,656 MR images in the multisite, multiscanner Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We present a comparison of three methods for estimating total intracranial volume to assess their robustness and correct the brain structure volumes using the residual method and the proportional (normalization by division) method. We then evaluated the distribution of brain structure volumes over the entire ADNI database before and after accounting for multiple covariates such as total intracranial volume, scanner field strength, sex, and age using two techniques: (a) Zscapes, a panoramic visualization technique to analyze the entire database and (b) empirical cumulative distributions functions. The results from this study highlight the importance of assessing the goodness of data harmonization as a necessary preprocessing step when pooling large data set with multiple covariates, prior to further statistical data analysis.
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