ArticleHuman brain mapping2018
Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging.
Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 101 citations in OpenAlex.
- Vascular brain injury modifies the relationship between sleep duration, cognition, and white matter hyperintensity burden in the Alzheimer's disease continuum.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Longitudinal changes in control energy of brain networks in older adults at familial risk for Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
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- Association between cardiovascular disease risk factors and white matter lesions: The Tohoku Medical Megabank Cohort Study.Journal of atherosclerosis and thrombosis · 2026Article
- Amyloid and tau pathologies are drivers of white matter damage in aging and Alzheimer's disease.GeroScience · 2026Article
- Cardiovascular risk factors, aging, and incidence of dementia (CAIDE) risk score and its association with cognitive performance and volumetric brain measures in mild cognitive impairment.IBRO neuroscience reports · 2026Article
- The influence of vascular risk factors and aging on choroid plexus enlargement.European radiology · 2026Article
- Characterizing spatiotemporal white matter hyperintensity pathophysiology in vivo to disentangle vascular and neurodegenerative contributions.Nature communications · 2026Article
- Age-inappropriate white matter injury reveals hidden cerebral small vessel disease burden and mortality risk.Cerebral circulation - cognition and behavior · 2026Article
- Amyloid and tau pathologies are drivers of white matter damage in aging and Alzheimer's disease.medRxiv : the preprint server for health sciences · 2025Article
- Vascular risk factors mediate the relationship between education and white matter hyperintensities.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Quantifying brain atrophy in Frontotemporal Dementia: a head-to-head comparison of neuroimaging techniques.medRxiv : the preprint server for health sciences · 2025Article
- Impact of white matter hyperintensities on α4β2 nicotinic acetylcholine receptor binding in the human brain.European journal of nuclear medicine and molecular imaging · 2025Article
- Association Between Small Vessel Disease and Migraine: A Study Based on Peak Width of Skeletonized Mean Diffusivity.Journal of clinical neurology (Seoul, Korea) · 2025Article
- Article
- Racial and ethnic differences in white matter hypointensities: The role of vascular risk factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Domain-specific longitudinal associations between brain volume, white matter lesions, and cognitive function changes.Heliyon · 2025Article
- Apathy progression is associated with brain atrophy and white matter damage in Parkinson's disease.Brain communications · 2025Article
- Cerebrovascular markers of WMH and infarcts in ADNI: A historical perspective and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- QRATER: a collaborative and centralized imaging quality control web-based application.Aperture neuro · 2024Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
introductionFluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints.
methodsIn this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information.
resultsThe WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r = .743, p < .001, ADNI2/GO: r = .904, p < .001), segmentations obtained from T1w + T2w + PD for ADNI1 (r = .888, p < .001) and T1w + FLAIR for ADNI2/GO (r = .969, p < .001), age (ADNI1: r = .391, p < .001, ADNI2/GO: r = .466, p < .001), and ADAS13 (ADNI1: r = .227, p < .001, ADNI2/GO: r = .190, p < 0.001), and NPI (ADNI1: r = .290, p < .001, ADNI2/GO: r = 0.144, p < .001), controlling for age.
conclusionOur results suggest that while T2w/PD and FLAIR provide more accurate estimates of the true WMHLs, T1w-Only segmentations can still provide estimates that hold strong correlations with the actual WMHLs, age, and performance on various cognitive/clinical scales, giving added value to datasets where T2w/PD or FLAIR are not available.
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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.