Anatomical Mapping of White Matter Hyperintensities (WMH)

DeCarli, Charles; Fletcher, Evan; Ramey, Vincent H.; Harvey, Danielle; Jagust, William J.

doi:10.1161/01.str.0000150668.58689.f2

Cited by 441 publications

(280 citation statements)

References 57 publications

Supporting

Mentioning

265

Contrasting

Unclassified

Order By: Relevance

“…We did not treat periventricular and deep WMH separately, as has been done in some previous studies, because these have been shown to be highly correlated [e.g. DeCarli et al, 2005 found the correlation to be around r = 0 .9] and their division may be arbitrary: for example, periventricular WMH are often contiguous with superior deep WMH.…”

Section: Methodsmentioning

confidence: 99%

Brain volumetric changes and cognitive ageing during the eighth decade of life

Ritchie

Dickie

Cox

et al. 2015

Human Brain Mapping

View full text Add to dashboard Cite

Later‐life changes in brain tissue volumes—decreases in the volume of healthy grey and white matter and increases in the volume of white matter hyperintensities (WMH)—are strong candidates to explain some of the variation in ageing‐related cognitive decline. We assessed fluid intelligence, memory, processing speed, and brain volumes (from structural MRI) at mean age 73 years, and at mean age 76 in a narrow‐age sample of older individuals (n = 657 with brain volumetric data at the initial wave, n = 465 at follow‐up). We used latent variable modeling to extract error‐free cognitive levels and slopes. Initial levels of cognitive ability were predictive of subsequent brain tissue volume changes. Initial brain volumes were not predictive of subsequent cognitive changes. Brain volume changes, especially increases in WMH, were associated with declines in each of the cognitive abilities. All statistically significant results were modest in size (absolute r‐values ranged from 0.114 to 0.334). These results build a comprehensive picture of macrostructural brain volume changes and declines in important cognitive faculties during the eighth decade of life. Hum Brain Mapp 36:4910–4925, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc

show abstract

Section: Methodsmentioning

confidence: 99%

Brain volumetric changes and cognitive ageing during the eighth decade of life

Ritchie

Dickie

Cox

et al. 2015

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…We made no distinction between deep and periventricular WML, as it has been shown that deep, periventricular, and total WML are highly correlated with each other (DeCarli et al, 2005). Furthermore, we chose to use total WML volume, as it has been suggested that categorical distinctions between periventricular and deep WML are arbitrary (DeCarli et al, 2005). According to current guidelines (Hachinski et al, 2006), WML volumes were normalized for ICV to correct for differences in head size.…”

Section: Assessment Of White Matter Lesionsmentioning

confidence: 99%

Total Cerebral Blood Flow, White Matter Lesions and Brain Atrophy: The SMART-MR Study

Appelman

Graaf

Vincken

et al. 2007

J Cereb Blood Flow Metab

View full text Add to dashboard Cite

We investigated whether total cerebral blood flow (CBF) was associated with brain atrophy, and whether this relation was modified by white matter lesions (WML). Within the Second Manifestations of ARTerial disease-magnetic resonance (SMART-MR) study, a prospective cohort study among patients with arterial disease, cross-sectional analyses were performed in 828 patients (mean age 58 610 years, 81% male) with quantitative flow, atrophy, and WML measurements on magnetic resonance imaging (MRI). Total CBF was measured with MR angiography and was expressed per 100 mL brain volume. Total brain volume and ventricular volume were divided by intracranial volume to obtain brain parenchymal fraction (BPF) and ventricular fraction (VF). Lower BPF indicates more global brain atrophy, whereas higher VF indicates more subcortical brain atrophy. Mean CBF was 52.0610.2 mL/min per 100 mL, mean BPF was 79.262.9%, and mean VF was 2.0360.96%. Linear regression analyses showed that lower CBF was associated with more subcortical brain atrophy, after adjusting for age, sex, vascular risk factors, intima-media thickness, and lacunar infarcts, but only in patients with moderate to severe WML (upper quartile of WML): Change in VF per s.d. decrease in CBF 0.18%, 95% CI: 0.02 to 0.34%. Our findings suggest that cerebral hypoperfusion in the presence of WML may be associated with subcortical brain atrophy.

show abstract

“…For example, in (Swartz, et al 2002), a 3D classification algorithm was applied to separate DWMHs from PVWMHs. Other investigators have used nonlinear image registration methods to convert the WMHs across subjects into a standard space (Taylor, et al 2003;DeCarli, et al 2005). …”

Section: Introductionmentioning

confidence: 99%

A fully automated method for quantifying and localizing white matter hyperintensities on MR images

Rosano

Butters

et al. 2006

Psychiatry Research: Neuroimaging

179

165

View full text Add to dashboard Cite

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies e.g. (Taylor, et al. 2003)), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large neuroimage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.

show abstract

Anatomical Mapping of White Matter Hyperintensities (WMH)

Abstract: Background and Purpose-MRI segmentation and mapping techniques were used to assess evidence in support of categorical distinctions between periventricular white matter hyperintensities (PVWMH) and deep WMH (DWMH

Cited by 441 publications

References 57 publications

Brain volumetric changes and cognitive ageing during the eighth decade of life

Brain volumetric changes and cognitive ageing during the eighth decade of life

Total Cerebral Blood Flow, White Matter Lesions and Brain Atrophy: The SMART-MR Study

A fully automated method for quantifying and localizing white matter hyperintensities on MR images

Contact Info

Product

Resources

About