Anatomical mapping of white matter hyperintensities (WMH) - Exploring the relationships between periventricular WMH, deep WMH, and total WMH burden

Department of Neurology, University of California at Davis, Davis, Calif, USA.
Stroke (Impact Factor: 5.72). 02/2005; 36(1):50-5. DOI: 10.1161/01.STR.0000150668.58689.f2
Source: PubMed


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). Qualitative MRI studies generally identify 2 categories of WMH on the basis of anatomical localization. Separate pathophysiologies and behavioral consequences are often attributed to these 2 classes of WMH. However, evidence to support these empirical distinctions has not been rigorously sought.
MRI analysis of 55 subjects included quantification of WMH volume, mapping onto a common anatomical image, and spatial localization of each WMH voxel. WMH locations were then divided into PVWMH and DWMH on the basis of distance from the lateral ventricles and correlations, with total WMH volume determined. Periventricular distance histograms of WMH voxels were also calculated.
PVWMH and DWMH were highly correlated with total WMH (R2>0.95) and with each other (R2>0.87). Mapping of all WMH revealed smooth expansion from around central cerebrospinal fluid spaces into more distal cerebral white matter with increasing WMH volume.
PVWMH, DWMH, and total WMH are highly correlated with each other. Moreover, spatial analysis failed to identify distinct subpopulations for PVWMH and DWMH. These results suggest that categorical distinctions between PVWMH and DWMH may be arbitrary, and conclusions regarding individual relationships between causal factors or behavior for PVWMH and DWMH may more accurately reflect total WMH volume relationships.

  • Source
    • "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 5 0.9] and their division may be arbitrary: for example, periventricular WMH are often contiguous with superior deep WMH. Blinded to all clinical and cognitive details, we manually checked all segmented images for accuracy, corrected errors, and excluded imaging-detected infarcts from WMH volumes. "
    [Show abstract] [Hide abstract]
    ABSTRACT: 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, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc
    Full-text · Article · Sep 2015 · Human Brain Mapping
  • Source
    • "For each individual, WML volumes were estimated using the subcortical FreeSurfer 5.1 segmentation (Fischl et al., 2002; Smith et al., 2011), rank-transformed to address the skewed distribution (Conover and Iman, 1981, 1982), and residualized for total FreeSurfer 5.1-generated intracranial volume (ICV) via regression. The Free- Surfer 5.1-derived WML volumes were validated using fluid attenuated inversion recovery images (available in 77 of 92 cases) with the application of a semi-automated WML segmentation algorithm (DeCarli et al., 2005). There was good agreement between the 2 ranktransformed and ICV-adjusted WML volumes as indicated by an intra-class correlation coefficient of 0.83 (95% CI: 0.73, 0.89). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study used path analysis to examine effects of cognitive activity and physical activity on cognitive functioning in older adults, through pathways involving beta-amyloid (Aβ) burden, cerebrovascular lesions, and neural injury within the brain regions affected in Alzheimer's disease (AD). Ninety-two cognitively normal older adults (75.2 ± 5.6 years) reported lifetime cognitive activity and current physical activity using validated questionnaires. For each participant, we evaluated cortical Aβ burden (using [(11)C] labeled Pittsburgh-Compound-B positron emission tomography), cerebrovascular lesions (using magnetic resonance imaging-defined white matter lesion [WML]), and neural integrity within AD regions (using a multimodal neuroimaging biomarker). Path models (adjusted for age, gender, and education) indicated that higher lifetime cognitive activity and higher current physical activity was associated with fewer WMLs. Lower WML volumes were in turn related to higher neural integrity and higher global cognitive functioning. As shown previously, higher lifetime cognitive activity was associated with lower [(11)C] labeled Pittsburgh-Compound-B retention, which itself moderated the impact of neural integrity on cognitive functioning. Lifestyle activity may thus promote cognitive health in aging by protecting against cerebrovascular pathology and Aβ pathology thought to be relevant to AD development.
    Full-text · Article · Feb 2014 · Neurobiology of aging
  • Source
    • "The use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. The within-scanner variability of global cortical thickness measurements reported in previous studies was 0.03–0.07 in average (DeCarli et al., 2005; Pennanen et al., 2005; Gronenschild et al., 2012). Scanner upgrade did not increase variability nor introduce bias while measurements across field strength were slightly biased (thicker at 3 T). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective: Temporo-parietal cortex thinning is associated to mild cognitive impairment (MCI) due to Alzheimer disease (AD). The increase of EEG upper/low alpha power ratio has been associated with AD-converter MCI subjects. We investigated the association of alpha3/alpha2 ratio with patterns of cortical thickness in MCI. Materials and Methods: Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording and high resolution 3D magnetic resonance imaging. Alpha3/alpha2 power ratio as well as cortical thickness was computed for each subject. Three MCI groups were detected according to increasing tertile values of upper/low alpha power ratio. Difference of cortical thickness among the groups was estimated. Pearson’s r was used to assess the topography of the correlation between cortical thinning and memory impairment. Results: High upper/low alpha power ratio group had total cortical gray matter volume reduction of 471 mm2 than low upper/low alpha power ratio group (p < 0.001). Upper/low alpha group showed a similar but less marked pattern (160 mm2) of cortical thinning when compared to middle upper/low alpha power ratio group (p < 0.001). Moreover, high upper/low alpha group had wider cortical thinning than other groups, mapped to the Supramarginal and Precuneus bilaterally. Finally, in high upper/low alpha group temporo-parietal cortical thickness was correlated to memory performance. No significant cortical thickness differences was found between middle and low alpha3/alpha2 power ratio groups. Conclusion: High EEG upper/low alpha power ratio was associated with temporo-parietal cortical thinning and memory impairment in MCI subjects. The combination of EEG upper/low alpha ratio and cortical thickness measure could be useful for identifying individuals at risk for progression to AD dementia and may be of value in clinical context.
    Full-text · Article · Oct 2013 · Frontiers in Aging Neuroscience
Show more