Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults

Abstract

White matter hyperintensities (WMH) are frequently divided into periventricular (PWMH) and deep (DWMH), and the two classes have been associated with different cognitive, microstructural, and clinical correlates. However, although this distinction is widely used in visual ratings scales, how to best anatomically define the two classes is still disputed. In fact, the methods used to define PWMH and DWMH vary significantly between studies, making results difficult to compare. The purpose of this study was twofold: first, to compare four current criteria used to define PWMH and DWMH in a cohort of healthy older adults (mean age: 69.58 ± 5.33 years) by quantifying possible differences in terms of estimated volumes; second, to explore associations between the two WMH sub-classes with cognition, tissue microstructure and cardiovascular risk factors, analysing the impact of different criteria on the specific associations. Our results suggest that the classification criterion used for the definition of PWMH and DWMH should not be considered a major obstacle for the comparison of different studies. We observed that higher PWMH load is associated with reduced cognitive function, higher mean arterial pressure and age. Higher DWMH load is associated with higher body mass index. PWMH have lower fractional anisotropy than DWMH, which also have more heterogeneous microstructure. These findings support the hypothesis that PWMH and DWMH are different entities and that their distinction can provide useful information about healthy and pathological aging processes.

Visceral obesity relates to deep white matter hyperintensities via inflammation

“…Interestingly, the notion that blood pressure and obesity (defined by BMI) are differentially associated with periventricular and deep WMH has been suggested recently using dichotomous WMH markers in the Whitehall study comprising~500 participants. 18…”

“…14 These vascular pathologies eventually lead to tissue damage that can impact brain function, histologically evident as (incomplete or complete) infarcts, demyelination, loss of oligodendrocytes, and/or axonal damage. 18 Also, alcohol intake and smoking were associated with higher WMH probability in selective regions according to a more fine-graded spatial WMH analysis. This issue is of highest relevance given that, so far, modification of vascular risk factors has shown only limited effects to slow the progression of WMH.…”

“…17 A recent study showed that dWMH and pvWMH differ not only in markers of tissue microstructure derived from diffusion weighted imaging, but also in correlated risk factors and cognitive effects. 18 Also, alcohol intake and smoking were associated with higher WMH probability in selective regions according to a more fine-graded spatial WMH analysis. 19 However, spatial distributions of WMH on magnetic resonance imaging (MRI) have largely been neglected.…”

“…37,38 In contrast, one morphological difference between dWMH and pvWMH is the underlying vessel supply, and it has been argued that deep white matter areas, such as the centrum semiovale, fed by medullary arteries arising from the cortical branches of middle cerebral arteries, are more likely to be sensitive to small-vessel disease and ischemia. 18 Considering white matter microstructure, we recently showed that lower fractional anisotropy, as a measure of directed diffusion, was related to visceral obesity in multiple white matter tracts throughout the brain using diffusion-weighted imaging. However, others criticize the dichotomization of WMH and argue for a continuous pathology, 38 and potential molecular pathways that link visceral obesity specifically to deep WMH need to be explored in future studies.…”

A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

“…Griffanti et al [6] proposed a method where correlation amongst images was identified using mean and standard deviation between various features like cognition, tissue microstructure, and so forth. This correlation was able to diagnose similar MR images with white matter disturbances.…”