Heterogeneity of Cerebral White Matter Lesions and Clinical Correlates in Older Adults

Abstract

Background and purpose: Cerebral white matter signal abnormalities (WMSAs) are a significant radiological marker associated with brain and vascular aging. However, understanding their clinical impact is limited because of their pathobiological heterogeneity. We determined whether use of robust reliable automated procedures can distinguish WMSA classes with different clinical consequences.

Methods: Data from generally healthy participants aged >50 years with moderate or greater WMSA were selected from the Human Connectome Project-Aging (n=130). WMSAs were segmented on T1 imaging. Features extracted from WMSA included total and regional volume, number of discontinuous clusters, size of noncontiguous lesion, contrast of lesion intensity relative to surrounding normal appearing tissue using a fully automated procedure. Hierarchical clustering was used to classify individuals into distinct classes of WMSA. Radiological and clinical variability was evaluated across the individual WMSA classes.

Results: Class I was characterized by multiple, small, lower-contrast lesions predominantly in the deep WM; class II by large, confluent lesions in the periventricular WM; and class III by higher-contrast lesions restricted to the juxtaventricular WM. Class II was associated with lower myelin content than the other 2 classes. Class II was more prevalent in older subjects and was associated with a higher prevalence of hypertension and lower physical activity levels. Poor sleep quality was associated with a greater risk of class I.

Conclusions: We classified heterogeneous subsets of cerebral white matter lesions into distinct classes that have different clinical risk factors. This new method for identifying classes of WMSA will be important in understanding the underlying pathophysiology and in determining the impact on clinical outcomes.

Keywords: brain; hypertension; risk factors; sleep; white matter.

Figure 1.. WMSA variable quantification procedure

(A) Segmentation and regional localization procedures of WMSA in whole brain slices. Each mask for regional localization was created using Freesurfer procedures of lateral ventricle segmentation, dilatation of 3 mm and 13 mm and subtracted and combined with resulting volumes. (B) Clustering procedure for WMSA number with non-contiguous voxels and each lesion volume measurement. Discrete lesions are labeled as different colors. (C) Segmentation of WMSA, border (inner and outer layers of WMSA) and NAWM. Lesion contrast is calculated with the average of intensity ratios of outer and inner layers of regionally segmented WMSA. T.vol: total WMSA volume, JV.vol: juxtaventricular WMSA volume, PV.vol: periventricular WMSA volume, D.vol: deep WMSA volume, le.no: number of non-contiguous lesion, le.vol: volume of each non-contiguous lesion, pdr: periventricular to deep WMSA volume ratio, le.ct: WMSA lesion contrast

Figure 2.. WMSA classification procedure

(A) Cluster dendrogram by hierarchical clustering analysis. Dendrogram shows three clusters with four factors including le.no, le.vol, pdr and le.ct with Euclidean distance and Ward linkage method. (B) Formation of three classes in 3D scatter plots based on hierarchical clustering analysis. Each class is specifically located in domains with le.vol, le.no, pdr, and le.ct. (C) Multinomial logistic regression modeling based on principal component analysis. (D) The 2D plot with PC1 and PC2 clearly draw the line between the classes. le.no: number of non-contiguous lesion, le.vol: volume of each non-contiguous lesion, pdr: periventricular to deep WMSA volume ratio, le.ct: WMSA lesion contrast

Figure 3.. Association between key WMSA variables and individual class risk.

WMSA class was predicted from a multinomial model with le.no + le.vol + pdr + le.ct for a hypothetical subject with WMSA. The stacked-area effect plots display relationships between le.no (A), le.vol (B), pdr (C), le.ct (D), and the predicted probabilities of each class. le.no: number of non-contiguous WMSA lesions, le.vol: volume for each noncontiguous lesion, pdr: periventricular to deep WMSA volume ratio, le.ct: WMSA lesion contrast

Figure 4.. WMSA distribution patterns in individual WMSA classes.

The map was generated by summed volumes from individuals of each class. Colors in each slice show lesion prevalence within the group (0-50%).