White matter hyperintensities in the forties: their prevalence and topography in an epidemiological sample aged 44-48

Abstract

White matter hyperintensities (WMHs) are a frequent finding on T2-weighted MRI of the brain in elderly individuals, but their prevalence and severity in younger asymptomatic populations is less well studied. We report the topography of WMHs on T2-weighted fluid inversion recovery (FLAIR) MRI in 428 individuals aged 44-48 years recruited randomly from a healthy community sample. WMHs were delineated from FLAIR and T1-weighted scans by using a computer algorithm, further verified and then classified using k-nearest neighbor (kNN) algorithm into deep WMH (DWMH), and periventricular WMH (PVWMH), which included extended periventricular "rims" and frontal and occipital "caps". Small caps and pencil-thin rims were not taken as WMHs for this analysis. The new computer algorithm was validated and compared with the scores of visual rating, and the correspondence between the two methods was high. We found that 218 (50.9%) subjects had WMHs. 146 of the 218 (34.1% of whole sample population of 428) subjects had deep white matter hyperintensities (DWMHs). The average number of WMH clusters (occurrences) per brain was 1.37 (0.94 for DWMH and 0.43 for pathological PVWMH) and the mean WMH tissue volume was 0.278 ml. There was no significant sex difference in the severity and distribution of WMHs. The study suggests that small punctate or focal WMHs are common in the brains of individuals in their 40s, and may represent an early stage of development of these lesions.

Figure 1

Preparation for kNN classification feature space: (a) A candidate WMH cluster is extracted from FLAIR image. The ratio of the mean intensity of this candidate WMH cluster over the intensity of brain tissue is then calculated. (b) The voxels that surround this candidate WMH cluster form a 3D “shell” around the cluster, and from the co‐registered T1‐weighted image the “shell” is extracted and its intensity information is calculated. WMH cluster size (number of voxels in the cluster) is also calculated. (c) The spatial information of this candidate WMH cluster with respect to the nearest edge of the lateral ventricles is obtained from this “ventricle distance map”. The voxel intensity of this ventricle distance map is the distance of that voxel to the nearest edge of the lateral ventricles. (d) Flowchart of kNN classification implementation. implementation. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

Figure 2

Some kNN classification results. Red: deep white matter hyperintensity (DWMH) clusters; yellow: extended rims or caps that are pathological periventricular WMH (pPVWMH) and both DWMH and pPVWMH are counted as WMHs in the data analysis; green and blue: pencil‐thin rims (blue) or caps (green), which are not considered as pathological WMHs and thus not included in the analysis). [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

Figure 3

The relationship between WMH volumes and the distance from the lateral ventricles, using (a) whole brain WMH volumes (mean) and (b) deep WMH volumes (mean). Note that these DWMH clusters were classified by kNN algorithm.

Figure 4

Spatial distribution of WMHs. Map of the sample (n = 218) with WMHs in the standard space. The WMHs were mapped into the standard space from their each individual acquisition space. The color bar denotes the number of WMH occurrences of the anatomical location. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]