Work

White matter hypointensities and hyperintensities have equivalent correlations with age and CSF β‐amyloid in the nondemented elderly

Year: 2019

Type: article

Abstract: INTRODUCTION: T1- and T2-weighted sequences from MRI often provide useful complementary information about tissue properties. Leukoaraiosis results in signal abnormalities on T1-weighted images, which are automatically quantified by FreeSurfer, but this marker is poorly characterized and is rarely used. We evaluated associations between white matter hyperintensity (WM-hyper) volume from FLAIR and white matter hypointensity (WM-hypo) volume from T1-weighted images and compared their associations with age and cerebrospinal fluid (CSF) β-amyloid and tau. METHODS: A total of 56 nondemented participants (68-94 years) were recruited and gave informed consent. All participants went through MR imaging on a GE 1.5T scanner and of these 47 underwent lumbar puncture for CSF analysis. WM-hypo was calculated using FreeSurfer analysis of T1 FSPGR 3D, and WM-hyper was calculated with the Lesion Segmentation Toolbox in the SPM software package using T2-FLAIR. RESULTS: WM-hyper and WM-hypo were strongly correlated (r = .81; parameter estimate (p.e.): 1.53 ± 0.15; p < .0001). Age was significantly associated with both WM-hyper (r = .31, p.e. 0.078 ± 0.030, p = .013) and WM-hypo (r = .42, p.e. 0.055 ± 0.015, p < .001). CSF β-amyloid levels were predicted by WM-hyper (r = .33, p.e. -0.11 ± 0.044, p = .013) and WM-hypo (r = .42, p.e. -0.24 ± 0.073, p = .002). CSF tau levels were not correlated with either WM-hyper (p = .9) or WM-hypo (p = .99). CONCLUSIONS: Strong correlations between WM-hyper and WM-hypo, and similar associations with age, abnormal β-amyloid, and tau suggest a general equivalence between these two imaging markers. Our work supports the equivalence of white matter hypointensity volumes derived from FreeSurfer for evaluating leukoaraiosis. This may have particular utility when T2-FLAIR is low in quality or absent, enabling analysis of older imaging data sets.

Source: Brain and Behavior

DOI: 10.1002/brb3.1457