Classifying white matter hyperintensities according to intensity and spatial localisation reveals specific association with cognition: Imaging correlates of cognition and biomarkers. (7th December 2020)

• Record Type: Journal Article
• Authors: Melazzini, Luca; Bordin, Valentina; Suri, Sana; Zsoldos, Enikő; Ebmeier, Klaus P; Jenkinson, Mark; Mackay, Clare; Sardanelli, Francesco; Griffanti, Ludovica
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.042751
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: White matter hyperintensities (WMH) on T2‐weighted images are imaging biomarkers of brain small vessel disease. When classified according to location (periventricular/deep), they have shown different associations with cognition. WMH can also appear hypointense on T1‐weighted (T1w) images as a possible sign of irreversible tissue damage. We hypothesise that sub‐classifying WMH combining intensity information and spatial localisation may provide better insight into the association with cognition, not detectable for the total WMH burden. Method: We analysed data from 684 subjects of the Whitehall II imaging sub‐study. A supervised machine learning method (BIANCA) was used to segment WMH. An automatic method based on cluster localisation and image intensity was then applied to classify WMH into 4 categories according to adjacency to the ventricles (periventricular/deep) and appearance on T1w images (either T1w‐hypointense or not) (Figure 1). Derived volumes were entered into a general linear model as predictors of the participants' cognitive scores on neuropsychological tests. Result: Periventricular T1w‐hypointense WMH were significantly related to worse performance in the trail‐making test A (p = 0.011), digit‐symbol (p = 0.028), and digit‐coding (p = 0.009) tests. When including only the total WMH burden in the model, we could not find any associations between WMH and cognition. Age, gender, years of education, systolic and diastolic blood pressure were used as covarietes in the statistical models. Conclusion: Sub‐classifying WMH according to both location and appearance on T1w images provided added value compared to total WMH burden alone. These are promising findings for WMH interpretation in the clinical practice and for the development of methods for analysing imaging biomarkers related to cognition.