Data‐driven approach reveals heterogeneity and region‐specific association of white matter hyperintensities with the APOE genotype: Doctor AI: Making computers explain their decisions. (7th December 2020)

• Record Type: Journal Article
• Authors: Habes, Mohamad; Sotiras, Aristeidis; Erus, Guray; Toledo, Jon B.; Janowitz, Deborah; Wolk, David A.; Shou, Haochang; Bryan, Nick; Doshi, Jimit; Voelzke, Henry; Schminke, Ulf; Hoffmann, Wolfgang; Resnick, Susan M.; Grabe, Hans J.; Davatzikos, Christos
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• 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.037342
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: We used a transparent machine learning algorithm to identify subtypes of white matter damage in aging and to characterize their clinical representations. White matter hyperintensities (WMH), a common finding on brain magnetic resonance imaging (MRI), are known to be associated with cognitive impairment and dementia. Previous studies have shown spatial heterogeneity of WMH appearance, but the contribution of Alzheimer disease (AD) dementia and related genetic risk factors to this heterogeneity remains unclear. We sought to investigate the heterogeneity of WMH, in the general population, leveraging state of the art pattern analysis methods and exploring whether the APOE genotype is contributing to this heterogeneity. Method: A sample of N=1836 people was obtained from the Study of Health in Pomerania (SHIP), Germany, covering a wide age range (22–84 years, median age 52.2±13.16). WMH were automatically segmented on multi‐modal MRI scans and decomposed into four spatially distinctive components using Non‐Negative Matrix Factorization (NNMF), a machine‐learning based structural covariance method. Associations of these components with the APOE genotype (available for n=303) was investigated in subjects older than 60 years of age, using a multivariable linear regression model for each component, with WMH component loadings as the outcome variable and APOE genotype (encoded as shown in Table 1) as the independent variable. Age, sex and ship sub‐cohort were included as covariates. All reported P‐values were Bonferroni corrected for multiple comparisons. Result: Data‐driven approach identified four WMH components, consistent with clinical categorization in deep and periventricular lesions, while further dividing periventricular WMH into posterior, frontal and dorsal components (Figure 1). APOE genotype was significantly associated with the dorsal periventricular WMH component (P=0.035, Bonferroni corrected), but not with the other three WMH components (Table 1). Conclusion: We obtained a high consistency of data‐driven WMH components and established clinical categorizations. Our data demonstrated that the appearance of WMH follows heterogeneous regional distribution patterns. Additionally, the APOE genotype, the strongest genetic risk factor for sporadic AD, is specifically associated with one of the components indicating spatial specificity and a potential link to early MRI changes in the predementia phase.