Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects

Abstract

Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals.

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

“…Demographic information for the Arizona APOE cohort and ADNI cohort are summarized in Table 1 . Within each cohort, group differences of gender and APOE-ε4 genotype were estimated using chi-square tests, group differences of age and education were calculated by t -tests ( Crivello et al, 2010 , Dong et al, 2019 ). Inferential analysis demonstrated that the CU progressors and non-progressors have no significant differences on sex, age, education, and relevant APOE genotype.…”

“…After we calculated the significant variations on the ventricular surface, we could apply directional analysis ( Yao et al, 2018 , Dong et al, 2019 ) to study the correspondence between VV enlargement and ventricular morphometry expansion. The direction (atrophy or expansion) of group differences were analyzed along the surface normal direction and within surfaces at each surface point.…”

“…Structural MRI biomarkers are the mainstay of AD imaging research as well as clinical diagnosis ( Sperling et al, 2011b , Tosun et al, 2016 ). MRI biomarkers include whole-brain ( Chen et al, 2007 , Frisoni et al, 2010 , Cuingnet et al, 2011 ), entorhinal cortex ( Cardenas et al, 2011 , Zhou et al, 2013 , Li et al, 2014 ), hippocampus ( Reiman et al, 1998 , Pardoe et al, 2009 , Shi et al, 2013a ; Li et al,2016 ; Saeed et al, 2018 , Dong et al, 2019 ), and temporal lobe volumes ( Hua et al, 2010 , Coupé et al, 2019 ), as well as ventricular expansion ( Jack et al, 2008 , Thompson et al, 2006 , Wang et al, 2011 ). Ventricular expansion reflects diffuse brain atrophy ( Madsen et al, 2013 , 2015).…”

“…This method gave better effect sizes for detecting ventricular morphometry differences than other TBM-based methods, including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix ( Wang et al 2010 , 2011). To comprehensively capture deformations along the surface normal directions and within surfaces, we developed multivariate morphometry statistics (MMS) combining mTBM and RD to detect brain abnormalities associated with neurodegenerative diseases ( Dong et al, 2019 , Shi et al, 2014 , Shi et al, 2015 , Wang et al, 2011 , Li et al,2016 ). Our previous studies ( Wang et al 2010 , 2011) demonstrated that surface-based MMS gained improved statistical power compared to other TBM-based methods.…”

“…Previous studies of ventricular morphometric modeling ( Thompson et al, 2004a , Ferrarini et al, 2008 , Chou et al, 2008 , Wang et al, 2011 , Apostolova et al, 2012 , Roussotte et al, 2014b ) mapped only part of anatomical ventricular surfaces, with coverage of inferior or posterior horns being incomplete. In this work, we propose a complete ventricular morphometry analysis system (VMAS), which is based on MMS proposed by our previous methods ( Wang et al 2010 , 2011), but includes an automated ventricular segmentation method ( Zhang et al 2016 ), together with an efficient morphometric expansion/atrophy visualization analysis module ( Yao et al, 2018 , Dong et al, 2019 ). The proposed VMAS can capture a whole connected 3D ventricular surface characteristic as well as subregional deformations.…”