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.

The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T2 test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.

Denitrification is the primary process that regulates the removal of bioavailable nitrogen (N) from aquatic ecosystems. Quantifying the capacity of N removal from aquatic systems can provide a scientific basis for establishing the relationship between N reduction and water quality objectives, quantifying pollution contributions from different sources, as well as recommending control measures. The Lake Taihu region in China has a dense river network and heavy N pollution; however, the capacity for permanent N removal by the river network is unknown. Here, we concurrently examined environmental factors and net N2 flux from sediments of two rivers in the Lake Taihu region between July 2012 and May 2013, using membrane inlet mass spectrometry, and then established a regression model incorporating the highly correlated factors to predict the N removal capacity of the river network in the region. To test the applicability of the regression model, 21 additional rivers surrounding Lake Taihu were sampled between July and December 2013. The results suggested that water nitrate concentrations are still the primary controlling factor for net denitrification even in this high N loading river network, probably due to multicollinearity of other relevant factors, and thus can be used to predict N removal from aquatic systems. Our established model accounted for 78% of the variability in the measured net N2 flux in these 21 rivers, and the total N removed through N2 production by the river network was estimated at 4 × 10(4) t yr(-1), accounting for about 43% of the total aquatic N load to the river system. Our results indicate that the average total N content in the river water discharged into Lake Taihu would be around 5.9 mg of N L(-1) in the current situation, far higher than the target concentration of 2 mg of N L(-1), given the total N load and the N removal capacity. Therefore, a much stronger effort is required to control the N pollution of the surface water in the region.

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