| Literature DB >> 27521741 |
Alexandra Badea1, Lauren Kane2, Robert J Anderson3, Yi Qi3, Mark Foster3, Gary P Cofer3, Neil Medvitz4, Anne F Buckley4, Andreas K Badea3, William C Wetsel5, Carol A Colton6.
Abstract
Multivariate biomarkers are needed for detecting Alzheimer's disease (AD), understanding its etiology, and quantifying the effect of therapies. Mouse models provide opportunities to study characteristics of AD in well-controlled environments that can help facilitate development of early interventions. The CVN-AD mouse model replicates multiple AD hallmark pathologies, and we identified multivariate biomarkers characterizing a brain circuit disruption predictive of cognitive decline. In vivo and ex vivo magnetic resonance imaging (MRI) revealed that CVN-AD mice replicate the hippocampal atrophy (6%), characteristic of humans with AD, and also present changes in subcortical areas. The largest effect was in the fornix (23% smaller), which connects the septum, hippocampus, and hypothalamus. In characterizing the fornix with diffusion tensor imaging, fractional anisotropy was most sensitive (20% reduction), followed by radial (15%) and axial diffusivity (2%), in detecting pathological changes. These findings were strengthened by optical microscopy and ultrastructural analyses. Ultrastructual analysis provided estimates of axonal density, diameters, and myelination-through the g-ratio, defined as the ratio between the axonal diameter, and the diameter of the axon plus the myelin sheath. The fornix had reduced axonal density (47% fewer), axonal degeneration (13% larger axons), and abnormal myelination (1.5% smaller g-ratios). CD68 staining showed that white matter pathology could be secondary to neuronal degeneration, or due to direct microglial attack. In conclusion, these findings strengthen the hypothesis that the fornix plays a role in AD, and can be used as a disease biomarker and as a target for therapy.Entities:
Keywords: Alzheimer's disease; Diffusion tensor imaging; Electron microscopy; Fornix; Mouse models
Mesh:
Substances:
Year: 2016 PMID: 27521741 PMCID: PMC5159324 DOI: 10.1016/j.neuroimage.2016.08.014
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 6.556