4.7 Article

Remote sites of structural atrophy predict later amyloid formation in a mouse model of Alzheimer's disease

Journal

NEUROIMAGE
Volume 50, Issue 2, Pages 416-427

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.12.070

Keywords

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Funding

  1. NIH/NCRR [U24 RR021760, P41-RR005959]
  2. NIH/NIA [K01-AG026144]
  3. Alzheimer's Association [NIRG 062582]
  4. NIH Director's New Innovator [DP2 OD001734]

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Magnetic resonance (MR) imaging can provide a longitudinal view of neurological disease through repeated imaging of patients at successive stages of impairment. Until recently, the difficulty of manual delineation has limited volumetric analyses of MR data sets to a few select regions and a small number of subjects. Increased throughput offered by faster imaging methods, automated segmentation, and deformation-based morphometry have recently been applied to overcome this limitation with mouse models of neurological conditions. We use automated analyses to produce an unbiased view of volumetric changes in a transgenic mouse model for Alzheimer's disease (AD) at two points in the progression of disease: immediately before and shortly after the onset of amyloid formation. In addition to the cortex and hippocampus, where atrophy has been well documented in AD patients, we identify volumetric losses in the pons and substantia nigra where neurodegeneration has not been carefully examined. We find that deficits in cortical volume precede amyloid formation in this mouse model, similar to presymptomatic atrophy seen in patients with familial AD. Unexpectedly, volumetric losses identified by MR outside of the forebrain predict locations of future amyloid formation, such as the inferior colliculus and spinal nuclei, which develop pathology at very late stages of disease. Our work provides proof-of-principle that MR microscopy can expand our view of AD by offering a complete and unbiased examination of volumetric changes that guide us in revisiting the canonical neuropathology. (C) 2009 Elsevier Inc. All rights reserved.

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