β-amyloid, hippocampal atrophy and their relation to longitudinal brain change in cognitively normal individuals

Recent literature has examined baseline hippocampal volume and extent of brain amyloidosis to test potential synergistic effects on worsening cognition and extent of brain atrophy. Use of hippocampal volume in prior studies was based on the notion that limbic circuit degeneration is an early manifestation of the Alzheimer's Disease (AD) pathophysiology. To clarify these interactions early in the AD process, we tested the effects of amyloid and baseline normalized hippocampal volume on longitudinal brain atrophy rates in a group of cognitively normal individuals. Results showed that the combination of elevated β-amyloid and baseline hippocampal atrophy is associated with increased rates specific to the limbic circuit and splenium. Importantly, this atrophy pattern emerged from a voxelwise analysis, corroborated by regression models over region of interests in native space. The results are broadly consistent with previous studies of the effects of amyloid and baseline hippocampal atrophy in normals, while pointing to accelerated atrophy of AD-vulnerable regions detectable at the preclinical stage.

[1]  J. Hodges,et al.  Focal posterior cingulate atrophy in incipient Alzheimer's disease , 2010, Neurobiology of Aging.

[2]  Christine Bastin,et al.  Relationships between brain metabolism decrease in normal aging and changes in structural and functional connectivity , 2013, NeuroImage.

[3]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[4]  Charles DeCarli,et al.  Heterogeneity of cognitive trajectories in diverse older persons. , 2010, Psychology and aging.

[5]  Robert I. Reid,et al.  White matter integrity determined with diffusion tensor imaging in older adults without dementia: influence of amyloid load and neurodegeneration. , 2014, JAMA neurology.

[6]  S. DeKosky,et al.  Kinetic Modeling of Amyloid Binding in Humans using PET Imaging and Pittsburgh Compound-B , 2005, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[7]  C. Jack,et al.  Biomarker Modeling of Alzheimer’s Disease , 2013, Neuron.

[8]  K. Ishii,et al.  Comparison of gray matter and metabolic reduction in mild Alzheimer’s disease using FDG-PET and voxel-based morphometric MR studies , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[9]  N. Volkow,et al.  Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data , 1996, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  P. Yger,et al.  An Optimized Blockwise Non Local Means Denoising Filter for 3D Magnetic Resonance Images , 2007 .

[11]  C. Studholme,et al.  An intensity consistent filtering approach to the analysis of deformation tensor derived maps of brain shape , 2003, NeuroImage.

[12]  Mary M. Reilly,et al.  Reply: The p.Ser107Leu in BICD2 is a mutation 'hot spot' causing distal spinal muscular atrophy. , 2015 .

[13]  C. Jack PART and SNAP , 2014, Acta Neuropathologica.

[14]  Michael Weiner,et al.  An Intensity Consistent Approach to the Cross Sectional Analysis of Deformation Tensor Derived Maps of Brain Shape , 2002, MICCAI.

[15]  C. Rowe,et al.  Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study , 2013, The Lancet Neurology.

[16]  Evan Fletcher,et al.  Early Brain Loss in Circuits Affected by Alzheimer’s Disease is Predicted by Fornix Microstructure but may be Independent of Gray Matter , 2014, Front. Aging Neurosci..

[17]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[18]  Pierrick Coupé,et al.  An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images , 2008, IEEE Transactions on Medical Imaging.

[19]  Evan Fletcher,et al.  Using Prior Information To Enhance Sensitivity of Longitudinal Brain Change Computation , 2014 .

[20]  James J. Pekar,et al.  Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease , 2009, NeuroImage.

[21]  Keith A. Johnson,et al.  Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. , 2014, JAMA neurology.

[22]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[23]  Andrew J. Saykin,et al.  Regionally specific atrophy of the corpus callosum in AD, MCI and cognitive complaints , 2006, Neurobiology of Aging.

[24]  P. Nestor,et al.  Diffusion tensor imaging in Alzheimer's disease: insights into the limbic-diencephalic network and methodological considerations , 2014, Front. Aging Neurosci..

[25]  W. Klunk,et al.  Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. , 2003, Journal of medicinal chemistry.

[26]  Evan Fletcher,et al.  Adaptive image segmentation for robust measurement of longitudinal brain tissue change , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  J. Hodges,et al.  Limbic hypometabolism in Alzheimer's disease and mild cognitive impairment , 2003, Annals of neurology.

[28]  Charles DeCarli,et al.  Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation. , 2015, Brain : a journal of neurology.

[29]  Michael S. Gazzaniga,et al.  Cortical Projection Topography of the Human Splenium: Hemispheric Asymmetry and Individual Differences , 2010, Journal of Cognitive Neuroscience.

[30]  Miranka Wirth,et al.  Vascular risk and Aβ interact to reduce cortical thickness in AD vulnerable brain regions , 2014, Neurology.

[31]  Guy B. Williams,et al.  Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease. , 2010, Brain : a journal of neurology.

[32]  J. Mazziotta,et al.  Regional Spatial Normalization: Toward an Optimal Target , 2001, Journal of computer assisted tomography.

[33]  Clifford R. Jack,et al.  Rates of β-amyloid accumulation are independent of hippocampal neurodegeneration , 2014, Neurology.

[34]  Evan Fletcher,et al.  Combining Boundary-Based Methods With Tensor-Based Morphometry in the Measurement of Longitudinal Brain Change , 2013, IEEE Transactions on Medical Imaging.

[35]  H. Braak,et al.  Evolution of the neuropathology of Alzheimer's disease , 1996, Acta neurologica Scandinavica. Supplementum.

[36]  D. Y. Lee,et al.  Sub-Regional Hippocampal Injury is Associated with Fornix Degeneration in Alzheimer’s Disease , 2012, Front. Ag. Neurosci..

[37]  Janna H. Neltner,et al.  Primary age-related tauopathy (PART): a common pathology associated with human aging , 2014, Acta Neuropathologica.

[38]  C. Jack,et al.  An operational approach to National Institute on Aging–Alzheimer's Association criteria for preclinical Alzheimer disease , 2012, Annals of neurology.

[39]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[40]  W. Jagust,et al.  The effect of amyloid β on cognitive decline is modulated by neural integrity in cognitively normal elderly , 2013, Alzheimer's & Dementia.

[41]  L. Maffei,et al.  Environmental enrichment strengthens corticocortical interactions and reduces amyloid-β oligomers in aged mice , 2013, Front. Aging Neurosci..

[42]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[43]  R. Dineen,et al.  The fornix in health and disease: an imaging review. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[44]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[45]  Jelle Jolles,et al.  Multiple indicators of age-related differences in cerebral white matter and the modifying effects of hypertension , 2010, NeuroImage.

[46]  R. Petersen Mild cognitive impairment as a diagnostic entity , 2004, Journal of internal medicine.

[47]  Daniel Rueckert,et al.  Diffeomorphic Registration Using B-Splines , 2006, MICCAI.

[48]  Michael Weiner,et al.  Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: An MRI study of 676 AD, MCI, and normal subjects , 2008, NeuroImage.

[49]  Michèle Allard,et al.  Distinctive alterations of the cingulum bundle during aging and Alzheimer’s disease , 2010, Neurobiology of Aging.