Alzheimer’s Disease Neuroimaging Initiative: A One-year Follow up Study Using Tensor-based Morphometry Correlating Degenerative Rates, Biomarkers and Cognition

Department Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
NeuroImage (Impact Factor: 6.36). 02/2009; 45(3). DOI: 10.1016/j.neuroimage.2009.01.004
Source: PubMed

ABSTRACT Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental status exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (Abeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.

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Available from: Alex Leow, Aug 16, 2015
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    • "ADNI began in 2005, after testing the feasibility and reproducibility of a range of scanning protocols. This led to standardized scanning methods implemented at 58 sites across North America (Leow et al. 2009; Jahanshad et al. 2010; Jack 2012; Zhan et al. 2012). Many other neuroimaging consortia have been established, including the functional Brain Imaging Research Network (FBIRN) (Potkin and Ford 2009) which has developed standardized methods for multi-center functional MRI studies (Glover et al. 2012) and the Mind Clinical Imaging Consortium (Gollub et al. 2013) focusing on schizophrenia, as well as research networks focusing on pediatric imaging (Evans 2006), autism (Ecker et al. 2013), HIV/AIDS (Cohen et al. 2010) and many others. "
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    • "For each subject, the local expansion factor of the 3D elastic warping transform , calculated as the determinant of the Jacobian matrix of the deformation , was plotted (Leow et al., 2005) to show relative volume differences between each individual and the common template, and reveal areas of structural volume deficits, or expansions, relative to the healthy population average. TBM was also applied to the longitudinal ADNI dataset by using a nonlinear registration algorithm to match 3D baseline structural MR images with follow-up images acquired 12 months later as explained in our earlier work (Leow et al., 2009). "
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    10/2012; 1(1). DOI:10.1016/j.nicl.2012.09.012
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    • "grDG: Granular layer of the dentate gyrus; h: hippocampus. methods both to animals and humans [12] [30] [31]. Previous studies in mouse models of AD have mainly focused on the A␤ burden [32] [33] [34], volumetric changes [18] [35], or even MRS parameters, as predominant markers for AD [36] [37]. "
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