Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type

In large-deformation diffeomorphic metric mapping (LDDMM), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. Thus, methods such as principal component analysis (PCA) of the initial momentum leads to analysis of anatomical shape and form in target images without being restricted to small-deformation assumption in the analysis of linear displacements. We apply this approach to a study of dementia of the Alzheimer type (DAT). The left hippocampus in the DAT group shows significant shape abnormality while the right hippocampus shows similar pattern of abnormality. Further, PCA of the initial momentum leads to correct classification of 12 out of 18 DAT subjects and 22 out of 26 control subjects

[1]  M. Miller,et al.  Statistical Analysis of Hippocampal Asymmetry in Schizophrenia , 2001, NeuroImage.

[2]  N. Schuff,et al.  Age effects on atrophy rates of entorhinal cortex and hippocampus , 2006, Neurobiology of Aging.

[3]  R. Rabbitt,et al.  3D brain mapping using a deformable neuroanatomy. , 1994, Physics in medicine and biology.

[4]  N. Schuff,et al.  Longitudinal volumetric MRI change and rate of cognitive decline , 2005, Neurology.

[5]  Darryl D. Holm,et al.  Soliton dynamics in computational anatomy , 2004, NeuroImage.

[6]  Michael I. Miller,et al.  Preclinical detection of Alzheimer's disease: hippocampal shape and volume predict dementia onset in the elderly , 2005, NeuroImage.

[7]  S. Joshi,et al.  Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus , 2000, Neurology.

[8]  V. Arnold Mathematical Methods of Classical Mechanics , 1974 .

[9]  Lei Wang,et al.  Correlations Between Antemortem Hippocampal Volume and Postmortem Neuropathology in AD Subjects , 2004, Alzheimer disease and associated disorders.

[10]  L. Younes,et al.  Evidence of Structural Remodeling in the Dyssynchronous Failing Heart , 2005, Circulation research.

[11]  Alain Trouvé,et al.  Diffeomorphisms Groups and Pattern Matching in Image Analysis , 1998, International Journal of Computer Vision.

[12]  J Philip Miller,et al.  Longitudinal course and neuropathologic outcomes in original vs revised MCI and in pre-MCI , 2006, Neurology.

[13]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[14]  M W Vannier,et al.  Three-dimensional hippocampal MR morphometry with high-dimensional transformation of a neuroanatomic atlas. , 1997, Radiology.

[15]  Q. Mcnemar Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[16]  L. Younes,et al.  Statistics on diffeomorphisms via tangent space representations , 2004, NeuroImage.

[17]  Alain Trouvé,et al.  Geodesic Shooting for Computational Anatomy , 2006, Journal of Mathematical Imaging and Vision.

[18]  Gary G. Koch,et al.  Categorical data analysis using the sas® system, 2nd edition , 2000 .

[19]  Michael I. Miller,et al.  Validation of semiautomated methods for quantifying cingulate cortical metrics in schizophrenia , 2004, Psychiatry Research: Neuroimaging.

[20]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[21]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[22]  C. Jack,et al.  Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI , 2005, Neurology.

[23]  Nick C Fox,et al.  A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. , 2003, Archives of neurology.

[24]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[25]  Michael I. Miller,et al.  Hierarchical brain mapping via a generalized Dirichlet solution for mapping brain manifolds , 1995, Optics & Photonics.

[26]  Paul Dupuis,et al.  Variational problems on ows of di eomorphisms for image matching , 1998 .

[27]  J. Morris,et al.  Current concepts in mild cognitive impairment. , 2001, Archives of neurology.

[28]  Chris Frost,et al.  The analysis of repeated ‘direct’ measures of change illustrated with an application in longitudinal imaging , 2004, Statistics in medicine.

[29]  M. Miller Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms , 2004, NeuroImage.

[30]  Michael I. Miller,et al.  Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging☆ , 2003, NeuroImage.

[31]  T. W. Anderson,et al.  An Introduction to Multivariate Statistical Analysis , 1959 .

[32]  Michael I. Miller,et al.  Gaussian Random Fields on Sub-Manifolds for Characterizing Brain Surfaces , 1997, IPMI.

[33]  M. Miller,et al.  Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change , 2004, NeuroImage.

[34]  U. Grenander,et al.  Statistical methods in computational anatomy , 1997, Statistical methods in medical research.

[35]  Gary G. Koch,et al.  Categorical Data Analysis Using The SAS1 System , 1995 .

[36]  Nick C. Fox,et al.  Differentiating AD from aging using semiautomated measurement of hippocampal atrophy rates , 2004, NeuroImage.

[37]  Norbert Schuff,et al.  Longitudinal stability of MRI for mapping brain change using tensor-based morphometry , 2006, NeuroImage.

[38]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[39]  Michael I. Miller,et al.  Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type , 2006, NeuroImage.

[40]  Nick C. Fox,et al.  Atrophy rates of the cingulate gyrus and hippocampus in AD and FTLD , 2007, Neurobiology of Aging.

[41]  Michael I. Miller,et al.  Brain Segmentation and the Generation of Cortical Surfaces , 1999, NeuroImage.

[42]  J. Price,et al.  Clinicopathologic studies in cognitively healthy aging and Alzheimer's disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. , 1998, Archives of neurology.

[43]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .