Patterns of structural complexity in Alzheimer's disease and frontotemporal dementia

The goal of this project was to utilize an information theoretic formalism for medical image analysis initially proposed in [Young et al. (2005): Phys Rev Lett 94:098701‐1] to detect and quantify subtle global and regional differences in spatial patterns in patients suffering from Alzheimer's disease (AD) and frontotemporal dementia (FTD) by estimating the structural complexity of anatomical brain MRI. The sensitivity and specificity of the results are compared with those of a recent analysis, currently considered state of the art for MR studies of neurodegeneration. The previous study used regional estimates of cortical thinning and/or volume loss to differentiate between normal aging, AD, and FTD. The analysis illustrates that the structural complexity estimation method, a general multivariate approach to the study of variation in brain structure which does not depend on highly specialized volumetric and thickness estimates, is capable of providing sensitive and interpretable diagnostic information. Human Brain Mapp 2009. © 2008 Wiley‐Liss, Inc.

[1]  N. Schuff,et al.  Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI , 2006, Neurology.

[2]  Norbert Schuff,et al.  Measuring structural complexity in brain images , 2008, NeuroImage.

[3]  J. Morris,et al.  Profound Loss of Layer II Entorhinal Cortex Neurons Occurs in Very Mild Alzheimer’s Disease , 1996, The Journal of Neuroscience.

[4]  H J Testa,et al.  Diagnostic patterns of regional atrophy on MRI and regional cerebral blood flow change on SPECT in young onset patients with Alzheimer's disease, frontotemporal dementia and vascular dementia , 2002, Acta neurologica Scandinavica.

[5]  R. Faber,et al.  Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. , 1999, Neurology.

[6]  D UllmanJeffrey,et al.  Introduction to automata theory, languages, and computation, 2nd edition , 2001 .

[7]  G. Frisoni,et al.  Hippocampus and entorhinal cortex in frontotemporal dementia and Alzheimer’s disease: a morphometric MRI study , 2000, Biological Psychiatry.

[8]  G. Frisoni,et al.  Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer’s disease , 1999, Neurology.

[9]  J. Gee,et al.  What's in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer's disease, frontotemporal dementia and corticobasal degeneration. , 2003, Brain : a journal of neurology.

[10]  J. Crutchfield,et al.  Structural information in two-dimensional patterns: entropy convergence and excess entropy. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Norbert Schuff,et al.  Summarizing complexity in high dimensions. , 2005, Physical review letters.

[12]  B L Miller,et al.  Patterns of brain atrophy in frontotemporal dementia and semantic dementia , 2002, Neurology.

[13]  G. Binetti,et al.  A brief neuropsychological assessment for the differential diagnosis between frontotemporal dementia and Alzheimer's disease , 2001, European journal of neurology.

[14]  J. Gee,et al.  Alzheimer's Disease And Frontotemporal Dementia Exhibit Distinct Atrophy-Behavior Correlates: , 2003 .

[15]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[16]  M J Campbell,et al.  Laminar and regional distributions of neurofibrillary tangles and neuritic plaques in Alzheimer's disease: a quantitative study of visual and auditory cortices , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  Nick C. Fox,et al.  MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease , 1998, IEEE Transactions on Medical Imaging.

[18]  Peter Lorenzen,et al.  Unbiased Atlas Formation Via Large Deformations Metric Mapping , 2005, MICCAI.

[19]  Sandra E. Black,et al.  Topographical Patterns of Lobar Atrophy in Frontotemporal Dementia and Alzheimer’s Disease , 2006, Dementia and Geriatric Cognitive Disorders.

[20]  Murray Grossman,et al.  Alzheimer's disease and frontotemporal dementia exhibit distinct atrophy-behavior correlates: a computer-assisted imaging study. , 2003, Academic radiology.

[21]  G. Tononi Consciousness, information integration, and the brain. , 2005, Progress in brain research.

[22]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[23]  Patrick R Hof,et al.  Changes in the structural complexity of the aged brain , 2007, Aging cell.

[24]  Alan C. Evans,et al.  Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy. , 2004, Cerebral cortex.

[25]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[26]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[27]  Young,et al.  Inferring statistical complexity. , 1989, Physical review letters.

[28]  E. Bigio,et al.  Lateralization on Neuroimaging Does Not Differentiate Frontotemporal Lobar Degeneration from Alzheimer’s Disease , 2004, Dementia and Geriatric Cognitive Disorders.

[29]  G K Wilcock,et al.  Anatomical correlates of the distribution of the pathological changes in the neocortex in Alzheimer disease. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Guang H. Yue,et al.  Quantifying degeneration of white matter in normal aging using fractal dimension , 2007, Neurobiology of Aging.

[31]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[32]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[33]  Paul M. Thompson,et al.  Automated brain tissue assessment in the elderly and demented population: Construction and validation of a sub-volume probabilistic brain atlas , 2005, NeuroImage.

[34]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[35]  S Kobashi,et al.  Frontotemporal dementia and Alzheimer disease: evaluation of cortical atrophy with automated hemispheric surface display generated with MR images. , 1998, Radiology.

[36]  Max A. Viergever,et al.  f-information measures in medical image registration , 2004, IEEE Transactions on Medical Imaging.

[37]  K. Worsley,et al.  Unified univariate and multivariate random field theory , 2004, NeuroImage.

[38]  J. Kril,et al.  Regional and cellular pathology in frontotemporal dementia: relationship to stage of disease in cases with and without Pick bodies , 2004, Acta Neuropathologica.

[39]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[40]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[41]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease: Report of the NINCDS—ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease , 2011, Neurology.

[42]  N. Schuff,et al.  Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. , 2006, Brain : a journal of neurology.