Diffusion Tensor Metrics as Biomarkers in Alzheimer's Disease

Background Although diffusion tensor imaging has been a major research focus for Alzheimer’s disease in recent years, it remains unclear whether it has sufficient stability to have biomarker potential. To date, frequently inconsistent results have been reported, though lack of standardisation in acquisition and analysis make such discrepancies difficult to interpret. There is also, at present, little knowledge of how the biometric properties of diffusion tensor imaging might evolve in the course of Alzheimer’s disease. Methods The biomarker question was addressed in this study by adopting a standardised protocol both for the whole brain (tract-based spatial statistics), and for a region of interest: the midline corpus callosum. In order to study the evolution of tensor changes, cross-sectional data from very mild (N = 21) and mild (N = 22) Alzheimer’s disease patients were examined as well as a longitudinal cohort (N = 16) that had been rescanned at 12 months. Findings and Significance The results revealed that increased axial and mean diffusivity are the first abnormalities to occur and that the first region to develop such significant differences was mesial parietal/splenial white matter; these metrics, however, remained relatively static with advancing disease indicating they are suitable as ‘state-specific’ markers. In contrast, increased radial diffusivity, and therefore decreased fractional anisotropy–though less detectable early–became increasingly abnormal with disease progression, and, in the splenium of the corpus callosum, correlated significantly with dementia severity; these metrics therefore appear ‘stage-specific’ and would be ideal for monitoring disease progression. In addition, the cross-sectional and longitudinal analyses showed that the progressive abnormalities in radial diffusivity and fractional anisotropy always occurred in areas that had first shown an increase in axial and mean diffusivity. Given that the former two metrics correlate with dementia severity, but the latter two did not, it would appear that increased axial diffusivity represents an upstream event that precedes neuronal loss.

[1]  Guanghua Xiao,et al.  Distinctive disruption patterns of white matter tracts in Alzheimer's disease with full diffusion tensor characterization , 2012, Neurobiology of Aging.

[2]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[3]  Yong He,et al.  Multiple diffusion indices reveals white matter degeneration in Alzheimer's disease and mild cognitive impairment: a tract-based spatial statistics study. , 2011, Journal of Alzheimer's disease : JAD.

[4]  Michael I. Miller,et al.  Multi-Modal MRI Analysis with Disease-Specific Spatial Filtering: Initial Testing to Predict Mild Cognitive Impairment Patients Who Convert to Alzheimer’s Disease , 2011, Front. Neur..

[5]  C. Jack,et al.  Anterior temporal lobes and hippocampal formations: normative volumetric measurements from MR images in young adults. , 1989, Radiology.

[6]  Luigi Mansi,et al.  Regional cortical dysfunction in Alzheimer's disease as determined by positron emission tomography , 1984, Annals of neurology.

[7]  Guy B. Williams,et al.  Comparative Reliability of Total Intracranial Volume Estimation Methods and the Influence of Atrophy in a Longitudinal Semantic Dementia Cohort , 2009, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[8]  S. F. Witelson Hand and sex differences in the isthmus and genu of the human corpus callosum. A postmortem morphological study. , 1989, Brain : a journal of neurology.

[9]  Matthias J. Müller,et al.  Functional relevant loss of long association fibre tracts integrity in early Alzheimer's disease , 2008, Neuropsychologia.

[10]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[11]  Susumu Mori,et al.  Diffusion Tensor Magnetic Resonance Imaging of Wallerian Degeneration in Rat Spinal Cord after Dorsal Root Axotomy , 2009, The Journal of Neuroscience.

[12]  A. Auchus,et al.  Diffusion Tensor Imaging of Normal Appearing White Matter and Its Correlation with Cognitive Functioning in Mild Cognitive Impairment and Alzheimer's Disease , 2007, Annals of the New York Academy of Sciences.

[13]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[14]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[15]  Hirofumi Sakurai,et al.  Diffusion-weighted and magnetization transfer imaging of the corpus callosum in Alzheimer’s disease , 1999, Journal of the Neurological Sciences.

[16]  David M. Thomasson,et al.  Reliability of fiber tracking measurements in diffusion tensor imaging for longitudinal study , 2010, NeuroImage.

[17]  Guy B. Williams,et al.  The relationship of topographical memory performance to regional neurodegeneration in Alzheimer's disease , 2012, Front. Ag. Neurosci..

[18]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[19]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[20]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[21]  Christian Beaulieu,et al.  Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment , 2007, NeuroImage.

[22]  C. Jack,et al.  Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment , 1999, Neurology.

[23]  D. Prvulovic,et al.  Multiple Indices of Diffusion Identifies White Matter Damage in Mild Cognitive Impairment and Alzheimer’s Disease , 2011, PloS one.

[24]  Matthias J. Müller,et al.  Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild cognitive impairment , 2005, Neurobiology of Aging.

[25]  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.

[26]  Guy B. Williams,et al.  Looking for the optimal DTI acquisition scheme given a maximum scan time: are more b-values a waste of time? , 2009, Magnetic resonance imaging.

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

[28]  C. Caltagirone,et al.  In vivo structural neuroanatomy of corpus callosum in Alzheimer's disease and mild cognitive impairment using different MRI techniques: a review. , 2010, Journal of Alzheimer's disease : JAD.

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

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

[31]  Stephen M. Smith,et al.  DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease , 2011, NeuroImage.

[32]  T. Wyss-Coray,et al.  Inflammation in Alzheimer disease-a brief review of the basic science and clinical literature. , 2012, Cold Spring Harbor perspectives in medicine.

[33]  N. Schuff,et al.  Headache and cerebral venous air embolism , 2007, Neurology.

[34]  F. Wilcoxon,et al.  Individual comparisons of grouped data by ranking methods. , 1946, Journal of economic entomology.

[35]  C. Wheeler-Kingshott,et al.  About “axial” and “radial” diffusivities , 2009, Magnetic resonance in medicine.

[36]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[37]  John R Hodges,et al.  The Addenbrooke's Cognitive Examination Revised (ACE‐R): a brief cognitive test battery for dementia screening , 2006, International journal of geriatric psychiatry.

[38]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[39]  V. Wedeen,et al.  Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo , 2003, Magnetic resonance in medicine.

[40]  Takashi Inoue,et al.  Selective reduction of diffusion anisotropy in white matter of Alzheimer disease brains measured by 3.0 Tesla magnetic resonance imaging , 2002, Neuroscience Letters.

[41]  Naoto Hayashi,et al.  Effect of scanner in longitudinal diffusion tensor imaging studies , 2012, Human brain mapping.

[42]  Norbert Schuff,et al.  White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI , 2009, Brain : a journal of neurology.

[43]  J. Molinuevo,et al.  Multiple DTI index analysis in normal aging, amnestic MCI and AD. Relationship with neuropsychological performance , 2012, Neurobiology of Aging.

[44]  Jens Frahm,et al.  Topography of the human corpus callosum revisited—Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging , 2006, NeuroImage.

[45]  K. Pearson Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia , 1896 .

[46]  Matthias J. Müller,et al.  Ultrastructural Hippocampal and White Matter Alterations in Mild Cognitive Impairment: A Diffusion Tensor Imaging Study , 2004, Dementia and Geriatric Cognitive Disorders.