Abstract
Alzheimer’s disease (AD) deficits may be due in part to declining white matter (WM) integrity and disrupted connectivity. Numerous diffusion-weighted MRI (dMRI) studies of AD report WM deficits based on tensor model metrics. New microstructural measures derived from additional dMRI models may carry different information about WM microstructure including the geometry of diffusion anisotropy, diffusivity, complexity, estimated number of distinguishable fiber compartments, number of crossing fibers and neurite dispersion. Here we aimed to find the most helpful dMRI metrics and brain regions from a set of 17 dMRI-derived feature maps, to predict diagnostic group (AD or healthy control). The best metrics for classification were non-tensor metrics in the hippocampus and temporal lobes, areas consistently implicated in AD.
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References
Alzheimer’s Disease Association: Alzheimer’s disease facts and figures. Alzheimers Dement. 8(2), 131–168 (2012)
Petersen, R.C., et al.: Current concepts in mild cognitive impairment. Arch. Neurol. 58(12), 1985–1992 (2001)
Bruscoli, M., Lovestone, S.: Is MCI really just early dementia? A systematic review of conversion studies. Int. Psychogeriatr. 16(2), 129–140 (2004)
Delbeuck, X., et al.: Alzheimer’s disease as a disconnection syndrome? Neuropsychol. Rev. 13, 79e92 (2003)
Jack Jr., C.R., et al.: Update on the magnetic resonance imaging core of the Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Dement. 6(3), 212–220 (2010)
Descoteaux, M., Poupon, C.: Diffusion-weighted MRI. In: Belvic, D., Belvic, K. (eds.) Comprehensive Biomedical Physics, vol. 3, no. 6, pp. 81–97. Elsevier, Oxford (2014)
Behrens, T.E.J., et al.: Probabilistic diffusion tractography with multiple fibre orientations. What can we gain? Neuroimage 34(1), 144–155 (2007)
Descoteaux, M.: High angular resolution diffusion MRI: from local estimation to segmentation and tractography. Ph.D. Thesis, Université de Nice (2008)
Xie, S., et al.: Voxel-based detection of white matter abnormalities in mild Alzheimer disease. Neurology 66(12), 1845–1849 (2006)
Canu, E., et al.: Microstructural diffusion changes are independent of macrostructural volume loss in moderate to severe Alzheimer’s disease. J. Alzheimers Dis. 19(3), 963–976 (2010)
Leow, A.D., et al.: The tensor distribution function. Magn. Reson. Med. 61(1), 205–214 (2009)
Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358–1372 (2004)
Tournier, J.D., et al.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23, 1176–1185 (2004)
Wedeen, V.J., et al.: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54, 1377–1386 (2005)
Zhang, H., et al.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000–1016 (2012)
Medina, D., et al.: White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol. Aging 27(5), 663–672 (2006)
Rose, S.E., et al.: Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment. J. Neurol. Neurosurg. Psychiatry 77(10), 1122–1128 (2006)
Zhang, Y., et al.: Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68(1), 13–19 (2007)
Kavcic, V., et al.: White matter integrity linked to functional impairments in aging and early Alzheimer’s disease. Alzheimers Dement. 4(6), 381–389 (2008)
Stebbins, G.T., Murphy, C.M.: Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behav. Neurol. 21(1), 39–49 (2009)
Graña, M., et al.: Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation. Neurosci. Lett. 502, 225e229 (2011)
Haller, S., et al.: Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J. Alzheimers Dis. 22, 315e327 (2010)
O’Dwyer, L., et al.: Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS One 7, e32441 (2012)
Nir, T., et al.: Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. Neuroimage Clin. 3, 180–195 (2013)
Holmes, C.J., et al.: Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22, 324–333 (1998)
Leow, A.D., et al.: Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration. IEEE Trans. Med. Imaging 26(6), 822–832 (2007)
Basser, P.J., et al.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)
Song, S.K., et al.: Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage 20, 1714–1722 (2003)
Song, S.K., et al.: Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage 26(1), 132–140 (2005)
Batchelor, P.G., et al.: A rigorous framework for diffusion tensor calculus. Magn. Reson. Med. 53, 221–225 (2005)
Ennis, D.B., Kindlmann, G.: Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn. Reson. Med. 55, 136–146 (2006)
Jian, B., et al.: A novel tensor distribution model for the diffusion-weighted MR signal. Neuroimage 37(1), 164–176 (2007)
Zhan, L., et al.: A novel measure of fractional anisotropy based on the tensor distribution function. Med. Image Comput. Comput. Assist. Interv. 12(Pt 1), 845–852 (2009)
Aganj, I., et al.: Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn. Reson. Med. 64(2), 554–466 (2010)
Zhang, H., et al.: Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. Neuroimage 56(3), 1301–1315 (2011)
Gutman, B., et al.: Creating Unbiased Minimal Deformation Templates for Brain Volume Registration. Organization for Human Brain Mapping, Barcelona (2010)
Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14(1), 11–22 (2004)
Patenaude, B., et al.: A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56, 907–922 (2011)
Mori, S., et al.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40(2), 570–582 (2008)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Statist. Soc. B 67(2), 301–320 (2005)
Brun, A., Englund, E.: White matter disorder in dementia of the Alzheimer type: a pathoanatomical study. Ann. Neurol. 19(3), 253–262 (1986)
Sjobeck, M., et al.: Decreasing myelin density reflected increasing white matter pathology in Alzheimer’s disease—a neuropathological study. Int. J. Geriatr. Psychiatry 20(10), 919–926 (2005)
Hua, X., et al.: 3D characterization of brain atrophy in Alzheimer’s disease and mild cognitive impairment using tensor-based morphometry. Neuroimage 41(1), 19–34 (2008)
Migliaccio, R., et al.: White matter atrophy in Alzheimer’s disease variants. Alzheimers Dement. 8(5 Suppl.), S78-87 e71-72 (2012)
Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)
Klöppel, S., et al.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131, 681–689 (2008)
Lerch, J.P., et al.: Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol. Aging 29(1), 23–30 (2008)
Magnin, B., et al.: Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51(2), 73–83 (2009)
Wee, C.Y., et al.: Enriched white matter connectivity networks for accurate identification of MCI patients. Neuroimage 54(3), 1812–1822 (2011)
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Nir, T.M. et al. (2016). Alzheimer’s Disease Classification with Novel Microstructural Metrics from Diffusion-Weighted MRI. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-28588-7_4
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DOI: https://doi.org/10.1007/978-3-319-28588-7_4
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