AMI  Vol.2 No.2 , April 2012
DTI and Structural MRI Classification in Alzheimer’s Disease
ABSTRACT
In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on diffusion tensor (DTI) and anatomical magnetic resonance imaging (MRI). We propose a new multimodal measure that combines anatomical and diffusivity measures at the voxel level. Our approach relies on whole-brain parcellation into 73 anatomical regions and the extraction of multimodal characteristics in these regions. Discriminative features are identified using different feature selection (FS) methods and used in a Support Vector Machine (SVM) for individual classification. Fifteen AD patients and 16 elderly controls were discriminated using mean diffusivity alone, combination of mean diffusivity and fractional anisotropy, and multimodal measures in the 73 ROIs and the overall accuracy obtained was 65.2%, 68.6% and 72% respectively. Overall accuracy reached 99% in multimodal measures when relevant regions were selected.

Cite this paper
L. Mesrob, M. Sarazin, V. Hahn-Barma, L. Souza, B. Dubois, P. Gallinari and S. Kinkingnéhun, "DTI and Structural MRI Classification in Alzheimer’s Disease," Advances in Molecular Imaging, Vol. 2 No. 2, 2012, pp. 12-20. doi: 10.4236/ami.2012.22003.
References
[1]   C. P. Ferri, M. Prince, C. Brayne, et al., “Global Prevalence of Dementia: A Delphi Consensus Study,” Lancet, Vol. 366, No. 9503, 2005, pp. 2112-2117. doi:10.1016/S0140-6736(05)67889-0

[2]   B. Dubois, H. H. Feldman, C. Jacova, et al., “Research Criteria for the Diagnosis of Alzheimer’s Disease: Revising the NINCDS-ADRDA Criteria,” The Lancet Neurology, Vol. 6, No. 8, 2007, pp. 734-746. doi:10.1016/S1474-4422(07)70178-3

[3]   N. C. Fox, E. K. Warrington, P. A. Freeborough, et al., “Presymptomatic Hippocampal Atrophy in Alzheimer’s Disease. A Longitudinal MRI Study,” Brain, Vol. 119, No. 6, 1996, pp. 2001-2007. doi:10.1093/brain/119.6.2001

[4]   C. R. J. Jack, R. C. Petersen, Y. C. Xu, et al., “Prediction of AD with MRI-Based Hippocampal Volume in Mild Cognitive Impairment,” Neurology, Vol. 52, No. 7, 1999, pp. 1397-1403.

[5]   B. C. Dickerson, I. Goncharova, M. P. Sullivan, et al., “MRI-Derived Entorhinal and Hippocampal Atrophy in Incipient and Very Mild Alzheimer’s Disease,” Neurobiology of Aging, Vol. 22, No. 5, 2001, pp. 747-754 doi:10.1016/S0197-4580(01)00271-8

[6]   P. J. Visser, F. R. Verhey, P. A. Hofman, et al., “Medial Temporal Lobe Atrophy Predicts Alzheimer’s Disease in Patients with Minor Cognitive Impairment,” Journal of Neurology Neurosurgery & Psychiatry, Vol. 72, No. 4, 2002, pp. 491-497

[7]   A. T. Du, N. Schuff, J. H. Kramer, et al., “Higher Atrophy Rate of Entorhinal Cortex than Hippocampus in AD,” Neurology, Vol. 62, No. 3, 2004, pp. 422-427

[8]   K. B. Walhovd, A. M. Fjell, L. Amlien, et al., “Multimodal Imaging in Mild Cognitive Impairment: Metabolism, Morphometry and Diffusion of the Temporal-Parietal Memory Network,” NeuroImage, Vol. 45, No. 1, 2009, pp. 215-233. doi:10.1016/j.neuroimage.2008.10.053

[9]   A. Cherubini, P. Peran, I. Spoletini, et al., “Combined Volumetry and DTI in Subcortical Structures of Mild Cognitive Impairment and Alzheimer’s Disease Patients,” Journal of Alzheimer’s Disease, Vol. 19, No. 4, 2010, pp. 1273-1282.

[10]   L. Wang, F. C. Goldstein, E. Veledar, et al., “Alterations in Cortical Thickness and White Matter Integrity in Mild Cognitive Impairment Measured by Whole-Brain Cortical Thickness Mapping and Diffusion TensorImaging,” American Journal of Neuroradiology, Vol. 30, No. 5, 2009, pp. 893-899. doi:10.3174/ajnr.A1484

[11]   U. Friese, T. Meindl, S. Herpertz, et al., “Diagnostic Utility of Novel MRI-Based Biomarkers for Alzheimer’s Disease: Diffusion Tensor Imaging and Deformation-Based Morphometry,” Journal of Alzheimer’s Disease, Vol. 20, No. 2, 2010, pp. 477-490.

[12]   M. J. Muller, D. Greverus, C. Weibrich, et al., “Diagnostic Utility of Hippocampal Size and Mean Diffusivity in Amnestic MCI,” Neurobiology of Aging, Vol. 28, No. 3, 2007, pp. 398-403. doi:10.1016/j.neurobiolaging.2006.01.009

[13]   S. E. Rose, A. L. Janke and J. B. Chalk, “Gray and White Matter Changes in Alzheimer’s Disease: A Diffusion Tensor Imaging Study,” Journal of Magnetic Resonance Imaging, Vol. 27, No. 1, 2008, pp. 20-26 doi:10.1002/jmri.21231

[14]   S. Xie, J. X. Xiao, G. L. Gong, et al., “Voxel-Based Detection of White Matter Abnormalities in Mild Alzheimer Disease,” Neurology, Vol. 66, No. 12, 2006, pp. 1845-1849. doi:10.1212/01.wnl.0000219625.77625.aa

[15]   H. Cho, D. W. Yang, Y. M. Shon, et al., “Abnormal Integrity of Corticocortical Tracts in Mild Cognitive Impairment: A Diffusion Tensor Imaging Study,” Journal of Korean Medical Science, Vol. 23, No. 3, 2008, pp. 477-483. doi:10.3346/jkms.2008.23.3.477

[16]   M. Zarei, B. Patenaude, J. Damoiseaux, et al., “Combining Shape and Connectivity Analysis An MRI Study of Thalamic Degeneration in Alzheimer’s Disease,” NeuroImage, Vol. 49, No. 1, 2010, pp. 1-8. doi:10.1016/j.neuroimage.2009.09.001

[17]   K. Kantarci, Y. Xu, M. M. Shiung, et al., “Comparative Diagnostic Utility of Different MR Modalities in Mild Cognitive Impairment and Alzheimer’s Disease,” Dementia and Geriatric Cognitive Disorders, Vol. 14, No. 4, 2002, pp. 198-207. doi:10.1159/000066021

[18]   Y. Zhang, N. Schuff, G. H. Jahng, et al., “Diffusion Tensor Imaging of Cingulum Fibers in Mild Cognitive Impairment and Alzheimer’s Disease,” Neurology, Vol. 68, No. 1, 2007, pp. 13-19. doi:10.1212/01.wnl.0000250326.77323.01

[19]   Y. Nakata, N. Sato, K. Nemoto, et al., “Diffusion Abnormality in the Posterior Cingulum and Hippocampal Volume: Correlation with Disease Progression in Alzheimer’s Disease,” Magnetic Resonance Imaging, Vol. 27, No. 3, 2009, pp. 347-354 doi:10.1016/j.mri.2008.07.013

[20]   B. B. Avants, P. A. Cook, L. Ungar, et al., “Dementia Induced Correlated Reductions in White Matter Integrity and Cortical Thickness: A Multivariate Neuroimaging Study with Sparse Canonical Correlation Analysis,” NeuroImage, Vol. 50, No. 3, 2010, pp. 1004-1016. doi:10.1016/j.neuroimage.2010.01.041

[21]   L. Wang, F. C. Goldstein, E. Veledar, et al., “Alterations in Cortical Thickness and White Matter Integrity in Mild Cognitive Impairment Measured by Whole-Brain Cortical Thickness Mapping and Diffusion Tensor Imaging,” American Journal of Neuroradiology, Vol. 30, No. 5, 2009, pp. 893-899. doi:10.3174/ajnr.A1484

[22]   B. Magnin, L. Mesrob, S. Kinkingnéhun, et al., “Support Vector Machine-Based Classification of Alzheimer’s Disease from Whole-Brain Anatomical MRI,” Neuroradiology, Vol. 51, No. 2, 2009, pp. 73-83. doi:10.1007/s00234-008-0463-x

[23]   C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, Vol. 20, No. 3, 1995, pp. 273-297.

[24]   B. Efron and R. Tibshirani, “An Introduction to the Bootstrap,” CRC Press, Boca Raton, 1993.

[25]   G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price and E. M. Stadlan, “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,” Neurology, Vol. 77, No. 7, 1884, pp. 939-944.

[26]   J. C. Morris, “The Clinical Dementia Rating (CDR): Current Version and Scoring Rules,” Neurology, Vol. 43, No. 11, 1993, pp. 2412-2414.

[27]   M. F. Folstein, S. E. Folstein and P. R. McHugh, “‘MiniMental State’: A Practical Method for Grading the Cognitive State of Patients for the Clinician,” Journal of Psychiatric Research, Vol. 12, No. 3, 1975, pp. 189-198.

[28]   B. Dubois, A. Slachevsky, I. Litvan and B. Pillon, “The FAB: A Frontal Assessment Battery at Bedside,” Neurology, Vol. 55, No. 11, 2000, pp. 1621-1626.

[29]   S. A. Montgomery and M. A. Asberg, “A New Depression Scale Designed to be Sensitive to Change,” British Journal of Psychiatry, Vol. 134, No. 4, 1979, pp. 382-389. doi:10.1192/bjp.134.4.382

[30]   S. Mattis, “Dementia Rating Scale,” Psychological Assessment Resources, Odessa, 1988.

[31]   E. Grober and H. Buschke, “Genuine Memory Deficits in Dementia,” Developmental Neuropsychology, Vol. 3, No. 1, 1987, pp. 13-36.

[32]   D. Wechsler, “The Wechsler Intelligence Scale,” 3rd Edition, The Psychological Corporation, San Antonio, 1997.

[33]   H. J. Riddoch and G. W. Humphreys, “Birmingham Object Recognition Battery,” Lawrence Erlbaum Associated Ltd., Mahwah, 1993.

[34]   B. Pillon, N. Gouider-Khouja, B. Deweer, et al., “The Neuropsychological Pattern of Corticobasal Degeneration: Comparison with Progressive Supranuclear Palsy and Alzheimer’s Disease,” Journal of Neurology Neurosurgery & Psychiatry, Vol. 58, No. 2, 1995, pp. 174-179. doi:10.1136/jnnp.58.2.174

[35]   F. Thuillard and G. Assal, “Données Neuropsychologiques Chez le Sujet Agé Normal,” In: M. Habib, Y. Joanette and M. Puel, Eds., Démences et Syndromes Démentiels. Approche Neuropsychologique, Masson, Paris, 1991, pp. 125-133.

[36]   T. Liu, G. Young, L. Huang and S. T. Wong, “76-Space Analysis of Grey Matter Diffusivity: Methods and Applications,” NeuroImage, Vol. 31, No. 1, 2006, pp. 51-65. doi:10.1016/j.neuroimage.2005.11.041

[37]   N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, et al., “Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain,” NeuroImage, Vol. 15, No. 1, 2002, pp. 273-289. doi:10.1006/nimg.2001.0978

[38]   I. Guyon, J. Weston, S. Barnhill and V. Vapnik, “Gene Selection for Cancer Classification Using Support Vector Machines,” Machine Learning, Vol. 46, No. 1-3, 2002, pp. 389-422. doi:10.1023/A:1012487302797

[39]   S. Kinkingnéhun, M. Sarazin, S Lehéricy, et al., “VBM Anticipates the Rate of Progression of Alzheimer Disease: A 3-Year Longitudinal Study,” Neurology, Vol. 70, No. 23, 2008, pp. 2201-2211. doi:10.1212/01.wnl.0000303960.01039.43

[40]   D. Zhang, Y. Wang, L. Zhou, et al., “Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment,” NeuroImage, Vol. 55, No. 3, 2011, pp. 856-867. doi:10.1016/j.neuroimage.2011.01.008

 
 
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