Improved methods for early diagnosis and non-invasive surrogates for the diagnosis of disease severity in Alzheimer’s disease (AD) are becoming the new challenge. Dementia can now be accurately determined through clinical evaluation, cognitive screening, basic laboratory evaluation and structural imaging. Magnetic resonance (MRI) techniques are being evaluated as possible surrogate measures to monitor disease progression. The purpose of this work is to correlate the results of combined advanced MR techniques with neuropsychological performance in order to identify a sensible and sensitive imaging approach to quantify neurodegenerative disease progression. One of the most relevant evidences in our study is the degeneration of the fibers of the corpus callosum in the pathogenesis of cognitive disorders in AD patients, as demonstrated by the relationship between altered neuropsychological tests and reduced FA (Fractional Anisotrophy) values of the corpus callosum in such patients. This data is also integrated by the evidence of anatomic reduction of the total volume of the corpus callosum assessed by FreeSurfer, thus supporting the hypothesis that the “brain disconnects” play a key role in the pathogenesis of AD. Statistical evaluation of regression consisting in the identification of different numerical coefficients that are multiplied by the thickness of the right fusiform value or by the volume of left inferoparietal region and left middle-temporal region, allows us to obtain the predictive numeric value of the related neuropsychological test. Combination of non-conventional magnetic resonance imaging, including morphometry, spectroscopy, MD (mean diffusivity) and FA evaluation, could be an alternative to clinic in the evaluation of neurodegeneration in AD.
McKhann, G., Drachman, D., Folstein, M., et al. (1984) 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, 34, 939-944. http://dx.doi.org/10.1212/WNL.34.7.939
Dubois, B., Feldman, H.H., Jacova, C., et al. (2007) Research Criteria for the Diagnosis of Alzheimer’s Disease: Revising the NINCDS-ADRDA Criteria. The Lancet Neurology, 6, 734-746. http://dx.doi.org/10.1016/S1474-4422(07)70178-3
Dubois, B., Feldman, H.H., Jacova, C., et al. (2010) Revising the Definition of Alzheimer’ Disease: A New Lexicon. The Lancet Neurology, 9, 1118-1127. http://dx.doi.org/10.1016/S1474-4422(10)70223-4
Frisoni, G.B., Winblad, B. and O’Brien, J.T. (2011) Revised NIA-AA Criteria for the Diagnosis of Alzheimer’s Disease: A Step Forward but Not Yet Ready for Widespread Clinical Use. International Psychogeriatrics, 23, 1191-1196. http://dx.doi.org/10.1017/S1041610211001220
Ghidoni, R., Benussi, L., Glionna, M., et al. (2008) Low Plasma Progranulin Levels Predict Progranulin Mutations in Frontotemporal Lobar Degeneration. Neurology, 71, 1235-1239. http://dx.doi.org/10.1212/01.wnl.0000325058.10218.fc
Jack Jr., C.R., Petersen, R.C., O’Brien, P.C., et al. (1992) MR based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology, 42, 183-188. http://dx.doi.org/10.1212/WNL.42.1.183
Frisoni, G.B., Bianchetti, A., Geroldi, C. and Trabucchi, M. (1994) Measures of Medial Temporal Lobe Atrophy in Alzheimer’s Disease. Journal of Neurology, Neurosurgery & Psychiatry, 57, 1438-1439. http://dx.doi.org/10.1136/jnnp.57.11.1438
Jack, C.R., Petersen, R.C., Xu, Y., et al. (1997) Medial Temporal Atrophy on MRI in Normal Aging and Very Mild Alzheimer’s Disease. Neurology, 49, 786-794. http://dx.doi.org/10.1212/WNL.49.3.786
Bobinski, M., deLeon, M.J., Convit, A., et al. (1999) MRI of Entorhinal Cortex in Alzheimer’ s Disease. The Lancet, 353, 38-40. http://dx.doi.org/10.1016/S0140-6736(05)74869-8
Chételat, G., Landeau, B., Eustache, F., et al. (2005) Using Voxel-Based Morphometry to Map the Structural Changes Associated with Rapid Conversion in MCI: A Longitudinal MRI Study. NeuroImage, 27, 934-946. http://dx.doi.org/10.1016/j.neuroimage.2005.05.015
Whitwell, J.L., Przybelski, S.A., Weigand, S.D., et al. (2007) 3D Maps from Multiple MRI Illustrate Changing Atrophy Patterns as Subjects Progress from Mild Cognitive Impairment to Alzheimer’s Disease. Brain, 130, 1777-1786.
Fennema-Notestine, C., Hagler Jr., D.J., McEvoy, L.K., Fleisher, A.S., Wu, E.H., Karow, D.S. and Dale, A.M. (2009) Structural MRI Biomarkers for Preclinical and Mild Alzheimer’s Disease. Human Brain Mapping, 30, 3238-3253. http://dx.doi.org/10.1002/hbm.20744
Devanand, D.P., Bansal, R., Liu, J., Hao, X.J., et al. (2012) MRI Hippocampal and Entorhinal Cortex Mapping in Predicting Conversion to Alzheimer’s Disease. Neuroimage, 60, 1622-1629. http://dx.doi.org/10.1016/j.neuroimage.2012.01.075
Clerx, L., Jacobs, H.I., Burgmans, S., et al. (2013) Sensitivity of Different MRI-Techniques to Assess Gray Matter Atrophy Patterns in Alzheimer’s Disease Is Region-Specific. Current Alzheimer Research, 10, 940-951. http://dx.doi.org/10.2174/15672050113109990158
Bitsch, A., Bruhn, H., Vougioukas, V., et al. (1999) Inflammatory CNS Demyelination: Histopathologic Correlation with in Vivo Quantitative Proton MR Spectroscopy. AJNR American Journal of Neuroradiology, 20, 1619-1627.
Jessen, F., Block, W., Traber, F., et al. (2000) Proton MR Spectroscopy Detects a Relative Decrease of N-Acetylaspartate in the Medial Temporal Lobe of Patients with AD. Neurology, 55, 684-688. http://dx.doi.org/10.1212/WNL.55.5.684
Schuff, N., Capizzano, A.A., Du, A.T., et al. (2002) Selective Reduction of N-Acetylaspartate in Medial Temporal and Parietal Lobes in AD. Neurology, 58, 928-935. http://dx.doi.org/10.1212/WNL.58.6.928
Watanabe, T., Shiino, A. and Akiguchi, I. (2010) Absolute Quantification in Proton Magnetic Resonance Spectroscopy Is Useful to Differentiate Amnesic Mild Cognitive Impairment from Alzheimer’s Disease and Healthy Aging. Dementia and Geriatric Cognitive Disorders, 30, 71-77. http://dx.doi.org/10.1159/000318750
Tumati, S., Martens, S. and Aleman, A. (2013) Magnetic Resonance Spectroscopy in Mild Cognitive Impairment: Systematic Review and Meta-Analysis. Neuroscience & Biobehavioral Reviews, 37, 2571-2586. http://dx.doi.org/10.1016/j.neubiorev.2013.08.004
Giugni, E., Vadalà, R., De Vincentiis, C., et al. (2010) The Brain’s Default Mode Network: A Mind “Sentinel” Role? Functional Neurology, 25, 189-190.
Chhatwal, J.P. and Sperling, R.A. (2012) Functional MRI of Mnemonic Networks across the Spectrum of Normal Aging, Mild Cognitive Impairment, and Alzheimer’s Disease. Journal of Alzheimer’s Disease, 31, S155-S167.
Weiler, M., Fukuda, A., Massabki, L.H., et al. (2014) Default Mode, Executive Function, and Language Functional Connectivity Networks Are Compromised in Mild Alzheimer’s Disease. Current Alzheimer Research, 11, 274-282. http://dx.doi.org/10.2174/1567205011666140131114716
Davatzikos, C., Bhatt, P., Shaw, L.M., et al. (2011) Prediction of MCI to AD Conversion, via MRI, CSF Biomarkers, and Pattern Classification. Neurobiology of Aging, 32, 2322.
Jenkinson, M. and Smith, S. (2001) A Global Optimisation Method for Robust Affine Registration of Brain Images. Medical Image Analysis, 5, 143-156. http://dx.doi.org/10.1016/S1361-8415(01)00036-6
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers, J., Zhang, Y.Y., De Stefano, N., Brady, J.M. and Matthews, P.M. (2004) Advances in Functional and Structural MR Image Analysis and Implementation as FSL. Neuroimage, 23, S208-S219. http://dx.doi.org/10.1016/j.neuroimage.2004.07.051
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M. and Behrens, T.E. (2006) Tract-Based Spatial Statistics: Voxelwise Analysis of Multi-Subject Diffusion Data. Neuroimage, 31, 1487-1505. http://dx.doi.org/10.1016/j.neuroimage.2006.02.024
Andersson, J.L.R., Jenkinson, M. and Smith, S. (2007) Non-Linear Optimisation. FMRIB Technical Report.
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O. and Hawkes, D.J. (1999) Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transactions on Medical Imaging, 18, 712-721. http://dx.doi.org/10.1109/42.796284
Nichols, T.E. and Holmes, A.P. (2002) Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. Human Brain Mapping, 15, 1-25. http://dx.doi.org/10.1002/hbm.1058
Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M.F. and Woolrich, M.W. (2007) Probabilistic Diffusion Tractography with Multiple Fibre Orientations: What Can We Gain? Neuroimage, 34, 144-155. http://dx.doi.org/10.1016/j.neuroimage.2006.09.018
Dale, A.M., Fischl, B. and Sereno, M.I. (1999) Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. Neuroimage, 9, 179-194. http://dx.doi.org/10.1006/nimg.1998.0395