White matter at various stages of Alzheimer’s disease
Background: To determine whether the brain’s white matter (WM) volume condition provides an accurate insight into the early diagnosis of Alzheimer’s disease (AD).
Objective: Using an automatic system to measure WM from MR images in order to check the potential of WM atrophy to affect the progression of the AD and whether it can be used as a good biomarker.
Methods: We used the Open Access Series of Imaging Studies (OASIS) database. The method consists of a series of morphological operations on the binary images to extract WM volume and calculation of volume and the statistical characteristic of segmented WM.
Result: There is a significant negative correlation between WM volume and CDR (r=-0.432, p<0.05). The correlation between WM volume and CDR indicates that the severity of the current state of disease is associated with the loss of WM volume. While the AD has mostly been considered as a GM disease, this study approved that AD is characterized by the relevant involvement of the WM, and WM is a cognitive change in the AD.
Conclusion: Our results confirmed that WM volume significantly contributes to the prediction of the AD. A robust and accurate segmentation of WM lesions from MR images can provide importantly
information about the disease status and progression.
 Brun A, Englund E: A white matter disorder in dementia of the Alzheimer type: a pathoanatomical study. (1986). Ann Neurol;19:253–262.
 Weiler M, Agosta F, Canu E, et al: Following the spreading of brain structural changes in Alzheimer’s disease: a longitudinal, multimodal MRI study. (2015). J Alzheimers Dis;47:995– 1007.
 Silbert LC, Lahna D, Promjunyakul NO, et. al, (2018). Risk Factors Associated with Cortical Thickness and White Matter Hyperintensities in Dementia Free Okinawan Elderly. J Alzheimers Dis. doi: 10.3233/JAD-171153.
 De la Monte SM: Quantitation of cerebral atrophy in preclinical and end-stage Alzheimer’s disease. Ann Neurol 1989;25:450–459, DOI:10.1002/ana.410250506.
 Birdsill A, L. Koscik R, M. Jonaitis E, et. all, Regional white matter hyperintensities: aging, Alzheimer's disease risk, and cognitive function, (2014). Neurobiology of Aging, Volume 35, Issue 4, Pages 769-776.
 Sjobeck M, Haglund M, Englund E: White matter mapping in Alzheimer’s disease: a neuropathological study (2006). Neurobiol Aging; 27:673–680.
 Caso F., Agosta F., Filippi M., Insights into White Matter Damage in Alzheimer's Disease: From Postmortem to in vivo Diffusion Tensor MRI Studies, (2016). Neurodegener Dis. ;16(1-2):26-33. doi: 10.1159/000441422. Epub 2015 Dec 1.
 De la Monte SM: Quantitation of cerebral atrophy in preclinical and end-stage Alzheimer’s disease, (1989). Ann Neurol;25:450–459.
 Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB (2016): Brain atrophy in Alzheimer’s Disease and aging. Ageing Res Rev 30:25–48.
 Bo-Lin Ho, Yi-Hui Kao, Mei-Chuan Chou,Yuan-Han Yang, Cerebral White Matter Changes on Therapeutic Response to Rivastigmine in Alzheimer’s Disease, (2016). Journal of Alzheimer’s Disease 54,351–357, DOI: 10.3233/JAD-160364, IOS Press.
 Oishi, K., Faria, A.: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer’s disease participants. Neuroimage 46(2), 486–499 (2009).
 Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 189–198 (1975)
 Dinomais M, Celle S, Duval GT, Roche F, Henni S, Bartha R, Beauchet O, Annweiler C, Anatomic Correlation of the Mini-Mental State Examination: A Voxel-Based Morphometric Study in Older Adults, (2016).PLoS One. 14;11(10):e0162889. doi: 10.1371/journal.pone.0162889. eCollection 2016.
 Patil R.B., Ramakrishnan S. (2014) Correlation of Diffusion Tensor Imaging Indices with MMSE Score in Alzheimer Patients: A Sub-anatomic Region Based Study on ADNI Database. In: Pham T.D., Ichikawa K., Oyama-Higa M., Coomans D., Jiang X. (eds) Biomedical Informatics and Technology. Communications in Computer and Information Science, vol 404. Springer, Berlin, Heidelberg
 Barnes J, Dickerson BC, Frost C, Jiskoot LC, Wolk D, van der Flier WM, (2015). Alzheimer's disease first symptoms are age dependent: Evidence from the NACC dataset. Alzheimers Dement;11(11):1349-57. doi: 10.1016/j.jalz.2014.12.007.
 Lozano F, Ortiz A, Munilla J, Peinado A (2017) Automatic computation of regions of interest by robust principal component analysis.Application to automatic dementia diagnosis, Knowledge-based systems, vol. 13, no. C, pp. 229-237.
 West J, Warntjes J. B. M, Lundberg P, Novel whole brain segmentation and volume estimation using quantitative MRI, (2012). Eur Radiol, 22:998–1007, DOI 10.1007/s00330-011-2336-7.
 Remika Mito R, Raffelt D., Dhollander T., N. Vaughan D., Tournier J.-D, Salvado O, Brodtmann A, C. Rowe C, Villemagne V L., and Connelly A, Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment, (2018). Brain. 2018 Jan 4. doi: 10.1093/brain/awx355.
 Amlien K, Fjell A. M, DIFFUSION TENSOR IMAGING OF WHITE MATTER DEGENERATION IN ALZHEIMER’S DISEASE AND MILD COGNITIVE IMPAIRMENT (2014).Neuroscience, Volume 276, Pages 206-215, https://doi.org/10.1016/j.neuroscience.2014.02.017.
 Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, Frisoni GB, Filippi M (2011) White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology 258:853–863. Alves GS, O’Dwyer L, Jurcoane A, Oertel-Kno¨chel V, Kno¨chel.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
- All contributor(s) agree to transfer the copyright of this article to EPH Journal.
- EPH Journal will have all the rights to distribute, share, sell, modify this research article with proper reference of the contributors.
- EPH Journal will have the right to edit or completely remove the published article on any misconduct happening.