Comparison of the Unified Segmentation Method and the New Segmentation Method on Detection of Grey Matter and White Matter Changes in Alzheimer’s Disease Based on Voxel-Based Morphometry

Article Preview

Abstract:

Voxel-based morphometry method (VBM) has been widely applied to detect the brain atrophy and achieved promising results; however, the effect of the segmentation step in VBM is not clear and the new segmentation method in SPM8 hasn’t been used in Alzheimer’s disease (AD) studies. The aim of this study is to investigate the locations and degrees of grey matter (GM), white matter (WM) atrophy and evaluate the results derived from two segmentation methods. Magnetic resonance imaging (MRI) was collected in 16 AD patients and 16 healthy controls (HC). Using two segmentation methods respectively, several reduction clusters of GM and WM were detected but the locations and degrees of reduction volumes were discrepant resulted from different segmentation methods. Our results suggest that VBM is an effective tool to analyze AD brain atrophy and based on VBM, the comparison of the locations and degrees of volume reduction among AD researches through different segmentation methods should be cautious.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 301-303)

Pages:

1189-1195

Citation:

Online since:

July 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Hauw JJ, Duyckaerts C, Delaere P, Lamy C, Henry P. Alzheimer's disease: neuropathological and etiological data. Biomed Pharmacother, (1989) , 43(7): p.469–482.

DOI: 10.1016/0753-3322(89)90107-8

Google Scholar

[2] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. 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, (1984).

DOI: 10.1212/wnl.34.7.939

Google Scholar

[3] Mendez MF. The accurate diagnosis of early-onset dementia. International Journal of Psychiatry Medicine, (2006) , 36 (4): p.401–412.

DOI: 10.2190/q6j4-r143-p630-kw41

Google Scholar

[4] Klafki HW, Staufenbiel M, Kornhuber J, Wiltfang J. Therapeutic approaches to Alzheimer's disease. Brain, (2006) , 129 (Pt 11): p.2840–55.

DOI: 10.1093/brain/awl280

Google Scholar

[5] Chetelat, G., Landeau, B., Eustache, F., Mezenge, F., Viader, F., de la Sayette, V., Desgranges, B., Baron, J.C., Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage, (2005).

DOI: 10.1016/j.neuroimage.2005.05.015

Google Scholar

[6] Anne Hämäläinen, Susanna Tervo, Marta Grau-Olivares, et al., Voxel-based morphometry to detect brain atrophy in progressive mild cognitive impairment. NeuroImage, (2007) , 37: p.1122–1131.

DOI: 10.1016/j.neuroimage.2007.06.016

Google Scholar

[7] Lehmann, M., et al., Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer's disease. Neurobiol. Aging, (2009).

Google Scholar

[8] Claudia Plant, Stefan J. Teipel, Annahita Oswald, et at., Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. NeuroImage, (2010) , 50: p.162–174.

DOI: 10.1016/j.neuroimage.2009.11.046

Google Scholar

[9] Prashanthi Vemuri, Stephen D. Weigand, David S. Knopman, et al., Time-to-event voxel-based techniques to assess regional atrophy associated with MCI risk of progression to AD. NeuroImage, (2011) , 54: p.985–991.

DOI: 10.1016/j.neuroimage.2010.09.004

Google Scholar

[10] Andrea Mechelli, Cathy J. Price, Karl J. Friston, John Ashburner, Voxel-Based Morphometry of the Human Brain: Methods and Applications. Current Medical Imaging Reviews, (2005), 1(1): pp.1-9.

DOI: 10.2174/1573405054038726

Google Scholar

[11] Ashburner, J., A fast diffeomorphic image registration algorithm. NeuroImage, (2007) t. 1538 (1), p.95–113.

DOI: 10.1016/j.neuroimage.2007.07.007

Google Scholar

[12] John AshburnerT , Karl J. Friston, Unified segmentation. NeuroImage, (2005) , 26: p.839– 851.

Google Scholar

[13] G. McKhann, D. Drachmann, M. Foldstein, R. Katzman, D. Price, Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under auspices of Department of Health and Human Services Task Force of Alzheimer's Disease. Neurology 34 (1984).

DOI: 10.1212/wnl.34.7.939

Google Scholar

[14] Duncan, J., Seitz, R.J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., Newell, F.N., Emslie, H., A neural basis for General Intelligence. Science, (2000), 289 (5478): p.457–460.

DOI: 10.1126/science.289.5478.457

Google Scholar

[15] Ged Ridgway, Voxel-Based Morphometry with Unified Segmentation. Course from http: /www. cs. ucl. ac. uk/staff/G. Ridgway/zurich.

Google Scholar