Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

Zhan, L., Nie, Z., Ye, J., Wang, Y., Jin, Y., Jahanshad, N., Prasad, G., de Zubicaray, G. I., McMahon, K. L., Martin, N. G., Wright, M. J. and Thompson, P. M. (2014). Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM). In: MICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, Boston, MA, USA, (35-44). September 18, 2014. doi:10.1007/978-3-319-11182-7_4


Author Zhan, L.
Nie, Z.
Ye, J.
Wang, Y.
Jin, Y.
Jahanshad, N.
Prasad, G.
de Zubicaray, G. I.
McMahon, K. L.
Martin, N. G.
Wright, M. J.
Thompson, P. M.
Title of paper Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)
Conference name MICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014
Conference location Boston, MA, USA
Conference dates September 18, 2014
Journal name Mathematics and Visualization   Check publisher's open access policy
Series Mathematics and Visualization
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-11182-7_4
Open Access Status Not yet assessed
ISBN 9783319111810
ISSN 2197-666X
1612-3786
Volume 39
Start page 35
End page 44
Total pages 10
Language eng
Abstract/Summary To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.
Subjects 2611 Modelling and Simulation
2608 Geometry and Topology
1704 Computer Graphics and Computer-Aided Design
2604 Applied Mathematics
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collection: Centre for Advanced Imaging Publications
 
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Created: Fri, 06 Apr 2018, 05:15:18 EST