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., & Thompson, P. M. (2014) Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM). In O'Donnell, Lauren, Nedjati-Gilani, Gemma, Rathi, Yogesh, Reisert, Marco, & Schneider, Torben (Eds.) Computational Diffusion MRI, Springer International Publishing, Boston, MA, pp. 35-44.

View at publisher


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.

Impact and interest:

3 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 85864
Item Type: Conference Paper
Refereed: No
DOI: 10.1007/978-3-319-11182-7_4
ISBN: 9783319111810
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2014 Springer
Deposited On: 20 Jul 2015 02:39
Last Modified: 01 Sep 2015 01:36

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page