Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer’s disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.
KeywordsResting-state functional magnetic resonance imaging (rs-fMRI) Alzheimer’s disease (AD) Mild cognitive impairment (MCI) Graph theory Machine learning Support vector machine (SVM)
Data used in this paper were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://ADNI.loni. usc.edu). The investigators within the ADNI, who can be found at http://ADNI.loni.usc.edu/study-design/ongoing-investigations, contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this article. This study was supported by the Children’s Foundation Research Institute, Le Bonheur Children’s Hospital, Memphis, TN.
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
We thank Dr. Amanda Preston for her assistance with manuscript preparation.
Compliance with Ethical Standards
This study was funded by the Children’s Foundation Research Institute, Le Bonheur Children’s Hospital, Memphis, TN.
Conflict of Interest
The authors declared that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Anderson, A., & Cohen, M.S. (2013). Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial. Frontiers in Human Neuroscience, 7.Google Scholar
- Bai, F., Zhang, Z., Yu, H., Shi, Y., Yuan, Y., Zhu, W., & Qian, Y. (2008). Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neuroscience Letters, 438(1), 111–115.CrossRefPubMedGoogle Scholar
- Bassett, D. S., Bullmore, E., Verchinski, B. A., Mattay, V. S., Weinberger, D. R., & Meyer-Lindenberg, A. (2008). Hierarchical organization of human cortical networks in health and schizophrenia. The Journal of Neuroscience, 28(37), 9239–9248. doi: 10.1523/JNEUROSCI.1929-08.2008.CrossRefPubMedPubMedCentralGoogle Scholar
- Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 106(28), 11747–11752. doi: 10.1073/pnas.0903641106.CrossRefPubMedPubMedCentralGoogle Scholar
- Binnewijzend, M. A., Adriaanse, S. M., Van der Flier, W. M., Teunissen, C. E., de Munck, J. C., Stam, C. J., & Wink, A. M. (2014). Brain network alterations in Alzheimer’s disease measured by eigenvector centrality in fMRI are related to cognition and CSF biomarkers. Human Brain Mapping, 35(5), 2383–2393. doi: 10.1002/hbm.22335.CrossRefPubMedGoogle Scholar
- Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. The Journal of Neuroscience, 29(6), 1860–1873.CrossRefPubMedPubMedCentralGoogle Scholar
- Celone, K. A., Calhoun, V. D., Dickerson, B. C., Atri, A., Chua, E. F., Miller, S. L., & Blacker, D. (2006). Alterations in memory networks in mild cognitive impairment and Alzheimer’s disease: an independent component analysis. The Journal of Neuroscience, 26(40), 10222–10231.CrossRefPubMedGoogle Scholar
- Dey, S., Rao, A.R., & Shah, M. (2012). Exploiting the brain’s network structure in identifying ADHD subjects. Frontiers in Systems Neuroscience, 6.Google Scholar
- dos Santos Siqueira, A., Biazoli Junior, C.E., Comfort, W.E., Rohde, L.A., & Sato, J.R. (2014). Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI Data. BioMed Research International, 2014.Google Scholar
- Duda, R.O., Hart, P.E., & Stork, D.G. (2012). Pattern classification. Wiley.Google Scholar
- Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678.CrossRefPubMedPubMedCentralGoogle Scholar
- Jie, B., Zhang, D., Suk, H.-I., Wee, C.-Y., & Shen, D. (2013). Integrating Multiple Network Properties for MCI Identification. In G. Wu, D. Zhang, D. Shen, P. Yan, K. Suzuki & F. Wang (Eds.), Machine Learning in Medical Imaging (Vol. 8184, pp. 9–16). Springer International Publishing.Google Scholar
- Madsen, S. K., Ho, A. J., Hua, X., Saharan, P. S., Toga, A. W., Jack, C. R., Jr., & Initiative, A. s. D. N. (2010). 3D maps localize caudate nucleus atrophy in 400 Alzheimer’s disease, mild cognitive impairment, and healthy elderly subjects. Neurobiology of Aging, 31(8), 1312–1325.CrossRefPubMedPubMedCentralGoogle Scholar
- Mccarthy, P., Benuskova, L., & Franz, E.A. (2014). The age-related posterior-anterior shift as revealed by voxelwise analysis of functional brain networks. Frontiers in Aging Neuroscience, 6. doi: 10.3389/fnagi.2014.00301.
- Mesrob, L., Magnin, B., Colliot, O., Sarazin, M., Hahn-Barma, V., Dubois, B., & Benali, H. (2008). Identification of atrophy patterns in Alzheimer’s disease based on SVM feature selection and anatomical parcellation. In T. Dohi, I. Sakuma, & H. Liao (Eds.), Medical imaging and augmented reality (Vol. 5128, pp. 124–132). Berlin Heidelberg: Springer.Google Scholar
- Miller, S. L., Celone, K., DePeau, K., Diamond, E., Dickerson, B. C., Rentz, D., & Sperling, R. A. (2008). Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proceedings of the National Academy of Sciences, 105(6), 2181–2186.CrossRefGoogle Scholar
- Newman, M. E. J. (2008). mathematics of networks. In S. N. Durlauf & L. E. Blume (Eds.), The new palgrave dictionary of economics. Palgrave Macmillan: Basingstoke.Google Scholar
- Rish, I., Cecchi, G.A., & Heuton, K. (2012). Schizophrenia classification using functional network features. Paper presented at the SPIE Medical Imaging.Google Scholar
- Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., & Stam, C. J. (2010). Loss of ‘small-world’ networks in Alzheimer's disease: graph analysis of fMRI resting-state functional connectivity. PloS One, 5(11), e13788. doi: 10.1371/journal.pone.0013788.CrossRefPubMedPubMedCentralGoogle Scholar
- Suk, H.-I., Lee, S.-W., & Shen, D. (2015a). Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Structure and Function, 1–19. doi: 10.1007/s00429-015-1059-y.Google Scholar
- Toussaint, P.-J., Maiz, S., Coynel, D., Doyon, J., Messé, A., de Souza, L. C., & Benali, H. (2014). Characteristics of the default mode functional connectivity in normal ageing and Alzheimer’s disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. NeuroImage, 101, 778–786. doi: 10.1016/j.neuroimage.2014.08.003.CrossRefPubMedGoogle Scholar
- Vapnik, V. (1998). Statistical learning theory. New York: Wiley.Google Scholar