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Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease

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Abstract

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.

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Acknowledgments

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

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Funding

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.

Ethical approval

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

Informed consent was obtained from all individual participants included in the study.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran

    Ali Khazaee & Ata Ebrahimzadeh

  2. Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA

    Abbas Babajani-Feremi

  3. Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA

    Abbas Babajani-Feremi

  4. Neuroscience Institute, Le Bonheur Children’s Hospital, Memphis, TN, USA

    Abbas Babajani-Feremi

Authors
  1. Ali Khazaee
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  2. Ata Ebrahimzadeh
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  3. Abbas Babajani-Feremi
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Corresponding author

Correspondence to Ali Khazaee.

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Khazaee, A., Ebrahimzadeh, A. & Babajani-Feremi, A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging and Behavior 10, 799–817 (2016). https://doi.org/10.1007/s11682-015-9448-7

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  • DOI: https://doi.org/10.1007/s11682-015-9448-7

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