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Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification

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Abstract

A brain network can be constructed from various imaging modalities such as magnetic resonance imaging (MRI), representing the functional or structural connectivity between brain regions. The challenge of brain network analysis is efficient dimensionality reduction while retaining feature interpretability. We propose a new method to extract features from graph-structured data based on maximum mutual information (MMI-GSD). First, we develop a novel equation for the feature extraction from GSD and evaluate the interpretability of the features. We establish a framework to optimize the extracted features using the MMI. We conduct experiments on synthetic networks to validate the effectiveness of the proposed MMI-GSD. Next, we conduct experiments on 119 cognitively normal (CN), 105 mild cognitive impairment (MCI), and 36 Alzheimer’s disease (AD) individuals from the Alzheimer’s Disease Neuroimaging Initiative. The classification performance of the proposed method is significantly better than using traditional network metrics and existing feature extraction methods. In the clinical interpretation, we discover discriminative brain regions showing significant differences between the MCI and AD groups and identify significant abnormal connections concentrated in the left hemisphere.

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Acknowledgements

This work was supported by Beijing Advanced Innovation Center for Big Data-based Precision Medicine. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). 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.; Cogstate; 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 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Jiawei Yang & Shaoping Wang

  2. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China

    Shaoping Wang

  3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University and ASU-Mayo Center for Innovative Imaging, Tempe, AZ, 85287, USA

    Teresa Wu

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Correspondence to Shaoping Wang.

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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Yang, J., Wang, S. & Wu, T. Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification. Appl Intell 53, 1870–1886 (2023). https://doi.org/10.1007/s10489-022-03528-x

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