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Discrimination of Alzheimer’s Disease using longitudinal information

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Abstract

Alzheimer’s Disease (AD) is a neurological disorder that leads to a loss of cognitive functioning, affecting older people as well as their families. Although a few treatments are available to slow down the progress of the disease, they are limited in effectiveness and should start at an early stage of the disease. Since an early diagnosis of AD is crucial, to maximize treatment effectiveness and prepare the families for the worsening of symptoms, researchers are studying biomarkers and Computer-aided diagnosis (CAD) systems. Hence, this manuscript proposes a new methodology to obtain an efficient CAD system by relying on [\(^{18}\)F]-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) scans, while taking into account the longitudinal information of a subject. The CAD system tries to identify regions of interest by simultaneously segmenting all the FDG-PET scans acquired over time for each subject and combining the segmentation result to find the most coherent information for all the subjects. Experimental results show that the proposed CAD system outperforms a state-of-the-art approach, either when only relying on baseline scans or in the follow-up classification, achieving, for instance, more than 82.0% accuracy in the discrimination between AD and Mild Cognitive Impairment (MCI). Finally, in a multi-class classification task, the proposed CAD system attains 59.0% accuracy at baseline and goes up to 69.4% in the follow-up.

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Notes

  1. http://adni.loni.usc.edu/methods/pet-analysis/pre-processing/.

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Acknowledgements

This work was supported by the Portuguese Foundation for Science and Technology, under Scholarship Number SFRH/BPD/103127/2014 and Grants PTDC/EEI-SII/7092/2014 and PTDC/EEI-SII/1937/2014. 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, Alzheimers Association; Alzheimers 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.

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Authors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Helena Aidos

  2. Instituto de Telecomunicações and Instituto Superior Técnico, Lisbon, Portugal

    Ana Fred

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  1. Helena Aidos
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For the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Helena Aidos.

Additional information

Responsible editor: Pierre Baldi.

The source code of the proposed algorithm is available at http://camoes.lx.it.pt/pia/FU_CAD_toolbox.zip.

Data used in the preparation of this article were obtained from the Alzheimers 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.

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Aidos, H., Fred, A. & For the Alzheimer’s Disease Neuroimaging Initiative. Discrimination of Alzheimer’s Disease using longitudinal information. Data Min Knowl Disc 31, 1006–1030 (2017). https://doi.org/10.1007/s10618-017-0502-5

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