Uncertainty-driven modality selection for data-efficient prediction of Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder. Early prediction of the risk of converting to AD for individuals at pre-dementia stages such as Mild Cognitive Impairment (MCI) is important. This could provide an opportunity for early intervention to slow down disease progression before significant irreversible neurodegeneration occurs. Neuroimaging datasets of different modalities such as MRI and PET have shown great promise. However, different data modalities are associated with varying acquisition costs/levels of accessibility to patients. We propose a machine learning (ML) framework, namely Uncertainty-driven Modality Selection (UMoS), that allows for sequentially adding data modalities for each patient on an as-needed basis, while at the same time achieving high prediction accuracy as if all the modalities were used. UMoS provides a tool to assist clinicians in deciding what data modalities/diagnostic exams each patient needs. We apply UMoS to a real-world dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) based on demographic/clinical data, MRI, and PET. UMoS shows high accuracy for predicting MCI conversion to AD, which has no significant difference from models based on the simultaneous use of all the modalities for each patient. The benefit of UMoS is significant data efficiency accomplished by saving a large percentage of patients from needing to acquire more costly/less accessible data modalities, thus lessening the burden on patients and the healthcare system.

Keywords:

• Machine learning
• multi-modality data
• computer-aided diagnosis

Funding

This research was primarily supported by NIH grant 2R42AG053149-02A1 and NSF grant DMS-2053170. This research was also supported by NIH grants RF1AG073424, R01AG069453 and P30AG072980, the State of Arizona, and Banner Alzheimer’s Foundation. 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.

Consent and approval statement

This study has been exempted from the requirement for approval by an institutional review board. The data corpus is publicly available.

Disclosure statement

The authors report no conflict of interest.