Discriminating Aging Cognitive Decline Spectrum Using PET and Magnetic Resonance Image Features

Article type: Research Article

Authors: Dartora, Caroline Machado^{a; *; 1} | de Moura, Luís Vinicius^b | Koole, Michel^c | Marques da Silva, Ana Maria^{a; b; d} | for the Alzheimer’s Disease Neuroimaging Initiative²

Affiliations: [a] PUCRS, School of Medicine, Porto Alegre, Brazil | [b] PUCRS, School of Technology, Porto Alegre, Brazil | [c] KU Leuven, Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Medical Imaging Research Center, Leuven, Belgium | [d] PUCRS, Brain Institute of Rio Grande do Sul (BraIns), Porto Alegre, Brazil

Correspondence: [*] Correspondence to: Caroline Machado Dartora, Av. Ipiranga 6681, Prédio 96A, Sala 103.10, Porto Alegre, Brazil. E-mail: caroline.dartora@acad.pucrs.br.

Note: [1] Present address: Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.

Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://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

Abstract: Background:The population aging increased the prevalence of brain diseases, like Alzheimer’s disease (AD). Early identification of individuals with higher odds of cognitive decline is essential to maintain quality of life. Imaging evaluation of individuals at risk of cognitive decline includes biomarkers extracted from brain positron emission tomography (PET) and structural magnetic resonance imaging (MRI). Objective:We propose investigating ensemble models to classify groups in the aging cognitive decline spectrum by combining features extracted from single imaging modalities and combinations of imaging modalities (FDG+AMY+MRI, and a PET ensemble). Methods:We group imaging data of 131 individuals into four classes related to the individuals’ cognitive assessment in baseline and follow-up: stable cognitive non-impaired; individuals converting to mild cognitive impairment (MCI) syndrome; stable MCI; and Alzheimer’s clinical syndrome. We assess the performance of four algorithms using leave-one-out cross-validation: decision tree classifier, random forest (RF), light gradient boosting machine (LGBM), and categorical boosting (CAT). The performance analysis of models is evaluated using balanced accuracy before and after using Shapley Additive exPlanations with recursive feature elimination (SHAP-RFECV) method. Results:Our results show that feature selection with CAT or RF algorithms have the best overall performance in discriminating early cognitive decline spectrum mainly using MRI imaging features. Conclusion:Use of CAT or RF algorithms with SHAP-RFECV shows good discrimination of early stages of aging cognitive decline, mainly using MRI image features. Further work is required to analyze the impact of selected brain regions and their correlation with cognitive decline spectrum.

Keywords: Aging, amyloid, atrophy, fluorodeoxyglucose F18, machine learning, multimodal imaging

DOI: 10.3233/JAD-215164

Journal: Journal of Alzheimer's Disease, vol. 89, no. 3, pp. 977-991, 2022