PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer’s Disease Feature Fusion and Multimodal Classification

Article type: Research Article

Authors: Shojaie, Mehdi^{a; *} | Tabarestani, Solale^a | Cabrerizo, Mercedes^a | DeKosky, Steven T.^{b; f} | Vaillancourt, David E.^{b; c; f} | Loewenstein, David^{d; f} | Duara, Ranjan^{e; f} | Adjouadi, Malek^{a; f}

Affiliations: [a] Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA | [b] Department of Neurology, University of Florida, Gainesville, FL, USA | [c] Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA | [d] Center for Cognitive Neuroscience and Aging, University of Miami Miller School of Medicine, Miami, FL, USA | [e] Wien Center for Alzheimer’s Disease & Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA | [f] 1Florida ADRC (Florida Alzheimer’s Disease Research Center), Gainesville, FL, USA

Correspondence: [*] Correspondence to: Mehdi Shojaie, 10555 W Flagler St., Miami, FL, 33174, USA. Tel.: +1 305 781 8163; E-mail: [email protected].

Abstract: Background:Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer’s disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. Objective:This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. Methods:From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Results:Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. Conclusion:The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

Keywords: Alzheimer’s disease, amyloid-β , classification, feature selection, information theory, machine-learning, multimodal imaging, tau

DOI: 10.3233/JAD-210064

Journal: Journal of Alzheimer's Disease, vol. 84, no. 4, pp. 1497-1514, 2021