Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

Article type: Research Article

Authors: Wu, Jianfeng^a | Su, Yi^b | Zhu, Wenhui^a | Jalili Mallak, Negar^a | Lepore, Natasha^c | Reiman, Eric M.^b | Caselli, Richard J.^d | Thompson, Paul M.^e | Chen, Kewei^b | Wang, Yalin^{a; *} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA | [b] Banner Alzheimer’s Institute, Phoenix, AZ, USA | [c] CIBORG Lab, Department of Radiology Children’s Hospital Los Angeles, Los Angeles, CA, USA | [d] Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA | [e] Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA

Correspondence: [*] Correspondence to: Dr. Yalin Wang, School of Computing and Augmented Intelligence, Arizona State University, P.O. Box 878809, Tempe, AZ 85287, USA. Tel.: +1 480 965 6871; Fax: +1 480 965 2751; E-mail: [email protected].

Note: [1] Data used in the 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 the 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:Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining ‘A’ and ‘T’ hallmarks of Alzheimer’s disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative (‘N’) biomarkers comprise the “ATN framework” of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). Objective:To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. Methods:With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. Results:We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. Conclusion:The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

Keywords: Alzheimer’s disease, amyloid deposition, Braak12 tau-SUVR, Braak34 tau-SUVR, Centiloid, dictionary and correntropy-induced sparse coding, hippocampal multivariate morphometry statistics, tau deposition

DOI: 10.3233/JAD-220812

Journal: Journal of Alzheimer's Disease, vol. 91, no. 2, pp. 637-651, 2023