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

• URL: http://arxiv.org/abs/2211.05235v1
• Date: Fri, 28 Oct 2022 03:39:55 GMT
• Title: Improved Prediction of Beta-Amyloid and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding
• Authors: Jianfeng Wu (1), Yi Su (2), Wenhui Zhu (1), Negar Jalili Mallak (1), Natasha Lepore (3), Eric M. Reiman (2), Richard J. Caselli (4), Paul M. Thompson (5), Kewei Chen (2), Yalin Wang (1) (for the Alzheimer's Disease Neuroimaging Initiative, (1) School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA, (2) Banner Alzheimer's Institute, Phoenix, USA, (3) CIBORG Lab, Department of Radiology Children's Hospital Los Angeles, Los Angeles, USA, (4) Department of Neurology, Mayo Clinic Arizona, Scottsdale, USA, (5) Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, USA)
• Abstract summary: We develop a non-invasive framework to quantitatively predict the amyloid and tau measurements. We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Beta-amyloid (A$\beta$) 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 A$\beta$/tau pathology include cerebrospinal fluid (CSF; invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (BBBM; 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 (ADNI). 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 (SPHARM). 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.

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