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

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') bio...

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Veröffentlicht in:Journal of Alzheimer's disease Jg. 91; H. 2; S. 637
Hauptverfasser: Wu, Jianfeng, Su, Yi, Zhu, Wenhui, Jalili Mallak, Negar, Lepore, Natasha, Reiman, Eric M, Caselli, Richard J, Thompson, Paul M, Chen, Kewei, Wang, Yalin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands 01.01.2023
Schlagworte:
Alzheimer Disease - metabolism
Amyloid beta-Peptides - metabolism
Atrophy - pathology
Biomarkers - cerebrospinal fluid
Hippocampus - pathology
Humans
Magnetic Resonance Imaging
Positron-Emission Tomography - methods
tau Proteins - metabolism
hippocampal multivariate morphometry statistics
Braak34 tau-SUVR
Alzheimer’s disease
Centiloid
dictionary and correntropy-induced sparse coding
tau deposition
amyloid deposition
Braak12 tau-SUVR
ISSN:1875-8908, 1875-8908
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Zusammenfassung: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). To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. 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. 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. 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1875-8908
1875-8908
DOI:10.3233/JAD-220812