Geometric Modeling of Hippocampal Tau Deposition: A Surface-Based Framework for Covariate Analysis and Off-Target Contamination Detection
Liangkang Wang, Akhil Ambekar, Ani Eloyan

TL;DR
This paper presents a geometric surface-based framework for analyzing hippocampal tau deposition in Alzheimer's disease, enabling detailed covariate analysis, subtype identification, and contamination detection in PET imaging.
Contribution
It introduces a novel surface modeling approach that captures tau spatial distribution and morphological changes, improving analysis precision and contamination detection in PET data.
Findings
Distinct tau dynamics identified for different AD subtypes
Structured spatial trajectory of tau expansion in hippocampus
Detection of off-target contamination from choroid plexus
Abstract
We introduce a framework combining geometric modeling with disease progression analysis to investigate tau deposition in Alzheimer's disease (AD) using positron emission tomography (PET) data. Focusing on the hippocampus, we construct a principal surface that captures the spatial distribution and morphological changes of tau pathology. By projecting voxels onto this surface, we quantify tau coverage, intensity, and thickness through bidirectional projection distances and interpolated standardized uptake value ratios (SUVR). This low-dimensional embedding preserves spatial specificity while mitigating multiple comparison issues. Covariate effects are analyzed using a two-stage regression model with inverse probability weighting to adjust for signal sparsity and selection bias. Using the SuStaIn model, we identify subtypes and stages of AD, revealing distinct tau dynamics: the…
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Taxonomy
TopicsDementia and Cognitive Impairment Research · Alzheimer's disease research and treatments · Functional Brain Connectivity Studies
