Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated
tau proteins in the form of neurofibrillary tangles are the two
neuropathological hallmarks of Alzheimer's disease (AD). In particular, the
accumulation of A$\beta$ plaques, as evinced by the A/T/N
(amyloid/tau/neurodegeneration) framework, marks the initial stage. Thus, the
identification of individuals with A$\beta$ positivity could enable early
diagnosis and potentially lead to more effective interventions. Deep learning
methods relying mainly on amyloid PET images have been employed to this end.
However, PET imaging has some disadvantages, including the need of radiotracers
and expensive acquisitions. Hence, in this work, we propose a novel multimodal
approach that integrates information from structural, functional, and diffusion
MRI data to discriminate A$\beta$ status in the AD continuum. Our method
achieved an accuracy of $0.762\pm0.04$. Furthermore, a \textit{post-hoc}
explainability analysis (guided backpropagation) was performed to retrieve the
brain regions that most influenced the model predictions. This analysis
identified some key regions that were common across modalities, some of which
were well-established AD-discriminative biomarkers and related to A$\beta$
deposition, such as the hippocampus, thalamus, precuneus, and cingulate gyrus.
Hence, our study demonstrates the potential viability of MRI-based
characterization of A$\beta$ status, paving the way for further research in
this domain.

通过结构、功能和扩散性 MRI 数据的多模态集成信息，可以准确判别 Aβ 血型在阿尔茨海默病连续性中的状态，为该领域的进一步研究铺平了道路。

多模态 MRI 基于的阿尔茨海默病相关淀粉样蛋白状态的检测

Multimodal MRI-based Detection of Amyloid Status in Alzheimer's Disease  Continuum

Motivation: Alzheimer's Disease hallmarks include amyloid-beta deposits and
brain atrophy, detectable via PET and MRI scans, respectively. PET is
expensive, invasive and exposes patients to ionizing radiation. MRI is cheaper,
non-invasive, and free from ionizing radiation but limited to measuring brain
atrophy.
Goal: To develop an 3D image translation model that synthesizes amyloid-beta
PET images from T1-weighted MRI, exploiting the known relationship between
amyloid-beta and brain atrophy.
Approach: The model was trained on 616 PET/MRI pairs and validated with 264
pairs.
Results: The model synthesized amyloid-beta PET images from T1-weighted MRI
with high-degree of similarity showing high SSIM and PSNR metrics
(SSIM>0.95&PSNR=28).
Impact: Our model proves the feasibility of synthesizing amyloid-beta PET
images from structural MRI ones, significantly enhancing accessibility for
large-cohort studies and early dementia detection, while also reducing cost,
invasiveness, and radiation exposure.

开发一种 3D 图像转换模型，能够从 T1 加权 MRI 合成淀粉样蛋白 PET 图像，通过模型训练和验证，证明了从结构性 MRI 图像中合成淀粉样蛋白 PET 图像的可行性，大大提高了大型队列研究和早期痴呆症检测的可访问性，同时降低成本、侵入性和辐射暴露。

基于条件生成对抗网络的结构性 MRI 三维淀粉样蛋白质 PET 合成

Three-Dimensional Amyloid-Beta PET Synthesis from Structural MRI with  Conditional Generative Adversarial Networks

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning
with amyloidosis, followed by neuronal loss and deterioration in structure,
function, and cognition. The accumulation of amyloid-beta in the brain,
measured through 18F-florbetapir (AV45) positron emission tomography (PET)
imaging, has been widely used for early diagnosis of AD. However, the
relationship between amyloid-beta accumulation and AD pathophysiology remains
unclear, and causal inference approaches are needed to uncover how amyloid-beta
levels can impact AD development. In this paper, we propose a graph varying
coefficient neural network (GVCNet) for estimating the individual treatment
effect with continuous treatment levels using a graph convolutional neural
network. We highlight the potential of causal inference approaches, including
GVCNet, for measuring the regional causal connections between amyloid-beta
accumulation and AD pathophysiology, which may serve as a robust tool for early
diagnosis and tailored care.

本文介绍了使用图卷积神经网络的图变系数神经网络 (GVCNet) 来估计连续治疗水平下的个体治疗效果，并强调因果推断方法包括 GVCNet 可用于衡量淀粉样 β 的沉积和 AD 病理生理学之间的区域性因果关系，这可能会成为早期诊断和定制护理的强有力工具。