arXiv:2606. 04066v1 Announce Type: cross Abstract: Understanding how structural connections are associated with tau propagation in Alzheimer's disease (AD) remains a central open question, yet existing computational models either rely heavily on biophysical assumptions or lack neurobiologically interpretable pathway maps.
By Jing Zhang, Norman Scheel, Minheng Chen, Tong Chen, Yanjun Lyu, David C. Zhu, Rong Zhang, Dajiang Zhu
arXiv:2607. 07401v1 Announce Type: cross Abstract: While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice.
By Chengbo Wang, Jiacheng Yu, Linjie Bian, Ming Qi, Xiaosheng Liu, Tongtong Che, Jichang Zhang, Shuyu Li, Shaoli Song, Xiuying Wang
While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice. Deep learning-based MRI translation provides a potential solution to reduce scan duration.
Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black-box forecasting task.
arXiv:2606. 15784v1 Announce Type: new Abstract: Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade.
By Nguyen Linh Dan Le
arXiv:2606. 17867v1 Announce Type: cross Abstract: Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research -- aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization -- the relationships among these modalities remain poorly understood.
By Antonio Scardace, Daniele Rav\`i
The paper introduces DB‑SUiT, a surface‑based diffusion bridge that translates cortical MRI to PET images directly on the cortical manifold. It employs a conditional spherical U‑shaped vision Transformer to capture multi‑scale surface features and long‑range dependencies while incorporating demographic and subcortical information. Evaluations on two dementia datasets show that the synthesized PET surfaces outperform MRI and PET volumes in automated classification and achieve high diagnostic accuracy in a blinded reader study.
By Yitong Li, Alexandra Samoylova, Fabian Bongratz, Timo Grimmer, Dennis M. Hedderich, Igor Yakushev, Christian Wachinger
arXiv:2606. 22216v2 Announce Type: replace-cross Abstract: While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical utility is constrained by high operational costs and cumulative radiation risks.
By Yongheng Sun, Minhui Yu, Mengqi Wu, Maureen Kohi, Mingxia Liu
arXiv:2607. 17442v1 Announce Type: new Abstract: Background.
By Navin Bondade
arXiv:2606. 25185v1 Announce Type: new Abstract: Accurately predicting the spatiotemporal evolution of amyloid-$\beta$ and tau proteins at the individual level is critical for improving the diagnosis and treatment of Alzheimer's disease.
By Xiaofeng Xu, Tingting Dan, Zifan Zhou, Bin Li, Guorong Wu, Wenrui Hao
arXiv:2410. 01244v2 Announce Type: replace-cross Abstract: We introduce a novel Wasserstein-1 ($W_1$) path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertainty Propagation (WUP) theorem that bounds the $W_1$ distance between terminal distributions by the proposed divergence, equivalently characterized by a weighted $L^2$ discrepancy between the underlying drifts and the $W_1$ distance between their initial measures.
By Ziyu Chen, Markos A. Katsoulakis, Benjamin J. Zhang
The paper introduces MCNet, a neural network that assigns a Modality Contribution Score (MCS) to each patient, indicating how much structural MRI versus amyloid PET drives the diagnostic decision for Alzheimer’s disease. Across 327 ADNI-3 participants, MCNet achieved strong three‑class staging (AUC = 0.881) and the MCS showed a clear, statistically significant increase in PET dominance from cognitively normal to AD. The method was validated on an independent OASIS‑3 cohort and compared favorably to SHAP, suggesting it can guide personalized imaging and clinical trial decisions.
By Dawa Chyophel Lepcha, Aaliya Ali, Sophie A. Martin, Deepika Koundal, Pierrick Coupe, Shabbir Syed-Abdul