Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
Washington University in St Louis（圣路易斯华盛顿大学）；Washington University School of Medicine in St Louis（圣路易斯华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines
Universiti Teknologi PETRONAS（马来西亚国油大学）；Hospital Universiti Sains Malaysia（马来西亚理科大学医院）；Universiti Sains Malaysia（马来西亚理科大学）；Ulster University（阿尔斯特大学）；University of Calgary（卡尔加里大学）；University of Bath（巴斯大学）
本文基于 324 名 BioFIND 参与者的 MEG 与 MRI 数据，通过交叉验证机器学习分类识别 MCI，发现融合未校正 MEG 与 z-score 校正 MRI 特征效果最佳。
Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection
Beijing JiaoTong University（北京交通大学）；Research Center of 6G Mobile Communications（6G移动通信研究中心）；School of Cyber Science and Engineering（网络空间与工程学院）；Huazhong University of Science and Technology（华中科技大学）
Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University（佐治亚州立大学神经影像学与数据科学转化研究三机构中心（TReNDS））；College of Pharmacy, University of Florida（佛罗里达大学药学院）
