Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.
An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
Department of Mathematics and Physics, Mathematics Major, Gangneung-Wonju National University（数学与物理系，数学专业，江陵-Wonju国立大学）；Department of Data Science, Gangneung-Wonju National University（数据科学系，江陵-Wonju国立大学）
An explainable framework for the relationship between dementia and glucose metabolism patterns
Department of Signal Processing and Biomedical Applications, University of Granada（信号处理与生物医学应用系，格拉纳达大学）；Department of Statistics and Operations Research, University of Valencia（统计与运筹学系，瓦伦西亚大学）；Department of Communication Engineering, University of Malaga（通信工程系，马拉加大学）
Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Washington University School of Medicine（华盛顿大学医学院）
目的阿尔茨海默病（Alzheimer disease，AD）是痴呆的最常见病因，是一种以严重的认知功能障碍为特征的综合征，严重影响日常生活活动。我们旨在对将机器学习（ML）方法应用于电子健康记录中的临床数据的研究进行系统性文献综述（SLR），以模拟AD痴呆进展的风险。材料与方法：我们检索了2010年1月1日至2020年5月31日期间发表在PubMed、Scopus、ScienceDirect、IEEE Explore Digital Library、Association for Computing Machinery Digital Library和arXiv上的文章。我们使用预定义的标准来选择相关文章，并根据ML分析的关键组成部分（如数据特征，计算算法和研究重点）进行总结。结果：在过去的5年中，使用基于ML的分析进行AD痴呆建模的研究论文数量有了相当大的增长。我们回顾了SLR中的64篇相关文章。结果表明，大多数现有研究都集中在使用包含神经成像和临床数据（神经行为状态检查评分、患者人口统计学数据、神经成像数据和实验室测试值）的公开数据集来预测AD痴呆的进展。讨论内容：识别有AD痴呆进展风险的个体可能有助于个性化疾病管理，以规划未来的护理。由结构化数据表和临床笔记组成的临床数据可以有效地用于基于ML的方法来建模AD痴呆进展的风险。数据共享和结果的可重复性可以增强本研究的影响力、适应…
Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
School of Computing Technologies RMIT University Melbourne VIC Australia；Department of Biological Sciences；Department of Computer Science \& Information Systems Birla Institute of Technology；Institute of Innovation, Science；Sustainability Federation University Australia Ballarat VIC Australia；RMIT University；Birla Institute of Technology；Federation University Australia
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI
Sharif University of Technology（谢里夫理工大学）；Iranian Brain Mapping Biobank（伊朗脑图谱生物库）；National Brain Mapping Laboratory（国家脑图谱实验室）；Virtual University of Medical Sciences（虚拟医科大学）
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers
Stanford University（斯坦福大学）；Carl Zeiss Meditec, Inc.（卡尔蔡司医疗科技公司）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Stanford University School of Medicine（斯坦福大学医学院）；Genentech（基因泰克）
Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
Department of Mathematical Sciences, University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）；Department of Plant Science and Landscape Architecture, University of Connecticut（康奈尔大学植物科学与景观建筑系）；Naveen Jindal School of Management, University of Texas at Dallas（德克萨斯大学达拉斯分校纳文·金达管理学院）；Zhejiang Provincial People’s Hospital（浙江省人民医院）
Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models
Department of Mathematical Sciences（数学科学系）；Naveen Jindal School of Management（奈文·金达管理学院）；Department of Biology（生物系）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Duke University（杜克大学）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院）；Department of Neurosurgery（神经外科系）
Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University（维克森林大学）；University of British Columbia（不列颠哥伦比亚大学）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；University of Nottingham（诺丁汉大学）
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
University College London（伦敦大学学院）；Inria（法国国家信息与自动化研究所）；Université Côte d'Azur（蔚蓝海岸大学）
在这里，我们提出了DIVE：数据驱动的顶点进化推理。DIVE是一种基于图像的疾病进展模型，具有单顶点分辨率，旨在从短期纵向数据集重建大脑病理学的长期模式。DIVE对在患者群体中具有类似时间动态的皮质表面上的逐顶点生物标志物测量进行聚类，并且同时估计每个聚类中的顶点测量的平均轨迹。DIVE独特地将皮质分割成具有共同进展模式的区域，从而为个体疾病提供新的签名。DIVE进一步估计每个受试者每次访视的疾病分期和进展速度，可能增强临床试验或管理的分层。在模拟数据上，DIVE可以恢复地面实况集群及其底层轨迹，前提是集群之间的平均轨迹有足够的差异。我们证明了DIVE的数据来自两个队列：阿尔茨海默病神经影像学倡议（ADNI）和痴呆症研究中心（DRC），英国，包含患者与后皮质萎缩（PCA）以及典型的阿尔茨海默病（tAD）。DIVE在两个独立的数据集（ADNI和DRC）中发现了tAD受试者的萎缩的相似空间模式，并进一步揭示了不同疾病（tAD vs PCA）和不同类型生物标志物数据的不同病理模式：来自磁共振成像（MRI）的皮质厚度vs来自正电子发射断层扫描（PET）的淀粉样蛋白负荷。最后，DIVE可用于使用任何种类的逐体素或逐顶点测量（包括雅可比压缩图、来自扩散成像的分数各向异性（FA）图或其他PET测量）来估计脑中病理的细粒度空间分布。DIVE源代码可在线获得：https://github.com/m…
Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
Department of Computer Science Emory University Atlanta GA USA（埃默里大学计算机科学系）；Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta GA USA（埃默里大学数据科学中心）；Department of Computer Science Georgia State University Atlanta GA USA（佐治亚州立大学计算机科学系）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Atlanta GA USA（神经影像与数据科学转化研究三机构中心（TReNDS））
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University（维克森林大学）；University of Victoria（维多利亚大学）；University of Nottingham（诺丁汉大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Department of Industrial and Systems Engineering, Rutgers University（鲁特大学工业与系统工程系）；Department of Psychiatry and Behavioral Sciences, University of Minnesota（明尼苏达大学精神病学与行为科学系）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国家阳明交通大学医学院脑科学研究所）
Gradient based Severity Labeling for Biomarker Classification in OCT
Georgia Institute of Technology（佐治亚理工学院）；Center for Signal and Information Processing CSIP（信号与信息处理中心）；School of Electrical and Computer Engineering（电气与计算机工程学院）
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；CIBM Center for Biomedical Imaging（CIBM生物医学影像中心）；UCLouvain（鲁汶大学）；Lausanne University and University Hospital（洛桑大学及大学医院）；National Institute of Neurological Disorders and Stroke, National Institutes of Health（美国国立卫生研究院国家神经疾病与卒中研究所）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Siemens Healthcare AG（西门子医疗股份公司）；Cliniques universitaires Saint-Luc, Université catholique de Louvain（圣吕克大学医院，天主教鲁汶大学）；CHUV（洛桑大学医院）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University Hospital Basel and University of Basel（巴塞尔大学医院及巴塞尔大学）；IRCCS San Raffaele Hospital and Vita-Salute San Raffaele University（圣拉斐尔医院及圣拉斐尔生命健康大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
A Multi-target Bayesian Transformer Framework for Predicting Cardiovascular Disease Biomarkers during Pandemics
Department of Computer Science, WPI（计算机科学系，WPI）；Department of Genetics and Computational Biology, UMass Chan Medical School（遗传学与计算生物学系，UMass Chan医学学院）；AI Exploration Center, Department of Data Science and Analytics, SUNY Polytechnic Institute（AI探索中心，数据科学与分析系，SUNY polytechnic学院）
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Biomedical Informatics（生物医学信息学系）；Department of Neurology（神经病学系）；Translational Data Analytics Institute（转化数据分析研究所）
