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）
Alwani Liyana Ahmad, Jose Sanchez-Bornot, Roberto C. Sotero, Damien Coyle, Zamzuri Idris, Ibrahima Faye
备注：
28 pages, 9 figures, 3 tables, 19 supplimetary material
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。
摘要：
MEG 是具有出色时间和空间分辨率的非侵入性神经成像技术，对于研究痴呆和阿尔茨海默病中的脑功能至关重要。它们可识别包括临床前和前驱期在内的不同阿尔茨海默病阶段的脑活动变化。MEG 可能在临床症状出现之前检测到病理变化，从而为干预提供潜在生物标志物。本研究评估了利用 MEG 特征区分 BioFIND 研究中健康对照者与轻度认知障碍参与者的分类技术。我们分别以及联合比较了基于 MEG 的生物标志物与基于 MRI 的解剖学特征。我们使用了来自 324 名 BioFIND 参与者的 3 Tesla MRI 和 MEG 数据；其中 158 名 MCI 和 166 名 HC。分析使用 MATLAB 以及 SPM12 和 OSL 工具箱完成。机器学习分析在传感器空间和源空间上进行，包括 100 次 Monte Carlo 重复的 10 折交叉验证。结合 MRI 与 MEG 特征取得了最佳性能；使用基于 LCMV 源的 MEG 时，GLMNET 的准确率为 0.76，AUC 为 0.82。仅使用 LCMV 和 eLORETA 的 MEG 分析也表现良好，表明将未经校正的 MEG 与经 z-score 校正的 MRI 特征相结合是最优的。
英文摘要：
MEG are non invasive neuroimaging techniques with excellent temporal and spatial resolution, crucial for studying brain function in dementia and Alzheimer Disease. They identify changes in brain activity at various Alzheimer stages, including preclinical and prodromal phases. MEG may detect pathological changes before clinical symptoms, offering potential biomarkers for intervention. This study evaluates classification techniques using MEG features to distinguish between healthy controls and mild cognitive impairment participants from the BioFIND study. We compare MEG based biomarkers with MRI based anatomical features, both independently and combined. We used 3 Tesla MRI and MEG data from 324 BioFIND participants;158 MCI and 166 HC. Analyses were performed using MATLAB with SPM12 and OSL toolboxes. Machine learning analyses, including 100 Monte Carlo replications of 10 fold cross validation, were conducted on sensor and source spaces. Combining MRI with MEG features achieved the best performance; 0.76 accuracy and AUC of 0.82 for GLMNET using LCMV source based MEG. MEG only analyses using LCMV and eLORETA also performed well, suggesting that combining uncorrected MEG with z-score-corrected MRI features is optimal.
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Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)（弗劳恩霍夫算法与科学计算研究所（SCAI））；University of Bonn（波恩大学）；Tel Aviv University（特拉维夫大学）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；University of Barcelona（巴塞罗那大学）；ImmunoBrain Checkpoint Ltd.（ImmunoBrain Checkpoint有限公司）；Sagol School of Neuroscience（萨戈尔神经科学学院）
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）
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）
A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks
McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；Complutense University of Madrid（马德里康普顿斯大学）；Harvard Medical School（哈佛医学院）
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University of Pennsylvania（宾夕法尼亚大学）；United States Naval Academy（美国海军学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Cedars Sinai Medical Center（西好莱坞 Cedars Sinai 医院）
Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Unravelling Causal Genetic Biomarkers of Alzheimer's Disease via Neuron to Gene-token Backtracking in Neural Architecture: A Groundbreaking Reverse-Gene-Finder Approach
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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）
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University Lausanne Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；Zhejiang University（浙江大学）；Zhejiang University School of Medicine（浙江大学医学院）
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Georgia Institute of Technology（佐治亚理工学院）；Georgia State University（佐治亚州立大学）；Emory University（埃默里大学）；Center for Translational Research in Neuroimaging and Data Science（神经影像学与数据科学转化研究中心）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Delft University of Technology（代尔夫特理工大学）
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
University of Pennsylvania（宾夕法尼亚大学）；Stony Brook University（石溪大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Medical University of South Carolina（南卡罗来纳医科大学）；Cedars-Sinai Medical Center（西格拉姆山医疗中心）
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
Machine Learning Driven Biomarker Selection for Medical Diagnosis
Fulton Schools of Engineering, Arizona State University（亚利桑那州立大学富尔顿工程学院）；Biodesign Center for Personalized Diagnostics, Arizona State University（亚利桑那州立大学生物设计个性化诊断中心）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）
A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment
Universiti Teknologi PETRONAS（国油科技大学）；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）
Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease
Taipei Medical University（台北医学大学）；University of Memphis（孟菲斯大学）；Chitkara University（奇特卡拉大学）；University College London（伦敦大学学院）；UPES（石油与能源研究大学）；University of Eastern Finland（东芬兰大学）；CNRS（法国国家科学研究中心）；University Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）
Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data
Lyon Neuroscience Research center（里昂神经科学研究中心）；INSERM（法国国家健康与医学研究院）；Functional Neurology and Epileptogy, HCL（里昂公立医院集团功能神经与癫痫科）；Univ Lyon（里昂大学）；CNRS（法国国家科学研究中心）；INSA Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CREATIS（里昂成像与生物技术研究中心）
