Normative modeling has recently been proposed as an alternative for the case-control approach in modeling heterogeneity within clinical cohorts. Normative modeling is based on single-output Gaussian process regression that provides coherent estimates of uncertainty required by the method but does not consider spatial covariance structure. Here, we introduce a scalable multi-task Gaussian process regression (S-MTGPR) approach to address this problem. To this end, we exploit a combination of a low-rank approximation of the spatial covariance matrix with algebraic properties of Kronecker product in order to reduce the computational complexity of Gaussian process regression in high-dimensional output spaces. On a public fMRI dataset, we show that S-MTGPR: 1) leads to substantial computational improvements that allow us to estimate normative models for high-dimensional fMRI data whilst accounting for spatial structure in data; 2) by modeling both spatial and across-sample variances, it provides higher sensitivity in novelty detection scenarios.
Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data
Radboud University（拉德堡德大学）；Radboud University Medical Centre（拉德堡德大学医学中心）；University of Oslo（奥斯陆大学）；Oslo University Hospital（奥斯陆大学医院）；King’s College London（伦敦国王学院）
Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders
Department of Computer Science and Engineering, Washington University in St. Louis, USA（计算机科学与工程系，华盛顿大学圣路易斯分校）；Institute for Informatics, Data Science and Biostatistics, Washington University in St.Louis, USA（信息学、数据科学与生物统计研究所，华盛顿大学圣路易斯分校）；Department of Radiology, Washington University in St.Louis, USA（放射学系，华盛顿大学圣路易斯分校）
Fusing multimodal neuroimaging data with a variational autoencoder
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory（三机构神经影像与数据科学转化研究中心（TReNDS），佐治亚州立大学、佐治亚理工学院、埃默里大学）；Faculty of Electrical Engineering, Mathematics & Computer Science, TU Delft（代尔夫特理工大学电气工程、数学与计算机科学学院）；Lambert High School（兰伯特高中）
Denoising diffusion networks for normative modeling in neuroimaging
Australian Institute for Machine Learning (AIML), Adelaide University（澳大利亚机器学习研究所（AIML），阿德莱德大学）；South Australian Health and Medical Research Institute (SAHMRI)（南澳大利亚健康与医学研究机构（SAHMRI））；School of Computer Science and Information Technology, Adelaide University（计算机科学与信息技术学院，阿德莱德大学）；School of Public Health, Adelaide University（公共卫生学院，阿德莱德大学）
Scalable Simulation-Based Model Inference with Test-Time Complexity Control
Machine Learning in Science, University of Tübingen；Tübingen AI Center, Tübingen, Germany；Max Planck Institute for Intelligent Systems, Department Empirical Inference, Tübingen, Germany；Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK；National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, United Kingdom
AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors
Institut national de la recherche scientifique (INRS)（国家科学研究研究所）；Université du Québec à Chicoutimi (UQAC)（魁北克大学希库蒂米分校）；McGill University（麦吉尔大学）；The Neuro (Montreal Neurological Institute-Hospital)（蒙特利尔神经学研究所/医院）；Douglas Hospital Research Centre（道格拉斯医院研究中心）
