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）
Seyed Mostafa Kia, Hester Huijsdens, Richard Dinga, Thomas Wolfers, Maarten Mennes, Ole A. Andreassen, Lars T. Westlye, Christian F. Beckmann, Andre F. Marquand
Clinical neuroimaging has recently witnessed explosive growth in data availability which brings studying heterogeneity in clinical cohorts to the spotlight. Normative modeling is an emerging statistical tool for achieving this objective. However, its application remains technically challenging due to difficulties in properly dealing with nuisance variation, for example due to variability in image acquisition devices. Here, in a fully probabilistic framework, we propose an application of hierarchical Bayesian regression (HBR) for multi-site normative modeling. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging data compared to widely used methods. This provides the possibility i) to learn the normative range of structural and functional brain measures on large multi-site data; ii) to recalibrate and reuse the learned model on local small data; therefore, HBR closes the technical loop for applying normative modeling as a medical tool for the diagnosis and prognosis of mental disorders.
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Technische Universität Berlin（柏林工业大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Central China Normal University（华中师范大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）
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）
A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets
University College London（伦敦大学学院）；Max Planck University College London Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）；Wellcome Centre for Human Neuroimaging（韦尔科姆人类神经影像中心）
Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Roche Pharma Research \& Early Development, F. Hoffmann-La Roche Ltd, Basel, Switzerland Université Paris-Saclay, Inria, CEA, Paris, Palaiseau, France Correspondance
