Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. In this paper, we study NeuroVNN, based on coVariance neural networks, as a paradigm for foundation model for the brain age prediction application. NeuroVNN is pre-trained as a regression model on healthy population to predict chronological age using cortical thickness features and fine-tuned to estimate brain age in different neurological contexts. Importantly, NeuroVNN adds anatomical interpretability to brain age and has a `scale-free' characteristic that allows its transference to datasets curated according to any arbitrary brain atlas. Our results demonstrate that NeuroVNN can extract biologically plausible brain age estimates in different populations, as well as transfer successfully to datasets of dimensionalities distinct from that for the dataset used to train NeuroVNN.
Infant Brain Age Classification: 2D CNN Outperforms 3D CNN in Small Dataset
Department of Biomedical Engineering, University of Tennessee Health Science Center（田纳西大学健康科学中心生物医学工程系）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Enanta Pharmaceuticals（Enanta制药公司）
MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning
Maastricht University Medical Centre +（马斯特里赫特大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Delft University of Technology（代尔夫特理工大学）；Erasmus MC - University Medical Center Rotterdam（伊拉斯姆斯大学医学中心鹿特丹分校）；Leiden University（莱顿大学）
Learning to synthesise the ageing brain without longitudinal data
University of Edinburgh（爱丁堡大学）；The BHF Centre for Cardiovascular Science（英国心脏基金会心血管科学中心）；The Alan Turing Institute（艾伦·图灵研究所）；Institute for Digital Communications, School of Engineering, University of Edinburgh（爱丁堡大学工程学院数字通信研究所）；The BHF Centre for Cardiovascular Science, Edinburgh（爱丁堡英国心脏基金会心血管科学中心）；The Alan Turing Institute, London（伦敦艾伦·图灵研究所）；Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK（爱丁堡大学工程学院数字通信研究所）
Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective
Department of Electrical and Computer Engineering at the University at Albany, SUNY（纽约州立大学阿尔巴尼分校电气与计算机工程系）；Department of Electrical and Computer Engineering at the University of Rochester（罗切斯特大学电气与计算机工程系）；Department of Electrical and Systems Engineering at the University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）
Estimating brain age based on a healthy population with deep learning and structural MRI
Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；Taub Institute for Research on Alzheimer’s Disease and the Aging Brain（陶布阿尔茨海默病与衰老大脑研究所）
