'Our work is biologically inspired computer science,' said Poggio, who is also co-director of the Center for Biological and Computational Learning. But in the last 50 years, computer science and AI (artificial intelligence) have developed independently of neuroscience.' 'That was Alan Turing's original motivation in the 1940s. 'People have been talking about computers imitating the brain for a long time,' said Tomaso Poggio, the Eugene McDermott Professor of Brain and Cognitive Sciences and a member of the McGovern Institute for Brain Research at MIT. The researchers were pleasantly surprised at the power of this new approach. On a publicly available clinical fMRI dataset, we compare the novelty detection performance of multivariate normative models estimated by the proposed NP approach to a baseline multi-task Gaussian process regression approach and show substantial improvements for certain diagnostic problems.For the first time, MIT scientists have applied a computer model of how the brain processes visual information to a complex, real world task: recognizing the objects in a busy street scene. In this scheme, predictive uncertainty can be approximated by sampling from the distribution of these global latent variables. This enables us to learn optimal feature representations and covariance structure for the random-effect and noise via global latent variables. To achieve this, we define a stochastic process formulation for mixed-effect models and show how NPs can be adopted for spatially structured mixed-effect modeling of neuroimaging data. In this paper, we propose a deep normative modeling framework based on neural processes (NPs) to solve these problems. Current implementations rely on Gaussian process regression, which provides coherent estimates of uncertainty needed for the method but also suffers from drawbacks including poor scaling to large datasets and a reliance on fixed parametric kernels. %X Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders. %C Proceedings of Machine Learning Research

%B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %T Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data On a publicly available clinical fMRI dataset, we compare the novelty detection performance of multivariate normative models estimated by the proposed NP approach to a baseline multi-task Gaussian process regression approach and show substantial improvements for certain diagnostic problems.Ĭite this =

Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders.

Neural processes