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• W2903848768 abstract "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. 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. In this paper, we propose a deep normative modeling framework based on neural processes (NPs) to solve these problems. 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. This enables us to learn optimal feature representations and covariance structure for the random-effect and noise via global latent variables. In this scheme, predictive uncertainty can be approximated by sampling from the distribution of these global latent variables. 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." @default.
• W2903848768 created "2018-12-22" @default.
• W2903848768 creator A5032648713 @default.
• W2903848768 creator A5054538140 @default.
• W2903848768 date "2018-12-12" @default.
• W2903848768 modified "2023-09-26" @default.
• W2903848768 title "Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data" @default.
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• W2903848768 hasPublicationYear "2018" @default.
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