Normative Modeling of Neuroimaging Data Using Scalable Multi-task Gaussian Processes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)


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.


Gaussian processes Multi-task learning Normative modeling Neuroimaging fMRI Clinical neuroscience Novelty detection 

Supplementary material

473976_1_En_15_MOESM1_ESM.pdf (82 kb)
Supplementary material 1 (pdf 82 KB)


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
  2. 2.Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands

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