Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data

Understand

Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge.

• A promising approach is to use probabilistic regression methods to estimate normative models of brain function using (f)MRI data then use these to map variation across individuals in clinical populations (e.g., via anomaly detection).
• To fully capture individual differences, it is crucial to statistically model the patterns of correlation across different brain regions and individuals.
• However, this is very challenging for neuroimaging data because of high-dimensionality and highly structured patterns of correlation across multiple axes.