Warped Bayesian linear regression for normative modelling of big data.

ABSTRACT: Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges. So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging mo

SUBMITTER: Fraza CJ 

PROVIDER: S-EPMC7613680 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature