A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets.

ABSTRACT: Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisti

SUBMITTER: Ferreira FS 

PROVIDER: S-EPMC8861855 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature