CJ Oates, T Carneiro da Costa and TE Nichols
Towards a Multi-Subject Analysis of Neural Connectivity
Abstract: Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose DAGs may differ but are likely to share many features. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates et al. (2014); in this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multi-subject experiment. Elicitation of hyperparameters requires care and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific DAGs, we simultaneously estimate relationships between the subjects themselves. A special case of the methodology provides a novel analogue of k-means clustering of subjects based on their DAG structure. It is anticipated that the exact algorithms discussed here will be widely applicable within neuroscience.