Bayesian temporal biclustering with applications to multi-subject neuroscience studies

Oregon Convention Center 

We consider the problem of analyzing multivariate time series collected on multiple subjects, with the goal of identifying groups of subjects exhibiting similar trends in their recorded measurements over time as well as time-varying groups of associated measurements. We propose a Bayesian model for temporal bi-clustering featuring nested partitions, where a time-invariant partition of subjects induces a time-varying partition of measurements. Our approach allows for data-driven determination of the number of subject and measurement clusters as well as estimation of the number and location of changepoints in measurement partitions. To efficiently perform model fitting and posterior estimation with Markov Chain Monte Carlo, we derive a blocked update of measurements' cluster-assignment sequences.
We illustrate the performance of our model in two applications to functional magnetic resonance imaging data and to an electroencephalogram (EEG) dataset. The results indicate that the proposed model can combine information from potentially many subjects to discover a set of interpretable, dynamic patterns.

Bayesian

Time Series

Neuroimaging

Clustering 

Section on Statistics in Imaging