Bayesian temporal biclustering with applications to multi-subject neuroscience studies
This work addresses the challenge of multi-subject neuroscience studies by enabling data-driven clustering of subjects and measurements over time, though it is incremental as it builds on existing biclustering and Bayesian methods.
The authors tackled the problem of analyzing multivariate time series from multiple subjects to identify groups with similar trends and time-varying measurement associations, proposing a Bayesian temporal biclustering model that efficiently discovers interpretable, dynamic patterns in neuroscience data, with experiments showing it performs well at identifying ground-truth clusters.
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. To this end, we propose a Bayesian model for temporal biclustering 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 dataset. The results indicate that the proposed model can combine information from potentially many subjects to discover a set of interpretable, dynamic patterns. Experiments on simulated data compare the estimation performance of the proposed model against ground-truth values and other statistical methods, showing that it performs well at identifying ground-truth subject and measurement clusters even when no subject or time dependence is present.