A Bayesian functional model with multilevel partition priors for group studies in neuroscience

The statistical analysis of group studies in neuroscience is particularly challenging due to the complex spatio-temporal nature of the data, its multiple levels and the inter-individual variability in brain responses. Traditional ANOVA-based studies and linear mixed effects models typically provide only a limited exploration of the dynamics of group brain activity and of the variability in individual responses, potentially leading to overly simplistic conclusions and/or missing more intricate patterns. In this study we propose a novel Bayesian model-based clustering approach for functional data that simultaneously assesses group effects and individual deviations across the main temporal features in the data. To this end, we develop a multilevel partition prior to model the functional scores arising from a functional Principal Components decomposition of neuroscientific recordings. The proposed approach enables a thorough exploration of group differences and individual deviations without compromising on the spatio-temporal nature of the data. Through a simulation study we demonstrate that the proposed approach achieves correct classification in different clustering scenarios under low and high noise levels in the data. Finally, we consider an Electroencephalogram case study involving an object recognition task, where the proposed approach provides new insights into the underlying brain mechanisms generating the data and their variability.