A Bayesian functional model with multilevel partition priors for group studies in neuroscience (2312.16739v2)

Abstract: 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. In this respect, traditional ANOVA-based studies and linear mixed effects models typically provide only limited exploration of the dynamic of the group brain activity and variability of the 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 method for functional data to simultaneously assess group effects and individual deviations over the most important temporal features in the data. To this aim, we develop an innovative multilevel partition prior to model the functional scores of a functional Principal Components decomposition of neuroscientific recordings; this approach returns a thorough exploration of group differences and individual deviations without compromising on the spatio-temporal nature of the data. By means of a simulation study we demonstrate that the proposed model returns correct classification in different clustering scenarios under low and high noise levels in the data. Finally we consider a case study using Electroencephalogram data recorded during an object recognition task where our approach provides new insights into the underlying brain mechanisms generating the data and their variability.