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The statistical analysis of group studies in neuroscience is particularlychallenging due to the complex spatio-temporal nature of the data, its multiplelevels and the inter-individual variability in brain responses. In thisrespect, traditional ANOVA-based studies and linear mixed effects modelstypically provide only limited exploration of the dynamic of the group brainactivity and variability of the individual responses potentially leading tooverly simplistic conclusions and/or missing more intricate patterns. In thisstudy we propose a novel method based on functional Principal ComponentsAnalysis and Bayesian model-based clustering to simultaneously assess groupeffects and individual deviations over the most important temporal features inthe data. This method provides a thorough exploration of group differences andindividual deviations in neuroscientific group studies without compromising onthe spatio-temporal nature of the data. By means of a simulation study wedemonstrate that the proposed model returns correct classification in differentclustering scenarios under low and high of noise levels in the data. Finally weconsider a case study using Electroencephalogram data recorded during an objectrecognition task where our approach provides new insights into the underlyingbrain mechanisms generating the data and their variability.

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