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 method based on functional Principal Components Analysis and Bayesian model-based clustering to simultaneously assess group effects and individual deviations over the most important temporal features in the data. This method provides a thorough exploration of group differences and individual deviations in neuroscientific group studies 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 of 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.

翻译：神经科学群体研究的统计分析尤为具有挑战性，原因在于数据的复杂时空特性、多水平结构以及个体间脑反应差异。传统基于方差分析的研究和线性混合效应模型通常仅能有限探索群体脑活动动态和个体反应变异性，可能导致过于简化的结论和/或遗漏更复杂的模式。本研究提出一种基于函数主成分分析和贝叶斯模型聚类的创新方法，用于同步评估数据中最重要时间特征上的群体效应与个体偏差。该方法在不牺牲数据时空特性的前提下，为神经科学群体研究中的群体差异与个体偏差提供深入探索。通过模拟研究，我们证明所提模型在低噪声和高噪声水平的不同聚类场景下均能实现正确分类。最终，我们以物体识别任务中记录的脑电图数据为例进行案例研究，该方法为生成数据及其变异性的潜在脑机制提供了新见解。