Project to be confirmed after rotation year

早期的大脑生长和发育涉及大脑连接和复杂行为的重大变化。然而，大脑网络发育与个体行为差异之间的关系尚不清楚。该项目旨在评估来自大型数据集的神经生理脑连接的个体偏差，并进一步了解这种偏差与行为结果的个体差异之间的关系。学生将学习使用先进的大脑连接和机器学习方法处理和分析纵向神经生理学数据，揭示大脑网络在行为和认知早期发展中的作用。

Early brain growth and development involve significant changes in brain connectivityand complex behaviours. However, the relationship between brain networkdevelopment and individual differences in behaviour remains unclear. This projectaims to assess individual deviations in neurophysiological brain connectivity from largenormative datasets, and further understand how such deviations relate to individualdifferences in behavioural outcomes. The student will learn to process and analyselongitudinal neurophysiological data using advanced brain connectivity and machinelearning methods, shedding light on the role of brain networks in the earlydevelopment of behaviour and cognition.