Investigating electrophysiological brain network dynamics and mechanisms underlying attentional processes during learning

在成人和儿童中，学习效率在很大程度上取决于认知控制和执行功能。在我的项目中，我将致力于研究这些过程中涉及的潜在大脑网络和机制。使用新开发的网络动力学机器学习模型，我将研究网络的连接性和连贯性模式，并将它们与使用静息状态和任务脑电图（EEG）数据的成人学习的行为指标相关联。稍后，我将收集更多的电生理数据集，采用新的行为范式和数据收集方法（如更高密度的EEG或OPM-MEG），并扩展用于模拟大脑网络动力学的计算方法。此外，我将使用收集的数据来建立我以前的工作，调查记忆重放和休息状态大脑网络之间的关系。了解生命的规则-变革性技术-生物科学促进对健康的综合了解”

In both adults and children, the efficiency of learning is heavily dependent on cognitive control and executive function. In my project, I will aim to study the underlying brain networks and mechanisms involved in these processes. Using newly developed machine learning models of network dynamics, I will investigate network connectivity and coherence patterns and relate them to behavioural indices of adult learning using resting state and task electroencephalography (EEG) data. Later, I will collect further electrophysiological datasets, employing new behavioural paradigms and data collection methods (such as higher density EEG or OPM-MEG) and extend computational methods for modelling brain network dynamics. Additionally, I will use collected data to build on my previous work investigating relationships between memory replay and resting-state brain networks.- Understanding the rules of life- Transformative technologies- Bioscience for an integrated understanding of health"