Age-related changes in brain connectivity and cognition studied through machine learning

这个博士项目旨在研究大脑/认知关系如何在整个生命周期中变化，使用包含600多名参与者（剑桥老龄神经科学中心）神经成像数据的开放数据档案。该项目将联合收割机从多种脑成像方法获得的信息：1。功能性磁共振成像（fMRI），通过对血管的影响间接测量大脑活动;2.脑磁图（MEG），测量大脑活动产生的头部周围的磁场，作为神经激活的直接测量;3.弥散加权磁共振成像（DWI），测量大脑的解剖“布线”;结构磁共振成像（sMRI），测量皮质折叠和皮质下结构的形状相关特性。为了将来自各种神经成像方式的信息联合收割机结合起来，将探索几种方法，其中包括基于复杂网络理论最新进展的方法，该方法采用多层网络同时描述多个相互作用的网络结构。机器学习方法，包括神经网络和相关向量机，将用于确定大脑网络的哪些方面（在研究的所有成像方式中）最能预测个体认知能力。最后，这种方法将被用来测试现有的理论，大脑网络如何重组与年龄有关的大脑侧化的变化和前后脑激活之间的转变的假设。

This PhD project aims to study how the brain/cognition relationship changes across lifespan, using an open data archive containing neuroimaging data from 600+ participants (Cambridge Centre for Ageing Neuroscience). The project will combine information obtained from multiple brain imaging methods:1. Functional MRI (fMRI), which measures brain activity indirectly through its effects on blood vessels;2. Magnetoencephalography (MEG), which measures the magnetic field around the head generated by brain activity as a direct measure of neural activation;3. Diffusion-weighted MRI (DWI), which measures the anatomical "wiring" of the brain; and4. Structural MRI (sMRI), which measures shape-related properties of cortical folding and subcortical structure.To combine information from the various neuroimaging modalities, several approaches will be explored, among them methods based on recent theoretical advances in complex networks that employ multilayer networks to describe multiple interacting network structures simultaneously. Machine learning methods, including neural networks and relevance vector machines, will be used to determine which aspects of brain networks (across all imaging modalities studied) best predict individual cognitive ability. Finally, this approach will be used to test existing theories of how brain networks reorganize with age, with hypotheses about age-related changes of brain lateralization and about shifts between anterior and posterior brain activation.