Dynamic analysis of the ageing functional brain

静息状态fMRI中的动态功能连接（dFC）有望为预测健康和疾病中的脑功能提供候选生物标志物。然而，dFC指标的可靠性和可解释性仍然存在争议。此外，这些还没有在临床前环境（如啮齿动物）中得到适当的验证，也没有与辅助神经生物学终点相结合。使用复杂性科学方法，该项目将使用大量基于阶段的dFC指标来量化大脑功能。这些工具将包括源自动力系统、随机和信息动力学方法的工具。此外，这些指标将用于在中观和宏观尺度上形成啮齿动物生命周期中大脑功能的新描述。我们处于一个独特的位置来实现这个项目的雄心，由于一个为期3年的bbsrc资助的项目，我们已经完成了对n=70只大鼠的结构和功能MRI数据的纵向采集，这些大鼠被跟踪了两年，每只至少有4个成像点。“积极生活方式”亚组在他们的早期和晚期生活中也经历了环境丰富和饮食限制。我们已经证明，将机器学习应用于结构数据可以预测该队列的寿命（business et al., 2022）。我们现在计划从功能磁共振成像数据中开发更准确的预测器。博士研究生将得到神经成像部门和大脑的工作人员的支持，特别是我们的临床前成像中心。指导教授：Turkheimer教授对脑功能标记物的开发非常感兴趣，并在该领域领导实验、数据采集和分析。第二位导师：Paul Expert博士是一位物理学家，对复杂系统和使用网络和统计学习的脑功能建模感兴趣。第三位导师：Diana Cash博士是临床前科学家，也是MRI， PET和啮齿动物行为，组织学和放射自显像等方法的专家

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to delivercandidate biomarkers for prediction of brain function in health and disease. However,the reliability and interpretability of dFC metrics remain contested. Moreover, thesehave not been properly validated in a pre-clinical context (e.g. rodents) and integratedwith ancillary neurobiological endpoints. Using a complexity-science approach, theproject will quantify brain function using a large battery of phase-based dFC metrics.These will include tools originated from dynamical systems, stochastic and informationdynamics approaches. Moreover, these metrics will be used to form novel descriptionsof rodent brain function during the life-cycle at the meso- and macro-scale. We are in aunique position to fulfil the ambition of this project as, thanks to a 3-year BBSRCfunded project, we have already completed the longitudinal acquisition of structuraland functional MRI data over n=70 rats that were followed for two years with at least 4imaging points each. An "active lifestyle" sub-group also underwent environmentalenrichment and dietary restriction between their early and late life. We have alreadyshown that machine learning applied to structural data can predict life-span in thiscohort (Busini et al., 2022). We now plan to develop even more accurate predictorsfrom fMRI data. The PhD student will have the support of the staff of the NeuroimagingDepartment and of BRAIN, our preclinical imaging centre and in particular. 1stSupervisor: Prof Turkheimer has a focused interest in the development of brainfunctional markers and leads experimentation, data acquisition and analytics in thisarea. 2nd Supervisor: Dr Paul Expert is a physicist with an interest in complex systemsand modelling of brain function using networks and statistical learning 3rd Supervisor:Dr Diana Cash is preclinical scientist and expert in MRI, PET and methods such asbehaviour, histology, and autoradiography in rodents