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

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多名参与者的神经成像数据的开放数据档案（http：//www.cam-can.org/）。该项目将联合收割机结合多种脑成像方法获得的信息：（1）功能性磁共振成像（fMRI），通过其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），测量大脑的解剖学“布线”。由于方法的组合，计划的方法超越了早期-主要是（f）基于MRI的-关于年龄相关脑变化的研究（Sowell等人，Nature Neurosci 2003; Salat等人，Cereb Cortex 2004; Dosenbach等人，Science 2010）。纳入MEG增加了一种具有高时间分辨率的方法，可以揭示多个频率范围内共存的静息态网络（Hillebrand等人，2012）。机器学习将用于确定大脑网络的哪些方面（在研究的所有成像模式中）最能预测个人认知能力。最后，我们将使用这种方法来测试现有的大脑网络如何随着年龄的增长而重组的理论，关于大脑侧化的年龄相关变化的假设（Cabeza et al. Psychol Ageing 2002）以及关于前后脑激活之间的变化（Davis et al. Cereb Cortex 2008）。对于博士候选人，基本的Matlab或Python编程技能和定量背景（物理，数学，计算机科学或工程）是可取的。监督团队结合了MEG/EEG分析、结构和功能MRI分析以及对从开放数据档案中获得的大型数据集进行机器学习的专业知识。建议阅读：Geerligs等人，（2018）Neurobiol Aging。72：106-120 doi：10.1016/j.neurobiolaging.2018.07.025 Mandke et al.（2017）Neuroimage 166：371-384 https://doi.org/10.1016/j.neuroimage.2017.11.016

The PhD project aims to study how the brain/cognition relationship changes across the lifespan, using an open data archive containing neuroimaging data from 600+ participants (http://www.cam-can.org/). The project will combine information obtained with 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; and (3) diffusion-weighted MRI (DWI), which measures the anatomical "wiring" of the brain. Due to the combination of methods, the planned approach goes beyond earlier - mostly (f)MRI-based - research on age-related brain changes (Sowell et al. Nature Neurosci 2003; Salat al. Cereb Cortex 2004; Dosenbach et al. Science 2010). The inclusion of MEG adds a method with high temporal resolution that can reveal co-existing resting-state networks in multiple frequency ranges (Hillebrand et al. 2012). Machine learning will be used to determine which aspects of brain networks (across all imaging modalities studied) best predict individual cognitive ability. Finally, we will use this approach to test existing theories of how brain networks reorganize with age, with hypotheses about age-related changes of brain lateralization (Cabeza et al. Psychol Ageing 2002) and about shifts between anterior and posterior brain activation (Davis et al. Cereb Cortex 2008). For the PhD candidate, basic Matlab or Python programming skills and a quantitative background (physics, mathematics, computer science or engineering) are desirable. The supervisory team combines expertise in MEG/EEG analysis, structural and functional MRI analysis, and machine learning on large datasets obtained from open data archives.Suggested reading: Geerligs et al., (2018) Neurobiol Aging. 72:106-120 doi: 10.1016/j.neurobiolaging.2018.07.025 Mandke et al. (2017) Neuroimage 166:371-384 https://doi.org/10.1016/j.neuroimage.2017.11.016