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

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