Potential PhD topics

Our research generally concerns the cognitive neuroscience of ageing and dementia. This involves relating various neuroimaging measures of the brain to various latent cognitive abilities, as well as lifestyles, to understand how ageing and neurodegenerative diseases like Alzheimer's impair cognition through affecting the brain, and how to mitigate these effects. We tend to use large open-access datasets like the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), BioFIND, NTAD/SHINE, UK BioBank, cohorts in the LifeBrain consortium and those on the DPUK portal, combined with advanced statistical techniques.

To hear an overview about our work on healthy ageing using the CamCAN cohort (Jan 2021), you could watch this video.

Some more specific suggestions are below, but note that the list below is not exhaustive; i.e, if you have a topic in mind that overlaps with our general interests, please do email to discuss!

1. Investigating the neural correlates of "cognitive reserve" - the intact cognitive performance of some older people despite clinical MRI evidence of typical age-related brain atrophy. Some examples include effects of mid-life activity (Chan et al., 2018) and functional segregation of brain networks (Raykov et al., 2023), as covered in this BNA Festive talk.

2. Investigating functional compensation while older adults perform cognitive tasks, for which we believe evidence is currently limited, at least for memory (Morcom et al., 2018) and motor control (Knights et al., 2021), but possibly occurs in problem-solving tasks (Knights et al., 2024), though this may reflect task-specific strategies, so common mechanisms need to be explored across multiple tasks.

3. Importance of vascular health for late-life cognition, including effects on white-matter, such as in this paper (King et al., 2023) and as summarised in second half of this talk.

4. Behavioural, fMRI and/or M/EEG studies on the effect of healthy ageing on memory, such as (Henson et al., 2016) or (Gellersen et al., 2022).

5. Investigation of MEG for early detection of dementia, particularly using machine learning classification methods, as in (Vaghari et al, 2022), summarised in this talk.

6. Investigation of the "cognitively frail", who score below cut-offs on conventional tests for dementia (such as MMSE/ACER) yet have not sought clinical advice, e.g. (Kocagoncu et al., 2022).

7. Further methodological development of analysis of fMRI/MEG/EEG data, particularly in relation to multimodal integration (e.g., Henson et al., 2011), as in this talk.