Physiology of cognitive changes in ageing and dementia

The brain changes as we age, but which changes should we consider normal, and which are warning signs that cognitive problems such as memory impairment may develop in future? How do we know whether someone is deviating from the course of healthy ageing and is on a path that will eventually lead to dementia, the most common diagnosis being Alzheimer's disease (AD)?

These are pressing issues, not least because the UK has a rapidly ageing population and cases of AD are set to double in the next 10-20-years worldwide.

My research is about identifying the earliest changes in brain physiology that give rise to cognitive impairment in later life. Research in animal models has identified many candidate mechanisms of brain ageing at the cellular level, while large-scale epidemiological studies have identified the cognitive changes that people typically experience as they age. To connect these findings, we need a mechanistic understanding of how age-related changes in the brain give rise to cognitive decline.

To address this, I will tackle a key technical challenge. Ageing and AD can simultaneously affect neurons, the brain's blood supply (vasculature) and the interface between them (neurovascular coupling). How can we disentangle these effects, and identify which of them are associated with cognitive dysfunction?

No single medical imaging method can achieve this. A standard approach for measuring the function of the brain, BOLD fMRI (Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging), can localise where in the brain there are effects of ageing with high spatial precision, however the signals it measures reflect a mixture of neural and vascular contributions.

Integrating other kinds of measurements could help to resolve this ambiguity. Arterial Spin Labelling (ASL) MRI provides a direct measure of blood perfusion (delivery of blood to the capillary bed), but it is slower than BOLD fMRI and therefore affords lower sensitivity. Direct electromagnetic recordings of the brain (electroencephalography, EEG or magnetoencephalography, MEG) provide insights into neural activity with exquisite temporal precision, but they are not sensitive to vasculature.

These methods - BOLD fMRI, ASL MRI and EEG/MEG - provide complementary perspectives on the brain, raising the question of how to integrate them to build a cohesive picture of ageing.

My solution, which I have developed in the lead up to this proposal, is to take a 'best of all worlds' approach. I analyse data from large-scale clinical studies conducted by my collaborators, where volunteers have undergone multiple kinds of neuroimaging. For example, they may start by having their neural activity measured with MEG while performing a simple task, and then they will perform the same task while undergoing MRI, to measure their blood oxygen and blood flow.

The novelty of my approach lies in the way these different kinds of data are integrated. For each volunteer, a biologically detailed mathematical model is specified that describes how their data were generated. This model includes unknown quantities, such as the strength of neural connections, which are estimated from the combined neuroimaging data.

The model thereby acts like a "mathematical microscope", for inferring the biological processes in the volunteer's brain that gave rise to their data.

I will apply this approach to identify the particular mixture of medical and health factors that are associated with age-related brain dysfunction. Using methods similar to weather forecasting, I will investigate which of these factors affect the long-term trajectory of cognitive decline. This could, in future, enable targeted early interventions to prevent or slow cognitive decline in ageing.