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Current investigations

Below are summaries of some of the ways we are currently investigating our research themes. We are continuing to assemble our cohort of 700 individuals, so example data presented are preliminary and for illustration purposes only.

Multi-modality:

How do different measures of brain structure and function tell us about the ageing process?

A number of neuroimaging methods are under development for this project which are aimed at addressing the challenges and potentials of this unique dataset. This includes overcoming a number of data challenges such as normalizing accurately when measuring grey matter integrity across a wide age range, and sensitively measuring the integrity of white matter networks. Functional networks are being assessed in both fMRI and MEG, allowing for the ability to compare network measures across modalities in the same individuals. Methods of combining neural data with multiple types of cognitive or background data are being developed using structural equation modelling, mediation models and naturalistic tasks that produce a rich, multidimensional dataset. Read more about some of these developments here.

Plasticity:

How is good cognitive performance supported by neural flexibility?

Many of our current investigations are focussed on understanding how patterns of activity change and develop gradually over the lifespan. See the neuroimaging methods pagefor more information on measures of connectivity and neural networks that will reveal important information about age-related changes to neural networks.

In addition to long-term neural flexibility optimising performance also requires flexibly over the short range, for example during an individual testing session. This kind of flexibility is being examined in examinations of motor control and priming during an episodic memory task.

Variability:

Which cognitive abilities and individuals have successful life-long trajectories?

Ongoing research demonstrates a number of dissociations between cognitive abilities that decline with age and those that don't. These dissociations emphasize how informative it is to examine processes that are not negatively affected by age. Critically, preserved abilities are neither simple, nor are they are obviously unrelated to abilities that do decline.

One example of this dissociation can be seen in tasks that are supported by frontal lobe function. Although the frontal lobes appear to be particularly affected by the normal ageing process, there seems to be a dissociation between abilities affected by posterior frontal lobes which decline with age, and preserved abilities that are supported by more anterior frontal lobes. Read more here.

Another dissociation between preserved and impaired function is evident from language performance. Current data support a well-supported dissociation between language production and comprehension during ageing. There is a strong effect of age on the ability to fluently produce words using a number of tasks. However, comprehending syntactic structure is preserved. Read more here.

Interactivity:

How do different cognitive domains interact with each other and other background factors?

Current investigations focus on gathering background measures that may impact on cognitive and neural health. For example, cognitive abilities may be impacted by general mental health, as reflected in measures of anxiety and depression, or by lifestyle factors like sleep patterns. Read more here.

Another kind of interaction is revealed by the measurement of multiple cognitive domains in the CamCAN testing sessions. For example, different aspects of learning are being examined with motor learning and episodic memory tasks. Likewise, emotion processing is being measured from a number of different angles including emotional memory, emotional regulation, and emotional face recognition. These different measures provide a richer understanding of processes like learning and emotion processing, and also provide the ability to link between disparate cognitive domains in the same individuals.