How age is affecting your mind

Prevailing theories of ageing suggest that higher cognitive abilities decline as part of a single process, due to changes in the frontal part of the brain. New research, part of the Cambridge Centre for Ageing and Neuroscience, instead shows that age-related changes in these abilities change at different rates and have unique relations to our constantly changing brains

Cognitive abilities change across the lifespan at different rates. Some abilities, such as language and general knowledge are hardly affected, and even improve across the lifespan. Others, such as memory, reaction time and goal maintenance often show declines. Moreover, certain people seem to age without noticeable adverse effects on their mental abilities, whereas others are more affected. Understanding why certain abilities age, and why some people age more healthily than others, is a key challenge for cognitive neuroscience.

Our team, based at the Medical Research Council (MRC) Cognition and Brain Sciences Unit and the University of Cambridge, studied a large cohort of almost 3000 healthy volunteers from the general population, as part of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN). This project funded by the UK Biotechnology and Biological Sciences Research Council, aimed to examine the neural and demographic underpinnings of healthy cognitive ageing¹. In this study, we focused on a subset of cognitive abilities known as ‘executive functions’, namely higher cognitive abilities such as reasoning, decision-making and planning. These skills are particularly important because they are closely related to other age-related problems such as living independently (e.g. paying your bills, planning a shopping trip³).

A smaller subgroup of the entire project took part in our study². Over 500 participants, ranging between 18 and 88 years old, were asked to perform cognitive tests. The first task measured fluid reasoning, or the ability to solve abstract puzzles that don’t require any particular knowledge beforehand (Figure 1a). This is the type of test often used to measure IQ. The second challenge was a multitasking task: people had to engage with a mock hotel environment (Figure 1b), where they had to juggle many managerial duties (arranging reservations, looking up phone numbers, organising loose change). Although each individual task wasn’t complicated, we were interested in how well people could maintain performance on all these duties simultaneously without losing track of the others. We were particularly interested in these two tasks, as previous work has shown they are dependent on the structure in the front of the brain.

[caption id="attachment_42595" align="aligncenter" width="500"] Figure 1. a) Example of a fluid reasoning task. Participants had to decide which of the possible responses best fit into the empty square. b) The mock hotel environment set up. Participants were asked to perform each of the tasks for equal amounts of time in total.[/caption]

After performing these tasks, participants were scanned using MRI, so that we could examine the neuroanatomy of their brains. We specifically focused on the frontal cortex, the area of the brain commonly associated with executive functions, which is known to decline with age at a rate faster than the rest of the brain. However, it was unknown whether different aspects of the frontal cortex – such as different sub-regions and different types of brain tissue (white matter and grey matter) – age at different rates, and how this impacted on people’s fluid intelligence and ability to multi-task.

We looked at two key neural properties: How much grey matter (cell bodies) people had in certain regions, and how well different regions of the brain were interconnected by bundles of axons (called white matter). Using a standard structural MRI scan and advanced computational methods to match brains across individuals, we quantified how much grey matter people had both in the frontopolar cortex (the green region in Figure 2), because this region is known to be involved in complex reasoning tasks, and in the Multiple Demand system, a set of regions (Figure 2, in blue) that are known to be active whenever people perform a cognitively engaging task of any type (e.g., linguistic or spatial). Second, we used a different MRI technique called Diffusion Tensor Imaging (DTI), which allows estimation of the integrity of major white matter bundles. We focused on two key white matter bundles again thought to be important on the basis of previous research: the Forceps Minor (red in Figure 2), which connects the left and right frontopolar regions, and the Anterior Thalamic Radiations (yellow in Figure 2), which connect parts of the frontal cortex (including the Multiple Demand regions) to the rest of the brain.

[caption id="attachment_42596" align="aligncenter" width="500"] Figure 2. Four brain regions of interest. Frontopolar cortex (green), the Multiple Demand system, (blue), the Forceps Minor (red) and the Anterior Thalamic Radiations (yellow).[/caption]

In addition to the differential ageing of multitasking and fluid reasoning, a key finding was that different brain structures aged at different rates, as can be seen in Figure 3. White matter aged more quickly than grey matter, and white matter right at the front of the brain aged most quickly of all. Moreover, a model that represented the integrity of the frontal cortex as a single dimension was rejected by our statistical test, confirming the multidimensionality of frontal ageing. This differential ageing in the brain might explain why some abilities decline with age whilst others such as language and general knowledge are preserved, or even improve, well into old age.

[caption id="attachment_42597" align="aligncenter" width="600"] Figure 3. Differential brain ageing within the frontal cortex. White matter regions (red and yellow) aged more quickly than grey matter regions, with the Forceps Minor (in red) ageing most quickly of all.[/caption]

We then used a statistical technique called Structural Equation Modelling to combine these behavioural and neural measurements in order to examine how differences between individuals in their cognitive abilities were related to differences in their brains. Several key findings emerged. Firstly, although we found that multitasking and fluid reasoning are correlated to some extent, our model showed that they are nonetheless best considered as two separate abilities. In particular, these two abilities showed different rates of age-related decline: Multitasking only showed a modest decline with age, whereas fluid reasoning was more strongly affected. Moreover, the brain measures showed independent contributions to each ability: Both grey and white matter within and between the frontopolar regions were important for fluid reasoning ability (but not the Multiple Demand regions, contrary to our expectations), whereas white matter connections from the frontal cortex to the rest of the brain (the Anterior Thalamic Radiations) predicted multitasking ability (Figure 4).

One interesting aspect of these findings is that grey and white matter in the frontal cortex play partially independent roles in supporting fluid reasoning. In other words, it is not enough to consider only the cell bodies (grey matter), or only the connections between them (white matter). This finding is important, as certain types of cognitive and physical exercise are known to have different effects on grey and white matter¹.

Next we asked how much of the age-related differences in cognitive abilities could be ascribed to individual differences in these structural brain measures. For this we used mediation models, which test whether the relationship between two variables (here between age and cognitive ability) is mediated, or caused by, concurrent differences in a third variable (in this case, changes in brain structure). These models revealed that the above differences in brain structure could account for approximately one third of the age-related decline in cognitive abilities. Given all the other sources of variability between people (e.g., in genetic make-up, lifetime experience, etc.), this contribution of just 1-2 brain measures towards age-related differences in cognitive ability is particularly impressive.

[caption id="attachment_42598" align="aligncenter" width="600"] Figure 4. The full structural equation model that best explained the brain-behaviour relationship across our sample. Key findings are the separation of the two behavioural tasks (shown in circles) and the specific brain-behaviour pathways (in green).[/caption]

Finally, we tested various constraints on the models to examine whether the relationship between brain and behaviour was identical when we considered only the youngest compared to the oldest in our sample. We found that for multitasking ability, the anterior thalamic radiations became more important in the elderly, suggesting that the integrity of this white matter structure is particularly important in maintaining, as people grow older, the ability to juggle multiple goals at the same time.

Taken together, our research indicates a more multifaceted view of aging, even within the limited domain of executive functions and the frontal cortex. Our findings fit into an emerging picture of ageing that is much more nuanced and positive than previously thought. Future work in this and other cohorts will no doubt include an even more comprehensive set of behavioural, lifestyle, genetic and neural measurements, in order to better understand who ages well, and why. By understanding how our brains change as we grow older, and what makes some people age better than others, we hope to be able to determine what lifestyle and behavioural habits are most important to ensure we age well, so that more people will age healthily, happily and independently.

References

1. Hötting, K., & Röder, B. (2013). Beneficial effects of physical exercise on neuroplasticity and cognition. Neuroscience & Biobehavioral Reviews, 37(9), 2243-2257.
2. Kievit, R. A., Davis, S. W., Mitchell, D. J., Taylor, J. R., Duncan, J., Henson, R. N., & Cam-CAN Research team. (2014). Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nature Communications, 5.
3. Salthouse, T. (2012). Consequences of age-related cognitive declines. Annual review of psychology, 63, 201.
4. Shafto, M. A., Tyler, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., ... & Matthews, F. E. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC neurology, 14(1), 204.

The authors

Rogier Kievit is a postdoctoral investigator scientist at the MRC Cognition and Brain Sciences Unit and Bye Fellow at Fitzwilliam College.

Professor Richard N. A. Henson is programme leader at the MRC Cognition and Brain Sciences Unit.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). We are grateful to the Cam-CAN respondents and their primary care teams in Cambridge for their participation in this study. We also thank colleagues at the MRC Cognition and Brain Sciences Unit MEG and MRI facilities for their assistance.