The cognitive neuroscience of ageing

Abstract

The availability of neuroimaging technology has spurred a marked increase in the human cognitive neuroscience literature, including the study of cognitive ageing. Although there is a growing consensus that the ageing brain retains considerable plasticity of function, currently measured primarily by means of functional MRI, it is less clear how age differences in brain activity relate to cognitive performance. The field is also hampered by the complexity of the ageing process itself and the large number of factors that are influenced by age. In this Review, current trends and unresolved issues in the cognitive neuroscience of ageing are discussed.

Figure 1. Increased brain activity in older adults may be associated with better or worse task performance

This figure summarizes the results of two studies that differ in how increased brain activity in older adults was associated with task performance. In one of these studies, several brain regions (indicated in blue) showed a correlation between more activity and more accurate performance on an task requiring inhibition of responses in a go/no-go task. In the other study the regions shown in green showed a correlation between more activity and slower reaction times on perceptual and working memory tasks. Note that some regions (colored blue and green) showed an association with better performance in Study 1 and the opposite effect in Study 2. This discrepancy highlights the complexity of trying to relate brain activity in older adults to their behaviour, and indicates that the specific relationships between regional brain activity and task performance in older adults depend on the task demands or on the behavioral measure that is assessed (or both). Abbreviations: MFg, middle frontal gyrus; IFg, inferior frontal gyrus; IPL, inferior parietal lobe; IT, inferior temporal cortex; Vc, visual cortex.

Figure 2. ‘Compensation-Related Utilization of Neural Circuits hypothesis’

The function relating the change in brain activity (measured by fMRI during a task of interest) to levels of cognitive load is shown for young adults, old adults with a low risk of developing AD and old adults with high risk of developing dementia. The function in low risk older adults would be shifted to the left relative to that seen in younger adults. At relatively low levels of cognitive load this shift would result in higher activity in older relative to younger adults (green shaded area). However, activity in older adults would reach its peak and level off while younger adults’ activity is still increasing, so that at higher load levels there would be no age difference in activity or younger adults would have higher activity (gray shaded area). A similar effect would be seen when high-risk older adults are compared to low-risk older adults – higher activity in high risk groups relative to low risk at low levels of cognitive load (blue shaded area), with the reverse seen at higher levels of load. This hypothesized set of load-dependent functions could explain why studies have reported both under- and over-recruitment in older adults compared to young adults, and in high-risk older adults compared to low- risk older adults.

Figure 3. The default network in young and older adults

The regions making up the default network (DN) are shown in this figure (brain areas shown in red). The DN was defined using a multivariate, whole-brain approach that identified regions where activity at rest was correlated with activity in the posterior cingulate cortex (PCC, a major node of the DN). The DN includes lateral inferior parietal regions (IPL), as well as ventromedial prefrontal cortex (vmPFC), superior frontal gyrus (SFG), and the medial temporal lobe (MTL). The green regions represent a subset of DN areas, both medial and lateral, with weaker resting functional connectivity in older compared to young adults (also see Box 1).

Figure 4. A hypothetical model of the various dimensions that can interact with aging

The model is intended to show the interplay among a wide array of physical and behavioral aspects (some of which are discussed in this review) and the aging process. The arrows are bidirectional to indicate that the influence can potentially arise from these factors on the aging process, or vice versa. For example, genetic factors could influence how an individual ages, and aging can enhance the effects of genes on specific behaviors. There are other factors that could be included here, such as risk factors for vascular disease or dementia, but this incomplete list gives a sense of how complex the study of aging is, and how difficult it would be to comprehensively assess these variables in a single experiment.