Reviews and perspectivesCognitive reserve☆
Section snippets
Brain reserve and cognitive reserve
Reserve can be roughly classified into passive and active models. Brain reserve (Katzman, 1993) is an example of a passive model, where reserve derives from brain size or neuronal count. Larger brains can sustain more insult before clinical deficit emerges, because sufficient neural substrate remains to support normal function. This approach to reserve has been codified in the threshold model (Satz, 1993), which revolves around the construct of “brain reserve capacity”. The model recognizes
Measures of reserve
For brain reserve, anatomic measures such as brain volume, head circumference, synaptic count, or dendritic branching are effective measures of reserve. Based on the epidemiologic evidence reviewed below, variables descriptive of lifetime experience are commonly used as proxies for CR. These include measures of socioeconomic status, such as income or occupational attainment, educational attainment and leisure activity. In some populations, degree of literacy might be a better marker for CR than
Epidemiologic evidence for CR
Fig. 1 illustrates the way in which cognitive reserve may mediate between AD pathology and its clinical expression. We assume that AD pathology slowly increases over time, and this is graphed on the x-axis. The y-axis represents cognitive function, in this case memory performance. AD pathology probably begins to develop many years before the disease is expressed clinically and slowly becomes more severe. At some point this developing pathology will begin to produce the initial cognitive changes
Evidence for CR from studies of regional cerebral blood flow
Our first imaging studies of CR were designed to test the hypothesis that at any given level of clinical AD severity an individual with a higher level of CR should have greater AD pathology. The logic behind this prediction is demonstrated in figure one. Note that, after the point of inflection, at any particular memory test score on the x-axis, pathology (on the y-axis) is more advanced in patients with higher CR. In these studies, we used resting regional cerebral blood flow (rCBF) as a
Neural mechanisms underlying CR
The epidemiologic and CBF at rest data provide evidence for the existence of CR. However, they cannot provide clues as to the neural mechanisms that may mediate CR. To pursue this question, my group turned to cognitive activation studies using O15 PET and fMRI.
I have suggested that the neural implementation of CR might take two forms: neural reserve and neural compensation (see Table 1) (Stern et al., 2005). The idea behind neural reserve is that there is natural inter-individual variability in
Exploring the neural mechanisms underlying cognitive reserve
In this section I will review the reasoning underlying the different approaches my group has taken when exploring the neural underpinnings of CR. In the next section I will review studies that demonstrate these approaches. In order to provide focus, the discussion and reviewed studies will focus on exploring CR in normal aging. For the most part, the questions to be asked in neuroimaging studies when exploring the neural basis of cognitive reserve in aging mirror those addressed in most
Review of imaging studies
In this section, I will review studies conducted by my group that address the questions and apply the approaches discussed above. These studies were not designed to definitively identify the neural substrates of CR. The goals are more limited. First, we wanted to develop methodology to address some of the difficulties in studying CR discussed above. Second, we wanted to explore techniques for supporting the concepts of neural reserve and neural compensation. Several data sets were analyzed more
Conclusions
There is strong epidemiologic evidence for the idea that cognitive reserve mediates between brain changes or pathology and the clinical manifestation of that pathology. The concept of cognitive reserve strongly relies on the idea that there can be individual differences in how tasks are processed that can allow some people to cope better than others with brain changes in general and aging in particular. I have tried to map out a systematic approach for exploring the neural implementation of CR.
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This work was supported by a grant from the National Institutes on Aging (RO1 AG26158).