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Review
. 2011 Jul;17(4):593-601.
doi: 10.1017/S1355617710001748.

Conceptual and measurement challenges in research on cognitive reserve

Affiliations
Review

Conceptual and measurement challenges in research on cognitive reserve

Richard N Jones et al. J Int Neuropsychol Soc. 2011 Jul.

Abstract

Cognitive reserve, broadly conceived, encompasses aspects of brain structure and function that optimize individual performance in the presence of injury or pathology. Reserve is defined as a feature of brain structure and/or function that modifies the relationship between injury or pathology and performance on neuropsychological tasks or clinical outcomes. Reserve is challenging to study for two reasons. The first is: reserve is a hypothetical construct, and direct measures of reserve are not available. Proxy variables and latent variable models are used to attempt to operationalize reserve. The second is: in vivo measures of neuronal pathology are not widely available. It is challenging to develop and test models involving a risk factor (injury or pathology), a moderator (reserve) and an outcome (performance or clinical status) when neither the risk factor nor the moderator are measured directly. We discuss approaches for quantifying reserve with latent variable models, with emphasis on their application in the analysis of data from observational studies. Increasingly latent variable models are used to generate composites of cognitive reserve based on multiple proxies. We review the theoretical and ontological status of latent variable modeling approaches to cognitive reserve, and suggest research strategies for advancing the field.

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Conflict of interest statement

The authors have no conflict of interest.

Figures

Fig. 1
Fig. 1
Reserve modifies the relationship between pathology and performance.
Fig. 2
Fig. 2
The relationship between pathology and performance is attenuated in high relative to low reserve.
Fig. 3
Fig. 3
A formative measurement model.
Fig. 4
Fig. 4
A reflective measurement model.
Fig. 5
Fig. 5
A formative measurement with an outcome.
Fig. 6
Fig. 6
A multiple regression model.

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