Abstract
Compensation in cognitive aging is a topic of recent interest. However, factors contributing to cognitive compensation in functions such as phonemic fluency (PF) are not completely understood. Using cross-sectional data, we investigated cognitive reserve (CR) and network efficiency in young (32-58 years) versus old (59-84 years) individuals with high versus low performance in PF. ANCOVA was used to investigate the interaction between CR, age, and performance in PF. Random forest and graph theory analyses were conducted to study the contribution of cognition to PF and efficiency measures, respectively. Higher CR increased performance in PF and reduced age-related differences in PF. A slightly higher number of cognitive functions contributed to performance in high CR groups. The networks were more integrated in high CR individuals, both in the older age and high-performance groups. The strength and segregation of the networks were decreased in high-performance groups with high CR. We conclude that PF decreases less with age in individuals with higher CR, possibly due to a greater capacity to recruit non-linguistic cognitive networks, and efficient use of language networks, thereby integrating information in a rapid way across less fragmented networks. High CR and network efficiency seem to be important factors for cognitive compensation.
Highlights
The overall goal of this study was to investigate how cognitive reserve (CR) and efficiency levels contribute to phonemic fluency differently in people with high versus low performance and in younger versus older individuals
We found that older adults performed worse than younger adults in verbal fluency, but this difference was minimized by high CR levels and high efficiency of cognitive networks
High CR levels have been associated with higher performance in phonemic fluency [21,22,23,24,25]
Summary
Many cognitive functions decline with age, language is one of the few functions that can resist the onslaught of aging [1, 2] An explanation for this is that language abilities are broadly distributed through different neural networks across the brain [3]. Compensation can be approached by investigating how different cognitive functions are associated with or contribute to language abilities [9]. Due to the complexity of human cognition, an interesting approach is to investigate the contribution of different cognitive functions to verbal fluency by using multivariate methods for data analysis. Lexical access, working memory, processing speed, and visuoconstructive abilities were the most contributing functions to performance in phonemic fluency. Further research is needed to elucidate the factors involved in these compensatory mechanisms
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