Department of Clinical Psychology, Psychobiology and Methodology, Faculty of Health Science, Section of Psychology and Speech Therapy, University of La Laguna, La Laguna, Tenerife, Spain.
Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.
Aging (Albany NY). 2020 Nov 17;12(22):23351-23378. doi: 10.18632/aging.202177.
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.
认知老化补偿是近期关注的一个话题。然而，对于语音流畅性（PF）等功能的认知补偿因素尚不完全清楚。本研究使用横断数据，调查了认知储备（CR）和网络效率在 PF 表现高（32-58 岁）与低（59-84 岁）个体中的作用。采用方差分析（ANCOVA）来研究 CR、年龄和 PF 表现之间的交互作用。随机森林和图论分析分别用于研究认知对 PF 和效率测量的贡献。较高的 CR 提高了 PF 表现，减少了 PF 与年龄相关的差异。在高 CR 组中，有较多的认知功能对 PF 表现有贡献。在高 CR 个体中，无论是在老年还是高表现组，网络的整合度都更高。在高 CR 高表现组中，网络的强度和分离度降低。我们得出结论，在 CR 较高的个体中，PF 随年龄下降的幅度较小，这可能是由于他们有更大的能力来招募非语言认知网络，并有效地利用语言网络，从而在较少碎片化的网络中以更快速的方式整合信息。高 CR 和网络效率似乎是认知补偿的重要因素。
