Cognitive Neuroscience Division, Columbia University, New York, New York, USA.
Taub Institute, Columbia University, New York, New York, USA.
Hum Brain Mapp. 2021 Feb 15;42(3):644-659. doi: 10.1002/hbm.25250. Epub 2020 Oct 27.
Previous studies have demonstrated that four latent variables, or reference abilities (RAs), can account for the majority of age-related changes in cognition: these being episodic memory, fluid reasoning, speed of processing, and vocabulary. In the current study, we focused on RA-selective functional connectivity patterns that vary with both age and behavior. We analyzed fMRI data from 287 community-dwelling adults (20-80 years) on a battery of tests relating to the four RAs (three tests per RA = 12 tests). Functional connectivity values were calculated between a pre-defined set of 264 ROIs (nodes). Across all participants, we (a) identified connections (edges) that correlated with an RA-specific indicator variable and, indexing only these edges; (b) performed linear regression analysis per edge, regressing indicator correlations (Model 1) and connectivity values (Model 2) on Age, Behavioral Performance, and the Interaction term; and (c) took the conjunction of significant edges between models. Results revealed a different subset of edges for each RA whose connectivity strength and domain-selectivity varied with age and behavior. Strikingly, the fluid reasoning RA was particularly vulnerable to the effects of age and displayed the most extensive connectivity and selectivity "footprint" for behavior. These findings indicate that different functional networks are recruited across RA, with fluid reasoning displaying a special status among them.
先前的研究已经表明，四个潜在变量或参考能力（RAs）可以解释认知与年龄相关的大部分变化：这些是情节记忆、流体推理、处理速度和词汇量。在当前的研究中，我们专注于 RA 选择性功能连接模式，这些模式随着年龄和行为而变化。我们分析了 287 名居住在社区的成年人（20-80 岁）的 fMRI 数据，这些成年人接受了与四个 RAs 相关的一系列测试（每个 RA 有三个测试= 12 个测试）。在一组预定义的 264 个 ROI（节点）之间计算功能连接值。在所有参与者中，我们 (a) 确定了与 RA 特定指标变量相关的连接（边），并仅对这些边进行索引；(b) 对每个边执行线性回归分析，根据年龄、行为表现和交互项对指标相关性（模型 1）和连接值（模型 2）进行回归；以及 (c) 在模型之间取显著边的交集。结果揭示了每个 RA 的不同边子集，其连接强度和域选择性随年龄和行为而变化。引人注目的是，流体推理 RA 特别容易受到年龄的影响，并显示出最广泛的连接和选择性“足迹”。这些发现表明，不同的功能网络在 RA 之间被招募，其中流体推理具有特殊地位。
