时间标签：数据日期 2026-11-05 · 生成时间 2026-11-05 05:30（Asia/Shanghai）｜ 检索窗口 PubMed edat 2026/11/02 – 2026/11/06 ｜ 情报发生日期 2026-11-05
研究问题
However, relatively few studies have examined metabolomic associations across specific cognitive domains, even fewer have evaluated whether these associations are shared relationships with functional measures of brain activity.Neural network activation pattern scores derived from the Reference Ability Neural Network (RANN) cohort offer a domain-specific characterization of brain function based on task-related functional neuroimaging (Habeck et al., 2016).；Examining the concordance of their significant metabolomic associations therefore provides insight into whether metabolic alterations reflect fundamental processes shared across levels of brain function or are specific to either early neural activation dysfunction or downstream cognitive expression.；Furthermore, comparing metabolite associations across domains may reveal whether metabolic alterations meaningfully reflect common mechanisms that support multiple cognitive processes or domain-specific pathways that differentially influence memory, language, reasoning, or processing speed.In this study, we leveraged untargeted high-resolution metabolomics to examine associations between circulating metabolite features and domain-specific cognitive performance as well as cognitive neural network activation pattern scores.
方法
However, relatively few studies have examined metabolomic associations across specific cognitive domains, even fewer have evaluated whether these associations are shared relationships with functional measures of brain activity.Neural network activation pattern scores derived from the Reference Ability Neural Network (RANN) cohort offer a domain-specific characterization of brain function based on task-related functional neuroimaging (Habeck et al., 2016).；Furthermore, comparing metabolite associations across domains may reveal whether metabolic alterations meaningfully reflect common mechanisms that support multiple cognitive processes or domain-specific pathways that differentially influence memory, language, reasoning, or processing speed.In this study, we leveraged untargeted high-resolution metabolomics to examine associations between circulating metabolite features and domain-specific cognitive performance as well as cognitive neural network activation pattern scores.；By comparing metabolite associations across these complementary measures, this work provides insight into the metabolic processes that underlie early and domain-specific changes in brain function.2Materials and methods2.1ParticipantsThe current study utilized data from participants of the Reference Abilities Neural Network (RANN) study of Columbia University.
核心结论
There were 0, 1, and 0 overlapping pathways for reasoning, speed, and language domains, respectively.4DiscussionIn this cross-sectional study of 112 participants, we found few metabolite features were significantly associated with the reasoning domain for neural network activation pattern score while no metabolite features were significantly associated with other cognitive or neural network outcomes after adjusting for multiple comparisons.；These findings suggest that the neural network for reasoning may be particularly sensitive specifically to disruptions in metabolic processes that support high energetic demand and cellular maintenance, aligning with the greater computational complexity and metabolic requirements of higher-order cognitive functions.Other domains showed comparatively few enriched pathways involved.；Speed was linked to a small number of pathways, including biopterin and pyruvate metabolism, while language demonstrated minimal metabolic signal overall, consistent with its characterization as a more stable, crystallized cognitive domain (Cattell, 1963).
局限
However, relatively few studies have examined metabolomic associations across specific cognitive domains, even fewer have evaluated whether these associations are shared relationships with functional measures of brain activity.Neural network activation pattern scores derived from the Reference Ability Neural Network (RANN) cohort offer a domain-specific characterization of brain function based on task-related functional neuroimaging (Habeck et al., 2016).；However, the order of the two sessions was counterbalanced across participants.；The tests in this battery, however, differed from those administered during the in-scanner assessment.
对本项目的价值
中等价值：属「脑老化与神经退行」方向；脑老化 / 神经退行 / 认知栏目可用作本项目文献库与综述素材。 ｜ 价值分 2.9/5（★★★☆☆）
全文来源
PubMed PMC JATS（正文 43,372 字符，题名指纹 0.86，已过 PubMed PMCID 官方关联 + 题名指纹 + 正文长度归属校验）
原文题录
- 中文题名：认知正常成人代谢组学特征与认知及神经网络的维度特异性关联
- 英文标题：Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults.
- 期刊与标识：Frontiers in neuroscience · PMID 42978722 · DOI 10.3389/fnins.2026.1898605 · PMC PMC13727718