将老龄化的计算认知特征与可分离的大脑和社会人口因素进行映射。

IF 4.1 Q2 GERIATRICS & GERONTOLOGY
Aleya A Marzuki, Kean Yung Wong, Jee Kei Chan, Sze Yie Na, Arjun Thanaraju, Paveen Phon-Amnuaisuk, Samira Vafa, Jie Yap, Wei Gene Lim, Wei Zern Yip, Annette Shamala Arokiaraj, Dexter Shee, Louisa Gee Ling Lee, Yook Chin Chia, Michael Jenkins, Alexandre Schaefer
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引用次数: 0

摘要

衰老与认知能力和大脑结构完整性的下降有关。然而,在以下几个方面却存在分歧:(1) 不同人群受影响领域的特异性;(2) 大脑结构退化相关模式的位置;(3) 导致 "不健康 "认知的社会人口因素。我们的目标是识别老年人的认知特征,并确定可能塑造这些特征的大脑和社会人口特征。一个东南亚老年人样本(N = 386)参加了一项包括认知测试、神经影像学和结构化访谈的多阶段研究。我们使用计算模型来提取认知灵活性和反应抑制的潜在机制。我们采用数据驱动方法,根据标准成绩测量和模型参数构建认知概况。我们还研究了灰质体积和机器学习得出的 "脑年龄"。结果表明,认知控制区域的灰质普遍减少,这与集群转移能力差和注意力不集中有关。反应迟钝的特征与高龄大脑有关。这两种特征都与社会经济地位低下和认知储备相关。我们发现,社会人口因素对认知特征的影响部分受灰质和白质总量以及背外侧前额叶和小脑体积的影响。这项研究加深了人们对认知障碍的不同衰老特征如何与特定和整体大脑退化相对应,以及社会经济因素对老年认知表现的重要影响的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping computational cognitive profiles of aging to dissociable brain and sociodemographic factors.

Aging is associated with declines in cognition and brain structural integrity. However, there is equivocality over (1) the specificity of affected domains in different people, (2) the location of associated patterns of brain structural deterioration, and (3) the sociodemographic factors contributing to 'unhealthy' cognition. We aimed to identify cognitive profiles displayed by older adults and determine brain and sociodemographic features potentially shaping these profiles. A sample of Southeast-Asian older adults (N = 386) participated in a multi-session study comprising cognitive testing, neuroimaging, and a structured interview. We used computational models to extract latent mechanisms underlying cognitive flexibility and response inhibition. Data-driven methods were used to construct cognitive profiles based on standard performance measures and model parameters. We also investigated grey matter volume and machine-learning derived 'brain-ages'. A profile associated with poor set-shifting and rigid focusing was associated with widespread grey matter reduction in cognitive control regions. A slow responding profile was associated with advanced brain-age. Both profiles were correlated with poor socioeconomic standing and cognitive reserve. We found that the impact of sociodemographic factors on cognitive profiles was partially mediated by total grey and white matter, and dorsolateral prefrontal and cerebellar volumes. This study furthers understanding of how distinct aging profiles of cognitive impairment uniquely correspond to specific vs. global brain deterioration and the significance of socioeconomic factors in informing cognitive performance in older age.

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