Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.
Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.
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DOI：
10.1002/hbm.25316
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发表时间：
2021-04-15
影响因子：
4.8
通讯作者：
de Lange AG
中科院分区：
文献类型：
--
作者：
Anatürk M;Kaufmann T;Cole JH;Suri S;Griffanti L;Zsoldos E;Filippini N;Singh-Manoux A;Kivimäki M;Westlye LT;Ebmeier KP;de Lange AG
大脑维护的概念是指在老年时保持大脑的完整性，而认知储备是指在神经退行性变或与衰老相关的大脑变化存在的情况下保持认知的能力。虽然这两种机制被认为有助于老年人认知功能的个体差异，但目前还没有衡量这些结构的“金标准”。使用机器学习方法，我们根据白厅II MRI子研究队列（N = 537，年龄范围= 60.34-82.76）中与标准老化模式的偏差估计了大脑和认知年龄，并测试了这些结构之间的对应程度，以及它们与发病前智商，教育和生活方式轨迹的关联。与强调智商作为认知储备的代表的现有文献一致，较高的病前智商与独立于脑年龄的较低认知年龄有关。没有强有力的证据表明，大脑或认知年龄和生活方式轨迹之间的联系，从中年到晚年的潜在类增长分析。然而，事后分析显示，累积的生活方式措施和独立于认知年龄的大脑年龄之间的关系。总之，我们提出了一种新的方法来表征大脑和认知维持老化，这可能是有用的，为未来的研究，寻求确定因素，有助于大脑的保护和认知储备机制在老年。使用机器学习，我们根据白厅II MRI子研究队列中与规范老化模式的偏差来估计大脑和认知年龄，并测试了这些结构之间的对应程度，以及它们与发病前智商，教育和生活方式轨迹的关联。这项研究提出了一种新的方法来表征衰老过程中的大脑和认知维持，这可能有助于未来的研究，以确定有助于老年大脑保护和认知储备的因素。
The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age. Using machine learning, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort, and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. The study presents a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve in older age.
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影响因子：
15.8
作者：
Clare L
通讯作者：
Clare L
DOI：
10.20982/tqmp.05.1.p011
发表时间：
2009-01-01
期刊：
TUTORIALS IN QUANTITATIVE METHODS FOR PSYCHOLOGY
影响因子：
--
作者：
Andruff, Heather;Carraro, Natasha;Louvet, Benoit
通讯作者：
Louvet, Benoit
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.7
作者：
de Lange AG;Anatürk M;Suri S;Kaufmann T;Cole JH;Griffanti L;Zsoldos E;Jensen DEA;Filippini N;Singh-Manoux A;Kivimäki M;Westlye LT;Ebmeier KP
通讯作者：
Ebmeier KP
影响因子：
1.9
作者：
Brathen, Anne Cecilie Sjoli;De Lange, Ann-Marie Glaso;Walhovd, Kristine B.
通讯作者：
Walhovd, Kristine B.