Geerligs Linda, Rubinov Mikail, Henson Richard N
Medical Research Council (MRC) Cognition and Brain Sciences Unit, Cambridge CB2 7EF, United Kingdom, Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 3EB, United Kingdom, Churchill College, University of Cambridge, Cambridge CB3 0DS, United Kingdom, and.
J Neurosci. 2015 Oct 14;35(41):13949-61. doi: 10.1523/JNEUROSCI.1324-15.2015.
Resting-state functional connectivity, as measured by functional magnetic resonance imaging (fMRI), is often treated as a trait, used, for example, to draw inferences about individual differences in cognitive function, or differences between healthy or diseased populations. However, functional connectivity can also depend on the individual's mental state. In the present study, we examined the relative contribution of state and trait components in shaping an individual's functional architecture. We used fMRI data from a large, population-based human sample (N = 587, age 18-88 years), as part of the Cambridge Centre for Aging and Neuroscience (Cam-CAN), which were collected in three mental states: resting, performing a sensorimotor task, and watching a movie. Whereas previous studies have shown commonalities across mental states in the average functional connectivity across individuals, we focused on the effects of states on the pattern of individual differences in functional connectivity. We found that state effects were as important as trait effects in shaping individual functional connectivity patterns, each explaining an approximately equal amount of variance. This was true when we looked at aging, as one specific dimension of individual differences, as well as when we looked at generic aspects of individual variation. These results show that individual differences in functional connectivity consist of state-dependent aspects, as well as more stable, trait-like characteristics. Studying individual differences in functional connectivity across a wider range of mental states will therefore provide a more complete picture of the mechanisms underlying factors such as cognitive ability, aging, and disease.
The brain's functional architecture is remarkably similar across different individuals and across different mental states, which is why many studies use functional connectivity as a trait measure. Despite these trait-like aspects, functional connectivity varies over time and with changes in cognitive state. We measured connectivity in three different states to quantify the size of the trait-like component of functional connectivity, compared with the state-dependent component. Our results show that studying individual differences within one state (such as resting) uncovers only part of the relevant individual differences in brain function, and that the study of functional connectivity under multiple mental states is essential to disentangle connectivity differences that are transient versus those that represent more stable, trait-like characteristics of an individual.
静息态功能连接性通过功能磁共振成像（fMRI）测量，通常被视为一种特质，例如用于推断认知功能的个体差异，或健康人群与患病群体之间的差异。然而，功能连接性也可能取决于个体的心理状态。在本研究中，我们考察了状态和特质成分在塑造个体功能结构中的相对贡献。我们使用了来自一个基于人群的大型人类样本（N = 587，年龄18 - 88岁）的fMRI数据，该样本是剑桥衰老与神经科学中心（Cam - CAN）的一部分，数据在三种心理状态下收集：静息、执行感觉运动任务以及观看电影。尽管先前的研究表明，个体间平均功能连接性在不同心理状态下存在共性，但我们关注的是状态对功能连接性个体差异模式的影响。我们发现，在塑造个体功能连接性模式方面，状态效应与特质效应同样重要，二者解释的方差量大致相等。当我们将衰老作为个体差异的一个特定维度进行研究时，以及当我们考察个体变异的一般方面时，都是如此。这些结果表明，功能连接性的个体差异既包括状态依赖的方面，也包括更稳定的、类似特质的特征。因此，在更广泛的心理状态范围内研究功能连接性的个体差异，将为认知能力、衰老和疾病等因素背后的机制提供更完整的图景。
大脑的功能结构在不同个体和不同心理状态下显著相似，这就是为什么许多研究将功能连接性用作特质测量。尽管存在这些类似特质的方面，但功能连接性会随时间以及认知状态的变化而变化。我们在三种不同状态下测量连接性，以量化功能连接性中类似特质成分与状态依赖成分相比的大小。我们的结果表明，仅研究一种状态（如静息）下的个体差异，只能揭示大脑功能中相关个体差异的一部分，而研究多种心理状态下的功能连接性对于区分短暂的连接性差异与代表个体更稳定、类似特质特征的差异至关重要。
