Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance.
Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance.
复制标题
人类智力测试表现个体差异的网络抽样理论的神经影像学证据。
DOI：
10.1038/s41467-021-22199-9
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发表时间：
2021-04-06
影响因子：
16.6
通讯作者：
Hampshire A
中科院分区：
文献类型：
--
作者：
Soreq E;Violante IR;Daws RE;Hampshire A
尽管经过了世纪的研究，人类的智力究竟应该作为一种主要的能力、几种主要的能力还是许多不同的能力来研究，以及这些能力与大脑的功能组织之间的关系，仍然不清楚。在这里，我们结合联合收割机心理测量和机器学习方法，以数据驱动的方式研究认知任务绩效中的因素结构和个体变异性与动态网络连接组学的关系。我们报告说，12个子任务，从一个既定的智力测试可以准确地多路分类（74%，机会8.3%）的基础上，他们唤起的网络状态。行为-心理测量空间中任务的接近性与其网络状态的相似性相关。此外，网络状态被更准确地分类为相对于表现较低的个体。这些结果表明，人类大脑使用高维网络采样机制来灵活地编码不同的认知任务。智力测试表现的群体变异性与这些任务优化网络状态表达的保真度有关。一个定义人类特征是执行各种认知挑战性任务的能力。作者表明，这种适应性与网络采样机制有关，其中全脑网络状态瞬时混合了不同任务所需的神经资源的独特组合。
Despite a century of research, it remains unclear whether human intelligence should be studied as one dominant, several major, or many distinct abilities, and how such abilities relate to the functional organisation of the brain. Here, we combine psychometric and machine learning methods to examine in a data-driven manner how factor structure and individual variability in cognitive-task performance relate to dynamic-network connectomics. We report that 12 sub-tasks from an established intelligence test can be accurately multi-way classified (74%, chance 8.3%) based on the network states that they evoke. The proximities of the tasks in behavioural-psychometric space correlate with the similarities of their network states. Furthermore, the network states were more accurately classified for higher relative to lower performing individuals. These results suggest that the human brain uses a high-dimensional network-sampling mechanism to flexibly code for diverse cognitive tasks. Population variability in intelligence test performance relates to the fidelity of expression of these task-optimised network states. A defining human characteristic is the ability to perform diverse cognitively challenging tasks. The authors show that this adaptability relates to a network sampling mechanism, where brain-wide network states transiently blend the unique combinations of neural resources required by different tasks.
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影响因子：
5.4
作者：
Bartholomew, David J.;Deary, Ian J.;Lawn, Martin
通讯作者：
Lawn, Martin
DOI：
10.1073/pnas.1220826110
发表时间：
2013-07-09
影响因子：
11.1
作者：
Crossley, Nicolas A.;Mechelli, Andrea;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
14.5
作者：
Jolly, Amy E.;Scott, Gregory T.;Hampshire, Adam H.
通讯作者：
Hampshire, Adam H.
DOI：
10.1098/rstb.2013.0526
发表时间：
2014-10-05
影响因子：
6.3
作者：
Krienen, Fenna M.;Yeo, B. T. Thomas;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
通讯作者：
Grafton, Scott T.