Department of Psychology I, Würzburg University, 97070 Würzburg, Germany.
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
Cereb Cortex. 2022 Sep 19;32(19):4172-4182. doi: 10.1093/cercor/bhab473.
Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for the effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used functional magnetic resonance imaging data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system and replicates in two independent samples (n = 138 and n = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multitask brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold - the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multitask brain network.
智力描述了一个人的一般认知能力水平。它是心理科学中最基本的概念之一，对于有效适应不断变化的环境需求的行为至关重要。已经表明，改变外部任务需求会引起功能大脑网络的重新配置。然而，不同任务之间的神经重新配置是否与智力有关尚未得到研究。我们使用来自 812 名受试者的功能磁共振成像数据表明，较高的一般智力得分与静息状态与七种不同任务状态之间以及任务之间的大脑网络重新配置较少有关。这种关联适用于除运动系统之外的所有功能大脑网络，并且在两个独立的样本（n = 138 和 n = 184）中得到复制。我们的研究结果表明，智力得分较高的个体的内在网络结构更接近各种认知需求所需的网络结构。因此，多任务大脑网络重新配置可能代表行为正流形的神经反映 - 一般智力概念的本质。最后，我们的研究结果支持认知能力的神经效率理论，并揭示了人类智力作为分布式多任务大脑网络的涌现属性的见解。
