Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Padova Neuroscience Center & Department of Neuroscience, University of Padova, Padua, PD, Italy.
Brain Struct Funct. 2022 Jan;227(1):49-62. doi: 10.1007/s00429-021-02388-4. Epub 2021 Dec 4.
Executive functions (EF) are a set of higher-order cognitive abilities that enable goal-directed behavior by controlling lower-level operations. In the brain, those functions have been traditionally associated with activity in the Frontoparietal Network, but recent neuroimaging studies have challenged this view in favor of more widespread cortical involvement. In the present study, we aimed to explore whether the network that serves as critical hubs at rest, which we term network reliance, differentiate individuals as a function of their level of EF. Furthermore, we investigated whether such differences are driven by genetic as compared to environmental factors. For this purpose, resting-state functional magnetic resonance imaging data and the behavioral testing of 453 twins from the Colorado Longitudinal Twins Study were analyzed. Separate indices of EF performance were obtained according to a bifactor unity/diversity model, distinguishing between three independent components representing: Common EF, Shifting-specific and Updating-specific abilities. Through an approach of step-wise in silico network lesioning of the individual functional connectome, we show that interindividual differences in EF are associated with different dependencies on neural networks at rest. Furthermore, these patterns show evidence of mild heritability. Such findings add knowledge to the understanding of brain states at rest and their connection with human behavior, and how they might be shaped by genetic influences.
执行功能(EF)是一组高级认知能力,通过控制较低层次的操作来实现目标导向的行为。在大脑中,这些功能传统上与额顶网络的活动有关,但最近的神经影像学研究挑战了这一观点,认为皮层的参与更为广泛。在本研究中,我们旨在探索作为关键枢纽在静息状态下的网络(我们称之为网络依赖)是否可以根据个体的 EF 水平来区分个体。此外,我们还研究了这些差异是由遗传因素还是环境因素驱动的。为此,我们分析了来自科罗拉多纵向双胞胎研究的 453 对双胞胎的静息态功能磁共振成像数据和行为测试。根据单因素/多样性模型获得了 EF 表现的单独指数,区分了代表以下三个独立成分的能力:一般 EF、转换特异性和更新特异性能力。通过对个体功能连接体进行逐步的模拟网络损伤的方法,我们表明 EF 的个体差异与静息状态下不同的神经网络依赖有关。此外,这些模式显示出轻微的遗传可能性。这些发现增加了对静息状态下大脑状态及其与人类行为的联系的理解,以及遗传因素如何影响这些状态。