Ferguson Michael A, Anderson Jeffrey S, Spreng R Nathan
Laboratory of Brain and Cognition, Human Neuroscience Institute, Department of Human Development, Cornell University, Ithaca, NY, 14853.
Departments of Bioengineering and Neuroradiology, University of Utah, Salt Lake City, UT, 84132.
Netw Neurosci. 2017 Jun 1;1(2):192-207. doi: 10.1162/NETN_a_00010. eCollection 2017 Spring.
Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain's intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data ( = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain's intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.
人类智力被概念化为一个由可分离的认知过程组成的复杂系统，然而，研究智力神经基础的研究通常强调离散脑区的贡献，或者更近一些，强调功能连接区域的特定网络的贡献。在这里，我们采用更广泛的系统视角，以研究智力是否是大脑内在网络结构中同步性的一种涌现特性。使用大量静息态功能磁共振成像和认知数据样本（ = 830），我们报告说，分布式脑网络内部和之间功能相互作用的同步性可靠地预测了流体智力和灵活智力功能。通过采用全脑、系统水平的方法，我们能够通过表征大脑内在网络结构的特征来可靠地预测人类智力的个体差异。这些发现为最终开发神经标记物以预测与神经发育、正常衰老和脑部疾病相关的智力功能变化带来了希望。
