Satary Dizaji Aslan, Vieira Bruno Hebling, Khodaei Mohmmad Reza, Ashrafi Mahnaz, Parham Elahe, Hosseinzadeh Gholam Ali, Salmon Carlos Ernesto Garrido, Soltanianzadeh Hamid
Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Inbrain Lab, Department of Physics, FFCLRP, University of São Paulo, Ribeirao Preto, Brazil.
Basic Clin Neurosci. 2021 Jan-Feb;12(1):1-28. doi: 10.32598/bcn.12.1.574.1. Epub 2021 Jan 1.
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.
人类智力一直是科学家们着迷的课题。自20世纪初斯皮尔曼提出一般智力以来，在刻画智力的不同方面及其与大脑结构和功能特征的关系方面取得了重大进展。近年来，使用扩散加权成像（DWI）和功能磁共振成像（fMRI）的先进脑成像设备的发明，使研究人员能够测试关于人类智力神经相关性的假设。本综述总结了关于人类智力与神经成像数据关联的最新发现。为此，首先，我们回顾将脑形态测量学与智力相关联的文献。接下来，我们详细阐述DWI和静息态fMRI在智力研究中的应用。然后，我们对使用多模态DWI-fMRI来阐明智力的文献进行综述。最后，我们讨论从神经成像数据进行智力个体化预测的现状，并指出未来的策略。未来的研究对于使用神经成像特征来估计人类智力的基于机器学习的预测框架具有广阔前景。
