Most studies indicate that intelligence (g) is positively correlated with cortical thickness. However, the interindividual variability of cortical thickness has not been taken into account. In this study, we aimed to identify the association between intelligence and cortical thickness in adolescents from both the group's mean and dispersion point of view, utilizing the structural brain imaging from the Adolescent Brain and Cognitive Development (ABCD) Consortium, the largest cohort in early adolescents around 10 years old. The mean and dispersion parameters of cortical thickness and their association with intelligence were estimated using double generalized linear models(DGLM). We found that for the mean model part, the thickness of the frontal lobe like superior frontal gyrus was negatively related to intelligence, while the surface area was most positively associated with intelligence in the frontal lobe. In the dispersion part, intelligence was negatively correlated with the dispersion of cortical thickness in widespread areas, but not with the surface area. These results suggested that people with higher IQ are more similar in cortical thickness, which may be related to less differentiation or heterogeneity in cortical columns.
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
AI Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学AI Virtanen分子科学研究所）；Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所）
Spherical Brownian Bridge Diffusion Models for Conditional Cortical Thickness Forecasting
Lab for AI in Medical Imaging, Technical University of Munich, Munich, Germany（人工智能医学影像实验室，慕尼黑技术大学，慕尼黑，德国）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）
Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography
Classifying histograms of medical data using information geometry of beta distributions
Université Paris 1 Panthéon Sorbonne（巴黎第一大学）；Université Côte d’Azur（蔚蓝海岸大学）；Inria Epione（法国国家信息与自动化研究所Epione团队）；Ecole Nationale de l’Aviation Civile（法国民航大学）；Toulouse NeuroImaging Center（图卢兹神经影像中心）；Université de Toulouse（图卢兹大学）
What do neuroanatomical networks reveal about the ontology of human cognitive abilities?
Hong Kong Baptist University（香港浸会大学）；Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学）；East China Normal University（华东师范大学）；Humboldt University at Berlin（柏林洪堡大学）；Zhejiang Normal University（浙江师范大学）；Zhejiang University（浙江大学）
Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease
University of Southern California（南加州大学）；University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；King's College London（伦敦国王学院）
Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（南加州大学凯克医学院马克与玛丽史蒂文斯神经成像与信息学研究所成像遗传学中心）；University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心、格罗宁根大学）；University of Cape Town（开普敦大学）
