Yadav Abhinav, Purushotham Archana
Institute for Stem Cell Science and Regenerative Medicine, Bangalore, India.
National Centre for Biological Sciences, Bangalore, India.
Brain Behav. 2025 May;15(5):e70531. doi: 10.1002/brb3.70531.
A growing number of studies implicate functional brain networks in intelligence, but it is unclear if network nodal structure relates to intelligence.
Using MRI, we studied the relationship of the general intelligence factor (g) with cortical thickness (CT), local gyrification index (LGI), and voxel-based morphometry in the nodes of the default mode network (DMN) and task-positive network (TPN) in a cohort of 44 young, healthy adults. Employing a novel strategy, we performed repeated analyses with multiple sets of g estimates to remove false positives.
CT and LGI in medial and temporal nodes of the DMN were reliably correlated with g (p < 0.05; Pearson's coefficient: ‑0.52 to ‑0.25 and 0.22 to 0.41, respectively). Linear regression models were developed with these parameters to estimate individual g scores, with a median adj. R of 0.25.
Cortical thickness and gyrification in key nodes of the Default Mode Network correlate with intelligence. Linear regression models with these cortical parameters may provide an estimate of the g factor.
越来越多的研究表明功能性脑网络与智力有关，但尚不清楚网络节点结构是否与智力相关。
我们使用磁共振成像（MRI），研究了44名年轻健康成年人队列中，默认模式网络（DMN）和任务积极网络（TPN）节点的一般智力因素（g）与皮质厚度（CT）、局部脑回指数（LGI）以及基于体素的形态学之间的关系。我们采用一种新策略，对多组g估计值进行重复分析以去除假阳性结果。
DMN内侧和颞叶节点的CT和LGI与g可靠相关（p < 0.05；皮尔逊系数分别为 -0.52至 -0.25和0.22至0.41）。利用这些参数建立线性回归模型以估计个体g分数，调整后R的中位数为0.25。
默认模式网络关键节点的皮质厚度和脑回化与智力相关。具有这些皮质参数的线性回归模型可能会提供g因素的估计值。
