The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Cuban Neuroscience Center, Havana, Cuba.
Hum Brain Mapp. 2020 Mar;41(4):906-916. doi: 10.1002/hbm.24848. Epub 2019 Nov 5.
Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract-based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white-matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS-III intelligence quotients and indices were obtained. Inspired by the "Watershed model" of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables.
研究智力的神经基础一直集中在将大脑成像变量与整体量表进行比较,而不是将整合这些量表或商数的认知领域进行比较。在这里,探讨了平均束内分数各向异性(mTBFA)与智力指数之间的关系。使用基于感兴趣区域的方法对 10 条白质束进行确定性束追踪,在此基础上计算 mTBFA。研究样本包括来自古巴人类大脑映射项目第二波的 83 名健康个体,获得了他们的韦氏智力测验 III 智力商数和指数。受“智力分水岭模型”的启发,我们采用正则化分层多指标、多原因模型(MIMIC)来评估 mTBFA 与智力分数的关联,由总结这些指数的潜在变量介导。由于样本量有限,正则化 MIMIC 通过弹性网络惩罚选择相关的 mTBFA,并实现了对数据的良好拟合。需要两个潜在变量来描述指数:流体智力(知觉组织和处理速度指数)和晶体智力(言语理解和工作记忆指数)。正则化 MIMIC 揭示了小钳状束对晶体智力的影响,以及上纵束对流体智力的影响。该模型还检测到年龄对两个潜在变量的显著影响。