Two sides of the same coin: distinct neuroanatomical patterns predict crystallized and fluid intelligence in adults

Hui Xu; Cheng Xu; Zhenliang Yang; Guanghui Bai; Bo Yin

doi:10.3389/fnins.2023.1199106

Two sides of the same coin: distinct neuroanatomical patterns predict crystallized and fluid intelligence in adults

Front Neurosci. 2023 May 25:17:1199106. doi: 10.3389/fnins.2023.1199106. eCollection 2023.

Authors

Hui Xu^{1

2}, Cheng Xu³, Zhenliang Yang⁴, Guanghui Bai⁵, Bo Yin¹

Affiliations

¹ Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
² Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada.
³ School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
⁴ Faculty of Psychology, Tianjin Normal University, Tianjin, China.
⁵ Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

Abstract

Background: Crystallized intelligence (Gc) and fluid intelligence (Gf) are regarded as distinct intelligence components that statistically correlate with each other. However, the distinct neuroanatomical signatures of Gc and Gf in adults remain contentious.

Methods: Machine learning cross-validated elastic net regression models were performed on the Human Connectome Project Young Adult dataset (N = 1089) to characterize the neuroanatomical patterns of structural magnetic resonance imaging variables that are associated with Gc and Gf. The observed relationships were further examined by linear mixed-effects models. Finally, intraclass correlations were computed to examine the similarity of the neuroanatomical correlates between Gc and Gf.

Results: The results revealed distinct multi-region neuroanatomical patterns predicted Gc and Gf, respectively, which were robust in a held-out test set (R² = 2.40, 1.97%, respectively). The relationship of these regions with Gc and Gf was further supported by the univariate linear mixed effects models. Besides that, Gc and Gf displayed poor neuroanatomical similarity.

Conclusion: These findings provided evidence that distinct machine learning-derived neuroanatomical patterns could predict Gc and Gf in healthy adults, highlighting differential neuroanatomical signatures of different aspects of intelligence.

Keywords: crystallized intelligence; elastic net regression; fluid intelligence; machine learning; morphometry; neuroanatomy.

Grants and funding

This study was funded by the Natural Science Foundation of Zhejiang Province (No. LY15H090016), Wenzhou Science and Technology Bureau in China (No. Y20140577), and Beijing New Health Industry Development Foundation (No. XM2020-02-002).