Volume 22, Issue 3 (September 2026)                   IJEEE 2026, 22(3): 3971-3971 | Back to browse issues page

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Parham E, Feshki M, Fallahi A, Soltanian-Zadeh H. Structural Connectivity of Brain Regions May Predict Human Intelligence. IJEEE 2026; 22 (3) :3971-3971
URL: http://ijeee.iust.ac.ir/article-1-3971-en.html
Structural Connectivity of Brain Regions May Predict Human Intelligence
Elahe Parham , Mohamad Feshki , Alireza Fallahi , Hamid Soltanian-Zadeh
Abstract:   (134 Views)
The discovery of relationships between brain connectivity and human intelligence is of great interest. In this study, we identify structural connections correlated with human intelligence and investigate the predictability of intelligence from brain structural connectivity. The study uses data from 137 healthy subjects from the Human Connectome Project (HCP, 1200 Subjects Release). Structural connectivity was estimated using tractography derived from diffusion tensor imaging (DTI) data. A connectivity matrix was constructed using the mean fractional anisotropy (FA) of white-matter pathways between 116 regions defined by the AAL atlas. Global graph measures and correlation analysis were applied to identify connections relevant to predicting fluid intelligence (Gf) and crystallized intelligence (Gc). For prediction, three regression models of linear regression, support vector regression (SVR), and multi-layer perceptron (MLP) were employed. Most connections associated with Gf or Gc were located in the right hemisphere. Connections originating from prefrontal, right temporal, limbic, and right occipital regions were related to Gf, whereas connections originating from prefrontal, temporal, and left parietal regions were related to Gc. Among the models, SVR showed superior performance, achieving R² values of 0.45 and 0.52 for Gf and Gc, respectively. No significant relationships were observed between global graph measures and Gf or Gc scores. These findings demonstrate that DTI-based structural connectivity can be used to predict both fluid and crystallized intelligence, with fine-grained regional definitions enabling more specific connectivity patterns than in previous studies.
Keywords: Structural Connectivity, Brain Connectivity, Diffusion Tensor Imaging (DTI), Intelligence, Prediction.
Full-Text [PDF 885 kb]   (62 Downloads)    
Type of Study: Research Paper | Subject: Biomedical Signal Processing
Received: 2025/06/07 | Revised: 2026/05/08 | Accepted: 2026/01/30
Supplementary file [PDF 538 KB]  (24 Download)
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.