en 4-Biomedical Signal Processing Biomedical Signal Processing Research Paper Research Paper <span style="font-size:10.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif">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 R&sup2; 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.</span></span> Structural Connectivity, Brain Connectivity, Diffusion Tensor Imaging (DTI), Intelligence, Prediction. 3971 3971 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-597-1&slc_lang=en&sid=1 Elahe Parham e.parham72@yahoo.com 1800319475328460017627 1800319475328460017627 No School of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran. Mohamad Feshki mohamadfeshki@gmail.com 1800319475328460017628 1800319475328460017628 No School of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran. Alireza Fallahi fallahi@hut.ac.ir 1800319475328460017629 1800319475328460017629 No Biomedical Engineering Department, Hamedan University of Technology, Hamedan, Iran. Hamid Soltanian-Zadeh hszadeh@ut.ac.ir 1800319475328460017630 1800319475328460017630 Yes School of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.