Relationship of IQ with Causal Connections Between Brain Salient Network Regions

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people’s lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value <0.05 were found to be significantly different, according to the order of the edges, this difference is in the relationship between rMCC-rvIPFC, rInsula-rvIPFC, rInsula-rPutamen, rInsulaIIPG, IIPG-rSFG, IIPG-rSFG. In the next step, we used the algorithms of support machines and nearest neighbors to classify these three groups. Finally, using the support vector regression algorithm, we predicted the IQ from the edge values with RMSE=5.41 and MAE=3.59. The results of this research indicate a significant relationship between IQ and the salient network of the brain.