Electroencephalography Connectome-based Predictive Modeling of Nonverbal Intelligence Level in Healthy Subjects.

Intelligence is increasingly recognized as a critical factor in successful behavioral and emotional regulation. Neuroimaging techniques coupled with machine learning algorithms have proven to be valuable tools for uncovering the neural foundations of individual cognitive abilities. Nevertheless, current electroencephalography (EEG) studies primarily focus on classification tasks to predict the intelligence category of subjects (e.g., high, medium, or low intelligence), rather than providing quantitative intelligence-level forecasts. Furthermore, the outcomes obtained are significantly impacted by the specific data processing pipeline chosen, which could potentially compromise result generalizability. In this study, we implemented a connectome-based predictive modeling approach on high-density resting-state EEG data from healthy participants to predict their nonverbal intelligence level. This method was applied to three independently collected data sets (n = 255) with different functional connectivity methods, parcellation atlases, threshold p values, and curve fitting orders used to ensure the reliability of the findings. Prediction accuracy, measured as correlation between predicted and observed values, varied significantly across pipeline configurations. The most consistent results across data sets were found in the alpha frequency band. Furthermore, we employed a computational lesioning approach to identify the valuable edges that made the most significant contribution to predicting intelligence. This analysis highlighted the crucial role of frontal and parietal regions in complex cognitive computations. Overall, these findings support and expand upon previous research, underscoring the close relationship between alpha rhythm characteristics and cognitive functions and emphasizing the critical consideration of method selection in result evaluation.