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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

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

Anton Pashkov^1,2,3, Ivan Dakhtin^4,5,6, Inna Feklicheva⁴

¹FSBI "Federal Center of Neurosurgery," Novosibirsk, Russia.

Journal of Cognitive Neuroscience

|July 11, 2025

Summary

This study used resting-state electroencephalography (EEG) to predict nonverbal intelligence quantitatively. Findings highlight the alpha frequency band and frontal-parietal regions as key predictors, emphasizing the need for robust methodology.

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Area of Science:

Neuroscience
Cognitive Science
Machine Learning

Background:

Intelligence is crucial for behavioral and emotional regulation.
Neuroimaging and machine learning reveal neural bases of cognition.
Current electroencephalography (EEG) studies often classify intelligence levels rather than predict them quantitatively.

Purpose of the Study:

To quantitatively predict nonverbal intelligence levels using resting-state EEG data.
To assess the reliability and generalizability of findings across different data processing pipelines.
To identify specific neural connections contributing to intelligence prediction.

Main Methods:

Applied connectome-based predictive modeling to high-density resting-state EEG data from 255 healthy participants.
Utilized three independent datasets and varied functional connectivity methods, parcellation atlases, and statistical thresholds.
Employed a computational lesioning approach to pinpoint critical neural connections.

Main Results:

Prediction accuracy for nonverbal intelligence varied significantly based on data processing pipeline configurations.
The alpha frequency band demonstrated the most consistent predictive results across datasets.
Computational lesioning identified frontal and parietal regions as critical for intelligence prediction.

Conclusions:

Resting-state EEG, particularly the alpha frequency band, can quantitatively predict nonverbal intelligence.
Frontal and parietal brain regions play a significant role in cognitive computations related to intelligence.
Methodological choices in data processing critically impact the reliability and generalizability of EEG-based intelligence prediction.