Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC 0.81 (congruent trials) and 0.86 (incongruent trials) for reaction time prediction

• Primary Outcome: Functional connectivity patterns predicting reaction time during cognitive control tasks

• Nnt Or Sensitivity: Classification accuracy 81-86% for identifying high-performing individuals

• Confidence Interval: Not reported

Clinical Application

Limited by requirement for specialised EEG equipment and advanced signal processing capabilities Relevant for Australian neuroscience research centres and cognitive assessment clinics, though validation in local populations needed Adults undergoing cognitive assessment, particularly in research or clinical neuropsychology settings

Abstract

OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health factors on task-related dynamic FC remains underexplored. We examined FC within and between seven core brain networks: Default Mode (DMN), Ventral Attention (VAN), Somatomotor (SMN), Dorsal Attention (DAN), Frontoparietal (FPN), Visual (VIN), and Limbic (LIN) during a modified Eriksen flanker task. We aimed to identify distinct network configurations linked to varying cognitive control demands and assess the influence of demographic and health factors. METHODS: Secondary analyses of source-localized EEG data from 255 adults (mean age 38.97 ± 11.65 years; BMI 30.21 ± 9.29 kg/m2) were conducted. A hidden Markov model extracted spatiotemporal dynamics, quantifying within- and between-network correlations. Extreme Gradient Boosting with recursive feature elimination identified connectivity metrics predicting reaction time (RT) of high performers (>80% accuracy). Regression models assessed effects of age, sex, BMI, and income on task-related FC. RESULTS: Classification accurately predicted RT in both congruent (AUC = 0.81) and incongruent (AUC = 0.86) trials. Key FC features included VAN-LIN, VIN-VAN, and SMN-DAN for congruent trials; SMN-DMN, LIN-FPN, and VIN-DAN for incongruent trials. Age was linked to reduced FC, while higher BMI showed modest positive FC associations in LIN-FPN and VIN-DAN. Sex and income were not significant predictors of RT or FC. CONCLUSIONS: These findings reveal the relevance of specific dynamic network interactions in cognitive control and highlight the need to consider age and BMI as contributors to brain connectivity during task performance.

References

1. 1.Parvez, A., Verma, S., Cerna, J., Holthaus, T., Kim, J., Pascual-Abreu, M., Kinder, C. J., & Khan, N. A. (2026). Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning. International Journal of Psychophysiology, 113352. https://doi.org/10.1016/j.ijpsycho.2026.113352