タスク誘発性脳ネットワークダイナミクス：ソース局在化EEGと機械学習を用いた認知的制御
Alfia Parvez1, Shreya Verma1, Jonathan Cerna2
1Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
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.
