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EEG source functional connectivity predicts individual-level cognitive control efficiency

Poster No:

1169

Submission Type:

Abstract Submission

Authors:

Yaqian Zhao¹, Hui Shen¹, Shuqi Zhao¹, Ziyu Zhao¹, Min Ou¹, Xueliang Yang¹

Institutions:

¹College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan

First Author:

Yaqian Zhao
College of Intelligence Science and Technology, National University of Defense Technology
Changsha, Hunan

Co-Author(s):

Hui Shen
College of Intelligence Science and Technology, National University of Defense Technology
Changsha, Hunan

Shuqi Zhao
College of Intelligence Science and Technology, National University of Defense Technology
Changsha, Hunan

Ziyu Zhao
College of Intelligence Science and Technology, National University of Defense Technology
Changsha, Hunan

Min Ou
College of Intelligence Science and Technology, National University of Defense Technology
Changsha, Hunan

Xueliang Yang
College of Intelligence Science and Technology, National University of Defense Technology
Changsha, Hunan

Introduction:

Cognitive control(CC) is fundamentally composed of updating, inhibition and cognitive flexibility（Uddin,2021）.The brain's dynamic network supports this complex functions via inter-region information exchange. Although fMRI and PET have significantly advanced the assessment of CC（Deck,2023；Muehllehner,2006）, their expense and limited availability have spurred interest in more cost-effective imaging methods. Electroencephalography(EEG) emerges as a promising and portable option. However, it remains uncertain whether EEG can be used to predict individual-level CC efficiency. In this study, we applied EEG source imaging(ESI) to investigate EEG's feasibility to predict CC efficiency at an individual level. Furthermore, we asked whether the predictive power differs across various frequency bands and EEG-derived metrics.

Methods:

This database includes 7 right-handed college students (6 males, 1 female) aged 22-24 years (mean age 23 ± 0.5774 years).EEG recordings were conducted using a 64-channel Neuroacle cap following the 10-20 system. We employed a hybrid experimental design (Deck, 2023) with high- and low-demand Navon and Stroop tasks (Fig. 1A). EEG data preprocessing involved eliminate unnecessary electrodes(VEOU,VEOL,ECG,HEOR,HEOL), filtering (0.1-45 Hz), downsampling (250 Hz), segmentation, and re-referencing. Analysis included: (a) High-density EEG data were processed with EEG source imaging (ESI) using dSPM and projected onto the DK atlas to obtain 68 source signals; (b) Source-Space Functional Connectivity(SSFC) was calculated by Coherence(COH), Phase Lag Index(PLI), Phase Locking Value(PLV) and Weighted Phase Lag Index(WPLI), respectively. Their differences between high- demand and low-demand tasks were correlated with behavioral indices to find the most predictive features. (c) Data were divided into five sets for model training and testing of Support Vector Regression (SVR).Model parameters were optimized using grid-search and cross-validation based on the coefficient of determination (R²) and the lowest average Mean Squared Error (MSE).Model validation was performed using Monte Carlo cross-validation (10 folds) with random permutation tests (1,000 times).

Results:

Fig.2(A-D) illustrate the feature selection process under the Navon task , showing that various metrics predict conversion efficiency across frequencies. Notably, the beta-band PLI combined with top 5% SSFC-behavior correlation predicts switching ability most accurately. The Stroop task yields equivalent findings. Thus, PLI at the beta frequency is chosen for predicting CC efficiency. Fig.2E and Fig.2G indicate that frontal lobe-related regions are more extensively involved in the prediction of cognitive switching and inhibition. This observation is consistent with the previous functional MRI studies showing that neural activity in the left inferior frontal junction and left prefrontal cortex is associated with individuals' cognitive flexibility and inhibitory abilities (Cubillo, 2010). Our results align with these findings and further identify a unique region for switching prediction, the right supramarginal gyrus, which is in line with the discovery that the right supramarginal is activated in cognitive tasks and considered a distinct functional unit within the prefrontal cortex (Meno, 2022).Enhanced activity in inhibitory task-related brain regions suggests greater inter-region communication and control demands compared to switching tasks.Fig.2 F and Fig.2 H confirm that beta-band PLI-based functional brain networks significantly predict cognitive switching and inhibition.

Conclusions:

In this study,we demonstrated the feasibility of using EEG to predict individual-level CC efficiency. Moreover, we found that the beta-band Phase Locking Index (PLI) significantly predicts CC efficiency, providing a novel perspective on how brain networks support complex cognitive functions.

Higher Cognitive Functions:

Executive Function, Cognitive Control and Decision Making

Modeling and Analysis Methods:

Classification and Predictive Modeling

Connectivity (eg. functional, effective, structural) ¹

EEG/MEG Modeling and Analysis ²

Exploratory Modeling and Artifact Removal

Keywords:

Machine Learning

Other - Electroencephalography, Functional connectivity, Cognitive control, EEG source imaging

^1|2Indicates the priority used for review

Abstract Information

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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Please indicate which methods were used in your research:

EEG/ERP

Provide references using APA citation style.

Uddin, L. Q. (2021). Cognitive and behavioural flexibility: neural mechanisms and clinical considerations.Nature Reviews Neuroscience,22(3), 167-179.
Muehllehner, G. (2006). Positron emission tomography. Physics in Medicine & Biology, 51(13), R117.
Deck, B. L. (2023). Individual-level functional connectivity predicts cognitive control efficiency. NeuroImage, 283, 120386.
Cubillo, A. (2010). Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood Attention-Deficit Hyperactivity Disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching. Journal of psychiatric research, 44(10), 629-639.
Menon, V.(2022). The role of PFC networks in cognitive control and executive function.Neuropsychopharmacology,47(1), 90-103.

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