Triple Configuration of Brain Networks Based on Recurrent Neural Networks: The Synergistic Effects of Exogenous Stimuli, Task Demands, and Spontaneous Activity

TL;DR

This study introduces a neural network-based framework to decode dynamic brain network configurations from EEG data, highlighting the parietal regions' role in cognitive flexibility and intelligence.

Contribution

It presents a novel RNN-based model that captures triple brain network configurations influenced by external stimuli, tasks, and spontaneous activity, emphasizing parietal regions' importance.

Findings

Parietal network identified as a key hub for multiple configurations.

Distinct functional roles of anterior and posterior parietal regions under different stimuli.

Framework separates latent brain dynamics factors.

Abstract

The foundation of cognitive flexibility and higher-order intelligence lies in the functional structure and activity of brain networks, which can be dynamically configured by both external environments and internal states. However, decoding these dynamics from high-dimensional neural data remains a challenge. In this study, we propose a computational framework using Recurrent Neural Networks (RNNs) with neural dynamic constraints to model source-localized resting-state EEG data from $114$ participants. We aim to clarify the "triple brain network configurations" driven by exogenous and endogenous factors, including external stimuli, information processing tasks, and spontaneous activities. Our model identifies the parietal network as a critical hub supporting these multiple configuration patterns. Furthermore, we reveal that the anterior and posterior parietal regions exhibit distinct functional specializations under different stimulus modalities. By formalizing a triple configuration framework, this work separates latent factors of brain dynamics and underscores the computational significance of parietal regions in orchestrating higher-order intelligence.