Task-Aware Effective Connectivity Modeling for Cognitive Function Prediction

Effective connectivity (EC) derived from resting-state Functional Magnetic Resonance Imaging (rs fMRI) has emerged as a critical tool for deepening our understanding of brain function in both health and dis ease. However, most studies estimate EC on an individual basis, treating it as a hidden parameter within the model and requiring retraining the model for each subject. They often overlook the valuable population-level information and limit their generalizability. Additionally, EC is typically obtained independently of downstream tasks, reducing its capacity to effectively capture task-specific variations. To address these limitations, we propose a flexible Task-Aware Effective Connectivity (TAEC) model, designed to construct individualized, task-aware, and nonlinear causal brain networks without requiring subject-specific retraining. In this framework, a Causal Discovery Module (CDM) is introduced to capture the implicit neural representation of the EC by a spatial-temporal attention mechanism, producing the estimation of an individual EC. Subsequently, we propose a Task-Aware Graph Neural Network (GNN) Predictor, which incorporates a task-aware penalty to enable end-to-end prediction, enhancing task performance and the identification of task-dependent EC patterns. Extensive experiments on twelve cognitive tasks from the Human Connectome Project (HCP) dataset demonstrate that the proposed method achieves state-of-the-art performance, validating its effectiveness in task-aware effective connectivity modeling. Furthermore, the framework discovers discriminative and task-specific EC patterns, which offer additional in-sights into cognitive functions.