CFCDBN: Personalized Directional Brain Network Modeling of Cross-Frequency Coupling Alterations in Adolescent Anxiety Disorders

Anxiety disorders (AD) are prevalent psychiatric conditions that profoundly impact adolescent neural development. Abnormal delta-beta cross-frequency coupling (CFC) has been identified as a key electrophysiological marker of altered neural dynamics in individuals with AD. However, most existing studies focus on static analysis within restricted brain regions and predefined frequency bands, which limits the understanding of large-scale dynamic neural communication. Therefore, we propose a novel cross-frequency coupling directed brain network (CFCDBN) framework, which integrates personalized CFC estimation and causal information flow modeling to capture the dynamic interactions of the brain network in AD. Personalized CFC significantly improves the precise representation of AD-related neural dynamics by adaptive frequency band division and individualized oscillation feature extraction, overcoming the limitations of traditional CFC methods. The analysis reveals significant delta-beta coupling abnormalities in the left hemisphere of AD, accompanied by disrupted directional pathways involving the thalamus, precuneus, and insula. These findings suggest impaired emotional and cognitive communication from the subcortical to cortical regions. To validate the efficacy of CFCDBN in distinguishing AD patients from healthy individuals, we developed a direction-aware graph neural network (DA-GNN) model that uses CFCDBN representations as input to capture dynamic neural patterns in causal brain connectivity. Experimental results show that the model consistently outperforms traditional machine learning methods and undirected GNN baselines in automatic AD identification, achieving a classification accuracy of 77.8%, and confirming the value of CFCDBN as a robust biomarker for AD-related network dysfunction. These findings not only deepen our understanding of the neural dynamics underlying AD, but also lay the foundation for personalized and mechanism-driven neuromodulation strategies. he core implementation of the CFCDBN framework is available on GitHub: https://github.com/wdxcjnb6/CFCDBN.