A Deep Learning Framework for Unraveling Toxicokinetic-Neuropsychiatric Interactions

Abstract

The neuropsychiatric consequences of toxicant exposure remain poorly understood due to the complexity of interactions between xenobiotics and the central nervous system. Traditional models lack the capacity to integrate high-dimensional biological data and temporal exposure patterns. This study proposes a novel deep learning framework that integrates toxicokinetic parameters with multimodal neurobiological data to predict and interpret toxin-induced neuropsychiatric outcomes. The framework utilizes convolutional neural networks (CNNs) for analyzing neuroimaging data, graph neural networks (GNNs) for capturing connectivity disruptions, and hidden Markov models (HMMs) for modeling the temporal progression of psychiatric symptoms. Preprocessing pipelines incorporate normalization and generative adversarial network (GAN)-based imputation to address data sparsity. Model outputs are interpreted using SHapley Additive exPlanations (SHAP) to ensure transparency. The proposed model achieved superior predictive performance (accuracy: 91.2%, AUC-ROC: 0.942) compared to traditional machine learning approaches. SHAP analysis highlighted key contributors to neurotoxicity, including dopamine transporter disruption and frontal cortex dysconnectivity. Personalized predictions based on individual exposure profiles demonstrate the framework's potential for real-world application in risk assessment and precision toxicology. This integrative deep learning approach represents a major advancement in bridging toxicology and neuroscience, offering novel insights into the mechanistic pathways of neurotoxicity and enabling proactive, individualized health interventions.