Spatiotemporal video of blood-brain barrier disruption in neuroinflammatory disorders.

Introduction: Understanding blood-brain barrier (BBB) disruption in neuroinflammatory disorders is crucial for advancing neurological diagnostics and therapy. Unlike prior work that focuses on static imaging or rule-based modeling, our approach introduces a principled, video-driven biomarker system with interpretable temporal dynamics, contextual adaptability, and patient-specific alignment. This represents a fundamental shift from handcrafted thresholding and static biomarker snapshots to real-time, trajectory-based modeling of BBB disruptions. Owing to the spatiotemporal complexity of BBB dynamics in diseases like multiple sclerosis and encephalitis, traditional assessment methods-such as contrast-enhanced MRI or CSF analysis-often fall short due to low temporal resolution, observer bias, and limited generalizability. These limitations hinder the detection of subtle or transient barrier perturbations with potential diagnostic value. Methods: In response to these obstacles, we present a novel paradigm employing spatiotemporal video-derived biomarkers to facilitate real-time, interpretable assessment of BBB integrity. Central to our approach is VidNet, a deep video modeling architecture that extracts latent biomarker trajectories from neuroimaging sequences using hierarchical attention to focus on physiologically meaningful patterns, such as microvascular compromise. Complementing this, CABRiS (Context-Aware Biomarker Refinement Strategy) integrates imaging context and patient-specific priors to enhance robustness, domain adaptability, and semantic consistency. This hybrid system-combining BioVidNet's trajectory encoding with CABRiS refinement-enables precise, individualized quantification of BBB dynamics. Results and discussion: Evaluation on benchmark and clinical datasets reveals superior detection of neurovascular disruptions and alignment with expert annotations compared to existing methods. By offering temporally resolved and personalized assessments, our framework supports goals in dynamic neuroimaging, including early intervention and mechanistic disease understanding. This work contributes a scalable, interpretable tool for precision neuromonitoring in neuroinflammatory conditions. Unlike previous approaches that primarily depend on static neuroimaging features, handcrafted thresholds, or disease_specific heuristics, our method introduces a principled end-to-end framework that integrates dynamic video-based biomarkers with interpretable deep modeling. By disentangling transient motion patterns and physiological rhythms within a unified latent space, and aligning biomarker trajectories through patient-specific contextual priors, our method uniquely captures personalized temporal dynamics of BBB disruption. This represents a marked advancement over conventional methods in both adaptability and clinical interpretability, offering a new paradigm for precision neuromonitoring in neuroinflammatory settings.