oward Causality-Driven Cognitive Connectomics: Integrating Molecular, Structural, and Computational Approaches for Real-Time Circuit Mapping

Abstract: Cognitive connectomics is undergoing a transformative evolution from static, descriptive brain maps to dynamic, causality-driven models of neural circuitry. Traditional methods captured the architecture of brain networks but often lacked the mechanistic insights necessary to decode real-time brain function. Integrating advances in molecular labeling technologies, high-resolution structural imaging, and computational causal inference offers unprecedented opportunities to map the brain’s operational dynamics with temporal precision. This paper critically synthesizes recent breakthroughs, including molecular barcoding techniques and electron microscopy innovations, and explores the convergence of machine learning-based causal discovery methods. We propose a comprehensive framework that unifies molecular, structural, and computational approaches to establish real-time, mechanistic cognitive connectomics, setting the foundation for next-generation neuroscience research and clinical applications. Keywords: Cognitive Connectomics, Causality, Real-Time Brain Mapping, Molecular Barcoding, Structural Imaging, Computational Neuroscience, Causal Inference, Machine Learning in Neuroscience, Circuit Dynamics, Neuroinformatics