A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data

Brain structure plays a pivotal role in shaping neural dynamics. Current models lack the anatomical and functional resolution needed to integrate whole-brain structure and dynamics within a unified computational framework. Here, we introduce the FEDE (high FidElity Digital brain modEl) pipeline, generating anatomically accurate brain digital twins from imaging data. Combining advanced techniques of finite-element analysis and biophysical modeling, FEDE reconstructs multi-scale brain structure with high spatial resolution, while also replicating whole-brain neural activity. We demonstrated FEDE’s application by creating the first brain digital twin of a toddler with autism spectrum disorder (ASD). Through parameter optimization, FEDE replicated experimental neural activity while reconstructing multi-scale structural features ranging from whole-brain connectivity to synaptic timescales. FEDE estimated possible patient-specific anomalies in synaptic transmission, consistent with ASD pathophysiology. Our pipeline represents a significant leap forward in brain modeling, paving the way for effective applications of digital twins in experimental and clinical settings.Author summary: Autism spectrum disorder (ASD) is a common neurodevelopmental condition that affects how people communicate, learn, and interact with others. Despite decades of research, the biological mechanisms underlying ASD remain difficult to pinpoint, partly because we cannot easily study how brain structure and activity interact in patients. In this study, we developed a new approach to bridge that gap based on a digital twin: a detailed computational replica of the patient’s brain. Our method, called FEDE, combines detailed anatomical modeling based on medical images with biophysical simulations of neural activity using finite-element techniques. This allows us to reconstruct both the structure and function of a patient’s brain within the same computational framework. We applied our approach to a young child with ASD, accurately reproducing their recorded brain activity and suggesting possible individualized alterations in synaptic transmission consistent with known ASD mechanisms. By linking anatomy and dynamics in a single model, our approach offers a new avenue for personalized investigation and treatment of brain disorders.