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

Abstract

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.