Reconstructing whole-brain structure and dynamics using imaging data and personalized modeling

Brain structure plays a pivotal role in shaping neural dynamics. Current models lack the anatomical and functional resolution needed to accurately capture both structural and dynamical features of the human brain. Here, we introduce the FEDE (high FidElity Digital brain modEl) pipeline, generating anatomically accurate brain digital twins from imaging data. Using advanced techniques of anatomical tissue segmentation and finite-element analysis, FEDE reconstructs brain structure with high spatial resolution, while also replicating whole-brain neural activity. We demonstrated its application by creating the first brain digital twin of a toddler with autism spectrum disorder (ASD). Through parameter optimization, FEDE replicated both time-frequency and spatial features of recorded neural activity. Notably, FEDE predicted patient-specific aberrant values of excitation to inhibition ratio, coherently with ASD pathophysiology. FEDE represents a significant leap forward in brain modeling, paving the way for more effective applications of digital twin in experimental and clinical settings.