The researchers developed FEDE (High-Fidelity Digital Brain Model), a computational pipeline that combines three MRI methods with finite element biophysical simulations to build patient-specific virtual brain models. The study, published in the journal PLOS Digital Health, demonstrated this approach by building what the authors describe as the first digital twin of the brain of a young child (age 2.4) with autism spectrum disorder (ASD). The model replicated electroencephalogram (EEG) recordings of the child’s brain activity and estimated patient-specific changes in excitation-to-inhibition (E/I) signals—nearly three times the level seen in the healthy brain—consistent with the known pathophysiology of autism spectrum disorder. Because the study included a single patient with no control group, the findings regarding ASD are proof-of-concept hypotheses. If validated in larger studies, FEDE could support individualized modeling of brain disorders and inform precision medicine approaches for developmental conditions.
