Integrating subject-specific conduction velocity into virtual brain models: advancing Brain Digital Twin technologies

Motivation: The Virtual Brain (TVB) is a neuroinformatic platform that simulates brain dynamics integrating subject-specific imaging data. The standard TVB fixes the conduction velocity (CV) of signals between regions, making it insensitive to variations in axonal diameter and myelination. Goal(s): A myelin-integrated TVB is presented to incorporate variable CV and improve brain dynamics simulations. Approach: CV-weighted connectomes are computed from MRI-derived myelin- and axonal-volume fraction maps, then integrated into TVB. Results: The computation of subject-specific CVs based on MRI-derived myelinations and axonal diameter improves TVB ability to predict empirical functional data, towards more accurate subject-specific simulations, representing an important step towards brain digital twins. Impact: Our study demonstrates that simulating brain activity with The Virtual Brain model improves when integrating subject-specific neural conduction values, derived from MRI-based measures of myelin content and axonal diameter. This approach advances the development of Brain Digital Twin technologies.