Abstract #1470

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

Eleonora Lupi¹, Fulvia Palesi¹, Anita Monteverdi², Marta Gaviraghi¹, Carolyn McNabb³, Pedro Luque Laguna³, Eirini Messaritaki³, Alessandro Daducci⁴, Marco Palombo^3,5, Mara Cercignani³, Egidio D’Angelo^1,2, and Claudia AM Gandini Wheeler-Kingshott^1,2,6

¹Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy, ³Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, ⁴Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy, ⁵School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom, ⁶NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom

Synopsis

Keywords: Structural Connectivity, Neuroscience, The virtual brain, modeling

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.

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