Personalized Digital Brain Twins (DBT) represent a front-edge approach able to simulate single-subjects brain activity both in healthy and pathological subjects. DBT will exploit some of the front-edge technologies of computational neuroscience: MRI features will be used to describe macroscopic pathological alterations and then combined with mean field models that will embed mesoscale structural and functional features to be adapted to reproduce the pathological activity of several neurological pathologies.
More in detail, brain MRI data will be used to quantify pathological alterations of microstructural proprieties (such as the density and orientation of neurites, the content of myelin, iron, and brain metabolites) and to create the subject-specific brain avatar. In parallel, pathological mean field models will be defined on the basis of mesoscopic alterations specific of the investigated disease. These elements will be combined to generate the personalized DBT that will allow to obtain realistic synthetic brain activity of specific brain networks for investigating the pathological mechanisms underlying human behavior and emergent functions. These personalized DBT will profile patients at different pathological stages and to help therapeutic intervention.
A PhD student will have the possibility to cover various aspects of the investigation of brain functioning with the aim of identifying strategies for diagnosis and personalized therapy. Students have the possibility to spend part of their research activity abroad since the projects are developed within a collaborative network, which comprises MNESYS, CN1, EBRAINS 2.0, and Virtual Brain Twin (VBT) projects.
Digital Brain Twins.
Personalized Digital Brain Twins (DBT) are built up combining multiscale data.
Macroscale data, such as MRI and EEG, are inputted in the model and retain pathological alterations at whole-brain level.
Further, mesoscopic computational models, like the cerebellar mean field, embed mesoscale structural and functional features which must be specifically adapted to account for pathological alterations of the microcircuit neural activity.
References
- Palesi F, Lorenzi RM, Casellato C, et al. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics. Front Cell Neurosci. 2020;14(July):1-11. doi:10.3389/fncel.2020.00240
- D’Angelo E, Jirsa V. The quest for multiscale brain modeling. Trends in Neurosciences. 2022; 45(10):777-790. doi.org/10.1016/j.tins.2022.06.007
- Monteverdi A, Palesi F, Costa A, et al. Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases. Front Aging Neurosci. 2022;14:1-17. doi:10.3389/fnagi.2022.868342
- Monteverdi A, Palesi F, Schirner M, et al. Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias. Front Aging Neurosci. 2023;(July):1-15. doi:10.3389/fnagi.2023.1204134