Going towards large-scale system level, data-driven computational models can be embedded in whole-brain simulators at multiple scales, to test the impact on brain dynamics. Mean field formalism can be customized to build up a mesoscale model of specific brain regions. Recently, a cerebellum-specific mean field has been developed and integrated in The Virtual Brain framework (TVB). The cerebellar mean field was built using a system of equations, where properties of neuronal populations (granule cells – GrC, Golgi cells – GoC, Purkinje cells – PC, Molecular Layer Interneurons – MLI) and topological parameters are embedded in inter-dependent transfer functions.
Neural masses or mean-field models of multiple brain regions can be interfaced with detailed models of specific microcircuits (e.g. spiking networks, at single neuron resolution), to study the brain at different granularities and bridge the gap across scales (“co-simulations” approach). A spiking version of the cerebellar network has been integrated in the mouse TVB. Global coupling and bi-directional interfaces between modes signals are tuned to match data on mouse resting-state functional connectivity and spiking rates, and task-dependent inputs are provided to the whole system.
A PhD student involved in this research topic will have the opportunity to learn informatic programming and mathematical frameworks in neuroscience, to deal with connectivity data and whole-brain signal propagation.
Cerebellar networks into whole-brain simulation. Different modules of cerebellar mean-field can be embedded and connected each other introducing functional and structural intra-cerebellar coupling parameters. The connection with cortical MFs is set considering structural parameters extracted from large-scale connectome (e.g. tract length) and functional parameters related to the signal propagation. On the right, the cosimulation framework is shown: the cerebellar nodes are represented by spiking neural networks with biophysically-defined cell populations.
References
- Lorenzi R, Geminiani A, Zerlaut Y, De Grazia M, Destexhe A, Gandini C.A.M, Palesi F, Casellato C and D’Angelo E. A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics. PLOS COMP BIO. 2023; doi: 1371/journal.pcbi.1011434
- Palesi F, Lorenzi R, Casellato C, Ritter P, Jirsa V, Gandini Wheeler-Kingshott C, D‘Angelo E. The importance of cerebellar connectivity on simulated brain dynamics, FRONT CELL NEUROSCI 2020; 14:240 doi: 10.3389/fncel.2020.00240
- Ciapponi C, Li Y, Osorio D.A., Rodarie D, Casellato C, Mapelli L, D’Angelo E. Variations on the theme: focus on cerebellum and emotional processing. FRONT SYST NEUROSCI. 2023; doi: 10.3389/fnsys.2023.1185752