A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics

Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions

Relationships among porcine and human P[6] rotaviruses: Evidence that the different human P[6] lineages have originated from multiple interspecies transmission events

AbstractPorcine rotavirus strains (PoRVs) bearing human-like VP4 P[6] gene alleles were identified. Genetic characterization with either PCR genotyping or sequence analysis allowed to determine the VP7 specificity of the PoRVs as G3, G4, G5 and G9, and the VP6 as genogroup I, that is predictive of a subgroup I specificity. Sequence analysis of the VP8* trypsin-cleavage product of VP4 allowed PoRVs to be characterized further into genetic lineages within the P[6] genotype. Unexpectedly, the strains displayed significantly higher similarity (up to 94.6% and 92.5% at aa and nt level, respectively) to human M37-like P[6] strains (lineage I), serologically classifiable as P2A, or to the atypical Hungarian P[6] human strains (HRVs), designated as lineage V (up to 97.0% aa and 96.1% nt), than to the porcine P[6] strain Gottfried, lineage II (<85.1% aa and 82.2 nt), which is serologically classified as P2B. Interestingly, no P[6] PoRV resembling the original prototype porcine strain, Gottfried, was detected, while Japanase P[6] PoRV clustered with the atypical Japanase G1 human strain AU19. By analysis of the 10th and 11th genome segments, all the strains revealed a NSP4B genogroup (Wa-like) and a NSP5/6 gene of porcine origin. These findings strongly suggest interspecies transmission of rotavirus strains and/or genes, and may indicate the occurrence of at least 3 separate rotavirus transmission events between pigs and humans, providing convincing evidence that evolution of human rotaviruses is tightly intermingled with the evolution of animal rotaviruses

Multiscale co-simulation of Cortico-Basal Ganglia interactions for Parkinson’s disease

This thesis aims to simulate a multiscale brain model able to describe the peculiar changes in brain dynamics which characterize Parkinson’s disease (PD), the most common basal ganglia (BG) related movement disorder and to test possible DBS configurations. My goal was to simulate a model which combines different scales of details. A previously validated data-driven spiking network model for BG has been placed in a whole-brain framework that uses a mean-field approach specifically tuned from neuroimaging data of some PD patients. The results have been analyzed by comparing the most relevant state variables which characterize the network dynamics between the PD case and the healthy case (HC). My results from the resting-state simulations are coherent with important findings from literature, especially the well-known hypotheses regarding PD patients’ decreased activity level in thalamic region is identified as the major cause of some relevant PD symptoms (bradykinesia or akinesia). Additionally, the model shows increased neural activity of the subthalamic nucleus according to the literature and many experimental findings. Some differences between PD and HC states in the resting state firing activity also emerged for some cortical regions. Interestingly, the validated models for PD and HC are used to explore different stimuli injection configurations which are assumed to simulate different kinds of brain perturbations for PD patients, clinically used to attenuate PD symptomatology. Starting from a subject-specific model, a method to evaluate the stimuli locations. The potential of this work is the possibility to evaluate stimulation placement and configuration before surgery and to improve the settings for individual patients

A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.

Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions