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.

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics.

The brain shows a complex multiscale organization that prevents a direct understanding of how structure, function and dynamics are correlated. To date, advances in neural modeling offer a unique opportunity for simulating global brain dynamics by embedding empirical data on different scales in a mathematical framework. The Virtual Brain (TVB) is an advanced data-driven model allowing to simulate brain dynamics starting from individual subjects' structural and functional connectivity obtained, for example, from magnetic resonance imaging (MRI). The use of TVB has been limited so far to cerebral connectivity but here, for the first time, we have introduced cerebellar nodes and interconnecting tracts to demonstrate the impact of cerebro-cerebellar loops on brain dynamics. Indeed, the matching between the empirical and simulated functional connectome was significantly improved when including the cerebro-cerebellar loops. This positive result should be considered as a first step, since issues remain open about the best strategy to reconstruct effective structural connectivity and the nature of the neural mass or mean-field models generating local activity in the nodes. For example, signal processing is known to differ remarkably between cortical and cerebellar microcircuits. Tackling these challenges is expected to further improve the predictive power of functional brain activity simulations, using TVB or other similar tools, in explaining not just global brain dynamics but also the role of cerebellum in determining brain states in physiological conditions and in the numerous pathologies affecting the cerebro-cerebellar loops.

Unsuspected Involvement of Spinal Cord in Alzheimer Disease.

Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer's disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients' classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD.