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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

Mean-field (MF) models can be used to summarize in a few statistical parameters the salient properties of an inter-wired neuronal network incorporating different types of neurons and synapses along with their topological organization. MF are crucial to efficiently implement the modules of large-scale brain models maintaining the specificity of local 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 network (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 satisfactorily reproduced the average dynamics of the different neuronal populations in response to various input patterns and predicted the modulation of 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 that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.

31Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has 32 not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the 33 cortex to the peripheral nervous system and viceversa, it is indeed a very interesting question to 34 assess whether it is affected by atrophy in a disease that is known for its involvement of cognitive 35 domains first and foremost, with motor symptoms being clinically assessed too. We therefore 36 hypothesize that Alzheimer Disease severe atrophy can affect the spinal cord too and that spinal 37 cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding 38 neurodegeneration associated with dementia. 39Methods: 3DT1 images of 31 Alzheimer's disease (AD) and 35 healthy control (HC) subjects were 40 processed to calculate volumes of brain structures and cross-sectional area (CSA) and volume 41 (CSV) of the cervical cord (per vertebra as well as the C2-C3 pair (CSA23 and CSV23)). Correlated 42 features (ρ>0.7) were removed, and best subset identified for patients' classification with the 43 Random Forest algorithm. General linear model regression was used to find significant differences 44 between groups (p<=0.05). Linear regression was implemented to assess the explained variance of 45 Spinal cord atrophy contribution to AD the Mini Mental State Examination (MMSE) score as dependent variable with best features as 46 predictors. 47 Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. 48 Patients classification reached 76% accuracy when including CSA23 together with volumes of 49 hippocampi, left amygdala, white and grey matter, with 74% sensitivity and 78% specificity. 50 CSA23 alone explained 13% of MMSE variance. 51 Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD 52 from HC, together with more established features. Results show that CSA23, calculated form the 53 same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a 54 considerable weight in classification tasks warranting further investigations. Together with recent 55 studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal 56 cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy 57 explains also cognitive scores, which could significantly impact how we model sensorimotor 58 control in degenerative diseases with a primary cognitive domain involvement. Prospective studies 59should be purposely designed to understand the mechanisms of atrophy and the role of the spinal 60 cord in AD. 61

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