Biology subjects

D'Angelo, E. U.

Publications and source records attributed to D'Angelo, E. U..

16 recordsLinked to original sources

bioRxiv2026

Directed Human Structural Connectome Reveals Hierarchical Organization and Shapes Large-Scale Brain Dynamics

The human structural connectome, most commonly derived from diffusion-weighted imaging (DWI) and tractography, provides a macroscopic description of whole-brain wiring and serves as the structural foundation of network neuroscience, large-scale brain simulations, and personalized digital brain twins. However, tractography-derived connectomes are fundamentally limited by their inability to distinguish afferent from efferent connections, yielding networks that are undirected and therefore blind to the hierarchical organization imposed by the directionality of anatomical connections. In this study, we introduce a directed human structural connectome (DHSC) by transferring tracer-derived projection patterns from macaque to human using cross-species connectivity blueprints. Topological analysis of the DHSC manifests biological plausibility, a small-world network organization, and a directionality-based hierarchy, which offer the hierarchical organization of human brain networks. In the context of brain dynamics, the introduction of directionality reshapes the propagation and persistence of sensory inputs. DHSC also best captures the empirical spatiotemporal dynamics of stimulus-evoked brain activity. The findings demonstrate that anatomical directionality is a critical determinant of large-scale brain organization and dynamics. This provides evidence that directed connectome may offer potential advantages in large-scale simulations of the human brain. The resulting DHSC, along with all related analyses and data are openly available.

neuroscience↗

bioRxiv2026

The Cerebellar Engine: Multiscale Digital Brain Co-simulations Reveal How Cerebellar Spiking Architecture Shapes Cortical Coherence

Cellular activities shape large-scale brain dynamics determining brain functioning and disease, yet the causal mechanisms across scales remain unclear. In particular, the cerebellum has been reported to modulate whole-brain dynamics during sensorimotor integration through unknown circuit interactions. To investigate the underlying mechanisms, we developed a novel multiscale digital brain simulator, in which a spiking neural network of the olivocerebellar microcircuit is embedded in a mean-field virtual mouse brain and wired using an atlas-based long-range connectome. Parameters were systematically tuned to match multiscale experimental data from primary sensory and motor cortices (S1 and M1) and cerebellum. We analyzed the role of cerebellar circuitry on sensorimotor integration by lesioning critical circuit connections in silico. Results suggested that Purkinje cell inhibition enhances the processing efficiency of the 'cerebellar engine' through decorrelation of cerebellar nuclei activity and that the pathway between mossy fibers and cerebellar nuclei is the specific pathway inside the microcircuit driving M1-S1 coherence. These results indicate a mechanistic link between cerebellar microcircuit and cortical sensorimotor processing. This novel framework opens new perspectives for the broader multiscale investigation of brain physiological and pathological states in relation to specific cellular and microcircuit properties.

neuroscience↗

bioRxiv2026

Brain digital twins reveal network changes in congenital and slowly progressive cerebellar ataxias

Cerebellar ataxias are a rare group of disorders manifesting with motor incoordination and cognitive-affective deficits of variable severity. Although neurogenetic has revealed multiple mutations, the study of ataxias still relies on clinical evaluation, while the underlying neural network changes remain unclear. It has been argued that the less severe symptoms in congenital (like Joubert syndrome, JS) than in slowly progressive (SP) ataxias reflect a different interplay of alteration and compensation but direct evidence is still lacking. Moreover, it is unclear why, in front of a wide heterogeneity of molecular alterations, SPs show common clinical symptoms. To address these questions, we created brain digital twins for each participant by combining volumetry, graph theory analysis of structural and functional connectivity, and dynamical simulations using the virtual brain. We studied 8 JS (3 females, 21{+/-}6years), 8 SP (3 females, 20{+/-}5years) and 11 healthy controls (HC; 5 females, 21{+/-}2years).Volumetry quantified atrophy, graph metrics (centrality, segregation and integration) characterized topology, and neural dynamical simulations estimated excitation/inhibition balance, providing anatomo-physiological parameters within the somatomotor (SMN) and ventral attention (VAN) networks. Anatomo-physiological parameters were correlated with clinical/neuropsychological scores, and unsupervised clustering was applied to assess whether network features can discriminate between JS and SP beyond clinical classification. MRI morphometry confirmed selective vermis reduction in JS and a widespread cerebellar atrophy in SP compared to HC. In both ataxia groups, SMN and VAN showed reduced volume and structural connectivity but with different patterns of topological and dynamical alterations. In the SMN of SP, reduced centrality and excitation/inhibition balance depressed information transfer through the network. In the VAN of JS, reduced centrality, segregation, and integration, were detrimental but coexisted with a higher number of functional core nodes and an increased large-scale excitatory coupling, supporting compensatory reorganisation in extracerebellar nodes. Clustering confirmed that SMN better differentiates SP, whereas VAN better clusters JS. Importantly, anatomo-physiological parameters of network volume, topology, and dynamics correlated with patients motor and cognitive performance. In conclusion, primary cerebellar damage secondarily impacts large-scale brain networks, altered in both ataxia groups but compensated only in JS. Similar clinical symptoms in SP reflects the similarity of network changes, while differential involvement of SMN and VAN in JS and SP reflects the connectivity pattern of the lesioned areas inside these large-scale brain circuits. Importantly, anatomo-physiological parameters are sufficient to explain individual motor and cognitive performance, offering a basis for improved patient profiling and personalized therapies.

neuroscience↗

bioRxiv2026

Automated derivation of mean field models from spiking neural networks for the simulation of brain dynamics

A mean field model (MFM) is a mesoscopic description of neuronal population dynamics that can reduce the complexity of neural microcircuits into equations preserving key functional properties. The generation of a MFM is a complex mathematical process that starts with the incorporation of single neuron input/output relationships and local connectivity. Once neuron electroresponsiveness and synaptic properties are defined, in principle, the process can be automatized. Here we develop a tool for automatic MFM derivation from biophysically grounded spiking networks (Auto-MFM) by performing micro-to-mesoscale parameter remapping, estimating input/output relationships specific for different neuronal populations (i.e., transfer functions), and optimizing transfer function parameters. Auto-MFM was tested using a spiking cerebellar circuit as a generative model. The cerebellar MFM derived with Auto-MFM accurately reproduced cerebellar population dynamics of the corresponding spiking network, matching mean and time-varying firing rates across a wide range of stimulation patterns. Auto-MFM allowed us to model and explore physiological and pathological circuit variants; indeed, it was used to map ataxia-related structural connectivity alterations of the cerebellar network, in which Purkinje cells with simplified dendritic structure altered the cerebellar connectivity. Furthermore, Auto-MFM was used to create a library of cerebellar MFMs by sweeping the level of the excitatory conductance at mossy fiber - granule cell synapse, which is altered in several neuropathologies. Auto-MFM is thus proving a flexible and powerful tool to generate region-specific MFMs of healthy and pathological brain networks to be embedded in brain digital models.

neuroscience↗

bioRxiv2025

Causal inference and functional dynamics of a visuomotor network demonstrate excitatory/inhibitory alterations in Multiple Sclerosis

Balanced excitation and inhibition are essential for brain dynamics, and their disruption can lead to network dysfunction in neurological diseases. Here, we present a conceptually unified multiscale brain modelling framework combining Dynamic Causal Modelling (DCM) applied to task and resting-state functional Magnetic Resonance Imaging (fMRI) data and The Virtual Brain (TVB) to characterise the excitatory/inhibitory balance of the brain. We applied the framework to a visuomotor brain subnetwork in a cohort of 9 healthy controls and 17 people with multiple sclerosis (pwMS). Acquired data included an event-related task fMRI experiment with variable grip force, resting-state fMRI, and diffusion-weighted imaging. The visuomotor network comprised the bilateral primary visual cortex (V1), left primary motor cortex (M1), supplementary motor and premotor cortex (SMAPMC), cingulate cortex (CC), superior parietal lobule (SPL), and right cerebellar lobule VI (CR). Results from DCM showed that while the overall network architecture was preserved in MS, there were significant alterations in the excitatory/inhibitory nature of effective connectivity: at rest, a statistical change was observed in CR-to-V1 connectivity, which was inhibitory in healthy volunteers but excitatory in MS. During task, effective connectivity feedback, including cerebellar self-connection, was positive in healthy volunteers but negative in MS and became increasingly dysregulated with higher motor demand. Alterations in functional and effective connectivity were associated with behavioural performance (task reaction time) and clinical measures (disability severity). At the overall group level, TVB parameters linked reduced NMDA-mediated excitatory gain to slower task responses. Moreover, integrating DCM and TVB demonstrated that higher global excitatory gain was associated with stronger task-engaged effective connectivity across sensorimotor and visuomotor pathways, linking network-level excitability captured by TVB to context-dependent reconfiguration of directed interactions and to connection-level strength revealed by DCM.

neuroscience↗

bioRxiv2025

Region-specific mean field models enhance simulations of local and global brain dynamics

Brain dynamics can be simulated using virtual brain models, in which a standard mathematical representation of oscillatory activity is usually adopted for all cortical and subcortical regions. However, some brain regions have specific microcircuit properties that are not recapitulated by standard oscillators. Moreover, magnetic resonance imaging (MRI)-based connectomes may not be able to capture local circuit connectivity. Region-specific models incorporating computational properties of local neurons and microcircuits have recently been generated using the mean field (MF) approach and proposed to impact large-scale brain dynamics. Here we have used a MF of the cerebellar cortex to generate a mesoscopic model of the whole cerebellum featuring a prewired connectivity of multiple cerebellar cortical areas with deep cerebellar nuclei. This multi-node cerebellar mean field model was then used to substitute the corresponding standard oscillators and build up a cerebellar mean field virtual brain (cMF-TVB), for a group of healthy human subjects. Simulations revealed that electrophysiological and fMRI signals generated by the cMF-TVB significantly improved the fitness of local and global dynamics with respect to a homogeneous model made solely of standard oscillators. The cMF-TVB reproduced the rhythmic oscillations and coherence typical of the cerebellar circuit and allowed to correlate electrophysiological and functional MRI signals to specific neuronal populations. In aggregate, region-specific models based on MF technology and pre-wired circuit connectivity can significantly improve virtual brain simulations fostering the generation of effective brain digital twins that could be used for physiological studies and clinical applications.

bioengineering↗

bioRxiv2023

Dopamine-dependent cerebellar dysfunction enhances beta oscillations and disrupts motor learning in a multiarea model

Parkinsons disease (PD) is a chronic degenerative disorder of the central nervous system that affects the motor system. The discovery that PD motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of PD research on the basal ganglia. However, recent findings point to an active involvement of the cerebellum in PD. Here, we have developed a multiscale computational model of the rodent brains basal ganglia-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of PD neural activity, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in PD. Overall, these results suggest a relevant involvement of cerebellum in PD motor symptoms. Significance StatementThis study highlights the role of cerebellum in Parkinsons disease (PD). While most studies on PD concentrate on dopaminergic mechanisms in the basal ganglia, here we show that dopamine depletion impacts also on the cerebellum, generating a complex dysfunctional interaction between the two subcortical circuits. To investigate this interaction, we developed de novo a multiarea multiscale network model that mechanistically addresses the effects of dopamine depletionon the cerebellum. Our study demonstrates that this aspect is crucial to reproduce experimental data, particularly the increased beta wave activity. Moreover, alterations in spike-time-dependent plasticity at the parallel fibre - Purkinje cell synapse of cerebellum can explain the link between dopamine depletion to motor learning impairment. These simulations indicate that the cerebellum warrants more attention in future PD research.

neuroscience↗

bioRxiv2022

Parallel processing relies on a distributed, low-dimensional cortico-cerebellar architecture

A characteristic feature of human cognition is our ability to multi-task - performing two or more tasks in parallel - particularly when one task is well-learned. How the brain supports this capacity remains poorly understood. Most past studies have focussed on identifying the areas of the brain - typically the dorsolateral prefrontal cortex - that are required to navigate information processing bottlenecks. In contrast, we take a systems neuroscience approach to test the hypothesis that the capacity to conduct effective parallel processing relies on a distributed architecture that interconnects the cerebral cortex with the cerebellum. The latter structure contains over half of the neurons in the adult human brain, and is well-suited to support the fast, effective, dynamic sequences required to perform tasks relatively automatically. By delegating stereotyped within-task computations to the cerebellum, the cerebral cortex can be freed up to focus on the more challenging aspects of performing the tasks in parallel. To test this hypothesis, we analysed task-based fMRI data from 50 participants who performed a task in which they either balanced an avatar on a screen ( Balance), performed serial-7 subtractions ( Calculation) or performed both in parallel ( Dual-Task). Using a set of approaches that include dimensionality reduction, structure-function coupling and time-varying functional connectivity, we provide robust evidence in support of our hypothesis. We conclude that distributed interactions between the cerebral cortex and cerebellum are crucially involved in parallel processing in the human brain.

neuroscience↗

bioRxiv2022

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and vigor of saccadic eye movements.

Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20-100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual end-point error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber - Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in optimizing saccade movement control: it turns out that LTD regulates the accuracy and LTP the speed (vigor) of the ballistic eye movement. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the accuracy and vigor regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements. Author summaryIt has been suggested that the cerebellum plays a crucial role in oculomotor adaptation. Computationally, the cerebellum is described as a supervised learner whose activity can be adjusted by synaptic changes proportional to the amount of mismatch between expected and actual movements outcomes (sensorimotor-errors). However, the spike-timing-dependent-plasticity (STDP) underlying adaptation has been so far modeled in behaviors where the error is continuously available as tracking-error. Such models depend on detailed tracking-errors to improve movement quality. We asked if the cerebellum can maintain good motor control even if the error is not completely available? This is important considering that error-dependent-STDP is only a subset among a family of STDP processes in the cerebellum. Moreover, even the physiological signals that were generally thought to carry sensorimotor-errors (called complex-spikes) are shown to encode multiple types of information regarding the movement. In this context, we characterize the role of cerebellar STDPs in saccade control, where the error information is constrained because of sensory suppression. We show that even in the absence of detailed error, the cerebellum can leverage two of its STDPs to increase movement quality. Hence, we emphasize the need to go beyond error-centric view to understand how the cerebellum improves motor behavior.

neuroscience↗

bioRxiv2022

Cross-comparison of state of the art neuromorphological simulators on modern CPUs and GPUs using the Brain Scaffold Builder

A variety of software simulators exist for neuronal networks, and a subset of these tools allow the scientist to model neurons in high morphological detail. The scalability of such simulation tools over a wide range in neuronal networks sizes and cell complexities is predominantly limited by effective allocation of components of such simulations over computational nodes, and the overhead in communication between them. In order to have more scalable simulation software, it is therefore important to develop a robust benchmarking strategy that allows insight into specific computational bottlenecks for models of realistic size and complexity. In this study, we demonstrate the use of the Brain Scaffold Builder (BSB; De Schepper et al., 2021) as a framework for performing such benchmarks. We perform a comparison between the well-known neuromorphological simulator NEURON (Carnevale and Hines, 2006), and Arbor (Abi Akar et al., 2019), a new simulation library developed within the framework of the Human Brain Project. The BSB can construct identical neuromorphological and network setups of highly spatially and biophysically detailed networks for each simulator. This ensures good coverage of feature support in each simulator, and realistic workloads. After validating the outputs of the BSB generated models, we execute the simulations on a variety of hardware configurations consisting of two types of nodes (GPU and CPU). We investigate performance of two different network models, one suited for a single machine, and one for distributed simulation. We investigate performance across different mechanisms, mechanism classes, mechanism combinations, and cell types. Our benchmarks show that, depending on the distribution scheme deployed by Arbor, a speed-up with respect to NEURON of between 60 and 400 can be achieved. Additionally Arbor can be up to two orders of magnitude more energy efficient.

neuroscience↗

bioRxiv2021

Brain-inspired spiking neural network controller for a neurorobotic whisker system

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

bioengineering↗

bioRxiv2021

Scaffold modelling captures the structure-function-dynamics relationship in brain microcircuits

1Modelling brain networks with complex configuration and cellular properties requires a set of neuroinformatic tools and an organized staged workflow. We have therefore developed the Brain Scaffold Builder (BSB), a new modeling framework embedding multiple strategies for cell placement and connectivity and a flexible management of cellular and network mechanisms. With BSB, for the first time, the mouse cerebellar cortex was reconstructed and simulated at cellular resolution, using morphologically realistic multi-compartmental single-neuron models. Embedded connection rules allowed BSB to generate the cerebellar connectome, unifying a collection of scattered experimental data into a coherent construct. Naturalistic background and sensory-burst stimulation were used for functional validation against recordings in vivo, monitoring the impact of subcellular mechanisms on signal propagation and spatio-temporal processing and providing a new ground-truth about circuit organization for the prediction of neural dynamics.

neuroscience↗

bioRxiv2020

BOLD response to multiple grip forces in MS: going beyond the main effect of movement in BA 4a and BA 4p

This study highlights the importance of looking beyond the main effect of movement to study alterations in functional response in the presence of central nervous system pathologies such as multiple sclerosis (MS). Data show that MS selectively affects regional BOLD (Blood Oxygenation Level Dependent) responses to variable grip forces (GF). It is known that the anterior and posterior BA 4 areas (BA 4a and BA 4p) are anatomically and functionally distinct. It has also been shown in Healthy volunteers that there are linear (1st order, typical of BA 4a) and non-linear (2nd-4th order, typical of BA 4p) BOLD responses to different levels of GF applied during a dynamic motor paradigm. After modelling the BOLD response with a polynomial expansion of the applied GFs, the particular case of BA 4a and BA 4p were investigated in Healthy Volunteers (HV) and MS subjects. The main effect of movement (0th order) analysis showed that the BOLD signal is greater in MS compared to healthy volunteers within both BA 4 sub-regions. At higher order, BOLD-GF responses were similar in BA 4a but showed a marked alteration in BA 4p of MS subjects, with those with greatest disability showing the greatest deviations from the healthy response profile. Therefore, the different behaviour in HV and MS could only be uncovered through a polynomial analysis looking beyond the main effect of movement into the two BA 4 sub-regions. Future studies will investigate the source of this pathophysiology, combining the present fMRI paradigm with blood perfusion and non-linear neuronal response analysis.

neuroscience↗

bioRxiv2020

Cellular-resolution mapping uncovers spatial adaptive filtering at the cerebellum input stage

Long-term synaptic plasticity, in the form of either potentiation or depression (LTP or LTD), is thought to provide the substrate for adaptive computations in brain circuits. Although molecular and cellular processes of plasticity have been clarified to a considerable extent at individual synapses, very little is known about the spatiotemporal organization of LTP and LTD in local microcircuits. Here, we have combined multi-spot two-photon laser microscopy and realistic modeling to map the distribution of plasticity in multi-neuronal units of the cerebellar granular layer activated by stimulating an afferent mossy fiber bundle. The units, composed by ~300 active neurons connected to ~50 glomeruli, showed potentiation concentrated in the core and depression in the periphery. This plasticity was effectively accounted for by an NMDA receptor and calcium-dependent induction rule and was regulated by local microcircuit mechanisms in the inhibitory Golgi cell loops. The organization of LTP and LTD created effective spatial filters tuning the time-delay and gain of spike retransmission at the cerebellum input stage and provided a plausible basis for the spatiotemporal recoding of input spike patterns anticipated by the motor learning theory.

neuroscience↗

bioRxiv2019

Cervical spinal cord atrophy and Alzheimer’s disease

ObjectiveBrain 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 viceversa, it is indeed a very interesting question to assess whether it is affected by atrophy in 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 Alzheimer Disease 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.\n\nMethods3DT1 images of 31 Alzheimers disease (AD) and 35 healthy control (HC) subjects were processed to calculate volumes 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 ({rho}>0.7) were removed, and 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 dependent variable with best features as predictors.\n\nResultsSpinal 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 grey matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance.\n\nDiscussionOur findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. Results show that CSA23, calculated form 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.

neuroscience↗

bioRxiv2018

Hyper-excitability and hyper-plasticity disrupt cerebellar signal transfer in the IB2 KO mouse model of autism

Autism spectrum disorders (ASD) are pervasive neurodevelopmental conditions that often involve mutations affecting synaptic mechanisms. Recently, the involvement of cerebellum in ASD has been suggested but the underlying functional alterations remained obscure. We investigated single-neuron and microcircuit properties in IB2 KO mice, which present a cerebellar phenotype associated with ASD. Granule cells showed a larger NMDA receptor-mediated current and enhanced intrinsic excitability raising the excitatory/inhibitory balance. Furthermore, the spatial organization of granular layer responses to mossy fibers shifted from a Mexican hat to stovepipe hat profile, with stronger excitation in the core and weaker inhibition in the surround. Finally, the size and extension of long-term synaptic plasticity was remarkably increased. These results show for the first time that hyper-excitability and hyper-plasticity disrupt signal transfer in the granular layer of IB2 KO mice supporting cerebellar involvement in the pathogenesis of ASD.

neuroscience↗