Targeted antisense oligonucleotide treatment rescues developmental alterations in spinal muscular atrophy organoids Irene Faravelli, Paola Rinchetti, Monica Tambalo, Illia Simutin, Lisa Mapelli, Sara Mancinelli, Matteo Miotto, Mafalda Rizzuti, Andrea D’Angelo, Chiara Cordiglieri, Giulia Forotti, Clelia Peano, Paolo Kunderfranco, Luca Calandriello, Giacomo P. Comi, Elvezia Paraboschi, Eleonora Pali, Francesca Beatrice, Egidio D’Angelo, Serge Przedborski, Monica Nizzardo, Simona Lodato, Stefania Corti Nature Communications, 2026 Spinal muscular atrophy (SMA) is a severe neurological disease caused by mutations in the SMN1 gene, characterized by early onset and degeneration of lower motor neurons. Understanding early neurodevelopmental defects in SMA is crucial for optimizing therapeutic interventions. Using spinal cord and cerebral organoids generated from multiple SMA type 1 male donors, we revealed widespread disease mechanisms beyond motor neuron degeneration. Single-cell transcriptomics uncovered pervasive alterations across neural populations, from progenitors to neurons, demonstrating SMN-dependent dysregulation of neuronal differentiation programs. Multi-electrode array (MEA) analysis identified consistent hyperexcitability in both spinal and brain organoids, establishing altered electrical properties as a central nervous system-wide feature of pathogenesis. Early administration of an optimized antisense oligonucleotide (ASO) that increased SMN levels rescued morphological and functional deficits in spinal cord organoids across different genetic backgrounds. Importantly, this early intervention precisely corrected aberrant splicing in here identified SMN1 targets enriched at critical nodes of neuronal differentiation. Our findings demonstrate that early developmental defects are core features of SMA pathogenesis that can be prevented by timely therapeutic intervention, providing insights for optimizing treatment strategies.
The Sense of Smell (SoS) Atlas: Its Creation and First Application to Investigate COVID-19 Related Anosmia With a Comprehensive Quantitative MRI Protocol Marta Gaviraghi, Eleonora Lupi, Elena Grosso, Andrea Fusari, Mattia Baiguera, Anita Monteverdi, Marco Battiston, Francesco Grussu, Baris Kanber, Ferran Prados Carrasco, Rebecca S. Samson, Janine Makaronidis, Marios C. Yiannakas, Michael S. Zandi, Rachel L. Batterham, Egidio D'Angelo, Fulvia Palesi, Claudia A. M. Gandini Wheeler‐Kingshott Journal of Magnetic Resonance Imaging, 2026 BackgroundThe loss of smell (anosmia) has been noted in numerous diseases, including COVID‐19. Inflammatory and microstructural alterations are possible underlying mechanisms of anosmia in COVID‐19. However, no atlas exists to study olfaction and the associated tissue property changes.PurposeTo develop the sense of smell (SoS) atlas, including gray matter regions and white matter tracts of the olfactory circuit, to investigate the underpinnings of COVID‐19 related anosmia.Study TypeRetrospective.SubjectsFor the SoS atlas, high‐resolution tractograms of 10 healthy controls (HC) of the Human Connectome Project (7 females, 22–35 years) were used. The SoS atlas was applied to 8 subjects with persistent anosmia following COVID‐19 (COVID‐P, 7 females, 52 ± 12 years), 19 subjects that recovered from COVID‐19 anosmia (COVID‐R, 14 females, 38 ± 13 years), and 17 HC (8 females, 39 ± 12 years).Field Strength/Sequence3 T, 3D inversion recovery, 3D fast field echo, and spin‐echo echo‐planar imaging sequences.AssessmentTo create the SoS atlas, regions were identified and tracts were extracted via tractography following biological constraints. MRI metrics sensitive to alterations in neuroinflammation, axonal degeneration, myelin and macromolecular density, and iron were analyzed.Statistical TestsRegion‐based analysis (p‐value < 0.05, false discovery rate (FDR) corrected) and voxel‐based analysis (p‐value < 0.001 uncorrected, FDR‐corrected cluster extent = 5 voxels) were performed on 15 multisequence‐MRI metrics between the three groups.ResultsThe SoS atlas consisted of 35 regions and, after anatomical curation, the initial 506 tracts were refined to 78. Compared to HC, COVID‐P presented alterations in neuroinflammation‐related (mean: 41% of total alterations) and axonal degeneration‐related (31%) MRI metrics, while COVID‐R presented alterations of myelin‐related metrics (68%). COVID‐P alterations mainly affected the hindbrain (56%), while COVID‐R the hindbrain (39%).Data ConclusionA novel tool, the SoS atlas, was developed to study the olfactory system and applied in combination with multisequence‐MRI metrics to investigate the mechanisms of COVID‐19 related anosmia.Evidence Level3.Technical EfficacyStage 1.
Editorial: Multiscale brain modelling Egidio D'Angelo Frontiers in Cellular Neuroscience, 2026 Addressing the multiscale brain organization is fundamental not only to understand its inherent mechanisms of function but also to answer neuropathological questions and promote the development of new technologies for AI and health. A review on the issue is presented here in the paper by Krejcar and Namazi (2025) and additional information can be found in D'Angelo and Jirsa (2022) and Wang et al. (2024). The Authors have then covered two main areas of research: theoretical models of neuronal excitability, network oscillations, and brain activity, and models applied to the study of Alzheimer's disease. Galinsky and Frank (2025) address the wave nature of the action potentials proposing an alternative framework to the standard Hodgkin-Huxley model for the action potential in axons. This is based on the Author's theory of electric field wave propagation in anisotropic and inhomogeneous brain tissues and addresses the limitations of the Hodgkin-Huxley model, including its inability to explain extracellular spiking, efficient brain synchronization, saltatory conduction along myelinated axons, and various other observed coherent macroscopic brain electrical phenomena. Pieramico et al. (2025) show how Hidden Markov Models can be used to analyze time series of neural activity. The study demonstrates that Time-Delay Embedded Hidden Markov Models performs better than Gaussian models in accurately detecting brain states from synthetic phase-coupled interaction data. Finally, Ghosh et al. (2025) present general trainable networks of Hopf oscillators to model high-dimensional electroencephalogram (EEG) signals across different sleep stages. The model, once embedded with a hidden layer, can faithfully predict the empirical EEG representing a step toward constructing a large-scale, biologically inspired model of brain dynamics.Two papers address Alzheimer disease. Fadel et al. (2025) present a model of functional connectivity changes with learning and memory in a mouse model based on data obtained from mutant mice. The APP/PS1 mice showed a pattern of hyperconnectivity, including the Default Mode Network, after learning. Modeling revealed functional connections that support learning and memory performance. These models show potential for early disease detection by identifying connectivity patterns associated with cognitive decline and may provide a means to understand how FC translates into learning and memory performance. Moravveji et al. (2025) show a sensitivity analysis of a mathematical model of Alzheimer's disease progression to unveil causal pathways. The study presents the first local sensitivity analysis of a multiscale ODE-based model of Alzheimer's Disease (AD) and captures the multifactorial nature of AD incorporating neuronal, pathological, and inflammatory processes at the nano, micro and macro scales. This detailed framework enables realistic simulation of disease progression and identification of key biological parameters that influence system behavior. This analysis identifies the key drivers of disease progression across patient profiles, providing insight into targeted therapeutic strategies.
Region-specific mean field models enhance simulations of local and global brain dynamics Roberta Maria Lorenzi, Fulvia Palesi, Claudia Casellato, Claudia A. M. Gandini Wheeler-Kingshott, Egidio D’Angelo Npj Systems Biology and Applications, 2025 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 MF 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 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.
Coincidence detection between apical and basal dendrites drives STDP in cerebellar Golgi cells Eleonora Pali, Stefano Masoli, Danila Di Domenico, Teresa Sorbo, Francesca Prestori, Egidio D’Angelo Communications Biology, 2025 Cerebellar Golgi cells (GoCs), segregate parallel fiber (pf), and mossy fiber (mf) inputs on apical and basal dendrites. Computational modeling predicted that this anatomical arrangement, coupled with a specific ionic channel localization, could be instrumental to drive STDP at mf-GoC synapses. Here, we test this hypothesis with GoC patch-clamp recordings in acute mouse cerebellar slices. Repeated mf-pf pairing on the theta-band within a ± 50 ms time window induces anti-symmetric Hebbian-STDP, with spike-timing long-term potentiation or depression (st-LTP or st-LTD) occurring when action potentials (APs) elicited by pf stimulation follow or precede the activation of mf synapses, respectively. Mf-GoC STDP induction requires AP backpropagation from apical to basal dendrites, NMDA receptor activation at mf-GoC synapses, and intracellular calcium changes. Importantly, STDP is inverted by inhibitory control. Thus, experimental evidence confirms and extends model predictions suggesting that GoC STDP can bind molecular layer to granular layer activity, regulating cerebellar computation and learning.
Cerebellar basket cell filtering of Purkinje cell responses elicited by low frequency parallel fibre transmission Stefano Masoli, Martina Francesca Rizza, Teresa Soda, Diana Sánchez-Ponce, Alberto Munoz, et al. Scientific Reports, 2025 Basket cells (BC) are inhibitory interneurons of the cerebellar molecular layer (ML) forming peri-somatic synapses on Purkinje cells (PC). BC physiological and computational properties remained poorly understood and not clearly differentiated from those of stellate cells (SC). We identified BCs in acute mouse cerebellar slices and measured their intrinsic excitability and synaptic responsiveness. BCs and SCs were similar in some respects, although BCs showed stronger and faster synaptic excitation in response to parallel fibre (pf) bursts. The analysis of BC inhibition of PCs was extended over a broad parameter space using accurate multi-compartmental computational models. During pf bursts, the BC reduced the PC response at low-frequency, while SCs did it at high-frequency. BC filtering was explained by the engagement of HCN1 channels, which activated slowly during low-frequency BC-PC GABAergic transmission. The increase of input conductance caused by HCN1 channels in the PC soma, by shunting excitatory currents elicited by pfs and travelling toward the axon initial segment (AIS), reduced the PC output frequency. These simulations predict that BC and SC operate in tandem, setting the frequency band of PC transmission through the regulation of PC frequency/response curves.
A Computational Model of the Respiratory CPG for the Artificial Control of Breathing Lorenzo De Toni, Federica Perricone, Lorenzo Tartarini, Giulia Maria Boiani, Stefano Cattini, Luigi Rovati, Dimitri Rodarie, Egidio D’Angelo, Jonathan Mapelli, Daniela Gandolfi Bioengineering, 2025 The human respiratory Central Pattern Generator (CPG) is a complex and tightly regulated network of neurons responsible for the automatic rhythm of breathing. Among the brain nuclei involved in respiratory control, excitatory neurons within the PreBotzinger Complex (PreBötC) are both necessary and sufficient for generating this rhythmic activity. Although several models of the PreBötC circuit have been proposed, a comprehensive analysis of network behavior in response to physiologically relevant external inputs remains limited. In this study, we present a computational model of the PreBötC consisting of 1000 excitatory neurons, divided into two functional subgroups: the rhythm-generating population and the pattern-forming population. To enable real-time closed-loop simulations, we employed parallelized multi-process computing to accelerate network simulation. The network, composed of asynchronous neurons, could produce bursting activity at a eupneic breathing frequency of 0.22 Hz, which could also reproduce the rapid and stable chemoreception of breathing activated in response to hypercapnia. Additionally, it successfully replicated rapid and stable respiratory responses to elevated carbon dioxide levels (hypercapnia), mediated through simulated chemoreception. External inputs from a carbon dioxide sensor were used to modulate the network activity, allowing the implementation of a real-time respiratory control system. These results demonstrate that a network of asynchronous, non-bursting neurons can emulate the behavior of the respiratory CPG and its modulation by external stimuli. The proposed model represents a step toward developing a closed-loop controller for breathing regulation.
Enhanced electrophysiological recordings in acute brain slices, spheroids, and organoids using 3D high-density multielectrode arrays Lisa Mapelli, Danila Di Domenico, Giacomo Sciacca, Francesco Mainardi, Alessandra Ottaviani, Anita Monteverdi, Mariateresa Tedesco, Chiara Rosa Battaglia, Simona Tritto, Mauro Gandolfo, Kilian Imfeld, Stefanie Kiderlen, Lukas Krainer, Chiara Cervetto, Manuela Marcoli, Anson Sing, Jimena Andersen, Fikri Birey, Steven A. Sloan, Alessandro Maccione, Egidio D'Angelo Plos One, 2025 Recent advances in three-dimensional (3D) biological brain models in vitro and ex vivo are creating new opportunities to understand the complexity of neural networks but pose the technological challenge of obtaining high-throughput recordings of electrical activity from multiple sites in 3D at high spatiotemporal resolution. This cannot be achieved using planar multi-electrode arrays (MEAs), which contact just one side of the neural structure. Moreover, the specimen adhesion to planar MEAs limits fluid perfusion along with tissue viability and drug application. Here, the efficiency of the tissue-sensor interface provided by advanced 3D high-density (HD)-MEA technology was evaluated in acute brain slices, spheroids, and organoids obtained from different brain regions. The 3D HD-MEA microneedles reached the inner layers of samples without damaging network integrity and the microchannel network between microneedles improved tissue vitality and chemical compound diffusion. In acute cortico-hippocampal and cerebellar slices, signal recording and stimulation efficiency proved higher with the 3D HD-MEA than with a planar MEA improving the characterization of network activity and functional connectivity. The 3D HD-MEA also resolved the challenge of recording from brain spheroids as well as cortical and spinal organoids. Our results show that 3D HD-MEA technology represents a valuable tool to address the complex spatiotemporal organization of activity in brain microcircuits, making it possible to investigate 3D biological models.
Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an action Roberta Maria Lorenzi, Gökçe Korkmaz, Adnan A. S. Alahmadi, Anita Monteverdi, Letizia Casiraghi, Egidio D’Angelo, Fulvia Palesi, Claudia A. M. Gandini Wheeler-Kingshott Cerebellum, 2025 The motor learning theory anticipates that cerebro-cerebellar loops perform sensorimotor prediction, thereby regulating motor control during action execution (AE) and observation (AO), but the causal interaction between the cerebellum and cerebral cortex remains unclear. Therefore, our aim was to understand what triggers neuronal activity between brain areas engaged in a visuo-motor task that involves cortico-cerebellar interactions, organised in loops. We used Dynamic Causal Modelling (DCM) to study functional MRI (fMRI) data obtained in healthy participants during a squeeze-ball task in either execution or observation conditions. In both cases, active regions included 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 cerebellum (CRBL). Networks supporting executing or observing an action showed the same effective connectivity, with pathways between regions wired in closed loops. However, the cerebellar communication towards the cerebral cortex switched from excitatory in execution to inhibitory in observation. Moreover, when executing the action signal modulation was non-linear from SMAPMC to CRBL and within the CRBL self-connection, supporting that the CRBL elaborates motor plans received from SMAPMC. Thus, the need for motor planning and the presence of a sensorimotor feedback in action execution discriminate the modality of forward control operated by the CRBL. Interestingly, this study also showed that the CRBL differentially controls the excitatory/inhibitory dynamics of inter-regional effective connectivity, depending on its functional engagement. These findings are fundamental for understanding brain dynamics in health and disease and for designing artificial sensorimotor controllers.
Linking cellular-level phenomena to brain architecture: the case of spiking cerebellar controllers Egidio D’Angelo, Alberto Antonietti, Alice Geminiani, Benedetta Gambosi, Cristiano Alessandro, Emiliano Buttarazzi, Alessandra Pedrocchi, Claudia Casellato Neural Networks, 2025 Linking cellular-level phenomena to brain architecture and behavior is a holy grail for theoretical and computational neuroscience. Advances in neuroinformatics have recently allowed scientists to embed spiking neural networks of the cerebellum with realistic neuron models and multiple synaptic plasticity rules into sensorimotor controllers. By minimizing the distance (error) between the desired and the actual sensory state, and exploiting the sensory prediction, the cerebellar network acquires knowledge about the body-environment interaction and generates corrective signals. In doing so, the cerebellum implements a generalized computational algorithm, allowing it "to learn to predict the timing between correlated events" in a rich set of behavioral contexts. Plastic changes evolve trial by trial and are distributed over multiple synapses, regulating the timing of neuronal discharge and fine-tuning high-speed movements on the millisecond timescale. Thus, spiking cerebellar built-in controllers, among various computational approaches to studying cerebellar function, are helping to reveal the cellular-level substrates of network learning and signal coding, opening new frontiers for predictive computing and autonomous learning in robots.
An Equivalent Single Spiking Neuron Model of the Working Memory Navya Ajith, Arathi Rajendran, Giovanni Naldi, Egidio D'Angelo, Shyam Diwakar 2025 International Conference on Cognitive Computing in Engineering Communications Sciences and Biomedical Health Informatics Ic3ecsbhi 2025, 2025
Author Correction: Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA (Scientific Reports, (2023), 13, 1, (17355), 10.1038/s41598-023-43706-6) Silvia De Francesco, Claudio Crema, Damiano Archetti, Cristina Muscio, Robert I. Reid, Anna Nigri, Maria Grazia Bruzzone, Fabrizio Tagliavini, Raffaele Lodi, Egidio D’Angelo, Brad Boeve, Kejal Kantarci, Michael Firbank, John-Paul Taylor, Pietro Tiraboschi, Alberto Redolfi, Maria Grazia Bruzzone, Pietro Tiraboschi, Claudia A. M. Gandini Wheeler-Kingshott, Michela Tosetti, Gianluigi Forloni, Alberto Redolfi, Egidio D’Angelo, Fabrizio Tagliavini, Raffaele Lodi, Raffaele Agati, Marco Aiello, Elisa Alberici, Carmelo Amato, Domenico Aquino, Filippo Arrigoni, Francesca Baglio, Laura Biagi, Lilla Bonanno, Paolo Bosco, Francesca Bottino, Marco Bozzali, Nicola Canessa, Chiara Carducci, Irene Carne, Lorenzo Carnevale, Antonella Castellano, Carlo Cavaliere, Mattia Colnaghi, Valeria Elisa Contarino, Giorgio Conte, Mauro Costagli, Greta Demichelis, Silvia De Francesco, Andrea Falini, Stefania Ferraro, Giulio Ferrazzi, Lorenzo Figà Talamanca, Cira Fundarò, Simona Gaudino, Francesco Ghielmetti, Ruben Gianeri, Giovanni Giulietti, Marco Grimaldi, Antonella Iadanza, Matilde Inglese, Maria Marcella Laganà, Marta Lancione, Fabrizio Levrero, Daniela Longo, Giulia Lucignani, Martina Lucignani, Maria Luisa Malosio, Vittorio Manzo, Silvia Marino, Jean Paul Medina, Edoardo Micotti, Claudia Morelli, Cristina Muscio, Antonio Napolitano, Anna Nigri, Francesco Padelli, Fulvia Palesi, Patrizia Pantano, Chiara Parrillo, Luigi Pavone, Denis Peruzzo, Nikolaos Petsas, Anna Pichiecchio, Alice Pirastru, Letterio S. Politi, Luca Roccatagliata, Elisa Rognone, Andrea Rossi, Maria Camilla Rossi-Espagnet, Claudia Ruvolo, Marco Salvatore, Giovanni Savini, Emanuela Tagliente, Claudia Testa, Caterina Tonon, Domenico Tortora, Fabio Maria Triulzi, and Scientific Reports, 2024
Multiscale modeling of neuronal dynamics in hippocampus CA1 Federico Tesler, Roberta Maria Lorenzi, Adam Ponzi, Claudia Casellato, Fulvia Palesi, Daniela Gandolfi, Claudia A. M. Gandini Wheeler Kingshott, Jonathan Mapelli, Egidio D'Angelo, Michele Migliore, Alain Destexhe Frontiers in Computational Neuroscience, 2024
Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA Silvia De Francesco, Claudio Crema, Damiano Archetti, Cristina Muscio, Robert I. Reid, Anna Nigri, Maria Grazia Bruzzone, Fabrizio Tagliavini, Raffaele Lodi, Egidio D’Angelo, Brad Boeve, Kejal Kantarci, Michael Firbank, John-Paul Taylor, Pietro Tiraboschi, Alberto Redolfi, Maria Grazia Bruzzone, Pietro Tiraboschi, Claudia A. M. Gandini Wheeler-Kingshott, Michela Tosetti, Gianluigi Forloni, Alberto Redolfi, Egidio D’Angelo, Fabrizio Tagliavini, Raffaele Lodi, Raffaele Agati, Marco Aiello, Elisa Alberici, Carmelo Amato, Domenico Aquino, Filippo Arrigoni, Francesca Baglio, Laura Biagi, Lilla Bonanno, Paolo Bosco, Francesca Bottino, Marco Bozzali, Nicola Canessa, Chiara Carducci, Irene Carne, Lorenzo Carnevale, Antonella Castellano, Carlo Cavaliere, Mattia Colnaghi, Valeria Elisa Contarino, Giorgio Conte, Mauro Costagli, Greta Demichelis, Silvia De Francesco, Andrea Falini, Stefania Ferraro, Giulio Ferrazzi, Lorenzo Figà Talamanca, Cira Fundarò, Simona Gaudino, Francesco Ghielmetti, Ruben Gianeri, Giovanni Giulietti, Marco Grimaldi, Antonella Iadanza, Matilde Inglese, Maria Marcella Laganà, Marta Lancione, Fabrizio Levrero, Daniela Longo, Giulia Lucignani, Martina Lucignani, Maria Luisa Malosio, Vittorio Manzo, Silvia Marino, Jean Paul Medina, Edoardo Micotti, Claudia Morelli, Cristina Muscio, Antonio Napolitano, Anna Nigri, Francesco Padelli, Fulvia Palesi, Patrizia Pantano, Chiara Parrillo, Luigi Pavone, Denis Peruzzo, Nikolaos Petsas, Anna Pichiecchio, Alice Pirastru, Letterio S. Politi, Luca Roccatagliata, Elisa Rognone, Andrea Rossi, Maria Camilla Rossi-Espagnet, Claudia Ruvolo, Marco Salvatore, Giovanni Savini, Emanuela Tagliente, Claudia Testa, Caterina Tonon, Domenico Tortora, Fabio Maria Triulzi, and Scientific Reports, 2023
Italian, European, and international neuroinformatics efforts: An overview Alberto Redolfi, Damiano Archetti, Silvia De Francesco, Claudio Crema, Fabrizio Tagliavini, Raffaele Lodi, Roberta Ghidoni, Claudia A. M. Gandini Wheeler‐Kingshott, Daniel C. Alexander, Egidio D'Angelo European Journal of Neuroscience, 2023
Granule Cells and Parallel Fibers Egidio D’Angelo Essentials of Cerebellum and Cerebellar Disorders A Primer for Graduate Students Second Edition, 2023
Myelin quantification in Magnetic Resonance Imaging Convegno Nazionale Di Bioingegneria, 2023
Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias Anita Monteverdi, Fulvia Palesi, Michael Schirner, Francesca Argentino, Mariateresa Merante, Alberto Redolfi, Francesca Conca, Laura Mazzocchi, Stefano F. Cappa, Matteo Cotta Ramusino, Alfredo Costa, Anna Pichiecchio, Lisa M. Farina, Viktor Jirsa, Petra Ritter, Claudia A. M. Gandini Wheeler-Kingshott, Egidio D’Angelo Frontiers in Aging Neuroscience, 2023
MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms Fulvia Palesi, Anna Nigri, Ruben Gianeri, Domenico Aquino, Alberto Redolfi, Laura Biagi, Irene Carne, Silvia De Francesco, Stefania Ferraro, Paola Martucci, Jean Paul Medina, Antonio Napolitano, Alice Pirastru, Francesca Baglio, Fabrizio Tagliavini, Maria Grazia Bruzzone, Michela Tosetti, Claudia A.M. Gandini Wheeler-Kingshott Physica Medica, 2022
Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network Anna Nigri, Stefania Ferraro, Claudia A. M. Gandini Wheeler-Kingshott, Michela Tosetti, Alberto Redolfi, Gianluigi Forloni, Egidio D'Angelo, Domenico Aquino, Laura Biagi, Paolo Bosco, Irene Carne, Silvia De Francesco, Greta Demichelis, Ruben Gianeri, Maria Marcella Lagana, Edoardo Micotti, Antonio Napolitano, Fulvia Palesi, Alice Pirastru, Giovanni Savini, Elisa Alberici, Carmelo Amato, Filippo Arrigoni, Francesca Baglio, Marco Bozzali, Antonella Castellano, Carlo Cavaliere, Valeria Elisa Contarino, Giulio Ferrazzi, Simona Gaudino, Silvia Marino, Vittorio Manzo, Luigi Pavone, Letterio S. Politi, Luca Roccatagliata, Elisa Rognone, Andrea Rossi, Caterina Tonon, Raffaele Lodi, Fabrizio Tagliavini, Maria Grazia Bruzzone, and Frontiers in Neurology, 2022
Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? Kurt G. Schilling, François Rheault, Laurent Petit, Colin B. Hansen, Vishwesh Nath, Fang-Cheng Yeh, Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, Thomas Yu, Elda Fischi-Gomez, Marco Pizzolato, Mario Ocampo-Pineda, Simona Schiavi, Erick J. Canales-Rodríguez, Alessandro Daducci, Cristina Granziera, Giorgio Innocenti, Jean-Philippe Thiran, Laura Mancini, Stephen Wastling, Sirio Cocozza, Maria Petracca, Giuseppe Pontillo, Matteo Mancini, Sjoerd B. Vos, Vejay N. Vakharia, John S. Duncan, Helena Melero, Lidia Manzanedo, Emilio Sanz-Morales, Ángel Peña-Melián, Fernando Calamante, Arnaud Attyé, Ryan P. Cabeen, Laura Korobova, Arthur W. Toga, Anupa Ambili Vijayakumari, Drew Parker, Ragini Verma, Ahmed Radwan, Stefan Sunaert, Louise Emsell, Alberto De Luca, Alexander Leemans, Claude J. Bajada, Hamied Haroon, Hojjatollah Azadbakht, Maxime Chamberland, Sila Genc, Chantal M.W. Tax, Ping-Hong Yeh, Rujirutana Srikanchana, Colin D. Mcknight, Joseph Yuan-Mou Yang, Jian Chen, Claire E. Kelly, Chun-Hung Yeh, Jerome Cochereau, Jerome J. Maller, Thomas Welton, Fabien Almairac, Kiran K Seunarine, Chris A. Clark, Fan Zhang, Nikos Makris, Alexandra Golby, Yogesh Rathi, Lauren J. O'Donnell, Yihao Xia, Dogu Baran Aydogan, Yonggang Shi, Francisco Guerreiro Fernandes, Mathijs Raemaekers, Shaun Warrington, Stijn Michielse, Alonso Ramírez-Manzanares, Luis Concha, Ramón Aranda, Mariano Rivera Meraz, Garikoitz Lerma-Usabiaga, Lucas Roitman, Lucius S. Fekonja, Navona Calarco, Michael Joseph, Hajer Nakua, Aristotle N. Voineskos, Philippe Karan, Gabrielle Grenier, Jon Haitz Legarreta, Nagesh Adluru, Veena A. Nair, Vivek Prabhakaran, Andrew L. Alexander, Koji Kamagata, Yuya Saito, Wataru Uchida, Christina Andica, Masahiro Abe, Roza G. Bayrak, Claudia A.M. Gandini Wheeler-Kingshott, Egidio D'Angelo, Fulvia Palesi, Giovanni Savini, Nicolò Rolandi, Pamela Guevara, Josselin Houenou, Narciso López-López, Jean-François Mangin, Cyril Poupon, Claudio Román, Andrea Vázquez, Chiara Maffei, Mavilde Arantes, José Paulo Andrade, Susana Maria Silva, Vince D. Calhoun, Eduardo Caverzasi, Simone Sacco, Michael Lauricella, Franco Pestilli, Daniel Bullock, Yang Zhan, Edith Brignoni-Perez, Catherine Lebel, Jess E Reynolds, Igor Nestrasil, René Labounek, Christophe Lenglet, Amy Paulson, Stefania Aulicka, Sarah R. Heilbronner, Katja Heuer, Bramsh Qamar Chandio, Javier Guaje, Wei Tang, Eleftherios Garyfallidis, Rajikha Raja, Adam W. Anderson, Bennett A. Landman, Maxime Descoteaux Neuroimage, 2021
Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum Petruţ A. Bogdan, Beatrice Marcinnò, Claudia Casellato, Stefano Casali, Andrew G.D. Rowley, Michael Hopkins, Francesco Leporati, Egidio D'Angelo, Oliver Rhodes Frontiers in Cellular Neuroscience, 2021
Frontal and Cerebellar Atrophy Supports FTSD-ALS Clinical Continuum Beatrice Pizzarotti, Fulvia Palesi, Paolo Vitali, Gloria Castellazzi, Nicoletta Anzalone, Elena Alvisi, Daniele Martinelli, Sara Bernini, Matteo Cotta Ramusino, Mauro Ceroni, Giuseppe Micieli, Elena Sinforiani, Egidio D’Angelo, Alfredo Costa, Claudia A. M. Gandini Wheeler-Kingshott Frontiers in Aging Neuroscience, 2020
Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts Alberto Redolfi, Silvia De Francesco, Fulvia Palesi, Samantha Galluzzi, Cristina Muscio, Gloria Castellazzi, Pietro Tiraboschi, Giovanni Savini, Anna Nigri, Gabriella Bottini, Maria Grazia Bruzzone, Matteo Cotta Ramusino, Stefania Ferraro, Claudia A. M. Gandini Wheeler-Kingshott, Fabrizio Tagliavini, Giovanni B. Frisoni, Philippe Ryvlin, Jean-François Demonet, Ferath Kherif, Stefano F. Cappa, Egidio D'Angelo Frontiers in Neurology, 2020
Unsuspected Involvement of Spinal Cord in Alzheimer Disease Roberta Maria Lorenzi, Fulvia Palesi, Gloria Castellazzi, Paolo Vitali, Nicoletta Anzalone, Sara Bernini, Matteo Cotta Ramusino, Elena Sinforiani, Giuseppe Micieli, Alfredo Costa, Egidio D’Angelo, Claudia A. M. Gandini Wheeler-Kingshott Frontiers in Cellular Neuroscience, 2020
GPU Parallelization of Realistic Purkinje Cells with Complex Morphology Emanuele Torti, Stefano Masoli, Giordana Florimbi, Egidio D'Angelo, Marta Ticli, Francesco Leporati Proceedings 27th Euromicro International Conference on Parallel Distributed and Network Based Processing Pdp 2019, 2019
Cerebellar learning properties are modulated by the CRF receptor Gili Ezra-Nevo, Francesca Prestori, Francesca Locatelli, Teresa Soda, Michiel M. ten Brinke, Mareen Engel, Henk-Jan Boele, Laura Botta, Dena Leshkowitz, Assaf Ramot, Michael Tsoory, Inbal E. Biton, Jan Deussing, Egidio D'Angelo, Chris I. De Zeeuw, Alon Chen Journal of Neuroscience, 2018
The role of the cerebellum in multiple sclerosis—150 years after Charcot Katrin Parmar, Christine Stadelmann, Maria A. Rocca, Dawn Langdon, Egidio D'Angelo, Marcus D’Souza, Jessica Burggraaff, Christiane Wegner, Jaume Sastre-Garriga, Alonso Barrantes-Freer, Jonas Dorn, Bernard M.J. Uitdehaag, Xavier Montalban, Jens Wuerfel, Christian Enzinger, Alex Rovira, Mar Tintore, Massimo Filippi, Ludwig Kappos, Till Sprenger Neuroscience and Biobehavioral Reviews, 2018
Specific patterns of white matter alterations help distinguishing Alzheimer's and vascular dementia Fulvia Palesi, Andrea De Rinaldis, Paolo Vitali, Gloria Castellazzi, Letizia Casiraghi, Giancarlo Germani, Sara Bernini, Nicoletta Anzalone, Matteo Cotta Ramusino, Federica M. Denaro, Elena Sinforiani, Alfredo Costa, Giovanni Magenes, Egidio D'Angelo, Claudia A. M. Gandini Wheeler-Kingshott, Giuseppe Micieli Frontiers in Neuroscience, 2018
Modeling the cerebellar microcircuit: New strategies for a long-standing issue Egidio D’Angelo, Alberto Antonietti, Stefano Casali, Claudia Casellato, Jesus A. Garrido, Niceto Rafael Luque, Lisa Mapelli, Stefano Masoli, Alessandra Pedrocchi, Francesca Prestori, Martina Francesca Rizza, Eduardo Ros Frontiers in Cellular Neuroscience, 2016
Brain-inspired sensorimotor robotic platform learning in cerebellum-driven movement tasks through a cerebellar realistic model Ijcci 2013 Proceedings of the 5th International Joint Conference on Computational Intelligence, 2013
Complex dynamics in the granual layer of the cerebellum: Large-scale computational reconstructions 4th International Conference on Cognitive Systems Cogsys 2010, 2010
Action potential detection by non linear microscopy Leonardo Sacconi, Jacopo Lotti, Rodney P. O’Connor, Jonathan Mapelli, Daniela Gandolfi, Egidio D'Angelo, Francesco S. Pavone Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2009
Cognitive memory control in borderline personality disorder patients M. Sala, E. Caverzasi, E. Marraffini, G. De Vidovich, M. Lazzaretti, G. d'Allio, M. Isola, M. Balestrieri, E. D'Angelo, F. Zappoli Thyrion, P. Scagnelli, F. Barale, P. Brambilla Psychological Medicine, 2009
Calcium-dependent chloride transient currents in the immature oocyte of the frog, Rana esculenta Archives Italiennes De Biologie, 1989
RECENT SCHOLAR PUBLICATIONS
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Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics FJ Sheiban, A Antonietti, MF Beyazyüz, R De Schepper, ... Communications Biology , 2026 2026
The Cerebellar Engine: Multiscale Digital Brain Co-simulations Reveal How Cerebellar Spiking Architecture Shapes Cortical Coherence A Geminiani, JM Meier, D Perdikis, S Ouertani, C Casellato, P Ritter, ... bioRxiv, 2026.04. 02.715849 , 2026 2026
Virtual brain and electroencephalography explain the variance of memory alterations in mild cognitive impairment A Monteverdi, MC Ramusino, F Conca, A Augello, C Totaro, PA Grasso, ... 2026
Brain digital twins reveal network changes in congenital and slowly progressive cerebellar ataxias M Gaviraghi, A Monteverdi, S Bulgheroni, M Mercati, A De Laurentiis, ... bioRxiv, 2026.03. 23.713380 , 2026 2026
Automated derivation of mean field models from spiking neural networks for the simulation of brain dynamics RM Lorenzi, M De Grazia, CAM Gandini Wheeler Kingshott, F Palesi, ... bioRxiv, 2026.03. 18.712631 , 2026 2026
Multiscale brain modelling E D'Angelo Frontiers in Cellular Neuroscience 20, 1783885 , 2026 2026
Emotional network modelling: whole brain simulations of fear conditioning in humans DA Osorio-Becerra, A Fusari, A Roy, D Benozzo, A Frick, E D'Angelo, ... JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
A Multi-Scale Virtual Mouse Brain for Investigating Cerebellar-Related Ataxic Alterations M De Grazia, E Bergamo, D Rodarie, AA Vergani, E D'angelo, ... JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
A Multi-Compartment Computational Approach to Cerebellar Circuit Dysfunction in Autism D Benozzo, MF Rizza, D Di Domenico, G Pellavio, F Marchetti, ... JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
Reconstruction and simulation of the mouse cerebellar declive: an atlas-based approach D Rodarie, D Osorio, E D'Angelo, C Casellato JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
Neural compensation drives functional resilience in a cerebellar model of schizophrenia AA Vergani, P Faris, C Casellato, M De Grazia, EU D'Angelo JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
Basket cell computational modeling predicts signal filtering of Purkinje cell responses MF Rizza, S Masoli, T Soda, F Prestori, E D'Angelo JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
Climbing fiber impact on human and mice Purkinje cell spines S Masoli, E D'angelo JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
Synaptic Plasticity Mechanisms and Dynamics in the Cerebellar Spiking Microcircuit AH Butt, M De Grazia, E Buttarazzi, D Rodarie, C Casellato, DA Egidio JOURNAL OF COMPUTATIONAL NEUROSCIENCE 54 (SUPPL 1) , 2026 2026
The Sense of Smell (SoS) Atlas: Its Creation and First Application to Investigate COVID‐19 Related Anosmia With a Comprehensive Quantitative MRI Protocol M Gaviraghi, E Lupi, E Grosso, A Fusari, M Baiguera, A Monteverdi, ... Journal of Magnetic Resonance Imaging 63 (2), 574-593 , 2026 2026 Citations: 2
A biologically-grounded cerebellar spiking network model with realistic synaptic transmission captures complex circuit dynamics. M De Grazia, D Benozzo, D Rodarie, F Marchetti, E D'Angelo, C Casellato bioRxiv, 2026.05. 12.724100 , 2026 2026
Virtual brain twins guide personalized treatment decision in schizophrenia G Preti, H Wang, A Ziaeemehr, M Woodman, P Prodan, P Triebkorn, ... medRxiv, 2026.05. 06.26352533 , 2026 2026
Cerebellar Cortical Circuitry in Schizophrenia: A Cellular Framework for Predictive Dysfunction and Psychosis P Faris, AA Vergani, IH Ortiz, S Masoli, P Fusar-Poli, E D’Angelo Preprint at https://doi. org/10.5281/zenodo 19337133 , 2026 2026 Citations: 1
Adaptive Cerebellar Networks in Sensorimotor Loops E Buttarazzi, M De Grazia, M Premi, E D'Angelo, A Antonietti, C Casellato Journal of Computational Neuroscience 54 (SUPPL1) , 2026 2026
MOST CITED SCHOLAR PUBLICATIONS
Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum C Hansel, DJ Linden, E D'Angelo Nature neuroscience 4 (5), 467-475 , 2001 2001 Citations: 771
Seeking a unified framework for cerebellar function and dysfunction: from circuit operations to cognition E D'Angelo, S Casali Frontiers in neural circuits 6, 116 , 2013 2013 Citations: 582
Timing and plasticity in the cerebellum: focus on the granular layer E D’Angelo, CI De Zeeuw Trends in neurosciences 32 (1), 30-40 , 2009 2009 Citations: 394
Theta-frequency bursting and resonance in cerebellar granule cells: experimental evidence and modeling of a slow k+-dependent mechanism E D'Angelo, T Nieus, A Maffei, S Armano, P Rossi, V Taglietti, A Fontana, ... Journal of Neuroscience 21 (3), 759-770 , 2001 2001 Citations: 346
Fibroblast growth factor homologous factors control neuronal excitability through modulation of voltage-gated sodium channels M Goldfarb, J Schoorlemmer, A Williams, S Diwakar, Q Wang, X Huang, ... Neuron 55 (3), 449-463 , 2007 2007 Citations: 334
Long-term potentiation of intrinsic excitability at the mossy fiber–granule cell synapse of rat cerebellum S Armano, P Rossi, V Taglietti, E D'Angelo Journal of Neuroscience 20 (14), 5208-5216 , 2000 2000 Citations: 316
Synaptic excitation of individual rat cerebellar granule cells in situ: evidence for the role of NMDA receptors. E D'Angelo, G De Filippi, P Rossi, V Taglietti The Journal of physiology 484 (2), 397-413 , 1995 1995 Citations: 305
Contralateral cortico-ponto-cerebellar pathways reconstruction in humans in vivo : implications for reciprocal cerebro-cerebellar structural connectivity in motor and … F Palesi, A De Rinaldis, G Castellazzi, F Calamante, N Muhlert, D Chard, ... Scientific reports 7 (1), 12841 , 2017 2017 Citations: 296
Contralateral cerebello-thalamo-cortical pathways with prominent involvement of associative areas in humans in vivo F Palesi, JD Tournier, F Calamante, N Muhlert, G Castellazzi, D Chard, ... Brain Structure and Function 220 (6), 3369-3384 , 2015 2015 Citations: 263
Physiology of the cerebellum E D'Angelo Handbook of clinical neurology 154, 85-108 , 2018 2018 Citations: 261
Evidence for NMDA and mGlu receptor-dependent long-term potentiation of mossy fiber–granule cell transmission in rat cerebellum E D'Angelo, P Rossi, S Armano, V Taglietti Journal of neurophysiology 81 (1), 277-287 , 1999 1999 Citations: 242
Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? KG Schilling, F Rheault, L Petit, CB Hansen, V Nath, FC Yeh, G Girard, ... NeuroImage 243, 118502 , 2021 2021 Citations: 218
Silencing the majority of cerebellar granule cells uncovers their essential role in motor learning and consolidation E Galliano, Z Gao, M Schonewille, B Todorov, E Simons, AS Pop, ... Cell reports 3 (4), 1239-1251 , 2013 2013 Citations: 200
The spatial organization of long-term synaptic plasticity at the input stage of cerebellum J Mapelli, E D'Angelo Journal of Neuroscience 27 (6), 1285-1296 , 2007 2007 Citations: 200
A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties S Solinas, T Nieus, E D‘Angelo Frontiers in cellular neuroscience 4, 903 , 2010 2010 Citations: 194
The quest for multiscale brain modeling E D’Angelo, V Jirsa Trends in neurosciences 45 (10), 777-790 , 2022 2022 Citations: 192
LTP regulates burst initiation and frequency at mossy fiber–granule cell synapses of rat cerebellum: experimental observations and theoretical predictions T Nieus, E Sola, J Mapelli, E Saftenku, P Rossi, E D'Angelo Journal of neurophysiology 95 (2), 686-699 , 2006 2006 Citations: 191
Stim and Orai proteins in neuronal Ca 2+ signaling and excitability F Moccia, E Zuccolo, T Soda, F Tanzi, G Guerra, L Mapelli, F Lodola, ... Frontiers in cellular neuroscience 9, 153 , 2015 2015 Citations: 187
Increased neurotransmitter release during long‐term potentiation at mossy fibre–granule cell synapses in rat cerebellum E Sola, F Prestori, P Rossi, V Taglietti, E D'Angelo The Journal of physiology 557 (3), 843-861 , 2004 2004 Citations: 176
Ionic mechanisms of autorhythmic firing in rat cerebellar Golgi cells LF Elisabetta Cesana, J Mapelli, E D'Angelo The Journal of physiology 574 (3), 711-729 , 2006 2006 Citations: 169