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.

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.

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.

In this paper, we propose a biologically plausible computational working memory (WM) model implemented using a spiking neuron model representing a predictable WM mechanism in a single neuron. Empirical evidence from single neuron animal brain recordings has shown that WM is processed in a neuron model by encoding associations and exhibiting persistent activity. The model implemented using an adaptive exponential integrate and fire neuron model, was able to replicate the dynamics observed in WM tasks, such as the Delayed Match to Sample (DMS) paradigm. The input patterns were encoded as numbers, representing the spike train patterns in the neurons, and the frequencies of transient discharges of corresponding neurons were the outputs. By simulating this task, the model demonstrated how cognitive processes such as encoding, maintaining, and retrieving information during the delay period could be performed by single neurons. The model was examined by modifying parameters including the duration of delay, number of inputs, and retrieval probe count attributed to cognitive load. Through this soft computing-based approach, our simulations allow us to elaborate equivalents in emergent dynamics, including persistent neuronal activity during the delay period.

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.

Cerebellar Purkinje cells are one of the most complex neurons in the central nervous system and are well known for their extensive dendritic tree dotted by dendritic spines. PC spines receive excitatory synapses from parallel and climbing fibers and, although their morphological properties are comparable to those of other neuronal types, they show distinct extracellular and intracellular regulatory properties. Purkinje cell spine protrusion and helical patterning do not require nearby axons, as e.g., in pyramidal cells. Instead, Purkinje cell spines require structural proteins located on parallel and climbing fibers for their stabilisation and maintenance. The total spine number is influenced by scaffold proteins and eventually reflects the total dendritic length and local spine density. Purkinje cell spines were supposed to range up to over 105 in rodents and 106 in humans, but recent experimental data show that spines are less numerous than initially thought. Instead, they are endowed with mechanisms designed to improve their efficiency and differentiation. Some spines are double-headed, thereby enhancing Purkinje cell responses when the companion parallel fiber is stimulated. Other spines are single-headed and presumably endowed with slow neurotransmission mechanisms. Latest experimental data showed that glial cells modulate spines activity after a task or learning. Eventually, these multiple mechanisms can make each spine crucial in its own way for synaptic pattern recognition. In this review, we present the most recent advancements on Purkinje cell spines spanning their biochemical, structural, and functional properties, both in mice and humans, and propose a recalculation of the effective complement of spines and their activation by parallel fibers.

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.

BACKGROUND:The 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. PURPOSE:To 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 TYPE:Retrospective. SUBJECTS:For 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/SEQUENCE:3 T, 3D inversion recovery, 3D fast field echo, and spin-echo echo-planar imaging sequences. ASSESSMENT:To 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 TESTS:Region-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. RESULTS:The 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 CONCLUSION:A 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 LEVEL:3. TECHNICAL EFFICACY:Stage 1.

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.

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.

The neural basis of emotional experience, both in neurotypical and clinical conditions, remains an open research topic. Historically, the cerebellum was considered a purely motor structure; however, studies since the mid-twentieth century and contributions like the cerebellar cognitive-affective syndrome, evidenced its role in emotion. This has led to an expansion of the paradigm, encouraging further research into the cerebellar role in emotion. Understanding this field's development is essential to assessing its current state, identifying knowledge gaps, and exploring emerging areas. This paper analyzes the evolution of scientific production, addressing how scientific interest has changed over time, factors driving growth, dominant topics, leading figures, and collaboration networks. This analysis identifies trends and opportunities, guiding strategies and advancing knowledge through a comprehensive view of the state-of-the-art in this research area. To achieve this, a systematic search was conducted in key databases, identifying 1,162 publications with which an exhaustive quantitative analysis was conducted using bibliometric techniques, network analysis, and visualization tools. The results show exponential growth in the field, evidenced by the increase in publications, researchers, funding sources, and the emergence of new topics. This interest, along with an interdisciplinary approach, has fostered collaboration, with large teams and multicenter projects emerging, although small, isolated teams still predominate. Research mainly focuses on neurological and affective disorders, with a predominance of studies in humans, followed by rodent models. Overall, the analysis reveals a highly interdisciplinary and expanding field. However, challenges remain, including unequal access to resources and limited exploration of some topics.

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.

Purkinje cells in the cerebellum are among the largest neurons in the brain and have been extensively investigated in rodents. However, their morphological and physiological properties remain poorly understood in humans. In this study, we utilized high-resolution morphological reconstructions and unique electrophysiological recordings of human Purkinje cells ex vivo to generate computational models and estimate computational capacity. An inter-species comparison showed that human Purkinje cell had similar fractal structures but were larger than those of mouse Purkinje cells. Consequently, given a similar spine density (2/μm), human Purkinje cell hosted approximately 7.5 times more dendritic spines than those of mice. Moreover, human Purkinje cells had a higher dendritic complexity than mouse Purkinje cells and usually emitted 2–3 main dendritic trunks instead of one. Intrinsic electro-responsiveness was similar between the two species, but model simulations revealed that the dendrites could process ~6.5 times (n = 51 vs. n = 8) more input patterns in human Purkinje cells than in mouse Purkinje cells. Thus, while human Purkinje cells maintained spike discharge properties similar to those of rodents during evolution, they developed more complex dendrites, enhancing computational capacity.

Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, 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 parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms.