Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder

Trimarco, Emiliano; Mirino, Pierandrea; Caligiore, Daniele

doi:10.3389/fnsys.2021.666649

Cited by 5 publications

(3 citation statements)

References 100 publications

(156 reference statements)

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“…For example, simulations predicted that alterations of synaptic plasticity in the cerebellar cortex could impair EBCC in different forms of cerebellar ataxia and that synaptic plasticity in the deep cerebellar nuclei was a possible compensatory mechanism ( Geminiani et al, 2018a ). Moreover, in autism spectrum disorder, simulations suggested that a higher CR learning rate could reflect a reduced number of Purkinje cells ( Trimarco et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model

Geminiani

Mockevičius

D’Angelo

et al. 2022

Front. Syst. Neurosci.

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Dystonia is a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive movements, postures, or both. Although dystonia is traditionally associated with basal ganglia dysfunction, recent evidence has been pointing to a role of the cerebellum, a brain area involved in motor control and learning. Cerebellar abnormalities have been correlated with dystonia but their potential causative role remains elusive. Here, we simulated the cerebellar input-output relationship with high-resolution computational modeling. We used a data-driven cerebellar Spiking Neural Network and simulated a cerebellum-driven associative learning task, Eye-Blink Classical Conditioning (EBCC), which is characteristically altered in relation to cerebellar lesions in several pathologies. In control simulations, input stimuli entrained characteristic network dynamics and induced synaptic plasticity along task repetitions, causing a progressive spike suppression in Purkinje cells with consequent facilitation of deep cerebellar nuclei cells. These neuronal processes caused a progressive acquisition of eyelid Conditioned Responses (CRs). Then, we modified structural or functional local neural features in the network reproducing alterations reported in dystonic mice. Either reduced olivocerebellar input or aberrant Purkinje cell burst-firing resulted in abnormal learning curves imitating the dysfunctional EBCC motor responses (in terms of CR amount and timing) of dystonic mice. These behavioral deficits might be due to altered temporal processing of sensorimotor information and uncoordinated control of muscle contractions. Conversely, an imbalance of excitatory and inhibitory synaptic densities on Purkinje cells did not reflect into significant EBCC deficit. The present work suggests that only certain types of alterations, including reduced olivocerebellar input and aberrant PC burst-firing, are compatible with the EBCC changes observed in dystonia, indicating that some cerebellar lesions can have a causative role in the pathogenesis of symptoms.

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Section: Introductionmentioning

confidence: 99%

Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model

Geminiani

Mockevičius

D’Angelo

et al. 2022

Front. Syst. Neurosci.

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“…The current model represents a first important step to study the possible integration of reinforcement and supervised learning processes in the brain. Future versions of the model could increase the realism and the quantitative matching of data of the model by including features that reproduce the interaction between cortical-subcortical learning mechanisms [70,71], and an architecture more closely reproducing the brain hierarchy underlying the acquisition and expression of motor movements with possibly a more explicit representation of targets [72][73][74].…”

Section: Discussionmentioning

confidence: 99%

Interactions between supervised and reinforcement learning processes in a neurorobotic model

Capirchio

Ponte

Baldassarre

et al. 2022

Preprint

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Several influential works propose that the acquisition of motor behavior involves different learning mechanisms in the brain, in particular supervised and reinforcement learning, that are respectively associated with cerebellar-thalamocortical and basal ganglia-thalamocortical networks. Despite increasing evidence suggesting anatomical and functional interactions between these circuits, the learning processes operating within them are studied in isolation, neglecting their strong interdependence. This article proposes a bio-inspired neurorobotic model implementing a possible cooperation mechanism between supervised and reinforcement learning. The model, validated with empirical data from healthy participants and patients with cerebellar ataxia, shows how the integration of the two learning processes could lead to benefit both learning performance and movement accuracy.

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“…These cerebellar neurons send inhibitory projections to the deep cerebellar nuclei (the output nuclei of the cerebellum) and the posterior lobe of the Vermis. Loss of these neurons is thought to lead to disinhibition of the deep cerebellar nuclei and of the uvula/nodulus lobules (Cerliani et al, 2015; Belmonte et al, 2004), which could explain the observed increased cerebellar integration and consequent abnormal eyes movement control in ASD patients (Trimarco et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Mesoscopic patterns of functional connectivity alterations in autism by contrast subgraphs

Lanciano¹,

Petri²,

Gili

et al. 2022

Preprint

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Despite the breakthrough achievements in understanding structural and functional connectivity alterations that underlie autism spectrum disorder (ASD), the exact nature and type of such alterations are not yet clear due to conflicting reports of hyper-connectivity, hypo-connectivity, and --in some cases-- combinations of both. In this work, we approach the debate about hyper- vs hypo-connectivity in ASD using a novel network comparison technique designed to capture mesoscopic-scale differential structures. In particular, we build on recent algorithmic advances in the sparsification of functional connectivity matrices, in the extraction of contrast subgraphs, and in the computation of statistically significant maximal frequent itemsets, and develop a method to identify mesoscale structural subgraphs that are maximally dense and different in terms of connectivity levels between the different sets of networks. We apply our method to analyse brain networks of typically developed individuals and ASD patients across different developmental phases and find a set of altered cortical-subcortical circuits between healthy subjects and patients affected by ASD. Specifically, our analysis highlights in ASD patients a significantly larger number of functional connections among regions of the occipital cortex and between the left precuneus and the superior parietal gyrus. At the same time, reduced connectivity characterised the superior frontal gyrus and the temporal lobe regions. More importantly, we can simultaneously detect regions of the brain that show hyper and hypo-connectivity in ASD in children and adolescents, recapitulating within a single framework multiple previous separate observations.

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Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder

Cited by 5 publications

References 100 publications

Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model

Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model

Interactions between supervised and reinforcement learning processes in a neurorobotic model

Mesoscopic patterns of functional connectivity alterations in autism by contrast subgraphs

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