Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.
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http://dx.doi.org/10.1371/journal.pcbi.1011008 | DOI Listing |
Maternal stress during pregnancy, or prenatal stress, is a risk factor for neurodevelopmental disorders in offspring, including autism spectrum disorder (ASD). In ASD, dorsal striatum displays abnormalities correlating with symptom severity, but there is a gap in knowledge about dorsal striatal cellular and molecular mechanisms that may contribute. Using a mouse model, we investigated how prenatal stress impacted striatal-dependent behavior in adult offspring.
View Article and Find Full Text PDFThe degeneration of midbrain dopamine (DA) neurons disrupts the neural control of natural behavior, such as walking, posture, and gait in Parkinson's disease. While some aspects of motor symptoms can be managed by dopamine replacement therapies, others respond poorly. Recent advancements in machine learning-based technologies offer opportunities for unbiased segmentation and quantification of natural behavior in both healthy and diseased states.
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January 2025
Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Electronic address:
C1q/TNF-related protein 14 (CTRP14), also known as C1q-like 1 (C1QL1), is a synaptic protein predominantly expressed in the brain. It plays a critical role in the formation and maintenance of the climbing fiber-Purkinje cell synapses, ensuring that only one single winning climbing fiber from the inferior olivary neuron synapses with the proximal dendrites of Purkinje cells during the early postnatal period. Loss of CTRP14/C1QL1 results in incomplete elimination of supernumerary climbing fibers, leading to multiple persistent climbing fibers synapsing with the Purkinje cells.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
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