Neuroscience
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Parkinson's disease (PD) is characterized by tremor, rigidity, and bradykinesia. PD is caused mainly by depletion of the nigrostriatal pathway. Conventional medications such as levodopa are highly effective in the early stage of PD; however, these medications fail to prevent the underlying neurodegeneration. ⋯ For example, although fetal ventral midbrain is efficacious in some patients, its ethical issues and the existence of graft-induced dyskinesias (GID) have prevented its use in large-scale clinical applications. ESCs have reliable isolation protocols and the potential to differentiate into dopaminergic progenitors. iPSCs and induced neural cells are suitable for autologous grafting. Here we review milestone improvements and emerging sources for cell-based PD therapy to serve as a framework for clinicians and a key reference to develop replacement therapy for other neurological disorders.
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The cerebellum forms regular neural network structures consisting of a few major types of neurons, such as Purkinje cells, granule cells, and molecular layer interneurons, and receives two major inputs from climbing fibers and mossy fibers. Its regular structures consist of three well-defined layers, with each type of neuron designated to a specific location and forming specific synaptic connections. During the first few weeks of postnatal development in rodents, the cerebellum goes through dynamic changes via proliferation, migration, differentiation, synaptogenesis, and maturation, to create such a network structure. ⋯ Therefore, it is reasonable that extracellular signaling via synaptic transmission, secreted molecules, and cell adhesion molecules, plays important roles in cerebellar network development. Although it is not yet clear as to how overall cerebellar development is orchestrated, there is indeed accumulating lines of evidence that extracellular signaling acts toward the development of individual elements in the cerebellar networks. In this article, we introduce what we have learned from many studies regarding the extracellular signaling required for cerebellar network development, including our recent study suggesting the importance of unbiased synaptic inputs from parallel fibers.
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Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. ⋯ In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.
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The cerebellum has long been conceptualized to control motor learning and motor coordination. However, increasing evidence suggests its roles in cognition and emotion behaviors. ⋯ To better understand the contribution of the cerebellum in ASD pathogenesis, we here discuss recent behavioral, genetic, and molecular studies from the human and mouse models. In addition, we raise several questions that need to be investigated in future studies from the point view of cerebellar dysfunction, cerebro-cerebellar connectivity and ASD.
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The cerebellum is thought to have a variety of functions because it developed with the evolution of the cerebrum and connects with different areas in the frontoparietal cortices. Like neurons in the cerebral cortex, those in the cerebellum also exhibit strong activity during planning in addition to the execution of movements. However, their specific roles remain elusive. ⋯ During a recently developed synchronized eye movement task, cerebellar nuclear neurons exhibited periodic preparatory activity for predictive synchronization. In all cases, the cerebellum generated preparatory activity lasting for several hundred milliseconds. These signals may regulate neuronal activity in the cerebral cortex that adjusts movement timing and predicts the timing of rhythmic events.