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Activity-Dependent Synapse Refinement: From Mechanisms to Molecules.
Nagappan-Chettiar, Sivapratha; Burbridge, Timothy J; Umemori, Hisashi.
Afiliação
  • Nagappan-Chettiar S; Department of Neurology, F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Burbridge TJ; Department of Neurology, F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Umemori H; Department of Neurology, F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Neuroscientist ; : 10738584231170167, 2023 May 04.
Article em En | MEDLINE | ID: mdl-37140155
The refinement of immature neuronal networks into efficient mature ones is critical to nervous system development and function. This process of synapse refinement is driven by the neuronal activity-dependent competition of converging synaptic inputs, resulting in the elimination of weak inputs and the stabilization of strong ones. Neuronal activity, whether in the form of spontaneous activity or experience-evoked activity, is known to drive synapse refinement in numerous brain regions. More recent studies are now revealing the manner and mechanisms by which neuronal activity is detected and converted into molecular signals that appropriately regulate the elimination of weaker synapses and stabilization of stronger ones. Here, we highlight how spontaneous activity and evoked activity instruct neuronal activity-dependent competition during synapse refinement. We then focus on how neuronal activity is transformed into the molecular cues that determine and execute synapse refinement. A comprehensive understanding of the mechanisms underlying synapse refinement can lead to novel therapeutic strategies in neuropsychiatric diseases characterized by aberrant synaptic function.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article