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Experience-dependent development of dendritic arbors in mouse visual cortex

Richards, SEV and Moore, AR and Nam, AY and Saxena, S and Paradis, S and van Hooser, SD (2020) Experience-dependent development of dendritic arbors in mouse visual cortex. In: Journal of Neuroscience, 40 (34). pp. 6536-6556.

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Official URL: https://dx.doi.org/10.1523/JNEUROSCI.2910-19.2020


The dendritic arbor of neurons constrains the pool of available synaptic partners and influences the electrical integration of synaptic currents. Despite these critical functions, our knowledge of the dendritic structure of cortical neurons during early postnatal development and how these dendritic structures are modified by visual experience is incomplete. Here, we present a large-scale dataset of 849 3D reconstructions of the basal arbor of pyramidal neurons collected across early postnatal development in visual cortex of mice of either sex. We found that the basal arbor grew substantially between postnatal day 7 (P7) and P30, undergoing a 45 increase in total length. However, the gross number of primary neurites and dendritic segments was largely determined by P7. Growth from P7 to P30 occurred primarily through extension of dendritic segments. Surprisingly, comparisons of dark-reared and typically reared mice revealed that a net gain of only 15 arbor length could be attributed to visual experience; most growth was independent of experience. To examine molecular contributions, we characterized the role of the activity-regulated small GTPase Rem2 in both arbor development and the maintenance of established basal arbors. We showed that Rem2 is an experience-dependent negative regulator of dendritic segment number during the visual critical period. Acute deletion of Rem2 reduced directionality of dendritic arbors. The data presented here establish a highly detailed, quantitative analysis of basal arbor development that we believe has high utility both in understanding circuit development as well as providing a framework for computationalists wishing to generate anatomically accurate neuronal models. Copyright © 2020 the authors

Item Type: Journal Article
Publication: Journal of Neuroscience
Publisher: Society for Neuroscience
Additional Information: The copyright of this article belongs to Society for Neuroscience
Department/Centre: Division of Biological Sciences > Biochemistry
Date Deposited: 11 Sep 2020 10:50
Last Modified: 11 Sep 2020 10:50
URI: http://eprints.iisc.ac.in/id/eprint/66477

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