Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.

Autor:	Schneider-Mizell CM; Allen Institute for Brain Science, Seattle, WA., Bodor AL; Allen Institute for Brain Science, Seattle, WA., Brittain D; Allen Institute for Brain Science, Seattle, WA., Buchanan J; Allen Institute for Brain Science, Seattle, WA., Bumbarger DJ; Allen Institute for Brain Science, Seattle, WA., Elabbady L; Allen Institute for Brain Science, Seattle, WA., Gamlin C; Allen Institute for Brain Science, Seattle, WA., Kapner D; Allen Institute for Brain Science, Seattle, WA., Kinn S; Allen Institute for Brain Science, Seattle, WA., Mahalingam G; Allen Institute for Brain Science, Seattle, WA., Seshamani S; Allen Institute for Brain Science, Seattle, WA., Suckow S; Allen Institute for Brain Science, Seattle, WA., Takeno M; Allen Institute for Brain Science, Seattle, WA., Torres R; Allen Institute for Brain Science, Seattle, WA., Yin W; Allen Institute for Brain Science, Seattle, WA., Dorkenwald S; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Bae JA; Princeton Neuroscience Institute, Princeton University, NJ.; Electrical and Computer Engineering Department, Princeton University., Castro MA; Princeton Neuroscience Institute, Princeton University, NJ., Halageri A; Princeton Neuroscience Institute, Princeton University, NJ., Jia Z; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Jordan C; Princeton Neuroscience Institute, Princeton University, NJ., Kemnitz N; Princeton Neuroscience Institute, Princeton University, NJ., Lee K; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology., Li K; Computer Science Department, Princeton University., Lu R; Princeton Neuroscience Institute, Princeton University, NJ., Macrina T; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Mitchell E; Princeton Neuroscience Institute, Princeton University, NJ., Mondal SS; Princeton Neuroscience Institute, Princeton University, NJ.; Electrical and Computer Engineering Department, Princeton University., Mu S; Princeton Neuroscience Institute, Princeton University, NJ., Nehoran B; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Popovych S; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Silversmith W; Princeton Neuroscience Institute, Princeton University, NJ., Turner NL; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Wong W; Princeton Neuroscience Institute, Princeton University, NJ., Wu J; Princeton Neuroscience Institute, Princeton University, NJ., Reimer J; Department of Neuroscience, Baylor College of Medicine, Houston, TX.; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine., Tolias AS; Department of Neuroscience, Baylor College of Medicine, Houston, TX.; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine.; Department of Electrical and Computer Engineering, Rice University., Seung HS; Princeton Neuroscience Institute, Princeton University, NJ.; Computer Science Department, Princeton University., Reid RC; Allen Institute for Brain Science, Seattle, WA., Collman F; Allen Institute for Brain Science, Seattle, WA., Maçarico da Costa N; Allen Institute for Brain Science, Seattle, WA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jan 06. Date of Electronic Publication: 2024 Jan 06.
DOI:	10.1101/2023.01.23.525290
Abstrakt:	Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy 1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu