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      Brainstem neurons that command mammalian locomotor asymmetries

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          Abstract

          Descending command neurons instruct spinal networks to execute basic locomotor functions, such as which gait and what speed. The command functions for gait and speed are symmetric, implying that a separate unknown system directs asymmetric movements—including the ability to move left or right. Here we report the discovery that Chx10-lineage reticulospinal neurons act to control the direction of locomotor movements in mammals. Chx10 neurons exhibit mainly ipsilateral projection, and their selective unilateral activation causes ipsilateral turning movements in freely moving mice. Unilateral inhibition of Chx10 neurons causes contralateral turning movements. Paired left/right motor recordings identified distinct mechanisms for directional movements mediated via limb and axial spinal circuits. Finally, we identify sensorimotor brain regions that project onto Chx10 reticulospinal neurons, and demonstrate that their unilateral activation can impart left/right directional commands. Together these data identify the descending motor system that commands left/right locomotor asymmetries in mammals.

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          Most cited references61

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            A mesoscale connectome of the mouse brain.

            Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
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              Genome-wide atlas of gene expression in the adult mouse brain.

              Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.
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                Author and article information

                Journal
                9809671
                Nat Neurosci
                Nat Neurosci
                Nature neuroscience
                1097-6256
                1546-1726
                04 March 2021
                01 June 2020
                11 May 2020
                01 April 2021
                : 23
                : 6
                : 730-740
                Affiliations
                [1 ]Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
                [2 ]The McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
                [3 ]Department of Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
                Author notes
                [* ]Corresponding author: Ole.Kiehn@ 123456sund.ku.dk
                Article
                EMS118466
                10.1038/s41593-020-0633-7
                7610510
                32393896
                b42bf154-fe9e-4eb5-b84b-c64668ba30d6

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