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      A cerebellar-thalamocortical pathway drives behavioral context-dependent movement initiation

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          Summary

          Executing learned motor behaviors often requires the transformation of sensory cues into patterns of motor commands that generate appropriately timed actions. The cerebellum and thalamus are two key areas involved in shaping cortical output and movement, but the contribution of a cerebellar-thalamocortical pathway to voluntary movement initiation remains poorly understood. Here, we investigated how an auditory “go cue” transforms thalamocortical activity patterns and how these changes relate to movement initiation. Population responses in dentate/interpositus-recipient regions of motor thalamus reflect a time-locked increase in activity immediately prior to movement initiation that is temporally uncoupled from the go cue, indicative of a fixed-latency feedforward motor timing signal. Blocking cerebellar or motor thalamic output suppresses movement initiation, while stimulation triggers movements in a behavioral context-dependent manner. Our findings show how cerebellar output, via the thalamus, shapes cortical activity patterns necessary for learned context-dependent movement initiation.

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          Highlights

          • DN/IPN thalamocortical activity conveys a reliable feedforward motor timing signal

          • Silencing DN/IPN or recipient regions of thalamus blocks movement initiation

          • Photostimulation of the DN/IPN thalamocortical pathway triggers movement

          • Thalamocortical activation drives behavioral context-dependent movement initiation

          Abstract

          Dacre et al. show the contribution of a cerebellar-thalamocortical pathway to movement initiation. Using gain- and loss-of-function manipulations they demonstrate that output from dentate/interpositus nuclei, via the thalamus, shapes cortical activity dynamics necessary for learned behavioral context-dependent movement initiation.

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          Most cited references121

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Ultra-sensitive fluorescent proteins for imaging neuronal activity

            Summary Fluorescent calcium sensors are widely used to image neural activity. Using structure-based mutagenesis and neuron-based screening, we developed a family of ultra-sensitive protein calcium sensors (GCaMP6) that outperformed other sensors in cultured neurons and in zebrafish, flies, and mice in vivo. In layer 2/3 pyramidal neurons of the mouse visual cortex, GCaMP6 reliably detected single action potentials in neuronal somata and orientation-tuned synaptic calcium transients in individual dendritic spines. The orientation tuning of structurally persistent spines was largely stable over timescales of weeks. Orientation tuning averaged across spine populations predicted the tuning of their parent cell. Although the somata of GABAergic neurons showed little orientation tuning, their dendrites included highly tuned dendritic segments (5 - 40 micrometers long). GCaMP6 sensors thus provide new windows into the organization and dynamics of neural circuits over multiple spatial and temporal scales.
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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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                Author and article information

                Contributors
                Journal
                Neuron
                Neuron
                Neuron
                Cell Press
                0896-6273
                1097-4199
                21 July 2021
                21 July 2021
                : 109
                : 14
                : 2326-2338.e8
                Affiliations
                [1 ]Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
                [2 ]Simons Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
                [3 ]Janelia Research Campus, HHMI, Ashburn, VA, USA
                Author notes
                []Corresponding author ian.duguid@ 123456ed.ac.uk
                [4]

                These authors contributed equally

                [5]

                Present address: Center for Integrative Physiology and Molecular Medicine, Saarland University, Homburg, Germany

                [6]

                Present address: UCL Sainsbury Wellcome Centre for Neural Circuits and Behaviour, London, UK

                [7]

                Present address: Brain and Behaviour Lab, Department of Bioengineering, Royal School of Mines, Imperial College London, UK

                [8]

                Present address: Center for Behavioral Brain Sciences, Institute for Cognitive Neurology and Dementia Research, Otto von Guericke University, Magdeburg, Germany

                [9]

                Present address: Eli Lilly and Company, Lilly Cambridge Innovation Center, Cambridge, MA, USA

                [10]

                Lead contact

                Article
                S0896-6273(21)00356-1
                10.1016/j.neuron.2021.05.016
                8315304
                34146469
                5511ed51-2cd4-4810-8ae2-50eab14096f0
                © 2021 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 23 September 2019
                : 7 April 2021
                : 11 May 2021
                Categories
                Article

                Neurosciences
                motor cortex,movement initiation,motor thalamus,cerebellar thalamocortical pathway,behavioural context,2-photon imaging,in vivo patch-clamp,photoactivation,cerebellar nuclei,motor timing

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