Cellular Anatomy of the Mouse Primary Motor Cortex

Muñoz-Castañeda, Rodrigo; Zingg, Brian; Matho, Katherine; Wang, Quanxin; Chen, Xiaoyin; Foster, Nicholas N.; Narasimhan, Arun; Li, Anan; Hirokawa, Karla E.; Huo, Bing-Xing; Bannerjee, Samik; Korobkova, Laura; Park, Chris Sin; Park, Young-Gyun; Bienkowski, Michael S.; Chon, Uree; Wheeler, Diek W.; Li, Xiangning; Wang, Yun; Kelly, Kathleen; An, Xu; Attili, Sarojini M.; Bowman, Ian; Bludova, Anastasiia; Çetin, Ali; Ding, Liya; Drewes, Rhonda; D’Orazi, Florence D.; Elowsky, Corey; Fischer, Stephan; Galbavy, William; Gao, Lei; Gillis, Jesse; Groblewski, Peter A.; Gou, Lin; Hahn, Joel D.; Hatfield, Joshua; Hintiryan, Houri; Huang, Jason H.; Kondo, Hideki; Kuang, Xiuli; Lesnar, Philip; Li, Xu; Li, Yaoyao; Lin, Meng-Kuan; Liu, Lijuan; Lo, D.C.W.; Mizrachi, Judith; Mok, Stéphanie; Naeemi, Maitham; Nicovich, Philip R.; Palaniswamy, Ramesh; Palmer, Jason; Qi, Xiaoli; Shen, Elise; Sun, Yuchi; Tao, Huizhong W.; Wakemen, Wayne; Wang, Yimin; Xie, Peng; Yao, Shenqin; Yuan, Jin; Zhu, Muye; Ng, Lydia; Zhang, Li I.; Lim, Byung Kook; Hawrylycz, Michael; Gong, Hui; Gee, James C.; Kim, Yongsoo; Peng, Hanchuan; Chuang, Kwanghun; Yang, Xia; Luo, Qi; Mitra, Partha P.; Zador, Anthony M.; Zeng, Hongkui; Ascoli, Giorgio A.; Huang, Zirui; Osten, Pavel; Harris, Julie A.; Dong, Hong‐Wei

doi:10.1101/2020.10.02.323154

Cited by 26 publications

(67 citation statements)

References 104 publications

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“…1f, ANOVA, F 1,10 = 8.38, p = 0.016). Finally, optogenetic activation of combined layer 5 MCtx FL output pathways (using the Rbp4-cre mouse line 5 ) was sufficient to invigorate movements (Extended Data Fig. 1).…”

Section: Resultsmentioning

confidence: 98%

“…Here we addressed the role of two major projection neuron subtypes in the motor cortex using a task that demands rescaling of movement amplitude. Although we attempted to produce a penetrant and robust inactivation of a broad class (Sim1+ 5,53,57 ) of PT neurons, it is nearly impossible to rule out a small subset that is critically important, but failed to be targeted. We did observe a modest, but significant change in trajectory during PT inactivation that was dissociated from a change in the scaling of movement amplitude or speed.…”

Section: Inactivation Of Pt and It Neurons Oppositely Affect Striatalmentioning

confidence: 99%

“…Cortical control of movement in vertebrates stems from two major classes of output pathways that arise from pyramidal and intratelencephalic tract neurons 1 . Individual pyramidal tract (PT) neurons project directly to brainstem and spinal cord, along with many other descending systems [2][3][4][5] . The other major class of deep motor cortical output projection are the intratelencephalic (IT) or corticostriatal projection neurons of layer 5 and layer 2/3 2,[5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

“…As a means of cell-type specific perturbation, we used expression of a potent optogenetic inhibitor (FLInChR 54 ) to inactivate IT or PT MCtx FL populations. Analogous to the labelling strategy for expression calcium indicators, we used a viral strategy with two mouse lines that label as broad a class as possible5,53,57 layer 5 IT (Tlx-cre) and PT (Sim1-cre) neurons (Extended dataFig. 2).…”

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confidence: 99%

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Descending neocortical output critical for skilled forelimb movements is distributed across projection cell classes

Park

Phillips

Martin

et al. 2021

Preprint

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Motor cortex is a key node in the forebrain circuits that enable flexible control of limb movements. The influence of motor cortex on movement execution is primarily carried by either pyramidal tract neurons (PT), which project directly to the brainstem and spinal cord, or intratelencephalic neurons (IT), which project within the forebrain. The logic of the interplay between these cell types and their relative contribution to the coordination and scaling of forelimb movements remains unclear. Here we combine large-scale neural recordings across all layers of motor cortex with cell-type specific perturbations in a cortex-dependent mouse behavior: kinematically-variable manipulation of a joystick. Our data demonstrate that descending neocortical motor commands are distributed across projection cell classes. IT neuron activity, in comparison to PT neurons, carries a larger fraction of information about gross movement kinematics. We find that forelimb movements are robust to optogenetic silencing of PT output, but substantially impaired by silencing Layer 5a IT projection neurons. Dorsal striatum is the unique extracortical integration point for IT and PT output pathways and its activity was more dependent upon IT input than PT input during movement execution. These data indicate that forebrain extrapyramidal pathways can be critical for regulating kinematics of motor skills.

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Section: Resultsmentioning

confidence: 98%

Section: Inactivation Of Pt and It Neurons Oppositely Affect Striatalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Descending neocortical output critical for skilled forelimb movements is distributed across projection cell classes

Park

Phillips

Martin

et al. 2021

Preprint

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“…The NeuroMorpho.Org database (Ascoli, 2006;Ascoli et al, 2007), which collects and indexes neuronal tracing data, currently hosts more than one hundred thousand arbors of diverse neurons from various animal species. Technological advances in large-scale electron (Helmstaedter et al, 2013;Kasthuri et al, 2015;Motta et al, 2019) and fluorescence microscopy (Gong et al, 2013;Winnubst et al, 2019;Abdeladim et al, 2019;Wang et al, 2019;Muñoz-Castañeda et al, 2020) facilitate the exploration of increasingly large volumes of brain tissue with ever improving resolution and contrast. These tridimensional (3D) imaging approaches are giving rise to a variety of model-centered trace sharing efforts such as the MouseLight (http://mouselight.janelia.org/, Winnubst et al, 2019), Zebrafish brain atlas (https://fishatlas.neuro.mpg.de/, Kunst et al, 2019) and drosophila connectome projects (https://neuprint.janelia.org/, Xu et al, 2020), among others.…”

Section: Introductionmentioning

confidence: 99%

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Phan

Matho

Beaurepaire

et al. 2020

Preprint

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The advent of large-scale microscopy along with advances in automated image analysis algorithms 1 is currently revolutionizing neuroscience. These approaches result in rapidly increasing libraries 2 of neuron reconstructions requiring innovative computational methods to draw biological insight 3 from. Here, we propose a framework from differential geometry based on scale-space representation 4 to extract a quantitative structural readout of neurite traces seen as tridimensional (3D) curves 5 within the anatomical space. We define and propose algorithms to compute a multiscale hierarchical 6 decomposition of traced neurites according to their intrinsic dimensionality, from which we deduce 7 a local 3D scale, i.e. the scale in microns at which the curve is fully 3D as opposed to being 8 embedded in a 2D plane or a 1D line. We applied our scale-space analysis to recently published 9 data including zebrafish whole brain traces to demonstrate the importance of the computed local 3D 10 scale for description and comparison at the single arbor levels and as a local spatialized information 11 characterizing axons populations at the whole brain level. The use of this broadly applicable approach 12 is facilitated through an open-source implementation in Python available through GeNePy3D, a 13 quantitative geometry library.14 Keywords Computational neuroanatomy · scale space · quantitative geometry · python 15 1 Introduction 16 Throughout the evolution of the field of neuroscience, neuroanatomy has played a key role via the analysis of neuronal 17 arbor traces, either at single cell or gross projection levels. The NeuroMorpho.Org database (Ascoli, 2006; Ascoli et al., 18 2007), which collects and indexes neuronal tracing data, currently hosts more than one hundred thousand arbors of 19 diverse neurons from various animal species. Thanks to technological advances in microscopy, through block-face 20 electron microscopy (Helmstaedter et al., 2013) and large-scale fluorescence microscopy (Abdeladim et al., 2019), we 21 are now able to image even larger tissue volumes with ever increasing resolution and contrast modalities. Crucially, the 22 coming of age of computer vision through the advent of deep learning is offering ways to automate the extraction of 23 neurite traces (Magliaro et al., 2019), a process that is both tedious and time consuming to do manually. As a result, we 24 are about to experience an exponential increase in the amount of neuronal traces extracted in diverse species, brain 25 regions, developmental stages and experimental conditions to answer key questions across neuroscience (Meinertzhagen, 26 2018). Methods from quantitative and computational geometry will play a major role in handling and analyzing the 27 A scale-space approach for 3D neuronal traces analysis growing body of neuronal reconstruction data, in its full tridimensional (3D) complexity, a requisite to linking neuronal 28 structure with development and function of brain circuits and systems. 29A vast array of geometric algorithmic met...

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Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area

Hahn

Gao

Boesen

et al. 2022

J of Comparative Neurology

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The macroscale neuronal connections of the lateral preoptic area (LPO) and the caudally adjacent lateral hypothalamic area anterior region (LHAa) were investigated in mice by anterograde and retrograde axonal tracing. Both hypothalamic regions are highly and diversely connected, with connections to >200 gray matter regions spanning the forebrain, midbrain, and rhombicbrain. Intrahypothalamic connections predominate, followed by connections with the cerebral cortex and cerebral nuclei. A similar overall pattern of LPO and LHAa connections contrasts with substantial differences between their input and output connections. Strongest connections include outputs to the lateral habenula, medial septal and diagonal band nuclei, and inputs from rostral and caudal lateral septal nuclei; however, numerous additional robust connections were also observed. The results are discussed in relation to a current model for the mammalian forebrain network that associates LPO and LHAa with a range of functional roles, including reward prediction, innate survival behaviors (including integrated somatomotor and physiological control), and affect. The present data suggest a broad and intricate role for LPO and LHAa in behavioral control, similar in that regard to previously investigated LHA regions, contributing to the finely tuned sensory‐motor integration that is necessary for behavioral guidance supporting survival and reproduction.

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Cellular Anatomy of the Mouse Primary Motor Cortex

Cited by 26 publications

References 104 publications

Descending neocortical output critical for skilled forelimb movements is distributed across projection cell classes

Descending neocortical output critical for skilled forelimb movements is distributed across projection cell classes

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area

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