scholarly journals Cellular anatomy of the mouse primary motor cortex

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 159-166
Author(s):  
Rodrigo Muñoz-Castañeda ◽  
Brian Zingg ◽  
Katherine S. Matho ◽  
Xiaoyin Chen ◽  
Quanxin Wang ◽  
...  

AbstractAn essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

Author(s):  
Rodrigo Muñoz-Castañeda ◽  
Brian Zingg ◽  
Katherine S. Matho ◽  
Quanxin Wang ◽  
Xiaoyin Chen ◽  
...  

AbstractAn essential step toward understanding brain function is to establish a cellular-resolution structural framework upon which multi-scale and multi-modal information spanning molecules, cells, circuits and systems can be integrated and interpreted. Here, through a collaborative effort from the Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based description of one brain structure - the primary motor cortex upper limb area (MOp-ul) of the mouse. Applying state-of-the-art labeling, imaging, computational, and neuroinformatics tools, we delineated the MOp-ul within the Mouse Brain 3D Common Coordinate Framework (CCF). We defined over two dozen MOp-ul projection neuron (PN) types by their anterograde targets; the spatial distribution of their somata defines 11 cortical sublayers, a significant refinement of the classic notion of cortical laminar organization. We further combine multiple complementary tracing methods (classic tract tracing, cell type-based anterograde, retrograde, and transsynaptic viral tracing, high-throughput BARseq, and complete single cell reconstruction) to systematically chart cell type-based MOp input-output streams. As PNs link distant brain regions at synapses as well as host cellular gene expression, our construction of a PN type resolution MOp-ul wiring diagram will facilitate an integrated analysis of motor control circuitry across the molecular, cellular, and systems levels. This work further provides a roadmap towards a cellular resolution description of mammalian brain architecture.


1988 ◽  
Vol 59 (3) ◽  
pp. 796-818 ◽  
Author(s):  
C. S. Huang ◽  
M. A. Sirisko ◽  
H. Hiraba ◽  
G. M. Murray ◽  
B. J. Sessle

1. The technique of intracortical microstimulation (ICMS), supplemented by single-neuron recording, was used to carry out an extensive mapping of the face primary motor cortex. The ICMS study involved a total of 969 microelectrode penetrations carried out in 10 unanesthetized monkeys (Macaca fascicularis). 2. Monitoring of ICMS-evoked movements and associated electromyographic (EMG) activity revealed a general pattern of motor cortical organization. This was characterized by a representation of the facial musculature, which partially enclosed and overlapped the rostral, medial, and caudal borders of the more laterally located cortical regions representing the jaw and tongue musculatures. Responses were evoked at ICMS thresholds as low as 1 microA, and the latency of the suprathreshold EMG responses ranged from 10 to 45 ms. 3. Although contralateral movements predominated, a representation of ipsilateral movements was found, which was much more extensive than previously reported and which was intermingled with the contralateral representations in the anterior face motor cortex. 4. In examining the fine organizational pattern of the representations, we found clear evidence for multiple representation of a particular muscle, thus supporting other investigations of the motor cortex, which indicate that multiple, yet discrete, efferent microzones represent an essential organizational principle of the motor cortex. 5. The close interrelationship of the representations of all three muscle groups, as well as the presence of a considerable ipsilateral representation, may allow for the necessary integration of unilateral or bilateral activities of the numerous face, jaw, and tongue muscles, which is a feature of many of the movement patterns in which these various muscles participate. 6. In six of these same animals, plus an additional two animals, single-neuron recordings were made in the motor and adjacent sensory cortices in the anesthetized state. These neurons were electrophysiologically identified as corticobulbar projection neurons or as nonprojection neurons responsive to superficial or deep orofacial afferent inputs. The rostral, medial, lateral, and caudal borders of the face motor cortex were delineated with greater definition by ICMS and these electrophysiological procedures than by cytoarchitectonic features alone. We noted that there was an approximate fit in area 4 between the extent of projection neurons and field potentials anti-dromically evoked from the brain stem and the extent of positive ICMS sites.(ABSTRACT TRUNCATED AT 400 WORDS)


2021 ◽  
Author(s):  
Kevin Patrick Cross ◽  
Douglas J Cook ◽  
Stephen H Scott

An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioural goal. Primary motor cortex (M1) is a key region involved in feedback processing, yet we know little about how different sources of feedback are processed by M1. We examined feedback-related activity in M1 to compare how different sources (visual versus proprioceptive) and types of information (limb versus goal) are represented. We found sensory feedback had a broad influence on M1 activity with ~73% of neurons responding to at least one of the feedback sources. Information was also organized such that limb and goal feedback targeted the same neurons and evoked similar responses at the single-neuron and population levels indicating a strong convergence of feedback sources in M1.


2013 ◽  
Vol 109 (3) ◽  
pp. 666-678 ◽  
Author(s):  
Emily R. Oby ◽  
Christian Ethier ◽  
Lee E. Miller

It is well known that discharge of neurons in the primary motor cortex (M1) depends on end-point force and limb posture. However, the details of these relations remain unresolved. With the development of brain-machine interfaces (BMIs), these issues have taken on practical as well as theoretical importance. We examined how the M1 encodes movement by comparing single-neuron and electromyographic (EMG) preferred directions (PDs) and by predicting force and EMGs from multiple neurons recorded during an isometric wrist task. Monkeys moved a cursor from a central target to one of eight peripheral targets by exerting force about the wrist while the forearm was held in one of two postures. We fit tuning curves to both EMG and M1 activity measured during the hold period, from which we computed both PDs and the change in PD between forearm postures (ΔPD). We found a unimodal distribution of these ΔPDs, the majority of which were intermediate between the typical muscle response and an unchanging, extrinsic coordinate system. We also discovered that while most neuron-to-EMG predictions generalized well across forearm postures, end-point force measured in extrinsic coordinates did not. The lack of force generalization was due to musculoskeletal changes with posture. Our results show that the dynamics of most of the recorded M1 signals are similar to those of muscle activity and imply that a BMI designed to drive an actuator with dynamics like those of muscles might be more robust and easier to learn than a BMI that commands forces or movements in external coordinates.


2020 ◽  
Vol 117 (23) ◽  
pp. 13078-13083 ◽  
Author(s):  
David J. Levinthal ◽  
Peter L. Strick

The central nervous system both influences and is influenced by the gastrointestinal system. Most research on this gut–brain connection has focused on how ascending signals from the gut and its microbiome alter brain function. Less attention has focused on how descending signals from the central nervous system alter gut function. Here, we used retrograde transneuronal transport of rabies virus to identify the cortical areas that most directly influence parasympathetic and sympathetic control of the rat stomach. Cortical neurons that influence parasympathetic output to the stomach originated from the rostral insula and portions of medial prefrontal cortex, regions that are associated with interoception and emotional control. In contrast, cortical neurons that influence sympathetic output to the stomach originated overwhelmingly from the primary motor cortex, primary somatosensory cortex, and secondary motor cortex, regions that are linked to skeletomotor control and action. Clearly, the two limbs of autonomic control over the stomach are influenced by distinct cortical networks.


Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 137-143 ◽  
Author(s):  
Meng Zhang ◽  
Stephen W. Eichhorn ◽  
Brian Zingg ◽  
Zizhen Yao ◽  
Kaelan Cotter ◽  
...  

AbstractA mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.


2007 ◽  
Vol 97 (5) ◽  
pp. 3736-3750 ◽  
Author(s):  
Ehud Trainin ◽  
Ron Meir ◽  
Amir Karniel

What determines the specific pattern of activation of primary motor cortex (M1) neurons in the context of a given motor task? We present a systems level physiological model describing the transformation from the neural activity in M1, through the muscle control signal, into joint torques and down to endpoint forces and movements. The redundancy of the system is resolved by biologically plausible optimization criteria. The model explains neural activity at both the population, and single neuron, levels. Due to the model's relative simplicity and analytic tractability, it provides intuition as to the most salient features of the system as well as a possible causal explanation of how these determine the overall behavior. Moreover, it explains a large number of recent observations, including the temporal patterns of single-neuron and population firing rates during isometric and movement tasks, narrow tuning curves, non cosine tuning curves, changes of preferred directions during a task, and changes of preferred directions due to different experimental conditions.


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