Mouse Primary Motor Cortex Research Articles

Sex differences in motor learning flexibility are accompanied by sex differences in mushroom spine pruning of the mouse primary motor cortex during adolescence.

Although males excel at motor tasks requiring strength, females exhibit greater motor learning flexibility. Cognitive flexibility is associated with low baseline mushroom spine densities achieved by pruning which can be triggered by α4βδ GABAA receptors (GABARs); defective synaptic pruning impairs this process. We investigated sex differences in adolescent pruning of mushroom spine pruning of layer 5 pyramidal cells of primary motor cortex (L5M1), a site essential for motor learning, using microscopic evaluation of Golgi stained sections. We assessed α4GABAR expression using immunohistochemical and electrophysiological techniques (whole cell patch clamp responses to 100 nM gaboxadol, selective for α4βδ GABARs). We then compared performance of groups with different post-pubertal mushroom spine densities on motor learning (constant speed) and learning flexibility (accelerating speed following constant speed) rotarod tasks. Mushroom spines in proximal L5M1 of female mice decreased >60% from PND35 (puberty onset) to PND56 (Pubertal: 2.23 ± 0.21 spines/10 μm; post-pubertal: 0.81 ± 0.14 spines/10 μm, P < 0.001); male mushroom spine density was unchanged. This was due to greater α4βδ GABAR expression in the female (P < 0.0001) because α4 -/- mice did not exhibit mushroom spine pruning. Although motor learning was similar for all groups, only female wild-type mice (low mushroom spine density) learned the accelerating rotarod task after the constant speed task (P = 0.006), a measure of motor learning flexibility. These results suggest that optimal motor learning flexibility of female mice is associated with low baseline levels of post-pubertal mushroom spine density in L5M1 compared to male and female α4 -/- mice.

Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint

We present a quantitative strategy to identify all projection neuron types from a given region with statistically different patterns of anatomical targeting. We first validate the technique with mouse primary motor cortex layer 6 data, yielding two clusters consistent with cortico-thalamic and intra-telencephalic neurons. We next analyze the presubiculum, a less-explored region, identifying five classes of projecting neurons with unique patterns of divergence, convergence, and specificity. We report several findings: individual classes target multiple subregions along defined functions; all hypothalamic regions are exclusively targeted by the same class also invading midbrain and agranular retrosplenial cortex; Cornu Ammonis receives input from a single class of presubicular axons also projecting to granular retrosplenial cortex; path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes; the identified classes have highly non-uniform abundances; and presubicular somata are topographically segregated among classes. This study thus demonstrates that statistically distinct projections shed light on the functional organization of their circuit.

Comparison of alterations in local field potentials and neuronal firing in mouse M1 and CA1 associated with central fatigue induced by high-intensity interval training and moderate-intensity continuous training.

The mechanisms underlying central fatigue (CF) induced by high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) are still not fully understood. In order to explore the effects of these exercises on the functioning of cortical and subcortical neural networks, this study investigated the effects of HIIT and MICT on local field potential (LFP) and neuronal firing in the mouse primary motor cortex (M1) and hippocampal CA1 areas. HIIT and MICT were performed on C57BL/6 mice, and simultaneous multichannel recordings were conducted in the M1 motor cortex and CA1 hippocampal region. A range of responses were elicited, including a decrease in coherence values of LFP rhythms in both areas, and an increase in slow and a decrease in fast power spectral density (PSD, n = 7-9) respectively. HIIT/MICT also decreased the gravity frequency (GF, n = 7-9) in M1 and CA1. Both exercises decreased overall firing rates, increased time lag of firing, declined burst firing rates and the number of spikes in burst, and reduced burst duration (BD) in M1 and CA1 (n = 7-9). While several neuronal firing properties showed a recovery tendency, the alterations of LFP parameters were more sustained during the 10-min post-HIIT/MICT period. MICT appeared to be more effective than HIIT in affecting LFP parameters, neuronal firing rate, and burst firing properties, particularly in CA1. Both exercises significantly affected neural network activities and local neuronal firing in M1 and CA1, with MICT associated with a more substantial and consistent suppression of functional integration between M1 and CA1. Our study provides valuable insights into the neural mechanisms involved in exercise-induced central fatigue by examining the changes in functional connectivity and coordination between the M1 and CA1 regions. These findings may assist individuals engaged in exercise in optimizing their exercise intensity and timing to enhance performance and prevent excessive fatigue. Additionally, the findings may have clinical implications for the development of interventions aimed at managing conditions related to exercise-induced fatigue.

Age-Dependent Modulation of Layer V Pyramidal Neuron Excitability in the Mouse Primary Motor Cortex by D1 Receptor Agonists and Antagonists

The primary motor cortex (M1) receives dopaminergic (DAergic) projections from the midbrain which play a key role in modulating motor and cognitive processes, such as motor skill learning. However, little is known at the level of individual neurons about how dopamine (DA) and its receptors modulate the intrinsic properties of the different neuronal subpopulations in M1 and if this modulation depends on age. Using immunohistochemistry, we first mapped the cells expressing the DA D1 receptor across the different layers in M1, and quantified the number of pyramidal neurons (PNs) expressing the D1 receptor in the different layers, in young and adult mice. This work reveals that the spatial distribution and the molecular profile of D1 receptor-expressing neurons (D1+) across M1 layers do not change with age. Then, combining whole-cell patch-clamp recordings and pharmacology, we explored ex vivo in young and adult mice the impact of activation or blockade of D1 receptors on D1+ PN intrinsic properties. While the bath application of the D1 receptor agonist induced an increase in the excitability of layer V PNs both in young and adult, we identified a distinct modulation of intrinsic electrical properties of layer V D1+ PNs by D1 receptor antagonist depending on the age of the animal.

Combinatorial quantification of distinct neural projections from retrograde tracing

Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. A source region projecting to n targets could have 2n-1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k target regions (k < n), one can experimentally count the cells expressing different color combinations in the source region. The neuronal counts for different color combinations from n-choose-k experiments provide constraints for a model that is robustly solvable using evolutionary algorithms. Here, we demonstrate this method’s reliability for 4 targets using simulated triple injection experiments. Furthermore, we illustrate the experimental application of this framework by quantifying the projections of male mouse primary motor cortex to the primary and secondary somatosensory and motor cortices.

Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts invivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.

Correlated Somatosensory Input in Parvalbumin/Pyramidal Cells in Mouse Motor Cortex.

In mammalian cortex, feedforward excitatory connections recruit feedforward inhibition. This is often carried by parvalbumin (PV+) interneurons, which may densely connect to local pyramidal (Pyr) neurons. Whether this inhibition affects all local excitatory cells indiscriminately or is targeted to specific subnetworks is unknown. Here, we test how feedforward inhibition is recruited by using two-channel circuit mapping to excite cortical and thalamic inputs to PV+ interneurons and Pyr neurons to mouse primary vibrissal motor cortex (M1). Single Pyr and PV+ neurons receive input from both cortex and thalamus. Connected pairs of PV+ interneurons and excitatory Pyr neurons receive correlated cortical and thalamic inputs. While PV+ interneurons are more likely to form local connections to Pyr neurons, Pyr neurons are much more likely to form reciprocal connections with PV+ interneurons that inhibit them. This suggests that Pyr and PV ensembles may be organized based on their local and long-range connections, an organization that supports the idea of local subnetworks for signal transduction and processing. Excitatory inputs to M1 can thus target inhibitory networks in a specific pattern which permits recruitment of feedforward inhibition to specific subnetworks within the cortical column.

A Low-Budget Photothrombotic Rodent Stroke Model.

A number of animal stroke models have been developed and used over the years to study the pathological mechanisms of this disorder and develop new therapies. Among them, the photothrombotic model of ischemicstroke has been central in various studies focusing on understanding of the basic biology of neural repair, identification and validation of key molecular targets involved in post-stroke recovery, and preclinical testing of various therapeutic approaches. To facilitate uniformity among various experimental groups using this expert-recommended mouse model of choice for stroke recovery studies, in this chapter we describe in detail a low-budget technique to induce photothrombosis in the mouse primary motor cortex. Additionally, we provide tips for conducting this procedure in other cerebral cortical regions of the mouse brain and in rats.

Correlation Analysis of Molecularly-Defined Cortical Interneuron Populations with Morpho-Electric Properties in Layer V of Mouse Neocortex.

Cortical interneurons can be categorized into distinct populations based on multiple modalities, including molecular signatures and morpho-electrical (M/E) properties. Recently, many transcriptomic signatures based on single-cell RNA-seq have been identified in cortical interneurons. However, whether different interneuron populations defined by transcriptomic signature expressions correspond to distinct M/E subtypes is still unknown. Here, we applied the Patch-PCR approach to simultaneously obtain the M/E properties and messenger RNA (mRNA) expression of >600 interneurons in layer V of the mouse somatosensory cortex (S1). Subsequently, we identified 11 M/E subtypes, 9 neurochemical cell populations (NCs), and 20 transcriptomic cell populations (TCs) in this cortical lamina. Further analysis revealed that cells in many NCs and TCs comprised several M/E types and were difficult to clearly distinguish morpho-electrically. A similar analysis of layer V interneurons of mouse primary visual cortex (V1) and motor cortex (M1) gave results largely comparable to S1. Comparison between S1, V1, and M1 suggested that, compared to V1, S1 interneurons were morpho-electrically more similar to M1. Our study reveals the presence of substantial M/E variations in cortical interneuron populations defined by molecular expression.

Motor learning drives dynamic patterns of intermittent myelination on learning-activated axons.

Myelin plasticity occurs when newly formed and pre-existing oligodendrocytes remodel existing patterns of myelination. Myelin remodeling occurs in response to changes in neuronal activity and is required for learning and memory. However, the link between behavior-induced neuronal activity and circuit-specific changes in myelination remains unclear. Using longitudinal in vivo two-photon imaging and targeted labeling of learning-activated neurons in mice, we explore how the pattern of intermittent myelination is altered on individual cortical axons during learning of a dexterous reach task. We show that behavior-induced myelin plasticity is targeted to learning-activated axons and occurs in a staged response across cortical layers in the mouse primary motor cortex. During learning, myelin sheaths retract, which results in lengthening of nodes of Ranvier. Following motor learning, addition of newly formed myelin sheaths increases the number of continuous stretches of myelination. Computational modeling suggests that motor learning-induced myelin plasticity initially slows and subsequently increases axonal conduction speed. Finally, we show that both the magnitude and timing of nodal and myelin dynamics correlate with improvement of behavioral performance during motor learning. Thus, learning-induced and circuit-specific myelination changes may contribute to information encoding in neural circuits during motor learning.

Neuropeptide S promotes maintenance of newly formed dendritic spines and performance improvement after motor learning in mice

Neuropeptide S (NPS), an endogenous neuropeptide consisting of 20 amino acids, selectively binds and activates G protein-coupled receptor named neuropeptide S receptor (NPSR) to regulate a variety of physiological functions. NPS/NPSR system has been shown to play a pivotal role in regulating learning and memory in rodents. However, it remains unclear that how NPS/NPSR system affects neuronal functions and synaptic plasticity after learning. We found that intracerebroventricular (i.c.v.) injection of NPS promoted performance improvement and reduced sleep duration after motor learning, which could be blocked by pre-treatment with intraperitoneal (i.p.) injection of NPSR antagonist SHA 68. Using intravital two-photon imaging, we examined the effect of NPS on the postsynaptic dendritic spines of layer V pyramidal neurons in the mouse primary motor cortex after motor learning. We found that i.c.v. injection of NPS strengthened learning-induce new spines and facilitated their survival over time. Furthermore, i.c.v. injection of NPS increased calcium activity of apical dendrites and dendritic spines of layer V pyramidal neurons in the mouse primary motor cortex during the running period. These findings suggest that activation of NPSR by NPS increases synaptic calcium activity and learning-related synapse maintenance, thereby contributing to performance improvement after motor learning.

Voluntary Behavior and Training Conditions Modulate in vivo Extracellular Glucose and Lactate in the Mouse Primary Motor Cortex.

Learning or performing new behaviors requires significant neuronal signaling and is metabolically demanding. The metabolic cost of performing a behavior is mitigated by exposure and practice which result in diminished signaling and metabolic requirements. We examined the impact of novel and habituated wheel running, as well as effortful behaviors on the modulation of extracellular glucose and lactate using biosensors inserted in the primary motor cortex of mice. We found that motor behaviors produce increases in extracellular lactate and decreases in extracellular glucose in the primary motor cortex. These effects were modulated by experience, novelty and intensity of the behavior. The increase in extracellular lactate appears to be strongly associated with novelty of a behavior as well as the difficulty of performing a behavior. Our observations are consistent with the view that a main function of aerobic glycolysis is not to fuel the current neuronal activity but to sustain new bio-infrastructure as learning changes neural networks, chiefly through the shuttling of glucose derived carbons into the pentose phosphate pathway for the biosynthesis of nucleotides.

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Isoform cell-type specificity in the mouse primary motor cortex

Full-length SMART-seq1 single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2 and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types—including isoform shifts between cell types that are masked in gene-level analysis—as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.

Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

A 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.

Cellular anatomy of the mouse primary motor cortex

An 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.

Short-Term Facilitation of Long-Range Corticocortical Synapses Revealed by Selective Optical Stimulation.

Short-term plasticity regulates the strength of central synapses as a function of previous activity. In the neocortex, direct synaptic interactions between areas play a central role in cognitive function, but the activity-dependent regulation of these long-range corticocortical connections and their impact on a postsynaptic target neuron is unclear. Here, we use an optogenetic strategy to study the connections between mouse primary somatosensory and motor cortex. We found that short-term facilitation was strong in both corticocortical synapses, resulting in far more sustained responses than local intracortical and thalamocortical connections. A major difference between pathways was that the synaptic strength and magnitude of facilitation were distinct for individual excitatory cells located across all cortical layers and specific subtypes of GABAergic neurons. Facilitation was dependent on the presynaptic calcium sensor synaptotagmin-7 and altered by several optogenetic approaches. Current-clamp recordings revealed that during repetitive activation, the short-term dynamics of corticocortical synapses enhanced the excitability of layer 2/3 pyramidal neurons, increasing the probability of spiking with activity. Furthermore, the properties of the connections linking primary with secondary somatosensory cortex resemble those between somatosensory-motor areas. These short-term changes in transmission properties suggest long-range corticocortical synapses are specialized for conveying information over relatively extended periods.

Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.

Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.

High-frequency repetitive transcranial magnetic stimulation enhances layer II/III morphological dendritic plasticity in mouse primary motor cortex

High-frequency repeated transcranial magnetic stimulation (HF-rTMS) is a safe non-invasive neuromodulatory technique and there is a body of evidence shows that it can modulate plasticity in different brain areas. One of the most interesting application of HF-rTMS is the modulation of plasticity in primary motor cortex (M1) to promote recovery after brain injuries. However, the underlying mechanism by which HF-rTMS modulates motor cortex plasticity remain to be investigated.In this study, we investigated the effects of HF-rTMS treatment on morphological plasticity of pyramidal neurons in layer II/III (L2/3) of the primary motor cortex in mice. Our results show that the treatment did not increase anxiety in mice in the open field test and the elevated plus-maze test. Treated mice displayed increased total spine density in apical and basal dendrites, with a predominance of thin spines. The treatment also increased dendritic complexity, as assessed by Sholl analysis at both apical and basal dendrites.Collectively, the results show that HF-rTMS induced remarkable changes in dendritic complexity in primary motor cortex L2/3 connections which may strengthen corticocortical connections increasing integration of information across cortical areas. The data support the use of HF-rTMS as a circuit-targeting neuromodulation strategy.

Phenotypic variation of transcriptomic cell types in mouse motor cortex

Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.