Motor cortex

During development, neural circuits are shaped continuously as we learn to control our bodies, with the goal of producing neural dynamics that enable the rich repertoire of behaviors we perform with our limbs. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories---supervised learning (SL) and reinforcement learning (RL)---that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of

The ability to flexibly time movements is crucial for behavior. The motor cortex, basal ganglia, and thalamus form a multiregional neural network, known as the cortico-basal ganglia thalamocortical (CBGTC) loop, which controls various aspects of motor behaviors, including the timing of actions [1]. For example, in mice performing a delayed licking task, average activity in the striatum and cortex ramp up to a threshold for executing a lick [2]. However, the distinct contributions from different cell types and regions within the CBGTC loop remain largely unknown due to the similarity in their r

…initialize movements in dynamic sensorimotor contingency, such as interception of motion targets. In the present study, we analyzed neural population dynamics recorded from motor cortex via Utah array while two macaques performed a delayed reach toward a target either static or moving circularly. Principal component analysis (PCA) showed that the directional tuning of preparatory latent fact

…of neural population activity in the motor cortex (MOp) of adult mice during natural feeding behavior. We focus on autism spectrum disorder (ASD) models, many cases of which are characterized by stereotypical motor impairments [3]. Specifically, we compare neural activity during a dexterous cued self-feeding task in two trans

Latent variable models are widely used in neuroscience to extract dynamical structure from high-dimensional neural activity, and provide valuable insights into neural computation. However, these models are typically inferred from a single recording session and exhibit limited generalization capabilities. This is primarily due to statistical heterogeneities in neural recordings across subjects and tasks, along with lack of inductive biases for disentangling shared and dataset-specific dynamical structure. In this work, we hypothesize that similar behavioral tasks admit a corresponding family of

…For example, in prefrontal cortex, the coding of a stimulus’s direction is different soon after stimulus presentation, versus during the delay period. Similarly, for a monkey performing a wrist movement task, the same linear mappings (decoders) cannot simultaneously predict movements when pushing against a spring, versus freely moving. While neural network-based decoders can have the capacity to accurately perform mappings across changing conditions, they are challengi

Nervous system enables flexible and adaptive control of behavior, allowing us to modify actions according to task demands. The associated neural population activity is often characterized by low-dimensional manifolds, orthogonal null and potent spaces, and rotations in neural trajectories. These features are conceptualized as “factors” or “latent dynamics” underlying movement preparation and execution, yet their origin and role in movement flexibility remain unclear. Current research attributes these factors to neural networks trained on specific tasks. However, unlike humans, these networks l

…The motor cortex (M1) is a key node in this network and likely plays a significant role in the expression of impaired movement in PD. Altered neuronal activity in M1 has been reported and characterized at the single-unit level in PD, however, results are difficult to reconcile under one theoretical model (Doudet et al., 1990; Watts \& Mandir, 1992; Pasquereau et al., 2016). One promising avenue toward a mechanistic understanding of M1 pathophysiology in PD is to inve

Spatiotemporal patterns of neuronal activity are hypothesized to be essential for information processing in the brain. They suggest the existence of cell assemblies, groups of co-active neurons that represent a distinct cognitive unit, potentially related to specific behaviors or representations. Conventionally, cell assemblies are defined by strong structural connections, such that the stimulation of a portion of its members should transiently activate the entire assembly. Their exact composition and computational role, however, have yet to be fully clarified. We present the detection of spik

The motor cortex can quickly adapt to perform variations of learned movements, for example by changing the speed or temporal order of muscle activations. The timescale of such adaptation is faster than can be explained by synaptic plasticity, making neuromodulation a prime suspect. Neuromodulation is typically modeled as gain changes at the level of somatic activation function. As a result, gain control is limited to the number of neurons N and often proves to be too coarse for multi-muscle coordination. Moreover, somatic gain modulation cannot easily accommodate the simultaneous impact of dif

…distinct dynamical patterns in motor cortex. However, we do not know what connectivity structures support this capability, nor the mechanism for selecting among the dynamics required for different tasks. We develop an analytical theory describing a network model capable of multi-task computation, where the connectivity is generated as a sum over a large number of low-rank matrices, each of which is specialized to perform one low-dimen

The acquisition of novel motor skills requires new patterns of neural activity and changes in synaptic weights, yet the precise rules governing synaptic plasticity in the motor cortex and how they give rise to the observed changes in activity are still unknown. Brain computer interfaces (BCIs) allow for task improvement to be directly linked to changes in neural activity. Recent experiments that pair BCIs with all-optical circuit mapping allow for the simultaneous tracking of in vivo changes in connectivity while task-relevant activity learning occurs. In silico models of learning allow for th

…circuits and behavior, with a focus on motor and somatosensory areas of the mouse’s cortex involved in controlling forelimb movements. In one line of investigation, we take a bottom-up, cellularly oriented approach and use optogenetics, electrophysiology, and related slice-based methods to dissect cell-type-specific circuits of corticospinal and other neurons in forelimb motor cortex. In another, we take a top-down ethologically oriented approach and analyze the kinematics and cortical correlates of “oromanual” dexterity as mice hand

Motor control is a complex, high-dimensional task. It has been suggested that the brain may reduce the dimensionality of this control problem by using a set of motor primitives (or muscle synergies) that encode frequently used patterns of muscle co-activations in the time and/or weight-domain (Bizzi et al., 1991; Kutch and Valero-Cuevas, 2012; Bizzi and Cheung, 2013; Alessandro et al., 2013). In this project we investigate how motor primitives may affect the learning of movement in motor cortex. We model motor cortex as a recurrent neural network (RNN) and compare two potential output scenario

…specific regions in the cortical motor system corresponding to the body parts involved in those actions. This activation follows the somatotopic mapping of these body parts. The variation in activation could suggest differences in how different types of symbols are processed semantically, or it might be a byproduct of activities related to imagining scenes associated with the meanings of these symbols. To address this debate, we simulated the semantic learning of realistic action words i

The stability of the relationship between neural activity and external variables is a matter of debate for many brain areas [1-5]. Whether neural representations are stable or "drifting" has significant implications for how downstream neural populations process these representations and incorporate them into computations driving perception, behavior, and memory. Most studies on the stability of neural coding for movements have focused on simple, stereotyped, and overtrained behaviors, finding that neural representations are stable [6-8]. However, it is unclear if this stability results from t

…activate pairs of neurons in the motor cortex (MC) or hippocampus CA3 to receive water rewards. We developed an open source pipeline th

Parkinson's disease (PD) is a neurodegenerative disease primarily characterized by severe motor symptoms that can be transiently relieved by medication (levodopa). While there is evidence of widespread alterations of the neuronal activities across the brain, reliable biomarkers are still lacking. To examine this question, we measured the resting state activities of 11 PD patients both with deep stimulation electrodes and with EEG electrodes placed in the motor areas, in order to provide simultaneous recording from both the subthalamic nucleus (STN) and the cortex. Then, we used a dynamical mea

In order to assess the recovery of neural activity after stroke, it is necessary to select tasks that can be performed in the early stages of recovery. Since hand flexion is the first functional recovery that can be observed, it is a promising task to evaluate the recovery process. fMRI has reported that the amplitude of neural activity varies with the intensity of movement, but the correlation between movement and neural activity itself has not been reported in functional near-infrared spectroscopy (fNIRS). To investigate this, the present study performed simultaneous fMRI and fNIRS measureme

…the SC, including pathways from the motor cortex (MC), barrel field of S1 (BC), and brainstem (Bs). Mapping the whisker-sensitive region of the SC in mice revealed a novel trans-collicular connectivity motif, w

Motor behaviors arise from dynamic interactions of interconnected neural populations across distributed brain areas. The underlying principles of information flow remain largely unknown. Here, we investigate the functional roles of motor cortex and intralaminar thalamus in driving specific cell populations of the striatum - the input nucleus of the basal ganglia - during movements. We recorded the activity of direct and indirect pathway medium spiny neurons (dMSNs and iMSNs) in the striatum as mice performed a skilled motor task, by in vivo two-photon calcium imaging through GRIN lens. Further

Motor imagery (MI) is defined as a mental simulation of movements without any actual motor action. In last decades, several studies had advanced our understanding of this mental process. They notably showed shared neuroanatomical substrates between MI and physical practice. These neuronal networks seem to be dependant of individual expertise. Although the scientific literature seems to agree for a reduced and focused brain activity as expertise increases, a few investigated complex whole-body movements because MI is usually studied through standardized common tasks (e.g., keyboard sequences).

…To comprehend local networks of the motor cortex and monitoring network changes during stimulation, we utilized subdurally positioned graphene electrodes for simultaneous recording and stimulation of the motor cortex in rhesus monkeys. Additionally, we implemented a wireless recordin

Glioblastoma Multiforme (GBM) is the most aggressive form of brain tumors. It affects 3 in 100,000 people and has a median survival of 15 months. GBM is known to infiltrate the surrounding brain parenchyma, interacting with neuronal tissue. Specifically, tumoral cells induce neuron hyper-excitability while peritumoral cells seem to regulate tumor proliferation and progression. This project aims to investigate how the plastic rearrangement of cortical areas occurs in glioma-bearing animals. We took advantage of Thy1-ChR2 transgenic mice, injected with two syngeneic lines of murine glioma cells