Topic: motor cortex

I’m interested in structure-function relationships in neural 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

The cell assembly hypothesis postulates that groups of coordinated neurons form the basis of information processing. Here, we test this hypothesis by analyzing massively parallel spiking activity recorded in monkey motor cortex during a reach-to-grasp experiment for the presence of significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a r

How does the motor cortex function in predictive sensorimotor control? This is important for understanding neural control of movement, but dauting for the tangling of high-dimensional sensory and motor information. To address this question, we recorded neural population activity from the motor cortex while the monkeys were performing a manual interception task. Interestingly, on the neural states of single trials, we observed a low-dimensional ring-like neural geometry. This geometry, featured with ordered reach-direction clusters and tilted target-speed rings, also emerges in a three-layer RN

How does the motor cortex (MC) achieve generalizable and purposeful movements from the complex non-linear musculoskeletal system? This understanding can help us uncover how does the cortical activity relates to the movement. To elucidate this relationship, we build a model of the MC with realistic properties. To mimic the recurrent connections between cortical neurons, we use recurrent neural networks (RNNs) to model the MC. The MC model receives sensory feedback and delivers muscle excitations to anatomically accurate musculoskeletal models to produce experimentally observed movements. The se

Moving a hand is like sailing: your motor cortex, the captain, receives hand location information to define movement parameters; and throughout the movement, your somatosensory areas, the localization equipment, gather data to ensure your hand is on the correct course, and to guide the captain in further commands. Models of information flow in primates suggest that during volitional movement, the sensory state information in Brodmann Area 3b (Ba3b) is spread to Ba4, then sent to muscles via motor commands, followed by muscle proprioceptive information through the thalamus to Ba2, and finally b

Behavior results from neural computations that occur at the level of populations of neurons, and understanding how a given computation produces a particular behavior requires a characterization of how distinct signals interact and evolve. For example, in motor cortex, discrete reaches are generated by a combination of ‘preparatory’ and ‘execution-related’ signals. While the number of computationally-relevant signals is much lower than the number of neurons in a population – and is therefore amenable to dimensionality-reduction methods – standard techniques, like principal components analysis,

Efficient coding has served as a guiding principle in understanding the neural code. To date, however, it has been explored mainly in the context of peripheral sensory cells with simple tuning curves. By contrast, ‘deeper’ neurons such as grid cells come with more complex tuning properties which imply a different, yet highly efficient, strategy for representing information. I will show that a highly efficient code is not specific to a population of neurons with finely tuned response properties: it emerges robustly in a shallow network with random synapses. Here, the geometry of population resp

It is widely known that motor learning leads to reorganization changes in the motor cortex. Recently, we have shown that using navigated transcranial magnetic stimulation (TMS) allows us to reliably trace interactions among motor cortical representations (MCRs) of different upper limb muscles. Using this approach, we investigate changes in the MCRs after fine finger movement training. Our preliminary results demonstrated that areas of the APB and ADM and their overlaps tended to increase after finger independence training. Considering the behavioral data, hand dexterity increased for both hand

While non-invasive Brain-to-Computer interface can accurately classify the lateralization of hand moments, the distinction of fingers activation in the same hand is limited by their local and overlapping representation in the motor cortex. In particular, the low signal-to-noise ratio restrains the opportunity to identify meaningful patterns in a supervised fashion. Here we combined Magnetoencephalography (MEG) recordings with advanced decoding strategy to classify finger movements at single trial level. We recorded eight subjects performing a serial reaction time task, where they pressed four

My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold compu

We have been developing Brain-Computer Interface (BCI) using electrocorticography (ECoG) [1] , which is recorded by electrodes implanted on brain surface, and magnetoencephalography (MEG) [2] , which records the cortical activities non-invasively, for the clinical applications. The invasive BCI using ECoG has been applied for severely paralyzed patient to restore the communication and motor function. The non-invasive BCI using MEG has been applied as a neurofeedback tool to modulate some pathological neural activities to treat some neuropsychiatric disorders. Although these techniques have bee

Motor adaptation is a widely-used paradigm for understanding short-term learning. However, it is unknown how existing skillsets acquired through the long-term learning process that begins in utero can affect motor adaptation. Long-term learning likely causes changes in neural connectivity, which may shape the neural dynamics that can be produced. To understand the interaction between circuit connectivity constraints and a neural population’s ability to change its activity patterns, we modeled the neural dynamics of the motor cortex during skill learning and subsequent adaptation using a recurr

A prevailing view of the role motor cortex plays in movement execution is that it’s limited to certain tasks, such as those involving novel muscle activation-patterns. Yet the relevant experimental support remains tenuous and the specific movement features that necessitate motor cortical involvement remain unclear. Classical studies often limited motor cortical function characterition to only a small number of qualitatively-defined movement types. Certain results suggest a broad motor cortical involvement in ensuring the smoothness/agility of many or all movements. Recent studies have raised t

Experimental and computational studies suggest that motor cortex acts as a feedback controller, allowing for ‘on-the-fly’ movement corrections in response to afferent sensory feedback. However, it remains unclear whether feedback control relates to longer-term learning, and how this would be implemented in neural circuitry. Here, we tackled these questions by testing how a recurrent neural network (RNN) can use feedback to control its own output, and whether this process can enable learning. We built an RNN that received feedback signaling the error between its intended and observed output. An

Intracortical brain-computer interfaces (iBCIs) can restore voluntary movement to people with paralysis by translating their brain activity into a control signal for an external device. Maintaining stable iBCI performance is challenging due to instabilities at the neural interface, such as shifts in electrode position in the brain or malfunctions of electrodes. Such instabilities result in a degradation of decoding performance, requiring that device use be interrupted to allow for the collection of recalibration data for the iBCI decoder. To address this challenge, an emerging class of methods

Animals constantly modify their behavior through experience. Flexible behavior is key to our ability to adapt to the ever-changing environment. My laboratory is interested in studying the activity of neuronal ensembles in behaving animals, and how it changes with learning. We have recently set up a paradigm where mice learn to associate sensory information (two different odors) to motor outputs (lick vs no-lick) under head-fixation. We combined this with two-photon calcium imaging, which can monitor the activity of a microcircuit of many tens of neurons simultaneously from a small area of the

Distinct synapses influence one another when they undergo changes, with unclear consequences for neuronal dynamics and function. Here we show that synapses can interact such that excitatory currents are naturally normalised and balanced by inhibitory inputs. This happens when classical spike-timing dependent synaptic plasticity rules are extended by additional mechanisms that incorporate the influence of neighbouring synaptic currents and regulate the amplitude of efficacy changes accordingly. The resulting control of excitatory plasticity by inhibitory activation, and vice versa, gives rise t

A central question in motor physiology has been whether motor cortex activity resembles muscle activity, and if not, why not? Over fifty years, extensive observations have failed to provide a concise answer, and the topic remains much debated. To provide a different perspective, we employed a novel behavioral paradigm that affords extensive comparison between time-evolving neural and muscle activity. Single motor-cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid ’tr

To make judgments, brain must be able to infer the state of the world based on often incomplete and ambiguous evidence. To probe neural circuits that perform the computations underlying such judgments, we developed a behavioral task for mice that required them to detect sustained increases in the speed of a continuously varying visual stimulus. In this talk, I will present evidence that the responses of secondary motor cortex to stimulus fluctuations in this task are consistent with updates of the animal’s state of belief that the change has occurred. These results establish a framework for me

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

Prior neuroimaging studies have shown that words associated with actions, like "grasp" and "kick," activate 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