The Movement Control & Neuroplasticity Research Group of KU Leuven offers a full-time position (18-24 months) for a postdoctoral candidate in motor control and learning from a lifespan perspective. This position is targeted at an integration of behavioral and electroencephalography techniques (EEG). We are looking for a dynamic and motivated individual with experience and enthusiasm in studying the human brain in relation to motor performance and in contributing to transparent and reproducible research. The candidate will also contribute to the supervision of master theses in the domain of motor control and motor learning. Apply here: https://www.kuleuven.be/personeel/jobsite/jobs/55968303

Join Us for the Memory Decoding Journal Club! A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience. This time, we’re diving into a groundbreaking paper: "Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

Sleep strongly affects synaptic strength, making it critical for cognition, especially learning and memory formation. Whether and how sleep deprivation modulates human brain physiology and cognition is poorly understood. Here we examined how overnight sleep deprivation vs overnight sufficient sleep affects (a) cortical excitability, measured by transcranial magnetic stimulation, (b) inducibility of long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity via transcranial direct current stimulation (tDCS), and (c) learning, memory, and attention. We found that sleep deprivation increases cortical excitability due to enhanced glutamate-related cortical facilitation and decreases and/or reverses GABAergic cortical inhibition. Furthermore, tDCS-induced LTP-like plasticity (anodal) abolishes while the inhibitory LTD-like plasticity (cathodal) converts to excitatory LTP-like plasticity under sleep deprivation. This is associated with increased EEG theta oscillations due to sleep pressure. Motor learning, behavioral counterparts of plasticity, and working memory and attention, which rely on cortical excitability, are also impaired during sleep deprivation. Our study indicates that upscaled brain excitability and altered plasticity, due to sleep deprivation, are associated with impaired cognitive performance. Besides showing how brain physiology and cognition undergo changes (from neurophysiology to higher-order cognition) under sleep pressure, the findings have implications for variability and optimal application of noninvasive brain stimulation.

Although bradykinesia, tremor, and rigidity are hallmark motor defects in Parkinson’s disease (PD) patients, they also experience motor learning impairments and non-motor symptoms such as depression. The neural basis for these different PD symptoms are not well understood. While current treatments are effective for locomotion deficits in PD, therapeutic strategies targeting motor learning deficits and non-motor symptoms are lacking. We found that distinct parafascicular (PF) thalamic subpopulations project to caudate putamen (CPu), subthalamic nucleus (STN), and nucleus accumbens (NAc). While PF-->CPu and PF-->STN circuits are critical for locomotion and motor learning respectively, inhibition of the PF-->NAc circuit induced a depression-like state. While chemogenetically manipulating CPu-projecting PF neurons led to a long-term restoration of locomotion, optogenetic long-term potentiation at PF-->STN synapses restored motor learning behavior in PD model mice. Furthermore, activation of NAc-projecting PF neurons rescued depression-like PD phenotypes. Importantly, we identified nicotinic acetylcholine receptors capable of modulating PF circuits to rescue different PD phenotypes. Thus, targeting PF thalamic circuits may be an effective strategy for treating motor and non-motor deficits in PD.

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 hands, but the amplitudes of voluntary contraction of the muscles for the APB and ADM did not change significantly. The behavioral results correspond with a previously described suggestion that hand strength and hand dexterity are not directly related as well as an increase in overlaps between MCRs of the trained muscles supports the idea that voluntary muscle relaxation is an active physiological process.

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation-based visual discrimination task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in decision tasks.

Learning is known to induce the formation of new dendritic spines, but despite decades of effort, the functional properties of new spines in vivo remain unknown. Here, using a combination of longitudinal in vivo 2-photon imaging of the glutamate reporter, iGluSnFR, and correlated electron microscopy (CLEM) of dendritic spines on the apical dendrites of L2/3 excitatory neurons in the motor cortex during motor learning, we describe a framework of new spines' formation, survival, and resulting function. Specifically, our data indicate that the potentiation of a subset of clustered, pre-existing spines showing task-related activity in early sessions of learning creates a micro-environment of plasticity within dendrites, wherein multiple filopodia sample the nearby neuropil, form connections with pre-existing boutons connected to allodendritic spines, and are then selected for survival based on co-activity with nearby task-related spines. Thus, the formation and survival of new spines is determined by the functional micro-environment of dendrites. After formation, new spines show preferential co-activation with nearby task-related spines. This synchronous activity is more specific to movements than activation of the individual spines in isolation, and further, is coincident with movements that are more similar to the learned pattern. Thus, new spines functionally engage with their parent clusters to signal the learned movement. Finally, by reconstructing the axons associated with new spines, we found that they synapse with axons previously unrepresented in these dendritic domains, suggesting that the strong local co-activity structure exhibited by new spines is likely not due to axon sharing. Thus, learning involves the binding of new information streams into functional synaptic clusters to subserve the learned behavior.

The primary motor cortex (M1) is a major output center for movement execution and motor learning, and its dysfunction contributes to the pathophysiology of Parkinson’s disease (PD). While human studies have indicated that a loss of midbrain dopamine neurons alters M1 activation, the mechanisms underlying this phenomenon remain unclear. Using a mouse model of PD, we uncovered several shifts within M1 circuitry following dopamine depletion, including impaired excitation by thalamocortical afferents and altered excitability. Our findings add to the growing body of literature highlighting M1 as a major contributor in PD, and provide targeted neural substrates for possible therapeutic interventions.

Motor learning is typically assessed in the lab, affording a high degree of control over the task environment. However, this level of control often comes at the cost of smaller sample sizes and a homogenous pool of participants (e.g. college students). To address this, we have designed a web-based motor learning experiment, making it possible to reach a larger, more diverse set of participants. As a proof-of-concept, we collected 1,581 participants completing a visuomotor rotation task, where participants controlled a visual cursor on the screen with their mouse and trackpad. Motor learning was indexed by how fast participants were able to compensate for a 45° rotation imposed between the cursor and their actual movement. Using a cross-validated LASSO regression, we found that motor learning varied significantly with the participant’s age and sex, and also strongly correlated with the location of the target, visual acuity, and satisfaction with the experiment. In contrast, participants' mouse and browser type were features eliminated by the model, indicating that motor performance was not influenced by variations in computer hardware and software. Together, this proof-of-concept study demonstrates how large datasets can generate important insights into the factors underlying motor learning.

The CNS is responsive to an ever-changing environment. Until recently, studies of neural plasticity focused almost exclusively on functional and structural changes of neuronal synapses. In recent years, myelin plasticity has emerged as a potential modulator of neural networks. Myelination of previously unmyelinated axons, and changes in the structure on already-myelinated axons, can have large effects on network function. The heterogeneity of the extent of how axons in the CNS are myelinated offers diverse scope for dynamic myelin changes to fine-tune neural circuits. The traditionally held view of myelin as a passive insulator of axons is now changing to one of lifelong changes in myelin, modulated by neuronal activity and experience. Myelin, produced by oligodendrocytes (OLs), is essential for normal brain function, as it provides fast signal transmission, promotes synchronization of neuronal signals and helps to maintain neuronal function. OLs differentiate from oligodendrocyte precursor cells (OPCs), which are distributed throughout the adult brain, and myelination continues into late adulthood. OPCs can sense neuronal activity as they receive synaptic inputs from neurons and express voltage-gated ion channels and neurotransmitter receptors, and differentiate into myelinating OLs in response to changes in neuronal activity. This lecture will explore to what extent myelin plasticity occurs in adult animals, whether myelin changes occur in non-motor learning tasks, especially in learning and memory, and questions whether myelin plasticity and myelin regeneration are two sides of the same coin.

Analogies are widely used by teachers and coaches of different movement disciplines, serving a role during the learning phase of a new skill, and honing one’s performance to a competitive level. In previous studies, analogies improved motor control in various tasks and across age groups. Our study aimed to evaluate the efficacy of analogies throughout the learning process, using kinematic measures for an in-depth analysis. We tested whether applying analogies can shorten the motor learning process and induce insight and skill improvement in tasks that usually demand many hours of practice. The experiment included a drawing task, in which subjects were asked to connect four dots into a closed shape, and a mirror game, in which subjects tracked an oval that moved across the screen. After establishing a baseline, subjects were given an analogy, explicit instructions, or no further instruction. We compared their improvement in overall skill, accuracy, and speed. Subjects in the analogy and explicit groups improved their performance in the drawing task, while significant differences were found in the mirror game only for slow movements between analogy and controls. In conclusion, analogies are an important tool for teachers and coaches, and more research is needed to understand how to apply them for maximum results. They can rapidly change motor control and strategy but may also affect only some aspects of a movement and not others. Careful thought is needed to construct an effective analogy that encompasses relevant movement facets, as well as the practitioner’s personal background and experience.

Animals control motor actions at multiple timescales. We use larval zebrafish and advanced optical microscopy to understand the underlying neural mechanisms. First, we examined the mechanisms of short-term motor learning by using whole-brain neural activity imaging. We found that the 5-HT system integrates the sensory outcome of actions and determines future motor patterns. Second, we established a method for recording spiking activity and membrane potential from a population of neurons during behavior. We identified putative motor command signals and internal copy signals that encode millisecond-scale details of the swimming dynamics. These results demonstrate that zebrafish provide a holistic and mechanistic understanding of the neural basis of motor control in vertebrate brains.

The songbird zebra finch is an exemplary model system in which to study trial-and-error learning, as the bird learns its single song gradually through the production of many noisy renditions. It is also a good system in which to study the maintenance of motor skills, as the adult bird actively maintains its song and retains some residual plasticity. Motor learning occurs through the association of timing within the song, represented by sparse firing in nucleus HVC, with motor output, driven by nucleus RA. Here we show through modeling that the small level of observed variability in HVC can result in a network which is more easily able to adapt to change, and is most robust to cell damage or death, than an unperturbed network. In collaboration with Carlos Lois’ lab, we also consider the effect of directly perturbing HVC through viral injection of toxins that affect the firing of projection neurons. Following these perturbations, the song is profoundly affected but is able to almost perfectly recover. We characterize the changes in song acoustics and syntax, and propose models for HVC architecture and plasticity that can account for some of the observed effects. Finally, we suggest a potential role for inputs from nucleus Uva in helping to control timing precision in HVC.

Using Systems Neuroscience Approaches to Understand Motor Learning & Recovery Post-Stroke

Studies of neural circuit plasticity focus almost exclusively on functional and structural changes of neuronal synapses. In recent years, however, myelin plasticity has emerged as a potential modulator of neuronal networks. Myelination of previously unmyelinated axons and changes in the structure on already-myelinated axons can have large effects on the function of neuronal networks. Yet myelination has been mostly studied in relation to its functional and metabolic activity. Myelin modifications are increasingly being implicated as a mechanism for sensory-motor learning and unpublished data from our lab indicate that myelination also occurs during cognitive non-motor learning. It is, however, unclear how specific these myelin changes are and even less is known of the underlying mechanisms of learning-evoked myelin plasticity. In this journal club, Dr Giulia Bonetto will provide a general overview on myelin plasticity. Additionally, she will present new data addressing the role of myelin plasticity in cognitive non-motor learning.

Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensori-motor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.

Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of-the-art neural interface performance. I’ll then survey recent work that highlights the likelihood that BMIs, much like natural motor learning, engages multiple distributed learning mechanisms that can be carefully interrogated with BMI.