scholarly journals Distinct roles for motor cortical and thalamic inputs to striatum during motor learning and execution

2019 ◽  
Author(s):  
Steffen B. E. Wolff ◽  
Raymond Ko ◽  
Bence P. Ölveczky
Keyword(s):  
Motor Cortex ◽  
Motor Learning ◽  
Learning Process ◽  
Brain Areas ◽  
Thalamic Neurons ◽  
Cortical Input ◽  
Motor Circuits ◽  
Motor Network ◽  
Motor Cortical ◽  
Cortical Inputs

AbstractThe acquisition and execution of learned motor sequences are mediated by a distributed motor network, spanning cortical and subcortical brain areas. The sensorimotor striatum is an important cog in this network, yet how its two main inputs, from motor cortex and thalamus respectively, contribute to its role in motor learning and execution remains largely unknown. To address this, we trained rats in a task that produces highly stereotyped and idiosyncratic motor sequences. We found that motor cortical input to the sensorimotor striatum is critical for the learning process, but after the behaviors were consolidated, this corticostriatal pathway became dispensable. Functional silencing of striatal-projecting thalamic neurons, however, disrupted the execution of the learned motor sequences, causing rats to revert to behaviors produced early in learning and preventing them from re-learning the task. These results show that the sensorimotor striatum is a conduit through which motor cortical inputs can drive experience-dependent changes in subcortical motor circuits, likely at thalamostriatal synapses.

scholarly journals Decorrelation of cortical inputs and motoneuron output

2011 ◽  
Vol 106 (5) ◽  
pp. 2688-2697 ◽  
Author(s):  
Francesco Negro ◽  
Dario Farina
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Small Population ◽  
Spectral Lines ◽  
Spinal Motoneurons ◽  
Force Output ◽  
Cortical Input ◽  
Motoneuron Pool ◽  
Discharge Rates ◽  
Cortical Inputs

Oscillations in the primary motor cortex are transmitted through the corticospinal tract to the motoneuron pool. This pathway is believed to produce an effective and direct command from the motor cortex to the spinal motoneurons for the modulation of the force output. In this study, we used a computational model of a population of motoneurons to investigate the factors that can influence the transmission of the cortical input to the output of motoneurons, since it can be quantified by coherence analysis. The simulations demonstrated that, despite the nonlinearity of the motoneurons, oscillations present in the cortical input are transmitted to the output of the motoneuron pool at the same frequency. However, the interference introduced by the nonlinearity of the system increases the variability of the oscillations in output, introducing spectral lines whose frequency depends on the input frequencies and the motoneuron discharge rates. Moreover, an additional source of synaptic input common to all motoneurons but independent from the corticospinal component decorrelates the cortical input and motoneuron output and, thus, decreases the magnitude of the estimated coherence, even if the effective cortical drive does not change. These results indicate that the corticospinal input can effectively be sampled by a small population of motoneurons. However, the transmission of a corticospinal drive to the motoneuron pool is influenced by the nonlinearity of the spiking processes of the active motoneurons and by synaptic inputs common to the motoneuron population but independent from the cortical input.

scholarly journals Somatosensory cortical excitability changes precede those in motor cortex during human motor learning

2019 ◽  
Vol 122 (4) ◽  
pp. 1397-1405 ◽  
Author(s):  
Hiroki Ohashi ◽  
Paul L. Gribble ◽  
David J. Ostry
Keyword(s):  
Motor Cortex ◽  
Motor Learning ◽  
Evoked Potentials ◽  
Somatosensory Cortex ◽  
Somatosensory Evoked Potentials ◽  
Motor Skill ◽  
Cortical Excitability ◽  
Motor Evoked Potentials ◽  
Human Motor ◽  
Motor Cortical

Motor learning is associated with plasticity in both motor and somatosensory cortex. It is known from animal studies that tetanic stimulation to each of these areas individually induces long-term potentiation in its counterpart. In this context it is possible that changes in motor cortex contribute to somatosensory change and that changes in somatosensory cortex are involved in changes in motor areas of the brain. It is also possible that learning-related plasticity occurs in these areas independently. To better understand the relative contribution to human motor learning of motor cortical and somatosensory plasticity, we assessed the time course of changes in primary somatosensory and motor cortex excitability during motor skill learning. Learning was assessed using a force production task in which a target force profile varied from one trial to the next. The excitability of primary somatosensory cortex was measured using somatosensory evoked potentials in response to median nerve stimulation. The excitability of primary motor cortex was measured using motor evoked potentials elicited by single-pulse transcranial magnetic stimulation. These two measures were interleaved with blocks of motor learning trials. We found that the earliest changes in cortical excitability during learning occurred in somatosensory cortical responses, and these changes preceded changes in motor cortical excitability. Changes in somatosensory evoked potentials were correlated with behavioral measures of learning. Changes in motor evoked potentials were not. These findings indicate that plasticity in somatosensory cortex occurs as a part of the earliest stages of motor learning, before changes in motor cortex are observed. NEW & NOTEWORTHY We tracked somatosensory and motor cortical excitability during motor skill acquisition. Changes in both motor cortical and somatosensory excitability were observed during learning; however, the earliest changes were in somatosensory cortex, not motor cortex. Moreover, the earliest changes in somatosensory cortical excitability predict the extent of subsequent learning; those in motor cortex do not. This is consistent with the idea that plasticity in somatosensory cortex coincides with the earliest stages of human motor learning.

scholarly journals Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
James M. Murray ◽  
G. Sean Escola
Keyword(s):  
Motor Cortex ◽  
Motor Learning ◽  
Motor Skills ◽  
Neural Mechanisms ◽  
Neural Basis ◽  
Brain Areas ◽  
Multiple Timescales ◽  
Learned Behaviors ◽  
Future Learning ◽  
Slow Learning

AbstractThe learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support motor learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.

scholarly journals Dendritic Na+ spikes enable cortical input to drive action potential output from hippocampal CA2 pyramidal neurons

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Qian Sun ◽  
Kalyan V Srinivas ◽  
Alaba Sotayo ◽  
Steven A Siegelbaum
Keyword(s):  
Action Potential ◽  
Hippocampal Neurons ◽  
Pyramidal Neurons ◽  
Brain Areas ◽  
Cortical Input ◽  
Strong Excitation ◽  
Dendritic Spikes ◽  
Efficient Coupling ◽  
Dendritic Arbors ◽  
Cortical Inputs

Synaptic inputs from different brain areas are often targeted to distinct regions of neuronal dendritic arbors. Inputs to proximal dendrites usually produce large somatic EPSPs that efficiently trigger action potential (AP) output, whereas inputs to distal dendrites are greatly attenuated and may largely modulate AP output. In contrast to most other cortical and hippocampal neurons, hippocampal CA2 pyramidal neurons show unusually strong excitation by their distal dendritic inputs from entorhinal cortex (EC). In this study, we demonstrate that the ability of these EC inputs to drive CA2 AP output requires the firing of local dendritic Na+ spikes. Furthermore, we find that CA2 dendritic geometry contributes to the efficient coupling of dendritic Na+ spikes to AP output. These results provide a striking example of how dendritic spikes enable direct cortical inputs to overcome unfavorable distal synaptic locale to trigger axonal AP output and thereby enable efficient cortico-hippocampal information flow.

scholarly journals Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing

2017 ◽  
Vol 118 (2) ◽  
pp. 1235-1243 ◽  
Author(s):  
Heather R. McGregor ◽  
Paul L. Gribble
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Motor Learning ◽  
Resting State ◽  
Brain Function ◽  
Motor Recovery ◽  
Resting State Functional Connectivity ◽  
Brain Areas ◽  
Motor Network ◽  
Sensory Motor

We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learning. Preobservation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory-motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke.

Changes in Brain Activity During Motor Learning Measured With PET: Effects of Hand of Performance and Practice

1998 ◽  
Vol 80 (4) ◽  
pp. 2177-2199 ◽  
Author(s):  
H. van Mier ◽  
L. W. Tempel ◽  
J. S. Perlmutter ◽  
M. E. Raichle ◽  
S. E. Petersen
Keyword(s):  
Motor Cortex ◽  
Motor Learning ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Brain Areas ◽  
Left Hand ◽  
Eyes Closed ◽  
Hand Performance ◽  
Right And Left Hand

van Mier, H., L. W. Tempel, J. S. Perlmutter, M. E. Raichle, and S. E. Petersen. Changes in brain activity during motor learning measured with PET: effects of hand of performance and practice. J. Neurophysiol. 80: 2177–2199, 1998. The aim of this study is to assess brain activity measured during continuous performance of design tracing tasks. Three issues were addressed: identification of brain areas involved in performing maze and square tracing tasks, investigation of differences and similarities in these areas related to dominant and nondominant hand performance, and most importantly, examination of the effects of practice in these areas. A total of 32 normal, right-handed subjects were instructed to move a pen with the dominant right hand (16 subjects) or nondominant left hand (16 subjects) continuously through cut-out maze and square patterns with their eyes closed during a 40-s positron emission tomography (PET) scan to measure regional blood flow. There were six conditions: 1) holding the pen on a writing tablet without moving it (rest condition); 2) tracing a maze without practice; 3) tracing the same maze after 10 min of practice; 4) tracing a novel maze; and tracing an easily learned square design at 5) high or 6) low speed. To identify brain areas generally related to continuous tracing, data analyses were performed on the combined data acquired during the five tracing scans minus rest conditions. Areas activated included: primary and secondary motor areas, somatosensory, parietal, and inferior frontal cortex, thalamus, and several cerebellar regions. Then comparisons were made between right- and left-hand performance. There were no significant differences in performance. As for brain activations, only primary motor cortex and anterior cerebellum showed activations that switched with hand of performance. All other areas, with the exception of the midbrain, showed activations that were common for both right- and left-hand performance. These areas were further analyzed for significant conditional effects. We found patterns of activation related to velocity in the contralateral primary motor cortex, related to unskilled performance in right premotor and parietal areas and left cerebellum, related to skilled performance in supplementary motor area (SMA), and related to the level of capacity at which subjects were performing in left premotor cortex, ipsilateral anterior cerebellum, right posterior cerebellum and right dentate nucleus. These findings demonstrate two important principles: 1) practice produces a shift in activity from one set of areas to a different area and 2) practice-related activations appeared in the same hemisphere regardless of the hand used, suggesting that some of the areas related to maze learning must code information at an abstract level that is distinct from the motor performance of the task itself.

scholarly journals Remembrance of things practiced: Fast and slow learning in cortical and subcortical pathways

2019 ◽  
Author(s):  
James M. Murray ◽  
G. Sean Escola
Keyword(s):  
Motor Cortex ◽  
Motor Skills ◽  
Learning Process ◽  
Neural Mechanisms ◽  
Neural Basis ◽  
Brain Areas ◽  
Multiple Timescales ◽  
Learned Behaviors ◽  
Future Learning ◽  
Slow Learning

The learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support such learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning, and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.

scholarly journals Shaping the Sensory–Motor Network by Short-Term Unresolvable Sensory–Motor Mismatch

2022 ◽  
Vol 12 ◽  
Author(s):  
Carsten M. Klingner ◽  
Fabian Kattlun ◽  
Lena Krolopp ◽  
Elisabeth Jochmann ◽  
Gerd F. Volk ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Motor Learning ◽  
Motor Adaptation ◽  
Brain State ◽  
Short Term ◽  
Motor Representation ◽  
Learning From Errors ◽  
Motor Network ◽  
Sensory Motor

Learning from errors as the main mechanism for motor adaptation has two fundamental prerequisites: a mismatch between the intended and performed movement and the ability to adapt motor actions. Many neurological patients are limited in their ability to transfer an altered motor representation into motor action due to a compromised motor pathway. Studies that have investigated the effects of a sustained and unresolvable mismatch over multiple days found changes in brain processing that seem to optimize the potential for motor learning (increased drive for motor adaptation and a weakening of the current implementation of motor programs). However, it remains unclear whether the observed effects can be induced experimentally and more important after shorter periods. Here, we used task-based and resting-state fMRI to investigate whether the known pattern of cortical adaptations due to a sustained mismatch can be induced experimentally by a short (20 min), but unresolvable, sensory–motor mismatch by impaired facial movements in healthy participants by transient facial tapping. Similar to long-term mismatch, we found plastic changes in a network that includes the striatal, cerebellar and somatosensory brain areas. However, in contrast to long-term mismatch, we did not find the involvement of the cerebral motor cortex. The lack of the involvement of the motor cortex can be interpreted both as an effect of time and also as an effect of the lack of a reduction in the motor error. The similar effects of long-term and short-term mismatch on other parts of the sensory–motor network suggest that the brain-state caused by long-term mismatch can be (at least partly) induced by short-term mismatch. Further studies should investigate whether short-term mismatch interventions can be used as therapeutic strategy to induce an altered brain-state that increase the potential for motor learning.

Sign in / Sign up

Export Citation Format

Share Document