scholarly journals Modulation of neural activity in frontopolar cortex drives reward-based motor learning

2021 ◽  
Author(s):  
Maria Herrojo Ruiz ◽  
Tom Maudrich ◽  
Benjamin Kalloch ◽  
Daniela Sammler ◽  
Rouven Kenville ◽  
...  
Keyword(s):  
Motor Learning ◽  
Learning Strategy ◽  
Computational Modelling ◽  
Learning Task ◽  
Motor Sequence ◽  
Healthy Human ◽  
Frontopolar Cortex ◽  
Human Participants ◽  
Efficient Learning ◽  
The Right

Abstract The frontopolar cortex (FPC) contributes to tracking the reward of alternative choices during decision making, as well as their reliability. Whether this FPC function extends to reward gradients associated with continuous movements during motor learning remains unknown. We used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants completed a motor sequence learning task using trialwise reward feedback to discover a hidden goal along a continuous dimension: timing. As additional conditions, we modulated the contralateral motor cortex (left M1) activity, and included a control sham stimulation. Right FPC-tDCS led to faster learning compared to lM1-tDCS and sham through regulation of motor variability. Computational modelling revealed that in all stimulation protocols, an increase in the trialwise expectation of reward was followed by greater exploitation, as shown previously. Yet, this association was weaker in lM1-tDCS suggesting a less efficient learning strategy. The effects of frontopolar stimulation were dissociated from those induced by lM1-tDCS and sham, as motor exploration was more sensitive to inferred changes in the reward tendency (volatility). The findings suggest that rFPC-tDCS increases the sensitivity of motor exploration to updates in reward volatility, accelerating reward-based motor learning.

scholarly journals Modulation of neural activity in frontopolar cortex drives reward-based motor learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Herrojo Ruiz ◽  
T. Maudrich ◽  
B. Kalloch ◽  
D. Sammler ◽  
R. Kenville ◽  
...  
Keyword(s):  
Motor Learning ◽  
Learning Strategy ◽  
Computational Modelling ◽  
Performance Goal ◽  
Healthy Human ◽  
Frontopolar Cortex ◽  
Human Participants ◽  
Efficient Learning ◽  
The Right ◽  
Control Sham

AbstractThe frontopolar cortex (FPC) contributes to tracking the reward of alternative choices during decision making, as well as their reliability. Whether this FPC function extends to reward gradients associated with continuous movements during motor learning remains unknown. We used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants practiced novel sequences of finger movements on a digital piano with corresponding auditory feedback. Their aim was to use trialwise reward feedback to discover a hidden performance goal along a continuous dimension: timing. We additionally modulated the contralateral motor cortex (left M1) activity, and included a control sham stimulation. Right FPC-tDCS led to faster learning compared to lM1-tDCS and sham through regulation of motor variability. Bayesian computational modelling revealed that in all stimulation protocols, an increase in the trialwise expectation of reward was followed by greater exploitation, as shown previously. Yet, this association was weaker in lM1-tDCS suggesting a less efficient learning strategy. The effects of frontopolar stimulation were dissociated from those induced by lM1-tDCS and sham, as motor exploration was more sensitive to inferred changes in the reward tendency (volatility). The findings suggest that rFPC-tDCS increases the sensitivity of motor exploration to updates in reward volatility, accelerating reward-based motor learning.

scholarly journals Modulation of neural activity in frontopolar cortex drives reward-based motor learning

2020 ◽  
Author(s):  
M Herrojo Ruiz ◽  
T Maudrich ◽  
B Kalloch ◽  
D Sammler ◽  
R Kenville ◽  
...  
Keyword(s):  
Decision Making ◽  
Motor Learning ◽  
Motor Skill ◽  
Brain Regions ◽  
Skill Learning ◽  
Motor Skill Learning ◽  
Motor Sequence ◽  
Performance Goal ◽  
Healthy Human ◽  
Frontopolar Cortex

AbstractDecision-making is increasingly being recognised to play a role in learning motor skills. Understanding the neural processes regulating motor decision-making is therefore essential to identify mechanisms that contribute to motor skill learning. In decision-making tasks, the frontopolar cortex (FPC) is involved in tracking the reward of different alternative choices, as well as their reliability. Whether this FPC function extends to reward landscapes associated with a continuous movement dimension remains unknown. Here we used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants completed a motor sequence learning task using trial-wise reward feedback to discover a hidden performance goal along a continuous dimension: timing. As a control condition, we modulated contralateral motor cortex (left M1) activity with tDCS, which has been shown to benefit motor skill learning but less consistently reward-based motor learning. Each active tDCS condition was contrasted to sham stimulation. Right FPC-tDCS led to faster learning primarily through a regulation of exploration, without concurrent modulation of motor noise. A Bayesian computational model revealed that following rFPC-tDCS, participants had a higher expectation of reward, consistent with their faster learning. These higher reward estimates were inferred to be less volatile, and thus participants under rFPC-tDCS deemed the mapping between movement and reward to be more stable. Relative to sham, lM1-tDCS did not significantly modulate main behavioral outcomes. The results indicate that brain regions previously linked to decision-making, such as the FPC, are relevant for motor skill learning.

scholarly journals Association of COMT val158met and DRD2 G>T genetic polymorphisms with individual differences in motor learning and performance in female young adults

2014 ◽  
Vol 111 (3) ◽  
pp. 628-640 ◽  
Author(s):  
Fatemeh Noohi ◽  
Nate B. Boyden ◽  
Youngbin Kwak ◽  
Jennifer Humfleet ◽  
David T. Burke ◽  
...  
Keyword(s):  
Individual Differences ◽  
Motor Learning ◽  
Genetic Polymorphisms ◽  
Sequence Learning ◽  
Learning Task ◽  
Striatal Dopamine ◽  
Motor Sequence Learning ◽  
Motor Sequence ◽  
Comt Val158met ◽  
Adaptation Task

Individuals learn new skills at different rates. Given the involvement of corticostriatal pathways in some types of learning, variations in dopaminergic transmission may contribute to these individual differences. Genetic polymorphisms of the catechol- O-methyltransferase (COMT) enzyme and dopamine receptor D2 (DRD2) genes partially determine cortical and striatal dopamine availability, respectively. Individuals who are homozygous for the COMT methionine ( met) allele show reduced cortical COMT enzymatic activity, resulting in increased dopamine levels in the prefrontal cortex as opposed to individuals who are carriers of the valine ( val) allele. DRD2 G-allele homozygotes benefit from a higher striatal dopamine level compared with T-allele carriers. We hypothesized that individuals who are homozygous for COMT met and DRD2 G alleles would show higher rates of motor learning. Seventy-two young healthy females (20 ± 1.9 yr) performed a sensorimotor adaptation task and a motor sequence learning task. A nonparametric mixed model ANOVA revealed that the COMT val-val group demonstrated poorer performance in the sequence learning task compared with the met-met group and showed a learning deficit in the visuomotor adaptation task compared with both met-met and val-met groups. The DRD2 TT group showed poorer performance in the sequence learning task compared with the GT group, but there was no difference between DRD2 genotype groups in adaptation rate. Although these results did not entirely come out as one might predict based on the known contribution of corticostriatal pathways to motor sequence learning, they support the role of genetic polymorphisms of COMT val158met (rs4680) and DRD2 G>T (rs 1076560) in explaining individual differences in motor performance and motor learning, dependent on task type.

scholarly journals Cognitive control affects motor learning through local variations in GABA within the primary motor cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuki Maruyama ◽  
Masaki Fukunaga ◽  
Sho K. Sugawara ◽  
Yuki H. Hamano ◽  
Tetsuya Yamamoto ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Motor Cortex ◽  
Motor Learning ◽  
Cognitive Control ◽  
Primary Motor Cortex ◽  
Resonance Spectroscopy ◽  
Motor Sequence ◽  
Sensorimotor Network ◽  
Preparatory Activity ◽  
The Right

AbstractThe primary motor cortex (M1) is crucial for motor learning; however, its interaction with other brain areas during motor learning remains unclear. We hypothesized that the fronto-parietal execution network (FPN) provides learning-related information critical for the flexible cognitive control that is required for practice. We assessed network-level changes during sequential finger tapping learning under speed pressure by combining magnetic resonance spectroscopy and task and resting-state functional magnetic resonance imaging. There was a motor learning-related increase in preparatory activity in the fronto-parietal regions, including the right M1, overlapping the FPN and sensorimotor network (SMN). Learning-related increases in M1-seeded functional connectivity with the FPN, but not the SMN, were associated with decreased GABA/glutamate ratio in the M1, which were more prominent in the parietal than the frontal region. A decrease in the GABA/glutamate ratio in the right M1 was positively correlated with improvements in task performance (p = 0.042). Our findings indicate that motor learning driven by cognitive control is associated with local variations in the GABA/glutamate ratio in the M1 that reflects remote connectivity with the FPN, representing network-level motor sequence learning formations.

scholarly journals Effect of Repetitive Passive Movement Before Motor Skill Training on Corticospinal Excitability and Motor Learning Depend on BDNF Polymorphisms

2021 ◽  
Vol 15 ◽  
Author(s):  
Manh Van Pham ◽  
Shota Miyaguchi ◽  
Hiraku Watanabe ◽  
Kei Saito ◽  
Naofumi Otsuru ◽  
...  
Keyword(s):  
Motor Learning ◽  
Index Finger ◽  
Learning Task ◽  
Passive Movement ◽  
Corticospinal Excitability ◽  
Homeostatic Plasticity ◽  
Learning Ability ◽  
Learning Efficiency ◽  
Learning Tasks ◽  
The Right

A decrease in cortical excitability tends to be easily followed by an increase induced by external stimuli via a mechanism aimed at restoring it; this phenomenon is called “homeostatic plasticity.” In recent years, although intervention methods aimed at promoting motor learning using this phenomenon have been studied, an optimal intervention method has not been established. In the present study, we examined whether subsequent motor learning can be promoted further by a repetitive passive movement, which reduces the excitability of the primary motor cortex (M1) before motor learning tasks. We also examined the relationship between motor learning and the brain-derived neurotrophic factor. Forty healthy subjects (Val/Val genotype, 17 subjects; Met carrier genotype, 23 subjects) participated. Subjects were divided into two groups of 20 individuals each. The first group was assigned to perform the motor learning task after an intervention consisting in the passive adduction–abduction movement of the right index finger at 5 Hz for 10 min (RPM condition), while the second group was assigned to perform the task without the passive movement (control condition). The motor learning task consisted in the visual tracking of the right index finger. The results showed that the corticospinal excitability was transiently reduced after the passive movement in the RPM condition, whereas it was increased to the level detected in the control condition after the motor learning task. Furthermore, the motor learning ability was decreased immediately after the passive movement; however, the motor performance finally improved to the level observed in the control condition. In individuals carrying the Val/Val genotype, higher motor learning was also found to be related to the more remarkable changes in corticospinal excitability caused by the RPM condition. This study revealed that the implementation of a passive movement before a motor learning tasks did not affect M1 excitatory changes and motor learning efficiency; in contrast, in subjects carrying the Val/Val polymorphism, the more significant excitatory changes in the M1 induced by the passive movement and motor learning task led to the improvement of motor learning efficiency. Our results also suggest that homeostatic plasticity occurring in the M1 is involved in this improvement.

Stress Modulates the Balance between Hippocampal and Motor Networks during Motor Memory Processing

2020 ◽  
Author(s):  
N Dolfen ◽  
B R King ◽  
L Schwabe ◽  
M A Gann ◽  
M P Veldman ◽  
...  
Keyword(s):  
Motor Learning ◽  
Sequence Learning ◽  
Learning Task ◽  
Motor Memory ◽  
Motor Sequence Learning ◽  
Motor Sequence ◽  
Memory Processing ◽  
Sleep Episode ◽  
Cortical Regions ◽  
The Impact

Abstract The functional interaction between hippocampo- and striato-cortical regions during motor sequence learning is essential to trigger optimal memory consolidation. Based on previous evidence from other memory domains that stress alters the balance between these systems, we investigated whether exposure to stress prior to motor learning modulates motor memory processes. Seventy-two healthy young individuals were exposed to a stressful or nonstressful control intervention prior to training on a motor sequence learning task in a magnetic resonance imaging (MRI) scanner. Consolidation was assessed with an MRI retest after a sleep episode. Behavioral results indicate that stress prior to learning did not influence motor performance. At the neural level, stress induced both a larger recruitment of sensorimotor regions and a greater disengagement of hippocampo-cortical networks during training. Brain-behavior regression analyses showed that while this stress-induced shift from (hippocampo-)fronto-parietal to motor networks was beneficial for initial performance, it was detrimental for consolidation. Our results provide the first experimental evidence that stress modulates the neural networks recruited during motor memory processing and therefore effectively unify concepts and mechanisms from diverse memory fields. Critically, our findings suggest that intersubject variability in brain responses to stress determines the impact of stress on motor learning and subsequent consolidation.

Rapid Modulation of GABA Concentration in Human Sensorimotor Cortex During Motor Learning

2006 ◽  
Vol 95 (3) ◽  
pp. 1639-1644 ◽  
Author(s):  
Anna Floyer-Lea ◽  
Marzena Wylezinska ◽  
Tamas Kincses ◽  
Paul M. Matthews
Keyword(s):  
Motor Learning ◽  
Sensorimotor Cortex ◽  
Sequence Learning ◽  
Learning Task ◽  
Resonance Spectroscopy ◽  
Motor Sequence Learning ◽  
Motor Sequence ◽  
Gaba Concentration ◽  
Primary Sensorimotor Cortex

Movement representations within the human primary motor and somatosensory cortices can be altered by motor learning. Decreases in local GABA concentration and its release may facilitate this plasticity. Here we use in vivo magnetic resonance spectroscopy (MRS) to noninvasively measure serial changes in GABA concentration in humans in a brain region including the primary sensorimotor cortex contralateral to the hand used for an isometric motor sequence learning task. Thirty minutes of motor sequence learning reduced the mean GABA concentration within a 2 × 2 × 2-cm3 voxel by almost 20%. This reduction was specific to motor learning: 30 min of similar, movements with an unlearnable, nonrepetitive sequence were not associated with changes in GABA concentration. No significant changes in GABA concentration were found in the primary sensorimotor cortex ipsilateral to the hand used for learning. These changes suggest remarkably rapid, regionally specific short-term presynaptic modulation of GABAergic input that should facilitate motor learning. Although apparently confined to the contralateral hemisphere, the magnitude of changes seen within a large spectroscopic voxel suggests that these changes occur over a wide local neocortical field.

scholarly journals Accumulation of Salient Perceptual Events Predicts Human Subjective Time

2020 ◽  
Author(s):  
Maxine T. Sherman ◽  
Zafeirios Fountas ◽  
Anil K. Seth ◽  
Warrick Roseboom
Keyword(s):  
Time Perception ◽  
Sensory Processing ◽  
Computational Modelling ◽  
Subjective Time ◽  
Clock Time ◽  
Healthy Human ◽  
Time Estimates ◽  
Experience Of Time ◽  
Human Participants ◽  
Computational Basis

AbstractHuman experience of time exhibits systematic, context-dependent deviations from objective clock time, for example, time is experienced differently at work than on holiday. However, leading explanations of time perception are not equipped to explain these deviations. Here we test the idea that these deviations arise because time estimates are constructed by accumulating the same quantity that guides perception: salient events. To test this, healthy human participants watched naturalistic, silent videos and estimated their duration while fMRI was acquired. Using computational modelling, we show that accumulated events in visual, auditory and somatosensory cortex all predict ‘clock time’, but duration biases reflecting human experience of time could only be predicted from the region involved in modality-specific sensory processing: visual cortex. Our results reveal that human subjective time is based on information arising during the processing of our dynamic sensory environment, providing a computational basis for an end-to-end account of time perception.

scholarly journals Theta and alpha power across fast and slow timescales in cognitive control

2020 ◽  
Author(s):  
Pieter Huycke ◽  
Pieter Verbeke ◽  
C. Nico Boehler ◽  
Tom Verguts
Keyword(s):  
Cognitive Control ◽  
Cognitive Processes ◽  
Learning Task ◽  
Neural Oscillations ◽  
Alpha Power ◽  
Exact Role ◽  
Healthy Human ◽  
Alpha Frequency ◽  
Human Participants ◽  
Slow Timescale

Theta and alpha frequency neural oscillations are important for learning and cognitive control, but their exact role has remained obscure. In particular, it is unknown whether they operate at similar timescales, and whether they support different cognitive processes. We recorded EEG in 30 healthy human participants while they performed a procedural learning task containing both novel (block-unique) and repeating stimuli. We investigated behavior and electrophysiology at both fast (i.e., within blocks) and slow (i.e., between blocks) time scales. Behaviorally, both response time and accuracy improved over both fast and slow timescales. At the same time, on the spectral level, theta power significantly decreased along the slow timescale, whereas alpha power instead significantly increased along the fast timescale. We thus demonstrate that theta and alpha both play a role during learning, but operate at different timescales. This result poses important empirical constraints for theories on learning, cognitive control, and neural oscillations.

scholarly journals Computational mechanisms of distributed value representations and mixed learning strategies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiva Farashahi ◽  
Alireza Soltani
Keyword(s):  
Learning Strategies ◽  
Learning Strategy ◽  
Learning Task ◽  
Neural Mechanisms ◽  
Inhibitory Neurons ◽  
Probabilistic Learning ◽  
Informative Feature ◽  
Human Participants ◽  
Alternative Solutions ◽  
Combining Estimates

AbstractLearning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations, connectivity, and lesioning of the RNNs, we demonstrate this mixed learning strategy relies on a distributed neural code and opponency between excitatory and inhibitory neurons through value-dependent disinhibition. Together, our results suggest computational and neural mechanisms underlying emergence of complex learning strategies in naturalistic settings.

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