Increasing human motor skill acquisition by driving theta-gamma coupling.
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ABSTRACT:
SUBMITTER: Akkad H
PROVIDER: S-EPMC8687660 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The primary motor cortex (M1) is critical for movement execution, but its role in motor skill acquisition remains elusive. Here, we examine the role of M1 intracortical circuits during skill acquisition. Paired-pulse transcranial magnetic stimulation (TMS) paradigms of short-interval intracortical facilitation (SICF) and inhibition (SICI) were used to assess excitatory and inhibitory circuits, respectively. We hypothesised that intracortical facilitation and inhibition circuits in M1 would be modulated to support acquisition of a novel visuomotor skill. Twenty-two young, neurologically healthy adults trained with their nondominant hand on a skilled and non-skilled sequential visuomotor isometric finger abduction task. Electromyographic recordings were obtained from the nondominant first dorsal interosseous (FDI) muscle. Corticomotor excitability, SICF, and SICI were examined before, at the midway point, and after the 10-block motor training. SICI was assessed using adaptive threshold-hunting procedures. Task performance improved after the skilled, but not non-skilled, task training, which likely reflected the increase in movement speed during training. The amplitudes of late SICF peaks were modulated with skilled task training. There was no modulation of the early SICF peak, SICI, and corticomotor excitability with either task training. There was also no association between skill acquisition and SICF or SICI. The findings indicate that excitatory circuitries responsible for the generation of late SICF peaks, but not the early SICF peak, are modulated in motor skill acquisition for a sequential visuomotor isometric finger abduction task.
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Project description:The plasticity of the human nervous system allows us to acquire an open-ended repository of sensorimotor skills in adulthood, such as the mastery of tools, musical instruments or sports. How novel sensorimotor skills are learned from scratch is yet largely unknown. In particular, the so-called inverse mapping from goal states to motor states is underdetermined because a goal can often be achieved by many different movements (motor redundancy). How humans learn to resolve motor redundancy and by which principles they explore high-dimensional motor spaces has hardly been investigated. To study this question, we trained human participants in an unfamiliar and redundant visually-guided manual control task. We qualitatively compare the experimental results with simulation results from a population of artificial agents that learned the same task by Goal Babbling, which is an inverse-model learning approach for robotics. In Goal Babbling, goal-related feedback guides motor exploration and thereby enables robots to learn an inverse model directly from scratch, without having to learn a forward model first. In the human experiment, we tested whether different initial conditions (starting positions of the hand) influence the acquisition of motor synergies, which we identified by Principal Component Analysis in the motor space. The results show that the human participants' solutions are spatially biased towards the different starting positions in motor space and are marked by a gradual co-learning of synergies and task success, similar to the dynamics of motor learning by Goal Babbling. However, there are also differences between human learning and the Goal Babbling simulations, as humans tend to predominantly use Degrees of Freedom that do not have a large effect on the hand position, whereas in Goal Babbling, Degrees of Freedom with a large effect on hand position are used predominantly. We conclude that humans use goal-related feedback to constrain motor exploration and resolve motor redundancy when learning a new sensorimotor mapping, but in a manner that differs from the current implementation of Goal Babbling due to different constraints on motor exploration.
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Project description:The stimulation of deep brain structures has thus far only been possible with invasive methods. Transcranial electrical temporal interference stimulation (tTIS) is a novel, noninvasive technology that might overcome this limitation. The initial proof-of-concept was obtained through modeling, physics experiments and rodent models. Here we show successful noninvasive neuromodulation of the striatum via tTIS in humans using computational modeling, functional magnetic resonance imaging studies and behavioral evaluations. Theta-burst patterned striatal tTIS increased activity in the striatum and associated motor network. Furthermore, striatal tTIS enhanced motor performance, especially in healthy older participants as they have lower natural learning skills than younger subjects. These findings place tTIS as an exciting new method to target deep brain structures in humans noninvasively, thus enhancing our understanding of their functional role. Moreover, our results lay the groundwork for innovative, noninvasive treatment strategies for brain disorders in which deep striatal structures play key pathophysiological roles.
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Project description:Current models of motor learning posit that skill acquisition involves both the formation and decay of multiple motor memories that can be engaged in different contexts. Memory formation is assumed to be context dependent, so that errors most strongly update motor memories associated with the current context. In contrast, memory decay is assumed to be context independent, so that movement in any context leads to uniform decay across all contexts. We demonstrate that for both object manipulation and force-field adaptation, contrary to previous models, memory decay is highly context dependent. We show that the decay of memory associated with a given context is greatest for movements made in that context, with more distant contexts showing markedly reduced decay. Thus, both memory formation and decay are strongest for the current context. We propose that this apparently paradoxical organization provides a mechanism for optimizing performance. While memory decay tends to reduce force output, memory formation can correct for any errors that arise, allowing the motor system to regulate force output so as to both minimize errors and avoid unnecessary energy expenditure. The motor commands for any given context thus result from a balance between memory formation and decay, while memories for other contexts are preserved.
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Project description:The development of novel strategies to augment motor training success is of great interest for healthy persons and neurological patients. A promising approach is the combination of training with transcranial electric stimulation. However, limited reproducibility and varying effect sizes make further protocol optimization necessary. We tested the effects of a novel cerebellar transcranial alternating current stimulation protocol (tACS) on motor skill learning. Furthermore, we studied underlying mechanisms by means of transcranial magnetic stimulation and analysis of fMRI-based resting-state connectivity. N = 15 young, healthy participants were recruited. 50 Hz tACS was applied to the left cerebellum in a double-blind, sham-controlled, cross-over design concurrently to the acquisition of a novel motor skill. Potential underlying mechanisms were assessed by studying short intracortical inhibition at rest (SICIrest) and in the premovement phase (SICImove), intracortical facilitation at rest (ICFrest), and seed-based resting-state fMRI-based functional connectivity (FC) in a hypothesis-driven motor learning network. Active stimulation did not enhance skill acquisition or retention. Minor effects on striato-parietal FC were present. Linear mixed effects modelling identified SICImove modulation and baseline task performance as the most influential determining factors for predicting training success. Accounting for the identified factors may allow to stratify participants for future training-based interventions.
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Project description:Hippocampal theta oscillations have been implicated in spatial memory function in both rodents and humans. What is less clear is how hippocampal theta interacts with higher frequency oscillations to support long-term memory. Here we asked 10 presurgical epilepsy patients undergoing intracranial EEG recording to perform a long-term spatial memory task in desktop virtual reality and found that increased theta power in two discrete bands ("low" 2-5 Hz and "high" 6-11 Hz) during cued retrieval was associated with improved task performance. Similarly, increased coupling between "low" theta phase and gamma amplitude during the same period was associated with improved task performance. Finally, low and high gamma amplitude appeared to peak at different phases of the theta cycle; providing a novel connection between human hippocampal function and rodent data. These results help to elucidate the role of theta oscillations and theta-gamma phase-amplitude coupling in human long-term memory.
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Project description:Amyloid precursor protein (APP) is critically involved in the pathophysiology of Alzheimer's disease, but its physiological functions remain elusive. Importantly, APP knockout (APP-KO) mice exhibit cognitive deficits, suggesting that APP plays a role at the neuronal network level. To investigate this possibility, we recorded local field potentials (LFPs) from the posterior parietal cortex, dorsal hippocampus and lateral prefrontal cortex of freely moving APP-KO mice. Spectral analyses showed that network oscillations within the theta- and gamma-frequency bands were not different between APP-KO and wild-type mice. Surprisingly, however, while gamma amplitude coupled to theta phase in all recorded regions of wild-type animals, in APP-KO mice theta-gamma coupling was strongly diminished in recordings from the parietal cortex and hippocampus, but not in LFPs recorded from the prefrontal cortex. Thus, lack of APP reduces oscillatory coupling in LFP recordings from specific brain regions, despite not affecting the amplitude of the oscillations. Together, our findings reveal reduced cross-frequency coupling as a functional marker of APP deficiency at the network level.
| S-EPMC4764939 | biostudies-literature
Project description:BackgroundHow oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes.ResultsFollowing learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance.ConclusionsFace discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.
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Project description:Theta and gamma rhythms and their cross-frequency coupling play critical roles in perception, attention, learning, and memory. Available data suggest that forebrain acetylcholine (ACh) signaling promotes theta-gamma coupling, although the mechanism has not been identified. Recent evidence suggests that cholinergic signaling is both temporally and spatially constrained, in contrast to the traditional notion of slow, spatially homogeneous, and diffuse neuromodulation. Here, we find that spatially constrained cholinergic stimulation can generate theta-modulated gamma rhythms. Using biophysically-based excitatory-inhibitory (E-I) neural network models, we simulate the effects of ACh on neural excitability by varying the conductance of a muscarinic receptor-regulated K+ current. In E-I networks with local excitatory connectivity and global inhibitory connectivity, we demonstrate that theta-gamma-coupled firing patterns emerge in ACh modulated network regions. Stable gamma-modulated firing arises within regions with high ACh signaling, while theta or mixed theta-gamma activity occurs at the peripheries of these regions. High gamma activity also alternates between different high-ACh regions, at theta frequency. Our results are the first to indicate a causal role for spatially heterogenous ACh signaling in the emergence of localized theta-gamma rhythmicity. Our findings also provide novel insights into mechanisms by which ACh signaling supports the brain region-specific attentional processing of sensory information.
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Project description:Oscillatory brain activity reflects different internal brain states including neurons' excitatory state and synchrony among neurons. However, characterizing these states is complicated by the fact that different oscillations are often coupled, such as gamma oscillations nested in theta in the hippocampus, and changes in coupling are thought to reflect distinct states. Here, we describe a new method to separate single oscillatory cycles into distinct states based on frequency and phase coupling. Using this method, we identified four theta-gamma coupling states in rat hippocampal CA1. These states differed in abundance across behaviors, phase synchrony with other hippocampal subregions, and neural coding properties suggesting that these states are functionally distinct. We captured cycle-to-cycle changes in oscillatory coupling states and found frequent switching between theta-gamma states showing that the hippocampus rapidly shifts between different functional states. This method provides a new approach to investigate oscillatory brain dynamics broadly.
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