Coherent theta oscillations in the cerebellum and supplementary motor area mediate visuomotor adaptation

The cerebellum and its interaction with cortical areas play a key role in our ability to flexibly adapt a motor program in response to sensory input. Current knowledge about specific neural mechanisms underlying the process of visuomotor adaptation is however lacking. Using a novel placement of EEG electrodes to record electric activity from the cerebellum, we studied local cerebellar activity, as well as its coupling with neocortical activity to obtain direct neurophysiological markers of visuomotor adaptation in humans. We found increased theta (4-8 Hz) power in "cerebellar" as well as cortical electrodes, when subjects first encountered a visual manipulation. Theta power decreased as subjects adapted to the perturbation, and rebounded when the manipulation was suddenly removed. This effect was observed in two distinct locations: a cerebellar cluster and a central cluster, which were localized in left cerebellar crus I (lCB) and right supplementary motor area (rSMA) using linear constrained minimum variance beamforming. Importantly, we found that better adaptation was associated with increased theta power in left cerebellar electrodes and a right sensorimotor cortex electrode. Finally, increased rSMA -> lCB connectivity was significantly decreased with adaptation. These results demonstrate that: (1) cerebellar theta power is markedly modulated over the course of visuomotor adaptation and (2) theta oscillations could serve as a key mechanism for communication within a cortico-cerebellar loop

EEG Characterization of Sensorimotor Networks: Implications in Stroke

The purpose of this dissertation was to use electroencephalography (EEG) to characterize sensorimotor networks and examine the effects of stroke on sensorimotor networks. Sensorimotor networks play an essential role in completion of everyday tasks, and when damaged, as in stroke survivors, the successful completion of seemingly simple motor tasks becomes fantasy. When sensorimotor networks are impaired as a result of stroke, varying degrees of sensorimotor deficits emerge, most often including loss of sensation and difficulty generating upper extremity movements. Although sensory therapies, such as the application of tendon vibration, have been shown to reduce the sensorimotor deficits after stroke, the underlying sensorimotor mechanisms associated with such improvements are unknown. While sensorimotor networks have been studied extensively, unanswered questions still surround their role in basic control paradigms and how their role changes after stroke. EEG provides a way to probe the high-speed temporal dynamics of sensorimotor networks that other more common imaging modalities lack. Sensorimotor network function was examined in controls during a task designed to differentiate potential mechanisms of arm stabilization and determine to what degree the sensorimotor network is involved. After sensorimotor network function was characterized in controls, we examined the effect of stroke on the sensorimotor network during rest and described the reorganization that occurs. Lastly, we explored tendon vibration as a sensory therapy for stroke survivors and determined if sensorimotor network mechanisms underlie improvements in arm tracking performance due to wrist tendon vibration. We observed cortical activity and connectivity that suggests sensorimotor networks are involved in the control of arm stability, cortical networks reorganize to more asymmetric, local networks after stroke, and tendon vibration normalizes sensorimotor network activity and connectivity during motor control after stroke. This dissertation was among the first studies using EEG to characterize the high-speed temporal dynamics of sensorimotor networks following stroke. This new knowledge has led to a better understanding of how sensorimotor networks function under ordinary circumstances as well as extreme situations such as stroke and revealed previously unknown mechanisms by which tendon vibration improves motor control in stroke survivors, which will lead to better therapeutic approaches

Motor adaptation and internal model formation in a robot-mediated forcefield

Background: Motor adaptation relies on error-based learning for accurate movements in changing environments. However, the neurophysiological mechanisms driving individual differences in performance are unclear. TMS-evoked potential can provide a direct measure of cortical excitability. Objective: To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield. Methods: 15 right-handed healthy participants (mean age 23 years) performed a robot-mediated forcefield perturbation task. There were 2 conditions: unperturbed non-adaptation and perturbed adaptation. Transcranial magnetic stimulation (TMS) was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex (left M1). EEG was continuously recorded, and cortical excitability was measured by TMS-evoked potential (TEP). Motor learning was quantified by the motor learning index. Results: Larger error-related negativity (ERN) in fronto-central regions was associated with improved motor performance as measured by a reduction in trajectory errors. Baseline TEP N100 peak amplitude predicted motor learning (p = 0.005), which was significantly attenuated relative to baseline (p = 0.0018) following motor adaptation. Conclusions: ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation. Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex. TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression

Practice changes beta power at rest and its modulation during movement in healthy subjects but not in patients with Parkinson\u27s disease

Abstract Background PD (Parkinson\u27s disease) is characterized by impairments in cortical plasticity, in beta frequency at rest and in beta power modulation during movement (i.e., event‐related ERS [synchronization] and ERD [desynchronization]). Recent results with experimental protocols inducing long‐term potentiation in healthy subjects suggest that cortical plasticity phenomena might be reflected by changes of beta power recorded with EEG during rest. Here, we determined whether motor practice produces changes in beta power at rest and during movements in both healthy subjects and patients with PD. We hypothesized that such changes would be reduced in PD. Methods We thus recorded EEG in patients with PD and age‐matched controls before, during and after a 40‐minute reaching task. We determined posttask changes of beta power at rest and assessed the progressive changes of beta ERD and ERS during the task over frontal and sensorimotor regions. Results We found that beta ERS and ERD changed significantly with practice in controls but not in PD. In PD compared to controls, beta power at rest was greater over frontal sensors but posttask changes, like those during movements, were far less evident. In both groups, kinematic characteristics improved with practice; however, there was no correlation between such improvements and the changes in beta power. Conclusions We conclude that prolonged practice in a motor task produces use‐dependent modifications that are reflected in changes of beta power at rest and during movement. In PD, such changes are significantly reduced; such a reduction might represent, at least partially, impairment of cortical plasticity

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Does extensive motor learning trigger local sleep?

After prolonged learning we all have experienced a reduction of alertness, resulting in errors that we would normally not make. Despite this being a common situation in everyday life, the reasons for this phenomenon are unclear. A possible explanation is that the regions of the brain which are involved in the learning, go off-line trying to partially recover. This event is defined as local sleep and it has been detected in animals and sleep-deprived humans performing learning tasks. Local sleep is a sleep-like electrophysiological activity occurring locally, while the rest of the brain is fully awake, and producing performance deterioration. However, since all the studies included both lack of sleep and learning, it is uncertain whether such phenomenon is related to sleep deprivation or if it is the consequence of prolonged learning. Further, local sleep has not been related to electrophysiological changes occurring during the task. This thesis aimed to assess, for the first time in well rested subjects, whether local sleep and performance decline occur because of prolonged learning. Specifically, the goal was to discriminate between sustained practice and learning, as to determine whether learning is required to cause local sleep. Also, a 90-minute nap was evaluated to establish whether sleep is necessary to counterbalance neuronal fatigue and performance decrease. The starting hypothesis was that local sleep is a plasticity-related phenomenon affecting performance and requiring learning to be triggered. Consequently, sleep would be a prerequisite to counterbalance performance and electrophysiological changes. High-Density EEG and behavioral data of 78 healthy young subjects were collected during and after two learning tasks performed for three hours: a visual sequence learning task, and a visuo-motor rotation task, randomly selected. Afterward, subjects were divided in two groups: those who slept for one hour and a half and those who remained awake and quietly rested for the same amount of time before being tested for electrophysiological and behavioral changes. Moreover, to discriminate between the effects of prolonged learning and practice, 11 additional subjects performed a control condition consisting in planar upper limb reaching movements instead of the above-mentioned learning tasks. In detail, the power spectrum of the EEG activity during the task and at rest with eyes opened was divided into five ranges to determine frequency changes of the EEG activity: delta 1 to 4 Hz; theta 4 to 8 Hz; alpha 8 to 13 Hz, beta 13 to 25 Hz, gamma 25 to 55 Hz. Additionally, movement-related beta activity of 35 young subjects was analyzed to find a relationship between task related oscillations and performance indices, as the modulatory activity during practice may reflect plasticity-related phenomena that can describe the occurrence of local sleep. Finally, 13 young subjects were compared to a dataset of 13 older participants who performed planar upper limb reaching movements to determine whether beta oscillations were affected by age. Specifically, beta activity was assessed during reaching movements in different brain regions, in terms of topography, magnitude, and peak frequency. Results demonstrated that sustained learning produced electrophysiological changes both at rest and during the task. In fact, resting state was characterized by a progressive slowing of the EEG activity over areas overlapping with those engaged during the task. Precisely, we detected task-related activity mainly in the high-frequency ranges (gamma and beta right temporo-parietal activity for the visual sequence learning task; alpha and beta activity over a fontal and left parietal areas for the visuo-motor rotation); the same areas were characterized by a progressive increase of the low frequency EEG activity at rest ranging from alpha, beta after one hour of practice, to theta after three one-hour blocks. The control task did not trigger such EEG slowing, as reaching movements without learning did only left an alpha, beta trace in the resting state over a cluster reflecting the motor area contralateral to the movement. Further, continuous learning triggered performance deteriorations only in tests sharing the same neural substrate of the previously performed task. In other words, the visuo-motor learning task only affected performance in a motor test consisting in random reaching movements; conversely, visual sequence learning altered performance on a visual working memory test, but did not influence reaching movements. Also, the control condition did not affect performance in any of the two exercises. Performance decline, learning ability and local sleep were partially renormalized by a 90-minute nap but not by an equivalent period of wake. As such, the global EEG activity, computed as the mean power of all the electrodes, was not affected by either 90 minutes of sleep or quiet wake. However, the regions characterized by low frequency at rest benefited from the sleep period, as the low frequencies content partially decreased after the nap but not after quiet wake. Task related beta activity during motor practice presented similar magnitude and timing patterns in different brain areas, with a progressive increase with practice, in both young and older subjects, despite the older subjects performing slower, less accurate movements. Intriguingly, the motor areas showed a post movement beta synchronization having a peak between 15 and 18 Hz, as opposed to a frontal area that has it between 23 and 29 Hz. Finally, results did not reveal any direct relationship between EEG beta oscillations and performance indices. Altogether, these results indicate that local sleep and performance decrease can be triggered by prolonged learning in well rested subjects; furthermore, some amount of sleep can partially renormalize learning ability, EEG activity and performance. Also, differences in the brainnoscillations during motor activity can express separate processes underlying motor planning, execution and skills acquisition. The present study adds some important knowledge in the field of local sleep; in fact, it suggests that such phenomenon is triggered by sustained learning rather than sleep deprivation, thus being a plasticity-related phenomenon. Finally, the role of sleep on counterbalancing local sleep has been proved, despite additional studies are required to establish whether a full night of sleep rather than a specific amount of time is needed to fully restore learning ability and electrophysiological activity. In conclusion, the present findings are of importance in all the fields where sustained learning is required, such as rehabilitative programs, sport and military trainings, and must be taken into account when plasticity plays a fundamental role in the acquisition of new skills

Upregulation of cortico-cerebellar functional connectivity after motor learning

Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity