Publications
62 results found
Gmaz JM, Keller JA, Dudman JT, et al., 2024, Integrating across behaviors and timescales to understand the neural control of movement., Curr Opin Neurobiol, Vol: 85
The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.
Safaie M, Chang JC, Park J, et al., 2023, Preserved neural dynamics across animals performing similar behaviour, Nature, Vol: 623, Pages: 765-771, ISSN: 0028-0836
Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.
Fortunato C, Bennasar-Vázquez J, Park J, et al., 2023, Nonlinear manifolds underlie neural population activity during behaviour., bioRxiv
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
Feulner B, Perich MG, Chowdhury RH, et al., 2022, Small, correlated changes in synaptic connectivity may facilitate rapid motor learning, Nature Communications, Vol: 13, ISSN: 2041-1723
Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, weused a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance onpreserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference betweenHinput and Hlocal, which could be exploited when designing future experiments.
Gallego-Carracedo C, Perich MG, Chowdhury RH, et al., 2022, Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner., Elife, Vol: 11
The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, whose activation we refer to as 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFPs). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.
Wimalasena LN, Braun JF, Keshtkaran MR, et al., 2022, Estimating muscle activation from EMG using deep learning-based dynamical systems models, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560
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- Citations: 4
Gallego JA, Makin TR, McDougle SD, 2022, Going beyond primary motor cortex to improve brain-computer interfaces, Trends in Neurosciences, Vol: 45, ISSN: 0166-2236
Brain-computer interfaces (BCIs) for movement restoration typically decode the user's intent from neural activity in their primary motor cortex (M1) and use this information to enable 'mental control' of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related information that is absent or otherwise difficult to resolve from M1 activity; and too much, in that motor commands are tangled up with nonmotor processes such as attention and feedback processing, potentially hindering decoding. Both challenges might be circumvented, we argue, by integrating additional information from multiple brain regions to develop BCIs that will better interpret the user's intent.
Gallego-Carracedo C, Perich MG, Chowdhury RH, et al., 2021, Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner
<jats:title>Abstract</jats:title><jats:p>The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the “latent dynamics”. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.</jats:p>
Feulner B, Perich M, Chowdhury R, et al., 2021, Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (H input ) and learning through local connectivity changes (H local ). Learning under H input produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, H local led to only slightly larger changes in neural activity and covariance compared to H input . This similarity is due to H local only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between H input and H local , which could be exploited when designing future experiments.
Gallego JA, Perich MG, Chowdhury RH, et al., 2020, Long-term stability of cortical population dynamics underlying consistent behavior, Nature Neuroscience, Vol: 23, Pages: 260-270, ISSN: 1097-6256
Animals readily execute learned behaviors in a consistent manner over long periods of time, and yet no equally stable neural correlate has been demonstrated. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which captures the dominant co-variation patterns within the neural population, must be preserved across time. We recorded from populations of neurons in premotor, primary motor and somatosensory cortices as monkeys performed a reaching task, for up to 2 years. Intriguingly, despite a steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. The stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based directly on the recorded neural activity degraded substantially. We posit that stable latent cortical dynamics within the manifold are the fundamental building blocks underlying consistent behavioral execution.
Salvador Lora-Millan J, Lopez-Blanco R, Alvaro Gallego J, et al., 2019, Mechanical vibration does not systematically reduce the tremor in essential tremor patients, SCIENTIFIC REPORTS, Vol: 9, ISSN: 2045-2322
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- Citations: 7
Puttaraksa G, Muceli S, Alvaro Gallego J, et al., 2019, Voluntary and tremorogenic inputs to motor neuron pools of agonist/antagonist muscles in essential tremor patients, Journal of Neurophysiology, Vol: 122, Pages: 2043-2053, ISSN: 0022-3077
Pathological tremor is an oscillation of body parts at 3–10 Hz, determined by the output of spinal motor neurons (MNs), which receive synaptic inputs from supraspinal centers and muscle afferents. The behavior of spinal MNs during tremor is not well understood, especially in relation to the activation of the multiple muscles involved. Recent studies on patients with essential tremor have shown that antagonist MN pools receive shared input at the tremor frequency. In this study, we investigated the synaptic inputs related to tremor and voluntary movement, and their coordination across antagonist muscles. We analyzed the spike trains of motor units (MUs) identified from high-density surface electromyography from the forearm extensor and flexor muscles in 15 patients with essential tremor during postural tremor. The shared synaptic input was quantified by coherence and phase difference analysis of the spike trains. All pairs of spike trains in each muscle showed coherence peaks at the voluntary drive frequency (1–3 Hz, 0.2 ± 0.2, mean ± SD) and tremor frequency (3–10 Hz, 0.6 ± 0.3) and were synchronized with small phase differences (3.3 ± 25.2° and 3.9 ± 22.0° for the voluntary drive and tremor frequencies, respectively). The coherence between MN spike trains of antagonist muscle groups at the tremor frequency was significantly smaller than intramuscular coherence. We predominantly observed in-phase activation of MUs between agonist/antagonist muscles at the voluntary frequency band (0.6 ± 48.8°) and out-of-phase activation at the tremor frequency band (126.9 ± 75.6°). Thus MNs innervating agonist/antagonist muscles concurrently receive synaptic inputs with different phase shifts in the voluntary and tremor frequency bands.
Farshchian A, Gallego JA, Miller LE, et al., 2019, Adversarial domain adaptation for stable brain-machine interfaces
Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of the brain. However, the inherent loss and turnover of recorded neurons requires repeated recalibrations of the interface, which can potentially alter the day-to-day user experience. The resulting need for continued user adaptation interferes with the natural, subconscious use of the BMI. Here, we introduce a new computational approach that decodes movement intent from a low-dimensional latent representation of the neural data. We implement various domain adaptation methods to stabilize the interface over significantly long times. This includes Canonical Correlation Analysis used to align the latent variables across days; this method requires prior point-to-point correspondence of the time series across domains. Alternatively, we match the empirical probability distributions of the latent variables across days through the minimization of their Kullback-Leibler divergence. These two methods provide a significant and comparable improvement in the performance of the interface. However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.
Farshchian A, Gallego JA, Miller LE, et al., 2019, Adversarial domain adaptation for stable brain-machine interfaces
© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of the brain. However, the inherent loss and turnover of recorded neurons requires repeated recalibrations of the interface, which can potentially alter the day-to-day user experience. The resulting need for continued user adaptation interferes with the natural, subconscious use of the BMI. Here, we introduce a new computational approach that decodes movement intent from a low-dimensional latent representation of the neural data. We implement various domain adaptation methods to stabilize the interface over significantly long times. This includes Canonical Correlation Analysis used to align the latent variables across days; this method requires prior point-to-point correspondence of the time series across domains. Alternatively, we match the empirical probability distributions of the latent variables across days through the minimization of their Kullback-Leibler divergence. These two methods provide a significant and comparable improvement in the performance of the interface. However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.
Perich MG, Gallego JA, Miller LE, 2018, A neural population mechanism for rapid learning, Neuron, Vol: 100, Pages: 964-+, ISSN: 0896-6273
Long-term learning of language, mathematics, and motor skills likely requires cortical plasticity, but behavior often requires much faster changes, sometimes even after single errors. Here, we propose one neural mechanism to rapidly develop new motor output without altering the functional connectivity within or between cortical areas. We tested cortico-cortical models relating the activity of hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices throughout adaptation to reaching movement perturbations. We found a signature of learning in the “output-null” subspace of PMd with respect to M1 reflecting the ability of premotor cortex to alter preparatory activity without directly influencing M1. The output-null subspace planning activity evolved with adaptation, yet the “output-potent” mapping that captures information sent to M1 was preserved. Our results illustrate a population-level cortical mechanism to progressively adjust the output from one brain area to its downstream structures that could be exploited for rapid behavioral adaptation.
Holobar A, Gallego JA, Kranjec J, et al., 2018, Motor unit-driven identification of pathological tremor in electroencephalograms, Frontiers in Neurology, Vol: 9
Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations. Methods: We exploit the coupling between the tremor-related cortical activity and motor unit population firings to build a linear minimum mean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here. Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinson's disease (PD) patients show a ~2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis. Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time.
Gallego JA, Perich MG, Chowdhury RH, et al., 2018, A stable, long-term cortical signature underlying consistent behavior
<jats:title>Abstract</jats:title><jats:p>Animals readily execute learned motor behaviors in a consistent manner over long periods of time, yet similarly stable neural correlates remained elusive up to now. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which capture the dominant co-variation patterns within the neural population, are preserved across time. We recorded from populations of neurons in premotor, primary motor, and somatosensory cortices for up to two years as monkeys performed a reaching task. Intriguingly, despite steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. Such stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based on the recorded neural activity degraded substantially. We posit that latent cortical dynamics within the manifold are the fundamental and stable building blocks underlying consistent behavioral execution.</jats:p>
Gallego JA, Perich MG, Naufel SN, et al., 2018, Cortical population activity within a preserved neural manifold underlies multiple motor behaviors, Nature Communications, Vol: 9, Pages: 1-13, ISSN: 2041-1723
Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task. While each task requires different patterns of muscle and single unit activity, we find unexpected similarities at the neural population level: the structure and activity of the neural modes is largely preserved across tasks. Furthermore, we find two sets of neural modes with task-independent activity that capture, respectively, generic temporal features of the set of tasks and a task-independent mapping onto muscle activity. This system of flexibly combined, well-preserved neural modes may underlie the ability of M1 to learn and generate a wide-ranging behavioral repertoire.
Lora-Millán JS, López-Blanco R, Gallego JÁ, et al., 2018, Mechanical vibration does not systematically reduce the tremor in Essential Tremor patients
<jats:title>Abstract</jats:title><jats:p>Essential tremor (ET) is a major cause of disability and is not effectively managed in half of the patients. We investigated whether mechanical vibration could reduce tremor in ET by selectively recruiting afferent pathways. We used piezoelectric actuators to deliver vibratory stimuli to the hand and forearm during long trials (4 min), while we monitored the tremor using inertial sensors. We analyzed the effect of four stimulation strategies, including different constant and variable vibration frequencies, in 18 ET patients. Although there was not a clear homogeneous response to vibration across patients and strategies, in most cases (50-72%) mechanical vibration was associated with an increase in the amplitude of their tremor. In contrast, the tremor was reduced in 5-22% of the patients, depending on the strategy. However, these results are hard to interpret given the intrinsic variability of the tremor: during equally long trials without vibration, the tremor changed significantly in 67% of the patients (increased in 45%; decreased in 22%). We conclude that mechanical vibration of the limb does not have a systematic effect on tremor in ET. Moreover, the observed intrinsic variability of the tremor should be taken into account when designing future experiments to assess tremor in ET and how it responds to any intervention.</jats:p>
Gallego JA, Hardwick RM, Oby ER, 2017, Highlights from the 2017 meeting of the Society for Neural Control of Movement (Dublin, Ireland), EUROPEAN JOURNAL OF NEUROSCIENCE, Vol: 46, Pages: 2141-2148, ISSN: 0953-816X
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- Citations: 3
Gallego JA, Perich MG, Naufel SN, et al., 2017, Multiple tasks viewed from the neural manifold: Stable control of varied behavior
<jats:title>Abstract</jats:title><jats:p>How do populations of cortical neurons have the flexibility to perform different functions? We investigated this question in primary motor cortex (M1), where populations of neurons are able to generate a rich repertoire of motor behaviors. We recorded neural activity while monkeys performed a variety of wrist and reach-to-grasp motor tasks, each requiring a different pattern of neural activity. We characterized the flexibility of M1 movement control by comparing the “neural modes” that capture covariation across neurons, believed to arise from network connectivity. We found large similarities in the structure of the neural modes across tasks, as well as striking similarities in their temporal activation dynamics. These similarities were only apparent at the population level. Moreover, a subset of these well-preserved modes captured a task-independent mapping onto muscle commands. We hypothesize that this system of flexibly combined, stable neural modes gives M1 the flexibility to generate our wide-ranging behavioral repertoire.</jats:p>
Gallego JA, Perich MG, Miller LE, et al., 2017, Neural Manifolds for the Control of Movement, NEURON, Vol: 94, Pages: 978-984, ISSN: 0896-6273
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- Citations: 195
Perich MG, Gallego JA, Miller LE, 2017, A neural population mechanism for rapid learning
<jats:title>Abstract</jats:title><jats:p>Long-term learning of language, mathematics, and motor skills likely requires plastic changes in the cortex, but behavior often requires faster changes, sometimes based even on single errors. Here, we show evidence of one mechanism by which the brain can rapidly develop new motor output, seemingly without altering the functional connectivity between or within cortical areas. We recorded simultaneously from hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices, and computed models relating these neural populations throughout adaptation to reaching movement perturbations. We found a signature of learning in the “null subspace” of PMd with respect to M1. Earlier experiments have shown that null subspace activity allows the motor cortex to alter preparatory activity without directly influencing M1. In our experiments, the null subspace planning activity evolved with the adaptation, yet the “potent” mapping that captures information sent to M1 was preserved. Our results illustrate a population-level mechanism within the motor cortices to adjust the output from one brain area to its downstream structures that could be exploited throughout the brain for rapid, online behavioral adaptation.</jats:p>
Gallego JA, Dideriksen JL, Holobar A, et al., 2017, Neural Control of Muscles in Tremor Patients, Biosystems and Biorobotics, Pages: 129-134
Essential tremor and Parkinson’s disease cause abnormal oscillatory activity in a variety of brain structures that is transmitted to spinal motoneurons and generates tremor. Because the motoneuron pool integrates synaptic inputs from descending and spinal circuits, the decoding of its activity provides a view on all the neural pathways involved in tremor generation. We investigated tremor mechanisms by analyzing the behavior of populations of motoneurons within a single muscle, across antagonist muscle pairs, and in relation to cortical activity. We observed that tremor is caused by a common cortical input projected to all motoneurons. We also found that spinal reflex pathways contribute fundamentally to shaping tremor properties. We posit that although ET and PD tremor are centrally generated, tremor properties are strongly determined by the interaction between descending and afferent inputs to the motoneuron pool.
Brzan PP, Gallego JA, Romero JP, et al., 2017, New Perspectives for Computer-Aided Discrimination of Parkinson's Disease and Essential Tremor, COMPLEXITY, ISSN: 1076-2787
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- Citations: 7
Dideriksen JL, Gallego JA, Holobar A, et al., 2015, One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor, JOURNAL OF NEURAL ENGINEERING, Vol: 12, ISSN: 1741-2560
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- Citations: 12
Ethier C, Gallego JA, Miller LE, 2015, Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery, CURRENT OPINION IN NEUROBIOLOGY, Vol: 33, Pages: 95-102, ISSN: 0959-4388
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- Citations: 38
Gallego JA, Dideriksen JL, Holobar A, et al., 2015, The phase difference between neural drives to antagonist muscles in essential tremor is associated with the relative strength of supraspinal and afferent input, The Journal of Neuroscience, Vol: 35, Pages: 8925-8937, ISSN: 0270-6474
The pathophysiology of essential tremor (ET), the most common movement disorder, is not fully understood. We investigated which factors determine the variability in the phase difference between neural drives to antagonist muscles, a long-standing observation yet unexplained. We used a computational model to simulate the effects of different levels of voluntary and tremulous synaptic input to antagonistic motoneuron pools on the tremor. We compared these simulations to data from 11 human ET patients. In both analyses, the neural drive to muscle was represented as the pooled spike trains of several motor units, which provides an accurate representation of the common synaptic input to motoneurons. The simulations showed that, for each voluntary input level, the phase difference between neural drives to antagonist muscles is determined by the relative strength of the supraspinal tremor input to the motoneuron pools. In addition, when the supraspinal tremor input to one muscle was weak or absent, Ia afferents provided significant common tremor input due to passive stretch. The simulations predicted that without a voluntary drive (rest tremor) the neural drives would be more likely in phase, while a concurrent voluntary input (postural tremor) would lead more frequently to an out-of-phase pattern. The experimental results matched these predictions, showing a significant change in phase difference between postural and rest tremor. They also indicated that the common tremor input is always shared by the antagonistic motoneuron pools, in agreement with the simulations. Our results highlight that the interplay between supraspinal input and spinal afferents is relevant for tremor generation.
Gallego JA, Dideriksen JL, Holobar A, et al., 2015, Influence of common synaptic input to motor neurons on the neural drive to muscle in essential tremor, JOURNAL OF NEUROPHYSIOLOGY, Vol: 113, Pages: 182-191, ISSN: 0022-3077
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- Citations: 47
Lambrecht S, Gallego JA, Rocon E, et al., 2014, Automatic real-time monitoring and assessment of tremor parameters in the upper limb from orientation data, FRONTIERS IN NEUROSCIENCE, Vol: 8, ISSN: 1662-453X
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- Citations: 10
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