Modeling movement disorders - CRPS-related dystonia explained by abnormal proprioceptive reflexes

AbstractHumans control their movements using adaptive proprioceptive feedback from muscle afferents. The interaction between proprioceptive reflexes and biomechanical properties of the limb is essential in understanding the etiology of movement disorders. A non-linear neuromuscular model of the wrist incorporating muscle dynamics and neural control was developed to test hypotheses on fixed dystonia. Dystonia entails sustained muscle contractions resulting in abnormal postures. Lack of inhibition is often hypothesized to result in hyperreflexia (exaggerated reflexes), which may cause fixed dystonia. In this study the model-simulated behavior in case of several abnormal reflex settings was compared to the clinical features of dystonia: abnormal posture, sustained muscle contraction, increased stiffness, diminished voluntary control and activity-aggravation.The simulation results were rated to criteria based on characteristic features of dystonia. Three abnormal reflex scenarios were tested: (1) increased reflex sensitivity—increased sensitivity of both the agonistic and antagonistic reflex pathways; (2) imbalanced reflex offset—a static offset to the reflex pathways on the agonistic side only; and (3) imbalanced reflex sensitivity—increased sensitivity of only the agonistic reflex pathways.Increased reflex sensitivity did not fully account for the features of dystonia, despite distinct motor dysfunction, since no abnormal postures occurred. Although imbalanced reflex offset did result in an abnormal posture, it could not satisfy other criteria. Nevertheless, imbalanced reflex sensitivity with unstable force feedback in one of the antagonists closely resembled all features of dystonia. The developed neuromuscular model is an effective tool to test hypotheses on the underlying pathophysiology of movement disorders

Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model

During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments

Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics

The ability of vertebrates to generate rhythm within their spinal neural networks is essential for walking, running, and other rhythmic behaviors. The central pattern generator (CPG) network responsible for these behaviors is well-characterized with experimental and theoretical studies, and it can be formulated as a nonlinear dynam- ical system. The underlying mechanism responsible for locomotor behavior can be expressed as the process of leaky integration with resetting states generating appropriate phases for changing body velocity. The low-dimensional input to the CPG model generates the bilateral pattern of swing and stance modulation for each limb and is consistent with the desired limb speed as the input command. To test the minimal configuration of required parameters for this model, we reduced the system of equations representing CPG for a single limb and provided the analytical solution with two complementary methods. The analytical and empirical cycle durations were similar (R2 = 0.99) for the full range of walking speeds. The structure of solution is consistent with the use of limb speed as the input domain for the CPG network. Moreover, the reciprocal interaction between two leaky integration processes representing a CPG for two limbs was sufficient to capture fundamental experimental dynamics associated with the control of heading direction. This analysis provides further support for the embedded velocity or limb speed representation within spinal neural pathways involved in rhythm generation

Peculiarities of the Tail-Withdrawal Reflex Circuit in Aplysia: a Model Study

The circuit of the tail-withdrawal reflex in Aplysia opens up possibilities to construct model systems allowing researchers to effectively investigate simple forms of learning and memory. Using the Python interface of the NEURON software, we simulated this reflex circuit and studied various characteristics of the latter. The phenomenon of spike frequency adaptation (SFA) and the period-adding bifurcation of the minimum were found in sensory neurons, when the latter were stimulated by square-wave stimuli. In all neurons of the circuit, variation of the stimulus strength first increased and then decreased the number of spikes in a burst. In addition, with decreases in the number of stimulated sensory neurons, a subliminal firing other than that in an intact burst appeared at the outputs of interneurons and motor neuron. Moreover, the potentials produced in the motor neuron induced corresponding oscillations of the muscle fiber force, which was indicative of a procedure of excitement-contraction coupling in the tail part of Aplysia. Finally, upon alteration of the conductance of synapses between interneurons and motoneuron, the duration of long-lasting responses increased regularly, implying synaptic plasticityОрганізація нервової мережі відсмикування „хвоста” в аплізії дозволяє побудувати модельну систему, за допомогою якої можна ефективно досліджувати прості форми навчання та пам’яті. Використовуючи інтерфейс Python та програмний засіб NEURON, ми змоделювали даний рефлекс та дослідили декілька властивостей модельної мережі. Феномени адаптації частоти розряду (SFA) та біфуркації з доданням періоду при мінімумі частоти спостерігалися в сенсорних нейронах в умовах стимуляції прямокутними стимулами. В усіх нейронах мережі зміни сили стимуляції призводили спочатку до збільшення числа піків у пачках, а потім до його зменшення. Окрім того, при зменшенні кількості стимульованих сенсорних нейронів на виходах інтернейронів та моторного нейрона з’являлася підпорогова кайма, що відрізнялася від такої в інтактних пачок. Більш того, потенціали, продуковані моторним нейроном, індукували відповідні осциляції сили, розвинутої м’язовим волокном, що свідчило про сполучення процесів збудження/скорочення у хвостовій частині аплізії. Нарешті, при змінах провідності синапсів між інтернейронами та мотонейронами тривалість „довгих” імпульсних відповідей закономірно збільшувалася, що вказувало на прояви синаптичної пластичності

A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms

A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve

abstract: Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms

Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand

Roboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to interact with unknown and changing environments as well as to learn and execute fine movements. In this paper, we review the system-level neurocomputational models of the human motor system, and we focus on biomimetic models simulating the functional activity of the cerebellum, the basal ganglia, the motor cortex, and the spinal cord, which are the main central nervous system areas involved in the learning, execution, and control of movements. We review the models that have been proposed from the early of 1970s, when the first cerebellar model was realized, up to nowadays, when the embodiment of these models into robots acting in the real world and into software agents acting in a virtual environment has become of paramount importance to close the perception-cognition-action cycle. This review shows that neurocomputational models have contributed to the comprehension and reproduction of neural mechanisms underlying reaching movements, but much remains to be done because a whole model of the central nervous system controlling musculoskeletal robots is still missing

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion

Études des mécanismes adaptatifs du maintien de l'équilibre orthostatique. Effets d'une fatigue musculaire, d'une douleur expérimentale et d'une perturbation externe.

Le maintien de l'équilibre orthostatique est une activité motrice primordiale parce qu'elle permet de préserver l'autonomie de chaque individu. Les études présentées dans cette thèse traitent comment diverses contraintes influencent les mécanismes de contrôle impliqués lors du maintien de l'équilibre en station debout. Cette thèse a donc pour objectifs de vérifier : (1) Les effets de la fatigue de certains muscles impliqués dans le contrôle du maintien orthostatique. (2) Les effets d'une douleur expérimentale sur les mécanismes de régulation de l'équilibre orthostatique. (3) Les effets d'une perturbation externe pouvant causer une perte d'équilibre.(4) La validité d'un modèle mathématique démontrant l'importance d'une troisième variable nécessaire pour prédire la stabilité en station debout : le temps de développement du moment de force aux chevilles.En conclusion, cette thèse permet d'éclaircir l'implication des mécanismes adaptatifs du système nerveux dans différents contextes.Premièrement, le système nerveux s'adapteraient à la fatigue des triceps suraux en augmentant la fréquence des ajustements posturaux afin d'éviter des déplacements plus excentriques du centre de masse du corps ou en augmentant les propriétés mécaniques des articulations (i.e. la rigidité). Deuxièmement, une stimulation des nocicepteurs altère principalement les processus sensori-moteurs du système de contrôle postural. La détérioration de la stabilité est fonction de la localisation et de l'intensité de la stimulation douloureuse. La perception de la douleur nécessite des ressources attentionnelles qui ne nuisent pas au contrôle du maintien de l'équilibre en station debout. Troisièmement, l'incertitude reliée à l'avènement probable d'une perturbation provoque une altération des processus de contrôle du maintien de l'équilibre dans les situations sans perturbation et avec perturbation. Quatrièmement, le temps de développement du momentde force aux chevilles contraint la capacité d'une personne à retrouver l'équilibre en station debout suite à une déstabilisation vers l'avant. En ajoutant cette variable à un modèle mathématique, celui-ci permet de prédire 73.3 % des chutes et 73.3 % des stabilisations observées expérimentalement