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

Decomposition of 3D joint kinematics of walking in Drosophila melanogaster

Animals exhibit a rich repertoire of locomotive behaviors. In the context of legged locomotion, i.e. walking, animals can change their heading direction, traverse diverse substrates with different speeds, or can even compensate for the loss of a leg. This versatility emerges from the fact that biological limbs have more joints and/or more degrees of freedom (DOF), i.e. independent directions of motions, than required for any single movement task. However, this further entails that multiple, or even infinitely many, joint configuration can result in the same leg stepping pattern during walking. How the nervous system deals with such kinematic redundancy remains still unknown. One proposed hypothesis is that the nervous system does not control individual DOFs, but uses flexible combinations of groups of anatomical or functional DOFs, referred to as motor synergies. Drosophila melanogaster represents an excellent model organism for studying the motor control of walking, not least because of the extensive genetic toolbox available, which, among others, allows the identification and targeted manipulation of individual neurons or muscles. However, their tiny size and ability for relatively rapid leg movements hampered research on the kinematics at the level of leg joints due to technical limitations until recently. Hence, the main objective of this dissertation was to investigate the three-dimensional (3D) leg joint kinematics of Drosophila during straight walking. For this, I first established a motion capture setup for Drosophila which allowed the accurate reconstruction of the leg joint positions in 3D with high temporal resolution (400 Hz). Afterwards, I created a kinematic leg model based on anatomical landmarks, i.e. joint condyles, extracted from micro computed-tomography scan data. This step was essential insofar that the actual DOFs of the leg joints in Drosophila were currently unknown. By using this kinematic model, I have found that a mobile trochanter-femur joint can best explain the leg movements of the front legs, but is not mandatory in the other leg pairs. Additionally, I demonstrate that rotations of the femur-tibia plane in the middle legs arise from interactions between two joints suggesting that the natural orientation of joint rotational axes can extent the leg movement repertoire without increasing the number of elements to be controlled. Furthermore, each leg pair exhibited distinct joint kinematics in terms of the joint DOFs employed and their angle time courses during swing and stance phases. Since it is proposed that the nervous system could use motor synergies to solve the redundancy problem, I finally aimed to identify kinematic synergies based on the obtained joint angles from the kinematic model. By applying principal component analysis on the mean joint angle sets of leg steps, I found that three kinematic synergies are sufficient to reconstruct the movements of the tarsus tip during stepping for all leg pairs. This suggests that the problem of controlling seven to eight joint DOFs can be in principle reduced to three control parameters. In conclusion, this dissertation provides detailed insights into the leg joint kinematics of Drosophila during forward walking which are relevant for deciphering motor control of walking in insects. When combined with the extensive genetic toolbox offered by Drosophila as model organism, the experimental platform presented here, i.e. the 3D motion capture setup and the kinematic leg model, can facilitate investigations of Drosophila walking behavior in the future

Preclinical evidence supporting the clinical development of central pattern generator-modulating therapies for chronic spinal cord-injured patients

Ambulation or walking is one of the main gaits of locomotion. In terrestrial animals, it may be defined as a series of rhythmic and bilaterally coordinated movement of the limbs which creates a forward movement of the body. This applies regardless of the number of limbs - from arthropods with six or more limbs to bipedal primates. These fundamental similarities among species may explain why comparable neural systems and cellular properties have been found, thus far, to control in similar ways locomotor rhythm generation in most animal models. The aim of this article is to provide a comprehensive review of the known structural and functional features associated with central nervous system (CNS) networks that are involved in the control of ambulation and other stereotyped motor patterns - specifically Central Pattern Generators (CPGs) that produce basic rhythmic patterned outputs for locomotion, micturition, ejaculation, and defecation. Although there is compelling evidence of their existence in humans, CPGs have been most studied in reduced models including in vitro isolated preparations, genetically-engineered mice and spinal cord-transected animals. Compared with other structures of the CNS, the spinal cord is generally considered as being well-preserved phylogenetically. As such, most animal models of SCI should be considered as valuable tools for the development of novel pharmacological strategies aimed at modulating spinal activity and restoring corresponding functions in chronic spinal cord-injured patients

Math Modeling of Interlimb Coordination in Cat Locomotion

Locomotion is an evolutionary adaptation that allows animals to move in 3-D space. The way that mammalian locomotion is controlled has been studied for generations. It remains unclear how the neuronal network that controls locomotion is structured and how the mammalian locomotor network keeps balance in the face of a changing environment. In this body of research, we build mathematical models of locomotion and fit our models to experimental data of walking cats to gain understanding of network connectivity and of balance control. Specifically, we test the biological plausibility of a particular connectivity of the mammalian locomotor network by matching network activity to phases of walking in different experimental conditions. We gain understanding of balance control with an inverted pendulum model that fits the center of mass oscillations during walking in different experimental conditions

The effect of physical and cognitive challenges on walking in younger and older adults

Changes in the gait pattern and gait speed are evident in older adults and are thought to occur due to age-related declines in physical and cognitive function. However, most studies describing age-related changes in gait are conducted in level, unobstructed environments. These assessments of gait do not consider the demands encountered when walking in the community. Community ambulation may be more difficult for older adults, as physically challenging environments such as slopes or stairs may be encountered, and individuals often need to perform secondary tasks concurrently while walking. The overarching aim of this thesis was to examine the effect of physical and cognitive challenges on gait in both older and younger adults. This thesis demonstrated that physical and cognitive demands influence gait differently in older compared to younger adults. Specifically, changes in gait speed when walking on an uphill or downhill slope, which are more physically demanding than walking on a level surface, were more strongly associated with age, health status and physical activity levels. Incorporating the 10MWT on a sloped surface will enhance the clinical utility of measuring gait speed to detect changes in healthy ageing. Despite changes in gait speed when walking downhill, older adults were observed to maintain similar inter-joint coordination and variability compared to younger adults during downhill walking. This finding indicated that older adults may only adjust their inter-joint coordination when they have insufficient physical abilities to meet the physical demands of the walking environment. The findings of the dual-task studies (Chapters 5 and 6) demonstrated that both the difficulty of the walking task and attention switching impact task prioritisation in older adults, validating and expanding upon models of task prioritisation. This thesis has demonstrated that the ageing process influences older adults who walk on sloped surfaces and have to perform secondary tasks that require attention switching, which are factors common to real-world environments. These factors may be included as part of assessment and rehabilitation procedures for older adults who walk in the community

Investigating a combination therapy of robot-driven rehabilitation techniques with viral delivery of brain-derived neurotrophic factor in treating adult spinal cord injury

A complete spinal cord injury (SCI) disrupts the normal architecture of the central nervous system, resulting in severe and irreversible impairment of the healthy functions of the body. SCI physically interrupts the neural networks used to relay descending motor information from and ascending sensory information to supraspinal structures in the brain separating circuits in spinal cord from brain supervision and recruitment. Depending on the location of the injury, complete SCI can lead to paraplegia or quadriplegia. In the injured individual, the loss of autonomy and mobility can severely decrease quality of life, as well as negatively impact health outcomes. As a result, locomotor rehabilitation is an area of interest for research for its potential translational benefits in the clinic. In previous work in our lab studying the rat model for SCI, we have demonstrated the efficacy of robotic technology in the rehabilitation of adult rats transected as neonates (NTX), which are unique in their ability to produce autonomous stepping after complete SCI without intervention. Using robotic assistance at the pelvis in our trunk-based rehabilitation paradigm, we have significantly improved locomotor function in such animals. Viewing the NTX model, thus, as a signpost for what is possible in recovery when using our robot in animals that can step after SCI, we have also shown that our robot can be used to drive epidural stimulation (ES) in the rat transected as an adult (ATX) to promote stepping patterns and increase body weight support. Recently, the use of neurotrophins, such as brain-derived neurotrophic factor (BDNF) has been investigated as a means to induce stepping and locomotor behaviors in the ATX model to varying levels of success. We believe that there are potentially synergistic benefits to combining our robot rehabilitation techniques with the use of BDNF to rehabilitate ATX animals. This thesis addresses this idea in depth. We first investigated how BDNF and our robot-assisted treadmill training might interact in the ATX model. Next, we added robot-driven epidural stimulation to the treatment regimen to further understand how the therapies might interact in rehabilitation. Finally, to elucidate the mechanisms underlying locomotor recovery following injury, we used intracortical microstimulation (ICMS) to map the motor cortex of successfully rehabilitated animals. Our results suggest that BDNF and robot technologies can be combined successfully to provide robust stepping patterns, characterized by body weight support and plantar stepping in the ATX model for rats. Furthermore, we show that epidural stimulation can be used to mitigate pathological sequelae that come from BDNF use. Finally, our work shows how active stepping using BDNF and robot rehabilitation in the ATX model may induce significant reorganization of the trunk motor cortex, providing more clues to the relationship between the cortex and the spinal cord in motor control and muscle synergy development.Ph.D., Biomedical Engineering -- Drexel University, 201

ヒト二足歩行の神経制御機序 : 大脳皮質と脊髄神経回路の役割

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 中澤 公孝, 東京大学准教授 柳原 大, 東京大学准教授 工藤 和俊, 東京大学准教授 吉岡 伸輔, 上武大学教授 関口 浩文University of Tokyo(東京大学

Motion representation with spiking neural networks for grasping and manipulation

Die Natur bedient sich Millionen von Jahren der Evolution, um adaptive physikalische Systeme mit effizienten Steuerungsstrategien zu erzeugen. Im Gegensatz zur konventionellen Robotik plant der Mensch nicht einfach eine Bewegung und führt sie aus, sondern es gibt eine Kombination aus mehreren Regelkreisen, die zusammenarbeiten, um den Arm zu bewegen und ein Objekt mit der Hand zu greifen. Mit der Forschung an humanoiden und biologisch inspirierten Robotern werden komplexe kinematische Strukturen und komplizierte Aktor- und Sensorsysteme entwickelt. Diese Systeme sind schwierig zu steuern und zu programmieren, und die klassischen Methoden der Robotik können deren Stärken nicht immer optimal ausnutzen. Die neurowissenschaftliche Forschung hat große Fortschritte beim Verständnis der verschiedenen Gehirnregionen und ihrer entsprechenden Funktionen gemacht. Dennoch basieren die meisten Modelle auf groß angelegten Simulationen, die sich auf die Reproduktion der Konnektivität und der statistischen neuronalen Aktivität konzentrieren. Dies öffnet eine Lücke bei der Anwendung verschiedener Paradigmen, um Gehirnmechanismen und Lernprinzipien zu validieren und Funktionsmodelle zur Steuerung von Robotern zu entwickeln. Ein vielversprechendes Paradigma ist die ereignis-basierte Berechnung mit SNNs. SNNs fokussieren sich auf die biologischen Aspekte von Neuronen und replizieren deren Arbeitsweise. Sie sind für spike- basierte Kommunikation ausgelegt und ermöglichen die Erforschung von Mechanismen des Gehirns für das Lernen mittels neuronaler Plastizität. Spike-basierte Kommunikation nutzt hoch parallelisierten Hardware-Optimierungen mittels neuromorpher Chips, die einen geringen Energieverbrauch und schnelle lokale Operationen ermöglichen. In dieser Arbeit werden verschiedene SNNs zur Durchführung von Bewegungss- teuerung für Manipulations- und Greifaufgaben mit einem Roboterarm und einer anthropomorphen Hand vorgestellt. Diese basieren auf biologisch inspirierten funktionalen Modellen des menschlichen Gehirns. Ein Motor-Primitiv wird auf parametrische Weise mit einem Aktivierungsparameter und einer Abbildungsfunktion auf die Roboterkinematik übertragen. Die Topologie des SNNs spiegelt die kinematische Struktur des Roboters wider. Die Steuerung des Roboters erfolgt über das Joint Position Interface. Um komplexe Bewegungen und Verhaltensweisen modellieren zu können, werden die Primitive in verschiedenen Schichten einer Hierarchie angeordnet. Dies ermöglicht die Kombination und Parametrisierung der Primitiven und die Wiederverwendung von einfachen Primitiven für verschiedene Bewegungen. Es gibt verschiedene Aktivierungsmechanismen für den Parameter, der ein Motorprimitiv steuert — willkürliche, rhythmische und reflexartige. Außerdem bestehen verschiedene Möglichkeiten neue Motorprimitive entweder online oder offline zu lernen. Die Bewegung kann entweder als Funktion modelliert oder durch Imitation der menschlichen Ausführung gelernt werden. Die SNNs können in andere Steuerungssysteme integriert oder mit anderen SNNs kombiniert werden. Die Berechnung der inversen Kinematik oder die Validierung von Konfigurationen für die Planung ist nicht erforderlich, da der Motorprimitivraum nur durchführbare Bewegungen hat und keine ungültigen Konfigurationen enthält. Für die Evaluierung wurden folgende Szenarien betrachtet, das Zeigen auf verschiedene Ziele, das Verfolgen einer Trajektorie, das Ausführen von rhythmischen oder sich wiederholenden Bewegungen, das Ausführen von Reflexen und das Greifen von einfachen Objekten. Zusätzlich werden die Modelle des Arms und der Hand kombiniert und erweitert, um die mehrbeinige Fortbewegung als Anwendungsfall der Steuerungsarchitektur mit Motorprimitiven zu modellieren. Als Anwendungen für einen Arm (3 DoFs) wurden die Erzeugung von Zeigebewegungen und das perzeptionsgetriebene Erreichen von Zielen modelliert. Zur Erzeugung von Zeigebewegun- gen wurde ein Basisprimitiv, das auf den Mittelpunkt einer Ebene zeigt, offline mit vier Korrekturprimitiven kombiniert, die eine neue Trajektorie erzeugen. Für das wahrnehmungsgesteuerte Erreichen eines Ziels werden drei Primitive online kombiniert unter Verwendung eines Zielsignals. Als Anwendungen für eine Fünf-Finger-Hand (9 DoFs) wurden individuelle Finger-aktivierungen und Soft-Grasping mit nachgiebiger Steuerung modelliert. Die Greif- bewegungen werden mit Motor-Primitiven in einer Hierarchie modelliert, wobei die Finger-Primitive die Synergien zwischen den Gelenken und die Hand-Primitive die unterschiedlichen Affordanzen zur Koordination der Finger darstellen. Für jeden Finger werden zwei Reflexe hinzugefügt, zum Aktivieren oder Stoppen der Bewegung bei Kontakt und zum Aktivieren der nachgiebigen Steuerung. Dieser Ansatz bietet enorme Flexibilität, da Motorprimitive wiederverwendet, parametrisiert und auf unterschiedliche Weise kombiniert werden können. Neue Primitive können definiert oder gelernt werden. Ein wichtiger Aspekt dieser Arbeit ist, dass im Gegensatz zu Deep Learning und End-to-End-Lernmethoden, keine umfangreichen Datensätze benötigt werden, um neue Bewegungen zu lernen. Durch die Verwendung von Motorprimitiven kann der gleiche Modellierungsansatz für verschiedene Roboter verwendet werden, indem die Abbildung der Primitive auf die Roboterkinematik neu definiert wird. Die Experimente zeigen, dass durch Motor- primitive die Motorsteuerung für die Manipulation, das Greifen und die Lokomotion vereinfacht werden kann. SNNs für Robotikanwendungen ist immer noch ein Diskussionspunkt. Es gibt keinen State-of-the-Art-Lernalgorithmus, es gibt kein Framework ähnlich dem für Deep Learning, und die Parametrisierung von SNNs ist eine Kunst. Nichtsdestotrotz können Robotikanwendungen - wie Manipulation und Greifen - Benchmarks und realistische Szenarien liefern, um neurowissenschaftliche Modelle zu validieren. Außerdem kann die Robotik die Möglichkeiten der ereignis- basierten Berechnung mit SNNs und neuromorpher Hardware nutzen. Die physikalis- che Nachbildung eines biologischen Systems, das vollständig mit SNNs implementiert und auf echten Robotern evaluiert wurde, kann neue Erkenntnisse darüber liefern, wie der Mensch die Motorsteuerung und Sensorverarbeitung durchführt und wie diese in der Robotik angewendet werden können. Modellfreie Bewegungssteuerungen, inspiriert von den Mechanismen des menschlichen Gehirns, können die Programmierung von Robotern verbessern, indem sie die Steuerung adaptiver und flexibler machen

Foot Placement Characteristics and Plantar Pressure Distribution Patterns during Stepping on Ground in Neonates

Stepping on ground can be evoked in human neonates, though it is rather irregular and stereotyped heel-to-toe roll-over pattern is lacking. Such investigations can provide insights into the role of contact- or load-related proprioceptive feedback during early development of locomotion. However, the detailed characteristics of foot placements and their association with motor patterns are still incompletely documented. We elicited stepping in 33 neonates supported on a table. Unilateral limb kinematics, bilateral plantar pressure distribution and EMG activity from up to 11 ipsilateral leg muscles were recorded. Foot placement characteristics in neonates showed a wide variation. In ~25% of steps, the swinging foot stepped onto the contralateral foot due to generally small step width. In the remaining steps with separate foot placements, the stance phase could start with forefoot (28%), midfoot (47%), or heel (25%) touchdowns. Despite forefoot or heel initial contacts, the kinematic and loading patterns markedly differed relatively to toe-walking or adult-like two-peaked vertical force profile. Furthermore, while the general stepping parameters (cycle duration, step length, range of motion of proximal joints) were similar, the initial foot contact was consistently associated with specific center-of-pressure excursion, range of motion in the ankle joint, and the center-of-activity of extensor muscles (being shifted by ~5% of cycle toward the end of stance in the “heel” relative to “forefoot” condition). In sum, we found a variety of footfall patterns in conjunction with associated changes in motor patterns. These findings suggest the potential contribution of load-related proprioceptive feedback and/or the expression of variations in the locomotor program already during early manifestations of stepping on ground in human babies

Understanding the modulation of walking speed and exploring how this differs in people with Parkinson’s disease.

Background: Parkinson's disease (PD) affects the ability of individuals to initiate movement and change muscle activity during gait initiation (GI) and during variations in walking speed. The present study aims to investigate the biomechanics parameters (kinetics and kinematics) and muscle activity characteristics during GI and variation in speed while walking on a treadmill and overground (OG) for PD-affected individuals and physically fit people. Methods: In this study, participants (n=17) included a physical fit (n= 11, aged 31.72 +/17.91 years) and a Parkinson’s (n= 6, aged 67.33 +/-11.57 years, disease duration 13.5 +/8.69). Both groups were evaluated while walking on the treadmill and over the ground for two phases. The first phase was Gait initiation, where the participants were asked to start walking at their comfortable speed for two gait cycles on the treadmill and OG. The second phase was speed variation, where the participants also walked at their comfortable speed, and increased their speed in response to visual instruction on screen. However, on the ground, they were asked to change their speed after their fifth walking step. A self-pacing treadmill synchronised with a virtual reality screen (MotekMedical, the Netherlands) and A 12-camera motion capture system (Vicon Motion Systems, UK) integrated with two embedded force plates and a wireless EMG system (Trigno, Delsys, USA) collected the biomechanical and muscle excitation data. Three gait cycles; before, during and immediately after the speed change was used for the analysis of the speed variation. Data were limited to lower limb joints and three muscles (tibialis anterior, gastrocnemius and soleus. Differences in the percentage of contraction and magnitude of muscle activation (area under the curve, AUC) were compared before and during the speed change. Results: PD-affected individuals spent less time on GI during treadmill walking (2.06 s ± 0.39) than the healthy reference group (2.25 s ± 0.42) but more time with OG walking (1.95s ±0.25) compared to the reference group (1.49s ±0.56). The reference group had a greater range of lower limb joint movement than the PD group during GI on both walking surfaces. The power produced at the hip and ankle joint by the reference group was higher than the overall PD group. The magnitude of muscle activation was lower in the PD group than the reference group, and the severity of the disease affected the magnitude of the muscle activation. At speed variation, both the reference and PD groups showed an increase in speed. Cadence declined in the reference group but elevated in the PD group. Soleus muscle activity increased with an increase in speed in PD-affected individuals, particularly in severely affected individuals compared to the reference group. Discussion/Conclusion: The mechanism for increasing speed appears to differ between PD-affected individuals and physically fit individuals. Soleus excitation during stance may be a control parameter for walking speed that is disturbed in PD, although age is likely to be a confounding factor. Further research is needed to understand the mechanisms underpinning these positive responses to interactive treadmill training and its impact on community walking. Keywords: Parkinson's disease, Gait initiation, Gait Cycles, treadmill walking, speed change.Background: Parkinson's disease (PD) affects the ability of individuals to initiate movement and change muscle activity during gait initiation (GI) and during variations in walking speed. The present study aims to investigate the biomechanics parameters (kinetics and kinematics) and muscle activity characteristics during GI and variation in speed while walking on a treadmill and overground (OG) for PD-affected individuals and physically fit people. Methods: In this study, participants (n=17) included a physical fit (n= 11, aged 31.72 +/17.91 years) and a Parkinson’s (n= 6, aged 67.33 +/-11.57 years, disease duration 13.5 +/8.69). Both groups were evaluated while walking on the treadmill and over the ground for two phases. The first phase was Gait initiation, where the participants were asked to start walking at their comfortable speed for two gait cycles on the treadmill and OG. The second phase was speed variation, where the participants also walked at their comfortable speed, and increased their speed in response to visual instruction on screen. However, on the ground, they were asked to change their speed after their fifth walking step. A self-pacing treadmill synchronised with a virtual reality screen (MotekMedical, the Netherlands) and A 12-camera motion capture system (Vicon Motion Systems, UK) integrated with two embedded force plates and a wireless EMG system (Trigno, Delsys, USA) collected the biomechanical and muscle excitation data. Three gait cycles; before, during and immediately after the speed change was used for the analysis of the speed variation. Data were limited to lower limb joints and three muscles (tibialis anterior, gastrocnemius and soleus. Differences in the percentage of contraction and magnitude of muscle activation (area under the curve, AUC) were compared before and during the speed change. Results: PD-affected individuals spent less time on GI during treadmill walking (2.06 s ± 0.39) than the healthy reference group (2.25 s ± 0.42) but more time with OG walking (1.95s ±0.25) compared to the reference group (1.49s ±0.56). The reference group had a greater range of lower limb joint movement than the PD group during GI on both walking surfaces. The power produced at the hip and ankle joint by the reference group was higher than the overall PD group. The magnitude of muscle activation was lower in the PD group than the reference group, and the severity of the disease affected the magnitude of the muscle activation. At speed variation, both the reference and PD groups showed an increase in speed. Cadence declined in the reference group but elevated in the PD group. Soleus muscle activity increased with an increase in speed in PD-affected individuals, particularly in severely affected individuals compared to the reference group. Discussion/Conclusion: The mechanism for increasing speed appears to differ between PD-affected individuals and physically fit individuals. Soleus excitation during stance may be a control parameter for walking speed that is disturbed in PD, although age is likely to be a confounding factor. Further research is needed to understand the mechanisms underpinning these positive responses to interactive treadmill training and its impact on community walking. Keywords: Parkinson's disease, Gait initiation, Gait Cycles, treadmill walking, speed change