Motor coordination

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In physiology, motor coordination is the orchestrated movement of multiple body parts as required to accomplish intended actions, like walking. This coordination is achieved by adjusting kinematic and kinetic parameters associated with each body part involved in the intended movement. The modifications of these parameters typically relies on sensory feedback from one or more sensory modalities (see multisensory integration), such as proprioception and vision.

Contents

Properties

Large degrees of freedom

Goal-directed and coordinated movement of body parts is inherently variable because there are many ways of coordinating body parts to achieve the intended movement goal. This is because the degrees of freedom (DOF) is large for most movements due to the many associated neuro-musculoskeletal elements. [1] Some examples of non-repeatable movements are when pointing [2] or standing up from sitting. [3] Actions and movements can be executed in multiple ways because synergies (as described below) can vary without changing the outcome. Early work from Nikolai Bernstein worked to understand how coordination was developed in executing a skilled movement. [1] In this work, he remarked that there was no one-to-one relationship between the desired movement and coordination patterns to execute that movement. This equivalence suggests that any desired action does not have a particular coordination of neurons, muscles, and kinematics.

Complexity

The complexity of motor coordination goes unnoticed in everyday tasks, such as in the task of picking up and pouring a bottle of water into a glass. This seemingly simple task is actually composed of multiple complex tasks. For instance, this task requires the following:

(1) properly reaching for the water bottle and then configuring the hand in a way that enables grasping the bottle.

(2) applying the correct amount of grip force to grasp the bottle without crushing it.

(3) coordinating the muscles required for lifting and articulating the bottle so that the water can be poured into the glass.

(4) terminating the action by placing the empty bottle back on the table.

Hand-eye coordination is also required in the above task. There is simultaneous coordination between hand and eye movement as dictated by the multi-sensory integration of proprioceptive and visual information. [4] Additional levels of coordination are required depending on if the person intends to drink from the glass, give it to someone else, or simply put it on a table. [5]

Types of motor coordination

Inter-limb

Inter-limb coordination is concerned about how movements are coordinated across limbs. In walking for instance, inter-limb coordination refers to the spatiotemporal patterns and kinematics associated with the movement of the legs. Prior work in vertebrates showed that distinct inter-limb coordination patterns, called gaits, occur at different walking speed ranges as to minimize the cost of transport. [6] Like vertebrates, drosophila change their interleg coordination pattern in a speed-dependent manner. However, these coordination patterns follow a continuum rather than distinct gaits. [7]

In bimanual tasks (tasks involving two hands), it was found that the functional segments of the two hands are tightly synchronized. One of the postulated theories for this functionality is the existence of a higher, "coordinating schema" that calculates the time it needs to perform each individual task and coordinates it using a feedback mechanism. There are several areas of the brain that are found to contribute to temporal coordination of the limbs needed for bimanual tasks, and these areas include the premotor cortex (PMC), the parietal cortex, the mesial motor cortices, more specifically the supplementary motor area (SMA), the cingulate motor cortex (CMC), the primary motor cortex (M1), and the cerebellum. [8]

Several studies have proposed that inter-limb coordination can be modeled by coupled phase oscillators, [9] [10] a key component of a central pattern generator (CPG) control architecture. In this framework, the coordination between limbs is dictated by the relative phase of the oscillators representing the limbs. Specifically, an oscillator associated with a particular limb determines the progression of that limb through its movement cycle (e.g. step cycle in walking). In addition to driving the relative limb movement in a forward manner, sensory feedback can be incorporated into the CPG architecture. This feedback also dictates the coordination between the limbs by independently modifying the movement of the limb that the feedback is acting on.

Intra-limb

Intra-limb coordination involves orchestrating the movement of the limb segments that make up a single limb. This coordination can be achieved by controlling/restricting the joint trajectories and/or torques of each limb segment as required to achieve the overall desired limb movement, as demonstrated by the joint-space model. [11] Alternatively, intra-limb coordination can be accomplished by just controlling the trajectory of an end-effector, such as a hand. An example of such concept is the minimum-jerk model proposed by Neville Hogan and Tamar Flash, [12] which suggests that the parameter the nervous system controls is the spatial path of the hand, ensuring that it is maximally smooth. Francesco Lacquaniti, Carlo Terzuolo and Paolo Viviani showed that the angular velocity of a pen's tip varies with the two-thirds power of the path curvature (two-thirds power law) during drawing and handwriting. [13] The two-thirds power law is compatible with the minimum-jerk model, but also with central pattern generators. It has subsequently been shown that the central nervous system is devoted to its coding. [14] [15] Importantly, control strategies for goal directed movement are task-dependent. This was shown by testing two different conditions: (1) subjects moved cursor in the hand to the target and (2) subjects move their free hand to the target. Each condition showed different trajectories: (1) straight path and (2) curved path. [16]

Eye-hand

Eye–hand coordination is associated with how eye movements are coordinated with and influence hand movements. Prior work implicated eye movement in the motor planning of goal-directed hand movement. [17]

Learning of coordination patterns

The following pages are recommended for understanding how coordination patterns are learned or adapted:

Quantifying inter-limb and intra-limb coordination

Muscle synergies

Nikolai Bernstein proposed the existence of muscle synergies as a neural strategy of simplifying the control of multiple degrees of freedom. [1] A functional muscle synergy is defined as a pattern of co-activation of muscles recruited by a single neural command signal. [18] One muscle can be part of multiple muscle synergies, and one synergy can activate multiple muscles. Synergies are learned, rather than being hardwired, like motor programs, and are organized in a task-dependent manner. In other words, it is likely that a synergy is structured for a particular action and not for the possible activation levels of the components themselves. Work from Emilio Bizzi suggests that sensory feedback adapts synergies to fit behavioral constraints, but may differ in an experience-dependent manner. [19] Synergies allow the components for a particular task to be controlled with a single signal, rather than independently. As the muscles of limb controlling movement are linked, it is likely that the error and variability are also shared, providing flexibility and compensating for errors in the individual motor components. The current method of finding muscle synergies is to use statistical and/or coherence analyses on measured EMG (electromyography) signals of different muscles during movements. [20] A reduced number of control elements (muscle synergies) are combined to form a continuum of muscle activation for smooth motor control during various tasks. [21] [22] Directionality of a movement has an effect on how the motor task is performed (i.e. walking forward vs. walking backward, each uses different levels of contraction in different muscles). [23] Moreover, it is thought that the muscle synergies limited the number of degrees of freedom by constraining the movements of certain joints or muscles (flexion and extension synergies). However, the biological reason for muscle synergies is debated. [24] In addition to the understanding of muscle coordination, muscle synergies have also been instrumental in assessing motor impairments, helping to identify deviations in typical movement patterns and underlying neurological disorders. [25]

Uncontrolled manifold hypothesis

Another hypothesis proposes that the central nervous system does not eliminate the redundant degrees of freedom, but instead uses them to ensure flexible and stable performance of motor tasks at the cost of motor variability. The Uncontrolled Manifold (UCM) Hypothesis provides a way to quantify a "muscle synergy" in this framework. [26] This hypothesis defines "synergy" a little differently from that stated above; a synergy represents an organization of elemental variables (degrees of freedom) that stabilizes an important performance variable. Elemental variable is the smallest sensible variable that can be used to describe a system of interest at a selected level of analysis, and a performance variable refers to the potentially important variables produced by the system as a whole. For example, in a multi-joint reaching task, the angles and the positions of certain joints are the elemental variables, and the performance variables are the endpoint coordinates of the hand. [26]

This hypothesis proposes that the controller (the brain) acts in the space of elemental variables (i.e. the rotations shared by the shoulder, elbow, and wrist in arm movements) and selects the feasible manifolds (i.e. sets of angular values corresponding to a final position). This hypothesis acknowledges that variability is always present in movement, and it categorizes it into two types: (1) bad variability and (2) good variability. Bad variability affects the important performance variable and causes large errors in the result of a motor task, and good variability keeps the performance task unchanged and leads to a successful outcome. An interesting example of the good variability was observed in the movements of the tongue, which are responsible for the speech production. [27] The stiffness level to the tongue's body creates some variability (in terms of the acoustical parameters of speech, such as formants), but this variability does not impair the quality of speech. [28] One of the possible explanations might be that the brain only works to decrease the bad variability that hinders the desired result, and it does so by increasing the good variability in the redundant domain. [26]

Other relevant pages

Related Research Articles

Motor learning refers broadly to changes in an organism's movements that reflect changes in the structure and function of the nervous system. Motor learning occurs over varying timescales and degrees of complexity: humans learn to walk or talk over the course of years, but continue to adjust to changes in height, weight, strength etc. over their lifetimes. Motor learning enables animals to gain new skills, and improves the smoothness and accuracy of movements, in some cases by calibrating simple movements like reflexes. Motor learning research often considers variables that contribute to motor program formation, sensitivity of error-detection processes, and strength of movement schemas. Motor learning is "relatively permanent", as the capability to respond appropriately is acquired and retained. Temporary gains in performance during practice or in response to some perturbation are often termed motor adaptation, a transient form of learning. Neuroscience research on motor learning is concerned with which parts of the brain and spinal cord represent movements and motor programs and how the nervous system processes feedback to change the connectivity and synaptic strengths. At the behavioral level, research focuses on the design and effect of the main components driving motor learning, i.e. the structure of practice and the feedback. The timing and organization of practice can influence information retention, e.g. how tasks can be subdivided and practiced, and the precise form of feedback can influence preparation, anticipation, and guidance of movement.

<span class="mw-page-title-main">Muscle memory</span> Consolidating a motor task into memory through repetition

Muscle memory is a form of procedural memory that involves consolidating a specific motor task into memory through repetition, which has been used synonymously with motor learning. When a movement is repeated over time, the brain creates a long-term muscle memory for that task, eventually allowing it to be performed with little to no conscious effort. This process decreases the need for attention and creates maximum efficiency within the motor and memory systems. Muscle memory is found in many everyday activities that become automatic and improve with practice, such as riding bikes, driving motor vehicles, playing ball sports, typing on keyboards, entering PINs, playing musical instruments, poker, martial arts, swimming, dancing, and drawing.

<span class="mw-page-title-main">Motor cortex</span> Region of the cerebral cortex

The motor cortex is the region of the cerebral cortex involved in the planning, control, and execution of voluntary movements. The motor cortex is an area of the frontal lobe located in the posterior precentral gyrus immediately anterior to the central sulcus.

Central pattern generators (CPGs) are self-organizing biological neural circuits that produce rhythmic outputs in the absence of rhythmic input. They are the source of the tightly-coupled patterns of neural activity that drive rhythmic and stereotyped motor behaviors like walking, swimming, breathing, or chewing. The ability to function without input from higher brain areas still requires modulatory inputs, and their outputs are not fixed. Flexibility in response to sensory input is a fundamental quality of CPG-driven behavior. To be classified as a rhythmic generator, a CPG requires:

  1. "two or more processes that interact such that each process sequentially increases and decreases, and
  2. that, as a result of this interaction, the system repeatedly returns to its starting condition."

Motor control is the regulation of movements in organisms that possess a nervous system. Motor control includes conscious voluntary movements, subconscious muscle memory and involuntary reflexes, as well as instinctual taxis.

<span class="mw-page-title-main">Scratch reflex</span> Response to activation of sensory neurons

The scratch reflex is a response to activation of sensory neurons whose peripheral terminals are located on the surface of the body. Some sensory neurons can be activated by stimulation with an external object such as a parasite on the body surface. Alternatively, some sensory neurons can respond to a chemical stimulus that produces an itch sensation. During a scratch reflex, a nearby limb reaches toward and rubs against the site on the body surface that has been stimulated. The scratch reflex has been extensively studied to understand the functioning of neural networks in vertebrates. Despite decades of research, key aspects of the scratch reflex are still unknown, such as the neural mechanisms by which the reflex is terminated.

Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms, computational models of biological neural networks and actual biological systems. Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures.

In physiology, an efference copy or efferent copy is an internal copy of an outflowing (efferent), movement-producing signal generated by an organism's motor system. It can be collated with the (reafferent) sensory input that results from the agent's movement, enabling a comparison of actual movement with desired movement, and a shielding of perception from particular self-induced effects on the sensory input to achieve perceptual stability. Together with internal models, efference copies can serve to enable the brain to predict the effects of an action.

<span class="mw-page-title-main">Proprioception</span> Sense of self-movement, force, and body position

Proprioception is the sense of self-movement, force, and body position.

<span class="mw-page-title-main">Motor program</span> Abstract representation of movement

A motor program is an abstract metaphor of the central organization of movement and control of the many degrees of freedom involved in performing an action. Biologically realistic alternatives to the metaphor of the "motor program" are represented by central pattern generators.p. 182 Signals transmitted through efferent and afferent pathways allow the central nervous system to anticipate, plan or guide movement. Evidence for the concept of motor programs include the following:p. 182

Sensory-motor coupling is the coupling or integration of the sensory system and motor system. Sensorimotor integration is not a static process. For a given stimulus, there is no one single motor command. "Neural responses at almost every stage of a sensorimotor pathway are modified at short and long timescales by biophysical and synaptic processes, recurrent and feedback connections, and learning, as well as many other internal and external variables".

Motor babbling is a process of repeatedly performing a random motor command for a short duration. It is similar to the vocal babbling of infants, where the brain learns the relation between vocal muscle activities and the resulting sounds. However, it was found that the general motor-control system is already exploring itself in the womb, in animals, in a similar way. Originally, the random spasms and convulsions of the embryo were seen as the non-functional consequences of growth. Later it was realized that the motor system is already calibrating its sensorimotor system before birth. After birth, motor babbling in primates continues in the random grasping movements towards visual targets, training the hand–eye coordination system. These insights are used since the early nineteen nineties in models of biological movement control and in robotics. In robotics, it is a system of robot learning whereby a robotic system can autonomously develop an internal model of its self-body and its environment. Early work is by Kuperstein (1991) using a robot randomly positioning a stick in its workspace, while being observed by two cameras, using a neural network to associate poses of the stick with joint angles of the arm. This type of research has led to the research field of developmental robotics.

In neuroscience and motor control, the degrees of freedom problem or motor equivalence problem states that there are multiple ways for humans or animals to perform a movement in order to achieve the same goal. In other words, under normal circumstances, no simple one-to-one correspondence exists between a motor problem and a motor solution to the problem. The motor equivalence problem was first formulated by the Russian neurophysiologist Nikolai Bernstein: "It is clear that the basic difficulties for co-ordination consist precisely in the extreme abundance of degrees of freedom, with which the [nervous] centre is not at first in a position to deal."

Gain field encoding is a hypothesis about the internal storage and processing of limb motion in the brain. In the motor areas of the brain, there are neurons which collectively have the ability to store information regarding both limb positioning and velocity in relation to both the body (intrinsic) and the individual's external environment (extrinsic). The input from these neurons is taken multiplicatively, forming what is referred to as a gain field. The gain field works as a collection of internal models off of which the body can base its movements. The process of encoding and recalling these models is the basis of muscle memory.

<span class="mw-page-title-main">Neuromechanics</span> Interdisciplinary field

Neuromechanics is an interdisciplinary field that combines biomechanics and neuroscience to understand how the nervous system interacts with the skeletal and muscular systems to enable animals to move. In a motor task, like reaching for an object, neural commands are sent to motor neurons to activate a set of muscles, called muscle synergies. Given which muscles are activated and how they are connected to the skeleton, there will be a corresponding and specific movement of the body. In addition to participating in reflexes, neuromechanical process may also be shaped through motor adaptation and learning.

As humans move through their environment, they must change the stiffness of their joints in order to effectively interact with their surroundings. Stiffness is the degree to a which an object resists deformation when subjected to a known force. This idea is also referred to as impedance, however, sometimes the idea of deformation under a given load is discussed under the term "compliance" which is the opposite of stiffness . In order to effectively interact with their environment, humans must adjust the stiffness of their limbs. This is accomplished via the co-contraction of antagonistic muscle groups.

The study of animal locomotion is a branch of biology that investigates and quantifies how animals move.

<span class="mw-page-title-main">Eberhard Fetz</span> American neuroscientist, academic and researcher

Eberhard Erich Fetz is an American neuroscientist, academic and researcher. He is a Professor of Physiology and Biophysics and DXARTS at the University of Washington.

Proprioception refers to the sensory information relayed from muscles, tendons, and skin that allows for the perception of the body in space. This feedback allows for more fine control of movement. In the brain, proprioceptive integration occurs in the somatosensory cortex, and motor commands are generated in the motor cortex. In the spinal cord, sensory and motor signals are integrated and modulated by motor neuron pools called central pattern generators (CPGs). At the base level, sensory input is relayed by muscle spindles in the muscle and Golgi tendon organs (GTOs) in tendons, alongside cutaneous sensors in the skin.

<span class="mw-page-title-main">Interlimb coordination</span> Coordination of the left and right limbs

Interlimb coordination is the coordination of the left and right limbs. It could be classified into two types of action: bimanual coordination and hands or feet coordination. Such coordination involves various parts of the nervous system and requires a sensory feedback mechanism for the neural control of the limbs. A model can be used to visualize the basic features, the control centre of locomotor movements, and the neural control of interlimb coordination. This coordination mechanism can be altered and adapted for better performance during locomotion in adults and for the development of motor skills in infants. The adaptive feature of interlimb coordination can also be applied to the treatment for CNS damage from stroke and the Parkinson's disease in the future.

References

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  25. ↑ Alnajjar, F.; Ozaki, K.; Matti, I.; Hiroshi, Y.; Masanori, T.; Ikue, U.; Masaki, K.; Maxime, T.; Chikara, N.; Alvaro, C.G.; Kensuke, O.; Aiko, O.; Izumi, K.; Shingo, S. (2020). "Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke". IEEE Access. 8 (6): 72138–72157. doi: 10.1109/ACCESS.2020.2987095 .
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  27. ↑ More precisely, the movements of tongue were modeled by means of a biomechanical tongue model, BTM, controlled by an optimum internal model, which minimizes the length of the path traveled in the internal space during the production of the sequences of tasks (see Blagouchine & Moreau).
  28. ↑ Iaroslav Blagouchine and Eric Moreau. Control of a Speech Robot via an Optimum Neural-Network-Based Internal Model with Constraints. IEEE Transactions on Robotics, vol. 26, no. 1, pp. 142—159, February 2010.
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