Part of the reason I enjoy cycling as my chosen sport now I am older is not just because it is beneficial from a health perspective, but because the apparent regularity of the rhythmical circular movement required for pedalling creates a sense of peace in me and paradoxically allows my mind to wander a bit away from its routine and usually work-focussed and life task orientated thoughts. I enjoy watching competitive darts, from the perspective of marvelling at how the folk participating in the competitions seem to so often hit the small area of the board they are aiming for with such precision, after fairly rapidly throwing their darts when it is their turn to do so. This week an old colleague and friend from University of Cape Town days, Dr Angus Hunter, published some interesting work on how the brain controls muscle activity during different experimental conditions, a field of which he is a world expert in, and it was great to read about his new research and innovative ideas as always. Some of the most fun times of my research career were spent in the laboratory working with Angus measuring muscle activity during movement related tasks, where one of our most challenging issues to deal with was the variability of the signal our testing devices recorded when measuring either the power output from, or electrical activity in, muscle fibres each time they contracted when a trial participant was asked to do the same task. A large part of the issue we had to solve then was whether this was signal ‘noise’ and an artefact of our testing procedures, or if it was part of the actual recruitment strategy the brain used to control the power output from the muscles. All of these got me thinking about motor control mechanisms, and how movement and activity is regulated in a way that gets tasks done in a seemingly smooth and co-ordinated way, often without us having to think about what we are doing, while when one measures individual muscle function it is actually very ‘noisy’ and variable, even during tasks which are performed with a high degree of accuracy, and how the brain either creates or ‘manages’ this variability and ‘noise’ to generate smooth and accurate rhythmical or target-focussed activity, as that which occurs when cycling and throwing darts respectively.
Some of the most interesting scientific work that I have ever read about was done by Nikolai Bernstein, a Russian neurophysiologist, who when working in the 1920’s at the somewhat euphemistically named Moscow Central Institute of Labour, examined motor control mechanisms during movement. As part of the communist government of the times centrally driven plans to improve worker productivity and output, Bernstein did research on manual labour tasks such as hammering and cutting, in order to try and understand how to optimise it. Using novel ‘cyclogram’ photography techniques, where multiple pictures were taken of a worker using a hammer or chisel to which a light source had been attached, he was able to produce the astonishing observation that each time the worker hit a nail or cut through metal, their arm movements were not identical each time they performed the action, and rather that there was a great degree of variability each time the similar action was performed, even though usually this variability in action produced an outcome which had a high degree of accuracy. He realized that each complete movement, such as moving the arm towards the target, is made up of a number of smaller movements of muscles around the shoulder, elbow and wrist joints, which together synergistically create the overall movement. Given how many muscles there are in the arm, working around three joints (and potentially more when one thinks of the finger joints and muscles controlling them), he suggested that were a very large number of potential combinations of muscle actions and joint positions that could be used for the same required action, and a different combination of these appeared to be ‘chosen’ by the brain each time it performed a repetitive task. From a motor control perspective, Bernstein deduced that this could potentially cause a problem for the brain, and whatever decision-making process decided on which movement pattern it would use to complete a task, given that it created a requirement for choosing a particular set of muscle synergies from a huge number of different options available, or in contrast not choosing all the other muscle synergistic options, each time the individual was required to perform a single task or continue performing a repetitive task. This would require a great amount of calculation and decision-making capacity on a repetitive basis by the brain / control processes, and he called this the motor redundancy, or degrees of freedom, problem.
Like a lot of work performed in the Stalin era in Russia, his fascinating work and observations did not become known to Western scientists until the 1960’s, when he published a text-book of his career in science, which was subsequently translated and taken forward by excellent contemporary movement control scientists like Mark Latash of the University of Pennsylvania State in the USA. Further studies have supported Bernstein’s earlier work, and it is astonishing how much variability there is in each movement trajectory of a complex action that is goal orientated. Mark has suggested that this is not a redundancy problem, but rather one of abundancy, with the multiple choices available being of benefit to the body of any individual performing repetitive tasks, potentially from a fatigue resistance and injury prevention perspective, which may occur if the same muscle fibres in the same muscle are used in the same way in a repetitive manner. Interestingly, when a person suffers a stroke or a traumatic limb injury, the quantity of movement variability appears to paradoxically reduce rather than increase after the stroke or injury, and this reduced variability of motor function is associated with a decrement in task performance accuracy and completion. Therefore, the high variability of movement patterns in healthy folk appears to paradoxically make task performance more accurate and not just more efficient.
How control processes choose a specific ‘pattern’ of muscle activity for a specific task is still not well known. A number of theories have been proposed (generally as a rule in science, the more theories there are about something, the more the likelihood there is that there is no clarity about it) with some quaint names, such as the equilibrium point hypothesis, which suggests that choice at the motor neuron level is controlled as part of the force-length relationship of the muscle; the uncontrolled manifold hypothesis, which suggests that the central nervous system focuses on the variables needed to control a task and ignores the rest (the uncontrolled manifold being those variables that do not affect task required activity); and the force control hypothesis, which suggests that the central nervous systems compares the required movement for the task against internal models, and then uses calculations and feedforward and feedback control mechanisms to direct activity against that set by the internal model; amongst others. All these are interesting and intellectually rigorous theories, but don’t tell us very much about exactly how the brain chooses a particular group of muscles to perform a task, and then subsequently a different group of muscles, which use a different flight trajectory, to perform the task again when it is repeated. It has been suggested that there are ‘synergistic sets’ of muscles which are chosen in their entirety for a single movement, and that the primitive reflexes or central pattern generators in the spinal cord may be involved. But the bottom line is that we just do not currently know exactly what control mechanism chooses a specific set of muscles to perform one movement of a repetitive task, why different muscles are chosen each time the same task is performed sequentially, or how this variable use of muscles for the same task is managed and controlled.
We have previously suggested that a number of other activities in the body beyond that of muscle control have similar redundancy (or abundancy) in how they are regulated, or at least in respect of which mechanisms are used to control them. For example, blood glucose concentrations can be controlled not only by changes in insulin concentrations, but also by that of glucagon, and can also be altered by changes in catecholamine (adrenaline or noradrenaline) or cortisol levels, and indeed by behavioural factors such as resisting the urge to eat. Each time blood glucose concentrations are measured, the concentrations of all these other regulatory hormones and chemicals will be different ratio-wise to each other, yet their particular synergistic levels at any one point in time maintains the level of blood glucose concentrations at homeostatically safe setpoint levels. The blood glucose level is maintained whatever the variability in the regulatory factor concentration ratios, and even though this variability in choice of control mechanisms similarly creates a potential for high computational load when managing blood glucose concentrations from a control perspective. Similarly, perception of mood state or emotions are thought to have redundancy in what factors ‘creates’ them. For example we can fairly accurately rate when we feel slightly, moderately or very fatigued, but underpinning the ‘feeling’ of fatigue at the physiological level can be changes in blood glucose, heart rate, ventilation rate, and a host of other metabolites and substrates in the body, each of which can be altered in a variable ratio way to make up the sensation of fatigue we rate as slightly, moderately or very high levels of fatigue. Furthermore, fatigue is a complex sensation made up of individual sensations such as breathlessness, pounding chest, sweating, pain, and occasionally confusion, dizziness, headache and pins and needles, amongst others, a combination of which can also be differently valenced to provide a similar general fatigue rating by whoever is perceiving the sensation of fatigue. To make it even more complex, the sensation of fatigue is related to inner voices which either rate the sensation of fatigue (the ‘I’ voice) or make a judgement on it related to social circumstances or family and environmental background (the ‘Me’ voice), and it is through the final combination of these that an individual finally rates their level of fatigue, which adds another level of redundancy, or abundancy, to the factors underpinning how the ‘gestalt’ sensation of fatigue is both created and perceived. There are therefore three potential ‘levels’ of redundancy / abundancy in the signals and factors which either individually or collectively make up the ‘gestalt’ sensation of fatigue, and a corresponding increased level of computational requirements potentially associated with its final genesis, and how this perceptual redundancy / abundancy is managed by the control mechanisms which generate them is still not well known.
In summary, therefore, the presence of variability during activities of daily living across a number of different body systems is not only ‘noise’ / artefacts of testing conditions which are challenges for us researchers to have to deal with, it also appears to be part of some very complex control mechanisms which must have some teleological benefit both for optimizing movement and activity, and ensuring the capacity to sustain it without fatigue or injury to the components of the mechanism which produces it. Each time I cycle on my bike and my legs move up and down to push the wheels forward, different muscles are being used in a different way during each rotation of the wheel. Each time a darts player throws a dart, different muscle synergies are used to paradoxically create the accuracy of their throw. There is real ‘noise’ that a researcher has to remove from their recorded traces after a testing session in a laboratory, such as that caused by the study participant sweating during the trial, which can affect electrophysiological signals, and there is always a degree of measurement error, and therefore some degree of ‘noise’ is present in the variability of the recorded output for any laboratory technique that measures human function. But, equally, Bernstein’s brilliant work and observations all those years ago helped us understand that variability is inherent in living systems, and after understanding this, each time I observe data, particularly that generated during electrophysiological work such as I have used for a number of experiments in my own research career, including electromyography (EMG), electroencephalography (EEG) or transcranial magnetic stimulation (TMS), which has low standard deviations in the results sections of published research articles, I do wonder at the validity of the data and whether it has been ‘paintbrushed’ by the researchers who describe it, as my old Russian neurophysiology research colleague Mikhail Lomarev used to describe it, when he or we thought data was ‘suspect’. The inherent variability in brain and motor control systems makes finding statistical significance in results generated using routine neurophysiological techniques more difficult. It also seems to create a huge increase in the requisite control-related calculations and planning for even a simple movement, though as Mark Latash suggested, the brain is likely to not be a micro-manager, but rather some effective parsing mechanism which can both generate and utilize a large number of synergistic movement patterns in a variable manner for any task, while not utilizing much decision making power using some sort of heuristic-based decision-making mechanism. Most importantly though, it fills one with a sense of awe at the ‘magic’ of our own body, and for the level of complexity involved in both its creation and operative management, when even a simple movement like striking an object with a hammer, or cutting a piece of metal, can be underpinned by such complex control mechanisms that our brains cannot currently comprehend or make sense of.
In a laboratory in the middle of Russia nearly a century ago, Nikolai Bernstein made some astonishing observations by doing exceptional research on basic motor control, while trying to increase the productivity of soviet-era industrial work. A century later we are still scratching our heads trying to understand what his findings mean from a motor control perspective. As I type these final sentences, I reflect on this, and wonder which synergistic composition of muscle activity in my fingers are responsible for creating the actions which lead to these words being generated, and realize that each time I do so, because of the concepts of variability, redundancy and abundancy, I will probably never use an identical muscle sequence when typing other ideas into words at another future point in time. But then again, I guess the words I will be writing in the future will also be different, and daily life, like motor control programs, will always vary, always change, even though the nail on the wall on which the picture hangs becomes a permanent ‘item’, as will this article become permanent when I hit the ‘send’ button to publish it. What is never to be seen again though are the traces in the ‘ether’ of the hammer blow which embedded the nail in the wall, and the exact movement of the individual muscles in the labourers arms and hands, and in my fingers as I typed which created these words. Like magic their variability was created, and like magic their pattern has dispersed, never to recur again in the same way or place, unless some brilliant modern day Bernstein can solve their magic and mystery, reproduce them in their original form using some as yet to be invented laboratory device, and publish them in a monograph. Let’s hope that if they do so, their great work does not languish unseen for forty years before being discovered by the rest of the world’s scientists, as was Bernstein’s wonderful observations of all those years ago!