Looking back on 2011, but through an academic lens, leaves the impossible task of trying to pick a research highlight. I guess in much the same way as your choice of a Sports Star of the Year would be influenced by your choice of sport (Messi, Djokovic, Cavendish or Wellington), the choice of most exciting or impactful sports science story of the year is heavily influenced by your particular focus within the sciences.
Similarly, within sports science, you may be heavily invested in physical activity and disease, molecular basis for injuries, applied physiology, or performance physiology.
My personal focus, at least during my PhD was fatigue, and specifically the role of the brain in the regulation of performance and pacing strategy. Therefore, my pick as the sports science story of 2011 is a series of studies out of Switzerland, which have provided the first evidence of how brain structures interact with one another during fatiguing exercise. To quote from the third of the three studies:
To the best of our knowledge, this is the first study to empirically demonstrate that muscle fatigue leads to changes in interaction between structures of a brain’s neural network
Background – the brain was clearly involved, but the “how” was missing
As I was finishing my PhD, the problem I encountered is that we were able to observe how performance and specifically pacing strategy was affected by various interventions (heat, high or low oxygen, energy supply, deception or manipulation of distance information), but we didn’t have the tools to measure the neural processes that were producing these changes.
Briefly, it was pretty clear that exercise performance was regulated by the brain, and over time, the theory evolved that the brain was monitoring all the physiological systems and ensuring that performance was optimized in the face of potentially limiting (or even harmful) changes in homeostasis. For example, it had been shown pretty clearly that when we hit a body temperature of around 40 degrees celsius, we stopped – limiting fatigue due to hyperthermia. Therefore, as soon as exercise was self-paced, the brain would monitor the rate at which the temperature was rising, and then regulate exercise intensity in order to prevent us from hitting this “limit” before the known end of exercise.
The same was true for exercise at altitude, with low glycogen stores, and when you lied to athletes about how much exercise remained – there was an anticipatory component to fatigue, so that fatigue was not merely the failure of physiology, but the process by which that potential failure (in performance, in this case), might be regulated.
The problem is that our ability to measure the neural contributions was limited. We were able to measure muscle activation levels, albeit crudely during dynamic exercise, but it gave a pretty clear picture of how the degree of muscle recruitment was altered by the brain over the course of exercise and with different situations. However, much had to be inferred from how power output or running speed changed as a function of changes in various physiological systems.
Therefore, at the conclusion of my PhD back in 2006, we had a theory, sometimes called the “central governor” model, which I believe accurately explained what was observed during exercise, but was in need of a mechanistic component. The theory began to evolve into the realms of philosophy (sometimes deliberate, other times out of ignorance). And one of the problems was this lent itself to gross misunderstandings. A very respected scientist came to me in Denver this year and mocked the theory because it meant there must “be a little man dancing around in your head telling you how to exercise”.
Of course, that is not part of any theory I’ve ever seen, but in the absence of measurements of brain function during exercise, it is, I suppose, the inevitable criticism. This lack of mechanistic explanation is one of the primary reasons that I looked elsewhere for future research, because we had taken our observations to a point where we had a model, a theory for how fatigue and physiology were inter-related, how pacing and performance were regulated, but we could not move beyond the hypothetical.
And so when, only a few months ago, a series of three studies on fatigue and the brain were published, it was an exciting breakthrough, the first, I suspect, of many, which will push the field of fatigue and exercise into the next phase of understanding.
The three studies: Building the model of fatigue
Science Daily have a really concise summary of the three studies, including some quotes from the scientists involved. I won’t rehash the translation of the science here, but rather direct you to their summary.
For those interested in the papers discussed in that article, they are at the followings links:
- Afferent pain information from the muscle contributes to inhibition of the motor cortex during fatiguing muscle contractions 
- The thalamus and insular cortex are involved in regulating exercise in response to afferent information from the muscle 
- Communication between brain areas during fatigue exercise 
The studies are certainly a breakthrough, but by no means a complete picture. For example, the first of the three studies produces a similar finding to a body of work by Markus Ammann (not in 2011, but over the last 4 or 5 years), which have shown a similar role of afferent (feedback) information from the muscle to the brain. The motor output (think muscle activation) is clearly influenced by this information, which should be obvious as soon as one accept that fatigue, and therefore performance, are regulated in the same way that any system is (blood glucose, body temperature etc – there are sensors, there is feedback, there is an effector).
What is needed next is to move this technology on from isolated muscle contractions and onto dynamic exercise. The above studies all used pretty isolated exercise (handgrips or leg extensions), or they use EEG during cycling (in Study 3). When we can measure brain activity using fMRI in different regions of the brain during a 10km running time-trial, for example, then we will have some extremely powerful information.
That breakthrough may be coming – at my University, some colleagues have done some great work and are in fairly advanced stages of being able to measure brain activity using fMRI during cycling activity, and that should unlock more secrets – the video is below.
Next step – decoding the “lights” and making sense of data
Once this can be done, then it’s a matter of understanding what it all means. The field of neuroscience has long ago evolved from a “black box” approach to understanding brain function, towards an integrated model. The danger for sports science is that the same may happen. Indeed, it already exists – this mindset has been another source of criticism for the central governor, in that people seem to expect it to be a distinct anatomical structure. Even the approach to studying fatigue has probably been held back by too specific approach to what is clearly a multi-faceted, complex phenomenon.
The reality is that it’s far too complex for that, and only many years of research will build the picture of how the brain integrates such vast complexity to regulate performance in the obvious way that it does!
2011 may have provided the first steps, but they are the first of many!
Next time: Sports stars of the year
- L. Hilty, K. Lutz, K. Maurer, T. Rodenkirch, C.M. Spengler, U. Boutellier, L. Jäncke, and M. Amann, “Spinal opioid receptor-sensitive muscle afferents contribute to the fatigue-induced increase in intracortical inhibition in healthy humans”, Experimental Physiology, pp. no-no, 2011. http://dx.doi.org/10.1113/expphysiol.2010.056226
- L. Hilty, L. Jäncke, R. Luechinger, U. Boutellier, and K. Lutz, “Limitation of physical performance in a muscle fatiguing handgrip exercise is mediated by thalamo-insular activity”, Human Brain Mapping, vol. 32, pp. 2151-2160, 2011. http://dx.doi.org/10.1002/hbm.21177
- L. Hilty, N. Langer, R. Pascual-Marqui, U. Boutellier, and K. Lutz, “Fatigue-induced increase in intracortical communication between mid/anterior insular and motor cortex during cycling exercise”, European Journal of Neuroscience, vol. 34, pp. 2035-2042, 2011. http://dx.doi.org/10.1111/j.1460-9568.2011.07909.x