ECR Spotlight – Cristian Riveros-Matthey Free

Cristian Riveros-Matthey

Describe your scientific journey and your current research focus

My scientific journey began with my studies in physical therapy at university, where I developed a strong interest in biomechanics. This fascination led me to pursue an exchange scholarship at the Biomechanics Laboratory of the University of León in Spain, where I gained valuable insights into human movement and physics. After completing my master's degree in clinical biomechanics, my focus shifted towards coding and instrument manipulation. The decision to pursue a PhD in biomechanics came about during a conference in Brisbane, Australia, in 2017, where I had the opportunity to immerse myself in one of the most influential schools of human movement research. Conversations with researchers in the field further solidified my interest in studying self-selected movement patterns. This eventually led me to join Professor Tim Carroll's lab at the School of Human Movement and Nutrition Sciences for my PhD. Throughout my research journey, I have been dedicated to unraveling the factors that drive the nervous system's selection of paces or cadences during various tasks, with a particular emphasis on cycling. However, I aspire to expand my investigations to other evolutionary tasks in the future. It has been an incredible journey, gaining insights into how our human system makes decisions based on objectives. This exploration of our remarkable machinery leaves me in awe, contemplating how technology and advancements in various fields are derived from our understanding of human movement.

How would you explain the main finding of your paper to a member of the public?

Our research investigated how people choose the speed at which they pedal while riding a bicycle. First, we discovered that as the intensity increased, people tended to pedal faster than what would be considered the most efficient for their energy consumption. We wanted to find out why this occurs and how it affects the muscles and joints while cycling. We measured the speed at which the lower limb muscle fibers shorten, and the joint power and muscle activation levels while participants cycled at different velocities and intensity levels. Notably, we found that the muscles were less active when cyclists self-selected their speed at normal intensity levels, rather than keeping the velocity constant at which the muscle fiber contracts. This suggests that cyclists may avoid muscle fatigue by reducing muscle activity at the self-selected speed. Overall, our study suggests that when people pedal at their preferred speed, their muscles may be adapting to generate optimal power, hence avoiding fatigue for the task at hand. This adaptation helps to explain why people choose to pedal at a faster rate than what might be considered the most energy efficient.

What are the potential implications of this finding for your field of research, and is there anything that you learned during this study that you wish you had known sooner?

These findings offer valuable insights into the underlying mechanisms governing the selection of preferred cadences during cycling. The observed relationship between minimized muscle activation and increasing fascicle shortening velocity at the cyclist's self-selected cadence aligns with the theoretical framework suggesting an optimal shortening velocity for maximizing power increases with the intensity of exercise and recruitment of fast twitch fibers. This association may indicate the nervous system's adaptive response aimed at mitigating muscle fatigue. Nonetheless, we acknowledge the existence of alternative cost functions derived from muscle activation that may not exclusively adhere to the criterion of fatigue avoidance. Further investigation is warranted to fully elucidate the complex interplay of factors influencing the selection of preferred movement patterns and their broader physiological implications.

Which part of this research project was the most rewarding/challenging?

I recall two moments of satisfaction during this study. The first was the completion of the processing of vastus lateralis muscle shortening velocities. Although the use of a personalized semi-automatic script facilitated the analysis, it required nearly 6 months of dedicated effort to derive the desired outcomes. Another significant milestone in our research journey was the moment we obtained the main findings of our study. Specifically, we observed the minimization of muscle activation (via EMG RMS) in the muscles surrounding the knee aligned to self-selected cadences. This pivotal finding served as a turning point in the investigation's progression.

Are there any important historical papers from your field that have been published in JEB?

There are so many, I think it's a bit unfair to select only one. But obeying the question, there is one titled ‘Energetics of Bipedal Running’ by Thomas J. Roberts, Rodger Kram, Peter G. Weyand and C. Richard Taylor (1998; doi:10.1242/jeb.201.19.2745). This definitely reformed the way to understand the metabolic consumption of running animals. The key findings of this historical paper revealed that the metabolic cost of generating force during running is disproportionately higher than previously assumed. This challenged the existing understanding of energy cost in running and shed light on the intricate relationship between force production and metabolic demand.

Are there any modern-day JEB papers that you think will be the classic papers of 2123?

The paper titled ‘Fast and slow processes underlie the selection of both step frequency and walking speed’ by Max Donelan's lab (2014; doi:10.1242/jeb.105270) exemplifies a future classic because of its significant contribution to understanding the complex dynamics of human locomotion and its interaction with the environment. The study employed an elaborate experimental design that treated human gait as a dynamic system, allowing for a comprehensive examination of how individuals adapt their walking patterns in response to environmental perturbations. These controlled inputs served as valuable tools to investigate the underlying adaptive processes governing the selection of step frequency and gait speed in dynamic settings. By systematically manipulating these factors, the researchers unveiled the intricate relationships between external conditions and the resulting adjustments in step frequency and walking speed. This research breakthrough provides valuable insights into the mechanisms driving human locomotion and sets the stage for future studies exploring the interplay between individuals and their surroundings in the context of gait.

What do you think experimental biology will look like 50 years from now?

I believe that experimental biology will undergo a profound transformation in the next 50 years as a result of the advancements in artificial intelligence (AI). AI technologies will revolutionize multiple aspects of experimental biology, enabling significant progress in molecular genetics and enhancing image processing capabilities. AI's computational power and data analysis capabilities will empower researchers to gain deeper insights into the intricate workings of biological systems. This will lead to new understanding of cellular processes, unravelling genetic interactions and accelerating the pace of drug discovery. Additionally, AI-driven image processing techniques will enhance our ability to visualize and comprehend complex biological structures and dynamic processes with unprecedented precision and accuracy. AI in experimental biology holds the potential for groundbreaking discoveries and advancements in this field.

If you had unlimited funding, what question in your research field would you most like to address?

If I had unlimited funds, I would like to address two main research questions. Firstly, I would explore the principles of optimality that govern human movement by testing different cost functions. To make the research more accurate, I would use simulated environments with multiple criteria to better understand how people perform different motor tasks. Secondly, I would investigate muscle mechanics, specifically looking at how the recruitment of muscle fibers affects (Henneman's size principle) the generation of muscle maximum power generation during dynamic tasks. This research will shed light on how optimal muscle condition might be reshaped as a function of external demands.