Mario Lavanga

Computational Neuroscientist, Data Scientist, Postdoctoral researcher at the Theoretical Neuroscience Group - INS - Aix-Marseille University

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Beyond prevention and good practices: how to explain individual variability in ageing

2021-01-21

The recent decades have seen a dramatic increase in life expectancy. A variety of factors are usually recognized to have contributed to the increased survival rates and lifespan of mankind. Among those, a diffused level of hygiene and the fight against infectious disease have been fundamental to a longer and healthier life. Paradoxically, Covid-19 has suddenly resumed dramatic memories of a forgotten past that strongly highlights these great achievements.

This longer lifespan does not only imply an increasing ageing population as the simple statistics would tell us, but it also reflects a dramatic increase in morbidities and disabilities, mostly related to chronic issues (such as cardiovascular and cancer diseases). Although everyday experience shows that the ageing is normally characterized by a decline of cognitive abilities, various cohort studies show that human ageing might have a greater variability in its later stages compared to the first decades of life. In layman terms, the last part of our lifespans could contain at the same time the brightest minds and cognitive impaired individuals.

This great amount of individual variability that ageing carries can be partially explained by personal factors such as genetics, environmental factors (education) or social factors (number of personal relationships). However, it is undeniable that the progress made in life expectancy during the first half of the XXth century has been less effective than the second half. The combination of an unexplained ageing variability and a slower dynamics of the life expectancy might imply an uneven distribution of a lower quality of life for the older population. On the one hand, the mean life expectancy keeps increasing at slower rate and on the other hand, the greater individual variability might mean a lower or higher life expectancy compared to the previous generation for different cohorts, especially in terms of quality of life. Therefore, one might want to engage in a personalized approach to explain the ageing process. In particular, in order to understand the brain dynamics during the ageing process, a digital twin might be envisaged such that the various trajectories of ageing for each individual can be explored. A possible approach consists in the design of a whole-brain model to replicate the functional neural activity based on the available anatomical information. Based on the different biological scenarios that might impact the anatomy of each individual, one can simulate the impact on the functional activity of the brain and possibly on the cognition.

This new field of research might have three beneficial effects. Firstly, we could have an in-silico version of a biological dataset and we could potentially summarize biological dynamics in a string of parameters. Secondly, this approach could provide a new toolbox to explain the relationship between the brain structure and the brain function during ageing. Ultimately, we could investigate how different structure degenerations could impact the ageing brain. Thirdly and most importantly, this approach could allow us to make causal statements about the cognitive decline of each patient.

Would this be enough? Probably not, like most of the scientific approaches that try to estimate and reproduce biological activity. Personalized brain-modelling can specifically lead-up to degeneracy, which might explain why people with very similar features (such as anatomy) could have different phenotypes (different brain functions or cognitions). Although we could aim for degeneracy in order to explain with one single model a variety of scenarios, this could entail that very different models could generate the same activity and the same model could generate very different phenotypes. However, the current pandemic has shown that the older population do not only need resilience against viral infections, but ageing requires resilience against other menaces, such as loneliness, pathological mental states and cognitive decline. In-silico modelling could steer the ageing research to improve the life expectancy of all population cohorts. And this is where the new frontiers of ageing research should lie in order to go well beyond the recipes of prevention and “happy-grannies” care facilities.