Resumen de Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling
The activity of the brain at rest reveals an intricate functional architecture and yet, the underlying mechanisms generating it are still not well understood. Whole-brain computational models have turned into fundamental tools for exploring these mechanisms as well as to uncover the relationship between structure and function both in the healthy and diseased brain. The results presented here aim to show that these models can be used as unique frameworks for understanding the origin of disease as well as to understand how local alterations have global and systemic effects. In These models can be used to alter local dynamics by means of artificial stimulation and lesioning with out the need of clinical interventions. Such alterations resonate with empirical observations, pinpointing to the origin of the mechanisms generating disease.
More specifically, in Chapter 1 I introduce specific concepts that help framing brain function as a global rather than a localized phenomenon as well as to argue why models are useful tools that clarify how brain function emerges. Chapter 2 and 3 are dedicated to show how models can help us understand brain disease as a systemic dysfunction as well as to predict outcomes by means of brain function tuning. Chapter 4 further develops this framework showing that models can be applied in single subjects, making clinical and personalized studies a possible scenario. Finally, Chapter 5 is dedicated to understand the outlook of brain modeling and brain function as well as to show that a global understanding of brain activity should be considered a central pillar in neuroscience.
In conclusion, the collection of results presented here showed the importance of computational models as tools for understanding disease. In one hand, it was shown that deep brain stimulation can be artificially applied in a simulated environment to find better and novel stimulation areas for Parkinson's disease (Saenger et al., 2017a). Further, models can also be used to artificially lesion the brain resembling a stroke while having remarkable similar results on global information flow (Saenger et al., 2017b). These results should pave the way for future studies trying to model brain disease.
References:
Saenger, V. M., Kahan, J., Foltynie, T., Friston, K., Aziz, T. Z., Green, A. L., ... & Mancini, L. (2017a). Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease. Scientific Reports, 7(1), 9882.
Saenger, V. M., Ponce-Alvarez, A., Adhikari, M., Hagmann, P., Deco, G., & Corbetta, M. (2017b). Linking entropy at rest with the underlying structural connectivity in the healthy and lesioned brain. Cerebral Cortex, 1-11.
