Neuroscience: a control theory and network science perspective

20 June 2017

Sérgio Pequito GRASP Lab / University of Pennsylvania

One fundamental challenge of our time is to understand the neuroscience of brain and it’s relation to neurological disease as well as human behaviors. In this talk, I will argue that tools from control systems, dynamics, and network science can have a very important role in both retrieving as well as interpreting data from brain sensors, such as functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). These tools will impact the way that analysis is conducted in neuroscience, and in brain-computer and brain-machine-brain interfaces. Furthermore, it will lead to new diagnosis and treatment of neural disorders, that ultimately will improve people’s life quality.

First, we address the problem of understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain: that is, a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines crisscrossing the cortex. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains the brain’s dynamics remains a mystery. Therefore, we introduce a high accuracy methodology to predict the functional connectivity of a subject at rest from his or her structural graph and unveil the role of indirect structural paths in the formation of functional correlations.

Next, we provide an overview of future challenges to be addressed, and how the outcome of this research can generate a paradigm shift. This paradigm will impact the way that analysis is conducted in neuroscience, and in brain-computer and brain-machine-brain interfaces. Furthermore, it will lead to new diagnostics and treatments of neural disorders, that ultimately will improve people’s life quality.



Sérgio Pequito is a postdoctoral researcher in General Robotics, Automation, Sensing & Perception Laboratory (GRASP Lab) at University of Pennsylvania. He obtained his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University and Instituto Superior Técnico, through the CMU-Portugal program, in 2014. Previously, he received his B.Sc. and M.Sc. in Applied Mathematics from the Instituto Superior Técnico in 2007 and 2009, respectively. Pequito's research consists of understanding the global qualitative behavior of large-scale systems from their structural or parametric descriptions and provide a rigorous framework for the design, analysis, optimization and control of large scale (real-world) systems. Currently, his interests span to neuroscience and biomedicine, where dynamical systems and control theoretic tools can be leveraged to develop new analysis tools for brain dynamics that, ultimately, will lead to new diagnostics and treatments of neural disorders. In addition, these tools can be used towards effective personalized medicine and improve brain-computer and brain-machine-brain interfaces that will improve people's life quality. Pequito was awarded the best student paper finalist in the 48th IEEE Conference on Decision and Control (2009). Also, Pequito received the ECE Outstanding Teaching Assistant Award from the Electrical and Computer Engineering Department at Carnegie Mellon University, and the Carnegie Mellon Graduate Teaching Award (University-wide) honorable mention, both in 2012. Also, Pequito was a 2016 EECI European Ph.D. Award on Control for Complex and Heterogeneous Systems finalist and received the 2016 O. Hugo Schuck Award in the Theory Category.