Analysis and Control of Functional Brain Networks
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ABSTRACT: During a cognitively demanding task or at rest, the brain exhibits a rich repertoire of large-scale functional patterns. These patterns are a measure of the coherence among the neural activities in different brain areas, reflect different cognitive functions, and can also be used as biomarkers for different psychiatric and neurological disorders. For example, while patterns of transient and partial coherence have been observed in healthy individuals, increased coherence in neural systems is often associated with degenerative diseases including Parkinson’s and Huntington’s disease, and epilepsy.
In this talk, I will discuss methods to model, analyze and control functional patterns in oscillatory brain networks. I will start by modeling the rhythmic activity of a neural system as the output of a network of diffusively-coupled oscillators, and use different synchronization notions as a proxy for functional patterns. I will derive conditions for the emergence of cluster synchronization, where independent groups of synchronized oscillators coexist in a network, and compare such conditions against empirical brain data. Finally, I will present a method to enforce desired synchronization patterns through minimally invasive and localized changes to the network structure, validate some of our findings using a well-accepted neurovascular model, and discuss future research directions.
BIO: Fabio Pasqualetti is a Professor of Mechanical Engineering at the University of California, Riverside. He completed a Doctor of Philosophy degree in Mechanical Engineering at the University of California, Santa Barbara, in 2012, a Laurea Magistrale degree (M.Sc. equivalent) in Automation Engineering at the University of Pisa, Italy, in 2007, and a Laurea degree (B.Sc. equivalent) in Computer Engineering at the University of Pisa, Italy, in 2004. He is the recipient of the 2017 Young Investigator Award from the Army Research Office and the 2019 Young Investigator Research Award from the Air Force Office of Scientific Research. His articles received the 2016 TCNS Outstanding Paper Award, the 2019 ACC Best Student Paper Award, the 2020 Control Systems Letters Outstanding Paper Award, the 2020 Roberto Tempo Best CDC Paper Award, and the 2021 O. Hugo Schuck Best Paper Award. His main research interests are in the areas of network systems, computational neuroscience, and machine learning.
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