Tracking dynamic brain networks during human perception and cognition

David Pascucci

Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland.

Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.


In the last decade, the rising field of network neuroscience has emphasized the need for advanced functional and effective connectivity measures, with the ultimate goal of relating brain network dynamics to human perception and cognition. Tracking dynamical brain systems that evolve at the sub-second scale of perceptual and cognitive processes, however, remains a major ongoing challenge.

In this talk, I will present an endeavour to characterize fast event-related dynamics in functional brain networks during perceptual and attentional tasks. I will first describe methods for modeling rapidly evolving directed connectivity patterns among multiple brain signals, using adaptive filtering techniques and electroencephalography source imaging. I will then discuss some of their potential pitfalls, and I will introduce a newly proposed algorithm, the Self-Tuning Optimized Kalman filter (STOK), as a promising tool for the fast and accurate tracking of dynamic brain connectivity patterns. After examining these methods, I will summarize some recent results of their application in the field of human perception and attention, focusing on how accurate models of time-varying brain connectivity could yield new fundamental insights into the dynamic and frequency-specific processes behind cognition and behaviour.