[BIC-announce] Dani Bassett talk | 11am Friday January 26th

Bratislav Misic, Dr. bratislav.misic at mcgill.ca
Wed Jan 10 12:47:21 EST 2024


Happy new year! Dani Bassett will give a talk in a couple of weeks. Details below. Tell your friends 🙂

11 am Friday February 26th
Jeanne Timmins Amphitheatre

Speaker:

Danielle Bassett. University of Pennsylvania. Website<https://complexsystemsupenn.com>. GoogleScholar<https://scholar.google.com/citations?user=siYpAPsAAAAJ&hl=en&oi=ao>.

Title:

The cost of brain state transitions

Abstract:

Neural tissue is a heterogeneous material characterized by fibrous and non-fibrous sectors. In the large-scale human brain, the fibrous tissue is composed of bundles of neuronal axons along which electrical signals can propagate. These so-called white matter tracts collectively form a fiber network, or structural connectome, that both supports and constrains complex activity dynamics. How does this constraint affect the cost of activity flow? In this talk, I will describe work that seeks to address this question using the formal mathematical approach of network control theory. Drawing on decades of work in systems engineering, network control theory provides a framework for calculating energy costs associated with network systems reaching, maintaining, and transitioning among brain states. Using this approach, I’ll address open modeling questions and validate key assumptions, demonstrate that theory-predicted energy costs align with biological measurements of glucose metabolism, show how the energetics of transitions depend upon brain states’ information content, and illustrate how these ideas help us to better understand processes elicited by mindfulness training. Taken together, the studies that I will describe characterize the role of the structural connectome in constraining the flow of activity, thereby determining a formal cost of brain state transitions that is biologically grounded and psychologically explanatory.

—
Bratislav Misic, PhD
Montreal Neurological Institute
https://netneurolab.github.io/
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