Simple - But Not Too Simple - Models to Investigate Complex Brain Dynamics

Explore a seminar on the complex relationship between structural and functional brain networks using statistical physics and minimalist mesoscopic models. Delve into a stochastic model for resting state brain activity that incorporates homeostatic plasticity mechanisms. Discover how the optimal inferred model for brain activity is poised at a critical state and how distance to criticality can serve as an individual-based marker for evaluating recovery of brain functional activity after stroke. Examine the concept of controllability in structural brain networks as a mechanism for understanding transitions between cognitive states, while considering important caveats and warnings. Learn about the implications of this research for clinical applications such as transcranial magnetic stimulation in stroke recovery.

Introduction
Background
Theory
Complexity
Structure network
Functional activity
Template
Functional connectivity
Measuring avalanches
Building simple models
Normalization
Data set
Critical point
Results
Stroke patients
TMS Trans Clinical Magnetic Stimulation
Bassett theory
International controlbility framework
Granon
equivalent condition
local controllability
average controllability
main result
linearization
analytical derivation
structural connectivity
conclusion