Abstract
Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions.
Highlights
Brain functioning depends on the interaction among different regions, which exchange information via a complex connectivity network and work together in a coordinated manner to realize cognitive tasks
In a recent work [12], we investigated the capacity of an important functional connectivity (FC) estimator to detect changes in connectivity using signals generated by Neural Mass Models (NMMs) of interconnected Regions of Interest (ROIs)
The aim of the present work was to evaluate the relationship between rhythms transmission among ROIs, network connectivity, and methods for FC estimation, laying emphasis on the possibility to detect the strength of the reciprocal connections and, above all, the effect of nonlinear alteration in neural activity
Summary
Brain functioning depends on the interaction among different regions, which exchange information via a complex connectivity network and work together in a coordinated manner to realize cognitive tasks. There is large consensus that connectivity is a primary means for understanding brain function at different levels of organization. Connectivity analysis has been assessed noninvasively in several recent studies, both starting from data obtained with magnetic resonance [5,6,7] or via neuroelectric imaging techniques (MEG or EEG) [8,9,10]; this analysis is of great value to encompass the relationships among the different areas involved, to unmask their specific role, and, to understand how these interactions produce cognition in a coordinated fashion. Both are different from the structural connectivity, defined as the presence of a physical connection among the regions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
