Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path

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Here we use computational modeling of fast neural dynamics to explore the relationship between structural and functional coupling in a population of healthy subjects. We use DTI data to estimate structural connectivity and subsequently model phase couplings from band-limited oscillatory signals derived from multichannel EEG data. Our results show that about 23.4% of the variance in empirical networks of resting-state fast oscillations is explained by the underlying white matter architecture. By simulating functional connectivity using a simple reference model, the match between simulated and empirical functional connectivity further increases to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational models of neural activity can explain missing links in the structure-function relationship.

Author: brokenbrilliant

I am a long-term multiple (mild) Traumatic Brain Injury (mTBI or TBI) survivor who experienced assaults, falls, car accidents, sports-related injuries in the 1960s, '70s, '80s, and '90s. My last mild TBI was in 2004, but it was definitely the worst of the lot. I never received medical treatment for my injuries, some of which were sports injuries (and you have to get back in the game!), but I have been living very successfully with cognitive/behavioral (social, emotional, functional) symptoms and complications since I was a young kid. I’ve done it so well, in fact, that virtually nobody knows that I sustained those injuries… and the folks who do know, haven’t fully realized just how it’s impacted my life. It has impacted my life, however. In serious and debilitating ways. I’m coming out from behind the shields I’ve put up, in hopes of successfully addressing my own (invisible) challenges and helping others to see that sustaining a TBI is not the end of the world, and they can, in fact, live happy, fulfilled, productive lives in spite of it all.

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