Computational and biophysical models of the brain

Widely distributed brain networks display highly coherent activity at rest. In this work, we combined bottom-up and top-down approaches to investigate the dynamics and underlying mechanisms of this spontaneous activity. We developed a realistic network model to simulate resting-state data, which incorporates biophysical regional dynamics, empirical brain connectivity, time delays, and background noise. At moderately weak coupling strengths, the model produces spontaneous metastable oscillatory states and a novel form of frequency depression, resulting in transient synchronizations between brain regions at reduced collective frequencies. We used fixed and sliding window correlation approaches on the power of band-limited MEG data, and show that brain regions exhibit significant functional connectivity (FC) in the alpha and beta frequency bands on slow (>1sec) time scales. We also show that temporal non-stationarity and bistability in FC occur in the same pairs of brain areas, and in the same frequency bands, as stationary measures of FC. We find that the network model reproduces the same frequency-dependency, time-scales, and non-stationary nature of FC as we found in real MEG data. Furthermore, seed-based correlations and independent component analysis also reveal a similar spatial profile of FC in empirical and simulated data, with the existence of widely distributed transient resting state networks in the same frequency bands. Finally, we used the network model simulations to evaluate a range of network estimation methods, and find that often the simplest linear measures perform best and some of the common non-linear measures can often give erroneous estimates. Overall, our results suggest that structured interactions between brain regions in the presence of delays and noise result in spontaneous synchronizations leading to the organized power fluctuations across brain regions, and some of the simplest statistical measures provide excellent estimates of this connectivity. Our work also highlights the potential of computational models in exploring neural mechanisms.