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. Author manuscript; available in PMC: 2018 Feb 1.
Published in final edited form as: Schizophr Res. 2016 Oct 23;180:70–77. doi: 10.1016/j.schres.2016.10.021

Figure 2. Computational models of large-scale resting-state dynamics.

Figure 2

(A) Modeling framework. Diffusion MRI-derived tractography provides the underlying structural connectivity among nodes in the model. Nodes simulate activity of local neural circuits, which interact through long-range connections. The model produces correlated spatiotemporal patterns in the simulated BOLD signal, which can be compared to empirical fMRI data, to optimize fitting of the biologically interpretable model parameters. Adapted from Deco et al. (2013). (B) Expansion of the model to incorporate hierarchical heterogeneity of local circuit properties, specifically stronger recurrent excitation (w) in association cortical networks compared to sensory cortical networks. (C) Elevated excitation-inhibition ratio increases preferential dysconnectivity in association networks in the model. Plotted is the difference between association and sensory measures (A–S) of within-network connectivity (covariance). Connectivity increases with three model parameters that all elevate the net E/I ratio. (D) Empirical measures of within-network connectivity in SCZ reveal preferential increase in connectivity in association networks, in line with model predictions. Adapted from Yang et al. (2016).