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Human brain dynamics can be profitably viewed through the lens of statisticalmechanics, where neurophysiological activity evolves around and between localattractors representing preferred mental states. Many physically-inspiredmodels of these dynamics define the state of the brain based on instantaneousmeasurements of regional activity. Yet, recent work in network neuroscience hasprovided initial evidence that the brain might also be well-characterized bytime-varying states composed of locally coherent activity or functionalmodules. Here we study this network-based notion of brain state to understandhow functional modules dynamically interact with one another to performcognitive functions. We estimate the functional relationships between regionsof interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI'spattern of allegiance to functional modules. Local minima in this modelrepresent attractor states characterized by specific patterns of modularstructure. The clustering of local minima highlights three classes of ROIs withsimilar patterns of allegiance to community states. Visual, attention,sensorimotor, and subcortical ROIs tend to form a single functional community.The remaining ROIs tend to form a putative executive control community or aputative default mode and salience community. We simulate the brain's dynamictransitions between these community states using a Markov Chain Monte Carlorandom walk. We observe that simulated transition probabilities between basinsresemble empirically observed transitions between community allegiance statesin resting state fMRI data. These results collectively offer a view of thebrain as a dynamical system that transitions between basins of attractioncharacterized by coherent activity in small groups of brain regions, and thatthe strength of these attractors depends on the cognitive computations beingperformed.

The Energy Landscape Underpinning Module Dynamics in the Human Brain Connectome