Abstract
Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states) reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm2 area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T = 0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep) and increased activation (hyper-activation) moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large-scale brain states was maximum, compatible with maximum information capacity—a presumed condition of consciousness.
Highlights
Cognitive functioning of the conscious human brain is thought to depend on the formation of dynamic patterns of neuronal coalitions and large-scale connectivity (Werner, 2009; Bressler and Menon, 2010)
We show that with the chosen constraints set by the connectivity matrix, the model predicts critical behavior at the optimal activation level at which metastable states occur
The high repertoire of states at critical T is consistent with the formerly postulated condition to support conscious cognition. The goal of this investigation was to demonstrate that long-range neuronal interactions based on empirical measurements in the human brain produce large-scale dynamic patterns of activity
Summary
Cognitive functioning of the conscious human brain is thought to depend on the formation of dynamic patterns of neuronal coalitions and large-scale connectivity (Werner, 2009; Bressler and Menon, 2010). The diversity or repertoire of distinct functional patterns reflects the information capacity of the brain that is thought to be central to consciousness (Tononi, 2008; Deco et al, 2014). The repertoire of brain states over time can be large if there is sufficient flexibility in the system to rapidly switch to new configurations and maintain these configurations for a finite amount of time. The time necessary for maintaining a configuration should roughly coincide with the duration of a conscious perceptual frame (Bachmann, 2013). The dynamic nature of the ongoing stream of consciousness may reflect this rapid sequence of state configurations (Werner, 2007). The disruption of the sequence of states may account for the anesthetic suppression of consciousness (Hudetz et al, 2014)
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