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Abstract
Characterizing anesthesia-induced alterations to brain network dynamics provides a powerful framework to understand the neural mechanisms of unconsciousness. To this end, increased attention has been directed at how anesthetic drugs alter the functional connectivity between brain regions as defined through neuroimaging. However, the effects of anesthesia on temporal dynamics at functional network scales is less well understood. Here, we examine such dynamics in view of the free-energy principle, which postulates that brain dynamics tend to promote lower energy (more organized) states. We specifically engaged the hypothesis that such low-energy states play an important role in maintaining conscious awareness. To investigate this hypothesis, we analyzed resting-state BOLD fMRI data from human volunteers during wakefulness and under sevoflurane general anesthesia. Our approach, which extends an idea previously used in the characterization of neuron-scale populations, involves thresholding the BOLD time series and using a normalized Hamiltonian energy function derived from the Ising model. Our major finding is that the brain spends significantly more time in lower energy states during eyes-closed wakefulness than during general anesthesia. This effect is especially pronounced in networks thought to be critical for maintaining awareness, suggesting a crucial cognitive role for both the structure and the dynamical landscape of these networks.
Highlights
While general anesthesia has a seemingly unambiguous behavioral endpoint— unconsciousness—the neural mechanisms by which this state is achieved are diverse and highly enigmatic (Alkire & Miller, 2005; Brown, Lydic, & Schiff, 2010; Brown, Purdon, & Van Dort, 2011; Mashour & Alkire, 2013)
The primary goal of this paper is to examine the effect of anesthesia on brain network dynamics through a statistical physics notion of energy and, to assess whether the state of unconsciousness is associated with an altered energy distribution relative to that of wakefulness
Our analyses focus on within-restingstate networks (RSNs) functional connectivity and energy dynamics
Summary
While general anesthesia has a seemingly unambiguous behavioral endpoint— unconsciousness—the neural mechanisms by which this state is achieved are diverse and highly enigmatic (Alkire & Miller, 2005; Brown, Lydic, & Schiff, 2010; Brown, Purdon, & Van Dort, 2011; Mashour & Alkire, 2013). It turns out that the maximum entropy model for a binary state system consisting of only pairwise interactions is known as the Ising model (Schneidman, Berry, Segev, & Bialek, 2006) This model was originally proposed to address phase transitions in networks of quantum spin states, but has since attracted considerable attention in computational neuroscience (Cocco, Leibler, & Monasson, 2009; Roudi, Tyrcha, & Hertz, 2009). Note that in this context, entropy is largely a static concept insofar as it describes a distribution of activation states, and it provides little information about the dynamics of how those states evolve over time. We will examine how the energy landscapes of brain networks, defined from functional magnetic resonance imaging (fMRI) data, vary between subjects who are awake and under general anesthesia
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