Energy landscapes of resting-state brain networks

Takamitsu Watanabe,Yasushi Miyashita,Naoki Masuda,Ichiro Shirouzu,Seiki Konishi,Toru Machida,Yoshio Imai,Hiroyuki Wada,Satoshi Hirose

doi:10.3389/fninf.2014.00012

Abstract

During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs) including the default-mode network (DMN) and frontoparietal network (FPN). Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics), the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant local minima were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states.

Highlights

In the last few decades, a line of neuroimaging studies have accumulated evidence supporting that spontaneous brain activity during rest is not random enough to be averaged out in statistical analysis (Biswal et al, 1995; Raichle et al, 2001; Greicius et al, 2003)
We investigate the energy landscapes of resting-state brain activity using the functional magnetic resonance imaging data previously collected by our group (Watanabe et al, 2013)
In the previous work based on these data, we demonstrated that the so-called pairwise maximum entropy model (MEM) (Schneidman et al, 2006; Shlens et al, 2006; Tang et al, 2008; Yu et al, 2008; Ohiorhenuan et al, 2010; Santos et al, 2010; Ganmor et al, 2011) described the activities of the default-mode network (DMN) and frontoparietal network (FPN) with high accuracy (Watanabe et al, 2013)

Summary

Introduction

In the last few decades, a line of neuroimaging studies have accumulated evidence supporting that spontaneous brain activity during rest is not random enough to be averaged out in statistical analysis (Biswal et al, 1995; Raichle et al, 2001; Greicius et al, 2003). The frontoparietal network (FPN), another RSN, is known to be recruited during cognitive tasks with relatively high loads that require continuous attention (Dosenbach et al, 2006; Corbetta et al, 2008; Fair et al, 2009). Experimental and computational studies indicate that within a RSN, a group of brain regions is activated within a specific time window, and that different groups of regions are activated during different time windows (Honey et al, 2007; Chang and Glover, 2010; Kiviniemi et al, 2011; Allen et al, 2012; Hutchison et al, 2013) Such spatio-temporal dynamics of the RSNs may facilitate, for example, the flexibility of human cognitive functions (Allen et al, 2012)

Methods

Results

Conclusion