Uncovering intrinsic connectional architecture of functional networks in awake rat brain

Intrinsic connectional architecture of the brain is a crucial element in understanding the governing principle of brain organization. To date, enormous effort has been focused on addressing this issue in humans by resting-state functional magnetic resonance imaging (rsfMRI). rsfMRI noninvasively measures functional connectivity without external stimulation, based on spontaneous low frequency fluctuations of the blood oxygenation level dependent (BOLD) signal. Using this technique, resting-state functional connectivity (RSFC) has been consistently revealed in multiple networks of the human brain, and has been shown to be altered by various factors such as sleep, anesthesia, as well as neurological and psychiatric disorders. 

Although much progress has been made in understanding connectional architecture of the human brain, this research area is significantly underexplored in animals, perhaps because of confounding effects of anesthetic agents used in most animal experiments on rsfMRI. To bridge this gap, we have systematically investigated the intrinsic connectional architecture by using a previously established awake-animal imaging model. The following results have been published recently (Liang et al., 2011).  

First, group independent component analysis (ICA) was applied to the rsfMRI data to extract elementary functional clusters of the brain. Most ICA components identified were located in specific anatomical regions, including bilateral caudate putamen, hippocampus, somatosensory cortex, thalamus and other cortical and subcortical regions. Therefore, this step provided a global layout of functional clusters in the awake rat brain. To our knowledge, this is currently the first study utilizing group ICA to study RSFC in rat brain, with only one previous rat ICA study at individual level (Hutchison et al., 2010).  

Subsequently, the connectional relationships between these clusters were evaluated by partial correlation analysis. Partial correlation coefficients between time courses of any two ICA components were calculated, controlling the rest of ICA components. Compared to widely used Pearson’s correlation in RSFC analysis, partial correlation could eliminate a large portion of connectivity that is mediated by other components, leaving largely direct connectivity.

The partial correlation coefficients matrices were then used to construct whole-brain functional neural network. Graph theoretical analysis was carried out for this network to examine two network characteristics: small-worldness and modular structure. This global network exhibited typical features of small-worldness, characterized by relatively high clustering coefficient and almost the same average shortest path length compared to random networks. Thus this result suggests that small-worldness is conserved in awake rat functional neural network, similar to results seen in human studies.

In addition, this network also exhibited strong community structure as seen in many biological and social networks. Modularity based community detection algorithm revealed significantly higher modularity (Q) value (Q =0.414) compared to random networks (p value<0.01), suggesting a significant modular structure. The whole-brain functional network was first partitioned into three modules. The first module predominantly extended across the cortical ribbon, indicating a strong intercortical communication across the cortex. The second module highlighted the olfactory pathway and its interaction with prefrontal cortex (PFC), and the integration of other sensory input, cognitive processing, and output in cortical and subcortical regions. Regions in the third module, including PFC, insular cortex, hypothalamus, and amygdala, are all key components subserving emotional and autonomic regulations. To address degeneracy issue of the modularity function, distributions of Q values and community structures were obtained for 20 repetitions. The result showed that the later two of the three modules previously identified were highly consistent across all repetitions with little variation, whereas the community structure of cortical regions was further divided into two sub-modules in the majority of repetitions.

Overall, the results of this work provided a functional atlas of intrinsic connectional architecture of the rat brain at both intraregional and interregional levels. More importantly, the current work revealed that functional networks in rats are organized in a nontrivial manner and conserve fundamental topological properties that are also seen in the human brain. Given the high psychopathological relevance of network organization of the brain, this study demonstrated the feasibility of studying mechanisms and therapies of multiple neurological and psychiatric diseases through translational research.