In this study, RSFC in awake rats was decomposed into 40 spatial components using group ICA. The direct connectional relationships between these components were evaluated using partial correlation, revealing a complex network linking different regions across the whole brain. This brain network was characterized by the features of small worldness with a large modularity, a large clustering coefficient and a small shortest path length. Furthermore, using a graph-theory approach, the whole-brain network was segregated into community structures.
To our knowledge, this is the first study utilizing group ICA to study RSFC in awake rats. ICA is well established in rsfMRI for decomposing functional clusters in the human brain. However, its application in the rat was rather limited. There is currently only one study that utilized ICA to analyze RSFC of individual
anesthetized rat without group analysis (Hutchison et al., 2010
). Lack of such effort has significantly limited the applicability of rsfMRI particularly in animal models. In the present study, images of all individual rats were aligned to a standard rat atlas, and thus allowed the group results to be obtained using group ICA. In addition, the awake condition avoided confounding effects of anesthesia. We found that the majority of components identified were located in anatomically well-defined regions, indicating a convergence between anatomical parcellation and functional systems. Some components such as bilateral somatosensory, motor, visual and auditory cortices are in excellent consistency with the literature (Peltier et al., 2005
; Lu et al., 2007
; Liu et al., 2010
). Spatial maps of subcortical regions including CPu, thalamus, hypothalamus and hippocampus also well agree with ICA results in individual anesthetized rats (Hutchison et al., 2010
), suggesting highly reproducible patterns of cortical and subcortical clustering across individuals. However, we also observed several less reported yet important clusters. For instance, there were components related to olfactory and executive functions. Olfaction is considered one of the most important sensory inputs in the rodent. Prominent components of olfactory bulb, AON and OT indicated functional significance of olfaction in awake rats. Moreover, PFC and AON were clustered into a single component, suggesting a close association between olfactory and executive functions (Cinelli et al., 1987
; Smith et al., 2010
To further evaluate inter-component connectional relationships, we applied partial correlation analysis on time courses of individual ICA components. Partial correlation analysis is an approach for estimating ‘direct’ statistical association by controlling out correlation mediated by other components. This analysis method essentially eliminated a large portion of connections that were mediated by other nodes with only ‘direct’ connections left. A recent study that evaluated various network modeling methods indicated that partial correlation performed very well in revealing network connections (Smith et al., 2010
). In addition, this analysis could reveal possible long-distance functional integration. Significant amount of direct connection identified in the present study is consistent with anatomical connections in the rat. For instance, direct connection between thalamus and hippocampus observed in the present study has been well documented in the literature using various techniques (Wouterlood et al., 1990
; Dolleman-Van Der Weel and Witter, 1996
). These two regions and their bi-directional connections are critical components of the anatomical system sub-serving spatial memory (Henry et al., 2004
). In addition, connections from the PFC to cingulate cortex and NAcc as shown in our data have been implicated in emotional processing (Hajos et al., 1998
). We also observed that thalamus bridges hippocampus and ACC. In accordance with this result, it was found that nucleus reuniens of the midline thalamus might serve as the link sending projection to the hippocampus from the medial PFC such as ACC (Vertes et al., 2007
With the global functional network constructed based on inter-component connections (), the first question to consider is whether the rat brain exhibits the same network characteristics reported in humans such as small-worldness. Human studies have indicated robust ‘small-world’ characteristics in both structural and functional connectivity networks. A small-world network is described by a high clustering coefficient and low minimum path length compared to random networks. Small-world networks allow high efficiency of information flow at a low wiring cost for both local (with a high clustering coefficient) and long distance (with a low minimum path length). Although small-worldness represents a crucial feature of brain organization in the human, there is a paucity of information regarding small-world networks in non-human subjects. Previous studies reported similar small-worldness of anatomical
networks in the macaque visual cortex and cat whole cortex (Hilgetag et al., 2000
). However, no study yet specifically addressed this question using functional connectivity in conscious rats. Our network metrics showed that in the rat brain, the whole-brain network is considerably more cliquish than random networks, while retaining approximately the same minimum path length. These results are quantitatively comparable to the human brain and suggest that small-worldness is conserved in the rat functional networks.
In addition to the small-world features, high modularity is also thought to be an important governing principle in brain networks. Several studies consistently reported that the resting-state brain network in humans exhibited robust community structure (He et al., 2009
; Meunier et al., 2009
). High modularity values of the rat whole-brain network obtained in our study indicated a robust community structure of the global network in the awake rat brain at the resting state. This result indicated that the rat brain shares basic topological characteristics with the human brain.
By utilizing Newman's spectral partitioning method, the rat whole-brain network was segregated into three modules. The first module predominantly extended across the cortical ribbon, indicating a strong inter-cortical communication across the cortex (Zhang et al., 2010
). The second module highlighted the olfactory pathway and its interaction with 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 sub-serving emotional and autonomic regulations (Paxinos, 2004
). Interestingly, using phMRI Schwarz and others reported very similar results with a module dominated by cortical regions and a second module primarily with subcortical regions (Schwarz et al., 2009
). Consistent with the intrinsic modular structure observed in the resting-state human brain, our rat results also showed long-distance interaction within modules.
To address the issue of degeneracy of the modularity function, distributions of Q values and community structures were obtained. The result showed that two of the three modules previously identified (yellow and green modules) were highly consistent across all repetitions with little variation, whereas the community structure of cortical regions was further divided into two sub-modules. We speculate that the relatively lower stability of this module might reflect higher complexity of the organization of cortical networks.
The ‘vertices’ in our graph are ICA components as oppose to individual voxels or anatomically defined ROIs in most other studies. The strategy of using ICA components to construct global networks is based on functionally segregated elements of the brain. Thus, we avoided anatomical restraint of ROI definitions. Recent evidence suggests that different anatomical parcellation schemes had significant influences on network topological properties (Wang et al., 2009
) and functionally inaccurate ROIs could severely damage the network estimation (Smith et al., 2010
). Therefore, our approach might have significant advantages in constructing the whole brain network compared to anatomical ROI-based approaches. Relative to voxel-by-voxel approaches, our approach is more computationally efficient.
There are several methodological limitations of the present study. First, an unweighted network was used in graph-theory analysis. Exploration on weighted networks should be interesting. Second, although rats were fully awake during RSFC scans, they were briefly anesthetized during setup. The effects of brief anesthesia on later RSFC need further investigation. Third, the ICA components number was arbitrary and other numbers can be used. In addition, negative inter-component partial correlation coefficients (approximately half of all correlation coefficients) were not analyzed but can potentially contain important information regarding neural networks. This information should be taken into consideration in future studies. Furthermore, although inter-component connectivity showed high consistency in the present study, individual variability particularly in topographical properties needs future examination. Our understanding of the brain function has substantially benefited from preclinical neurobiological investigation in animal models, primarily in rodents. The present study systematically investigated resting-state functional networks in the awake rat brain. It provided a functional atlas of the intrinsic connectional architecture of the rat brain at both intra- and inter-region levels. More investigations are still needed to further characterize connectional architecture in the rat brain. For example, it is unknown whether functional networks in rats are organized differently at different spatial scales, or whether significant community structure exists within each module. It is also unknown whether the rat brain has the default mode network found in humans and primates (Raichle et al., 2001
; Vincent et al., 2007
). Nevertheless, the current work revealed that the conscious rat brain conserved topological properties like small-worldness as observed in human. Combined with various invasive procedures, pharmacological interventions and genetic manipulations, it will serve as a prelude to future applications of RSFC in animal models.