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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neurosci. Author manuscript; available in PMC 2011 September 9.
Published in final edited form as:
PMCID: PMC3073070

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 combining resting-state functional magnetic resonance imaging (rsfMRI) with other techniques. However, this research area is significantly underexplored in animals, perhaps due to confounding effects of anesthetic agents used in most animal experiments on functional connectivity. To bridge this gap, we have systematically investigated the intrinsic connectional architecture in the rodent brain by using a previously established awake animal imaging model. First, group independent component analysis was applied to the rsfMRI data to extract elementary functional clusters of the brain. The connectional relationships between these clusters evaluated by partial correlation analysis were then used to construct a graph of whole-brain neural network. This network exhibited typical features of small-worldness and strong community structures as shown in the human brain. Finally, the whole-brain network was segregated into community structures using a graph-based analysis. The results of this work provided a functional ‘atlas’ of intrinsic connectional architecture of the rat brain at both intra- and inter-region levels. More importantly, the current work revealed that functional networks in rats are organized in a non-trivial manner and conserved fundamental topological properties as the human brain. Given the high psychopathological relevance of network organization of the brain, this study demonstrated the feasibility to study mechanisms and therapies of multiple neurological and psychiatric diseases through translational research.

Keywords: functional connectivity, rat, independent component analysis (ICA), graph theory, community structure, brain network


The effort to understand the connectional architecture of the brain has benefited tremendously from the advent of resting-state functional magnetic resonance imaging (rsfMRI). rsfMRI is a technique that non-invasively measures functional connectivity without external stimulation based on spontaneous low-frequency fluctuations of the fMRI signal (Biswal et al., 1995; Fox and Raichle, 2007). Using this technique, resting-state functional connectivity (RSFC) was consistently revealed in multiple networks of the human brain (Biswal et al., 1995; Greicius et al., 2003; Fox et al., 2005), and was altered by effects of sleep, anesthesia and ageing (Stevens et al., 2008; Horovitz et al., 2009). Recent studies have also delineated significant influences of various pathological conditions on RSFC (Greicius et al., 2007), indicating vital neurobiological and psychopathological relevance (Kennedy et al., 2006; Albert et al., 2009).

Well-documented properties of intra- and inter-regional connectivity make it extremely intriguing to extend the RSFC research at local brain regions to global brain networks. Using graph-based analysis separately identified brain networks sub-serving different functions in humans were found to topologically organize in a non-trivial manner to support efficient information processing (Wang et al., 2010). Graph theoretical approaches in rsfMRI uses anatomically or functionally defined regions of interest (ROIs) as ‘vertices’, and connectivity between ROIs as ‘edges’. These approaches have revealed that the human brain's networks are characterized by properties of small-world topology, highly connected hub and high modularity (Bullmore and Sporns, 2009). These findings are crucial because: (i) they identified the governing principle of the network organization of the human brain; and (ii) the same methods can be used to examine alterations of topological configuration of the brain in response to external stimulation or in different pathological conditions (Liu et al., 2008; Bassett and Bullmore, 2009). Therefore, these methods may serve as a potential biomarker of various mental disorders.

To date, the majority of studies on intrinsic connectional organization of the brain are conducted in humans. Systematic investigations of this issue in different animal models have been significantly underexplored (Vincent et al., 2007; Pawela et al., 2008; Schwarz et al., 2009), partially attributed to confounding effects of anesthetic agent used in animal studies on RSFC (Massimini et al., 2005; Lu et al., 2007; Liu et al., 2010). Consequently, it is very important to explore RSFC in awake animals because it can not only provide invaluable information regarding intrinsic connectional architecture of the animal brain and its reconfiguration in response to cognitive and emotional stimuli, but also may provide a unique window to explore comparative functional anatomy between species. Moreover, understanding connectional architecture in animals will allow us to investigate multiple psychiatric and neurological diseases using translational models. Recently, we have successfully demonstrated the feasibility of mapping RSFC in awake rats (Zhang et al., 2010) based on an awake animal imaging model that has been well established in our laboratory (King et al., 2005; Ferris et al., 2006). Using the same animal model here we have characterized the intrinsic network architecture in the awake rat.

Materials and Methods


Sixteen adult male Long-Evans (LE) rats (350 – 450 g for adult rats) were obtained from Charles River Laboratories. Animals were housed in Plexiglas cages (two per cage) and maintained in ambient temperature (22-24°C) on a 12-h light:12-h dark schedule. Food and water were provided ad libitum. All studies were approved by IACUC Committee of the University of Massachusetts Medical School.

Acclimation procedure

All rats were acclimated to MRI restraint and noise as previously described (King et al., 2005; Ferris et al., 2006). Briefly, rats were anesthetized with isoflurane and secured in Plexiglas stereotaxic head holder using plastic ear-bars. EMLA cream was applied tropically to minimize pain of mechanical restraint. Animals were then placed into black opaque tube ‘mock scanner’ with tape-recorded scanner noises. Animals were acclimated for eight days, one session per day. The time for exposure was increased from 15 minutes on the first day to 90 minutes on days 6, 7 and 8 with an increment of 15 minutes per day (King et al., 2005).

Animal preparation

Under short-acting isoflurane gas the animal was fitted into a head restrainer with a built-in coil. The head was placed into the cylindrical head-holder with the canines secured over a bite bar, the nose secured with a nose clamp, and ears positioned inside the head-holder with adjustable screws fitted into lateral sleeves. The body of the animal was placed into a body restrainer that allowed unrestricted respiration. After the animal was set up, the isoflurane gas was removed and the restraining system was positioned in the magnet. Animals were fully conscious within 10-15 min.

MR experiments

All experiments were carried out on a Bruker 4.7T/40cm horizontal magnet (Oxford, UK) interfaced with a Biospec Bruker console. A dual 1H radiofrequency (RF) coil configuration (Insight NeuroImaging Systems, Worcester, MA) consisting of a volume coil for exciting MRI signal and a surface coil for receiving MRI signal was used. The volume and surface coils were actively tuned and detuned to prevent mutual coil coupling.

For each session, anatomical images were acquired with a fast spin-echo sequence (RARE) with the following parameters: TR = 2125ms, RARE factor = 8, TE = 50ms, matrix size = 256×256, FOV = 3.2cm×3.2cm, slice number = 18, slice thickness = 1mm. T2*-weighted gradient-echo images coving the whole brain were then acquired using the echo-planar imaging (EPI) sequence with following parameters: TR = 1s, TE = 30ms, flip angle = 60°, matrix size = 64×64, FOV = 3.2cm×3.2cm, slice number=18, slice thickness = 1mm. Two hundred EPI volumes were acquired for each run, and six runs were obtained for each session. Rats were in resting state during all imaging sessions.

Pre-processing of imaging data

Imaging data was preprocessed using Medical Image Visualization and Analysis (MIVA,, Statistical Parametric Mapping (SPM8) software (Wellcome Department of Cognitive Neurology, London, UK) and MATLAB (Mathworks, Inc., Sherborn, MA). All images were first aligned and co-registered with MIVA as previously described (Zhang et al., 2010). After registration, all functional images were pre-processed with steps of motion correction, spatial smoothing (FWHM = 1mm), and voxel-wise linear detrending and 0.002-0.1Hz band-pass filtering. Data sets with excessive motion (>0.25 mm, 8 runs in total) were discarded, resulting in a total number of 88 runs for subsequent analysis.

Independent component analysis

Group ICA (Calhoun et al., 2001) was performed using GIFT toolbox ( The number of components was set at 40 (Hutchison et al., 2010). The infomax algorithm was used to perform spatial ICA and independent components were scaled to z-scores. Time courses of individual components for individual scans were extracted. Among the spatial maps of all 40 components, two were located at cerebrospinal fluid (CSF) areas and were identified as artifactual components.

Direct connectivity and graph theory analysis

Time courses of 40 components were used in direct connectivity analysis. For each individual RSFC run, the partial correlation coefficient between time courses of each pair of components was calculated, conditioning on time courses of the other 38 components. This step yielded a 40×40 partial correlation matrix for each run. Partial correlation coefficients (r values) were transformed to z scores and then averaged across all runs and across all animals. The final partial correlation matrix was generated by transforming the averaged z scores back to the r values. Each element of this matrix represented the strength of direct connectivity between two components. We only focused on positive partial correlation coefficients although negative coefficients were also detected. The significance of direct connectivity was calculated by using one sample t-test and thresholded at p-value < 0.01 (n = 88, uncorrected) based on all 88 partial correlation matrices. The two artifactual components did not show significant connections with other components, and thus were eliminated in further graph-theory analysis. As a result, a 38×38 adjacency matrix A was generated with each element aij describing the significant direct connection between each two components based on the p-value:

aij={1,if compoent i and j are conncted(i.e. p<0.01)0,otherwise

Based on this adjacency matrix, the community structure of the rat brain was obtained by using the spectral partitioning method (Newman, 2006). Modularity Q is defined as follows:


where m is the total number of edges in the network, and ki and kj are the degree of each vertex; ci is the group to which vertex i belongs and δ(ci, cj) is the Kronecker delta symbol.

The partitioning analysis followed the procedure in a previous work (Newman, 2006) and consisted of two steps. In the first step, we obtained a single solution of partitioning by using the spectral approach based on the leading eigenvector of the modularity matrix (Newman, 2006). This step, as pointed out by Newman, gave an excellent guide to the general form that the communities should take. In the second step, we combined the spectral method and the fine-tuning method described in Newman's study to further optimize modularity (Newman, 2006). Considering the fact that the modularity function Q generated by combining the spectral method and the fine-tuning method is degenerate (Good et al., 2010), in the second step we computed a distribution of Q values and a distribution of partitions by permuting the order of nodes in the adjacency matrix before feeding it into the optimization algorithm. Only the solution consistent over this distribution was reported. The degeneracy of Q goes approximately as 2k where k is the number of modules (Good et al., 2010). Since k=3 modules were found in the first step, >2k (20) repetitions were made to form the distribution of Q values and partitions. All analyses in the second step were performed using Brain Connectivity Toolbox (BCT) (Rubinov and Sporns, 2010). After partitioning, components belonging to the same module were displayed in the same colors in the figures.

Clustering coefficient and shortest path length

The averaged local clustering coefficient was calculated as


where Ej is the number of edges connecting neighbors of vertex j, and Vj is the number of neighbors of vertex j. Pure random networks with same numbers of nodes and edges were constructed based on Erdős–Rényi model with 100 repetitions. Random networks with the same distribution of degrees as the current rat-brain network were constructed using BCT with 100 repetitions. The averaged minimum path length was calculated as


where min_path is the shortest path length between vertices j and k.

Reproducibility of inter-component direct connectivity

To estimate the reliability of inter-component connectivity across animals, we randomly divided data from all animals into two subgroups. The strength of inter-component connectivity (defined as the amplitude of partial correlation coefficient between two components) between the two subgroups was quantitatively compared using the correlation of inter-component connectional strength between the two subgroups. This procedure was repeated 100 times and the correlation value averaged across 100 repetitions was reported.


Elementary clusters of RSFC revealed by group ICA

Group ICA results were obtained from 16 conscious rats. Most components identified were located in specific anatomical regions as displayed in Figure 1. Fig. 1a showed a component located at anatomically well defined bilateral caudate putamen (CPu). Fig1b-e represented functional structures of bilateral hypothalamus, thalamus, hippocampus and somatosensory (SS) cortex, respectively. In addition, functionally related regions also tended to cluster into single components. Fig1f showed a component including bilateral prefrontal cortex (PFC) and anterior olfactory nucleus (AON), showing well-known reciprocal functional connections of the olfactory bulbs and other olfactory related areas with the prefrontal cortex in conscious rats (Cinelli et al., 1987). Another olfactory-related component was located at olfactory tubercle (OT) (Fig 1g). Fig 1h showed a complex component composed of anterior cingulate cortex (ACC), prelimbic (PL) and infralimbic (ILA) cortices, together being considered as extended areas of PFC in the rat.

Figure 1
Spatial maps of individual components identified by ICA. (a-h): Examples of ICA components. Left columns are atlas images. Anatomic regions corresponding to individual ICA components are annotated. Middle columns are individual ICA components overlaid ...

Figure 2 showed 38 ICA components (excluding two artifactual components) overlaid on anatomical images, revealing the global clustering pattern of RSFC in the rat brain. Bilateral components were dominant of all ICA components identified (24 out of 38). In cortical regions, bilateral components (13 in total) were also dominant. The numbers of left and right cortical components were approximately equal (5 for left lateral components and 6 for right components).

Figure 2
The spatial pattern of 38 group ICA components (excluding two artifactual components). Individual components are displayed with distinct colors. Distance to Bregma (mm) for each imaging slice is labeled at the bottom of each image.

Direct connectivity between RSFC clusters calculated by partial correlation

To evaluate inter-component connectional relationships, we calculated the direct connectivity between individual components by using partial correlation analysis. The partial correlation coefficient matrix of 40 components averaged across all animals was displayed in Figure 3a. Statistical comparison at the group level revealed the pattern of direct connections between different RSFC clusters (one sample t-test, p < 0.01). To estimate the reliability of inter-component connectivity across animals, we randomly divided data from all animals into two subgroups. Fig. 3b showed a high correlation of inter-component connectional strength between the two subgroups (r=0.71, p<10-6), suggesting great reproducibility in direct connectivity between RSFC clusters. This result did not change when we repeated the same process for 100 times (averaged correlation coefficient of 100 repetitions ravg = 0.68).

Figure 3
Inter-component connectional relationships. (a) The partial correlation coefficient matrix averaged across all rats. Partial correlation coefficients (r values) were first transformed to z scores and then averaged across all runs and across all animals. ...

Graph-theory based analysis of the rat brain networks

The graph demonstration of significant direct connections between ICA components was shown in Fig. 4a. The total edge number was 78, yielding the connection density of 5.55%.The spectral partitioning algorithm based on the leading eigenvector (Newman, 2006) was applied to this graph (the first step of partitioning, see Methods) and revealed that the rat whole-brain network was segregated into three modules to achieve maximum modularity (Q = 0.414, Fig. 4). This modularity value was significantly higher than both random networks with same nodes and edges and random networks with same degree distribution (p<0.01 for both types of random networks), suggesting a prominent modular structure of intrinsic connectional architecture of the rat brain. Of the three modules, module 1 was dominated by cortical regions including the dorsal olfactory bulb, motor cortex, somatosensory cortex, insular cortex and visual cortex as shown in Fig 4b, indicating strong ‘direct’ communications across the cortical ribbon in the rat (Zhang et al., 2010). Module 2 included the olfactory system, PFC, ACC, CPu, posterior somatosensory cortex, thalamus, hypothalamus, hippocampus and auditory cortex. This module highlighted the integration of sensory input, cognitive processing and output (Paxinos, 2004). Module 3 consisted of the PFC, insular cortex, amygdala, hypothalamus and auditory cortex. This module might be related to emotion and autonomic regulation in the conscious rat (Paxinos, 2004).

Figure 4
Segregation of the whole-brain network of the awake rat brain. (a). The global functional network constructed based on significant inter-component connections. Each node represents an ICA component labeled with its corresponding anatomy and the ICA number. ...

To further maximize the final value of modularity, fine-tuning stages described in Newman's spectral partitioning analysis (Newman, 2006) were included in the 2nd step of the partitioning procedure. Considering that the modularity function Q is degenerate and leads to multiple solutions of graph partitioning (Good et al., 2010), we computed the distribution of Q values and partitions. The distribution of Q values ranged from 0.392 to 0.429 with the mean value of 0.416, which only slightly improved the Q value of 0.414 obtained in the first step. In all repetitions, the majority yielded 4 modules (12 of 20 repetitions). The major pattern of partitioning showed very high stability. Consistent with the partitioning result from the first step, two modules identical to the ‘green’ and ‘yellow’ modules as shown in Fig 4 were highly consistent in all 20 partitions with minimal variation. The ‘yellow’ module was found in all repetitions and the ‘green’ module was found in 19 of 20 repetitions. However, the ‘red’ module was less stable and tended to be further divided into two submodules as shown in Figure 5. The first submodule was found in 14 of 20 repetitions and the second submodule was found in 13 or 20 repetitions. This reduced stability of the cortical module might indicate higher complexity of cortical network organization.

Figure 5
Community structures dominant in 20 repetitions of the spectral partitioning method combined with the fine-tuning method. Distance to Bregma (mm) is labeled at the bottom of each image. The yellow and green modules are almost identical to those shown ...

Furthermore, the connectional architecture of the rat brain showed typical features of small-worldness characterized by high clustering coefficient and short minimum path length. When comparing to pure random networks with the same numbers of nodes and edges, the ratio of clustering coefficient (C/Crandom) was 1.7 and the ratio of minimum path length (L/L random) is 1.08, indicating a higher level of clustering and a similar minimum path length than pure randomized networks. The ratios of these two metrics compared to a random network with the same distribution of degrees showed similar results, C/Crandom=1.5, and L/Lrandom=1.02. These comparisons collectively suggest that the rat brain is a small-world network (Watts and Strogatz, 1998).


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 (Fig.4a), 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.


We thank Dr Wei Huang for her technical assistance. We also thank the reviewers for their insightful comments. This publication was made possible by the NIH Grant Number 1R01 MH067096-02 and 5R01DA021846-02 from the National Institute of Health, and the institutional fund from the University of Massachusetts Medical School. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.


  • Albert NB, Robertson EM, Miall RC. The resting human brain and motor learning. Curr Biol. 2009;19:1023–1027. [PMC free article] [PubMed]
  • Bassett DS, Bullmore ET. Human brain networks in health and disease. Curr Opin Neurol. 2009;22:340–347. [PMC free article] [PubMed]
  • Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–541. [PubMed]
  • Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–198. [PubMed]
  • Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp. 2001;14:140–151. [PubMed]
  • Cinelli AR, Ferreyra-Moyano H, Barragan E. Reciprocal functional connections of the olfactory bulbs and other olfactory related areas with the prefrontal cortex. Brain Res Bull. 1987;19:651–661. [PubMed]
  • Dolleman-Van Der Weel MJ, Witter MP. Projections from the nucleus reuniens thalami to the entorhinal cortex, hippocampal field CA1, and the subiculum in the rat arise from different populations of neurons. J Comp Neurol. 1996;364:637–650. [PubMed]
  • Ferris CF, Febo M, Luo F, Schmidt K, Brevard M, Harder JA, Kulkarni P, Messenger T, King JA. Functional magnetic resonance imaging in conscious animals: a new tool in behavioural neuroscience research. J Neuroendocrinol. 2006;18:307–318. [PMC free article] [PubMed]
  • Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8:700–711. [PubMed]
  • Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–9678. [PubMed]
  • Good BH, de Montjoye YA, Clauset A. Performance of modularity maximization in practical contexts. Phys Rev E Stat Nonlin Soft Matter Phys. 2010;81:046106. [PubMed]
  • Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100:253–258. [PubMed]
  • Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007;62:429–437. [PMC free article] [PubMed]
  • Hajos M, Richards CD, Szekely AD, Sharp T. An electrophysiological and neuroanatomical study of the medial prefrontal cortical projection to the midbrain raphe nuclei in the rat. Neuroscience. 1998;87:95–108. [PubMed]
  • He Y, Wang J, Wang L, Chen ZJ, Yan C, Yang H, Tang H, Zhu C, Gong Q, Zang Y, Evans AC. Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One. 2009;4:e5226. [PMC free article] [PubMed]
  • Henry J, Petrides M, St-Laurent M, Sziklas V. Spatial conditional associative learning: effects of thalamo-hippocampal disconnection in rats. Neuroreport. 2004;15:2427–2431. [PubMed]
  • Hilgetag CC, Burns GA, O'Neill MA, Scannell JW, Young MP. Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. Philos Trans R Soc Lond B Biol Sci. 2000;355:91–110. [PMC free article] [PubMed]
  • Horovitz SG, Braun AR, Carr WS, Picchioni D, Balkin TJ, Fukunaga M, Duyn JH. Decoupling of the brain's default mode network during deep sleep. Proc Natl Acad Sci U S A. 2009;106:11376–11381. [PubMed]
  • Hutchison RM, Mirsattari SM, Jones CK, Gati JS, Leung LS. Functional networks in the anesthetized rat brain revealed by independent component analysis of resting-state FMRI. J Neurophysiol. 2010;103:3398–3406. [PubMed]
  • Kennedy DP, Redcay E, Courchesne E. Failing to deactivate: resting functional abnormalities in autism. Proc Natl Acad Sci U S A. 2006;103:8275–8280. [PubMed]
  • King JA, Garelick TS, Brevard ME, Chen W, Messenger TL, Duong TQ, Ferris CF. Procedure for minimizing stress for fMRI studies in conscious rats. J Neurosci Methods. 2005;148:154–160. [PMC free article] [PubMed]
  • Liu X, Zhu XH, Zhang Y, Chen W. Neural Origin of Spontaneous Hemodynamic Fluctuations in Rats under Burst-Suppression Anesthesia Condition. Cereb Cortex 2010 [PMC free article] [PubMed]
  • Liu Y, Liang M, Zhou Y, He Y, Hao Y, Song M, Yu C, Liu H, Liu Z, Jiang T. Disrupted small-world networks in schizophrenia. Brain. 2008;131:945–961. [PubMed]
  • Lu H, Zuo Y, Gu H, Waltz JA, Zhan W, Scholl CA, Rea W, Yang Y, Stein EA. Synchronized delta oscillations correlate with the resting-state functional MRI signal. Proc Natl Acad Sci U S A. 2007;104:18265–18269. [PubMed]
  • Massimini M, Ferrarelli F, Huber R, Esser SK, Singh H, Tononi G. Breakdown of cortical effective connectivity during sleep. Science. 2005;309:2228–2232. [PubMed]
  • Meunier D, Lambiotte R, Fornito A, Ersche KD, Bullmore ET. Hierarchical modularity in human brain functional networks. Front Neuroinformatics. 2009;3:37. [PMC free article] [PubMed]
  • Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci U S A. 2006;103:8577–8582. [PubMed]
  • Pawela CP, Biswal BB, Cho YR, Kao DS, Li R, Jones SR, Schulte ML, Matloub HS, Hudetz AG, Hyde JS. Resting-state functional connectivity of the rat brain. Magn Reson Med. 2008;59:1021–1029. [PMC free article] [PubMed]
  • Paxinos G. The Rat Nervous System. Sydney, Australia: Elsevier Academic Press; 2004.
  • Peltier SJ, Kerssens C, Hamann SB, Sebel PS, Byas-Smith M, Hu X. Functional connectivity changes with concentration of sevoflurane anesthesia. Neuroreport. 2005;16:285–288. [PubMed]
  • Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–682. [PubMed]
  • Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059–1069. [PubMed]
  • Schwarz AJ, Gozzi A, Bifone A. Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI. Neuroimage. 2009;47:302–311. [PubMed]
  • Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, Woolrich MW. Network modelling methods for FMRI. Neuroimage 2010 [PubMed]
  • Stevens WD, Hasher L, Chiew KS, Grady CL. A neural mechanism underlying memory failure in older adults. J Neurosci. 2008;28:12820–12824. [PMC free article] [PubMed]
  • Vertes RP, Hoover WB, Szigeti-Buck K, Leranth C. Nucleus reuniens of the midline thalamus: link between the medial prefrontal cortex and the hippocampus. Brain Res Bull. 2007;71:601–609. [PubMed]
  • Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007;447:83–86. [PubMed]
  • Wang J, Zuo X, He Y. Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci. 2010;4:16. [PMC free article] [PubMed]
  • Wang J, Wang L, Zang Y, Yang H, Tang H, Gong Q, Chen Z, Zhu C, He Y. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp. 2009;30:1511–1523. [PubMed]
  • Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440–442. [PubMed]
  • Wouterlood FG, Saldana E, Witter MP. Projection from the nucleus reuniens thalami to the hippocampal region: light and electron microscopic tracing study in the rat with the anterograde tracer Phaseolus vulgaris-leucoagglutinin. J Comp Neurol. 1990;296:179–203. [PubMed]
  • Zhang N, Rane P, Huang W, Liang Z, Kennedy D, Frazier JA, King J. Mapping resting-state brain networks in conscious animals. J Neurosci Methods. 2010;189:186–196. [PMC free article] [PubMed]