Resting state networks (RSNs) in humans is becoming a tool to investigate brain basal states in health and disease 
. Using fMRI, neuronal baseline activity of the brain is thought to measure low frequency fluctuations that are related to functionally relevant networks 
. In humans these network patterns seem to be consistent and reproducible across subjects and over time 
. Using this approach it is possible to characterize the brain networks in terms of functionally relevant circuits that subserve specific actions such as environmental monitoring, action-execution, and sensory-perception domains 
Until recently, RSNs have not been studied in rodents. Consequently, healthy state basal networks are not well understood. Most rodent studies have been carried out on anesthetized animals. Furthermore, these studies use a seed-based approach in which specific questions are tested by using a particular brain region as a seed and determining what else in the brain displays similar temporal pattern. Kannurpatti et al. 
used the primary somatosensory cortex (S1) as seed and found a network that consisted of bilateral S1. Zhao et al. 
(using medetomidine anesthesia) and Majeed et al. 
(alpha-chloralose) found S1 as well as bilateral Caudate Putamen networks (CPu) connectivity. Pawela et al., 
found an extended cortical network including primary/secondary sensorimotor regions under medetomidine anesthesia. Two studies (Lu et al. 
with alpha-chloralose and Wang et al. 
with isoflorane) explored the effects of levels of anesthesia on networks and determined that connectivity decreased with increased levels of anesthesia. Similarly, in anesthetized monkeys, the networks observed in humans are also preserved 
. These results have raised issues relating to the significance of the networks to reflect meaningful biology 
since consciousness should be impaired under anesthesia. In a recent study by Liu et al. 
, in which fMRI (BOLD and CBF) as well as electrophysiology was used, they reported that while the level of anesthesia was light, good correlation between electrical activity and fMRI signals was observed. At deep levels of anesthesia, most burst electrical activity was suppressed but brain networks were still observed, although weaker than with light anesthesia. These findings were interpreted to reflect that at deep levels of anesthesia a loss of consciousness occurs due to a collapse of cortical activity patterns as reflected in the disappearance of electrical burst activity. The underlying coherent fluctuations that remain at deep levels of anesthesia are the ones that appear in fMRI studies, although they might not reflect brain activity. Hence, it is probable that varying types and levels of anesthetics affect spontaneous brain activity and the resulting RSNs.
To the best of our knowledge, there is only one other study that explores RSNs in awake rodents 
with a seed-based correlation method. The study used 3 seeds located at the prefrontal cortex, thalamus and retrosplenial cortex. There was a significant amount of overlap in the observed networks for the 3 seeds. The cortical ribbon encompassing sensorimotor regions was in all of them, suggesting that seed regions might contain temporal information of two or more networks, perhaps due to the size of the region or because of intrinsic connectivity in many networks.
An alternative approach in determining RSNs is the application of independent component analysis (ICA - 
) commonly used in humans. ICA does not need a-priori specifications of seed regions. It determines the optimal components that can explain the spatio-temporal patterns in the data. The obtained components might provide information that is different to the seed-based approach. For instance, a seed could be based on a brain region belonging to two or more networks. In this case, the time course extracted from this seed will be a mixture of two time courses - one from each network. A seed-based analysis of such a region might provide a resulting network that is a combination of other networks. For instance, in awake rats a seed- based connectivity analysis 
with somatosensory cortex as a seed produced a network consisting of the sensorimotor cortex. An ICA study of awake rodents 
had that particular network split into 2 (as we have found in this article) indicating that a structure could have activity belonging to different networks. Two studies have been reported utilizing ICA's: one in anesthetized rodents 
and the other in awake rats 
. Hutchison used low levels of isoflurane (1%) and determined 20 components of which they selected, visually, 12 networks for the brain structures they encompassed. Some of the networks observed by Hutchison (S1 network, S2 network, visual, auditory) appear as a single one in Zhang's awake connectivity analysis, indicating that perhaps they are separate networks. The ICA study by Liang et al. 
also applied graph theory with the aim of understanding the connectivity of the observed networks. They determined 40 ICA components and from them selected 8 that included cortical and subcortical structures (however, the cortical ribbon network observed by their seed analysis in their previous paper seems to be split into different components). Through a graph-based analysis, they derived 3 modules that seem to correspond to sensorimotor, integration/cognitive processing, and emotion/autonomic regulation. Thus, while the field has clearly advanced the issue of RSN selection would be improved if it were determined by a more robust or objective approach.
Optimally, determination of the basic RSNs should be carried out on awake rodents to eliminate the confounding effects of anesthesia. Additionally, a model-free analysis approach such as ICA should be used to define networks. However, as mentioned above, the selection process of significant components in an ICA analysis is rather arbitrary.
We have recently extended a technique (RAICAR - 
) to be able to statistically determine the most robust and reproducible components of an ICA-based analysis 
In this article, we have acquired baseline fMRI data in conscious rats and utilized ICA to determine robust reproducible RSNs. Our results indicate that 7 components meet the criteria and their putative role in brain function is discussed.