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 applica- tion of independent component analysis (ICA - [11]) 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 compo- nents 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 [26] with somatosensory cortex as a seed produced a network consisting of the sensorimotor cortex. An ICA study of awake rodents [27] 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 [28] and the other in awake rats [27]. 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. [27] 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 - [29]) to be able to statistically determine the most robust and reproducible components of an ICA-based analysis [29]). 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. Methods Ethics Statement All methods and experiments were approved by the Massa- chusetts General Hospital Subcommittee on Research Animal Care (IACUC-Protocol #2008N00192). Animals were caged 2 per cage and on a 12/12 hours light/dark cycle. Food and water were available ad libitum. Animals were inspected daily for welfare or signs of disease. No animal displayed excessive signs of stress (vocalizing trashing around) during training or imaging session. Resting State Networks Awake Rats Rat imaging Imaging methods have been described elsewhere [30]: rats were adapted to the imaging set-up by consecutive restraining over 3 days, one hour each day in a holding device similar to the one used for imaging set in a box with speakers playing the scanner noises, they were lightly anesthetized with 2% isoflorane for loading. On the imaging day, rats were slightly anesthetized with 2% isoflorane for loading into the imaging cradle; they were left to recover from isoflorane for 30 minutes before imaging to avoid any remnant effects of anesthesia. Fifteen Sprague-Dawley rats (males, weight, 300–350 g) were scanned in a 4.7T scanner (Bruker, Billerica, MA) in total darkness. Gradient Echo EPI images consisted of 12 slices 1.5 mm thick with a field of view of 30 mm and in-plane resolution of 64664 voxels covering from the front of the brain up to most of the cerebellum. Images were acquired with TE/ TR = 12 ms/3 s, 90u flip angle and 120 time points (6 minutes) per rat. We used a shorter TE to reduce susceptibility-induced distortions around the ear canals. However, a short TE reduces the BOLD contrast and might impair reproducibility of some networks. Two rats displayed excessive head-motion (.0.5 mm) and were excluded from further analysis. Analysis Analysis was performed using tools from the FMRIB software library (http://www.fmrib.ox.ac.uk/fsl/). The following pre-pro- cessing steps were carried out: (i) Motion correction and spike detection: Each rat was subjected to motion correction using FSL’s motion correction algorithm MCFLIRT and spike detection using FSL’s fsl_motion_outliers script. (ii) Brain extraction: After motion correction, for each rat masks were drawn to delineate the brain tissue from the background. Brain extraction was done manually because FSL’s brain extraction tool BET was not able to automatically remove the non-brain tissue in this case. (iii) Data cleanup: Motion and spike related artifacts were removed from the data by regressing out simultaneously the 6 motion parameters and the spike explanatory variable (EVs) created in step (i). The spike EVs are all 0 except at the volume location where a spike is detected. (iv) Spatial smoothing and band pass filtering: The cleaned data from step (iii) was preprocessed by applying a spatial smoothing using a Gaussian kernel FWHM of 0.9 mm, a grand mean intensity normalization of the entire data set using a single multiplicative factor, and band pass temporal filtering using filter cutoffs of 100 and 10 s (0.01–0.1 Hz) (similar to Liang et al. 2011). (v) Normalization to standard space: Registration of fMRI space data to standard space was carried out using FSL’s flirt using a 12 degrees of freedom affine transform. The standard space was chosen to be an anatomical scan from 1 rat of size 2566128624 with voxel size 3 0.11760.11761 mm . To reduce computational burden in the resting state analysis the 4-D filtered datasets from step (iv) were resampled to spatial size 100675648 with voxel size 0.360.260.5 mm3, a similar approach used for human analysis [11]. Resting State Analysis Resting state analysis was performed using spatial independent component analysis (ICA) using the tool MELODIC from FSL. There are 2 sources of variability when doing ICA. First, ICA results are produced by optimization of a non-convex function, which means that the results are not unique. Indeed, running ICA several times on the same dataset gives different results. Second, there is inter-subject variability in the ICA results. Given these sources of variability, we would like to find ICA components that are good descriptors of the resting state as well being reproducible across runs and across subjects. PLoS ONE | www.plosone.org 2 October 2011 | Volume 6 | Issue 10 | e25701