Given our initial population of 13 rats (2 out of 15 were excluded due to excessive motion), we randomly picked 5 rats without replacement out of 13 to create a group of 5 rats. We repeated this process to create 50 different groups of 5 rats. Each group of 5 rats was subjected to a temporal con-catenation based group ICA (concat-ICA). The number of independent compo- nents in concat-ICA was fixed at 20 (similar to human RSN studies [12]). One advantage of group concat-ICA analysis is that we constrain the group to have the same spatial maps but allow flexibility in modeling subject specific associated time- courses. For reproducibility of RSN analysis we adopted the following approach. The 50 group ICA maps from individual 5 subject concat-ICAs were subjected to a ranking and reproducibility analysis (RAICAR, [29]). The reproducibility analysis is a very efficient technique for filtering out only those ICA components that are reproducible across the input data sets. In our case, since the 50 groups of 5 are chosen randomly from 13 rats, the reproducibility is over multiple runs and subjects simultaneously. In the original RAICAR algorithm, there is no well-defined cutoff on reproducibility. Hence, we modified the RAICAR algorithm to have an automatically determined reproducibility cutoff [31]. Briefly, the cross-realization correlation matrix (CRCM) in RAICAR was randomly permuted 100 times and the RAICAR analysis was repeated for each random permutation. This generated a null-distribution for the reproducibility index, which was used to compute reproducibility p-values for each independent component. For group averaging and thresholding, the reproducible components (e.g., p-value,0.05) across the 50 groups ICA runs were automatically preserved as described above. These compo- nents were then analyzed voxel-wise using a group mean general linear model (GLM). A mixture model (MM) was fitted to the resulting t-statistic maps using algorithms developed in MATLAB [32] to determine corrected thresholds. The ‘‘activated’’ and ‘‘deactivated’’ components of the MM were modeled by a Gamma distribution and a ‘‘flipped’’ Gamma distribution, respectively. The distribution was modeled using a Student t distribution. Non-background components were detected by thresholding the posterior probability maps for ‘‘activation’’ and ‘‘deactivation’’ at posterior probability .0.5. Note that because of ‘‘sign ambiguity’’ in ICA ‘‘positive’’ or ‘‘negative’’ activation simply indicates reversed polarity of modulation. Component (Network) Identification Each component was analyzed to determine brain structures, or parts thereof, that were covered by the network. In addition to the component average statistical value for each brain structure, the percentage of the volume of the structure in the network was calculated. These two metrics were used to classify brain structures within a component in order of statistical significance and relative fraction of the structure involved; components were assigned a specific biological function based on the most salient brain structures in the network. Brain structures were identified on MRI slices that correspond to a standard Atlas [33,34]. Results Methodological Two out of 15 rats were excluded from further analysis due to excessive head motion. None were removed because of signs of stress such as vocalization. Figure 1 depicts head motion correction parameters for 2 typical rats and for the excluded rats. Resting State Networks Awake Rats In our previous study of thermal pain in other group of awake rodents [35], we demonstrated that heart rates during scanning stays within the normal range. Figure 2 shows an illustrative output of the RAICAR_N analysis. The top panel shows the sorted normalized reproduc- ibility values for each observed component. A 90% cutoff line corresponding to a p-value of 0.1 is also shown. The bottom panel depicts the normalized reproducibility with the corresponding 90% threshold line. In the present analysis, 7 components survive at a p-value of 0.1. Robust Reproducible Awake Rat RSNs Figure 3 depicts selected slices of the individual maps for each of the 7 components defined using RAICAR_N, for full maps see (Figure S1). For each component, details of structures involved in the RSN are noted in corresponding Tables S1, S2, S3, S4, S5, S6, S7. Figure 3 also depicts several of the brain structures identified in the MRI images. The 7 com- ponents depict networks that can be classified according to the group of structures they include based on their level of significance of activation as well as relative size of activation (see Methods: Component Identification). These networks are noted below and are listed in order of their ranking of the RAICAR-N evaluation below. Component 1 - Cerebellar Network. This network involved structures mostly in the cerebellum (cerebellar lobules 2,3,4,5) and the brainstem (periaqueductal grey (PAG) raphe nuclei), (Table S1, Figure 3-C1). Additional brain regions included the thalamus, hippocampus, cingulate cortex, olfactory cortex and insular cortex. These have been also observed in parts of ICA [27,28] or seed- based analysis [19,20,26]. Component 2 – (Visuo-) Spatial System. The predominant regions involved are in cortical and subcortical regions with essentially only the PAG showing some brainstem involvement (Table S2, Figure 3-C2). Cortical regions included the orbitofrontal, cingulate, and retrosplenial cortices as well as insular, motor and somatosensory cortices. Similar regions were found as part of the Prefrontal Cortex network defined in awake rats by [26]. Subcortical regions included a large number of thalamic regions (anterior, ventroanterior, intralaminar and midline, and ventrolateral thalamic nuclei) and septal regions. Component 3 - Basal Ganglia-Hypothalamus Network. This component is comprised mainly of subcortical structures that include components of the basal ganglia (globus pallidus, extension of the amygdala (exA), septal region, CPu, claustrum), thalamus, and hypothalamus (Table S3 and Figure 3-C3). Component 4 - Basal Ganglia-Thalamus-Hippocampal Network. Regions involved in this component are shown in Table S4 and Figure 3-C4 and are predominantly subcortical. Based on the regions observed, including the thalamus (ventral lateral, ventral medial, ventroposterior, mediodorsal nuclei), hypothalamus, lateral globus pallidus, medial division of exA, and hippocampus. Component 5 - Autonomic Network. This network is also predominantly subcortical in nature (Table S5, Figure 2-C5). As noted in the table, it encompasses mainly the hypothalamus, hippocampus (CA3), ventral tegmentum, and thalamic nuclei (VP complex, auditory, VM). Component 6 – Sensory (Exteroceptive) Network. This component is essentially only cortical in nature (Table S6, Figure 2-C6). The cortical regions include primary sensory and motor cortices and cortical regions involved in auditory and olfactory processing. In addition, the orbitofrontal and insular cortices are also part of this network. Notably, this network PLoS ONE | www.plosone.org 3 October 2011 | Volume 6 | Issue 10 | e25701