Multiple lines of evidence indicate that spontaneous brain activity plays an essential role in brain function (Raichle and Mintun, 2006; Zhang and Raichle, 2010). For instance, intrinsic neuronal signaling consumes the vast majority of brain energy (Raichle, 2006, 2010). Investigation of spontaneous brain activity, predominantly conducted using resting-state functional magnetic resonance imaging (rsfMRI) (Biswal et al., 1995; Fox and Raichle, 2007), has provided critical insight into the intrinsic organization of the brain network. Using spontaneously fluctuating blood-oxygenation-level dependent (BOLD) signal measured by rsfMRI, resting-state functional connectivity (RSFC) between brain regions can be gauged by statistical interdependence of their rsfMRI signals over the period of data acquisition (Fox and Raichle, 2007). Based on this quantity, multiple brain networks of functionally-related regions have been identified in both humans and animals, which convey the information of stable functional connectivity organization of the brain (Beckmann et al., 2005; Fox et al., 2005; Damoiseaux et al., 2006; Smith et al., 2009; Allen et al., 2011; Liang et al., 2011).

Conventional rsfMRI studies generally focus on steady features of RSFC by assuming that RSFC is stationary during the acquisition period. However, meaningful temporal variability of RSFC at shorter time scales has also been discovered (Chang and Glover, 2010). This initial research and its follow-up studies revealed dynamic properties of RSFC, indicating that the stationarity assumption of RSFC would be overly simplistic for understanding spontaneous brain activity (Hutchison et al., 2013a; Preti et al., 2017; Chang et al., 2016a). Indeed, using sliding window analysis and clustering methods, temporally alternating but spatially repeatable RSFC patterns have been identified (Allen et al., 2014). In addition, Liu and Duyn (2013) developed a method that examined instantaneous co-activations of BOLD signal at single rsfMRI frames and found that BOLD co-activation patterns well corresponded brain connectivity configurations (Liu and Duyn, 2013). With this method, the default mode network, which is a single network under the assumption of stationary RSFC, can be decomposed into multiple sub-networks with distinct spatiotemporal characteristics and functional relevance (Liu and Duyn, 2013). Notably, the neurophysiologic relevance of dynamic RSFC has been validated in multiple studies using simultaneous electrophysiology and rsfMRI acquisitions (Tagliazucchi et al., 2012; Chang et al., 2013; Keilholz, 2014; Liu et al., 2015b).

In parallel with blossoming dynamic RSFC studies in humans, dynamic RSFC studies in animal models have also been conducted. Animals’ brain preserves fundamental organizational properties as the human brain (Liang et al., 2011; Ma et al., 2016), and can serve as a translational model for studying complicated brain dynamics. Using either the sliding window or co-activation pattern approach, dynamic RSFC patterns have been found in both awake and anesthetized rats, as well as in anesthetized monkeys (Majeed et al., 2011; Hutchison et al., 2013b; Keilholz et al., 2013; Mohajerani et al., 2013; Liang et al., 2015; Grandjean et al., 2017; Ma et al., 2017). These results suggest that dynamics in RSFC might be a general feature in the mammalian brain.

Despite the critical advancement, aforementioned dynamic rsfMRI studies have mainly focused on revealing the spatial characteristics of RSFC patterns that were non-stationary, while the temporal relationship between these RSFC patterns is still unclear. Particularly, although the existence of temporal transitions between characteristic RSFC patterns has been established, it remains elusive whether these transitions are random or organized in an orderly manner (Majeed et al., 2011; Zalesky et al., 2014; Mitra et al., 2015; Preti et al., 2017). Lack of such information highlights a gap in elucidating the temporal relationship of separate brain connectivity configurations, and thus hinders the comprehensive characterization of spatiotemporal dynamics of spontaneous brain activity.

To address this issue, in the present study we studied the temporal transitions of intrinsic brain activity in both awake rats and humans. The reproducibility of the RSFC pattern transitions was examined. In addition, the organization of RSFC pattern transitions in rats and humans were respectively studied using graph theory analysis. RSFC patterns that were pivotal in temporal transitions were further identified.

In this study, we investigated the temporal transitions between spontaneous brain activity patterns in awake rats and humans. In rat data, we first obtained a library of 40 characteristic RSFC patterns, using seed-based correlational analysis with seeds defined by parcels in a whole-brain RSFC-based parcellation (Ma et al., 2016). These characteristic RSFC patterns were used as the reference patterns. Subsequently, based on the notion that the BOLD co-activation patterns of single rsfMRI frames represent their RSFC patterns (Liu et al., 2013; Liu and Duyn, 2013), each rsfMRI frame was matched to one of the 40 reference RSFC patterns that had the highest spatial similarity to the BOLD co-activation pattern of the frame. This step generated a time sequence of characteristic RSFC patterns for each rsfMRI run. Temporal transitions between every pair of RSFC patterns were then counted, which created a RSFC pattern transition matrix. A weighted directed transition network was constructed by thresholding this transition matrix, and was analyzed using graph theory. The same approach was also applied to human rsfMRI data to examine the translational value of the findings in rats. A schematic illustration of these procedures is shown in Figure 1.

Figure 1

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Characteristic RSFC patterns in the awake rat brain

An example of a characteristic RSFC pattern is shown in Figure 2, and the other 39 characteristic RSFC patterns are shown in Figure 2—figure supplements 1–5. As the whole-brain parcellation scheme we adopted maximized within-parcel and minimized cross-parcel RSFC profile similarity, these 40 group-level seed-based RSFC maps represented a set of characteristic RSFC patterns in the awake rat brain, and were used as the reference patterns. Notably, the number 40 was arbitrarily selected as an example of low-dimensionality parcellation of the rat brain. Similar analysis can be applied using other parcel numbers.

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Figure 2 (right panel) also shows the averaged pattern of rsfMRI frames that were matched to the reference RSFC pattern, which demonstrated high reminiscence between the BOLD co-activation pattern of single rsfMRI frames and the RSFC pattern it corresponded to (correlation coefficient = 0.91).

Reproducible temporal transitions between RSFC patterns

We first demonstrated that temporal transitions between RSFC patterns were highly reproducible at the group level. We randomly split all rats into two subgroups and obtained the transition matrix for each subgroup. Both matrices exhibited high similarity (Figure 3a), reflected by a significant correlation (r = 0.86, p ≈ 0) between the corresponding off-diagonal entries. To control for the possible bias that transitions between similar RSFC patterns may have a higher chance to occur in both subgroups, which can inflate the reproducibility, we regressed out the spatial similarities between reference RSFC patterns from both transition matrices. The reproducibility remained high after regression, with a significant correlation value of 0.77 (p ≈ 0, Figure 3b). Taken together, these results suggest that transitions between RSFC patterns are not random but follow specific temporal sequences in awake rats, and these transition sequences are not dictated by the similarity between RSFC patterns.

Figure 3 with 3 supplements see all

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To further examine whether reproducible RSFC pattern transitions were dominated by a small portion of rats, we assessed the reproducibility of RSFC pattern transitions for each individual animal by computing Pearson correlation between each individual-level transition matrix and the group-level transition matrix. Fisher Z-transformed correlation values were then averaged across rats. Our data showed a significant individual-level reproducibility (mean (±SD)=0.57 (±0.14), p ≈ 0). These results collectively indicate that nonrandom RSFC pattern transitions are a characteristic feature in awake rats.

To rule out the possibility that RSFC pattern transitions were caused by head motion (Laumann et al., 2016), we conducted several additional analyses. First, we re-evaluated the reproducibility of RSFC transitions between two subgroups of rats with relatively high and low motion, respectively. Rats in the first subgroup all had the motion level below the median, quantified by framewise displacement (FD). Rats in the second subgroup all had the motion level above the median. The mean (±SD) FD of the below- and above-median subgroups were 0.037 (±0.024) mm and 0.054 (±0.033) mm, respectively. Transition matrices were obtained in these two subgroups, respectively. Comparing these two transition matrices yielded a reproducibility of 0.786 with regression of RSFC pattern similarities, and 0.872 without regression of RSFC pattern similarities (Figure 3—figure supplement 1), which is similar to the reproducibility assessed based on a random division to subgroups (0.77 and 0.86 with and without regression of RSFC pattern similarities, respectively, Figure 3). In addition, the transition matrices from both subgroups were highly consistent with those in subgroups randomly divided (Figure 3). To statistically test whether the reproducibility using motion-based division to subgroups was different from that based on random divisions, we repeated random subgroup divisions 10000 times. Mean reproducibility (±SD) across all 10000 trials was 0.876 (±0.010) and 0.791 (±0.017) without and with regression of RSFC pattern similarities, respectively. Figure 3—figure supplement 2 shows the distributions of the reproducibility between randomly divided subgroups across all trials. This data demonstrated that the reproducibility using motion-based division to subgroups was not statistically different from the reproducibility based on random division regardless whether RSFC pattern similarities were regressed out (p=0.75) or not (p=0.67). These results indicate that high reproducibility in RSFC pattern transitions was not attributed to head motion.

In the second analysis, we directly compared the motion level between rsfMRI frames involved in RSFC pattern transitions versus those that were not in transitions. All rsfMRI frames analyzed were categorized into two groups. The first group included frames whose preceding and/or successive frame corresponded to a different RSFC pattern (i.e. in transitions). The second group included frames whose preceding and successive frames were the same RSFC pattern (i.e. not in transition). These two groups of rsfMRI frames showed consistent motion levels, quantified by their FD values (p=0.44, two-sample t-test), again indicating that RSFC pattern transitions were not triggered by head motion. To further test whether some RSFC pattern transitions were induced by head motion, we measured the head motion during each transition and compared the mean head motion level for each transition sequence and the occurrence count of this transition sequence. Specifically, we calculated the mean FD for transitions between every two RSFC patterns. This calculation yielded a 40 × 40 matrix, in which each element quantified the mean FD for each transition sequence (e.g. element (i,j) of this matrix measured the mean FD for the transition from RSFC pattern i to RSFC pattern j). Our data showed that the correlation between this transition FD matrix and the RSFC pattern transition matrix was minimal (r = −0.034), suggesting that the mean head motion level during each transition sequence did not predict the occurrence count of this transition sequence. This result further supported that RSFC pattern transitions were independent of head motion, and RSFC transitions did not follow head motion.

To examine whether our results were dependent on the motion censoring threshold selected (FD <0.2 mm), we reanalyzed our data using a more stringent censoring threshold (FC <0.1 mm). At this threshold and also keeping all other motion control criteria identical, very similar RSFC pattern transition matrices were obtained (Figure 3—figure supplement 3). The correlations between the RSFC pattern transition matrices at FD <0.1 mm and those at FD <0.2 mm were 0.83 and 0.88 with and without regression of RSFC pattern similarities, respectively, suggesting that our results were robust and insensitive to the motion censoring threshold applied.

Within- and between-brain system transitions

Figure 4 shows the group-level transition matrix thresholded using a permutation test (p<0.05, FDR corrected). Rows/columns in the transition matrix were arranged based on the brain system that the seed region of the reference RSFC pattern belonged to. Transitions between RSFC patterns tended to occur within the same brain system, as shown by a relatively denser distribution of near-diagonal nonzero elements in the matrix. However, cross-system transitions such as striatal-thalamic, striatal-somatosensory, striatal-prefrontal, striatal-hippocampal, hippocampal-amygdala, amygdala-motor transitions were observed.

Figure 4

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Organization of RSFC pattern transitions

A directed weighted graph of the RSFC pattern transition network was constructed based on the group-level thresholded transition matrix (Figure 4), as shown in Figure 5. The number of edges was 242, yielding a connection density of 15.5%. The transition network exhibited a prominent community structure with nine modules identified using the Louvain community detection algorithm (Vincent et al., 2008), suggesting that RSFC patterns belonging to the same modules had a higher probability to transit between each other than RSFC patterns across modules. The corresponding seed regions of RSFC patterns were color coded based on the community affiliations (Figure 5 inlet). Module one primarily covered hippocampal and retrohippocampal networks as well as caudal visual networks. Module two included caudal midbrain networks. Module three was comprised of brainstem and rostral midbrain networks. Module four covered rostral visual, amygdala, hypothalamic as well as motor and olfactory networks. Module five was dominated by auditory and somatosensory networks. Module six captured posterior ventral thalamic networks. Module seven included anterior thalamic networks. Module eight covered striatal and prefrontal networks. Module nine mainly included anterior cingulate cortex network. Networks from the same system usually fell into the same community, again indicating transitions between RSFC patterns frequently occurred within the same brain system. However, networks from different systems were also observed in the same modules, which highlights the importance of cross-system transitions.

Figure 5

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By quantifying the node-specific graph measures of node strength, betweenness centrality, characteristic path length and local clustering coefficient, hub nodes (i.e. pivotal RSFC patterns) in the graph were identified. Six RSFC patterns were identified as hubs in rats (hub score ≥3) including the networks of retrosplenial cortex, dorsal superior and inferior colliculi, hippocampus, anterior ventral thalamus, striatum and motor cortex. Figure 6 shows the seed regions of RSFC patterns with hub score ≥1, color coded based on the hub score. No color was given to seed regions of RSFC patterns with hub score = 0.

Figure 6

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Figure 7 shows RSFC pattern transitions of four representative hubs (red nodes), demonstrating the pivotal role of these patterns in RSFC temporal transitions. The majority of transitions between hubs and other RSFC patterns were bidirectional. Figure 7a shows transitions of the hub network of the superior and inferior colliculi with the networks of the periaqueductal gray, hippocampus, dorsal thalamus, hypothalamus, caudal visual cortex and motor areas. Figure 7b demonstrates the transitions between the hippocampus network (hub) and the RSFC networks of the superior and inferior colliculi, hippocampus/retrohippocampus, caudal visual cortex, as well as prefrontal and orbital cortices. Figure 7c displays transitions between the hub network of the anterior ventral thalamus and the RSFC networks of the brainstem, midbrain, dorsal CA1, dorsal thalamus, posterior ventral thalamus, dorsal caudate-putamen (CPu), olfactory tubercle, and orbital cortex. Figure 7d illustrates the transitions between the hub network of the ventral CPu, and the RSFC networks of the brainstem and olfactory tubercle, as well as infralimbic, prelimbic, and orbital cortices. Taken together, these results indicate that hub RSFC patterns were centralized patterns that play a pivotal role in transitions with other RSFC patterns involving multiple brain systems.

Figure 7

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RSFC pattern transitions in humans

To assess whether temporal transitions between RSFC patterns were also nonrandom in humans, we applied the same analysis to rsfMRI data from 812 human subjects in the HCP. Each frame was matched to one of 333 characteristic RSFC patterns defined by a well-established RSFC-based parcellation in humans (Gordon et al., 2016), and the number of transitions between every two RSFC patterns was counted for each subject. All subjects were then randomly split into two subgroups (406 subjects in each subgroup). The reproducibility between two subgroups was 0.9955 (without regression of seed map similarities, Figure 8a), and 0.9954 (with the regression of seed map similarities, Figure 8b). To assess the reproducibility at the individual level, the correlation between the transition matrix of each individual subject versus the group-level transition matrix was calculated. The mean correlation (±SD) across all subjects was 0.60 (±0.05). All these results were highly consistent with our findings in awake rats, suggesting that nonrandom transitions between RSFC patterns are conserved across species and might represent a characteristic feature of the mammalian brain.

Figure 8

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The group-level transition matrix obtained from all 812 human subjects was thresholded using the same permutation test (p<0.05, FDR corrected), as shown in Figure 9. Rows/columns of the RSFC transition matrix were grouped based on the brain system of the seed region. Similar to RSFC pattern transitions in rats, transitions between RSFC patterns in humans tended to occur within the same brain system, indicated by a denser distribution of near-diagonal nonzero elements in the matrix. However, considerable across-system RSFC pattern transitions were also evident.

Figure 9

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The hub score of each RSFC pattern was calculated in the same way as the rat data. Figure 10 showed seed regions of RSFC patterns with hub score ≥1, color coded based on the hub score. Seed regions of RSFC patterns with hub score = 0 were not given any color. Our results demonstrated that the human RSFC pattern transitions contain multiple hubs (hub score ≥3) in separate brain systems including default-mode (five nodes), cingulo-opercular (eight nodes), dorsal attention (two nodes), ventral attention (four nodes), fronto-parietal (four nodes), parietal memory (one node) and visual (five nodes) networks. Interestingly, these hub patterns were predominantly integrative networks.

Figure 10

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In the present study, we investigated temporal sequential transitions between intrinsic brain activity patterns in the awake rat and human brain. We showed that transitions between RSFC patterns exhibited high reproducibility across animals and were significantly above chance (Figures 3 and 4). In addition, the RSFC pattern transition network was constructed using the thresholded transition matrix (Figure 4), and its topological organization including the community structure (Figure 5) and hubness (Figure 6) was evaluated. Moreover, the transitions of four representative hub RSFC patterns in rats were demonstrated (Figure 7). Importantly, non-random RSFC pattern transitions were also observed in humans (Figure 8), and the organization of the human transition network was further analyzed using the same graph analysis approach (Figures 9 and 10). Taken together, the present study for the first time characterized the temporal organization between successive brain connectivity configurations. It demonstrates that spontaneous brain activity was not only far from random spatially, but also far from random temporally. Similar results in rats and humans indicate that this feature might be well conserved across species. These data collectively have provided new insight into understanding the spatiotemporal dynamics of spontaneous activity in the mammalian brain.

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Author details

1. Zhiwei Ma

   Department of Biomedical Engineering, The Huck Institutes of Life Sciences, The Pennsylvania State University, State College, United States

   Present address

   Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, Bethesda, United States

   Contribution

   Software, Formal analysis, Validation, Visualization, Writing—original draft

   Competing interests

   No competing interests declared

2. Nanyin Zhang

   Department of Biomedical Engineering, The Huck Institutes of Life Sciences, The Pennsylvania State University, State College, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   nuz2@psu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5824-9058

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