Spatio-temporal dynamics of cortical excitability are constantly impacted by fluctuations of internally generated activity, even in the absence of external inputs. The resting-state of the cortical functional architecture is characterized by canonical ‘default-mode networks’ (Fox and Raichle, 2007; Mitra and Raichle, 2016; Raichle, 2015) which have been identified in humans (Raichle, 2011), primates (Chauvette et al., 2011) and rodents (Gozzi and Schwarz, 2016; Jonckers et al., 2011). However, the spatio-temporal features of these networks are not static (Deco et al., 2013). Alternating states of excitability, manifesting as different activity states of the brain, can dynamically occur, even in different regions at the same time, as seen in the awake condition (Guo et al., 2004). Different states of brain activation can influence responses upon incoming sensory afferents (Pachitariu et al., 2015; Schwalm et al., 2017) and are accompanied by alterations in the spatiotemporal pattern of neuronal activity in many brain areas (Pais-Roldán et al., 2020). These alterations can be observed in local electrophysiological (Constantinople and Bruno, 2011; Deco et al., 2013; Gozzi and Schwarz, 2016) and optical (Jonckers et al., 2011; Matsui et al., 2016) recordings, as well as in large-scale readouts of brain activity, such as BOLD fMRI (Kalthoff et al., 2013; Lewis et al., 2009; Staresina et al., 2013). The ubiquity of fluctuating activity states which influence ongoing brain dynamics calls for a state-dependent assessment of resting state functional connectivity (Tagliazucchi and Laufs, 2014).

Recently, using local and brain-wide readouts, we have identified two activity states which were shown to occur under different types of anesthesia used for small animal functional magnetic resonance imaging (fMRI) studies (Schwalm et al., 2017). In a state of persistent activity, which can occur during periods of light anesthesia, sedation or during awake periods (Constantinople and Bruno, 2011), neurons are rather depolarized, sparsely active, leading to temporally dynamic, modality specific, network configurations (Barth and Poulet, 2012; D'Souza et al., 2014). In contrast to persistent activity, stands the bimodal activity pattern of slow oscillations, or slow waves which have been extensively described under various conditions (Chauvette et al., 2011; Ma and Zhang, 2018; Petersen et al., 2003; Sanchez-Vives et al., 2017; Sanchez-Vives and McCormick, 2000; Schwalm et al., 2017; Seamari et al., 2007; Stroh et al., 2013; Zucca et al., 2017), but only most recently in the framework of BOLD fMRI (Chang et al., 2016; Liu et al., 2011; Mitra et al., 2015; Schwalm et al., 2017). Their corresponding low-frequency component ranges at 0.2–1 Hz, reflecting alternating activity patterns: active (‘up’) periods in which cells are depolarized and fire action potentials in temporally restricted periods, and silence (‘down’) periods with rather hyperpolarized membrane potentials and an almost complete absence of neuronal activity (Destexhe et al., 2007; Steriade et al., 2001). Slow waves can occur rather locally (Nir et al., 2011) or can spread over the entire cortex (Ma and Zhang, 2018; Schwalm et al., 2017), eventually allowing activity to propagate (Sanchez-Vives et al., 2017). In our previous work, we found that independent component analysis (ICA) for data obtained during persistent activity revealed canonical resting-state associated networks, such as the default mode (Lu et al., 2012; Schwalm et al., 2017) or different sensory networks (Liang et al., 2013; Pawela et al., 2008). Defining different states of brain activity by their local and global features of network dynamics by no means entails that their behavioral correlates, as for example in an anesthetized condition versus natural sleep, would be identical. However, defining features of slow wave activity, such as its bimodality, are resembled in a plethora of different conditions (Bullmore and Sporns, 2009; Ma and Zhang, 2018; Mitra et al., 2018; Stroh et al., 2013), likely because the fundamental difference between fast and slow timescale cortical dynamics is regulated by distinct mechanisms at the single neuron level (Okun et al., 2019).

Functional connectivity analysis for resting-state fMRI is used to identify and characterize neural networks in health and disease (Buckner et al., 2005; Woodward et al., 2011), during learning (Lewis et al., 2009) or memory consolidation (Staresina et al., 2013). The possibility to combine resting-state fMRI with invasive recordings of brain activity constitutes a pivotal branch of translational research (2018). Despite evidence of subtle changes in anesthesia level being able to dramatically change brain connectivity (Grandjean et al., 2014; Hutchison et al., 2014; Zhang et al., 2019), little has been investigated on how the brain’s current state impacts functional connectivity derived from blood oxygenation level-dependent (BOLD) activity. While variations in functional connectivity have been related to cognitive state in humans (Finn et al., 2017), the effect of neurophysiologically defined states on functional connectivity in the cortex remains unknown. Directly relating functional connectivity to defined brain states may have explanatory value for a broad range of functional brain connectivity studies. We propose that connectivity measures in different conditions such as different anesthetics (Bukhari et al., 2017; Kalthoff et al., 2013; Liu et al., 2013; Nasrallah et al., 2014; Paasonen et al., 2018; Shin et al., 2016) can be classified based on the respective activity state of the brain. Based on these classifications, meaningful comparisons can be drawn both between functional connectivity signature in rodents as well as in humans.

Here, we find distinct BOLD functional connectivity patterns for a state of persistent activity and for a state dominated by the occurrence of slow waves. We identified BOLD correlates revealing compartmentalized network activity during the persistent activity state and a cortex-wide component during slow wave activity. Furthermore, we show a cortex-wide functional coupling of brain activity during slow wave activity which has its origin in transitions from population down to population up states which occur during ongoing slow wave activity. Our data reveals the imminent consequences of each slow wave event for the configuration of dynamic functional networks during this type of brain-wide activity.

We conducted ICA of rat resting-state fMRI signals during isoflurane-induced slow wave activity and in a medetomidine-induced persistent activity. For slow wave activity data, we found a characteristic cortex-wide component as characterized previously (Schwalm et al., 2017). Conversely, during persistent activity, this cortex-wide component was absent and canonical independent components of well-compartmentalized default mode networks were prevailing. While we found a cortex-wide component in all animals during isoflurane-induced slow wave activity (Figure 1A), the identified components for persistent activity resembled those described by others for awake (Becerra et al., 2011; Paasonen et al., 2018) or sedated rodents (Bukhari et al., 2018; Schwalm et al., 2017). We identified default mode network activation in 14 of 15 animals (Figure 1B), a bilateral auditory component in 11 of 15 medetomidine-sedated animals (Figure 1C) and a bilateral component of the insula in 9 of 15 animals (Figure 1D). All the selected components of each animal can be observed in Figure 1—figure supplement 1. Similarly, as it has been reported in previous work (Hsu et al., 2016), we identified four default mode network regions: left and right posterior parietal cortex, anterior and posterior insular cortex and orbital/prelimbic cortex. To corroborate the difference in the network organization between both brain states, we then manually defined nine anatomical regions of interests (ROIs) based on the z-scores maps of the persistent activity components. We run a functional connectivity analysis of each ROI for each individual and plotted the resulting matrix. When both matrices are compared, it can be observed that there is a wide distribution of significant connectivity among the different networks (delineated by the black squares) during slow wave activity (Figure 1E) compared with the high specificity showed during persistent activity (Figure 1E). These results indicate that there are marked differences in the network configurations between both types of activity.

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Persistent and slow wave activity display different functional connectivity networks

To assess whether the cortical connectivity patterns previously described belong to those particular networks or are illustrating a general cortical phenomenon, we obtained the functional connectivity signature of the cortical networks during slow wave and persistent activity. We parcellated the brain based on anatomical landmarks using the Valdes-Hernandez et al template for rats (Valdés-Hernández et al., 2011) and correlated the BOLD signal of 96 cortical structures using the average signal from the white matter structures, ventricles and the animal’s breathing rate as covariables in both conditions (Figure 2A). The name and order of cortical areas can be found in Figure 2—figure supplement 1. We plotted the cumulative distribution function (CDF) for the r-values of each matrix (n = 15) to establish whether a difference in the number and strength of significant correlations can be found. The cutoff was calculated for each matrix using FDR (mean 0.379 ± 0.041 s.e.m.). The CDF values differed significantly between both activity states (paired t-test, t(14) = 6.01 p<0.001) at the mean of the cutoff (Figure 2B). The number of correlated pairs for each individual at its particular cutoff was significant (paired t-test, t(14) = 6.502, p<0.001) (Figure 2C). These results suggest that slow wave and persistent activity are related to different functional connectivity patterns. To illustrate this point, we plotted connectivity patterns between regions in both brain states (Figure 2C and D), from this diagram it is possible to infer that during persistent activity, functional connectivity is more compartmentalized than during slow wave activity.

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Population activity during the presence of slow waves is characterized by long periods of quiescence followed by short lapses of neural activity corresponding to a propagating wave (Stroh et al., 2013). Frequency and power distributions of the BOLD signal and their relationships to functional connectivity have been studied before with respect to specific anesthetic conditions (Grandjean et al., 2014; Paasonen et al., 2018). fALFF analysis allows to determine the fractional power of the BOLD signal for a particular region of interest (ROI) (Zou et al., 2008). To rule out that the connectivity values observed during slow wave activity are due to the spurious consequence of an increase in power, we performed a fALFF analysis to investigate the differences in the BOLD power (Figure 3A), showing that during slow wave activity there is a significant shift to lower power values in the distribution of the fALFF curves which were created with the 96 cortical ROIs of each animal (paired t-test, t(14) = 3.11, p=0.008). To test the dependence of the strength in connectivity from the distance of cortical areas, we correlated the Euclidean distance between pairs with the significant r-scores of the correlation of those pairs (Figure 3B). The closer to 90° angle the slope of those correlations gets, the more dependent of the Euclidean distance their correlation becomes. We found, that this is indeed the case for slow wave activity recordings (Figure 3C), whose slopes are significantly lower than those of data recorded during persistent activity (paired t-test, t(14) = 5.43, p<0.001). These results corroborate that the BOLD signal during slow wave activity is governed by different dynamics than the BOLD signal during persistent activity. Furthermore, during slow wave activity, BOLD-driven connectivity seems to be less compartmentalized and shows a stronger dependency on the distance between areas than it is the case for persistent activity.

Figure 3

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Cortical small world network architecture characterizes persistent activity and disappears during slow wave activity evidenced by graph theory analysis

To assess the organizational nature of the cortical networks identified by our previous analyses, we applied graph theory methods (Rubinov and Sporns, 2010). We first computed the modularity degree of the cortical networks identified during persistent and slow wave activity respectively. Modularity is a quality index for a partition of a network into non-overlapping communities (Schwarz et al., 2008). Therefore, in a brain with more compartmentalized networks this measure is higher than in one that shows a less selective network configuration. When we compared modularity values from networks identified for slow wave activity to those of networks identified for persistent activity, we indeed observed significantly higher values for persistent activity (paired t-test, t(14) = 4.01, p=0.001; Figure 4A), resembling results previously reported by others (D'Souza et al., 2014). Global and local efficiency are two important characteristics to differentiate small world from random networks. Small world networks exhibit high values for both measurements, whilst randomized networks show high values in global but low values for local efficiency (Latora and Marchiori, 2001). When we compared these measures, we did not find significant differences in the global efficiency scores (Figure 4B; paired t-test, t(14) = 0.389, p=0.703), but we found significantly lower scores for slow wave activity than for persistent activity in the local efficiency measurement (Figure 4C; paired t-test, t(14) = 5.869, p<0.001). It is well documented that both, small world and random networks show a short characteristic path length, whilst only small world networks show a high clustering coefficient (Watts and Strogatz, 1998). In our analyses, we found a significantly lower clustering coefficient for networks dominated by slow wave activity (Figure 4D; paired t-test, t(14) = 3.789, p=0.002), but no significant differences between networks in slow wave and persistent activity for characteristic path length (Figure 4E; paired t-test, t(14) = 0.636, p=0.535). This suggests that during slow wave activity the cortex rather behaves like a random network, whilst during persistent activity it exhibits small world network features (Figure 4F). In summary, graph theory analysis indicated a different functional connectivity pattern for persistent and slow wave activity. For persistent activity canonical well compartmentalized, small world networks seem to play a dominating role, while during slow wave activity a highly interconnected randomized network is prevalent.

Figure 4

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Different activity states of the brain are reflected by changes in functional connectivity

Functional connectivity patterns in the rodent brain can change dramatically depending on the anesthetics used (Bukhari et al., 2017; Matsui et al., 2016; Wu et al., 2017). In order to demonstrate our effects to be independent of the anesthetic agent, we compared the medetomine-induced sedation persistent activity to a condition of low isoflurane anesthesia (0.5% - 0.7%) that prevents animals of entering slow wave activity (n = 6 animals), hypothesizing that both protocols lead to a similar functional state, that is persistent activity. In the fiber photometry recordings (Figure 5A), it becomes apparent that the population dynamics recorded under this low isoflurane anesthesia regime resemble the ones obtained under medetomidine, rather than showing similarity to the ones recorded under higher isoflurane concentrations (Figure 5B). To quantify whether this observation is meaningful for the BOLD signal, we computed the functional connectivity analysis in the same way as described for the previous experiments and obtained the connectivity matrices (Figure 5C). We computed matrix similarity analysis for each of the six matrices against the mean matrix for slow wave and persistent activity to determine if the connectivity patterns obtained under the low isoflurane concentration rather resemble the ones previously measured under medetomidine, (persistent activity) or the ones measured under higher isoflurane concentrations (slow wave activity). Similarity values can be compared extracting the eigen values of the matrix and then using Frobenius norm in order to calculate their similarity. The lower the obtained value, the greater is the similarity between two matrices. In this case, the resulting values related to persistent activity induced by low isoflurane, were significantly lower when compared with the ones for persistent activity induced by medetomidine sedation than when compared to the ones related to slow wave activity under high isoflurane concentration (Figure 5D; paired t-test, t(5) = 5.19, p=0.003). We used the same matrix similarity computation in a dynamic connectivity analysis. Therefore, we compared the initial 5 min of recordings with a moving average of 5 min in steps of 1 min. The results show that in the case of persistent activity induced by medetomidine and by low isoflurane concentrations, network configurations were significantly more stable than in the case of slow wave activity induced by high isoflurane concentrations (Figure 5E). This indicates that the network dynamics during slow wave activity might be related to a constant reorganization of connectivity patterns, probably due to the frequent changes in the rate of population down-up transitions.

Figure 5

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Population down-up transitions drive functional connectivity during slow wave activity

Slow wave activity has been proposed to constitute the default activity pattern of the cortex, which is present during deep slow wave sleep or when connections with the thalamus are physically removed (Sanchez-Vives et al., 2017). Local occurrences of slow wave activity have also been shown to take place in cortical circuits during awake periods (Vyazovskiy et al., 2011). This bimodal pattern – periods of neural quiescence interrupted by short bursts of activity - generates waves of activity that can propagate across the cortex (Busche et al., 2015; Stroh et al., 2013). We recently showed that these waves generate a cortex-wide increase in BOLD activity (Schwalm et al., 2017) which may also be responsible for a cortex-wide increase of functional connectivity during slow wave activity. To test this, we analyzed the previously obtained functional connectivity results during slow wave activity while using the cortex-wide component as a covariable, since this component represents a direct correlate of local slow waves (Schwalm et al., 2017). If the high connectivity signature found during slow wave activity is related to population down-up transitions, the cortex-wide component as a covariable should remove any correlation that occurs due to such down-up transitions observable in the population signal. Following this rationale, we expected that once the down-up transition covariable is removed from the connectivity analysis, correlation values should significantly drop (Figure 6Ai). Therefore, the cumulative distribution function of the r-values obtained using the cortex-wide component as a covariable (CWCcov) should be shifted to the left compared with those obtained using the original cortex inverted signal of the cortex-wide component (CWCinv). The CDF data (same analysis as in Figure 2B) showed significantly higher values for the CWCcov condition at the mean of the cutoff (Figure 6Aii) (paired t-test¸ t(14) = 4.96, p<0.001). Furthermore, the amount of significantly correlated pairs for each individual at its particular cutoff was also significantly less in the CWCcov condition. (paired t-test, t(14) = 5.435, p<0.001).This suggests a relationship between slow wave activity-related down-up transitions in the population signal and the strength of cortical functional connectivity. Additionally, we co-registered calcium photometry (Adelsberger et al., 2014; Grienberger et al., 2012) and fMRI BOLD signals during slow wave activity in five animals (Figure 6Bi). We quantified the number of population down-up transitions in a period of 3 min in the fluorescence signal emitted by a neural population in visual cortex (V1) (Figure 6Bii, upper panel) and correlated it with the number of significant pairs from the functional connectivity analysis of the BOLD signal for the same time period (Figure 6Bi, lower panel). We quantified these values for 30 min-long recordings using samples of 3 min and a gap of 1 min in between them. The correlation between the amount of down-up transitions in the fluorescence signal and the BOLD pairs was significant for five of the six animals (p<0.001) (Figure 6Bii). In order to overcome a potential source of a spurious correlation, we run a permutation test on each resulting correlation. The permutation test allows to establish the baseline correlation between points and to calculate the distance of the obtained results from that correlation. In the five animals, the permutation test shows that the correlation r-scores are in the 5% of significant values (Figure 6Bii, normal distribution plots). Finally, we calculated the statistical value for the whole dataset using the recently described repeated measures correlation (Bakdash and Marusich, 2017) a statistical technique for determining the inter-individuals correlation for intra-subject multiple measurements (r(196) = 0.534, p<10e⁻¹⁵). These results strongly suggest that slow wave activity-related down-up transitions are directly linked to the increase in cortical functional connectivity during the prevalence of slow waves.

Figure 6

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Dual-cortical calcium photometry during slow wave activity shows increased signal correlation in down-up transitions

While the previous results demonstrated the role of down-up transitions for functional connectivity during slow wave activity, they are based on hemodynamic signals rather than on a direct measure of neural responses. The occurrence of slow waves in a spatially confined neural population can be detected using fiber photometry measuring fluorescent signals emitted by cells previously stained with a calcium indicator (Adelsberger et al., 2014; Grienberger et al., 2012). To corroborate our results on the neural population level, we conducted experiments employing simultaneous optic-fiber-based calcium recordings in somatosensory (S1) and visual (V1) cortex (Figure 7A,B). Slow wave related down-up transitions can be easily identified and quantified (Seamari et al., 2007) and have a duration of 2 s on average (Figure 7C). The amplitude of the cross-correlation between these two signals is significant (Figure 7D), with a delay matching the propagation time of the wave propagating from V1 to S1 ((n = 6) 51.2 mm/s ± 8.1 s.e.m). To probe if down-up transitions are underlying the increase of functional cortical connectivity in our slow wave activity data, we filtered the fluorescence and the BOLD signal at different frequency intervals before running the cross-correlation analysis (Figure 7B,D). Filtering the signal induced a partial abolishment of the slow waves in particular bandwidths (Figure 7C) without interfering with the correlation of both cortical signals in other frequencies. Therefore, one should expect a decrease in the signal correlation on those frequencies when the slow wave activity is abolished. Results of six recordings in three animals showed that the cross-correlation peaks significantly decrease in the frequency interval for which the down-up transitions were filtered out (Figure 7B,E). The cross-correlation values were significantly lower when data was filtered between 0.01 and 0.4 Hz (paired t-test: t(5) = 4.26, p=0.008), 0.2–0.6 Hz (t(5) = 7.99, p<0.001), and 0.3–0.7 (t(5) = 7.28, p<0.001). These results support the ones obtained previously with BOLD connectivity analyses at the cortical population level, demonstrating down-up transitions to be the main contributor to an increase of cortical functional connectivity during slow wave activity.

Figure 7

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We demonstrated that different states of brain activity can modify properties of the functional connectome. ICA showed distinct functional networks for each of the investigated activity states. In the case of slow wave activity, a cortex-wide component was found in all animals, while for persistent activity, a clear compartmentalization of the cortical functional structures gave origin to well-determined functional networks, as the default mode or the auditory network. Notably, the relation of population activity to functional connectivity was not dependent on a specific anesthetic agent, suggesting that not the type of anesthesia used, but the induced activity state influenced resting-state networks. The functional connectivity analyses using cortical parcellation corroborated this concept, showing an increase in the connectivity r-scores as well as in the number of significant correlations during slow wave activity. Population activity dominated by slow waves is characterized by long periods of quiescence followed by short lapses of neural activity corresponding to a propagating wave (Stroh et al., 2013), as demonstrated by the simultaneous dual cortical calcium recordings. To rule out that the connectivity values observed during slow wave activity are due to the spurious consequence of an increase in power, we performed fALFF analysis and found significantly lower values for data obtained under slow wave activity, probably due to the long periods of quiescence observed in population slow wave activity.

All data generated or analysed during this study are included in the manuscript and supporting files. All source data files are available on Dryad Digital repository (https://doi.org/10.5061/dryad.vmcvdncqk). All custom Matlab codes used in these analyses are available at https://github.com/Strohlab/connectivityelife (copy archived at https://github.com/elifesciences-publications/connectivityelife; Aedo-Jury & Stroh, 2020).

1. 1. Aedo-Jury F
   2. Schwalm M
   3. Hamzehpour L
   4. Stroh A

   (2020) Dryad Digital Repository

   Brain states govern the spatio-temporal dynamics of resting-state functional connectivity.

   https://doi.org/10.5061/dryad.vmcvdncqk

1. 1. Adelsberger H
   2. Grienberger C
   3. Stroh A
   4. Konnerth A

   (2014) In vivo calcium recordings and channelrhodopsin-2 activation through an optical fiber

   Cold Spring Harbor Protocols 2014:pdb.prot084145.

   https://doi.org/10.1101/pdb.prot084145

   • PubMed
   • Google Scholar
2. Software

   1. Aedo-Jury F
   2. Stroh A

   (2020) Brain states govern the spatio-temporal dynamics of resting-state functional connectivity, version 1.0

   Github.

   https://github.com/Strohlab/connectivityelife
3. 1. Bakdash JZ
   2. Marusich LR

   (2017) Repeated measures correlation

   Frontiers in Psychology 8:456.

   https://doi.org/10.3389/fpsyg.2017.00456

   • PubMed
   • Google Scholar
4. 1. Barth AL
   2. Poulet JF

   (2012) Experimental evidence for sparse firing in the neocortex

   Trends in Neurosciences 35:345–355.

   https://doi.org/10.1016/j.tins.2012.03.008

   • PubMed
   • Google Scholar
5. 1. Becerra L
   2. Pendse G
   3. Chang PC
   4. Bishop J
   5. Borsook D

   (2011) Robust reproducible resting state networks in the awake rodent brain

   PLOS ONE 6:e25701.

   https://doi.org/10.1371/journal.pone.0025701

   • PubMed
   • Google Scholar
6. 1. Belloy ME
   2. Naeyaert M
   3. Abbas A
   4. Shah D
   5. Vanreusel V
   6. van Audekerke J
   7. Keilholz SD
   8. Keliris GA
   9. Van der Linden A
   10. Verhoye M

   (2018) Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic patterns and illustrates their relationship with the global signal

   NeuroImage 180:463–484.

   https://doi.org/10.1016/j.neuroimage.2018.01.075

   • PubMed
   • Google Scholar
7. 1. Boveroux P
   2. Vanhaudenhuyse A
   3. Bruno MA
   4. Noirhomme Q
   5. Lauwick S
   6. Luxen A
   7. Degueldre C
   8. Plenevaux A
   9. Schnakers C
   10. Phillips C
   11. Brichant JF
   12. Bonhomme V
   13. Maquet P
   14. Greicius MD
   15. Laureys S
   16. Boly M

   (2010) Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness

   Anesthesiology 113:1038–1053.

   https://doi.org/10.1097/ALN.0b013e3181f697f5

   • PubMed
   • Google Scholar
8. 1. Buckner RL
   2. Snyder AZ
   3. Shannon BJ
   4. LaRossa G
   5. Sachs R
   6. Fotenos AF
   7. Sheline YI
   8. Klunk WE
   9. Mathis CA
   10. Morris JC
   11. Mintun MA

   (2005) Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory

   Journal of Neuroscience 25:7709–7717.

   https://doi.org/10.1523/JNEUROSCI.2177-05.2005

   • PubMed
   • Google Scholar
9. 1. Bukhari Q
   2. Schroeter A
   3. Cole DM
   4. Rudin M

   (2017) Resting state fMRI in mice reveals anesthesia specific signatures of brain functional networks and their interactions

   Frontiers in Neural Circuits 11:5.

   https://doi.org/10.3389/fncir.2017.00005

   • PubMed
   • Google Scholar
10. 1. Bukhari Q
    2. Schroeter A
    3. Rudin M

    (2018) Increasing isoflurane dose reduces homotopic correlation and functional segregation of brain networks in mice as revealed by resting-state fMRI

    Scientific Reports 8:10591.

    https://doi.org/10.1038/s41598-018-28766-3

    • PubMed
    • Google Scholar
11. 1. Bullmore E
    2. Sporns O

    (2009) Complex brain networks: graph theoretical analysis of structural and functional systems

    Nature Reviews Neuroscience 10:186–198.

    https://doi.org/10.1038/nrn2575

    • PubMed
    • Google Scholar
12. 1. Busche MA
    2. Grienberger C
    3. Keskin AD
    4. Song B
    5. Neumann U
    6. Staufenbiel M
    7. Förstl H
    8. Konnerth A

    (2015) Decreased amyloid-β and increased neuronal hyperactivity by immunotherapy in Alzheimer's models

    Nature Neuroscience 18:1725–1727.

    https://doi.org/10.1038/nn.4163

    • PubMed
    • Google Scholar
13. 1. Busche MA
    2. Wegmann S
    3. Dujardin S
    4. Commins C
    5. Schiantarelli J
    6. Klickstein N
    7. Kamath TV
    8. Carlson GA
    9. Nelken I
    10. Hyman BT

    (2019) Tau impairs neural circuits, dominating amyloid-β effects, in alzheimer models in vivo

    Nature Neuroscience 22:57–64.

    https://doi.org/10.1038/s41593-018-0289-8

    • PubMed
    • Google Scholar
14. 1. Calhoun VD
    2. Adali T
    3. Pearlson GD
    4. Pekar JJ

    (2001) A method for making group inferences from functional MRI data using independent component analysis

    Human Brain Mapping 14:140–151.

    https://doi.org/10.1002/hbm.1048

    • PubMed
    • Google Scholar
15. 1. Capone C
    2. Rebollo B
    3. Muñoz A
    4. Illa X
    5. Del Giudice P
    6. Sanchez-Vives MV
    7. Mattia M

    (2019) Slow waves in cortical slices: how spontaneous activity is shaped by laminar structure

    Cerebral Cortex 29:319–335.

    https://doi.org/10.1093/cercor/bhx326

    • PubMed
    • Google Scholar
16. 1. Chang C
    2. Leopold DA
    3. Schölvinck ML
    4. Mandelkow H
    5. Picchioni D
    6. Liu X
    7. Ye FQ
    8. Turchi JN
    9. Duyn JH

    (2016) Tracking brain arousal fluctuations with fMRI

    PNAS 113:4518–4523.

    https://doi.org/10.1073/pnas.1520613113

    • PubMed
    • Google Scholar
17. 1. Chauvette S
    2. Crochet S
    3. Volgushev M
    4. Timofeev I

    (2011) Properties of slow oscillation during slow-wave sleep and anesthesia in cats

    Journal of Neuroscience 31:14998–15008.

    https://doi.org/10.1523/JNEUROSCI.2339-11.2011

    • PubMed
    • Google Scholar
18. 1. Constantinople CM
    2. Bruno RM

    (2011) Effects and mechanisms of wakefulness on local cortical networks

    Neuron 69:1061–1068.

    https://doi.org/10.1016/j.neuron.2011.02.040

    • PubMed
    • Google Scholar
19. 1. D'Souza DV
    2. Jonckers E
    3. Bruns A
    4. Künnecke B
    5. von Kienlin M
    6. Van der Linden A
    7. Mueggler T
    8. Verhoye M

    (2014) Preserved modular network organization in the sedated rat brain

    PLOS ONE 9:e106156.

    https://doi.org/10.1371/journal.pone.0106156

    • PubMed
    • Google Scholar
20. 1. Deco G
    2. Jirsa VK
    3. McIntosh AR

    (2013) Resting brains never rest: computational insights into potential cognitive architectures

    Trends in Neurosciences 36:268–274.

    https://doi.org/10.1016/j.tins.2013.03.001

    • PubMed
    • Google Scholar
21. 1. Destexhe A
    2. Hughes SW
    3. Rudolph M
    4. Crunelli V

    (2007) Are corticothalamic 'up' states fragments of wakefulness?

    Trends in Neurosciences 30:334–342.

    https://doi.org/10.1016/j.tins.2007.04.006

    • PubMed
    • Google Scholar
22. 1. Finn ES
    2. Scheinost D
    3. Finn DM
    4. Shen X
    5. Papademetris X
    6. Constable RT

    (2017) Can brain state be manipulated to emphasize individual differences in functional connectivity?

    NeuroImage 160:140–151.

    https://doi.org/10.1016/j.neuroimage.2017.03.064

    • PubMed
    • Google Scholar
23. 1. Fox MD
    2. Raichle ME

    (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

    Nature Reviews Neuroscience 8:700–711.

    https://doi.org/10.1038/nrn2201

    • PubMed
    • Google Scholar
24. 1. Garaschuk O
    2. Milos RI
    3. Konnerth A

    (2006) Targeted bulk-loading of fluorescent indicators for two-photon brain imaging in vivo

    Nature Protocols 1:380–386.

    https://doi.org/10.1038/nprot.2006.58

    • PubMed
    • Google Scholar
25. 1. Gozzi A
    2. Schwarz AJ

    (2016) Large-scale functional connectivity networks in the rodent brain

    NeuroImage 127:496–509.

    https://doi.org/10.1016/j.neuroimage.2015.12.017

    • PubMed
    • Google Scholar
26. 1. Grandjean J
    2. Schroeter A
    3. Batata I
    4. Rudin M

    (2014) Optimization of anesthesia protocol for resting-state fMRI in mice based on differential effects of anesthetics on functional connectivity patterns

    NeuroImage 102 Pt 2:838–847.

    https://doi.org/10.1016/j.neuroimage.2014.08.043

    • PubMed
    • Google Scholar
27. 1. Grandjean J
    2. Preti MG
    3. Bolton TAW
    4. Buerge M
    5. Seifritz E
    6. Pryce CR
    7. Van De Ville D
    8. Rudin M

    (2017) Dynamic reorganization of intrinsic functional networks in the mouse brain

    NeuroImage 152:497–508.

    https://doi.org/10.1016/j.neuroimage.2017.03.026

    • PubMed
    • Google Scholar
28. 1. Grienberger C
    2. Adelsberger H
    3. Stroh A
    4. Milos RI
    5. Garaschuk O
    6. Schierloh A
    7. Nelken I
    8. Konnerth A

    (2012) Sound-evoked network calcium transients in mouse auditory cortex in vivo

    The Journal of Physiology 590:899–918.

    https://doi.org/10.1113/jphysiol.2011.222513

    • PubMed
    • Google Scholar
29. 1. Guo K
    2. Benson PJ
    3. Blakemore C

    (2004) Pattern motion is present in V1 of awake but not anaesthetized monkeys

    European Journal of Neuroscience 19:1055–1066.

    https://doi.org/10.1111/j.1460-9568.2004.03212.x

    • PubMed
    • Google Scholar
30. 1. Gutierrez-Barragan D
    2. Basson MA
    3. Panzeri S
    4. Gozzi A

    (2019) Infraslow state fluctuations govern spontaneous fMRI network dynamics

    Current Biology 29:2295–2306.

    https://doi.org/10.1016/j.cub.2019.06.017

    • PubMed
    • Google Scholar
31. 1. Hagmann P
    2. Cammoun L
    3. Gigandet X
    4. Meuli R
    5. Honey CJ
    6. Wedeen VJ
    7. Sporns O

    (2008) Mapping the structural core of human cerebral cortex

    PLOS Biology 6:e159.

    https://doi.org/10.1371/journal.pbio.0060159

    • PubMed
    • Google Scholar
32. 1. Hiltunen T
    2. Kantola J
    3. Abou Elseoud A
    4. Lepola P
    5. Suominen K
    6. Starck T
    7. Nikkinen J
    8. Remes J
    9. Tervonen O
    10. Palva S
    11. Kiviniemi V
    12. Palva JM

    (2014) Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI

    Journal of Neuroscience 34:356–362.

    https://doi.org/10.1523/JNEUROSCI.0276-13.2014

    • PubMed
    • Google Scholar
33. 1. Hsu LM
    2. Liang X
    3. Gu H
    4. Brynildsen JK
    5. Stark JA
    6. Ash JA
    7. Lin CP
    8. Lu H
    9. Rapp PR
    10. Stein EA
    11. Yang Y

    (2016) Constituents and functional implications of the rat default mode network

    PNAS 113:E4541–E4547.

    https://doi.org/10.1073/pnas.1601485113

    • PubMed
    • Google Scholar
34. 1. Hutchison RM
    2. Womelsdorf T
    3. Allen EA
    4. Bandettini PA
    5. Calhoun VD
    6. Corbetta M
    7. Della Penna S
    8. Duyn JH
    9. Glover GH
    10. Gonzalez-Castillo J
    11. Handwerker DA
    12. Keilholz S
    13. Kiviniemi V
    14. Leopold DA
    15. de Pasquale F
    16. Sporns O
    17. Walter M
    18. Chang C

    (2013) Dynamic functional connectivity: promise, issues, and interpretations

    NeuroImage 80:360–378.

    https://doi.org/10.1016/j.neuroimage.2013.05.079

    • PubMed
    • Google Scholar
35. 1. Hutchison RM
    2. Hutchison M
    3. Manning KY
    4. Menon RS
    5. Everling S

    (2014) Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain's functional architecture

    Human Brain Mapping 35:5754–5775.

    https://doi.org/10.1002/hbm.22583

    • PubMed
    • Google Scholar
36. 1. Jonckers E
    2. Van Audekerke J
    3. De Visscher G
    4. Van der Linden A
    5. Verhoye M

    (2011) Functional connectivity fMRI of the rodent brain: comparison of functional connectivity networks in rat and mouse

    PLOS ONE 6:e18876.

    https://doi.org/10.1371/journal.pone.0018876

    • PubMed
    • Google Scholar
37. 1. Kalthoff D
    2. Po C
    3. Wiedermann D
    4. Hoehn M

    (2013) Reliability and spatial specificity of rat brain sensorimotor functional connectivity networks are superior under sedation compared with general anesthesia

    NMR in Biomedicine 26:638–650.

    https://doi.org/10.1002/nbm.2908

    • PubMed
    • Google Scholar
38. 1. Krishnan GP
    2. González OC
    3. Bazhenov M

    (2018) Origin of slow spontaneous resting-state neuronal fluctuations in brain networks

    PNAS 115:6858–6863.

    https://doi.org/10.1073/pnas.1715841115

    • PubMed
    • Google Scholar
39. 1. Latora V
    2. Marchiori M

    (2001) Efficient behavior of small-world networks

    Physical Review Letters 87:198701.

    https://doi.org/10.1103/PhysRevLett.87.198701

    • PubMed
    • Google Scholar
40. 1. Laumann TO
    2. Snyder AZ
    3. Mitra A
    4. Gordon EM
    5. Gratton C
    6. Adeyemo B
    7. Gilmore AW
    8. Nelson SM
    9. Berg JJ
    10. Greene DJ
    11. McCarthy JE
    12. Tagliazucchi E
    13. Laufs H
    14. Schlaggar BL
    15. Dosenbach NUF
    16. Petersen SE

    (2017) On the stability of BOLD fMRI correlations

    Cerebral Cortex 27:4719–4732.

    https://doi.org/10.1093/cercor/bhw265

    • PubMed
    • Google Scholar
41. 1. Leonardi N
    2. Van De Ville D

    (2015) On spurious and real fluctuations of dynamic functional connectivity during rest

    NeuroImage 104:430–436.

    https://doi.org/10.1016/j.neuroimage.2014.09.007

    • PubMed
    • Google Scholar
42. 1. Lewis CM
    2. Baldassarre A
    3. Committeri G
    4. Romani GL
    5. Corbetta M

    (2009) Learning sculpts the spontaneous activity of the resting human brain

    PNAS 106:17558–17563.

    https://doi.org/10.1073/pnas.0902455106

    • PubMed
    • Google Scholar
43. 1. Liang Z
    2. King J
    3. Zhang N

    (2012) Intrinsic organization of the anesthetized brain

    Journal of Neuroscience 32:10183–10191.

    https://doi.org/10.1523/JNEUROSCI.1020-12.2012

    • PubMed
    • Google Scholar
44. 1. Liang Z
    2. Li T
    3. King J
    4. Zhang N

    (2013) Mapping thalamocortical networks in rat brain using resting-state functional connectivity

    NeuroImage 83:237–244.

    https://doi.org/10.1016/j.neuroimage.2013.06.029

    • PubMed
    • Google Scholar
45. 1. Liu X
    2. Zhu XH
    3. Zhang Y
    4. Chen W

    (2011) Neural origin of spontaneous hemodynamic fluctuations in rats under burst-suppression anesthesia condition

    Cerebral Cortex 21:374–384.

    https://doi.org/10.1093/cercor/bhq105

    • PubMed
    • Google Scholar
46. 1. Liu X
    2. Zhu XH
    3. Zhang Y
    4. Chen W

    (2013) The change of functional connectivity specificity in rats under various anesthesia levels and its neural origin

    Brain Topography 26:363–377.

    https://doi.org/10.1007/s10548-012-0267-5

    • PubMed
    • Google Scholar
47. 1. Lu H
    2. Zou Q
    3. Gu H
    4. Raichle ME
    5. Stein EA
    6. Yang Y

    (2012) Rat brains also have a default mode network

    PNAS 109:3979–3984.

    https://doi.org/10.1073/pnas.1200506109

    • PubMed
    • Google Scholar
48. 1. Ma Z
    2. Zhang N

    (2018) Temporal transitions of spontaneous brain activity

    eLife 7:e33562.

    https://doi.org/10.7554/eLife.33562

    • PubMed
    • Google Scholar
49. 1. Massimini M
    2. Huber R
    3. Ferrarelli F
    4. Hill S
    5. Tononi G

    (2004) The sleep slow oscillation as a traveling wave

    Journal of Neuroscience 24:6862–6870.

    https://doi.org/10.1523/JNEUROSCI.1318-04.2004

    • PubMed
    • Google Scholar
50. 1. Matsui T
    2. Murakami T
    3. Ohki K

    (2016) Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity

    PNAS 113:6556–6561.

    https://doi.org/10.1073/pnas.1521299113

    • PubMed
    • Google Scholar
51. 1. Mitra A
    2. Snyder AZ
    3. Tagliazucchi E
    4. Laufs H
    5. Raichle ME

    (2015) Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep

    eLife 4:e10781.

    https://doi.org/10.7554/eLife.10781

    • PubMed
    • Google Scholar
52. 1. Mitra A
    2. Kraft A
    3. Wright P
    4. Acland B
    5. Snyder AZ
    6. Rosenthal Z
    7. Czerniewski L
    8. Bauer A
    9. Snyder L
    10. Culver J
    11. Lee JM
    12. Raichle ME

    (2018) Spontaneous Infra-slow brain activity has unique spatiotemporal dynamics and laminar structure

    Neuron 98:297–305.

    https://doi.org/10.1016/j.neuron.2018.03.015

    • PubMed
    • Google Scholar
53. 1. Mitra A
    2. Raichle ME

    (2016) How networks communicate: propagation patterns in spontaneous brain activity

    Philosophical Transactions of the Royal Society B: Biological Sciences 371:20150546.

    https://doi.org/10.1098/rstb.2015.0546

    • Google Scholar
54. 1. Nasrallah FA
    2. Lew SK
    3. Low AS
    4. Chuang KH

    (2014) Neural correlate of resting-state functional connectivity under α2 adrenergic receptor agonist, medetomidine

    NeuroImage 84:27–34.

    https://doi.org/10.1016/j.neuroimage.2013.08.004

    • PubMed
    • Google Scholar
55. 1. Nir Y
    2. Staba RJ
    3. Andrillon T
    4. Vyazovskiy VV
    5. Cirelli C
    6. Fried I
    7. Tononi G

    (2011) Regional slow waves and spindles in human sleep

    Neuron 70:153–169.

    https://doi.org/10.1016/j.neuron.2011.02.043

    • PubMed
    • Google Scholar
56. 1. Okun M
    2. Steinmetz NA
    3. Lak A
    4. Dervinis M
    5. Harris KD

    (2019) Distinct structure of cortical population activity on fast and infraslow timescales

    Cerebral Cortex 29:2196–2210.

    https://doi.org/10.1093/cercor/bhz023

    • PubMed
    • Google Scholar
57. 1. Paasonen J
    2. Stenroos P
    3. Salo RA
    4. Kiviniemi V
    5. Gröhn O

    (2018) Functional connectivity under six anesthesia protocols and the awake condition in rat brain

    NeuroImage 172:9–20.

    https://doi.org/10.1016/j.neuroimage.2018.01.014

    • PubMed
    • Google Scholar
58. 1. Pachitariu M
    2. Lyamzin DR
    3. Sahani M
    4. Lesica NA

    (2015) State-dependent population coding in primary auditory cortex

    Journal of Neuroscience 35:2058–2073.

    https://doi.org/10.1523/JNEUROSCI.3318-14.2015

    • PubMed
    • Google Scholar
59. 1. Pais-Roldán P
    2. Takahashi K
    3. Sobczak F
    4. Chen Y
    5. Zhao X
    6. Zeng H
    7. Jiang Y
    8. Yu X

    (2020) Indexing brain state-dependent pupil dynamics with simultaneous fMRI and optical fiber calcium recording

    PNAS 117:6875–6882.

    https://doi.org/10.1073/pnas.1909937117

    • PubMed
    • Google Scholar
60. 1. Pawela CP
    2. Biswal BB
    3. Cho YR
    4. Kao DS
    5. Li R
    6. Jones SR
    7. Schulte ML
    8. Matloub HS
    9. Hudetz AG
    10. Hyde JS

    (2008) Resting-state functional connectivity of the rat brain

    Magnetic Resonance in Medicine 59:1021–1029.

    https://doi.org/10.1002/mrm.21524

    • PubMed
    • Google Scholar
61. 1. Petersen CC
    2. Hahn TT
    3. Mehta M
    4. Grinvald A
    5. Sakmann B

    (2003) Interaction of sensory responses with spontaneous depolarization in layer 2/3 barrel cortex

    PNAS 100:13638–13643.

    https://doi.org/10.1073/pnas.2235811100

    • PubMed
    • Google Scholar
62. Software

    1. R Development Core Team

    (2010) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
63. 1. Raichle ME

    (2011) The restless brain

    Brain Connectivity 1:3–12.

    https://doi.org/10.1089/brain.2011.0019

    • PubMed
    • Google Scholar
64. 1. Raichle ME

    (2015) The restless brain: how intrinsic activity organizes brain function

    Philosophical Transactions of the Royal Society B: Biological Sciences 370:20140172.

    https://doi.org/10.1098/rstb.2014.0172

    • Google Scholar
65. 1. Rubinov M
    2. Sporns O

    (2010) Complex network measures of brain connectivity: uses and interpretations

    NeuroImage 52:1059–1069.

    https://doi.org/10.1016/j.neuroimage.2009.10.003

    • PubMed
    • Google Scholar
66. 1. Ruiz-Mejias M
    2. Ciria-Suarez L
    3. Mattia M
    4. Sanchez-Vives MV

    (2011) Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse

    Journal of Neurophysiology 106:2910–2921.

    https://doi.org/10.1152/jn.00440.2011

    • PubMed
    • Google Scholar
67. 1. Sanchez-Vives MV
    2. Massimini M
    3. Mattia M

    (2017) Shaping the default activity pattern of the cortical network

    Neuron 94:993–1001.

    https://doi.org/10.1016/j.neuron.2017.05.015

    • PubMed
    • Google Scholar
68. 1. Sanchez-Vives MV
    2. McCormick DA

    (2000) Cellular and network mechanisms of rhythmic recurrent activity in neocortex

    Nature Neuroscience 3:1027–1034.

    https://doi.org/10.1038/79848

    • PubMed
    • Google Scholar
69. 1. Schmid F
    2. Wachsmuth L
    3. Schwalm M
    4. Prouvot PH
    5. Jubal ER
    6. Fois C
    7. Pramanik G
    8. Zimmer C
    9. Faber C
    10. Stroh A

    (2016) Assessing sensory versus optogenetic network activation by combining (o)fMRI with optical ^Ca2+ recordings

    Journal of Cerebral Blood Flow & Metabolism 36:1885–1900.

    https://doi.org/10.1177/0271678X15619428

    • PubMed
    • Google Scholar
70. 1. Schwalm M
    2. Schmid F
    3. Wachsmuth L
    4. Backhaus H
    5. Kronfeld A
    6. Aedo Jury F
    7. Prouvot PH
    8. Fois C
    9. Albers F
    10. van Alst T
    11. Faber C
    12. Stroh A

    (2017) Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves

    eLife 6:e27602.

    https://doi.org/10.7554/eLife.27602

    • PubMed
    • Google Scholar
71. 1. Schwarz AJ
    2. Gozzi A
    3. Bifone A

    (2008) Community structure and modularity in networks of correlated brain activity

    Magnetic Resonance Imaging 26:914–920.

    https://doi.org/10.1016/j.mri.2008.01.048

    • PubMed
    • Google Scholar
72. 1. Seamari Y
    2. Narváez JA
    3. Vico FJ
    4. Lobo D
    5. Sanchez-Vives MV

    (2007) Robust off- and online separation of intracellularly recorded up and down cortical states

    PLOS ONE 2:e888.

    https://doi.org/10.1371/journal.pone.0000888

    • PubMed
    • Google Scholar
73. 1. Shin TJ
    2. Cho D
    3. Ham J
    4. Choi DH
    5. Kim S
    6. Jeong S
    7. Kim HI
    8. Kim JG
    9. Lee B

    (2016) Changes in thalamo-frontal interaction under different levels of anesthesia in rats

    Neuroscience Letters 627:18–23.

    https://doi.org/10.1016/j.neulet.2016.05.045

    • PubMed
    • Google Scholar
74. 1. Spoormaker VI
    2. Schröter MS
    3. Gleiser PM
    4. Andrade KC
    5. Dresler M
    6. Wehrle R
    7. Sämann PG
    8. Czisch M

    (2010) Development of a large-scale functional brain network during human non-rapid eye movement sleep

    Journal of Neuroscience 30:11379–11387.

    https://doi.org/10.1523/JNEUROSCI.2015-10.2010

    • PubMed
    • Google Scholar
75. 1. Stamatakis EA
    2. Adapa RM
    3. Absalom AR
    4. Menon DK

    (2010) Changes in resting neural connectivity during propofol sedation

    PLOS ONE 5:e14224.

    https://doi.org/10.1371/journal.pone.0014224

    • PubMed
    • Google Scholar
76. 1. Staresina BP
    2. Alink A
    3. Kriegeskorte N
    4. Henson RN

    (2013) Awake reactivation predicts memory in humans

    PNAS 110:21159–21164.

    https://doi.org/10.1073/pnas.1311989110

    • PubMed
    • Google Scholar
77. 1. Steriade M
    2. Dossi RC
    3. Nuñez A

    (1991) Network modulation of a slow intrinsic oscillation of cat thalamocortical neurons implicated in sleep Delta waves: cortically induced synchronization and brainstem cholinergic suppression

    The Journal of Neuroscience 11:3200–3217.

    https://doi.org/10.1523/JNEUROSCI.11-10-03200.1991

    • PubMed
    • Google Scholar
78. 1. Steriade M
    2. Timofeev I
    3. Grenier F

    (2001) Natural waking and sleep states: a view from inside neocortical neurons

    Journal of Neurophysiology 85:1969–1985.

    https://doi.org/10.1152/jn.2001.85.5.1969

    • PubMed
    • Google Scholar
79. 1. Stevner ABA
    2. Vidaurre D
    3. Cabral J
    4. Rapuano K
    5. Nielsen SFV
    6. Tagliazucchi E
    7. Laufs H
    8. Vuust P
    9. Deco G
    10. Woolrich MW
    11. Van Someren E
    12. Kringelbach ML

    (2019) Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

    Nature Communications 10:1035.

    https://doi.org/10.1038/s41467-019-08934-3

    • PubMed
    • Google Scholar
80. 1. Storey JD

    (2002) A direct approach to false discovery rates

    Journal of the Royal Statistical Society: Series B 64:479–498.

    https://doi.org/10.1111/1467-9868.00346

    • Google Scholar
81. 1. Stroh A
    2. Adelsberger H
    3. Groh A
    4. Rühlmann C
    5. Fischer S
    6. Schierloh A
    7. Deisseroth K
    8. Konnerth A

    (2013) Making waves: initiation and propagation of corticothalamic Ca2+ waves in vivo

    Neuron 77:1136–1150.

    https://doi.org/10.1016/j.neuron.2013.01.031

    • PubMed
    • Google Scholar
82. Book

    1. Stroh AE

    (2018) Optogenetics: A Roadmap

    Humana Press.

    https://doi.org/10.1007/978-1-4939-7417-7

    • Google Scholar
83. 1. Tagliazucchi E
    2. Laufs H

    (2014) Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep

    Neuron 82:695–708.

    https://doi.org/10.1016/j.neuron.2014.03.020

    • PubMed
    • Google Scholar
84. 1. Turchi J
    2. Chang C
    3. Ye FQ
    4. Russ BE
    5. Yu DK
    6. Cortes CR
    7. Monosov IE
    8. Duyn JH
    9. Leopold DA

    (2018) The basal forebrain regulates global Resting-State fMRI fluctuations

    Neuron 97:940–952.

    https://doi.org/10.1016/j.neuron.2018.01.032

    • PubMed
    • Google Scholar
85. 1. Valdés-Hernández PA
    2. Sumiyoshi A
    3. Nonaka H
    4. Haga R
    5. Aubert-Vásquez E
    6. Ogawa T
    7. Iturria-Medina Y
    8. Riera JJ
    9. Kawashima R

    (2011) An in vivo MRI template set for morphometry, tissue segmentation, and fMRI localization in rats

    Frontiers in Neuroinformatics 5:26.

    https://doi.org/10.3389/fninf.2011.00026

    • PubMed
    • Google Scholar
86. 1. Vincent JL
    2. Patel GH
    3. Fox MD
    4. Snyder AZ
    5. Baker JT
    6. Van Essen DC
    7. Zempel JM
    8. Snyder LH
    9. Corbetta M
    10. Raichle ME

    (2007) Intrinsic functional architecture in the anaesthetized monkey brain

    Nature 447:83–86.

    https://doi.org/10.1038/nature05758

    • PubMed
    • Google Scholar
87. 1. Vyazovskiy VV
    2. Olcese U
    3. Hanlon EC
    4. Nir Y
    5. Cirelli C
    6. Tononi G

    (2011) Local sleep in awake rats

    Nature 472:443–447.

    https://doi.org/10.1038/nature10009

    • Google Scholar
88. 1. Watts DJ
    2. Strogatz SH

    (1998) Collective dynamics of ‘small-world’ networks

    Nature 393:440–442.

    https://doi.org/10.1038/30918

    • Google Scholar
89. 1. Woodward ND
    2. Rogers B
    3. Heckers S

    (2011) Functional resting-state networks are differentially affected in schizophrenia

    Schizophrenia Research 130:86–93.

    https://doi.org/10.1016/j.schres.2011.03.010

    • PubMed
    • Google Scholar
90. 1. Wu T
    2. Grandjean J
    3. Bosshard SC
    4. Rudin M
    5. Reutens D
    6. Jiang T

    (2017) Altered regional connectivity reflecting effects of different anaesthesia protocols in the mouse brain

    NeuroImage 149:190–199.

    https://doi.org/10.1016/j.neuroimage.2017.01.074

    • PubMed
    • Google Scholar
91. 1. Yin D
    2. Zhang Z
    3. Wang Z
    4. Zeljic K
    5. Lv Q
    6. Cai D
    7. Wang Y
    8. Wang Z

    (2019) Brain map of intrinsic functional flexibility in anesthetized monkeys and awake humans

    Frontiers in Neuroscience 13:174.

    https://doi.org/10.3389/fnins.2019.00174

    • PubMed
    • Google Scholar
92. 1. Zang YF
    2. He Y
    3. Zhu CZ
    4. Cao QJ
    5. Sui MQ
    6. Liang M
    7. Tian LX
    8. Jiang TZ
    9. Wang YF

    (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI

    Brain & Development 29:83–91.

    https://doi.org/10.1016/j.braindev.2006.07.002

    • PubMed
    • Google Scholar
93. 1. Zhang Z
    2. Cai DC
    3. Wang Z
    4. Zeljic K
    5. Wang Z
    6. Wang Y

    (2019) Isoflurane-Induced burst suppression increases intrinsic functional connectivity of the monkey brain

    Frontiers in Neuroscience 13:296.

    https://doi.org/10.3389/fnins.2019.00296

    • PubMed
    • Google Scholar
94. 1. Zou QH
    2. Zhu CZ
    3. Yang Y
    4. Zuo XN
    5. Long XY
    6. Cao QJ
    7. Wang YF
    8. Zang YF

    (2008) An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF

    Journal of Neuroscience Methods 172:137–141.

    https://doi.org/10.1016/j.jneumeth.2008.04.012

    • PubMed
    • Google Scholar
95. 1. Zucca S
    2. D'Urso G
    3. Pasquale V
    4. Vecchia D
    5. Pica G
    6. Bovetti S
    7. Moretti C
    8. Varani S
    9. Molano-Mazón M
    10. Chiappalone M
    11. Panzeri S
    12. Fellin T

    (2017) An inhibitory gate for state transition in cortex

    eLife 6:e26177.

    https://doi.org/10.7554/eLife.26177

    • PubMed
    • Google Scholar

Author details

1. Felipe Aedo-Jury

   1. Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany
   2. Leibniz Institute for Resilience Research, Mainz, Germany

   Contribution

   Conceptualization, Data curation, Writing

   For correspondence

   felipe.aedo@lir-mainz.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8642-6955

2. Miriam Schwalm

   1. Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany
   2. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Formal analysis, Writing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4162-2298

3. Lara Hamzehpour

   Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany

   Contribution

   Data curation, Software, Writing - review and editing

   Competing interests

   No competing interests declared

4. Albrecht Stroh

   1. Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany
   2. Leibniz Institute for Resilience Research, Mainz, Germany

   Contribution

   Conceptualization, Funding acquisition, Writing

   For correspondence

   albrecht.stroh@unimedizin-mainz.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9410-4086

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