TY - JOUR
AU1 - Tu,, Wenyu
AU2 - Ma,, Zilu
AU3 - Ma,, Yuncong
AU4 - Dopfel,, David
AU5 - Zhang,, Nanyin
AB - Abstract The default mode network (DMN) is a principal brain network in the mammalian brain. Although the DMN in humans has been extensively studied with respect to network structure, function, and clinical implications, our knowledge of DMN in animals remains limited. In particular, the functional role of DMN nodes, and how DMN organization relates to DMN-relevant behavior are still elusive. Here we investigated the causal relationship of inactivating a pivotal node of DMN (i.e., dorsal anterior cingulate cortex [dACC]) on DMN function, network organization, and behavior by combining chemogenetics, resting-state functional magnetic resonance imaging (rsfMRI) and behavioral tests in awake rodents. We found that suppressing dACC activity profoundly changed the activity and connectivity of DMN, and these changes were associated with altered DMN-related behavior in animals. The chemo-rsfMRI-behavior approach opens an avenue to mechanistically dissecting the relationships between a specific node, brain network function, and behavior. Our data suggest that, like in humans, DMN in rodents is a functional network with coordinated activity that mediates behavior. awake, default mode network, DREADD, rat, resting-state fMRI Introduction Default mode network (DMN) is a large-scale brain network composed of a group of distributed brain regions (Raichle et al. 2001; Greicius et al. 2003). DMN exhibits high activity at rest (Gusnard, Raichle, et al. 2001), which is believed to support internally oriented processes such as self-referential mental activity in humans (Gusnard, Akbudak, et al. 2001). During externally directed or attention-demanding tasks, DMN activity is suppressed, and the level of DMN suppression is reported to be associated with task performance (Kelly et al. 2008; Hampson et al. 2010). In addition, functional abnormality of DMN has been linked to numerous brain disorders such as schizophrenia and Alzheimer’s disease (Greicius et al. 2004; Whitfield-Gabrieli et al. 2009). Taken together, DMN represents one of the most important functional networks in health and disease. DMN has also been discovered in other species including rodents and primates, suggesting that this network might be well conserved in the mammalian brain (Vincent et al. 2007; Lu et al. 2012; Stafford et al. 2014; Barks et al. 2015; Zhou et al. 2016). However, our knowledge of DMN in animals is mostly limited to their anatomical resemblance to human DMN. For instance, homologs of the medial prefrontal cortex (mPFC), posterior cingulate cortex and orbital frontal cortex, which all anatomically locate along the midline of the brain, are commonly found in DMNs in humans, primates, and rodents (Vincent et al. 2007; Lu et al. 2012; Stafford et al. 2014). Despite this key information, the functional role of individual DMN nodes and how their activity and connectivity architecture relate to DMN-relevant behavior in animals remain elusive. Revealing these relationships is important because it not only helps us understand DMN function from an evolutionary perspective, but also lays the foundation of using animals to model DMN-related brain disorders. These issues in principle can be tackled by manipulating the activity of a DMN node and examining the corresponding changes in DMN network properties and related behavior. This task can be achieved by combining resting-state functional magnetic resonance imaging (rsfMRI), a tool that measures brain-wide resting-state functional connectivity (RSFC), a neural modulation technique like designer receptors exclusively activated by designer drugs (DREADDs), as well as animal behavioral tests. DREADDs allow for reversible in vivo manipulation of neuronal activity of a selective brain region for hours (Armbruster et al. 2007), and rsfMRI provides comprehensive assessment of brain network architecture (Biswal et al. 1995; Liang et al. 2011). This chemo-rsfMRI-behavior approach allows for characterizing DMN reconfiguration as the neural activity of a defined network element is perturbed, and determining how this perturbation leads to behavioral change. Here we investigate the causal impact of suppressing the dorsal anterior cingulate cortex (dACC), a key node in the rat DMN, on DMN activity, organization, and DMN-related behavior using DREADDs and awake rodent rsfMRI established in our lab (Zhang et al. 2010; Ma et al. 2018; Ma and Zhang 2018; Dopfel et al. 2019). The dACC is selected because it is a pivotal node in DMN in multiple species, including rodents (Heilbronner and Hayden 2016; Jing et al. 2017; Nair et al. 2018). Rodent dACC is homologous to Brodmann area 24b in humans (Vogt and Paxinos 2014; Fillinger et al. 2018), which is involved in high order cognitive function such as reward encoding and monitoring, motor control and fear learning (Heilbronner and Hayden 2016). Imaging awake rodents avoids the confounding effects of anesthesia (Liang et al. 2012a; Liang et al. 2015; Gao et al. 2017; Hamilton et al. 2017; Ma et al. 2017), and permits linking imaging data to behavior (Liang et al. 2014; Dopfel et al. 2019). Our data show that suppressing the dACC disrupts activity and connectivity across the whole DMN. The DMN activity/connectivity changes are correlated with altered DMN-related behavior. All these changes are absent in sham rats or when a non-DMN region is suppressed. These data suggest that, like in humans, DMN in rats is a well-organized functional network with coordinated activity that mediates behavior. Materials and Methods Animals Data in the present study were obtained from 44 adult male Long-Evans rats (300–500 g), which were housed in Plexiglas cages with food and water provided ad libitum. The housing room was kept under a 12 h light:12 h dark cycle with the ambient temperature maintained at 22–24°C. All experiments were approved by the Pennsylvania State University Institutional Animal Care and Use Committee. Surgery Aseptic stereotaxic surgeries were performed for viral injections. The rat was first briefly anesthetized with isoflurane, followed by intramuscular (IM) injections of ketamine (40 mg/kg) and xylazine (12 mg/kg) to maintain the anesthesia. In addition, anti-inflammatory drug dexamethasone (0.5 mg/kg) and antibiotics Baytril (2.5 mg/kg) were administered. The animal was then tracheal intubated and ventilated with oxygen (PhysioSuite, Kent Scientific Corporation). During the surgery, the heart rate and SpO2 were monitored using a pulse oximetry (MouseSTAT® Jr, Kent Scientific Corporation). A warming pad (PhysioSuite, Kent Scientific Corporation) was placed underneath the animal to maintain the body temperature. In DREADD groups, AAV8-hSyn-hM4Di-mCherry (1 μL at titer ≥3 × 1012 vg/mL, Addgene, Watertown, MA) was bilaterally injected into the dACC (coordinates: anterior/posterior (AP) +2, medial/lateral (ML) ±0.5, dorsal/ventral (DV) −1; n = 25) and the primary visual cortex (coordinates: AP −6.5, ML ±3.5, DV −1; n = 8), respectively. Sham rats received a control null virus (AAV8-hSyn-GFP, 1 μL at titer ≥3 × 1012 vg/mL, Addgene, Watertown, MA, n = 8) in the dACC. In addition, AAV8-hSyn-hM4Di-mCherry (1 μL at titer ≥3 × 1012 vg/mL, Addgene, Watertown, MA) was unilaterally injected in the superior colliculus (SC; coordinates: AP −7, ML +1.5, DV −3; n = 3) for the visual stimulation experiment. Rats were given at least 4 weeks to recover after surgery. fMRI Experiment To minimize stress and motion during imaging, animals first underwent an acclimation procedure for 7 days. To better adapt the animal to the magnetic resonance imaging (MRI) scanning environment, the daily acclimation period was gradually increased (15 min for day 1, 30 min for day 2, 45 min for day 3, 60 min per day for days 4–7). Details of the acclimation procedure can be found in publications from our lab (Liang et al. 2012b; Dopfel and Zhang 2018) and other groups (Bergmann et al. 2016; Chang et al. 2016; Yoshida et al. 2016). A total of 30 min before imaging, clozapine N-oxide (CNO, 1 mg/kg in saline, dissolved in dimethyl sulfoxide (DMSO), Sigma-Aldrich, St. Louis, MO), or saline (with DMSO) was intraperitoneal (IP) injected. CNO and saline injections were carried out at a random order with at least 3 days apart. All functional magnetic resonance imaging (fMRI) experiments were conducted on a 7 T Bruker 70/30 BioSpec running ParaVision 6.0.1 (Bruker, Billerica, MA) using a birdcage coil at the high field MRI facility at the Pennsylvania State University. T2*-weighted gradient-echo rsfMRI images were obtained using an echo planar imaging sequence with the following parameters: repetition time = 1000 ms; field of view = 3.2 × 3.2 cm2; matrix size = 64 × 64; echo time = 15 ms; flip angle = 60; slice number = 20; slice thickness = 1 mm; 600 volume each run. Three runs were acquired for each rsfMRI session. The number of animals in each experiment is summarized in Supplementary Table S1. rsfMRI Data Analysis rsfMRI data analysis was performed using MATLAB. First, the motion of each rsfMRI volume was assessed using the relative framewise displacement (FD). Volumes with FD > 0.25 mm and their adjacent preceding and following volumes were scrubbed. Additionally, the first 10 volumes of every scan were removed to ensure steady-state magnetic resonance (MR) signal. Any scans with > 15% volumes discarded were excluded from further analysis. Subsequently, data were preprocessed using the pipeline described in our previous publications consisting of co-registration to a defined atlas, motion correction (SPM12), spatial smoothing (Gaussian kernel, full-width at half-maximum = 1 mm), voxelwise nuisance regression of motion parameters and the signals from the white matter and ventricles, as well as bandpass filtering (0.01–0.1 Hz) (Liang et al. 2013; Ma et al. 2018). DMN was identified using group independent component analysis (ICA) with the GIFT toolbox (Group ICA Of fMRI Toolbox, https://trendscenter.org/software/gift/, (Calhoun et al. 2001)). Data were decomposed into 20 components using the infomax algorithm. ICA was repeated 20 times with ICASSO to ensure the reliability of results. A single ICA component was identified as DMN based on its spatial similarity to the rat DMN pattern reported in the literature (Lu et al. 2012; Raichle 2015; Hsu et al. 2016). For each scan, the spatial map and time course of the DMN component were back-reconstructed using dual regression. Voxelwise one sample t-tests on z values were conducted to generate the final map of DMN (P < 0.05, df = 77, linear mixed model, false discovery rate [FDR] corrected). To examine the potential impact of the total component number predefined in the ICA analysis, other component numbers including 14, 15, 16, and 18 were also tested. For each voxel, fractional amplitude of low-frequency fluctuations (fALFF) was calculated by the ratio between the square root of power in the band of 0.01–0.08 Hz and that across the entire frequency range (Zou et al. 2008). Region of interest (ROI)-wise fALFF changes after DREADD inhibition were obtained using two-sample t-tests with a linear mixed model between data acquired after CNO injection versus saline injection. The Linear mixed model was used to take into account both inter-subject and within-subject variances, given that multiple scans were collected in the same animal. We fit the data to a linear mixed model with the formula “Response variable ~ treatment + (1|Rat)” using the MATLAB function “fitlme.” Response variable is fALFF. Treatment is either CNO or saline. Rat is the rat ID. In addition to fALFF, amplitude of low-frequency fluctuations (ALFF) (Zang et al. 2007) and root mean square (RMS) amplitude (i.e., standard deviation) of relative rsfMRI signal change were calculated and tested using the same statistical model. Functional connectivity was obtained by calculating the Pearson correlation coefficients between regionally averaged time courses of each pair of ROIs in DMN. Correlation coefficients were Fisher transformed, averaged across all scans in each group. Two sample t-test was applied using the same linear mixed model with the random effect of rats and the fixed effect of treatments. FDR correction for multiple comparisons (Genovese et al. 2002) was performed using the MATLAB function “mafdr” based on the FDR algorithm reported in (Storey 2002). Behavioral Test Animals were tested in two behavioral sessions with either a CNO (1 mg/kg) or saline injection. The two sessions were arranged at a random order, separated by at least 7 days. Each session was composed of a 45 min homecage test. Before the test, the animal was habituated to the behavioral room for 15 min, followed by the CNO/saline injection. The animal was then put back to its homecage in the behavioral room for 45 min. Behaviors including the total distance traveled, mean speed, time of mobility, and time of immobility for at least 2 s were recorded by an infrared camera and quantified by behavioral tracking software (ANY-maze, Stoelting Co., Wood Dale, IL). Electrophysiology Electrophysiology recordings were conducted in animals with the same inhibitory DREADDs expressed in the SC. Rats were initially anesthetized by IM injections of ketamine (40 mg/kg) and xylazine (12 mg/kg). NeuroNexus 16-channel electrode was slowly inserted to the SC (AP −7, ML: +1.5, DV: −3) to measure the neural response to visual stimulation. The reference wires from the electrode were connected to a stainless screw that was implanted in the cerebellum. The grounding wire was connected to the stereotaxic frame. Before recording, rats were injected with either saline or CNO in each session. During recording, light anesthesia was maintained using isoflurane (~0.75%). Visual stimulation was produced by a blue laser (473 nm, Opto Engine) coupled with an optic fiber, which was placed 5 cm away from the contralateral eye. In each trial, one light flash (100 ms) or five flashes (100 ms each flash, 100 ms inter-flash interval) were presented every 10 s, and each session included 15 trials. The laser light was controlled by a custom Labview program. The electrophysiological signal was sampled at 20 kHz and amplified using a Neuronexus recording system (Neuronexus, Ann Arbor, MI). Electrophysiology signal from the channel displaying the largest evoked spiking response to light stimulation was used in analysis. Data analysis was conducted with custom-written scripts in MATLAB. Raw data were bandpass filtered (multi-unit activity (MUA): 300–3000 Hz, local field potential [LFP]: 3–300 Hz) with the MATLAB functions “butter” and “filtfilt.” A notch filter with the frequencies of 60 ± 0.5 Hz was applied to remove the stationary power line interference. Spikes with the amplitude larger than three times of the standard deviation of electrophysiological signal were detected and clustered at a bin size of 50 ms in peristimulus-timed histograms. The number of spikes detected within 1 s after the onset of light stimulation was calculated and compared between saline and CNO injections using two-sample t-test. In addition, spikes and LFP of spontaneous activity, measured by electrophysiology signal 5 s before the onset of light stimulation were analyzed and compared between saline and CNO injections using two-sample t-test. Spectrograms were generated by Fourier transform of the LFP data using the MATLAB function “spectrogram.” Histology At the end of the experiment, the animal was perfused with saline and then 4% PFA solution. The brain was removed carefully and stored in the solution with 4% paraformaldehyde and 20% sucrose. After fixation, the brain was cut into slices of 60 μm thickness. Fluorescent expression in injection sites was imaged using microscope. Results Experimental animals were stereotactically injected with adeno-associated viruses (AAVs) expressing inhibitory hM4Di with a pan-neuronal synapsin promoter. Sham animals were injected with a control null virus. Animals were given at least 4 weeks for recovery and protein expression (Supplementary Fig. S1) and then subject to electrophysiology recording, rsfMRI scanning, or behavioral testing. 30 min before each experiment, a systematic injection of either CNO or saline was administered. There was no difference in motion levels during awake imaging between CNO and saline conditions in both the DREADD and sham groups (for all six motion parameters, P > 0.4, df = 76 in DREADD rats, and P > 0.2, df = 39, in sham rats). The experimental procedure is summarized in Figure 1. Figure 1 Open in new tabDownload slide Schematic diagram of the experimental paradigm. Figure 1 Open in new tabDownload slide Schematic diagram of the experimental paradigm. DREADDs Suppressed Both Evoked and Spontaneous Neural Activities We first validated the inhibitory effect of DREADDs using a visual stimulation experiment (Fig. 2). AAVs expressing inhibitory DREADDs (AAV8.hSyn.hM4Di.mCherry) were injected into the SC. After recovery and DREADD expression, neural activity in the SC was evoked by visual stimuli (Fig. 2A–D, 1 flash/trial and 5 flashes/trial, 100 ms per flash, 10 sec per trial, 15 trials per session) and recorded by an electrode inserted in the injected site. SC firing rates were significantly reduced 30 min after CNO injection relative to saline injection (two-sample t-tests, 1 flash/trial, t88 = 10.30, P = 8.8 × 10−17, Fig. 2B,D; 5 flashes/trial, t88 = 13.48, P = 4.1 × 10−23, Fig. 2C,D). In addition, spontaneous neural activities including spiking activity and LFP, quantified by the electrophysiology signal 5 sec before the onset of visual stimulation in each trial, were significantly dampened after CNO injection (MUA: t88 = 16.15, P = 1.041 × 10−36; LFP: t88 = 5.84, P = 8.6 × 10−8; Fig. 2E–G). The LFP spectrograms after saline and CNO injections in a representative rat were shown in Supplementary Figure S2. These data collectively confirmed the inhibitory effect of DREADDs on evoked and spontaneous neural activities in rats. Figure 2 Open in new tabDownload slide Validation of the inhibitory effect of DREADDs. (A) Visual stimulation paradigm and histology with the corresponding coordinate and electrode position; spiking activity in the SC in response to (B) one light flash (100 ms/flash) and (C) five flashes (100 ms/flash, 100 ms inter-flash interval) after saline and CNO injections (15 trials per animal, per condition, number of animals = 3); (D) DREADDs suppressed evoked firings (two sample tests, one flash/trial, t88 = 10.30, P = 8.8 × 10−17; five flashes/trial, t88 = 13.48, P = 4.1 × 10−23); (E) DREADDs suppressed spontaneous firings during 5 s before visual stimulation (t88 = 16.15, P = 1.041 × 10−36); (F) averaged spectrograms after saline (left) and CNO (right) injections; and (G) DREADD inhibition reduced LFP power (3–300 Hz) in the SC (t88 = 5.84, P = 8.6 × 10−8). ***P < 0.005. Figure 2 Open in new tabDownload slide Validation of the inhibitory effect of DREADDs. (A) Visual stimulation paradigm and histology with the corresponding coordinate and electrode position; spiking activity in the SC in response to (B) one light flash (100 ms/flash) and (C) five flashes (100 ms/flash, 100 ms inter-flash interval) after saline and CNO injections (15 trials per animal, per condition, number of animals = 3); (D) DREADDs suppressed evoked firings (two sample tests, one flash/trial, t88 = 10.30, P = 8.8 × 10−17; five flashes/trial, t88 = 13.48, P = 4.1 × 10−23); (E) DREADDs suppressed spontaneous firings during 5 s before visual stimulation (t88 = 16.15, P = 1.041 × 10−36); (F) averaged spectrograms after saline (left) and CNO (right) injections; and (G) DREADD inhibition reduced LFP power (3–300 Hz) in the SC (t88 = 5.84, P = 8.6 × 10−8). ***P < 0.005. Suppressing dACC Altered the Activity and Organization of DMN We examined the causal impact of inactivating the dACC on DMN activity and organization in awake rodents. The DMN was mapped using ICA, a method previously established to reveal the spatial pattern of DMN in humans, rats, and mice (Smith et al. 2009; Lu et al. 2012; Stafford et al. 2014; Hsu et al. 2016). Brain regions highlighted in the awake rat DMN included cortical regions of the dACC (i.e., cg1), ventral anterior cingulate cortex (vACC, cg2), dorsal, and ventral mid-cingulate cortex (dMCC and vMCC), retrosplenial cortex (RSC), prelimbic cortex (PL), infralimbic cortex (IL), orbital cortex (Orb), as well as subcortical regions of the basal forebrain (BF) and hippocampus (Hipp), as shown in Figure 3A. This DMN pattern well agreed with the rodent DMN pattern reported in the literature (Lu et al. 2012; Stafford et al. 2014; Hsu et al. 2016). Notably, the BF was found to be a prominent node in the awake rat DMN, which was not observed in other DMN mapping studies in anesthetized rodents (Lu et al. 2012; Stafford et al. 2014; Hsu et al. 2016), but was reported in a recent electrophysiology study, which demonstrated that the BF was a key DMN node regulating DMN-related behavior in awake rats (Nair et al. 2018). Highly consistent DMN patterns were also obtained with different predefined component numbers in ICA analysis (Supplementary Fig. S3). Figure 3 Open in new tabDownload slide Suppressing dACC alters activity and connectivity across DMN. (A) DMN constructed using ICA. DMN activity after (B) saline and (C) CNO injections; (D) ROI-wise fALFF after saline and CNO injections, respectively (*P < 0.05; ***P < 0.005; #P = 0.11; df = 76); (E) RSFC between DMN nodes in the DREADD group after saline and CNO injections, respectively; (F) RSFC changes after dACC inhibition (P < 0.05, df = 76, linear mixed model, FDR corrected, left panel). All connections exhibiting RSFC changes are overlaid on a glass rat brain (right panel). Red represents increased RSFC, whereas blue represents decreased FC. Figure 3 Open in new tabDownload slide Suppressing dACC alters activity and connectivity across DMN. (A) DMN constructed using ICA. DMN activity after (B) saline and (C) CNO injections; (D) ROI-wise fALFF after saline and CNO injections, respectively (*P < 0.05; ***P < 0.005; #P = 0.11; df = 76); (E) RSFC between DMN nodes in the DREADD group after saline and CNO injections, respectively; (F) RSFC changes after dACC inhibition (P < 0.05, df = 76, linear mixed model, FDR corrected, left panel). All connections exhibiting RSFC changes are overlaid on a glass rat brain (right panel). Red represents increased RSFC, whereas blue represents decreased FC. Figure 3B,C showed voxelwise amplitude of DMN activity in DREADD animals after receiving saline and CNO, respectively. DMN activity was quantified using fALFF, defined by voxelwise low-frequency spectrum power (0.01–0.08 Hz) normalized by the full-spectrum power (Zou et al. 2008) of the rsfMRI signal. This well-established method measures the amplitude of regional spontaneous brain activity (Zou et al. 2008). Intriguingly, suppressing one node in the DMN (i.e., dACC) dampened the activity of virtually the entire network, reflected by reduced BOLD fALFF in the dACC, vACC, dMCC, RSC, PL, IL, Hipp, and BF after CNO injection, relative to saline injection, in DREADD rats (Fig. 3D, P < 0.05, df = 76). Consistent results were also obtained using other quantities of rsfMRI amplitude including ALFF and RMS (Supplementary Figs S4 and S5). No region displayed reduced spontaneous activity in sham rats after CNO injection (Fig. 4A–C). In addition, the suppression of a non-DMN node, the primary visual cortex (V1), did not affect fALFF in the DMN (Supplementary Fig. S6). These two experiments suggest that the dampening of DMN activity in dACC-suppressed rats did not result from the off-target effect of CNO or a systematic bias of DREADD suppression. Figure 4 Open in new tabDownload slide DMN activity and connectivity in sham rats. DMN activity, measured by fALFF after (A) saline and (B) CNO injections; (C) ROI-wise fALFF after saline and CNO injections, respectively (*P < 0.05; ***P < 0.005; df = 39). (D) RSFC between DMN nodes in the sham group after saline and CNO injections, respectively; (E) RSFC difference between saline and CNO injections (P < 0.05, df = 39, linear mixed model, FDR corrected, left panel). All connections are overlaid on a glass rat brain (right panel). Red represents increased RSFC. Figure 4 Open in new tabDownload slide DMN activity and connectivity in sham rats. DMN activity, measured by fALFF after (A) saline and (B) CNO injections; (C) ROI-wise fALFF after saline and CNO injections, respectively (*P < 0.05; ***P < 0.005; df = 39). (D) RSFC between DMN nodes in the sham group after saline and CNO injections, respectively; (E) RSFC difference between saline and CNO injections (P < 0.05, df = 39, linear mixed model, FDR corrected, left panel). All connections are overlaid on a glass rat brain (right panel). Red represents increased RSFC. We also calculated RSFC between every pair of DMN nodes in both DREADD and sham rats after saline and CNO injections, respectively (Figs 3E,F and 4D,E). In DREADD rats, multiple cortical nodes displayed significantly reduced RSFC (P < 0.05, df = 76, FDR corrected), whereas the BF showed increased RSFC with the RSC and IL after dACC suppression (Fig. 3F). In contrast, only one connection (Hipp–vACC) showed increased RSFC after CNO injection in sham rats (Fig. 4D,E, P < 0.05, df = 39, FDR corrected). Taken together, these data revealed that DMN was significantly reorganized when dACC activity was suppressed, suggesting that the rodent DMN is a functional network with coordinated activity, and the dACC is a pivotal node in this network. Suppressing dACC Altered DMN-Related Behavior in Animals Given that suppressing dACC significantly altered DMN activity and connectivity in awake rats, we hypothesize that it also changes DMN-related behavior, measured by quiet restfulness. Nair et al. (2018) demonstrated that quiet restfulness in homecage is characteristic behavior to DMN activity, evidenced by elevated neural activity in DMN nodes, including the ACC and BF, during this behavioral state. Robust DMN activation during quiet restfulness was also reported in chimpanzees (Barks et al. 2015). Therefore, we evaluated quiet restfulness in homecage, defined by continuous immobility for at least 2 s, in our animals. After either CNO or saline injection, the rat was put in the homecage for 45 min and video recorded. The animal’s behavior was analyzed using behavioral tracking software (ANY-maze, Stoelting Co., Wood Dale, IL). After CNO injection, quiet restfulness was significantly reduced in the rat (Fig. 5A–E), reflected by significantly lower immobile time for at least 2 s (t26 = 3.991, P = 0.0009), but higher distance traveled (t26 = 4.294, P = 0.0004), mean speed (t26 = 4.811, P = 0.0001), and total mobile time (t26 = 3.991, P = 0.0009). Rats also displayed significantly higher rearing after CNO injection (t26 = 2.80, P = 0.01), likely reflecting an increase in vigilance and/or exploratory behavior. Reduced quiet restfulness remained consistent during the last 15 min of testing (Supplementary Fig. S7), suggesting that these behavioral changes were not due to the environmental change at the beginning of the test. Figure 5 Open in new tabDownload slide Suppressing dACC changed DMN-related behavior. (A) Total distance traveled in homecage (45 min), (B) mean speed, (C) total mobile time, (D) total immobile time for at least 2 s, and (E) total rearing behavior. *P < 0.05; **P < 0.01; ***P < 0.005; df = 26, n = 14. (F) Correlations between changes in dMCC activity and DMN-related behaviors. (G) Correlations between changes in PL activity and DMN-related behaviors. (H) Correlations between changes in functional connectivity and duration of quiet restfulness in the connections of dACC-dMCC, vACC-dMCC, dMCC-PL, dMCC-IL, vMCC-RSC, and vMCC-PL, n = 8. Figure 5 Open in new tabDownload slide Suppressing dACC changed DMN-related behavior. (A) Total distance traveled in homecage (45 min), (B) mean speed, (C) total mobile time, (D) total immobile time for at least 2 s, and (E) total rearing behavior. *P < 0.05; **P < 0.01; ***P < 0.005; df = 26, n = 14. (F) Correlations between changes in dMCC activity and DMN-related behaviors. (G) Correlations between changes in PL activity and DMN-related behaviors. (H) Correlations between changes in functional connectivity and duration of quiet restfulness in the connections of dACC-dMCC, vACC-dMCC, dMCC-PL, dMCC-IL, vMCC-RSC, and vMCC-PL, n = 8. We also examined whether DMN activity and connectivity changes could explain altered DMN-related behavior. In eight animals, both behavioral and imaging data were collected after DREADDs expression. Notably, both data were collected in the awake state, which permits linking results of these two measures. We found that fALFF changes in the dMCC and PL were significantly correlated to changes in all measures of DMN-related behavior (Fig. 5F,G) including distance traveled (dMCC: r = 0.80, P = 0.016; PL: r = 0.79, P = 0.019), mean speed (dMCC: r = 0.85, P = 0.0077; PL: r = 0.84, P = 0.0095), mobile time (dMCC: r = 0.74, P = 0.036; PL: r = 0.80, P = 0.017), and immobile time (dMCC: r = 0.74, P = 0.036; PL: r = 0.80, P = 0.017). In addition, RSFC changes in the connections of dACC–dMCC, vACC–dMCC, dMCC–PL, dMCC–IL, vMCC–RSC, and vMCC–PL were significantly correlated to the change of quiet restfulness across animals (R > 0.71, P < 0.05, Fig. 5H). These data collectively demonstrate that the impact of suppressing the dACC could propagate posteriorly to the mid-cingulate cortex (MCC) as well as inferiorly to the mPFC (i.e., PL & IL) within the DMN, leading to activity and connectivity changes in these regions, and these activity/connectivity changes were associated with alterations in DMN-related behavior in animals (Fig. 6). Figure 6 Open in new tabDownload slide DMN regions (with dashed lines) and connections displaying activity and RSFC changes, respectively, that are associated with changes in DMN-related behavior (inlet) after dACC suppression. Red lines indicate positive correlations, whereas the blue line indicates a negative correlation. Figure 6 Open in new tabDownload slide DMN regions (with dashed lines) and connections displaying activity and RSFC changes, respectively, that are associated with changes in DMN-related behavior (inlet) after dACC suppression. Red lines indicate positive correlations, whereas the blue line indicates a negative correlation. Discussion For over a century, the neuroscience field has been tremendously moved forward by the effort of examining the relationship between the damage of a specific brain region and loss of certain brain function. Classical examples include the discovery of the inferior frontal gyrus in speech production by Paul Broca, and the left posterior temporal cortex in language comprehension by Carl Wernicke. Despite the vital advancement, converging evidence suggests that a large number of complex tasks involve numerous cognitive processes that require integrated activity from multiple brain regions (Liegeois et al. 2019). In the present study, we have extended previous work and investigated functional characteristics of DMN in rodents. By combining DREADDs, rsfMRI, and behavioral test in an awake rodent model, we have mechanistically dissected the causal impact of disabling a key node (i.e., dACC) in rodent DMN on DMN organization and its relevant behavior. We have shown that disrupting the activity of a DMN node profoundly changes the activity and connectivity of the entire network. These changes in brain function are further associated with altered DMN-related behavior. These results reveal that, like in humans, DMN in awake rats is a functionally integrated network that mediates behavior. This study has filled the knowledge gap in functional characteristics of DMN in animals, and laid the foundation of using rodents to model DMN-related brain disorders. Probing the Causal Relationship Between Brain Network Activity and Behavior Although it is well known that brain networks are tightly related to behavior (Liegeois et al. 2019), research on the network-behavior relationship is primarily at a correlational or descriptive level, whereas the causal impact of network perturbation on behavior is rarely investigated. Understanding such impact is critical as network alterations are at the core of psychopathology (Menon 2011; Fornito et al. 2015). For instance, neuroimaging studies demonstrate that abnormalities in DMN activity and connectivity are characteristic to schizophrenia and depression, and these DMN changes are likely responsible for intensive self-reference and impaired attention in patients with schizophrenia, as well as negative rumination in patients with depression (Whitfield-Gabrieli and Ford 2012). However, to mechanistically determine these relationships, a method that allows for manipulating and monitoring network activity and behavior in the same individuals is needed. Given that nodes in a brain network are inter-connected, the network activity/connectivity in theory can be manipulated by controlling the activity of a specific node. To test this notion, here we suppressed the neural activity of a key node of DMN (i.e., dACC) using DREADDs and examined the corresponding changes in DMN properties as well as behavior in awake animals. DREADDs are an ideal tool to use with rsfMRI. This is because RSFC quantification relies on the temporal coherence of spontaneous neural activity, and therefore it typically requires rsfMRI data to be collected at a steady state. Unlike other neural modulation tools like optogenetics, DREADDs can reversibly manipulate activity in a brain region for hours, and thereby induce steady-state changes. Our data demonstrate that when a key node is inactivated, the activity of the entire DMN is altered, and so is the network organization. More importantly, these network-level changes are strongly associated with behavioral alterations. These results collectively suggest that DMN can be manipulated by controlling the activity in one node, and the chemo-rsfMRI-behavior method is a valuable tool for causally determining network-behavior relationship in animals. DREADDs versus Lesion Lesion is also a well-established approach that can induce steady-state changes in the brain. However, there are several advantages of choosing the DREADDs over the lesion method for our research purpose. First, DREADDs allow us to specifically and reversibly inhibit a brain region with minimal damage. In addition, DREADDs can be used to make within-animal comparisons with vehicle controls. On the other hand, lesion-induced long-term damage in brain tissue can decrease metabolism and alter local vascular structure. There can also be excessive blood flow in surrounding regions (Rorden and Karnath 2004). All these factors will impact fMRI data and can confound RSFC results. Furthermore, lesions can have significant off-target effects (Otchy et al. 2015). Long-term lesion studies can be compounded by post-lesion neural plasticity changes resulting from homeostatic compensatory mechanisms (i.e., secondary effects) (Otchy et al. 2015). Notably, most psychiatric disorders are linked to the loss of function without tissue damage, making the DREADD method a better translational model. Rodent DMN—Beyond the Anatomical Resemblance with Other Species Although the DMN in humans has been extensively studied including its network structure, function, and clinical implications, our understanding of the DMN in rodents is limited. The discovery of rodent DMN was mainly based on the anatomical resemblance of the network structure with the DMNs in humans and primates (Lu et al. 2012; Stafford et al. 2014), and yet its functional role in behavior remains unclear. Nair and colleagues showed that the rat BF exhibited pronounced gamma oscillations during DMN-related behavior and this activity was suppressed during active exploration of an unfamiliar environment (Nair et al. 2018). They further demonstrated that the BF controlled DMN-related behavior by affecting neural activity in the ACC. These results suggest that the BF and ACC might be key DMN nodes that regulate DMN activity. These results are highly consistent with our findings. First, we also observed prominent involvement of the BF in the rat DMN at baseline, which was missing in previous DMN mapping studies in anesthetized rodents (Lu et al. 2012; Hsu et al. 2016). Notably, the BF has strong connectivity with the cortex, and these connected regions are substantially overlapped with the DMN revealed in our study (Gielow and Zaborszky 2017; Agostinelli et al. 2019). Second, suppressing the dACC considerably dampened the DMN activity, confirming the critical role of ACC in the rat DMN (Nair et al. 2018). This result also agrees with the report that DMN hubs are mostly fragile (Gollo et al. 2018). Beyond the previous work, our data reveal that changes in DMN-related behavior resulting from dACC suppression were correlated to the activity and connectivity changes in other DMN nodes including MCC and PL. These results collectively support that, like in humans and primates, the DMN in rodents is a functional network with coordinated neural activity from distributed brain regions, and this network might support behavior related to internally oriented brain states. Regulation of DMN Relevant Behavior Might be MEDIATED Through mPFC and MCC in DMN Our data show that dACC suppression led to reduced activities in the PL and dMCC, which were significantly correlated to altered behavior of quiet restfulness. These results hint that the regulation of this behavioral change might be mediated through these two DMN nodes (Fig. 6). Despite strong anatomical connectivity, the MCC is functionally distinct from the ACC according to the cytology (Vogt et al. 1995), basal glucose metabolism (Vogt 2009), and connectivity (Vogt 2009). The MCC is also anatomically overlapped with the rostral cingulate motor area (Vogt 2016), which further projects to the spinal cord, dorsal striatum, as well as the primary motor, premotor, and supplementary motor cortices (Morecraft and Tanji 2009; Shackman et al. 2011). These anatomical structures provide physical basis that links MCC to the function of motor planning (Vogt 2016; Rolls 2019). Similarly, the mPFC is involved in the control of action, likely influenced by internal states (Gusnard, Akbudak, et al. 2001a; Miller and Cohen 2001; Miller and D'Esposito 2005). In rodents, the dorsal mPFC including PL can exert an inhibitory effect on the motor cortex, which is linked to controlling motion execution (Narayanan et al. 2006). Lesion or inactivation of PL leads to inappropriate response to cues (Kolb 1984; Risterucci et al. 2003; Narayanan et al. 2006). Given that there is no monosynaptic connection between the PL and motor cortex (Vogt et al. 1995), the PL-to-motor cortex control is likely mediated through the MCC (Rolls 2019). These studies all highlight the critical role of PL and MCC in motion control. This notion is further corroborated by our observation that reduced activity in both MCC and PL activity is associated with increased mobile time. Importantly, it has been found that DMN controls the transition from a resting state to a movement state via functional coupling with sensorimotor cortex (Treserras et al. 2009; Bazan et al. 2015). Taken together, our data suggest that the regulation of quiet restfulness is possibly mediated through MCC and mPFC. Potential Pitfalls There are a few potential pitfalls in the present study. First, we cannot exclude the involvement of upstream and/or downstream regions of the dACC in the observed effects. In the present study, we used AAV8 and allowed for at least 4 weeks for virus expression. It is likely that viral vectors transported retrogradely and anterogradely (Aschauer et al. 2013; Castle et al. 2014; Smith et al. 2016). Since CNO was injected systematically, upstream and/or downstream regions could be involved in DMN activity/connectivity and behavioral changes. Therefore, our main findings here are that suppressing dACC is sufficient, but may not be necessary to modulate the DMN activity and DMN-related behavior, and all these changes are essentially a brain-network phenomenon. Second, several studies have reported possible pharmacological off-target effects of CNO and its metabolites at relatively high doses (e.g., 10 mg/kg) (MacLaren et al. 2016; Gomez et al. 2017). Our study minimized these potential off-target effects in three major aspects. First, CNO was used at a relatively low dose (1 mg/kg). This dosage was found to ensure successful activation of DREADDs with negligible off-target effects (Baerentzen et al. 2019). Second, we used a sham group with a control null virus. All results in the sham group were reported and compared with those from the experimental group. These data demonstrated that CNO had minimal effects on fMRI signals and functional connectivity. Third, we also used a second control group with DREADD virus infused in a non-DMN region following the same procedures. Our data show that the suppression of a non-DMN node did not affect fALFF or FC in the DMN (Supplementary Fig. S6), ruling out any systematic bias of CNO in our results. The third potential pitfall is the usage of DMSO. DMSO vehicle was shown to have hemodynamic effects, which can compound the fMRI signal (Levett et al. 1987; Giorgi et al. 2017). The main purpose of using DMSO in the present study is to facilitate dissolving CNO powder into saline (Allen et al. 2019). Notably, we only used 0.5% DMSO when making the CNO solution, and this concentration was considerably lower than those used in studies reporting hemodynamic effects (Levett et al. 1987). For example, the study by Levett et al. (1987) used a bonus injection at 500 mg/kg followed with a 40-60 mL/h infusion. In addition, in those studies the hemodynamic effect started to exhibit 1 h after injection (Levett et al. 1987), whereas our experiment was already completed within 1 h. More importantly, we also added the same concentration of DMSO in saline solution in the vehicle group to control for the solvent effects. Therefore, the reported results from the experimental group should not be ascribed to the hemodynamic effects of DMSO. Summary In the present study, we applied the chemo-rsfMRI-behavior approach to investigate how perturbation of one node in DMN alters network activity, connectivity, and relevant behavior in awake rats. Given the cell-type specificity of DREADDs, this approach can be useful for further investigating the functional role of individual cell populations (e.g., excitatory neurons) in each DMN node in future studies. In addition, this study confirms that rodents can be useful as a translatable preclinical model to study DMN-related brain disorders. Funding National Institute of Neurological Disorders and Stroke (grant R01NS085200 to N.Z.); National Institute of Mental Health (grant RF1MH114224 to N.Z.). Notes We would like to thank Yikang Liu for his technical support. Conflict of Interest None declared. Conflict of Interest None declared. References Agostinelli LJ , Geerling JC, Scammell TE. 2019 . Basal forebrain subcortical projections . Brain Struct Funct . 224 : 1097 – 1117 . Google Scholar Crossref Search ADS PubMed WorldCat Allen DC , Carlson TL, Xiong Y, Jin J, Grant KA, Cuzon Carlson VC. 2019 . A comparative study of the pharmacokinetics of clozapine N-oxide and clozapine N-oxide hydrochloride salt in rhesus macaques . J Pharmacol Exp Ther . 368 : 199 – 207 . Google Scholar Crossref Search ADS PubMed WorldCat Armbruster BN , Li X, Pausch MH, Herlitze S, Roth BL. 2007 . Evolving the lock to fit the key to create a family of G protein-coupled receptors potently activated by an inert ligand . Proc Natl Acad Sci U S A . 104 : 5163 – 5168 . Google Scholar Crossref Search ADS PubMed WorldCat Aschauer DF , Kreuz S, Rumpel S. 2013 . Analysis of transduction efficiency, tropism and axonal transport of AAV serotypes 1, 2, 5, 6, 8 and 9 in the mouse brain . PLoS One . 8 :e76310. Google Scholar OpenURL Placeholder Text WorldCat Baerentzen S , Casado-Sainz A, Lange D, Shalgunov V, Tejada IM, Xiong M, L'Estrade ET, Edgar FG, Lee H, Herth MM, et al. 2019 . The chemogenetic receptor ligand clozapine N-oxide induces in vivo neuroreceptor occupancy and reduces striatal glutamate levels . Front Neurosci . 13 : 187 . Google Scholar Crossref Search ADS PubMed WorldCat Barks SK , Parr LA, Rilling JK. 2015 . The default mode network in chimpanzees (pan troglodytes) is similar to that of humans . Cereb Cortex . 25 : 538 – 544 . Google Scholar Crossref Search ADS PubMed WorldCat Bazan PR , Biazoli CE Jr, Sato JR, Amaro E Jr. 2015 . Motor readiness increases brain connectivity between default-mode network and motor cortex: impact on sampling resting periods from fMRI event-related studies . Brain Connect . 5 : 631 – 640 . Google Scholar Crossref Search ADS PubMed WorldCat Bergmann E , Zur G, Bershadsky G, Kahn I. 2016 . The Organization of Mouse and Human Cortico-Hippocampal Networks Estimated by intrinsic functional connectivity . Cereb Cortex . 26 : 4497 – 4512 . Google Scholar Crossref Search ADS PubMed WorldCat Biswal B , Yetkin FZ, Haughton VM, Hyde JS. 1995 . Functional connectivity in the motor cortex of resting human brain using echo-planar MRI . Magn Reson Med . 34 : 537 – 541 . Google Scholar Crossref Search ADS PubMed WorldCat Calhoun VD , Adali T, Pearlson GD, Pekar JJ. 2001 . A method for making group inferences from functional MRI data using independent component analysis . Hum Brain Mapp . 14 : 140 – 151 . Google Scholar Crossref Search ADS PubMed WorldCat Castle MJ , Gershenson ZT, Giles AR, Holzbaur EL, Wolfe JH. 2014 . Adeno-associated virus serotypes 1, 8, and 9 share conserved mechanisms for anterograde and retrograde axonal transport . Hum Gene Ther . 25 : 705 – 720 . Google Scholar Crossref Search ADS PubMed WorldCat Chang PC , Procissi D, Bao Q, Centeno MV, Baria A, Apkarian AV. 2016 . Novel method for functional brain imaging in awake minimally restrained rats . J Neurophysiol . 116 : 61 – 80 . Google Scholar Crossref Search ADS PubMed WorldCat Dopfel D , Perez PD, Verbitsky A, Bravo-Rivera H, Ma Y, Quirk GJ, Zhang N. 2019 . Individual variability in behavior and functional networks predicts vulnerability using an animal model of PTSD . Nat Commun . 10 : 2372 . Google Scholar Crossref Search ADS PubMed WorldCat Dopfel D , Zhang N. 2018 . Mapping stress networks using functional magnetic resonance imaging in awake animals . Neurobiol Stress . 9 : 251 – 263 . Google Scholar Crossref Search ADS PubMed WorldCat Fillinger C , Yalcin I, Barrot M, Veinante P. 2018 . Efferents of anterior cingulate areas 24a and 24b and midcingulate areas 24a' and 24b' in the mouse . Brain Struct Funct . 223 : 1747 – 1778 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Fornito A , Zalesky A, Breakspear M. 2015 . The connectomics of brain disorders . Nat Rev Neurosci . 16 : 159 – 172 . Google Scholar Crossref Search ADS PubMed WorldCat Gao YR , Ma Y, Zhang Q, Winder AT, Liang Z, Antinori L, Drew PJ, Zhang N. 2017 . Time to wake up: studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal . Neuroimage . 153 : 382 – 398 . Google Scholar Crossref Search ADS PubMed WorldCat Genovese CR , Lazar NA, Nichols T. 2002 . Thresholding of statistical maps in functional neuroimaging using the false discovery rate . Neuroimage . 15 : 870 – 878 . Google Scholar Crossref Search ADS PubMed WorldCat Gielow MR , Zaborszky L. 2017 . The input-output relationship of the cholinergic basal forebrain . Cell Rep . 18 : 1817 – 1830 . Google Scholar Crossref Search ADS PubMed WorldCat Giorgi A , Migliarini S, Galbusera A, Maddaloni G, Mereu M, Margiani G, Gritti M, Landi S, Trovato F, Bertozzi SM, et al. 2017 . Brain-wide mapping of endogenous serotonergic transmission via chemogenetic fMRI . Cell Rep . 21 : 910 – 918 . Google Scholar Crossref Search ADS PubMed WorldCat Gollo LL , Roberts JA, Cropley VL, Di Biase MA, Pantelis C, Zalesky A, Breakspear M. 2018 . Fragility and volatility of structural hubs in the human connectome . Nat Neurosci . 21 : 1107 – 1116 . Google Scholar Crossref Search ADS PubMed WorldCat Gomez JL , Bonaventura J, Lesniak W, Mathews WB, Sysa-Shah P, Rodriguez LA, Ellis RJ, Richie CT, Harvey BK, Dannals RF, et al. 2017 . Chemogenetics revealed: DREADD occupancy and activation via converted clozapine . Science . 357 : 503 – 507 . Google Scholar Crossref Search ADS PubMed WorldCat Greicius MD , Krasnow B, Reiss AL, Menon V. 2003 . Functional connectivity in the resting brain: a network analysis of the default mode hypothesis . Proc Natl Acad Sci U S A . 100 : 253 – 258 . Google Scholar Crossref Search ADS PubMed WorldCat Greicius MD , Srivastava G, Reiss AL, Menon V. 2004 . Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI . Proc Natl Acad Sci U S A . 101 : 4637 – 4642 . Google Scholar Crossref Search ADS PubMed WorldCat Gusnard DA , Akbudak E, Shulman GL, Raichle ME. 2001a . Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function . Proc Natl Acad Sci U S A . 98 : 4259 – 4264 . Google Scholar Crossref Search ADS WorldCat Gusnard DA , Raichle ME, Raichle ME. 2001b . Searching for a baseline: functional imaging and the resting human brain . Nat Rev Neurosci . 2 : 685 – 694 . Google Scholar Crossref Search ADS WorldCat Hamilton C , Ma Y, Zhang N. 2017 . Global reduction of information exchange during anesthetic-induced unconsciousness . Brain Struct Funct . 222 : 3205 – 3216 . Google Scholar Crossref Search ADS PubMed WorldCat Hampson M , Driesen N, Roth JK, Gore JC, Constable RT. 2010 . Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance . Magn Reson Imaging . 28 : 1051 – 1057 . Google Scholar Crossref Search ADS PubMed WorldCat Heilbronner SR , Hayden BY. 2016 . Dorsal anterior cingulate cortex: a bottom-up view . Annu Rev Neurosci . 39 : 149 – 170 . Google Scholar Crossref Search ADS PubMed WorldCat Hsu LM , Liang X, Gu H, Brynildsen JK, Stark JA, Ash JA, Lin CP, Lu H, Rapp PR, Stein EA, et al. 2016 . Constituents and functional implications of the rat default mode network . Proc Natl Acad Sci U S A . 113 : E4541 – E4547 . Google Scholar Crossref Search ADS PubMed WorldCat Jing W , Guo D, Zhang Y, Guo F, Valdes-Sosa PA, Xia Y, Yao D. 2017 . Reentrant information flow in electrophysiological rat default mode network . Front Neurosci . 11 : 93 . Google Scholar Crossref Search ADS PubMed WorldCat Kelly AM , Uddin LQ, Biswal BB, Castellanos FX, Milham MP. 2008 . Competition between functional brain networks mediates behavioral variability . Neuroimage . 39 : 527 – 537 . Google Scholar Crossref Search ADS PubMed WorldCat Kolb B . 1984 . Functions of the frontal cortex of the rat: a comparative review . Brain Res . 320 : 65 – 98 . Google Scholar Crossref Search ADS PubMed WorldCat Levett JM , Johns LM, Grina NM, Mullan BF, Kramer JF, Mullan JF. 1987 . Effects of dimethyl sulfoxide on systemic and cerebral hemodynamic variables in the ischemic canine myocardium . Crit Care Med . 15 : 656 – 660 . Google Scholar Crossref Search ADS PubMed WorldCat Liang Z , King J, Zhang N. 2011 . Uncovering intrinsic connectional architecture of functional networks in awake rat brain . J Neurosci . 31 : 3776 – 3783 . Google Scholar Crossref Search ADS PubMed WorldCat Liang Z , King J, Zhang N. 2012a . Anticorrelated resting-state functional connectivity in awake rat brain . Neuroimage . 59 : 1190 – 1199 . Google Scholar Crossref Search ADS WorldCat Liang Z , King J, Zhang N. 2012b . Intrinsic organization of the anesthetized brain . J Neurosci . 32 : 10183 – 10191 . Google Scholar Crossref Search ADS WorldCat Liang Z , King J, Zhang N. 2014 . Neuroplasticity to a single-episode traumatic stress revealed by resting-state fMRI in awake rats . Neuroimage . 103 : 485 – 491 . Google Scholar Crossref Search ADS PubMed WorldCat Liang Z , Li T, King J, Zhang N. 2013 . Mapping thalamocortical networks in rat brain using resting-state functional connectivity . Neuroimage . 83 : 237 – 244 . Google Scholar Crossref Search ADS PubMed WorldCat Liang Z , Liu X, Zhang N. 2015 . Dynamic resting state functional connectivity in awake and anesthetized rodents . Neuroimage . 104 : 89 – 99 . Google Scholar Crossref Search ADS PubMed WorldCat Liegeois R , Li J, Kong R, Orban C, Van De Ville D, Ge T, Sabuncu MR, BTT Y. 2019 . Resting brain dynamics at different timescales capture distinct aspects of human behavior . Nat Commun . 10 :2317. Google Scholar OpenURL Placeholder Text WorldCat Lu H , Zou Q, Gu H, Raichle ME, Stein EA, Yang Y. 2012 . Rat brains also have a default mode network . Proc Natl Acad Sci U S A . 109 : 3979 – 3984 . Google Scholar Crossref Search ADS PubMed WorldCat Ma Y , Hamilton C, Zhang N. 2017 . Dynamic connectivity patterns in conscious and unconscious brain . Brain Connect . 7 : 1 – 12 . Google Scholar Crossref Search ADS PubMed WorldCat Ma Z , Perez P, Ma Z, Liu Y, Hamilton C, Liang Z, Zhang N. 2018 . Functional atlas of the awake rat brain: a neuroimaging study of rat brain specialization and integration . Neuroimage . 170 : 95 – 112 . Google Scholar Crossref Search ADS PubMed WorldCat Ma Z , Zhang N. 2018 . Temporal transitions of spontaneous brain activity . Elife . 7 :e33562 Google Scholar OpenURL Placeholder Text WorldCat MacLaren DA , Browne RW, Shaw JK, Krishnan Radhakrishnan S, Khare P, Espana RA, Clark SD. 2016 . Clozapine N-oxide administration produces behavioral effects in Long-Evans rats: implications for designing DREADD experiments . eNeuro . 3 :ENEURO.0219-16.2016. Google Scholar OpenURL Placeholder Text WorldCat Menon V . 2011 . Large-scale brain networks and psychopathology: a unifying triple network model . Trends Cogn Sci . 15 : 483 – 506 . Google Scholar Crossref Search ADS PubMed WorldCat Miller BT , D'Esposito M. 2005 . Searching for "the top" in top-down control . Neuron . 48 : 535 – 538 . Google Scholar Crossref Search ADS PubMed WorldCat Miller EK , Cohen JD. 2001 . An integrative theory of prefrontal cortex function . Annu Rev Neurosci . 24 : 167 – 202 . Google Scholar Crossref Search ADS PubMed WorldCat Morecraft RJ , Tanji J. 2009 . Cingulofrontal Interactions and the Cingulate Motor Areas. In: Vogt BA (Ed.), Cingulate neurobiology and disease . New York : Oxford University Press , 113 – 144 . Nair J , Klaassen AL, Arato J, Vyssotski AL, Harvey M, Rainer G. 2018 . Basal forebrain contributes to default mode network regulation . Proc Natl Acad Sci U S A . 115 : 1352 – 1357 . Google Scholar Crossref Search ADS PubMed WorldCat Narayanan NS , Horst NK, Laubach M. 2006 . Reversible inactivations of rat medial prefrontal cortex impair the ability to wait for a stimulus . Neuroscience . 139 : 865 – 876 . Google Scholar Crossref Search ADS PubMed WorldCat Otchy TM , Wolff SB, Rhee JY, Pehlevan C, Kawai R, Kempf A, Gobes SM, Olveczky BP. 2015 . Acute off-target effects of neural circuit manipulations . Nature . 528 : 358 – 363 . Google Scholar Crossref Search ADS PubMed WorldCat Raichle ME . 2015 . The brain's default mode network . Annu Rev Neurosci . 38 : 433 – 447 . Google Scholar Crossref Search ADS PubMed WorldCat Raichle ME , MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. 2001 . A default mode of brain function . Proc Natl Acad Sci U S A . 98 : 676 – 682 . Google Scholar Crossref Search ADS PubMed WorldCat Risterucci C , Terramorsi D, Nieoullon A, Amalric M. 2003 . Excitotoxic lesions of the prelimbic-infralimbic areas of the rodent prefrontal cortex disrupt motor preparatory processes . Eur J Neurosci . 17 : 1498 – 1508 . Google Scholar Crossref Search ADS PubMed WorldCat Rolls ET . 2019 . The cingulate cortex and limbic systems for emotion, action, and memory . Brain Struct Funct . 224 : 3001 – 3018 . Google Scholar Crossref Search ADS PubMed WorldCat Rorden C , Karnath HO. 2004 . Using human brain lesions to infer function: a relic from a past era in the fMRI age? Nat Rev Neurosci . 5 : 813 – 819 . Google Scholar Crossref Search ADS PubMed WorldCat Shackman AJ , Salomons TV, Slagter HA, Fox AS, Winter JJ, Davidson RJ. 2011 . The integration of negative affect, pain and cognitive control in the cingulate cortex . Nat Rev Neurosci . 12 : 154 – 167 . Google Scholar Crossref Search ADS PubMed WorldCat Smith KS , Bucci DJ, Luikart BW, Mahler SV. 2016 . DREADDS: use and application in behavioral neuroscience . Behav Neurosci . 130 : 137 – 155 . Google Scholar Crossref Search ADS PubMed WorldCat Smith SM , Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, et al. 2009 . Correspondence of the brain's functional architecture during activation and rest . Proc Natl Acad Sci U S A . 106 : 13040 – 13045 . Google Scholar Crossref Search ADS PubMed WorldCat Stafford JM , Jarrett BR, Miranda-Dominguez O, Mills BD, Cain N, Mihalas S, Lahvis GP, Lattal KM, Mitchell SH, David SV, et al. 2014 . Large-scale topology and the default mode network in the mouse connectome . Proc Natl Acad Sci U S A . 111 : 18745 – 18750 . Google Scholar Crossref Search ADS PubMed WorldCat Storey JD . 2002 . A direct approach to false discovery rates . J R Stat Soc Series B Stat Methodology . 64 : 479 – 498 . Google Scholar Crossref Search ADS WorldCat Treserras S , Boulanouar K, Conchou F, Simonetta-Moreau M, Berry I, Celsis P, Chollet F, Loubinoux I. 2009 . Transition from rest to movement: brain correlates revealed by functional connectivity . Neuroimage . 48 : 207 – 216 . Google Scholar Crossref Search ADS PubMed WorldCat Vincent JL , Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME. 2007 . Intrinsic functional architecture in the anaesthetized monkey brain . Nature . 447 : 83 – 86 . Google Scholar Crossref Search ADS PubMed WorldCat Vogt BA . 2009 . Regions and subregions of the cingulate cortex . Oxford : Oxford University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Vogt BA . 2016 . Midcingulate cortex: structure, connections, homologies, functions and diseases . J Chem Neuroanat . 74 : 28 – 46 . Google Scholar Crossref Search ADS PubMed WorldCat Vogt BA , Nimchinsky EA, Vogt LJ, Hof PR. 1995 . Human cingulate cortex: surface features, flat maps, and cytoarchitecture . J Comp Neurol . 359 : 490 – 506 . Google Scholar Crossref Search ADS PubMed WorldCat Vogt BA , Paxinos G. 2014 . Cytoarchitecture of mouse and rat cingulate cortex with human homologies . Brain Struct Funct . 219 : 185 – 192 . Google Scholar Crossref Search ADS PubMed WorldCat Whitfield-Gabrieli S , Ford JM. 2012 . Default mode network activity and connectivity in psychopathology . Annu Rev Clin Psychol . 8 : 49 – 76 . Google Scholar Crossref Search ADS PubMed WorldCat Whitfield-Gabrieli S , Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, Shenton ME, Green AI, Nieto-Castanon A, LaViolette P, et al. 2009 . Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia . Proc Natl Acad Sci U S A . 106 : 1279 – 1284 . Google Scholar Crossref Search ADS PubMed WorldCat Yoshida K , Mimura Y, Ishihara R, Nishida H, Komaki Y, Minakuchi T, Tsurugizawa T, Mimura M, Okano H, Tanaka KF, et al. 2016 . Physiological effects of a habituation procedure for functional MRI in awake mice using a cryogenic radiofrequency probe . J Neurosci Methods . 274 : 38 – 48 . Google Scholar Crossref Search ADS PubMed WorldCat Zang YF , He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, Tian LX, Jiang TZ, Wang YF. 2007 . Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI . Brain Dev . 29 : 83 – 91 . Google Scholar Crossref Search ADS PubMed WorldCat Zhang N , Rane P, Huang W, Liang Z, Kennedy D, Frazier JA, King J. 2010 . Mapping resting-state brain networks in conscious animals . J Neurosci Methods . 189 : 186 – 196 . Google Scholar Crossref Search ADS PubMed WorldCat Zhou ZC , Salzwedel AP, Radtke-Schuller S, Li Y, Sellers KK, Gilmore JH, Shih YY, Frohlich F, Gao W. 2016 . Resting state network topology of the ferret brain . Neuroimage . 143 : 70 – 81 . Google Scholar Crossref Search ADS PubMed WorldCat Zou QH , Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, Wang YF, Zang YF. 2008 . An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF . J Neurosci Methods . 172 : 137 – 141 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Suppressing Anterior Cingulate Cortex Modulates Default Mode Network and Behavior in Awake Rats
JF - Cerebral Cortex
DO - 10.1093/cercor/bhaa227
DA - 2021-01-01
UR - https://www.deepdyve.com/lp/oxford-university-press/suppressing-anterior-cingulate-cortex-modulates-default-mode-network-gLqz4SwP8F
SP - 312
EP - 323
VL - 31
IS - 1
DP - DeepDyve
ER -