Norepinephrine-mediated arousal fluctuations drive inverted U-shaped functional connectivity dynamics

Domain: physics
Published: 2025-12-13
Editorial outcome: minor_revision

Abstract
Arousal states dynamically shape brain function and behavioral performance, as posited by the Yerkes-Dodson law. Yet, functional network substrates underlying this inverted U-shaped pattern remain unknown. Here, by integrating functional magnetic resonance imaging (fMRI) with simultaneous electroencephalography (EEG) across humans and awake mice, we found arousal modulated inverted U-shaped global functional connectivity (FC) dynamics, peaking at middle arousal level. Such inverted U-shaped FC exhibited significant correlation with arousal modulated behavioral performance, recapitulating the Yerkes-Dodson framework at the functional network level. Further combining invasive multimodal neural recording and manipulations of locus coeruleus-norepinephrine (LC-NE) neurons with awake mouse EEG-fMRI, we revealed the causal contribution of LC-NE system to arousal modulated FC dynamics, in which the inverted U-shaped pattern was dependent on the baseline arousal level. To summarize, we uncovered the functional network basis of the Yerkes-Dodson law, which was causally driven by the NE-mediated arousal fluctuations.

Introduction
Arousal states affect how we process information and respond to our environment with profound and complex regulatory effects on global brain dynamics1. The Yerkes-Dodson law2 posits that human moderate arousal optimizes decision-making and sensory integration, whereas both hypo- or hyper-arousal impair behavioral performance. Rodent studies also revealed behavioral performance peaked at moderate arousal level and degraded at extreme arousal levels in various behavioral paradigms, such as the detection task3,4. This cross-species conserved behavioral principle underscored arousal’s dual role as both facilitator and disruptor of adaptive functions. However, the underlying functional network substrates are yet to be clearly undefined. Crucially, whether this behavioral principle arises from arousal-dependent reconfiguration of whole-brain functional networks, and whether such dynamics persist across both resting and task-evoked states, remain unclear.

Extensive studies have examined the relationship between arousal and functional networks, highlighting the arousal-dependent functional network dynamics5–12. However, contradictory results of arousal effects on functional networks dynamics have been reported. Human functional magnetic resonance imaging (fMRI) studies revealed increased whole-brain functional connectivity (FC) with decreased arousal level from eye-open to eye-closed state5–8. In contrast, human fMRI studies with simultaneous electroencephalography (EEG) recording showed strengthened whole-brain FC with increased arousal from non-rapid eye movement (NREM) sleep to wakefulness9–11. Nevertheless, our awake marmoset fMRI work identified inverted U-shaped FC gradient dynamics with increasing arousal, indexed by both pupil size and fMRI-based index12. This discrepancy likely stemmed from heterogeneous arousal quantification methods, including pupillometry5–8, EEG spectra9–11, and fMRI-based arousal index12. Such diverse quantification of arousal levels renders above results not quantitatively comparable. Furthermore, for vast majority of fMRI studies lacking arousal monitoring, unaccounted arousal fluctuations may confound interpretations of FC alterations13, therefore demanding further exploration and clarification of arousal effects on FC dynamics.

One of potential regulators for mediating the arousal effects on FC dynamics is the locus coeruleus-norepinephrine (LC-NE) system14, which is the predominant noradrenergic source in mammalian brain15–18. LC-NE neurons exhibit both tonic and phasic firing patterns that modulate attention and arousal. Several studies have proposed that variations along these modes can contribute to an inverted U-shaped relation between noradrenergic drive and performance14,15,19. However, the extent, mechanisms, and generalizability of this relationship remain debated and likely depend on task context, receptor engagement, and brain state. Moreover, rodent fMRI combining precise neuromodulation techniques, such as optogenetics and chemogenetics, provides a platform to probe the causal relationship between LC-NE system and FC dynamics. After chemogenetic activation of LC-NE neurons in anesthetized mice20,21, significant increase of FC was observed. Nevertheless, how LC-NE modulated FC increases observed under low-arousal anesthesia mapped onto the inverted U-shaped relationship between arousal and behavior in the awake state remains unclear.

Therefore, it is crucial to establish the causal relationship among LC-NE system, arousal, and whole-brain functional networks. In this study, combining awake mouse and human fMRI in resting and task-evoked states with simultaneous EEG, i.e., gold standard to quantify arousal level, we revealed arousal modulated inverted U-shaped global FC across both species and behavioral states, exhibiting significant correlation with behavioral performance with arousal fluctuations. When causally up and down regulating LC-NE neuronal activity using optogenetics and chemogenetics fMRI, we demonstrated LC-NE system causally drove arousal-dependent FC dynamics, and such NE effect on global FC dynamics was dependent on the baseline arousal level and the direction of arousal-FC change. In summary, by integrating cross-species EEG-fMRI with causal LC-NE regulations, we demonstrated NE mediated arousal fluctuations drive inverted U-shaped functional connectivity dynamics. Our work outlined a cross-species, baseline-dependent inverted U-shaped relationship between arousal and global FC, with LC-NE activity serving as a causal driver of FC dynamics.

Results
Arousal modulated inverted U-shaped whole-brain FC in human resting state and task-evoked state
To dissect the role of arousal fluctuations in shaping whole-brain functional network dynamics and behavioral performance, we first utilized the openly accessible human detection task fMRI dataset22 (Fig. 1A). Participants were engaged in a spatial detection task to quickly determine the location of high-salience image22. Pupil size was simultaneously acquired and used as a behavioral arousal index (Fig. 1A). After our highly optimized fMRI denoising preprocessing12,23,24 (Supplementary Fig. 1) and regressing out the task block structure from BOLD signals, we employed the dynamic conditional correlation (DCC) approach25 to estimate whole-brain dynamic FC. Then, whole-brain dynamic FC matrices were binned into 8 intervals according to the pupil-based arousal index, ranging from low to high arousal levels (Supplementary Fig. 2). As expected, the reaction time during behavioral tasks peaked at the middle arousal level (Fig. 1B), whereas a weak but still significant inverted U-shaped trend of performance accuracy along with increasing pupil size. Importantly, arousal fluctuations showed an inverted U-shaped relationship with global FC changes (Fig. 1C). Moreover, such arousal-modulated inverted U-shaped FC dynamics were significantly correlated with participants’ behavioral performance (Fig. 1D, E), suggesting the arousal-modulated inverted U-shaped whole-brain FC could serve as an attractive indicator for explaining variations in task-related behavioral performance.

To explore whether the inverted U-shaped relationship also existed in resting state fMRI, we utilized the simultaneous EEG-fMRI dataset of human natural sleep26 with behavioral monitoring through eye-tracking and button-pressing (Fig. 1F). Continuous EEG-fMRI recording revealed substantial arousal fluctuations spanning awake and NREM sleep states, accompanied by notable fMRI signal dynamics (Supplementary Fig. 2). To quantify the arousal level, the ratio of the power in the alpha band (7–13Hz) to the theta band (3–7Hz) was defined as the human EEG-based arousal index27, achieving a refined second-by-second arousal estimation. Considering the potential systematic hemodynamic differences in the sleep-wake cycle28–30, only EEG-fMRI data in awake state was included in analysis. Dynamic FC matrices were binned into 30 intervals based on the EEG-based arousal index, ranging from low (drowsy) to high (alert) arousal levels (Supplementary Fig. 2). The matrices within each bin were averaged to determine global FC (Fig. 1G). Along with increasing arousal level, the monotonic elevation of pupil size and button-pressing rate, i.e., external behavioral proxy of arousal, demonstrated the accuracy and validity of our EEG-based arousal index (Fig. 1G, H). Notably, the global FC exhibited an inverted U-shaped relationship with arousal (Fig. 1G, H, Averaged global FC: Low v.s. Middle, p=0.0014; Middle v.s. High, p=0.0130; Low v.s. High, p=0.6601; Pupil size: Low v.s. Middle, p=0.0042; Middle v.s. High, p=0.0102; Low v.s. High, p=0.0024; Button-press rate: Low v.s. Middle, p=0.0220; Middle v.s. High, p=0.2864; Low v.s. High, p=0.0427). Such inverted U-shaped arousal-FC relationship, i.e., increasing from low to middle arousal level but decreasing at higher arousal level (Fig. 1G, H), resembled that during human detection task (Fig. 1C). Region-wise comparison showed predominately global, yet regionally differentiated, spatial patterns (Fig. 1I), which was in line with the inverted U-shaped pattern for global FC (Fig. 1G).

Of note, head motion could potentially bias estimates of arousal-FC relationship if not treated carefully. After applying our preprocessing pipeline, i.e., “6rp+6Δrp+MPCs” regression, we observed the correlation between arousal indices and frame-wise displacement (FD) of preprocessed fMRI signals became statistically non-significant (Supplementary Fig. 3), underscoring the effectiveness of our nuisance signal regression-based denoising strategy in mitigating motion-related confounds. To further verify the inverted U-shaped pattern was not affected by preprocessing strategy and the methodology of dynamic FC estimation, we compared the dynamic FC using different combinations of nuisance regressors, e.g., global signal (Supplementary Fig. 4A–C), and the sliding window approach with different window sizes (Supplementary Fig. 4D–F). Importantly, the inverted U-shaped pattern was consistent across all control analyses (Supplementary Fig. 4G), demonstrating minimal computational bias in our primary results.

Cross-species and cross-modal conserved arousal modulated inverted U-shaped pattern in mice
Next, we utilized our previous mouse sleep fMRI dataset24, which captured a broad spectrum of arousal fluctuations across awake, NREM, and rapid eye movement (REM) sleep states (Fig. 2A) using intracranial EEG (iEEG)-fMRI. Considering the relatively low signal-to-noise ratio (SNR) of raw fMRI images from mouse iEEG-fMRI dataset, we firstly conducted a seed-based connectivity analysis revealing canonical mouse functional networks (Supplementary Fig. 5), demonstrating the data were of sufficient quality to capture resting-state network organization. Then, similar to the above human analysis, only data in awake state was included. The inverse of the power in 2–10Hz band was defined as mouse iEEG-based arousal index1 (Fig. 2A). We found a consistent arousal modulated inverted U-shaped pattern of whole-brain FC in awake mice (Fig. 2B, C, Averaged global FC: Low v.s. Middle, p=0.0032; Middle v.s. High, p=0.0268; Low v.s. High, p=0.4875). Similarly, these arousal effects on FC dynamics were predominately global, yet regionally differentiated (Fig. 2D and Supplementary Fig. 6), highlighting a cross-species conserved pattern between humans and mice.

Moreover, we conducted a series of parallel analyses to demonstrate whether the inverted arousal-FC relationship was contingent on a single, narrow-band definition. Accordingly, we systematically varied EEG frequency band edges, i.e., off by one, two, or more Hz, and re-derived the arousal index for humans (Supplementary Fig. 7A–C) and mice (Supplementary Fig. 7D, E). Across all sets, the inverted U-shaped global FC-arousal relationship persisted with consistent negative quadratic patterns, indicating our result was not driven by an arrow, idiosyncratic choice of (i)EEG frequency bands.

To further confirm the neural basis of such inverted U-shaped pattern, wide-field calcium imaging data in mice31 was utilized to simultaneously record neural activities, EEG and pupil size (Supplementary Fig. 8A, B) across natural wake and sleep states. Using data from awake state, the global calcium-based FC exhibited an inverted U-shaped relationship with arousal (Supplementary Fig. 8C, D, Averaged global calcium-based FC: Low v.s. Middle, p=0.0226; Middle v.s. High, p<0.0001; Low v.s. High, p=0.5559; Pupil size: Low v.s. Middle, p=0.0012; Middle v.s. High, p<0.0001; Low v.s. High, p<0.0001), supporting above fMRI results in mice and humans (Figs. 1 and 2). And the spatial pattern of arousal modulated FC dynamics was also predominantly global and regionally differentiated (Supplementary Fig. 8E). Importantly, such calcium-based FC exhibited significant spatial correlation with fMRI-based FC in awake mice (Supplementary Fig. 8F), further demonstrating the underlying neural basis of arousal modulated inverted U-shaped whole-brain FC.

Meanwhile, based on the wide-field calcium imaging results in mice, we first identified the arousal bin with the highest calcium-based global FC (Supplementary Fig. 9A–D) to explore the tonic-like arousal effect on global FC dynamics. We found highest global FC in DMN-like regions occurred at lower arousal level, whereas highest global FC in primary sensorimotor areas occurred at higher arousal level. However, such arousal binning emphasized the absolute tonic-like arousal level and did not distinguish the phase of arousal cycle, i.e., rise, peak, and fall. Then, we applied phase-aligned analysis for EEG-based arousal index and calcium-based FC. Briefly, we segmented phasic-like arousal cycles in awake mice, temporally aligned the EEG-based arousal index to yield a canonical rise-peak-fall trajectory, and averaged the corresponding global calcium-based FC within phase-aligned arousal (Supplementary Fig. 9E, F). Spatially, highest FC in primary sensorimotor areas occurred in the peak phase of arousal (highest arousal), whereas highest FC in DMN-like and visual regions occurred in the falling phase (decreasing arousal) (Supplementary Fig. 9C, F). Moreover, we did not observe apparent regions showing highest FC in the rise phase of the arousal cycle. Thus, such phase-dependent phenomenon, i.e., all highest FC occurred in the peak and falling phase of the arousal cycle, indicated that increasing arousal preceded global FC elevation within an arousal cycle, rather than FC changes preceding arousal fluctuations.

Contribution of LC-NE system to arousal-modulated inverted U-shaped whole-brain FC
Although above (i)EEG-fMRI results showed globally distributed spatial patterns between dynamic FC and arousal, the regional heterogeneity still existed. Therefore, we set out to explore whether a cluster of core regions or connections predominantly contributed to the inverted U-shaped pattern (Fig. 3A). Based on EEG-fMRI results from humans (Fig. 1) and mice (Fig. 2), we calculated the correlation between each dynamic FC edge and the global FC, and defined such correlation coefficient as the sensitivity to arousal fluctuations (Fig. 3B). For each edge, higher correlation coefficient indicated higher sensitivity to arousal. The sensitivity of each node (brain region) was calculated as averaged sensitivity with other nodes (Fig. 3C). In human fMRI, we found the top 10% nodes with highest sensitivity primarily located in sensorimotor, dorsal attention and salience networks (Fig. 3D). Importantly, arousal also highly modulated the FC of dorsal raphe (DR), LC and parabrachial nucleus (PB) in the brainstem (Fig. 3D), which are known key regions in arousal modulation32. To quantitatively evaluate whether these nodes with higher sensitivity contributed more to the inverted U-shaped pattern, we permuted the dynamic FC edges across arousal bins and tested whether the inverted U-shape persisted or not. After orderly permuting brain regions from the highest to lowest sensitivity, the arousal modulated inverted U-shaped whole-brain FC architecture collapsed at a faster rate than random permutations without considering their sensitivity (Fig. 3E).

Similar results were observed in mice (Fig. 3F), in which the most sensitive nodes located in visual, motor, salience33, and brainstem areas (Fig. 3G). Specifically, these arousal-modulated regions in mouse brainstem showed large overlap with the ascending neuromodulatory system32, including LC, DR, substantia nigra (SNr), ventral tegmental area (VTA), hypothalamus ventral premammillary nucleus (PMv), and basal forebrain (BF) (Fig. 3G). Permutation analysis in mice (Fig. 3H) resembled those conducted in humans, again highlighting the key contribution of these highest sensitive nodes. Notably, the spatial profile of arousal-modulated nodes correlated strongly with axonal projections of neuromodulatory cell types (Fig. 3I). Among these, the anatomical projection from LC-NE neurons exhibited the highest spatial correlation compared to other types (Fig. 3I, LC-NE: r=0.42, p=0.0003; BF-Ach: r=0.29, p=0.0261; DR-5HT: r=0.18, p=0.0602; VTA-DA: r=0.08, p=0.1415; PMv-HA: r=0.30, p=0.0118), indicating the LC-NE system may play a crucial role in the arousal-modulated whole-brain FC dynamics.

To experimentally explore the contribution of LC-NE system, simultaneous fiber photometry recording of GCaMP6s-based calcium signal of LC-NE neurons and extracellular NE signals in prefrontal cortex (PFC) and primary somatosensory cortex (SSp-bfd) using the NE biosensor34,35 GRAB NE-2h (Supplementary Fig. 10) was further incorporated into our sleep iEEG-fMRI setup24 using DBH-Cre mice (Fig. 4A). Continuous recordings covering multiple sleep-awake cycles (Fig. 4B) revealed a notable infra-slow arousal fluctuation accompanied by cortical NE dynamics. As expected, peaks of LC-NE neuronal activity lead to the sequential increase of cortical extracellular NE concentrations (~2.5s delay) and iEEG-based arousal index (~5.2s delay) (Fig. 4C), suggesting the LC-NE activity preceded the arousal change. Combing the phase-aligned calcium-FC findings (Supplementary Fig. 9), we could extend this temporal sequence from LC-NE to arousal, then to global FC dynamics. Again, the whole-brain FC exhibited an inverted U-shaped profile with increasing NE concentration and LC-NE neuronal activity from low to high arousal level (Fig. 4D, E, Averaged global FC: Low v.s. Middle, p=0.0349; Middle v.s. High, p=0.0484; Low v.s. High, p=0.8456; LC-NE neuron Ca2+: Low v.s. Middle, p=0.0447; Middle v.s. High, p=0.2624; Low v.s. High, p=0.0008; NE sensor: Low v.s. Middle, p=0.4054; Middle v.s. High, p=0.1823; Low v.s. High, p=0.0082). In conclusion, these findings demonstrated that the NE-mediated arousal fluctuations exhibited an inverted U-shaped relationship with whole-brain FC (Fig. 4F).

Casual contribution of LC-NE system to arousal-modulated whole-brain FC
The above results suggested a close relationship between NE and whole-brain FC dynamics. To further explore such relationship causally, we manipulated NE concentrations through up and down-regulations of LC-NE neuronal activity using optogenetics and chemogenetics fMRI in awake and anesthetized mice (Figs. 5–7). First, stabilized step-function opsin (SSFO) hChR2(C128S D156A)-mCherry or mCherry-only virus was expressed in bilateral LC-NE neurons (Fig. 5A and Supplementary Fig. 11) served as SSFO or Sham group, respectively. Periodic blue and yellow light was delivered to bilateral LC for selectively activating or terminating such activation of NE neurons (Fig. 5B) during simultaneous recording of cortical NE concentration, iEEG, and fMRI signals in awake mice. SSFO-based activation of LC-NE neurons significantly elevated cortical NE concentration (Fig. 5C, SSFO v.s. Sham: p=0.0031) and the iEEG-based arousal index (SSFO ON v.s. OFF: p=0.0231, Sham ON v.s. OFF: p=0.5632) from the middle to high arousal level with ~1min delay (Fig. 5D and Supplementary Fig. 12), compared to the Sham group. Consistent with the right half of the inverted U-shape (from middle to high arousal level), we observed significant decreases of global FC after SSFO-based activation (Fig. 5E, F, SSFO v.s. Sham: p=0.0136), demonstrating that increased NE led to higher arousal level with a corresponding decrease of whole-brain FC in awake mice (Fig. 5G).

Next, the inhibitory opsin stGtACR2-mCherry or mCherry-only was expressed in bilateral LC-NE neurons (Supplementary Fig. 11), respectively, with similar awake mouse multimodal fMRI setup (Fig. 6A, B) to the above SSFO experiment. stGtACR2-based inactivation of LC-NE neurons significantly decreased cortical NE concentration (Fig. 6C, stGtACR2 v.s. Sham: p=0.0362) and the iEEG-based arousal index from the middle to low arousal level with ~2min delay (Fig. 6D, stGtACR2 ON v.s. OFF: p=0.0026, Sham ON v.s. OFF: p=0.5636), compared to the Sham group. Consistent with the left half of the inverted U-shape (from middle to low arousal level), we observed significant decreases of global FC after stGtACR2-based inactivation (Fig. 6E, F, stGtACR2 v.s. Sham: p=0.0188), demonstrating that decreased NE led to lower arousal level with a corresponding decrease of whole-brain FC in awake mice (Fig. 6G).

Moreover, chemogenetic fMRI was also employed to activate LC-NE neurons in both awake and isoflurane anesthetized mice (Fig. 7A). The hM3Dq-mCherry or mCherry-only was expressed in bilateral LC-NE neurons (Supplementary Fig. 11) served as hM3Dq or Sham group, respectively. In awake mice, after the injection of clozapine, EEG-based arousal index significantly increased from a middle to high arousal level (Fig. 7B, hM3Dq pre v.s. post: p=0.0007, Sham pre v.s. post: p=0.0031), and whole-brain FC decreased compared to the Sham group (Fig. 7C–E, hM3Dq v.s. Sham, p=0.0126). In isoflurane anesthetized mice, after the injection of clozapine, EEG-based arousal index also significantly increased, but from a low to middle arousal level (Fig. 7F, hM3Dq pre v.s. post: p=0.0003, Sham pre v.s. post: p=0.0018), accompanied by higher whole-brain FC compared to the Sham group20 (Fig. 7G–I, hM3Dq v.s. Sham, p=0.0079). Notably, the “middle” arousal under isoflurane anesthesia should not be assumed physiologically equivalent to the “middle” arousal in the awake state (Fig. 7). Isoflurane can shift neuromodulatory tone, vascular baselines, and neural excitability. Thus, in our study, the isoflurane condition was used to extend sampling of arousal spectrum into the qualitatively low arousal state. The integrated schematic diagram (Fig. 7) should be carefully viewed as a conceptual synthesis across arousal spectrum rather than a single continuous curve measured under one condition. Therefore, above two chemogenetics fMRI experiments demonstrated that the effect of NE upregulation on global FC dynamics is dependent on the baseline arousal level and the direction of arousal-FC change.

In conclusion, our findings demonstrate that arousal bidirectionally modulates whole-brain FC through an inverted U-shaped relationship, which is conserved in both humans and mice and can be driven by LC-NE system (Fig. 8). Global FC peaked at middle arousal level and decreased at the lower or higher arousal level. Such inverted U-shaped relationship from LC-NE activity to FC and behaviors highlighted the delicate balance required for optimal brain function with arousal. Understanding this nonlinearity provided critical insights into both normal cognition and neuropsychiatric disorders, paving the way for targeted interventions that restore equilibrium in the LC-NE system.

Methods
Human detection task fMRI dataset
The human detection task fMRI dataset22 was obtained from the OpenNeuro platform (ds001242). The study enrolled 28 healthy younger adults (Mean age=24.39 years, age range=18–34; 9 females) and 24 healthy older adults (Mean age=66.95 years, age range=55–75; 9 females). Participants provided informed consent approved by the University of Southern California Institutional Review Board and were paid for their participation. The experiments comprised several scanning sessions, but we only used anatomical scan and spatial detection task scans in the current analysis. MRI data were acquired using a 3T Siemens Magnetom Trio scanner. Anatomical image were acquired using a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence with the following parameters: repetition time (TR)=1950 ms, echo time (TE)=2.26 ms, flip angle (FA)=7°; field of view (FOV)=256 mm, resolution=1×1×1 mm3. Functional images for spatial detection task were acquired using an echoplanar imaging (EPI) sequence with 139 volumes using the following parameters: TR=2000 ms, TE=25 ms, FA=90°; matrix size=64×64, in-plane resolution=4×4 mm2, slice thickness=4 mm (interleaved, no skip), and number of slices=41.

Briefly, two auditory tones (500 Hz and 800 Hz) served as conditioned shocks (CSs). For the spatial detection task, each trial began with simultaneous presentation of a fixation cross and either the CS+ or CS− tone. The tone was played for 0.7 s, then the fixation cross remained on the screen for an additional 2 s. Next, a pair of images, i.e., one depicting a place and the other an object, were displayed in placeholder frames simultaneously for 0.6 s. The salient image had a higher contrast level (80%) than the paired non-salient image (20%). And to further increase its salience, it was highlighted with a yellow border for 0.1 s. Participants were required to identify the location of the salient image. The inter-trial interval was randomly jittered (2.5, 3.5, 4.5, and 5.5 s). For each run, 16 CS+ trials (8 place salient and 8 place non-salient trials) and 16 CS– trials presented in a random order. Across five runs, a total of 160 trials were conducted. Pupil diameter changes were recorded simultaneously at a 60 Hz infrared sampling rate.

Human simultaneous EEG–fMRI dataset of natural sleep
The human sleep EEG-fMRI dataset was obtained from the OpenNeuro platform (ds003768) and acquisition details can be found in the original publication26. Briefly, this data enrolled 33 healthy participants (age: 22.1±3.2 years; male/female: 17/16) with provided informed written consent. All experimental procedures have been approved by the Institutional Review Board at the Pennsylvania State University. The experiments comprised several scanning sessions, and we only used the data from the anatomical scan, two 10-min resting state scans, and several 15-min sleep scans. The participants’ eye states were monitored during the scanning. Participants were instructed to press a button on a joystick approximately once per second as a behavioral measurement of wakefulness. They were allowed to stop the button pressing as they felt falling asleep.

MRI data were obtained using a 3T Siemens Prisma scanner. Anatomical image was acquired using a MPRAGE sequence with the following parameters: TR=2300 ms, TE=2.28 ms, inversion time (TI)=900 ms; FA=8°; FOV=256 mm, resolution=1×1×1 mm3, and acceleration factor=2. Functional images were acquired using an EPI sequence with the following parameters: TR=2100 ms, TE=25 ms, FA=90°; FOV=240 mm, in-plane resolution=3×3 mm2, slice thickness=4 mm, and number of slices=35. The 32-channel EEG data were simultaneously collected at a sampling rate of 5000 Hz with a pass-band of 0–250 Hz. Sleep stages have been classified based on EEG signatures determined by a Registered Polysomnographic Technologist according to the AASM-recommended EEG exploratory derivations.

Mouse sleep fMRI dataset
The simultaneous electrophysiology and fMRI dataset in non-anesthetized mice was obtained from our previous study24. In total, 46 sessions of 4-h simultaneous ECoG/LFP-fMRI were recorded from 24 wild-type C57BL/6J mice. The neurophysiological signals were sampled at a rate of 24414 Hz with a high-pass filtering at 0.1 Hz and notch filtering at 50, 100, and 150 Hz to mitigate noise. MRI trigger signals were recorded simultaneously at a sampling rate of 1024 Hz. All MRI data were acquired with a Bruker BioSpec 9.4T scanner. An 86 mm volume coil was utilized for transmission and a 1 cm diameter single loop head coil was used for receiving. Mouse was head-fixed as described in our previous study24 with no anesthesia. A T2-weighted anatomical image was acquired for coregistration purpose using a rapid acquisition with relaxation enhancement (RARE) sequence with the following parameters: TR=3200 ms, TE=34 ms, matrix size=256×128, FOV=18×9 mm2, slice thickness=400 μm, in-plane resolution=70×70 μm2, and number of slices=22. After local shimming using Mapshim, functional images were acquired using an EPI sequence with the following parameters: TR=2000 ms, TE=14 ms, FA=70°, matrix size=90×45, nominal in-plane resolution=200×200 μm2, slice thickness=400 μm, number of slices=22, and 7200 EPI volumes. All 7200 volumes (4 h) were collected in a single EPI scan.

Mouse wide-field calcium imaging dataset
The mouse wide-field calcium imaging data were adopted from a previous study31. This dataset included 8 Thy1-GCaMP6s transgenic adult male mice (JAX024275), which express the calcium indicator GCaMP6s in excitatory pyramidal neurons. To effectively eliminate the hemodynamics signals, the 470 nm excitation light and 405 nm light were alternately applied frame by frame. The fluorescence acquired under the 405 nm light served as the control, as it is largely independent of the calcium concentration.

The wide-field imaging data were captured at a sampling frequency of 20 Hz for each wavelength (Supplementary Fig. 8A). An infrared camera was utilized for tracking the mice’s pupil and facial whisker movements. EEG and electromyogram (EMG) signals were recorded at a sample rate of 1000 Hz with low-pass filtering below 200 Hz. In total, 19 sessions of wide-field imaging recording were collected. The states of Wake, NREM sleep, and REM sleep have been manually classified based on EEG/EMG characteristics31. The wide-field calcium imaging data have been preprocessed, including (1) motion correction, (2) hemodynamics correction, and (3) registration to the CCFv3 mouse brain atlas.

Animals
For all data acquired in this study, all mouse experiments were approved by the Animal Care and Use Committee of the Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China. DBH-cre mice (MMRRC #032081-UCD STOCK Tg(Dbh-cre) KH212Gsat/Mmucd) were used for targeted expression of AAV virus in norepinephrine (NE) neurons in locus coeruleus (LC) for awake mouse fMRI experiments. Mice were group-housed (5–6/cage) in the standard laboratory condition (temperature: 23±1°C; humidity: 50–70%) under a 12-h light/dark cycle (light on from 7 a.m. to 7 p.m.) with food and water ad libitum. Experiments were conducted at 8–10 weeks of ages, weighted 18–30 g for both male and female mice. Considering the study was designed to identify a cross-species mechanism of the inverted U-shaped relationship between arousal and global FC, sex of mice was not considered a primary variable of interest. Number of mice used in each experiment was detailed in the Table 1. No statistical method was employed to predetermine sample sizes, but the sample size in this study was similar to our previous publication23.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I continue to be impressed by the scope and rigor of this work—collecting and analyzing such multimodal datasets across species is no small task, and the authors have done so with commendable care. The manuscript now reads stronger and more cohesive. While the link between arousal and LC is well established, this study contributes important new insight by contextualizing the inverted-U relationship with fMRI global connectivity. That said, the broader implication regarding how this finding advances the practical use of fMRI to understand arousal or vice versa remains somewhat open (see my comments #10). The authors have acknowledged this being out of scope, and I appreciate their thoughtful clarification. Overall, despite this notable boundary, I view the study as an important and technically impressive contribution that is building the foundation for future efforts in linking fMRI dynamics with arousal state.", "reviewer_2": "I have reviewed the revised version of the manuscript and would like to commend the authors for the substantial improvements made. They have addressed my comments in a satisfactory manner, and the quality of the paper has clearly benefited not only from their careful revisions but also from the valuable feedback provided by my fellow reviewers.\nI have personally verified the data and would like to make a minor remark. In the table1 associated with Figure 4, the authors indicate that there are eight complete datasets. However, this statement is only partially accurate, as within the \"Resting State\" dataset there are six recordings showing photometry signals between LC and GRABNE, four with LC GCaMP and two including only EEG signals. If this information is correct, this point should be clarified more explicitly in the table to avoid misunderstandings.\nAnother remark on data quality. The fMRI acquisitions, although the authors now successfully demonstrating the presence of resting-state networks, appear to me to be of somewhat lower quality compared to other studies. Nevertheless, this is acceptable given the complexity and the number of simultaneous recordings involved. It should be noted, however, that some regions show major artifacts, particularly in the frontal cortical areas, and this limitation should be explicitly mentioned in the manuscript.\nOverall, I believe the manuscript has been significantly strengthened and can now be considered suitable for publication, pending minor clarifications as noted above.", "reviewer_3": "The authors adequately addressed the comments and questions I raised in the first review. I have no further questions.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response:\nWe thank the reviewer for their meticulous examination of our data. We have now clarified this point in the legend of Figure 4, as the comment relates directly to the data presented in this figure, as follows:\n“Data from N = 8 mice. Among the data, 6 sessions included LC-GCaMP photometry, and 4 sessions included cortical GRAB photometry. All sessions included iEEG recordings.”\nAnother remark on data quality. The fMRI acquisitions, although the authors now successfully demonstrating the presence of resting-state networks, appear to me to be of somewhat lower quality compared to other studies. Nevertheless, this is acceptable given the complexity and the number of simultaneous recordings involved. It should be noted, however, that some regions show major artifacts, particularly in the frontal cortical areas, and this limitation should be explicitly mentioned in the manuscript.\nResponse:\nWe thank the reviewer for this remark. As reviewer already mentioned, acquiring fMRI images with high SNR and minimal distortion is extremely challenging with our complex setup involving simultaneous multimodal recordings (e.g., EEG, fiber photometry) in awake mice at 9.4T. Now we add more discussion of this limitation in our manuscript, as follows:\n“Given the complexity of the current multimodal awake mouse fMRI setup with iEEG and fiber photometry, the quality of functional images with regards to both SNR and image distortion was inevitably affected. Nevertheless, we demonstrated the data quality (Supplementary Fig. 5) was sufficient for our purpose. Future technical development for improved MR compatibility of simultaneous electrophysiological and fiber photometry recording will help to mitigate this issue.”"}]

Decision letter