Introduction

Despite considerable investigation into the molecular targets, neural circuits, and functional connectivity associated with various anesthetics, our comprehension of their effects on overall brain activity continues to be limited and incomplete [1]. At the molecular level, ketamine (KET) and isoflurane (ISO) interact with N-methyl-D-aspartate (NMDA) and gamma-aminobutyric acid type A (GABAa) receptors, respectively, modulating neuronal excitability and ultimately leading to a loss of consciousness [2]. In systems neuroscience, the neural mechanisms of anesthetic induced unconsciousness involve both top-down and bottom-up processes [3, 4]. As evidenced by in vivo electrophysiology or functional magnetic resonance imaging (fMRI) studies, the top-down paradigm illustrates that anesthetics induce unconsciousness by disrupting corticocortical and corticothalamic circuits responsible for neural information integration, while peripheral sensory information can still be conveyed to the primary sensory cortex [5, 6]. The bottom-up approach, exemplified by ISO, involves the activation of sleep-promoting nuclei like ventral lateral preoptic nucleus (VLPO) and inhibition of arousal centers in the brainstem and diencephalon, supporting the shared circuits of sleep and anesthesia [7, 8]. However, the limited spatial resolution of fMRI studies and the inability of EEG to capture specific brainstem nuclei hinder the acquisition of comprehensive whole-brain information. Although a substantial body of knowledge has been amassed, our understanding of the reciprocal responses among different brain regions during general anesthesia remains relatively sparse and fragmented. To bridge these gaps, further investigation using advanced techniques that can capture the whole-brain dynamics is needed to elucidate the complex interactions and shared mechanisms between various anesthetics.

Neuronal extracellular stimulation typically results in the elevation of adenosine 3’,5’-cyclic monophosphate (cAMP) levels and calcium influx, ultimately leading to the upregulation of immediate early genes (IEGs) such as c-fos [9, 10]. The translation product of c-fos, c-Fos protein, offers single-cell spatial resolution and has been utilized as a biomarker to identify anesthetic-activated brain regions [11]. Previous investigations of c-Fos expression throughout the brain demonstrated that GABAergic agents inhibited cortical activity while concurrently activating subcortical brain regions, including the VLPO, median preoptic nucleus (MnPO), lateral septal nucleus (LS), Edinger-Westphal nucleus (EW), and locus coeruleus (LC) [12–15]. In contrast, KET was shown to provoke wake-like c-Fos expression and intense augmentation of c-Fos expression in various brain regions at clinical dosages (75-100 mg/kg) [13]. It is important to note that these experiments administered KET at lights-on and GABAa receptor agonists at lights-off, potentially introducing circadian influences for direct comparison of ISO and KET. Moreover, it has been revealed that state of general anesthesia is not determined by activity in individual brain areas, but emerges as a global change within the brain. This change involves the activation of lateral habenular nucleus (LHb), VLPO, supraoptic nucleus (SON), and central amygdaloid nucleus (CeA), which are essential for anesthetics induced sedation, unconsciousness, or analgesia [7, 16–18]. However, brain-wide mapping and comparison of distinct anesthetic (KET and ISO) activated nuclei at a cellular level have not been fully elucidated.

In this study, we examined the distribution of nuclei activated by ISO and KET in 987 brain regions using immunochemical labeling and a customized MATLAB software package, which facilitated signal detection and registration to the Allen Mouse Brain Atlas reference [19]. We compared whole-brain c-Fos expression induced by KET and ISO through principal component analysis (PCA) and calculated inter-regional correlations by determining the covariance across subjects for each brain region pair. We then extracted significantly positively correlated brain regions to construct functional networks and performed graph theory-based network analyses to identify hub nodes. Our results uncovered distinct yet overlapping whole-brain activation patterns for KET and ISO.

Results

A comparison of the activation patterns of c-Fos in 53 brain areas in response to ISO and KET

To examine the pattern of c-Fos expression throughout the brain, 1.5% ISO was continuously ventilated, or 100 mg/kg KET was administered 90 minutes before harvesting (Figure 1A). Concurrently, to quantitatively assess brain states across the four groups, we utilized electroencephalography (EEG) and electromyography (EMG) recordings (Figure 1—figure supplement 1). The depth of anesthesia was evaluated based on the power ratio of the EEG within the delta (0.5–4 Hz) and theta (6–10 Hz) frequency bands, combined with the changes in the EMG [20]. Our findings showed that ISO and KET notably elevated EEG power ratio while reducing EMG power, suggesting both drugs induced a loss of consciousness. The raw images of brain slices were aligned to the Allen Mouse Brain Atlas (Figure 1B). Based on the standard mouse atlas available at http://atlas.brain-map.org/, the mouse brain was segmented into nine hierarchical levels, totaling 987 regions. The primary level consists of grey matter, the secondary of the cerebrum, brainstem, and cerebellum, and the tertiary includes regions like the cerebral cortex and cerebellar nuclei, among others. Some regions extend to the 8th and 9th levels. Our initial statistical comparison was conducted on the fifth level, which is composed of 53 subregions, as detailed in Supplementary Table 1 (Figure 1B) [21]. To differentiate c-Fos expression patterns across groups, we calculated the c-Fos density for each brain region, divided it by the overall brain c-Fos density, and then applied a logarithmic transformation. The transformed values were used for principal component analysis (PCA) (Figure 2A). The KET and ISO groups were distinctly delineated by the top two principal components. Notably, the KET-induced c-Fos expression manifested a positive association with principal component 1 (PC1), clustering around +2.5, which represented 30.72% of the total variance. In contrast, it exhibited a negative correlation with principal component 2 (PC2), clustering near −2.0, which contributed to 25.89% of the variance. On the other hand, ISO displayed a positive correlation with PC2, clustering at approximately +2.5, while its association with PC1 was neutral, near 0. Figure 2B illustrates the component coefficients of PC1 and PC2 for each brain region. Dots represent the top 25% of brain regions based on the absolute values of their component coefficients, signifying the most influential regions. KET, predominantly positively correlated with PC1, is associated with the activation of cortical regions (as evidenced by positive PC1 coefficients in MOB, AON, MO, ACA, and ORB) and the inhibition of subcortical areas (indicated by negative coefficients). On the other hand, ISO, with its notable association with PC2, showed top 25% positive coefficients in PALc, LSX of CNU, cortical ILA, and hypothalamic PVZ, while its negative coefficients were found primarily in cortical regions. These observations underscore that KET and ISO exhibit distinct patterns of brain region activation. KET displays cortical activation and subcortical inhibition, whereas ISO shows a contrasting preference, activating the cerebral nucleus (CNU) and the hypothalamus while inhibiting cortical areas. To reduce inter-individual variability, we normalized c-Fos⁺ cell counts in each brain area by dividing them by the total c-Fos⁺ cells in the entire brain (Figure 2C), Subsequently, we directly compared the differences between ISO and KET with their respective control groups. In line with PCA results, KET significantly activated the isocortex (16.55 ± 2.31 vs. 40.13 ± 1.97, P < 0.001) and reduced the proportion of c-Fos⁺ cells in the hypothalamus (14.88 ± 2.18 vs. 4.03 ± 0.57, P = 0.001) and midbrain (21.87 ± 2.74 vs. 6.48 ± 1.06, P = 0.005) compared to the saline group. In contrast, the proportion of c-Fos⁺ cells induced by ISO were mainly observed in the striatum and hypothalamus compared to the home cage group, specifically in the periventricular zone (PVZ) (2.330 ± 0.759 vs. 6.63 ± 0.84, P = 0.015) and lateral zone (LZ) (2.82 ± 0.3 vs. 5.59 ± 0.75, P = 0.002) of the hypothalamus, as well as STRv (0.59 ± 0.10 vs. 2.77 ± 0.40, P < 0.002) and LSX (1.93 ± 0.59 vs. 5.11 ± 0.41, P = 0.004) of the striatum (Figure 2C and Table 1). In summary, our findings reveal that KET predominantly affects cortical regions, whereas isoflurane mainly targets subcortical areas, specifically the striatum, and hypothalamus. These results demonstrate distinct patterns of brain region activation for each anesthetic agent.

Figure 1.

Figure 2.

Similarities and differences in ISO and KET activated c-Fos brain areas

To elucidate the c-Fos expression patterns induced by ISO and KET more clearly, we refined our focus from the initial 53 brain regions to a more granular examination of subregions, culminating in a comprehensive assessment of 201 distinct areas. We conducted a PCA on the logarithmic ratio of c-Fos density across these regions to discern variance among the groups (Figure 3). The KET group predominantly clustered around +7 on PC1 and approximately 0 on PC2, whereas the ISO group clustered around +4 on PC2 and −3 on PC1. KET displayed a strong positive correlation with PC1, accounting for 28.97% of the variance. In contrast, ISO negatively correlated with PC1 and positively with PC2, which accounted for 15.54% of the variance (Figure 3A). These findings underscore the distinct features between the ISO and KET groups, with the top two principal components effectively separating them. Further analysis of PC coefficients in each brain region showed (Figure 3B) that KET, positively associated with PC1, mainly activates the cerebral cortex (CTX) areas including the visceral area (VISC), claustrum (CLA), dorsal peduncular area (DP), orbital area (ORB), and temporal association areas (TEA), while inhibiting the hypothalamus, midbrain, and hindbrain. In contrast, ISO is negatively correlated with PC1, suggesting that the cortical areas activated by KET are inhibited by ISO. Additionally, ISO has a positive association with PC2 and is predominantly related to the activation of hypothalamic regions, such as the ventrolateral preoptic nucleus (VLPO), suprachiasmatic nucleus (SCH), tuberal nucleus (TU), medial preoptic area (MPO), and supraoptic nucleus (SON), as well as certain nuclei in the central nucleus (CNU). KET primarily activates regions within the cerebral cortex (CTX) and inhibits areas within the hypothalamus, midbrain, and hindbrain, aligning with the top-down mechanism suggesting anesthetic agents influence cortical and thalamocortical circuits [4]. Conversely, ISO activates hypothalamic areas while suppressing the cortex, midbrain, and hindbrain, consistent with the bottom-up mechanism. Figure 3C summarizes these distinctive responses, implying that anesthetic agents like KET and ISO modulate consciousness by targeting particular brain regions and neural circuits.

Figure 3.

Identification of brain regions activated by KET

We further employed quantitative methods to identify the brain regions activated by KET across the entire brain. Our findings concur with previous studies: numerous cortical regions associated with somatosensory, auditory, visual, and movement were activated (Figure 4A and C, Supplementary Figure 1, Supplementary Table 2). Additionally, we identified several innovative observations that enrich the current understanding in this field. To provide a clearer overview of our findings, we categorized the activated brain areas into different functional groups: (1) Arousal and REM sleep regulation: Several nuclei associated with arousal were activated, including the prelimbic area (PL), infralimbic cortex (ILA), paraventricular nucleus of the thalamus (PVT), dorsal raphe (DR), and sublaterodorsal nucleus (SLD). Prior evidence indicates that the PL/ILA and PVT regions play a role in regulating arousal in both cortical and subcortical areas[22, 23]. Additionally, dopaminergic neurons within the DR have been identified as vital components of wake-promoting pathways [24]. In the context of REM sleep, the sublaterodorsal nucleus (SLD) stands out for its role in stabilization[25]. (2) Pain modulation, KET significantly activated pain-related areas such as the anterior cingulate cortex (ACA) in the cortex [26], the anterior pretectal nucleus (APN) in the thalamus, which is known to be involved in managing chronic pain [27], and the anterior periaqueductal gray (PAG) region and medial prefrontal cortex (mPFC), both part of the endogenous pain inhibitory pathway [28, 29]. Additionally, the activation of the locus coeruleus (LC) in the midbrain may contribute to KET’s analgesic effects [30]. (3) Neuroendocrine regulation: The paraventricular hypothalamic nucleus (PVH) and supraoptic nucleus (SON), which are neuroendocrine-related regions, were also activated [18, 31]. (4) Movement: Subcortical nuclei associated with movement, such as the subthalamic nucleus (STN) and nucleus incertus (NI), were prominently activated by KET administration [32, 33]. (5) Connectivity: We observed significant activation of the nucleus reuniens (Re) located in the thalamus, which receives substantial afferent input from limbic structures and serves as a connector linking the hippocampus to the medial prefrontal cortex [34]. While our functional categorization offers a simplified overview, it’s worth noting that each activated brain region may have multifaceted roles beyond this classification. In summary, our study identified extensive activation of cortical and subcortical nuclei during KET anesthesia, encompassing regions related to arousal, pain modulation, neuroendocrine regulation, movement, and connectivity.

Figure 4.

Identification of brain regions activated by ISO

In our study, we aimed to identify brain regions activated by ISO and compare these with those activated by KET. We began by summarizing previously reported ISO activated nuclei, including PIR, LSd/LSv, CeA in the cortex and striatum, and VLPO, MnPO, EW, NTS, LC, ventral group of the dorsal thalamus (VeN), and area postrema (AP) in the hypothalamus and midbrain (Supplementary Table 3). We subsequently conducted a comprehensive MATLAB-based analysis of c-Fos expression throughout the entire brain, uncovering previously undetected activated nuclei (Figure 5A, Supplementary Figure 2). Newly identified activated nuclei in the CTX and the CNU included PL/ILA and ENT, aPVT in the thalamus, TU, ARH, PVi, and PVH in the hypothalamus, and PB in the hindbrain. All nuclei activated by ISO in this study were functionally classified and depicted in Figure 5C. Our results confirmed the activation of several brain regions involved in arousal-related nuclei, such as the prelimbic area (PL) and infralimbic areas (ILA), and the paraventricular nucleus (PVT) [22, 23]. Additionally, we observed activation in previously reported analgesia-related nuclei, including CeA and LC, as well as the parabrachial nucleus (PB) [18, 30, 35]. We also found activation in neuroendocrine function-related nuclei of the hypothalamus, such as TU, PVi, ARH, PVH, and SON. Moreover, we identified activations related to ISO induced side effects, such as in the piriform cortex (PIR) [36] and ENT [37], which may be stimulated by ISO odor, and the solitary tract nucleus (NTS), potentially responsible for ISO induced vomiting [38]. The only activated nucleus in the midbrain was the Edinger-Westphal nucleus (EW). Recent research has found that sevoflurane activates EW and is involved in sleep induction and maintenance of anesthesia, suggesting its crucial role in general anesthesia [39]. By comparing the ISO and KET induced c-Fos expression, we summarized the brain regions activated by both anesthetics in Figure 5D. Despite variations in molecular targets, the coactivation of regions such as PL/ILA, aPVT, CeA, PVH, SON, EW, PB, LC, and NTS by both ISO and KET suggests an overlapping neuronal circuitry that influences sleep-wake regulation, analgesia, and neuroendocrine functions. This shared neural circuitry may potentially offer a common mechanism across the two anesthetics for the maintenance of general anesthesia.

Figure 5.

Network generation and Hub identification

Previous research has established that general anesthesia is mediated by various brain regions [7, 16–18]. c-Fos expression serves as an indicator of neuronal activity, providing a single index of activation per region per animal. By examining the covariance of c-Fos expression across animals within each group, we can infer interactions between brain regions and identify functional networks engaged during general anesthesia [40]. Consequently, we initially incorporated a total of 63 brain regions, including nuclei that were significantly activated by ISO and KET (as indicated by positive findings in Figures 4 and 5) and those previously reported to be associated with the mechanisms of general anesthesia and sleep-wakefulness [41]. Highly correlated activities across different brain regions are presumed to constitute components of a functional network, reflecting the complex interplay of brain areas during general anesthesia. Using c-Fos expression as a marker for neuronal activity and calculated a comprehensive set of interregional correlations for four groups. The matrices exhibited interregional correlations for the number of c-Fos-positive cells in each condition (Figure 6A). Network graphs were generated by extracting Pearson’s coefficients > 0.82, as well as significant positive correlations (P < 0.05), from these matrices to construct functional brain networks (Figure 6B). Compared to the control group, isoflurane (ISO) slightly decreased interconnections between regions (network density: 0.13 vs. 0.10; edges: 267 vs. 198), with no significant difference in mean interregional correlation coefficients between the ISO group and the home cage group (Fisher Z = −0.018, P = 0.98). Conversely, ketamine (KET) significantly increased the network’s connectivity density (network density: 0.13 vs. 0.47; edges: 265 vs. 1008) and showed a significant increase in mean interregional correlation coefficients compared to the saline group (Fisher Z = 3.54, P < 0.001) (Figure 6C). These findings suggest that ISO may exert a mild inhibitory effect on functional network connectivity, whereas KET appears to enhance interregional correlation following administration (Figure 6D).

Figure 6.

Hubs are nodes that occupy critical central positions within the network, enabling the network to function properly. To determine the brain regions that serve as the hubs for ISO and KET induced functional networks, we calculated each node’s degree (the number of links that connect it) and betweenness centrality (the fraction of all shortest paths in the network that contain a given node). Nodes with high degree and betweenness centrality values typically exhibit many connections. We ranked each node according to its degree and betweenness centrality and extracted nodes with a rank greater than the 80th percentile in its network (Figure 7). Additionally, we segmented the functional network into non-overlapping modules using a spectral community detection algorithm and calculated the within-modal degree Z-score (the degree of nodes within a module, which indicates within-module connectivity) as well as participation coefficients (the distribution of the edges of a node between other modules, which indicates within-module connectivity) [42]. Nodes with relatively high values for both parameters were considered relatively significant within and outside the network module. We classified nodes with participation coefficients > 0.4 and Z scores > 1 as connector hubs [43]. The LC, exhibiting elevated degree and betweenness centrality as well as relatively higher Z-scores and participation coefficients, may play a pivotal role in mediating isoflurane-induced general anesthesia, given its known involvement in arousal, attention, and analgesia. In contrast, the somatosensory cortex (SS) functions as a connector hub in the KET group, indicating its integrative and coordinating role in ketamine-induced dissociative anesthesia (Figure 7D). The LSc served as the central hub in the saline group (Figure 7C). No significant findings were observed in the within-modal degree Z-score and participation coefficient analyses for the home cage group. However, the analysis did reveal a relatively high degree and betweenness centrality of APNs (Figure 7A), suggesting that the within-module connections are relatively independent, or that the pathways of inter-module information transmission do not depend on specific connector nodes.

Figure 7.

Figure 8.

Discussion

In this study, we conducted a comparative analysis of the effects of two general anesthetics, isoflurane (ISO) and ketamine (KET), on c-Fos expression throughout the brain. By employing principal component analysis (PCA), we were able to thoroughly examine the responses of individual brain regions to each anesthetic agent. Our findings reveal that KET dominantly activates the cerebral cortex yet suppresses subcortical regions, reflecting a top down mechanism of action, while ISO predominantly stimulates subcortical brain regions with relative cortical inhibition, substantiating its bottom up mechanism of action [3, 4]. Further functional analysis of brain networks, based on c-Fos expression, identified the somatosensory cortex (SS) and the locus coeruleus (LC) as central nodes for KET and ISO, respectively, highlighting the crucial roles of LC and SS under ISO and KET induced unconsciousness.

Our results demonstrate that ISO activates the sleep-promoting VLPO nucleus, the aPVT-infralimbic loop [23], and broadly inhibits the cortex, supporting a bottom-up mechanism of ISO induced unconsciousness. Nonetheless, our findings also reveal the activation of arousal related nuclei, such as PB and LC, which implies that the influence of isoflurane on consciousness may not solely rely on suppressing arousal centers, but rather through a more intricate relationship than formerly recognized. Identifying cell types and their dynamic changes during anesthesia will be crucial for clarifying their role in ISO induced unconsciousness.

While c-Fos-based functional network analysis offers lower temporal resolution, its single-cell resolution across the entire brain allows for the inclusion of midbrain and hindbrain regions, supplementing previous fMRI analyses. Our observations reveal that ISO mildly inhibits network density, and through graph theoretical analysis, we identify the LC as a highly connected hub, highlighting the critical role of the brainstem in ISO induced general anesthesia. The LC performs a wide range of functions in mice, including arousal, pain modulation, attention, stress response, and neuroprotection. Studies have shown that chemical activation of the LC increases whole-brain functional connectivity, attributed to its role as the primary source of norepinephrine (NE) and its extensive influence on nearly the entire brain [44]. The significant LC activation and its central position within the functional network underlying ISO induced unconsciousness suggest that the LC plays a crucial part in maintaining and integrating the entire unconsciousness functional network, emphasizing the involvement of LC in the bottom-up paradigm of ISO induced unconsciousness.

Mashour et al. proposed that anesthesia-induced unconsciousness encompasses not only the modulation of lower-level brain activity but also top down neural processing [3, 4]. Within this top down framework, anesthetics diminish consciousness by interfering with cortical and thalamocortical circuits responsible for neural information integration. Our study discovered that KET administration substantially activated cortical and subcortical arousal-promoting nuclei while concurrently causing relative thalamic suppression, with only the RE and TRS exhibiting activation. This suggests that thalamic inhibition may lead to a reduction in thalamocortical communication, which is characterized by the inability to perceive the external environment and results in disconnection from reality. Graph theoretical analysis also identified the somatosensory cortex (SS) as the hub node of the KET induced functional network. As a critical cortical area, SS is responsible for sensory processing, motor control, and cognitive functions [45]. Previous studies have demonstrated that local KET administration to SS recapitulates the effects of systemic KET on both the switch in pyramidal cell activity and dissociative-like behavior, implying that SS may serve as a key target for KET induced dissociation [46]. Our findings that SS acts as a hub node suggest that KET may modulate brain network function by influencing connectivity between SS and other brain regions, thereby affecting the behavior and cognitive states of mice. This further supports the significance of cortical areas during KET anesthesia.

Identifying shared neural features between KET and ISO is essential for understanding anesthetic-induced unconsciousness. The coactivation of sleep-wake regulation-related regions, such as PL/ILA and aPVT, along with analgesia-related nuclei like CeA, PB, and LC, suggests a shared mechanism for sleep-wake regulation and the common pathways for pain relief. This observation provides valuable insights into the fundamental mechanisms of anesthesia-induced hypnosis and analgesia. Additionally, the coactivation of neuroendocrine-related nuclei, including PVH and SO in the hypothalamus, raises questions about the potential influence of anesthetics on hormonal release and homeostatic regulation. Other coactivated nuclei, such as EW and NTS, warrant further investigation of their roles in anesthesia. In summary, the coactivated nuclei imply a potential shared neuronal circuitry for general anesthesia, encompassing common features like unconsciousness, analgesia, and autonomic regulation, regardless of the specific molecular targets of each drug. Future research could examine coactivated brain regions by the two anesthetics or manipulate identified hub nodes to further understand the mechanisms of general anesthesia. In summary, our study reveals distinct and shared neural mechanisms underlying isoflurane and ketamine anesthesia using c-Fos staining and network analysis. Our findings support “top-down” and “bottom-up” paradigms, and the identification of hub nodes and coactivated brain regions suggests shared neurocircuitry for general anesthesia, providing insights into the mechanisms underlying anesthetic-induced unconsciousness and analgesia.

Methods

Animals

All animal experiments were conducted in accordance with the National Institutes of Health guidelines and were approved by the Chinese Academy of Sciences’ Institute of Neuroscience. Adult male wild-type (WT) mice (C57BL/6J) (8-10 weeks old, weight from 22 to 26 g) were purchased from institute-approved vendors (LingChang Experiment Animal Co., China). Mice were individually housed and maintained on a 12 h:12 h light/dark cycle (lights on at 07:00 a.m. and off at 07:00 p.m.) with food and water available ad libitum.

Drug administration

All experiments occurred between 13:00-14:30 (ZT6–ZT7.5). We adapted mice to handling and the anesthesia chamber (10×15×15 cm) for several days to minimize experimental confound-induced c-Fos induction. Adult male mice were handled for the KET group for 10 min per day with normal saline (NS) injected intraperitoneally (i.p.) for three consecutive days at 13:00. On day five, a randomly chosen mouse received an injection of Ketamine (Gutian Medicine, H35020148), and the control groups (n=8) received the same volume of saline. ISO group (n=6) mice were handled and inhaled 1.5% isoflurane (RWD Life Science, 1903715) at 13:00 on day four in the chamber. Meanwhile, the control groups (n=6) were left undisturbed in their home cages prior to sampling. We confirmed the loss of righting reflex at 5 min after anesthetics exposure. For 90 min after KET injection or ISO inhalation, mice were deeply anesthetized with 5% ISO and transcardially perfused with 35 ml 0.1 M phosphate-buffered saline (PBS) followed by 35 ml 4% paraformaldehyde (PFA). The brains were then removed and postfixed overnight with 4% PFA. Following fixation, the brains were dehydrated for 48 hours with 30% sucrose (wt/vol) in PBS. Coronal sections (50 µm) of the whole brain were cut using a cryostat (HM525 NX, Thermo Scientific) after being embedded with OCT compound (NEG-50, Thermo Scientific) and freezing.

Anesthesia depth measurement

EEG and EMG recordings were conducted as described previously[20]. Specifically, a stainless steel electrode was positioned over the frontal cortex for EEG measurements, while an insulated EMG electrode was inserted into the neck muscles. Additionally, a reference electrode was placed above the cerebellum. The EEG/EMG data were captured using TDT system-3 amplifiers, specifically RZ5 + RA16PA and RZ2 + PZ5 configurations. To measure the depth of anesthesia using EEG/EMG signals, a fast Fourier transform spectral analysis was employed, featuring a frequency resolution of 0.18 Hz. Data was noted every 5 seconds through a MATLAB tool and subsequently verified manually by experts. The assessment was based on EMG power and the ratio of EEG’s δ power (0.5-4Hz) to θ power (6-10Hz). EEG and EMG power values within 30 minutes post-administration were normalized to a 5-minute pre-administration baseline.

Immunohistochemistry

One out of every three brain slices (100 µm intervals) of each whole brain was washed three times with 0.1 M phosphate-buffered saline (PBS) for 10 min and permeabilized for 30 minutes at room temperature with 0.3% Triton X-100 in PBS (PBST). Slices were incubated for 2 hours at room temperature with 2% normal donkey serum (Sigma, G6767) in PBS overnight at 4°C with c-Fos primary antibodies (226003, Synaptic Systems; 1:500) diluted in PBS with 1% donkey serum. After three washes with PBST, slices were incubated with the Cy3 donkey anti-rabbit (711165152, Jackson; 1:200) secondary antibody for 2 hours at room temperature. Immunostained slices were mounted with VECTASHIELD mounting medium with DAPI and then scanned under a fluorescent microscope equipped with a 10× objective (VS120, Olympus) or a confocal microscope with a 20× objective (FV300, Olympus). Due to the limited scope of a single field of view, either 2 or 4 adjacent fields of view were stitched together to offer a comprehensive representation of specific brain regions, including the PL, ILA, LSc, LSr, SS, and VISC.

Quantification of c-Fos positive cells

The procedures used for c-Fos analysis were based on previous research [19]. A custom-written software package was employed for cellular quantification within brain images. The software consists of three components: atlas rotation, image registration, and signal detection.

Atlas rotation module

Utilizing the Allen Mouse Brain Atlas, this module allows rotations in both horizontal and vertical dimensions to align with mouse brain samples. To determine the appropriate rotation angles, we manually pinpointed anatomical landmarks corresponding to the samples. For the left-right axis rotation, we chose points from the CA3 pyramidal layer in both hemispheres and the posterior slice of the dentate gyrus, specifically the granule cell layer. For the dorsal-ventral axis rotation, we identified key anatomical landmarks. These include the initial connections of the anterior commissure and corpus callosum between the hemispheres, as well as ventral regions like the interpeduncular nucleus and the suprachiasmatic nucleus. After determining these rotation angles, we adjusted the reference atlas to match our samples.

Image registration module

This module uses a tool that aligns brain slice images with a reference atlas, facilitating the alignment of overall brain region distribution. Registration starts by matching the coronal plane of the sample sections to the atlas. After defining the external boundaries of the brain section, the system performs geometric transformations on the section to optimize its fit with the atlas.

Signal detection module

The detection module is designed to automatically label c-Fos⁺ cells. Post-detection, a manual verification was performed on each digitized brain section image to ensure the accuracy and precision of the c-Fos⁺ cell markings.

Principal components analysis (PCA) of the activity patterns at whole brain

We utilized arithmetic methods to compute a logarithm ratio of the c-Fos density in a specific brain region for PCA analysis. Specifically, the cell density of a single brain region (defined as the number of c-Fos positive cells in a brain region divided by the volume of that region) was divided by the global c-Fos cell density (calculated as the total number of c-Fos positive cells throughout the entire brain divided by the total brain volume). Subsequently, the logarithm of this ratio value was taken. The formula is as follows:

Nr: The number of c-Fos+ cells in each brain region. Vr: The volume of each brain region. Principal components analysis was performed on the concatenated matrix containing data from four conditions, using singular value decomposition. We then selected the first two principal components (PCs), which accounted for 56.6% of the variance in Figure 2A and 44.5% in Figure 3A.

Network generation

To evaluate how functional connectivity changed under general anesthetics in WT mice, we extracted 63 brain regions from major brain subdivisions (cerebral cortex, cerebral nuclei, thalamus, hypothalamus, midbrain, and hindbrain) listed in Supplementary Table 2. Correlation matrices were generated by computing Pearson correlation coefficients from interregional c-Fos expression in the 63 regions. Mean correlations were calculated to assess changes in functional connectivity between these major subdivisions of the brain. Weighted undirected networks were constructed by considering correlations with Pearson’s r ≥0.82, corresponding to a one-tailed significance level of P<0.05 (uncorrected). The nodes in the networks represent brain regions, and the correlations that survived thresholding were considered connections. Theoretical graph analysis was performed using Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/, version 2019-03-03) in MATLAB R2021 (he MathWorks Inc.) [47]. Network visualization was performed using Cytoscape (version 3.2.1) [48].

Hub identification

Network centrality was evaluated using degree, betweenness, within-module z-scores (representing within-module connectivity), and participation coefficient (indicating between-module connectivity). These measures were computed for all nodes to identify potential hub regions [49]. Modularity was assessed employing Newman’s spectral community detection algorithm [50]. Degree represents the number of edges connected to a node, while betweenness denotes the number of shortest paths traversing a given node. Nodes with elevated betweenness centrality are involved in numerous shortest paths.

Statistical analysis

Sample size was determined based on prior studies [13, 14]. Data are presented as mean ± SEM, and all statistical tests were two-sided. For the c-Fos cell percentages in Figure 2C, we employed the Mann-Whitney test with a false discovery rate correction, setting a Q-value at 0.05. In Figures 4 and 5, differences in c-Fos cell counts for both KET and ISO were evaluated using an independent t-test. P-values were adjusted using the procedure by Benjamini, Krieger, and Yekutieli with Q set at 0.05 [51]. Figure 6C and Supplementary Figure 1 utilized a one-way ANOVA followed by a post-hoc Tukey correction. Pearson correlation coefficients (R) were transformed into Z-scores using the Fisher’s Z transformation before computing group means and making statistical comparisons. All statistical analyses were conducted using GraphPad Prism 9.0 (GraphPad Software, USA) and MATLAB R2021 (Mathworks Inc.). A P-value of less than 0.05 was deemed statistically significant.

Acknowledgements

We express our gratitude to our interns, Chuhang Wong from Imperial College London and Jiale Huang from ShanghaiTech University, for their assistance in cell counting.

Funding

This study was funded by the NSFC (grants 82271292, 81730031 to Y.W.; 82101350 to M.L.) and the Shanghai Municipal Key Clinical Specialty (grant shslczdzk06901 to Y.W.).

Author contributions

Yue Hu, Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing; Jiangtao Qi, Conceptualization, Software, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing; Zhao Zhang, Resources, Investigation, Supervision, Writing— review and editing; Mengqiang Luo, Resources, Supervision, Funding acquisition, Project administration, Writing— review and editing; Yingwei Wang, Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing—review and editing.

Supplementary Information

Supplementary Figure 1.

Supplementary Figure 2.

Supplementary Figure 3.

Supplementary Table 1:

Supplementary Table 2.

Supplementary Table 3.

Supplementary Table 4.

Supplementary Table 5.