Introduction

The use of fMRI is becoming increasingly important in animal studies because of its noninvasive imaging without sacrificing animals. During fMRI scanning, animals should be anesthetized to mitigate stress and minimize head-movement artifacts that can distort the images1,2,3,4. However, the choice of anesthesia can impact the results due to its varied effects on cerebral blood flow and brain function5,6,7. Numerous studies have indicated that the type, dosage, and timing of anesthesia can influence fMRI outcomes, such as baseline blood oxygen level-dependent (BOLD) signals, responses to stimulation, and functional connectivity8,9,10,11,12,13,14,15.

Various anesthetic agents have been used, including isoflurane, medetomidine, α-chloralose, urethane, ketamine, and thiobutabarbital, in rodents. Moreover, medetomidine, either alone or in conjunction with isoflurane, has been repeatedly and successfully used for evoked mouse fMRI studies10,16,17,18. Medetomidine is a selective alpha-2 adrenergic agonist that is commonly used as an anesthetic adjunct. Unlike many other sedatives and anesthetics, which deeply suppress central nervous system activity, medetomidine has been shown to preserve neural activity and maintain functional connectivity networks more closely resembling wakefulness16,19,20,21. In addition, it provides a stable sedative state characterized by a state of “arousable sedation” where patients are easily rousable and responsive to stimuli. The effects of medetomidine dosage on fMRI outcomes have been systematically evaluated9,10,22. For example, a gradual rise in the BOLD response with higher stimulation frequency serves as an indicator of sedation depth and may be adjusted by the dosage of medetomidine infusion10. To ensure a stable sedation state over an extended period using medetomidine, a high dosage is required, but it is associated with side effects that disrupt the synchrony in the brain22. In general, numerous studies have adopted the continuous medetomidine infusion of less than 0.2 mg/kg/h12,17,23.

While anesthesia generally suppresses brain activity24,25,26certain parts of the brainstem and limbic system, such as the amygdala, hippocampus, and anterior cingulate cortex, demonstrate less suppression than other brain areas in specific situations27,28. This is reasonable because the brainstem governs critical activities, such as heart rate and respiration, while the limbic system may contribute to the modulation of emotional responses and memory formation by remaining less suppressed during anesthesia. Moreover, the anterior cingulate cortex is involved in various functions, such as cognitive control, emotional regulation, pain processing, decision-making, and innate fear responses.

Notably, innate fear can be triggered instantaneously, even without conscious awareness, due to its crucial role in survival. The innate fear response is controlled by the basolateral amygdala and the anterior cingulate cortex29with the orbitofrontal cortex and insula also involved in controlling fear memory to predatory threats30,31,32,33,34,35. Since the anterior cingulate cortex receives information from the orbitofrontal cortex, it is expected that the anterior cingulate cortex and orbitofrontal cortex may remain less suppressed during the anesthetized state, ensuring the maintenance of vital functions. However, little is known about the specific effects of anesthesia agents on these areas and how anesthesia concentration influences emotional and cognitive processes. We focused on the fear-related regions derived from the electrical stimulation of the mouse forepaw, which is commonly used to test neural activation in the somatosensory cortex, as electrical shocks can activate fear-processing brain regions, such as the anterior cingulate cortex29.

Therefore, this study aimed to use functional connectivity to examined whether the anterior cingulate cortex and orbitofrontal cortex may be affected by the use of medetomidine doses. We examined functional connectivity changes in the fear-related regions composed of the anterior cingulate cortex and orbitofrontal cortex before and after electrical stimulation that can induce pain and lead to fear response. Utilizing structural and functional MRI data36 and Allen Mouse Brain Atlas37we constructed the fear-related regions. We then explored the functional connectivity changes in the fear-related regions between pre- and post-stimulations according to medetomidine dosages. To further test the functional connectivity changes at the network level, we conducted a graph-based network analysis and compared global network properties in the fear-related regions. We predicted the dosage of medetomidine affects functional pathways of fear processing.

Methods

Data acquisition

We obtained structural MRI and resting-state fMRI of the mice under different anesthesia dosages and electrical stimulation conditions from the data repository at the University of Queensland (https://doi.org/10.48610/3b35b94)38. All experimental protocols were approved by the Institutional Animal Ethics Committee at the University of Queensland (IACUC number: QBI/SCMB/089/16/QBI). All experiments were prepared in accordance with ARRIVE guidelines39and carried out following the Australian code of practice for the care and use of animals for scientific purposes40. All methods were performed in accordance with the relevant guidelines and regulations of the Korea Brain Research Institute.

Electrical stimulation was applied to the forepaw using sub-dermal electrodes inserted near the second and fourth digits of the left dorsal forepaw. To deliver consistent and mild stimulation, a current source (Isostim A320, World Precision Instrument, USA) was used with the following parameters: 6 Hz pulse frequency, 0.3 ms pulse width, and 0.2 mA current. These parameters were chosen to ensure non-noxious stimulation.

The obtained data were composed of low-dose (0.1 mg/kg/h, n = 9) and high-dose (0.3 mg/kg/h, n = 9) medetomidine conditions that medetomidine does not typically induce epileptic seizures in mice41. We excluded one mouse due To no acquisition of structural MRI images, and 8 mice were used for the analysis. A sample size of 8 mice is within the typical range for mouse fMRI studies, where sample sizes of n = 6–13 are commonly employed18,42,43,44,45. This range is often based on both practical constraints and ethical considerations, which limit the number of animals that can be used in such experiments.

Notably, no animal was exposed to any other anesthesia before the first session. All animals were exposed to repeated anesthesia, as this was vital to the experimental design, allowing for a paired t-test statistical analysis design. The effects and potential bias of prior exposures on the conclusion and analysis of the differential effects of different doses of anesthesia were controlled by randomizing the dose administered to each animal in the first session. In the second session, which was conducted at least a week later, the alternate dose was used. A brief experimental procedure is shown in Fig. 1a and detailed information on MRI acquisition is available in To and Nasrallah’s study36,38. Briefly, medetomidine anesthesia was initiated with an intraperitoneal bolus injection (0.5 mg/kg for the low-dose condition; 0.15 mg/kg for the high-dose condition), followed by continuous infusion at the designated dose (0.1 or 0.3 mg/kg/h). Isoflurane was tapered off gradually and maintained at ~ 0.5% during imaging. Functional scans were initiated approximately 55 min after medetomidine onset to ensure a stable anesthetic state. Throughout scanning, physiological parameters including respiration rate (90–180 breaths/min) and rectal temperature (36.5 ± 0.5 °C) were continuously monitored. No animals exhibited signs of arousal or distress during stimulation, and no imaging sessions required exclusion due To motion artifacts. Structural and functional MRI data were acquired using a 9.4T MRI scanner (Bruker Biospin, Germany) with a cryogenically cooled transmit and receive coil. The MRI parameters for the structural T2-weighted image were as follows: Turbo Rapid Acquisition with Refocused Echoes (TurboRARE), acquisition Matrix of 192× 192, field of view of 19.2 mm, 52 contiguous slices, voxel size of 0.1 × 0.1 × 0.3 mm³, repetition time (TR) of 7200 ms, and echo time (TE) of 39 ms, averages = 2, RARE factor = 8. The functional MRI parameters were as follows: a 2D gradient-echo echo-planar-imaging (GE-EPI), acquisition Matrix of 64× 64, field of view of 19.2 mm, 36 interleaved slices, voxel size of 0.3 × 0.3 × 0.6 mm³, TR for 1000 ms, TE of 14 ms, flip angle of 70°, and 600 volumes (total scan time = 10 min)36.

fMRI data preprocessing

Resting-state fMRI data were preprocessed using statistical parametric mapping (SPM12, http://www.fil.ion.ucl.ac.uk/spm)46 and FMIRB Software Library (https://fsl.fmrib.ox.ac.uk/fsl/)47. As used in our previous study48the fMRI data underwent seven preprocessing steps, orientation correction, voxel scaling (×10)49,50slice timing correction, head motion correction, distortion correction, co-registration, and spatial normalization to the template space Allen Mouse Brain Common Coordinate Framework (CCFv3)37, using non-linear transformation. The normalized data were interpolated to 0.8 × 0.8 × 0.8 mm3 voxels, which is equivalent to 0.08 mm with respect to the actual size of the mouse brain. Spatial smoothing was not conducted to avoid spill-over effects between voxels51. For the preprocessing of the fMRI time series, six rigid motion parameters and their derivatives, three principal components of the white Matter, and cerebrospinal fluid masks, Linear and quadratic regressors were regressed out. Band-pass filtering from 0.01 to 0.3 Hz was applied. We calculated Pearson correlation coefficients (r-values) between the filtered time series of two reference regions. Functional connectivity was defined by converting the r-values to Fisher z scores.

Construction of fear-related and sensory-motor-related brain regions

To investigate the topological characteristics of functional connectivity by electrical stimulation, we constructed the fear-related and sensory-motor-related regions of the mouse (Fig. 1). According to previous studies30,31,32,33,52,53we defined the fear-related regions with the anterior cingulate area (ACA), orbital area (ORB), and dentate gyrus (DG) of hippocampus of the Allen Mouse Brain Atlas (Supplementary Fig. 1). We excluded basolateral amygdala because of signal loss in the ventral brain areas around the amygdala54. We also defined the sensory-motor-related regions with the somatosensory areas (SS) and somatomotor areas (MO).

Fig. 1
figure 1

Overview of experimental procedure and subnetworks for functional connectivity analysis. (A) A mice fMRI study under different anesthesia dosage and stimulation conditions was conducted in To and Nasrallah’s work36. Some images were used with permission from https://biorender.com/z75s689 under a CC BY license. (B) The fear-related and sensory-motor-related regions were constructed with the following brain regions: ACAd1: Anterior cingulate area, dorsal part, layer 1; ACAv1: Anterior cingulate area, ventral part, layer 1; ORBm2: Orbital area, medial part, layer 2; DG/mo: Dentate gyrus, molecular layer; MOs1: Secondary motor area, layer 1; MOp1: Primary motor area, layer 1; SSp-ul1: Primary somatosensory area, upper Limb, layer 1; SSp-ul4: Primary somatosensory area, upper Limb, layer 4; SSp-ul6a: Primary somatosensory area, upper Limb, layer 6a; SSp-un1: Primary somatosensory area, unassigned, layer 1; SSp-m1: Primary somatosensory area, mouth, layer 1; SSp-n1: Primary somatosensory area, nose, layer 1; SSp-bfd1: Primary somatosensory area, barrel field, layer 1; SSs1: Supplemental somatosensory area, layer 1; SSs4: Supplemental somatosensory area, layer 4, SSs6a: Supplemental somatosensory area, layer 6a.

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Network analysis of fear-related and sensory-motor-related brain regions

To test whether functional connectivity changes can be observed at the network level, we analyzed functional connectivity with graph theoretical methods using the Brain Connectivity Toolbox (BCT, brain-connectivity-toolbox.net)55. The BCT toolbox, a MATLAB toolbox for complex brain-network analysis, can measure functional segregation and integration, quantify the importance of individual brain regions, and test the optimization of brain networks. Using the BCT toolbox, we calculated three global network properties: (i) global node degree (to test how a node is important), (ii) global node strength (to test how a node is strongly connected to other nodes), and (iii) global efficiency (to test how the optimization of functional network).

In the functional connectivity matrix (\(\:G\)), node degree (\(\:d\)) is calculated based on the number of edges connected between a node (\(\:i\)) and other nodes (\(\:j\)), and global node degree (\(\:D\)) is calculated by dividing the sum of node degrees with the total number (\(\:N\)) of nodes.

$$\:D=\frac{1}{N}\sum\:_{j\in\:G}d(i,j)$$
(1)

Node strength (\(\:s\)) is calculated based on the sum of the weights of edges connected between a node (\(\:i\)) and other nodes (\(\:ii\)). Global node strength (\(\:S\)) is calculated by dividing the sum of node strengths by the total number (\(\:N\)).

$$\:S=\frac{1}{N}\sum\:_{j\in\:G}w(i,j)$$
(2)

where \(\:w\) indicates the weight of edges between nodes \(\:i\) and \(\:j\).

Node efficiency is calculated by the mean of inverse shortest-path distance from a node to other nodes and global efficiency is defined by averaging all node efficiencies56. Global efficiency (\(\:E\)) is calculated as below:

$$\:E=\frac{1}{N\left(N-1\right)}\sum\:_{i\ne\:j\in\:G}\frac{1}{gd\left(i,j\right)}$$
(3)

where \(\:gd\) is the geodesic distance between nodes \(\:i\) and \(\:j\).

Results

Figure 2 displays the functional connectivity of the fear-related regions before and after electrical stimulation according to low- and high-dose conditions. The functional connections averaged across the group are shown in Figs. 2a and b. In the high-dose condition, functional connections were increased after electrical stimulation. Statistical differences in node strengths of functional connectivity between pre- and post-stimulation were found using a paired t-test and a false discovery rate (FDR) < 0.01 (Fig. 2b; Table 1). Significantly increased edges (FDR-adjusted p = 0.0018) were found between the ventral part of ACA, the lateral part of ORB, and DG (molecular layer) (Fig. 2c; Table 2).

Fig. 2
figure 2

Functional connectivity in the fear network and its difference between pre- and post-stimulation in low- and high-dose conditions. (A) Group-average functional connectivity matrices, (B) group-average functional networks, stimulation difference in (C) node strength, and (D) edge strength. ACA: Anterior cingulate area, ORB: Orbital area, DG: Dentate gyrus.

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Table 1 Statistical differences between the pre- and post-stimulation conditions in node strengths of the fear-related regions. Brain regions with statistically significant differences between the conditions in local node strength were determined with a threshold corresponding to a false discovery rate (FDR) of 0.05. Mean (standard deviation). *p < 0.05. ACAd1: anterior cingulate area, dorsal part (layer 1), ACAd2/3: anterior cingulate area, dorsal part (layer 2/3), ACAd5: anterior cingulate area, dorsal part (layer 5), ACAd6a: anterior cingulate area, dorsal part (layer 6a), ACAv1: anterior cingulate area, ventral part (layer 1), ACAv2/3: anterior cingulate area, ventral part (layer 2/3), ACAv5: anterior cingulate area, ventral part (layer 5), ACAv6a: anterior cingulate area, ventral part (6a), ORBl1: orbital area, lateral part (layer 1), ORBl2/3: orbital area, lateral part (layer 2/3), ORBl5: orbital area, lateral part (layer 5), ORBl6a: orbital area, lateral part (layer 6a), lateral part (layer 6b), ORBm1: orbital area, medial part (layer 1), ORBm2/3: orbital area, medial part (layer 2/3), ORBm5: orbital area, medial part (layer 5), ORBm6a: orbital area, medial part (layer 6a), ORBm6b: orbital area, medial part (layer 6b), ORBvl1: orbital area, ventrolateral part (layer 1), ORBvl2/3: orbital area, ventrolateral part (layer 2/3), ORBvl5: orbital area, ventrolateral part (layer 5), ORBvl6a: orbital area, ventrolateral part (layer 6a), ORBvl6b: orbital area, ventrolateral part (layer 6b), dg/mo: dentate gyrus, molecular layer, dg/po: dentate gyrus, polymorph layer, dg/sg: dentate gyrus, granule cell layer.
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Table 2 Statistical difference between the pre- and post-stimulation conditions in the edge strength of the fear-related brain regions. Edges with a false discovery rate (FDR) of 0.01 were determined. Mean (standard deviation). *p < 0.05. ACAd2/3: anterior cingulate area, dorsal part (layer 2/3), ACAv1: anterior cingulate area, ventral part (layer 1), ACAv2/3: anterior cingulate area, ventral part (layer 2/3), ACAv5: anterior cingulate area, ventral part (layer 5), ORBl1: orbital area, lateral part (layer 1), ORBl2/3: orbital area, lateral part (layer 2/3), ORBvl2/3: orbital area, ventrolateral part (layer 2/3), ORBvl6a: orbital area, ventrolateral part (layer 6a), ORBvl6b: orbital area, ventrolateral part (layer 6b), ORBl1: orbital area, lateral part (layer 1), ORBm1: orbital area, medial part (layer 1), ORBm2/3: orbital area, medial part (layer 2/3), ORBm5: orbital area, medial part (layer 5), ORBm6a: orbital area, medial part (layer 6a), dg/sg: dentate gyrus, granule cell layer, dg/po: dentate gyrus, polymorph layer.
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Our principal findings were that functional connectivity of the fear-related regions was significantly increased post-stimulation in high-dose condition, but not in low-dose condition. Based on these findings, we further questioned whether the increase in functional connectivity after post-stimulation is specific to the fear-related regions or not. To answer this question, we investigated global network properties in the sensory-motor-related regions that are engaged in the feeling of being afraid.

Figure 3 shows the global network properties (global node degree, global node strength, and global efficiency) in the fear and sensory-motor-related regions. In the fear-related regions, the high-dose condition showed significantly increased global network properties in post-stimulation compared to those in pre-stimulation. However, in the low-dose condition, there was no significant difference in global network properties between pre- and post-stimulations (Fig. 3a; Table 3).

Table 3 Global network properties of the fear-related and sensory-motor-related brain regions. Mean (standard deviation). *p < 0.05.
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To test whether the neuromodulation effect is significantly larger in high-dose conditions than in low-dose conditions, we conducted a two-way 2 (Stimulation: Pre-stim, Post-stim) × 2 (Dosage: Low-dose, High-dose) repeated measures analysis of variance (ANOVA). As a post-hoc analysis, paired t-tests were applied to assess the differences in global network properties between pre- and post-stimulation.

In the ANOVA analysis, global node degree, global node strength, and global efficiency showed significant interaction between Stimulation and Dosage (global node degree: F1,14 = 11.92, p = 0.0039; global node strength: F1,14 = 5.75, p = 0.0310; global efficiency: F1,14 = 6.66, p = 0.0218) as well as significant main effects of Stimulation (global node degree: F1,14 = 7.39, p = 0.0167; global node strength: F1,14 = 9.37, p = 0.0085; global efficiency: F1,14 = 13.73, p = 0.0024). However, there were no significant main effects of Dosage in the global network properties (global node degree: F1,14 = 0.40, p = 0.5352; global node strength: F1,14 = 0.45, p = 0.5121; global efficiency: F1,14 = 0.40, p = 0.5369). In the post-hoc analysis, significantly higher global network properties post-stimulation were observed in the high-dose condition compared to those pre-stimulation (Table 3). However, there were no significant differences in global network properties between pre- and post-stimulation in the low-dose condition.

In contrast, regardless of concentrations, global network properties of the sensory-motor-related regions did not show any significant differences between pre- and post-stimulations (Fig. 3b; Table 1). In addition, the two-way 2 × 2 ANOVA analysis revealed non-significant interaction effects for Stimulation × Dosage in the global network properties (global node degree: F1,14 = 0.82, p = 0.3803; global node strength: F1,14 = 1.75, p = 0.2065; global efficiency: F1,14 = 1.61, p = 0.2248) as well as no significant main effects for Stimulation (global node degree: F1,14 = 1.37, p = 0.2618; global node strength: F1,14 = 0.59, p = 0.4558; global efficiency: F1,14 = 0.53, p = 0.4791). However, there were significant main effects for Dosage in global node strength (F1,14 = 6.2, p = 0.0259) and global efficiency (F1,14 = 7.8, p = 0.0144) but not in global degree (F1,14 = 1.37, p = 0.2618). These results indicate a significant neuromodulation effect in the high-dose condition of the fear-related regions, not the sensory-motor-related regions.

Fig. 3
figure 3

Global network properties of functional connectivity in the fear-related and sensory-motor-related regions. Significant interactions between Stimulation (Pre-stim, Post-stim) × Dosage (Low-dose, High-dose) of the global network properties were observed in (A) the fear-related regions, not in (B) the sensory-motor-related regions. In the fear-related regions, global node degree, global node strength, and global efficiency were increased in the high-dose condition but not in the low-dose condition after electrical stimulation. However, in the sensory-motor-related regions, there were no significant increases after electrical stimulation in both high- and low-dose conditions.

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Discussion

In the current study, we tested how functional connectivity in the fear-related regions changes after electrical stimulation according to the medetomidine doses. This is important for animal fMRI studies, as brain functions may be differently represented in cortical and subcortical regions according to the dosage of anesthesia. In fact, the use of low-dose medetomidine has several advantages for sensory study such as sedation57preservation of sensory processing18,58less impairment of motor function12,59reduced cardiovascular and respiratory effects60and longitudinal study facilitation61. In contrast, the use of high-dose medetomidine can maintain sedation and physiological parameters10 and block somatosensory-evoked potentials62shift frequency power spectrum toward the higher frequency63,64and cause loss of consciousness65. Therefore, while higher-order cognitive networks may be disrupted with loss of consciousness, specific brain networks related to basic physiological functions may still be active in anesthesia. In this respect, we focused on innate fears because functional connectivity changes in the fear-related regions could explain the effects of medetomidine doses.

It is generally expected that the use of low-dose medetomidine may be advantageous in representing cognitive function, whereas the use of high-dose medetomidine may be difficult in representing cognitive function due to the loss of consciousness. This is likely because a lower medetomidine dose is better, considering the stable sedation of the functional network before and after stimulation, which has often been used in previous studies66,67. In the current study, we observed change (increase) in functional connectivity after electrical stimulation in the fear-related regions. In particular, the difference was larger for the high-dose condition than for the low-dose condition (Fig. 2a). Similarly, increased node strengths and edge strengths of the fear-related regions following electrical stimulation were only observed in the high-dose condition (Figs. 2b and c). Our findings are consistent with those of previous studies demonstrating neural activation in the fear-related brain regions by electrical stimulation33.

In the network analysis of functional connectivity, we also found that when high-dose medetomidine was used, global node degree, global node strength, and global efficiency after electrical stimulation were increased in the fear-related regions but not in the sensory-motor-related regions (Fig. 3). Previous studies on rodent resting-state fMRI have repeatedly observed that lower dose of medetomidine is consistently associated with higher BOLD signal and functional connectivity strength in cortical and subcortical regions9,12,63. Similarly, we observed significant main effects for the dosage of medetomidine in global node strength (F1,14 = 6.2, p = 0.0259) and global efficiency (F1,14 = 7.8, p = 0.0144) in global network properties of the sensory-motor-related regions. However, irrespective of medetomidine concentrations, no significant differences were observed in the global network properties of the sensory-motor-related regions before and after stimulations. Our findings are in line with those of previous studies reporting that under different levels of medetomidine, stimulation-induced neural activity in the somatosensory areas has no changes22and neurovascular coupling in the somatosensory cortex is not affected19. Interestingly, medetomidine dose-dependent anesthesia did not affect the changes in functional connectivity in the sensory-motor-related regions, but the fear-related regions remained functionally dependent on the anesthesia state. These findings show that changes in functional connectivity in the high-dose condition may be specific to individual networks.

However, upon comparing global network properties between low- and high-dose conditions, we found that high-dose medetomidine suppressed the functional connectivity in both the fear-related regions (two-sample t-tests, global node degree: low-dose = 19.16 ± 1.32, high-dose = 14.51 ± 1.26, p = 0.0232; global node strength: low-dose = 9.76 ± 0.77, high-dose = 7.36 ± 0.52, p = 0.0243; global efficiency: low-dose = 0.41 ± 0.02, high-dose = 0.35 ± 0.02, p = 0.0286) and sensory-motor-related regions (global node strength: low-dose = 13.79 ± 0.42, high-dose = 10.94 ± 0.66, p = 0.0025; global efficiency: low-dose = 0.36 ± 0.01, high-dose = 0.31 ± 0.01, p = 0.0013). These results are consistent with a previous finding that a higher dosage of medetomidine reduces functional connectivity strength43. Our findings also indicate that high dosage may disrupt functional synchrony in the brain with strong sedation.

Our main findings were that the functional network properties of the fear-related regions with high-dose medetomidine were increased after stimulation. These increases seem to represent the normalization of functional connectivity by neuromodulation. To clarify this, we additionally compared global network properties post-stimulation in low- and high-dose conditions in the fear-related and the sensory-motor-related regions. As a result, we observed no significant differences in global network properties between low- and high-dose medetomidine in both the fear-related regions (two-sample t-tests, global node degree: p = 0.3359; global node strength: p = 0.6087; global efficiency: p = 0.6657) and the somatosensory network (global node degree: p = 0.6416; global node strength: p = 0.3089; global efficiency: p = 0.2250). Furthermore, upon comparing global network properties in the pre-stimulus phase for the low- and high-dose conditions, we observed significant differences in global node degree (p = 0.0232), global node strength (p = 0.0243), and global efficiency (p = 0.0286) in the fear-related regions (Supplementary Fig. 2). Considering the baseline differences, the changes observed between pre- and post-stimulation are likely due to electrical stimulation.

These results indicate that the fear-related and sensory-motor-related brain regions are stable because of no changes in the functional network properties before and after stimulation in the low-dose condition. Considering these results, we conclude that the lower dosage of medetomidine is preferable for various fMRI studies, promoting the consistent maintenance of brain networks, as supported by previous research66,67. Conversely, our results suggest that a higher dosage increases the network’s responsiveness to external stimuli, similar to electrical stimulation in this study, facilitating the identification of brain regions responsive to such stimuli.

The hippocampus plays important roles in fear processing, as do the anterior cingulate cortex29,68,69,70,71 and orbitofrontal cortex32,72. In particular, the anterior cingulate cortex is connected to the basolateral amygdala29periaqueductal gray73nucleus accumbens74bed nucleus of the stria terminalis75and superior colliculus motor region76 to form a circuit or work cooperatively to control fear, pain-related defensive behavior, or pain avoidance response. It seems to be that neural activity in the anterior cingulate cortex and orbitofrontal cortex was less suppressed during the anesthetized state, allowing it to respond actively to stimuli at high-dose anesthesia conditions in line with a previous study77.

This study had some Limitations that should be considered for future studies. First, no fMRI data were available at 90 min in the high-dose medetomidine condition, which would have allowed to examine changes in functional connectivity over time. Second, actual fear response could not be measured from the mouse even though it is expected to respond similarly to humans.

In conclusion, this study is concerned with functional connectivity changes focusing on the fear-related regions that have not been previously addressed36,38. Our findings demonstrate the effects of medetomidine dosage on electrical stimulation and how it affects functional connectivity in the fear-related regions. Awake rodent fMRI scanning has many advantages when suitably performed78,79,80. However, when the use of awake fMRI is precluded, our findings remain important for planning animal fMRI studies that require anesthesia. Importantly, functional connectivity can be increased or decreased according to the agent and dosage of anesthesia used.