Relationship between individual differences in pain empathy and task- and resting-state EEG

Pain empathy is a complex form of psychological inference that enables us to understand how others feel in the context of pain. Since pain empathy may be grounded in our own pain experiences, it exhibits huge inter-individual variability. However, the neural mechanisms behind the individual differences in pain empathy and its association with pain perception are still poorly understood. In this study, we aimed to characterize brain mechanisms associated with individual differences in pain empathy in adult participants (n = 24). The 32-channel electroencephalography (EEG) was recorded at rest and during a pain empathy task, and participants viewed static visual stimuli of the limbs submitted to painful and nonpainful stimulation to solicit empathy. The pain sensitivity of each participant was measured using a series of direct current stimulations. In our results, the N2 of Fz and the LPP of P3 and P4 were affected by painful pictures. We found that both delta and alpha bands in the frontal and parietal cortex were involved in the regulation of pain empathy. For the delta band, a close relationship was found between average power, either in the resting or task state, and individual differences in pain empathy. It suggested that the spectral power in Fz's delta band may reflect subjective pain empathy across individuals. For the alpha band, the functional connectivity between Fz and P3 under painful picture stimulation was correlated to individuals' pain sensitivity. It indicated that the alpha band may reflect individual differences in pain sensitivity and be involved in pain empathy processing. Our results suggested the distinct role of the delta and alpha bands of EEG signals in pain empathy processing and may deepen our understanding of the neural mechanisms underpinning pain empathy.


Introduction
Pain empathy refers to the perception, judgement, and emotional response of an individual to the pain of others (Bernhardt and Singer, 2012;Smith et al., 2021), which varies significantly across situations and individuals (Eres et al., 2015;Stietz et al., 2019).Studies showed reproducible findings of an association between the rating of pain empathy and pain sensitivity (Baudic et al., 2022;Li et al., 2020;Ren et al., 2020;Zhao et al., 2022), suggesting that pain perception may be one of the factors affecting individual differences in pain empathy.However, the neural mechanism that accounts for variability in pain empathy and its relationship with pain across individuals is still largely unclear.
Insights from electroencephalography (EEG) experiments have suggested that emotions in others can trigger activity in the brain regions associated with emotions or feelings (Goldstein et al., 2018).For example, event-related potentials (ERP) obtained through classic picture-response experiments may reflect pain empathy processing to some extent (Li et al., 2020;Zhuo et al., 2022), and pain empathy could also induce brain oscillations that differ from neutral conditions at certain frequency bands (Mu et al., 2008).Several studies also showed that differences in subjective reports of empathy elicited by the pain picture were reflected in the magnitude of the specific ERP components (Fan and Han, 2008;Han et al., 2008;Li et al., 2020), indicating that individual differences in empathy exist objectively and can be expressed on EEG (van den Brink et al., 2012;Wang et al., 2022).Additionally, event-independent resting-state EEG was frequently used to study the underlying mechanisms of individual differences in cognitive control and information processing (Ambrosini and Vallesi, 2016;Nash et al., 2022).As an indicator reflecting the inherent activity pattern in an individual's brain, the resting-state EEG has attracted considerable attention in empathy research (Knyazev et al., 2018;Zhang et al., 2021).For example, power changes or asymmetries of the resting EEG at subjects' frontal and anterior cingulate cortex have been shown to affect their performance on empathic tasks (Kim et al., 2014;Koller-Schlaud et al., 2020).Hence, combine task-state and resting-state signals may offer some extra insight about individual differences in pain empathy.
Psychophysical studies have shown that affective states can be affected by a number of psychological factors, many of which are related to how painful the feeling is (Bernhardt and Singer, 2012;Vlaeyen and Linton, 2000).Neuroimaging studies suggested that brain electrical activity evoked by pain empathy may originates in the sensorimotor cortex and frontoparietal operculum, including the insula and secondary somatosensory cortex, as well as the mid-and anterior cingulate cortex (Kim et al., 2014;Riečanský et al., 2015;Schott, 2015;Wang et al., 2021b).These areas partially overlap with those that are frequently activated by the direct perception of pain (Bernhardt and Singer, 2012;Singer et al., 2004).Patients with persistent/chronic pain also tend to show higher levels of pain sensitivity and pain empathy (Diatchenko et al., 2005;Lumley et al., 2011).For example, our previous study observed that women with primary dysmenorrhea had abnormal higher pain empathy during both the menstrual and luteal phases compare healthy participants (Wang et al., 2021a).Additionally, we found that long-term menstrual pain may lead to maladaptive neuroplasticity in the brain and may further enhance pain empathy (Mu et al., 2021).Therefore, individual differences in pain empathy may be related to an individual's pain sensitivity.
In this study, we hypothesized that an individual's pain empathy may be associated with both task and resting state EEG signals, and we further attempted to explore the relationships among an individual's pain sensitivity, task-and resting-state EEG signals, and pain empathy.Pain empathy was measured using a series of visual stimuli (pain or neutral pictures), and 32-channel EEGs were recorded at rest and in both painful and non-painful experimental conditions.Pain sensitivity was measured using a series of direct current stimulation, and the current intensity with a stable pain score was recorded.

Methods
The research aims and experimental procedures had been fully explained to all participants, and the written informed consent of all participants was obtained.All studies were conducted in accordance with the Declaration of Helsinki and were approved by the Institutional Review Board of the Affiliated Hospital of North Sichuan Medical College.

Participants
Healthy participants were recruited from the local community.Exclusion criteria were: (1) individuals with any physical illness, such as a brain tumor, hepatitis, or epilepsy as assessed according to clinical evaluations and medical records; (2) those with the existence of chronic pain conditions (e.g., tension-type headache, fibromyalgia, etc.); (3) those with the existence of a neurological disease or psychiatric disorder; (4) pregnancy; (5) those using prescription medications within the last month; (6) those with alcohol, nicotine, or drug abuse; and (7) those with claustrophobia.

Stimulus and experimental design
Fig. 1 gives an overview of our experimental design, including current intensity measurement, resting-state EEG acquisition, and task-state EEG acquisition.
Current intensity measurement.At least 24 h before the EEG recording, pain sensitivity was measured by generating constant-current squarewave electrical pulses through an electrical stimulator to give nociceptive stimulus (pulse duration: 50 ms; SXC-4A, Sanxia Technique Inc., China).The electrical pulse travels through a pair of surface electrodes placed on the upper side of the proximal phalanx of the left ring finger.The intensity of the stimulation was calibrated specifically for each participant: the initial intensity was 300 μA, and the electrical stimulation was gradually increased with the step length of 100 μA (up to 9900 μA), until the participants' numerical rating scale (NRS; 0 = no pain, 10 = unbearable pain) score for the intensity of the same stimulus reached 6 three times in a row.The NRS score 6 was used as the intensity of suprathreshold pain, which refers to the amount of pain that a participant perceives after a stimulus with an intensity higher than the participant's pain threshold (Abrishami et al., 2011), and its corresponding electrical intensity was recorded as a measure of pain sensitivity.
Resting-state EEG acquisition.Participants were instructed to sit in a comfortable chair in a silent, temperature-controlled room (28 • C) and try to relax, looking directly at the center of a black computer screen for four minutes, and EEG data were recorded.The middle three minutes were used as the resting-state EEG data.
Task-state EEG acquisition.To solicit pain empathy from participants, a pain empathy paradigm with static visual stimuli was used, and this stimulation paradigm had been previously used in our recent studies (Wang et al., 2021a).The priming stimulus was a set of 60 emotional pictures from the International Affective Picture System (IAPS).As can be seen from Fig. 1, these pictures depicted a body part (i.e., a hand, a forearm, or a foot) under daily scenarios in painful pictures (PP, e.g., using a knife incorrectly and one hand was cut; 30 pictures) or non-painful pictures (NP, e.g., using a knife properly to cut vegetables; 30 pictures).In terms of luminance, contrast, and color, painful and non-painful pictures were well matched (Meng et al., 2013).All pictures were 7.08 × 5.32 inches wide and tall, with a pixel density of 100.
To get familiar with the procedure, participants were instructed to complete a 6-trial task in a training session, in which all pictures were different from those of the test session.In the test session, pictures were presented at the center of a computer screen with a black background, each picture at a viewing distance of about 80 cm.As illustrated in Fig. 1, each trial began with a fixation cross presented for 2000 ms, and then a painful or non-painful picture was presented for 1000 ms, followed by a blank screen for 1000 ms.Subsequently, participants were required to verbally rate the unpleasantness of the pictures (0 = no feeling, 10 = unbearable discomfort) on the 11-point numerical rating scale within 2000 ms.It should be noted that ratings of unpleasantness refer to participants' own experiences when observing the pictures instead of the experience of the subject in the pictures.The intertrial interval was 2000 ms, and the EEG recorded a total of 30 trials per block, with a 3-5 min rest time between the two blocks.All the pictures were pseudorandom, and neither PP nor NP appeared three times in a row.For each participant, the pain empathy score was calculated as the difference between the average of ratings for each PP and the average of ratings for each NP.

EEG recording and preprocessing
EEG data were recorded by TMSi (Netherlands) SAGA 32-channel EEG recording equipment and its supporting software Polybench (pass band: 1-100 Hz; sampling rate: 1000 Hz) according to the extended 10-20 system.The tip of the nose was used as the online reference electrode.Electrode impedances were kept below 10 kΩ.EEG data were preprocessed using EEGLAB (Delorme and Makeig, 2004), an open-source toolbox running in the MATLAB environment (R2021a; MathWorks, USA).
The Reference Electrode Standardization Technique (REST) (Dong et al., 2017) was used for re-reference and band-pass filtered between 1 and 30 Hz (Basic FIR filter, cutoff frequency (− 6 dB): [0.5 30.5]Hz).Then, task-state EEG epochs were extracted using a window analysis time of 1200 ms (200 ms before and 1000 ms after the onset of the stimulus), and the baseline was corrected using the pre-stimulus interval.Epochs contaminated by gross artifacts (e.g., large muscle activity) were removed.Meanwhile, the participants' ratings of the pictures at this epoch will also be deleted and will not be counted when calculating the empathy score.Eyeblinks and movements were corrected using an independent component analysis algorithm (Ren et al., 2020).
Resting-state EEG data were down-sampled to 250 Hz.The REST was used as a re-reference and band-pass filtered between 1 and 30 Hz.Then, resting-state EEG epochs were extracted using a window analysis time of 2000 ms, and the baseline correction was based on its own average.Similarly, artifacts and contamination were removed as much as possible (Zhang et al., 2021).

EEG feature extraction
For the task-state EEG data, we first calculated ERPs in painful and nonpainful conditions.Epochs belonging to the same condition were averaged, thus yielding two average waveforms for each participant.We identified Fz, P3, and P4 as electrodes of interest based on the potential distribution of scalp topographic maps in the time range of 0-1000 ms after stimulation.Then, all subjects and all conditions (NP and PP) were chosen for a one-way repeated measure analysis of variance (ANOVA) at each time point in three channels (Boly et al., 2011).Consistent with previous studies (Fan and Han, 2008;Han et al., 2008), the mean amplitudes of N1, N2 (from Fz), P300 and LPP (from the average of P3, P4) components were extracted for each participant.Second, the preprocessed EEG epochs and a standard fast Fourier transform (FFT) algorithm were used for EEG power spectrum analysis, and the Power Spectrum Density (PSD) was solved (Wang et al., 2014).The power spectrum of different frequency bands represents the power distribution of the signal in a specific frequency band.In the signal feature extraction phase, we used PSD to calculate the spectral power over the four frequency bands of Fz, P3, and P4 for each participant (delta/δ 1-3 Hz, theta/θ 4-7 Hz, alpha/α 8-13 Hz, and beta/β 14-30 Hz).
The phase locking value (PLV) is a common index of EEG functional connectivity based on phase synchronization, which has the advantage of being independent of the amplitude of the two neural oscillatory activities but only dependent on the phase (Gonuguntla et al., 2013).In the current study, time-frequency conversion (STFT, Hanning window width 100 ms) was performed on the data to obtain the time-frequency matrix of stored phase information for all epochs.For each time-frequency point, the consistency of the phase difference matrix of all epochs of the two electrodes at that time-frequency point was calculated, and the PLV of the time-frequency point was obtained (Lachaux et al., 1999).In the current study, the Fz, P3, and P4 were used as seed points, and the PLV of the seed electrode to other electrodes was calculated in different frequency bands (Time range: [0 1000] ms, the baseline limits: [− 150 − 50] ms).Further, we narrowed the time and frequency range to calculate PLV within a time-frequency region of interest (ROI) (Taesler and Rose, 2016).
For the resting-state EEG data, the calculation of the average power was similar to that in the task-state.For the calculation of PLV, a bandpass filter was applied to the frequency band of interest, and the Hilbert transform was used to obtain the phase difference at each time point between the two electrodes.All epochs were then averaged to obtain PLV between the whole set of electrodes.

Statistical analyses
To study the differences between PP and NP, paired sample T-tests were used for pain empathy scores and EEG features.Cohen's d was calculated to reflect the effect size for the T-tests.A log transform (log10) was used to normalize the EEG spectral power and the electrical threshold into normal distributions.The electrical threshold after the log transformation is taken as the current intensity.The false discovery rate (FDR) was used for multiple paired T-tests in the analysis of functional connectivity.The significance threshold for the FDR was set at p < 0.05.
To investigate the relationship among the pain empathy score, taskstate EEG features, and resting-state EEG features, mediation analyses were performed.Mediating analysis used task-state EEG features as the mediating variable, resting EEG features as the independent variable, and subjective score or pain sensitivity as the dependent variable.Pearson correlation was used to study the relationship among pain empathy scores, pain threshold scores, and EEG features, respectively.The mediation analyses were performed with SPSS software, and confidence intervals were verified with 5000 bootstrap tests.

Demographic characteristics
A priori power analysis conducted using the G*Power 3 revealed that 23 participants were required to reach a good statistical power of 0.95 to have effect of 0.8 with an alpha value of 0.05 for a paired T-test.To account for possible dropouts or errors during the study, a total of 30 healthy participants were recruited.Six participants were excluded for incomplete experimental procedures or serious data contamination.Thus, 24 healthy participants with an average age of 19.7 ± 0.3 years were selected (Table 1).The empathy scores were 2.8 ± 0.3.The current intensity was 3.2 ± 0.1.Paired sample T-tests revealed that the ratings in PP (3.8 ± 0.4) were significantly larger than those in NP (1.1 ± 0.2) (t (23) = 8.73, p < 0.0001, Cohen's d = 1.76).

ERP features
As shown in Fig. 2A, the ERP of Fz responses displayed two distinct negative components at early latencies (N1 and N2 components) and concentrated in the frontal region.Paired sample T-tests showed N2 absolute amplitudes in PP (5.33 ± 0.41) were significant larger than those in NP (4.00 ± 0.38) (Fig. 2B, t(23) = 2.44, p = 0.0226, Cohen's d = 0.69) but there was no significant difference between PP and NP in N1 absolute amplitude (p > 0.05).
As shown in Fig. 2C, the average ERP of P3 and P4 responses exhibited a major positive component in late latency (the P300 component), followed by a long late positive potential (the LPP component), and were most concentrated in the parietal region.The LPP component had the mean value of 600-900 ms amplitude.Paired sample T-tests showed that LPP amplitudes in PP (2.87.± 0.52) were significantly larger than those in NP (2.05 ± 0.50) (Fig. 2E, t(23) = 2.64, p = 0.0217, Cohen's d = 0.33).There was no significant difference between PP and NP in P300 amplitude (p > 0.05).

Spectral power features
As shown in Fig. 3A, the individual-level average power of each frequency band of the Fz was calculated.Paired-sample T-tests showed that the average power of the delta band in PP (4.15 ± 0.30) was significantly larger than that in NP (3.75 ± 0.23) (t(23) = 2.86, p = 0.0088, Cohen's d = 0.30), and the average power of the alpha band in PP (− 0.26 ± 0.04) was significantly less than that in NP (− 0.22 ± 0.04) (t(23) = 2.14 p = 0.0434, Cohen's d = − 0.21).For the delta bands, correlation analysis showed that individuals' pain empathy scores were positively correlated with the delta power in PP (Fig. 3B, r = 0.4667, p = 0.0215) and the resting delta power (Fig. 3C, r = 0.4932, p = 0.0143), respectively.A significant correlation was also found between the delta power in PP and the resting delta power (Fig. 3D, r = 0.7351, p < 0.0001).However, the results of the mediating analysis did not reveal any statistically significant findings among the delta power in PP, the resting delta power, and the empathy score.For the alpha bands, correlation analysis showed that the PP alpha power was related to the resting alpha power (r = 0.6408, p = 0.0007), but the correlation with empathy score was not statistically significant for both the alpha power in task and resting states (Figure S1 in the supplement).Additionally, no significant correlation with pain sensitivity was found for the average power of either delta or alpha.
The average power of each frequency band of P3 and P4 was also calculated (Figure S2 in the supplement).Paired-sample T-tests showed that the average power of the delta band in PP (0.78 ± 0.04) was significantly larger than that in NP (0.73 ± 0.03) (t(23) = 2.45, p = 0.0222, Cohen's d = 0.30), and the average power of the alpha band in PP (− 0.05 ± 0.04) was significantly less than that in NP (− 0.01 ± 0.05) (t(23) = 2.64 p = 0.0146, Cohen's d = − 0.18).No significant correlations were found between the empathy scores and delta/alpha power in task, and between the empathy scores and delta/alpha power in resting (Figure S3 in the supplement).

Functional connectivity features
For the delta and alpha band, the average PLV of Fz, P3, and P4 were significantly different between NP and PP (Figure S4 in the supplement).Paired sample T-tests were performed for each PLV at all time-frequency points in the delta (1~3 Hz, 0~1000 ms, 3003 points in total) or alpha (8~13 Hz, 0~1000 ms, 6006 points in total) band of the entire epoch of these connectivities.Only the time-frequency matrix of Fz-P3 showed significant differences (Fig. 4A and Figure S5 in the supplement).Specifically, time-frequency points within the red region were significant at p < 0.05 FDR corrected, and this region was considered as an ROI for the following analysis.
The current intensity of pain stimulation was positively correlated with the average PLV difference between PP and NP in ROI (Fig. 4B, r = 0.4277, p = 0.0371) and was positively correlated with the average PLV of Fz-P3 in the alpha band of the resting state (Fig. 4C, r = 0.4454, p = 0.0292).However, no correlation between the resting state and task state was found, and no significant associations were found between the pain empathy score and functional connectivity of Fz-Cz in the task-and resting-state.

Discussion
In this study, task-and resting-state EEG analyses were used to investigate individual differences in pain empathy and their relationship with individual pain sensitivity.Three main findings were obtained.First, as compared to NP in the pain empathy paradigm, the N2 and LPP amplitudes were significantly higher in the PP condition.Second, significant between-group differences (PP vs. NP) in the average spectral power of the Fz were found in the delta and alpha bands.There was a significant correlation between Fz's delta power in PP and the empathy score.Third, for the alpha band, functional connectivity between Fz and P3 showed significant between-group differences (PP vs. NP).There was a significant correlation between functional connectivity of Fz-P3 in task-state and pain sensitivity.Our results suggested that the spectral power in Fz's delta band may reflect subjective pain empathy across individuals, and the alpha band may reflect individual differences in pain sensitivity and be involved in pain empathy processing.

The electrical activity of the scalp corresponding to the Fz, P3, and P4 electrodes is closely related to pain empathy
In the current study, the ERP components can help us determine when neural activities started to differentiate between the perception of painful and neutral stimuli and whether task demands modulated this differentiation (Fan and Han, 2008).Previously, the features of EEG signals associated with pain empathy have been widely found to include the components of N1, P2, N2, P300, and LPP in the time domain ERP (Fan and Han, 2008;Ren et al., 2020).The P300 and LPP components in the parietal and occipital regions were thought to reflect an elaborative cognitive appraisal of pain empathy (Meng et al., 2013;Ren et al., 2020).The early N1, P2, and N2 components that mainly appeared in the frontal region were suggested to represent the emotional components of pain empathy (Cui et al., 2017;Xu et al., 2015).Our experiment induced emotional empathy in participants using a series of painful or neutral picture stimuli and recorded their unpleasantness to calculate empathy scores.One of our main findings was that the absolute amplitude of N2 and LPP was larger in PP than NP.Researchers pointed out that N2 may be an indicator of early automatic processing of stimuli and play an important component of emotional cognition in pain empathy (Cui et al., 2017;Vecchio and De Pascalis, 2023).For example, Cui et al. had participants with low or high working memory loads view pictures of others in painful or non-painful situations, and found that the greater the degree of emotional sharing, the greater the N2 amplitude induced.
One previous study has suggested that the LPP could potentially serve as an indicator of self-regulation processes associated with empathy (Ikezawa et al., 2014).Furthermore, the LPP may be separate from early automatic emotional responses, only regulated by negative stimuli (McCrackin and Itier, 2021).And with the increase in age, emotional arousal gradually decreased, cognitive evaluation gradually increased, and the LPP differential wave showed a greater gain (Cheng et al., 2014).Our results were consistent with these studies, indicating an association between pain empathy and the electrical activity of the scalp at Fz, P3, and P4 electrodes.

Spectral power in Fz's delta band may reflect subjective pain empathy across individuals
Spectral analysis is one of the standard methods used for quantification of the EEG (Dressler et al., 2004).By using FFT to transform time-domain EEG signals into frequency-domain signals, power spectral analysis allows for the separation of frequency bands that reflect different brain activity and the detection of responses that are delayed or not tightly synchronized to a stimulus (Goldfine et al., 2011).One study suggested that the delta band was deemed to be related to the relevance of the material being processed and to the degree of attention involved in the processing of visual stimuli (Balconi and Maria Elide Vanutelli, 2017).In some cases, it was shown that delta could be a marker of emotional cues (Fernández et al., 1998).The second primary discovery of our study revealed a significant difference in spectral power in the delta band between NP and PP on Fz, P3, and P4.Additionally, participants who had higher empathy scores tended to have higher delta power in PP on Fz, but not P3 and P4.In studies of emotional empathy, the delta power of the frontal region was shown to be responsive to empathic situations where emotional behavior was involved (Balconi and Maria Elide Vanutelli, 2017;León et al., 2014).For example, in Balconi et al.'s experiment, all participants were asked to look at emotional images depicting real interpersonal situations and generate empathy, and they found that the delta powers increased in response to positive and negative emotional interactions compared to neutral   interactions.Consistent with these findings, our results indicated that the delta power on Fz may reflect individual differences in pain empathy.The delta power on P3 and P4 was affected by painful pictures, but it may not reflect individual differences in emotional empathy.
In the absence of any task or stimulation, brain activity in the restingstate can mirror some task-induced activity patterns (Smith et al., 2009).Researchers used resting-state EEG to explore individual differences in pain empathy, and found that its power, functional connectivity, and microstate can reflect individual cognitive control and emotional empathy (Ambrosini and Vallesi, 2016;Koller-Schlaud et al., 2020;Zhang et al., 2021).Our results found a significant correlation between pain empathy and resting-state EEG in delta power and a significant correlation between the resting-state and task-state EEG in Fz delta power.However, the mediating effect between resting state, task state, and the empathy score was not observed.We inferred that this lack of mediation may be attributed to the presence of substantial collinearity between the resting and task states.Some studies have shown that differences in resting-state EEG affect the performance of task-states, and a significant increase in delta resting brain oscillatory activity could improve arousal and behavioral performance in emotional empathy tasks (Kim et al., 2014;Lapomarda et al., 2022).Our findings suggested that resting-state frontal delta rhythm brain activity may reflect an individual's sensitivity to others' pain expression and could influence empathy generation, leading to differences in individual pain empathy.It should be noted that we found no association between delta power and pain perception, possibly suggesting that this feature may be more correlated with individual differences in empathic responses than pain processing.

Alpha bands of Fz, P3, and P4 may be involved in processing pain empathy and associated with individual differences in pain perception
The frontal alpha oscillations were proposed to reflect the origin of top-down control regulating perceptual gains and modulations of parietal alpha oscillations were proposed to relates to intersensory reorienting (Misselhorn et al., 2019).Frontal alpha power has been widely used as an index of individual differences in emotional processing and affective style (Benz et al., 2013;Crabbe and Dishman, 2004;Simon et al., 2011).In the present study, the alpha power of PP on Fz, P3, and P4 was lower than that in NP, suggesting that frontal and parietal alpha rhythms were inhibited during receiving visual stimuli that involve the pain of others.One study found that participants passively observing pain expressed in virtual reality induced alpha power inhibition in the sensorimotor cortex region, and participants with strong empathy had higher alpha inhibition (Joyal et al., 2018).However, the association between alpha power and pain empathy was not found in our study.Whether the alpha power could reflect individual differences in pain empathy needs to be further considered.
Growing evidence suggested that rhythmic brain oscillations and their synchronization/ desynchronization may serve as indicators of dynamic and flexible communication between and across the cerebral networks underlying a specific behavior (Fries, 2009;Hanslmayr et al., 2016).Pain empathy may be based on the functional interplay of neuronal assemblies dispersed within and across different specialized brain regions (Betti et al., 2009).As a connection metric, PLV's reduction in the alpha band was suggested to represent phase desynchronization in response to visual stimuli (Burgess, 2013;Nunez et al., 1997).In our findings, the desynchronization level of electrode pair Fz-P3 in the alpha band was lower in PP than NP.However, no significant association was found between PLV and individual pain empathy scores.It suggested that the degree of phase synchronization between Fz and P3 is affected by pain empathy but could not reflect differences in the ability to empathize between individuals.
Many previous EEG studies have shown that various features of frontal alpha may be affected by participants' pain perception (Del Percio et al., 2006;Pascoal-Faria et al., 2015).For example, researchers reported greater frontal connectivity of the resting-state alpha rhythm in patients with chronic pain (Topaz et al., 2023;Ye et al., 2019).Combined with the effect of pain empathy on task state PLV, our findings indicated that Fz-P3 functional connectivity may reflect individual pain sensitivity, and related brain regions may also be activated due to pain empathy.In addition, participants' pain perception or expectations altered the alpha-band functional connectivity of the prefrontal to the somatosensory cortex (Bott et al., 2023;Nickel et al., 2020).We found that the increase in PLV within the ROI induced by pain empathy was positively correlated with current intensity and resting-state PLV.Thus, our results suggested that PP stimuli may have a pain-like effect on the prefrontal-sensorimotor network, thereby enabling individuals' pain sensitivity to influence their pain empathy.

Limitation
There are several issues that should be discussed in future research.First of all, the number of participants included in our experiment is small, which may lead to insignificant or accidental results from correlation analysis.Secondly, since mental fatigue occurs after performing a demanding task for a prolonged time, we did not correct the EEG signals during the electrical stimulation of pain.The direct relationship between brain oscillations of pain and pain empathy was not investigated in the current study.In our findings, significant associations were found among individuals' pain intensity scores and the functional connectivity of Fz-Cz in both task-state and resting-state.It suggested that there may be a potential association between pain perception and pain empathy.Future studies should consider a more comprehensive experimental design.Thirdly, healthy participants were recruited from the local college.The gender imbalance among recruited participants is a limitation of our study.Women are commonly regarded as being more empathetic than men.One recent study highlighted the importance of the anterior insula in mediating the sex difference in trait empathy (Wu et al., 2023).Future research should investigate the potential existence of a sex difference in the engagement of delta and alpha rhythms in the processing of pain empathy.

Fig. 1 .
Fig. 1.Experimental design.The experiment is divided into three parts: current intensity measurement, resting-state EEG acquisition, and task-state EEG acquisition.

Fig. 3 .
Fig. 3.The average power of the Fz.The comparisons for average power in each frequency band between NP and PP (A).Correlation analysis between empathy score, PP δ power and resting δ power (B~D).PP = painful pictures; NP = nonpainful pictures.

Fig. 4 .
Fig. 4. The PLV of electrode pair Fz-P3 alpha band.Paired sample T-tests for the PLV time-frequency matrix (blue: no significant; yellow: p<0.05, uncorrected; red: p<0.05,FDR corrected) (A).The correlation between the PLV difference in ROI and the current intensity of pain stimulation (B).The correlation between the PLV difference in ROI and the PLV in the resting state (C).PP = painful pictures; NP = nonpainful pictures.

Table 1
Demographic characteristics of all participants.
Data were reported as mean ± standard error unless otherwise indicated.NP = nonpainful picture; PP = painful picture.Z.Pan et al.