Li et al. Journal of Physiological Anthropology
https://doi.org/10.1186/s40101-023-00351-2(2024) 43:4

Journal of
Physiological Anthropology

Open Access

ORIGINAL ARTICLE

Altered neurophysiological responses
during empathy for pain in insomnia: evidence
from an EEG study in non‑clinical samples
Siyu Li1, Meiheng He1, Li Lin1, Qingwei Chen2,3* , Taotao Ru2,3* and Guofu Zhou2,3

Abstract
Background This study aims to investigate the behavioral and neurophysiological changes accompanying
the empathy for pain among individuals with insomnia in nonclinical samples, which has been scarcely exploredin the existing literature despite the deleterious effects of sleep disturbance on social behavior, and interactions had
been well-documented.
Methods Twenty-one individuals with insomnia in nonclinical samples and 20 healthy individuals as normal controls
participated in the study. Electroencephalograph (EEG) was continuously recorded, while the participants underwent
an empathy for pain task.
Results Subjective ratings of pain for painful and non-painful images revealed no statistically significant differences
between the insomnia and control groups. The painful images induced a smaller P2 compared to non-painful images
in the insomnia group, whereas no such difference was revealed for the controls. Moreover, a higher power densityof the alpha and theta2 bands in the posterior brain regions was found in the insomnia group compared to the control group.
Conclusion These findings suggest that individuals with insomnia exhibit altered neurophysiological responses
to pain stimuli and a lower capacity to share empathy for pain. These alterations may be associated with changes
in attentional mechanisms.
Keywords Insomnia, Empathy for pain, Event-related potentials, Time-frequency analysis*Correspondence:
Qingwei Chen
qingwei.chen@m.scnu.edu.cn
Taotao Ru
taotao.ru@m.scnu.edu.cn
1School of Psychology, South China Normal University,
Guangzhou 510631, China
2
Lab of Light and Physio‑Psychological Health, National Center
for International Research On Green Optoelectronics, South China Normal
University, Guangzhou 510006, China
3
Guangdong Provincial Key Laboratory of Optical Information Materials
and Technology & Institute of Electronic Paper Displays, South China
Academy of Advanced Optoelectronics, South China Normal University,
Guangzhou 510006, China

BackgroundAs one of the most common sleep disorders, insomnia
is characterized by difficulty falling asleep, maintaining
sleep, early awakening, poor sleep quality, and cognitive
impairment during the day [1], which is identified as arisk factor for multiple undesirable mental and physicaloutcomes [2, 3]. With the higher academic and employment pressures, a growing number of college students
experienced insomnia symptoms and subsequently
impaired physiological and psychological function. Findings from a meta-analysis study reported that the prevalence of insomnia among college students in China
ranged from 13.0 to 30.3% [4].

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Li et al. Journal of Physiological Anthropology(2024) 43:4

Sleep disturbance has been well established not only
disrupts emotional and cognitive functioning at the individual level [5], but also affects complex socio-emotional
functioning in terms of decreasing pro-social behavior[6]. Empathy is the process by which individuals understand and share the emotions and thoughts of others,
and it plays an important role in human social interaction [7]. Empathy for pain, as the most typical form of
empathy, refers to an individual’s perception, judgment,
and emotional response to others’ pain [8, 9], which is a
so-called empathic the state of empathy. It has been previously shown that insomnia-induced sleep impairment
had adverse impacts on mood [10]. Meanwhile, empathy for pain itself involves both cognitive and emotional
processing [11]. It thus would expect a direct effect of
insomnia on empathy for pain due to the common affective nature. However, whether and the extent to which
insomnia-induced chronic sleep disturbance influences
the ability to share and understand others’ emotional
feelings in individuals with insomnia have been scarcely
investigated.
Several previous studies have investigated the relationship between sleep and empathy. For instance, a questionnaire survey conducted by Brand et al. [12] among
adolescents reported that subjective perception of poor
sleep correlated with distinct deficiencies in emotional
competence and empathy. Rong et al. [13] found that
extended nighttime sleep duration was positively associated with the capacity of cognitive empathy, whereas
sleep disturbances were positively associated with emotional empathy capacity in preschoolers. Peretti et al.
[14] found that sleep deprivation induced a decreased
empathic response in both emotional and cognitive
empathy measures. Individuals suffering from severe
sleep deprivation have been shown to have difficulties
with emotion regulation and empathy [15]. Nonetheless,a few studies have directly investigated the relationshipbetween sleep and empathy for pain. One early fMRI
study reported that the core neural networks of empathy(anterior insula and anterior cingulate cortex) remained
unaffected with acute sleep restriction; however, theolder participants perceived the painful images as more
unpleasant after sleep restriction, but it was not the casefor the young participants [16]. Recently, one ERP study
by Duan et al. [17] revealed that acute sleep deprivation
resulted in decreased amplitude of the early components(N2, N340) and reduced power density of theta2 band,suggesting the ability to empathy for pain was impaired
following sleep deprivation. The authors argued thatsleep deprivation induced deficits in particular components of empathic and emotional processing. However, it
is still unclear whether insomnia-induced chronic sleep
loss and/or impaired sleep quality would directly regulate

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empathy response to pain. Considering that most of theresearch on sleep and empathy for pain has focused on
experimental depriving nocturnal sleep to cause acutesleep disturbance, there has been a notable gap in investigating the effects of insomnia-induced chronic sleep
impairment on neural oscillations related to empathy
for pain in individuals with insomnia. Thus, the primary
purpose of the current study is to investigate the effects
of insomnia on empathy for pain and further to explore
the temporal dynamics of neurophysiological changes
accompanying the empathy for pain among individuals
with insomnia.
Referring to the existing ERP studies on empathy for
pain and emotional processing, the ERP components
were particularly focused with interest on P2, N2, andLPC. P2 is mainly found in the frontal, central, and parietal areas, indicating the neuronal responses to groupbias within empathy [9, 18]. Research has indicated that
individuals with insomnia may show alterations in P2
waves, reflecting differences in cognitive processing ofstimuli and emotional regulation. For instance, Kertesz
et al. [19] found that insomnia patients demonstrated significantly smaller P2 amplitudes during audio stimulation
tasks compared to good sleepers. N2 is localized in thefrontal, central, and temporal regions [9], and the amplitude of N2 represents the degree of emotional sharingof empathy for others’ pain [20]. Additionally, Ling et al.
[21] discovered reduced N2 amplitudes in insomniacs
during the Go/No-go task, suggesting potential deficits
in attentional and inhibitory control. LPC, localized in
the posterior parietal lobe, represents a stage of cognitive
processing and evaluation of others’ pain like that of P3[20]. These findings highlight the potential utility of ERPs
in assessing and understanding the cognitive aspects ofinsomnia.
In addition, previous studies have reported that empathy for pain was characterized by frequency domain-specific neural activities. For instance, both the theta-band(3–8 Hz) synchronization and the alpha-band (9–14 Hz)
desynchronization were associated with empathy for
pain [22, 23]. Alpha oscillations underlined the sensory
qualities of others’ pain and were involved in the processing of somatic aspects of empathy for pain [24], whereas
emotional sharing and regulation during empathy for
pain were regulated by theta oscillations [23]. To provide
the persistence of dynamic neuronal oscillations in continuous signals, the current study also aims to use a TF
decomposition analysis to explore the effect of insomnia
on the frequency-domain measurements of neural activities associated with empathy for pain.
Hence, the current study aims to explore whether
insomnia would affect empathy for pain by contrasting the response of individuals with insomnia to painfulLi et al. Journal of Physiological Anthropology(2024) 43:4

images to those who were healthy. Furthermore, thedifferences in the temporal domain and frequency
domain-specific neural oscillations involving empathy
for painful images between individuals with insomnia
and the healthy are also investigated. We hypothesizedthat participants in the control group responded tothe painful images faster than those in the insomnia
group. Participants in the insomnia group would show
the decreased amplitude of the early and mid-late ERP
components (i.e., P2, N2, and LPC) elicited in alternative painful processing. Meanwhile, neural oscillations
involving empathic responses to painful images would
also be regulated by insomnia.

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education level. Table 1 presents the demographic characteristics of participants in both the insomnia and control groups. It is important to note that all employedparticipants had normal vision and had not taken any
sleep-affecting medications in the month leading up to
the study, ensuring a consistent baseline for analysis.
Participants in the insomnia group were screenedin non-clinical samples according to the criteria of theInternational Classification of Sleep Disorders (ICSD) [1],which may not necessarily align with those individuals
clinically diagnosed with insomnia by healthcare professionals or clinicians, including the following:(1) The Pittsburgh Sleep Quality Index (PSQI, [26]) > 7(2) Insomnia Severity Index (ISI, [27]) > 7(3) Sleep problems last longer than 1 month and occur
at least three times per week.
(4) The Beck Depression Inventory-II (BDI-II, [28]) < 12

Methods
DesignThis study followed a between-within subject design with
the Sleep group (between-subject factor: insomnia groupvs. control group) and Painful materials (within-subject
factor: painful, non-painful) as independent factors. Eachparticipant visited the lab once, and both painful andnon-painful images were administered alternately.
Participants and screening

The expected sample size was calculated using the
G*Power 3.1.6 software [25], indicating that a sample sizeof 17 for each group yields 80% power to detect a moderate effect size (0.25) with an error probability of 0.05.
Therefore, 21 participants were initially recruited foreach group, leading to a total of 42 participants. However,one participant withdrew from the experiment for personal reasons. In the end, a total of 21 participants withinsomnia (7 males) and 20 control healthy participants (8males) participated in the laboratory study (see Table 1).
Participants were recruited for the study throughonline advertisements at a local university. The participants included in the control group and insomnia
group were carefully matched in their age, gender, and

The inclusion criteria for participants in the control
group were as follows: (1) PSQI < 7 and ISI < 7, (2) content with their regular sleep patterns and have not experienced any insomnia symptoms in the past 3 months,(3) sleep latency < 30 min and total nighttime sleep
time ≥ 7 h, and (4) BDI-II < 12. The participants employedin the control group were matched with those in the
insomnia group in terms of age, gender, and educationlevel and had none of the diagnostic criteria of insomnia
met. All participants provided written informed consent,
and the study was approved by the ethical committee at a
local university.
Procedure

An online screening questionnaire was sent to all potential participants 1 week before the laboratory study. Participants who met all the screening criteria were required
to complete sleep diaries [29] to monitor their regularsleep quality and sleep–wake schedule. After arriving in

Table 1 Demography of participants in the control and insomnia groups
Controls (N = 20)
M

Insomnia (N = 21)
SD

M

t

p

SD

Male

8

7

Female

12

14

Age

20.7

2.01

21.7

2.82

− 0.86

0.4

Years of education

14.8

2.41

16.2

2.32

− 0.98

0.52

PSQI

3.3

1.63

11.71

2.8

− 11.7< 0.001

ISI

2.7

2.01

12.71

2.76

− 13.13< 0.001

BDI-II

6.89

3.13

8.23

3.57

− 2.67< 0.05

M mean, SD standard deviation, PSQI Pittsburgh Sleep Quality Index, ISI Insomnia Severity Index, BDI-II Beck Depression Inventory-II

Li et al. Journal of Physiological Anthropology(2024) 43:4

the lab, participants were briefly interviewed and filledout questionnaires regarding their demographics, baseline mood, and sleep quality of the preceding night.
Afterward, electrode caps were fitted to participants. To
maintain fixation at the fixation crosses and limit head
movements during EEG recording, participants were
instructed to blink as little and in the absence of a stimulus as possible. The formal experiment started with a few
practice trials. Then, two blocks of images were administered, with one block containing painful images and theother one containing non-painful images.
Stimuli and empathy for pain task

One hundred images (50 painful and 50 non-painful
images) were selected from a previous study by Meng
et al. [9]. This set of images was also widely used insubsequent pain-related studies [30–35]. The painfulintensity, valance, arousal, and motion of all painful andnon-painful images were subjectively assessed in a study
by Meng et al. [9], suggesting the manipulations of all
images were valid. The descriptive statistics of employedpainful and non-painful images in the current study are
shown in Table 2. It should be noted that all the images
are presented in pairs, with one representing a painful
scenario and the other a non-painful scenario (see Fig. 1).
Each image was manipulated with the same dimensionsof 9 cm × 6.76 cm (width × height) and a precision of l00
pixels per inch. The brightness, contrast, and color of the

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images were matched between the painful and non-painful images.
Each trial started with a 500-ms fixation “ + ” onthe screen center followed by a blank screen for
500–1000 ms, and then one of the paired images was
randomly displayed for 1000 ms (see Fig. 2). The participants were asked to press the “F” key if they thought
the person in the image was painful; otherwise, press
the “J” key. Afterward, the participants were asked tosubjectively rate the pain level of the person in theimage on a 9-point Likert scale ranging from 1 “nopainful” to 9 “very painful” and the degree of self-rating
on a 9-point Likert scale ranging from 1 “no unpleasantness” to 9 “very unpleasantness”; the two slices for
subjective ratings were run with a random interval of
500–1000 ms and were terminated after participants’
responses. Random intervals aim to ensure task independence and minimize biases by making the start ofeach task unpredictable. This helps prevent participants from forming temporal patterns or expectations.
A total of 100 trials were presented in two blocks, each
including half of the trials.
Baseline sleep and mood

PSQI was used to assess participants’ baseline sleep quality. The Self-rating Anxiety Scale (SAS) [36] and the Positive Affective and Negative Affective Schedule (PANAS)[37] were employed to assess the baseline mood.

Table 2 The descriptive statistics of painful and non-painful images employed in the current study
Painful (N = 50)
M

Non-painful (N = 50)
SD

M

t

p

SD

Painful intensity

5.71

0.59

1.76

0.46

− 39.98< 0.001

Valance

3.41

0.35

4.50

0.49

13.86

< 0.001

Arousal

5.47

0.48

2.39

0.49

− 34.11< 0.001

Motion

4.92

0.52

4.75

0.40

0.92

0.576

M mean, SD standard deviation

Fig. 1 Examples of image materials used in the experiment. A Painful stimulus. B Non-painful stimulusLi et al. Journal of Physiological Anthropology(2024) 43:4

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Fig. 2 Flow chart of the experimentElectroencephalogram (EEG) recording and preprocessing

EEG was collected with an 88-channel EEG amplifier (ANT EEGO; eemagine Medical Imaging Solutions
GmbH, Berlin, Germany) with 64 electrodes placed on
the scalp in accord with the requirements of the international 10–20 system. All channels were referenced during recording to a reference electrode positioned at CPz,and an electrode positioned at the FPz and Fz served
as ground. The electrooculogram (EOG) channels forrecording vertical eye electricity were located 1 cm below
the left orbit. All electrode impedances were kept below 5kΩ, and the bandpass filter was set at 0.01–100 Hz with asampling rate of 1 kHz.
EEG data were processed offline using MATLAB (v.
R2014a; the MathWorks Inc.) and with the EEGLAB
toolbox (version 13.5.4b). To exclude low-frequency drift
and high-frequency noise signal interference, the data isfiltered using a digital zero-phase bandpass filter with aslope of 24 dB/octave from 0.01 to 30 Hz. Subsequently,
the reference was converted to the average of the bilateral
mastoid signals, and continuous EEG data were reference converted. Eye artifact correction was accomplishedseparately for each participant by subjecting the EEG
data recorded to independent components analysis (ICA;[38]). Eye artifacts were removed by identifying their
topography. Subsequently, continuous EEG data weredivided into segments (− 200 ms before stimulus initiation to 800 ms after stimulus presentation), and baseline
correction was performed using 200 ms (− 200 to 0 ms)
before stimulus presentation as a baseline.
The current study is particularly interested in the neural activities involved in the ERP components, including painful stimulus-elicited early components (P2, N2)
and late components (LPC). Based on visual inspectionof the grand averages, scalp topographies, and previous

research, electrode grand averages were calculated separately for P2 waveform (F3, F4, FC1, and FC2 electrodes),N2 (FC5, FC6, and Cz electrodes), and LPC (FC1, FC2,
Cz, and FCz electrodes; 300 − 600 ms). Peak amplitude and latency were detected as local maxima andminima in certain time windows (P2: 120 − 180 ms; N2:
230 − 330 ms). Regarding time–frequency-domain indicators, the total EEG power was analyzed by following
these steps, and the specific electrodes were determined.
First, single-trial data were used to estimate the oscillatory power via the Morlet continuous wavelet transform(MCWT, [39]. The parameters of central frequency (ω)
and restriction(σ) in MCWT were 5 and 0.15, respectively [40]. Time–frequency representations (TFRs were
explored in the range of 1–40 Hz in steps of 0.5 Hz. Second, single-trial TFRs were averaged to obtain the averaged TFRs of every participant under each condition.
Third, the averaged TFRs were subsequently cut in length(− 500 to 1500 ms to reduce the edge effects. Fourth, thepower was normalized by conversion to a decibel (dB
scale [10_ log10 (power/baseline]. The baseline power
was computed as the average power across all experimentconditions, from − 500 to − 200 ms.
Based on previous studies [17, 22, 23], the current
study identified three time–frequency regions of interest(TF-ROIs): the alpha-band (8–13 Hz, 100–650 ms) theta1(2–4 Hz, 200–500 ms) and theta2 (5–7 Hz, 200–500 ms),
and the density power was averaged to obtain non-phaselocked components [41]. Meanwhile, the spatial regionsof interest (S-ROIs): the FC1, FCz, and FC2 electrodes,
were selected and collapsed by averaging their values toobtain an indicator of anterior activity; the P1, Pz, and P2
electrodes were selected and collapsed by averaging theirvalues as an indicator of posterior activity.

Li et al. Journal of Physiological Anthropology(2024) 43:4

Statistical analysis

Linear mixed model (LMM) analyses were performed
to separate the effects between individuals and within
individuals with R Studio (version 4.0.3) (“psych” and“plyr” packages were used for the preparatory analyses, “emmeans,” “Sjstats,” “lme4,” and “lmerTest” packages were used for the statistical analysis). In addition,
the “sjstats” toolkit was used to calculate the effect sizesof the LMM models, and this study used the effect size
indicator ηP2 to estimate and describe the effect sizes.
To account for the two levels (individual level and trial
level) in the data, we used a random intercept model, thatis, in addition to the fixed effects, we added a random
effect with “participants” as a categorical variable. This
allowed each participant to have their own unique intercept, reflecting the differences between individuals, andimproved the fit and accuracy of the model parameters.
The data from two participants in the insomnia groupwere excluded due to their excessive EEG artifacts, andthe data from 39 participants were included in the formal
data analysis.
First, independent-sample t-tests were used to verify
whether there were any potential differences in baseline
measures of sleep variables and mood because the baseline sleep quality and mood (anxiety and positive andnegative affects) were measured once for each group.
These baseline measures were considered as covariates insubsequent analyses to control for their potential effects.
An LMM analysis for accuracy rate (ACC) and
response time (RT) was conducted to explore behavioral
differences in empathy for pain, with “Participants” as arandom intercept to cluster the data per participant, with
Sleep group (insomnia vs. control), and Painful materials(painful vs. non-painful) as a fixed factor, and the interaction between Sleep group and Material was added. In
the LMM analyses, to explore the group differences in
ERP components, the Sleep group and Painful materials were added as fixed factors, and the P2, N2, and LPC
were added as dependent variables, respectively. With
respect to the time–frequency domain indicators, Brainregion (anterior vs. posterior) was added as an additional
fixed factor and the alpha, theta1, and theta2 band poweras dependent variables. The nature of all interactions

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and comparisons between different pain materials was
explored using Bonferroni correction.
To explore the link between neural and behavioral
response to empathy for pain, the Pearson correlational
analyses between time-domain indicators (mean amplitude of N2 and LPC, peak amplitude of P2) and RT and
between frequency-domain indicators (alpha, theta2)
and RT were performed for the two groups (control andinsomnia group) across all Painful materials conditions,
respectively.

Results
Baseline sleep and mood

The independent-sample t-tests revealed significant differences in the anxiety and negative mood between the
insomnia and control groups (ps < 0.05); participants in
the insomnia group showed average worse sleep quality,
more anxiety, and negative mood than those in the control group (see Table 3).
Subjective ratings and behavioral performance of empathy
for pain

The descriptive results of behavioral indicators are shownin Table 4. LMM analyses for RT and ACC revealed thatthe main effects and interaction effects did not reach significance [ACC: FGroup (1, 39) = 0.61, ηP2 = 0.012, p = 0.44;
FMaterials (1, 39) = 0.46, ηP2 = 0.009, p = 0.50; FInteraction (1,39) = 2.25, ηP2 = 0.04, p = 0.14; RT: FGroup (1, 39) = 2.59,
ηP2 = 0.052, p = 0.12; FMaterials (1, 39) = 2.29, ηP2 = 0.046,
Table 4 Descriptive results of behavioral indicators
Controls (N = 20)

Insomnia (N = 21)

Non-painful Painful

Non-painful Painful

M ± SEM ± SEM ± SE

ACC​

0.910 ± 0.02

0.919 ± 0.02 0.949 ± 0.02

0.925 ± 0.02

RT820 ± 100.7834 ± 100.7 1102 ± 97.6

1104 ± 97.6

Cognitive
empathy

1.10 ± 0.34

5.07 ± 0.34

1.39 ± 0.34

5.41 ± 0.34

Emotional
empathy

1.14 ± 0.34

5.43 ± 0.34

1.46 ± 0.34

5.81 ± 0.34

M ± SEM mean, SE standard error, ACC​accuracy rate, RT response time

Table 3 Baseline comparison of sleep quality and mood between the two groups
Controls (N = 20)
M

Insomnia (N = 21)
SD

M

t

p

SD

Positive mood

31.1

6.42

29.81

6.36

0.65

0.52

Negative mood

17.25

6.12

21.71

7.85

− 2.02

0.04

SAS

36

5.76

47.32

9.2

− 4.7

< 0.001

M mean, SD standard deviation, SAS Self-rating Anxiety ScaleLi et al. Journal of Physiological Anthropology(2024) 43:4

p = 0.14; FInteraction (1, 39) = 1.27, ηP2 = 0.026, p = 0.27].
Painful materials revealed a significant main effect [F (1,19) = 273.83, ηP2 = 0.891, p < 0.001], with the subjective
ratings of emotional pain for the painful images being
higher than the ratings for the non-painful images. Meanwhile, LMM analyses for cognitive empathy revealed a significant main effect of Painful materials [F (1.19) = 186.11,
p < 0.001, ηP2 = 0.835], with the participants’ cognitive
empathy scores for the painful images being higher thanthose for the non-painful images. LMM analyses foremotional empathy revealed a similar case with cognitive empathy. Painful materials also yielded a significant
main effect [F (1, 19) = 273.83, p < 0.001, ηP2 = 0.872],with the participants’ emotional empathy scores for thepainful images being higher than those for the non-painful images. These findings suggested that participants
showed more empathy for the painful images regardlessof whether they were insomnia or not.

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ERP results

The ERP waveforms and the topographies for theempathy for pain task can be seen in Figs. 3 and 4,
respectively, and the descriptive statistical data is
included in the supplementary materials. As for N2
peak amplitude, the Sleep group and Painful materials
yielded no significant main effects (FGroup (1, 43) = 2.13,
ηP2 = 0.044, p = 0.15; FMaterials (1, 39) = 0.08, ηP2 = 0.008,
p = 0.59), and the interaction effect between Sleep
group and Painful materials was not significant (F (1,39) = 0.30, ηP2 = 0.006, p = 0.59). The mean amplitudeof N2 also yielded no significant main effects of Sleep
group and Painful materials (FGroup (1, 43) = 2.26,
ηP2 = 0.048, p = 0.14; FMaterials (1, 39) = 0.03, ηP2 = 0.001,
p = 0.86), and the interaction effect was also not significant (F (1, 39) = 0.69, ηP2 = 0.015, p = 0.41). Similarly,
there were no significant main effects of Sleep group,
Painful materials, nor their interaction effect on theFig. 3 ERP waveforms for the empathy for pain task. The red- and blue-shaded areas represent the standard error of the mean (SEM); *p < 0.05.
***p < 0.001. A N2 component, time windows 230–330 ms. B P2 component, time windows 120–180 ms. C LPC component, time windows
300–600 ms

Li et al. Journal of Physiological Anthropology(2024) 43:4

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Fig. 4 The topographies under different conditions. The control group on the left, the insomnia group on the rightpeak latency (FGroup (1, 56) = 0.41, ηP2 = 0.007, p = 0.53;
FMaterials (1, 39) = 2.99, ηP2 = 0.051, p = 0.09; FInteraction (1,39) = 1.39, ηP2 = 0.023, p = 0.25).
The peak amplitude of P2 revealed a significant interaction effect between Sleep group and Painful materials [F (1, 39) = 5.51, p = 0.04, ηP2 = 0.09]. The post hoc
analyses indicated that participants in the insomnia
group showed significantly smaller P2 for the painful
images than the non-painful images (EMMpainful = 0.39,
SE = 0.92; EMMnon-painful = 1.71, SE = 0.92, p = 0.02), while
the amplitude of P2 elicited by painful images did not differ with that elicited by non-painful images in the control group (p = 0.68). No significant main effects wererevealed (FGroup (1, 45) = 0.38, ηP2 = 0.007, p = 0.54; FMateri2als (1, 39) = 2.40, ηP = 0.048, p = 0.13). For both the mean
amplitude and peak latency of P2, no significant main
effects nor interaction effects were found (mean amplitude: FGroup (1, 43) = 0.37, ηP2 = 0.008, p = 0.55; FMaterials(1, 39) = 0.94, ηP2 = 0.021, p = 0.34; FInteraction (1, 39) = 2.06,
ηP2 = 0.046, p = 0.16; peak latency: FGroup (1, 56) = 3.12,

ηP2 = 0.054, p = 0.08; FMaterials (1, 39) = 0.91, ηP2 = 0.016,
p = 0.35; FInteraction (1, 39) = 2.35, ηP2 = 0.041, p = 0.13).
The mean amplitude of LPC revealed a significant main
effect of Painful materials [F (1, 39) = 15.53, p < 0.001,
ηP2 = 0.25], with the painful images eliciting larger
LPC than the non-painful images (EMMpainful = 3.23,
SE = 0.70; EMMnon-painful = 1.46, SE = 0.70, p < 0.001). The
main effect of Sleep group and interaction effect between
Sleep group and Painful materials did not reach significance (FGroup (1, 41) = 0.20, ηP2 = 0.003, p = 0.66; FInteraction(1, 39) = 3.43, ηP2 = 0.06, p = 0.07).
Correlation between task performance and ERP indicators

The correlational analyses revealed that the mean amplitude of N2 was significantly positively correlated with RT
in the control group across all Painful material conditions(r = 0.18, p = 0.007), while it was not the case for the
insomnia group (r = −0.01, p = 0.85). The peak amplitudeof P2 was significantly correlated with RT in the control
group (r = 0.41, p < 0.001), while no such correlation wasLi et al. Journal of Physiological Anthropology(2024) 43:4

revealed in the insomnia group (r = −0.04, p = 0.55).
Meanwhile, the mean amplitude of LPC was positively
correlated with RT in both groups (rcontrol = 0.35, rinsomnia= 0.30, ps < 0.001) (see Fig. 5).
Time–frequency‑domain results

The frequency-domain analyses in four conditions(insomnia-painful; insomnia-non-painful, control-painful, control-non-painful) consistently revealed that thelower frequency band (< 8 Hz) showed the maximum
event-related synchronization (ERS) at 0–600 ms, while

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the higher frequency band showed the maximum eventrelated desynchronization (ERD) at 500–1000 ms (seeFig. 6). Thus, the relative power of four spectrum evokedin these time-windows was further investigated.
The relative power of the alpha band revealed a significant main effect of Brain region [F (1, 117) = 46.159,
p < 0.001, ηP2 = 0.26], with the larger alpha power being
observed in the anterior regions than the posterior regions(EMManterior =  − 0.98, SE = 0.30; EMMposterior =  − 1.83,
SE = 0.30, p < 0.001). The interaction effect between Sleep
group and Brain region was significant [F (1, 117) = 12.19,

Fig. 5 Scatter plot of the correlation coefficient between task performance and ERP amplitude. A Scatter plot of the peak amplitude of N2
and response time for the control and insomnia groups. B Scatter plot of the peak amplitude of P2 and response time for the control and insomnia
groups. C Scatter plot of the mean amplitude of LPC and response time for the control and insomnia groupsLi et al. Journal of Physiological Anthropology(2024) 43:4

Page 10 of 14

Fig. 6 EEG power responses. A Grand-averaged power is produced by painful and non-painful arguments in the anterior regions (the anterior
region of interest is the superimposed average of FC1, FCz, and FC2 electrode points). B Grand-averaged power is produced by painfuland non-painful arguments in the posterior regions (the posterior region of interest is the superimposed average of P1, Pz, and P2 electrode points)

p = 0.001, ηP2 = 0.07]; the post hoc analyses indicated thatthe participants in insomnia group showed larger alphapower in the posterior regions than participants in thecontrol group (EMMinsomnia =  − 1.24, SE = 0.42; EMMcontrol =  − 2.44, SE = 0.41, p = 0.047), whereas no such difference was revealed in the anterior regions (p = 0.59).
No other two-way interaction nor three-way interaction
effects were revealed (ps > 0.05).
The relative power of the theta1 band revealed a significant main effect of Brain region [F (1, 117) = 11.78,
p = 0.001, ηP2 = 0.09], with the larger theta1 power inthe posterior regions than the anterior regions (EMManSE = 0.16; EMMposterior = 1.89, SE = 0.16,
terior = 1.51,
p < 0.001). In addition, no other main nor interaction
effects reached significance (ps > 0.05).
The relative power of the theta2 band revealed significant main effects of Painful materials and Brain region[F (1, 117) = 7.05, p = 0.009, ηP2 = 0.05; F (1, 117) = 4.54,
p = 0.04, ηP2 = 0.03]. The non-painful images induced
larger theta2 power than the painful images (EMMpainful = 0.96, SE = 0.21; EMMnon-painful = 1.31, SE = 0.21,
p = 0.001), and the larger theta2 power was revealed in
the posterior regions than the anterior regions (EMManterior = 1.00, SE = 0.30; EMMposterior = 1.28, SE = 0.30,
p = 0.04). The interaction effect between Sleep group

and Brain region was significant [F (1, 117) = 5.68,
p = 0.02, ηP2 = 0.04]. The post hoc analyses indicatedthat participants in the insomnia group showed larger
theta2 power in the posterior regions than participantsin the control group (EMMinsomnia = 1.70, SE = 0.30;
EMMcontrol = 0.86, SE = 0.29, p = 0.049), while the theta2
power did not differ in the anterior regions between
two groups (p = 0.61). No other two-way interaction nor three-way interaction effects were revealed(ps > 0.05).
Correlation between task performanceand time‑frequency‑domain indicators

As the reported significant Sleep group related interactions for alpha and theta2, thus the correlations
between these two bands and RT were calculated.
Results showed that the relative power of the alphaband was significantly correlated with RT for painfuland non-painful images in the control group (r = 0.42,
p < 0.001) but not in the insomnia group (r = 0.13,
p = 0.29). The relative power of theta2 significantly
correlated with RT in the insomnia group (r = 0.31,
p = 0.007) but not in the control group (r = 0.09,
p = 0.45) (see Fig. 7).

Li et al. Journal of Physiological Anthropology(2024) 43:4

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Fig. 7 Scatter plot of the correlation coefficient. A Scatter plot of alpha frequency band and response time for two groups. B Scatter plot of theta2
frequency band and response time for two groups. Note: The shaded area indicates the confidence interval estimated from the standard error. The
alpha power is the energy intensity after decibel conversion relative to the baseline component and therefore has negative values

Discussion
In the current study, the behavioral and neurophysiological responses in empathy for pain in individuals
with insomnia in non-clinical samples were investigatedsimultaneously by using the electroencephalogram technique. The findings revealed no statistically significant
differences in behavioral performance on empathy for
pain task between the insomnia and control groups,
whereas individuals with insomnia showed altered time–
frequency domain-specific neural activities involved in
empathic processing of pain.
The current study employed the empathy for pain task
to investigate whether individuals with insomnia showeda difference in perception and emotional response topainful and non-painful materials compared to thehealthy. The findings revealed that the subjective ratingof painful materials did not significantly differ between
the insomnia and control groups. This might be due tothe ceiling effect that such easier-to-process painful
materials were employed in the current study, and thus,

both groups were able to accurately and quickly detect
whether the person in images was painful or not. With
the painful images being perceived as more painful than
the non-painful images in both insomnia and control
groups, it could be speculated that the painful images
could be easily distinguished from non-painful images
and that the current manipulations of the images werevalid. One other potential reason for null effects might
be due to the relatively young age of the participants [16],and thus, future explorations could illuminate whether
age played a critical role in these findings.
By recording the stimulus-locked ERPs, the current
findings revealed insomnia-specific changes in earlybottom-up attention to painful images, with smaller P2
amplitude to painful images than to non-painful images
being showed for the insomnia group but not for the control group. The early components (i.e., P2) were important neural indicators of empathic reactions to otherpeople’s pain and indicated stimulus-driven bottom-up
attentional processing [9]. The P2 amplitude represents

Li et al. Journal of Physiological Anthropology(2024) 43:4

enhanced attention [42]. Research on empathy for painin the healthy consistently revealed that P2 is more sensitive to pain stimuli [20, 32]. The reported smaller P2 topainful images versus non-painful images in the insomnia group—however—is consistent with a previous
study reporting that non-painful images elicited larger
P2 amplitudes than painful images did in sleep-deprived
subjects [17]. This could be explained by the fact that
painful stimuli which were inherently potentially threatening elicited stronger avoidance motivation and thus
shifted more attention resources to non-painful stimuli[43, 44].
Meanwhile, the current findings revealed that both themean amplitude of N2 and peak amplitude of P2 werepositively correlated with the behavioral response time inthe control group but not in the insomnia group, suggesting that the increased attention focusing on painful or
non-painful stimulus in the early processing stage would
delay response time, but this was not the case for individuals with insomnia. This result would also suggest that
attentional cues may exert differential modulatory effects
on empathy-related processing of pain in individuals withinsomnia and healthy individuals.
Besides ERP analyses, the TF analyses were used to
explore whether the empathy for pain was differently
characterized by frequency domain-specific neural activities between the insomnia group and the control group.
The results showed that relatively larger alpha power inposterior brain regions was elicited in the insomnia vs.
control group. Alpha power reflects cognitive inhibitionand has been considered a reverse activation measure[45]. These findings suggested that participants in the
insomnia group showed less cognitive inhibition to painful stimuli compared to those in the control group, and it
may reflect a deficiency in the empathic process in individuals with insomnia.
Furthermore, the relatively larger power density ofthe theta2 band in the posterior brain regions was elicited in participants in the insomnia group than those in
the control group. This is consistent with previous findings that an increase in theta power was revealed in adultand adolescent hyperactivity disorder (ADHD) patients
when compared to normal controls [46, 47]. The theta
oscillation is an indicator of selective voluntary attention [48]. Thus, increased theta2 oscillations may indicatethat individuals with insomnia require more attentional
resources to process the sharing of emotions in response
to others’ pain. Meanwhile, the painful images induced
smaller theta2 power than the non-painful images, while
no significant difference was revealed on theta1 in thecurrent study, which contrasts with the previous findings reporting stronger theta1 power was elicited bypain images than non-pain images [17]. This discrepancy

Page 12 of 14

might be probably due to the differences in sample characteristics and experimental manipulation. More specifically, Duan et al. [17] recruited individuals with normal
sleep and manipulated the experimental condition by
acute sleep deprivation, while we recruited both individuals with chronic insomnia and healthy controls andcompared the characteristics of empathy for pain of bothgroups. The correlational analyses between the responsetime of subjective ratings and neural oscillations revealed
that the relative power of the alpha band positively correlated with response times in the control group, but thedelta2 positively correlated with response time in the
insomnia group, suggesting that the alpha band and thetheta band may be the possible neural inhibition mechanisms behind the influence of insomnia on empathy
for pain to some extent. These findings suggest that theneural manifestation of empathy for pain might change
and thus result in behavioral bias for those with chronic
insomnia. However, this possibility is tentative and needs
to be tested more systematically in the future.
Some limitations need to the mentioned in the current study. Firstly, objective sleep parameters (i.e., sleep
architecture, sleep EEG) were not collected in the current study. Thus, the relationship between objective
sleep outcomes and the behavioral and neural responseto empathy for pain cannot be calculated. Secondly, personal traits such as empathy trait were not assessed in thecurrent study, and whether it would modulate neurobehavioral response to empathy for pain remains unknown.
In addition, it should be noted that the interaction on
the P2 component was significant only at the peak level,
which might be particularly susceptible to noise. Furthermore, the recruited participants in the insomnia
group were non-clinical young people, which limits thegeneralization of the current findings to other populations. One potential reason for the null results found in
the current study might be due to the fact that the nonclinical insomnia disorder might not be as severe as clinical insomnia disorder, which might obscure the actual
effects of insomnia on empathy for pain. Hence, a reexamination of the current findings in the clinical insomnia disorder group is recommended.

Conclusion
The current study evidenced that individuals withinsomnia do not have a behavioral bias in the subjective rating of pain for painful and non-painful images,
whereas the insomnia-specific differences in timedomain and frequency-domain neurophysiologicalactivities are revealed. Insomnia individuals but not thehealthy controls showed smaller P2 amplitude to painful versus non-painful images. Additionally, the powerin the alpha band and theta2 band in the posteriorLi et al. Journal of Physiological Anthropology(2024) 43:4

brain regions was greater for individuals with insomnia. These findings suggested that individuals withinsomnia exhibit altered neurophysiological responses
to pain stimuli and a diminished ability to empathizefor pain, potentially involving changes in attentional
mechanisms.
Abbreviations
ERP	Event-related potential
LPC	Late positive component
PSQI	Pittsburgh Sleep Quality Index
ISI	Insomnia Severity IndexSAS	Self-rating Anxiety ScalePANAS	Positive affective and negative affective schedule
M	MeanSD	Standard deviation
EEG	Electroencephalogram
EOG	Electrooculogram
ICA	Independent component analysis
ICSD	International Classification of Sleep Disorders
MCWT​	Morlet continuous wavelet transform
TFRs	Time-frequency representations
TF-ROIs	Time-frequency regions of interest
S-ROIs	Spatial regions of interest
LMM	Linear mixed model
ACC​	Accuracy rate
RT	Response time
EMM	Estimated marginal mean
ERD	Event-related desynchronization
ERS	Event-related synchronization
SE	Standard error

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​023-​00351-2.
Additional file 1: STable1. LMM analysis results of peak amplitude ofN2 components. STable2. LMM analysis results of mean amplitude ofN2 components. STable3. LMM analysis results of peak amplitude of P2
components. STable4. LMM analysis results of mean amplitude of P2
components. STable5. LMM analysis results of mean amplitude of LPC
components. STable6. Descriptive statistical results of ERP components. STable7. Descriptive statistical results of Time-frequency-domain
indicators.
Acknowledgements
The authors are grateful to all participants who participated in the study.
Additionally, special thanks are extended to Dr. Mingze Sun for providing theEEG technology support.
Authors’ contributions
SYL: conceptualization, investigation, methodology, formal analysis, data
curation, visualization, writing—original draft, and writing—review and editing. MHH: investigation and data curation. LL: writing—review and editing.
QWC: methodology, writing—review and editing, and conceptualization.
TTR: conceptualization, methodology, writing—review and editing, funding
acquisition, and supervision. GFZ: resources, project administration, funding
acquisition, and supervision.
Funding
This work was funded by the National Key R&D Program of China (No.
2021YFB3600601), Science and Technology Program of Guangzhou (No.
2019050001), MOE International Laboratory for Optical Information Technologies,
and the 111 Project. The authors also gratefully acknowledge the support of the 2022
Guangdong-Hong Kong-Macao Greater Bay Area Exchange Programs of SCNU.

Page 13 of 14

Availability of data and materials
The datasets used and/or analyzed during the current study are available fromthe corresponding authors upon reasonable request.

Declarations
Ethics approval and consent to participate
The ethics board of the South China Normal University examined andapproved the project (Ethics No. SCNU-PSY-2022–241). All employed participants gave their written informed consent to participate in this study.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 27 July 2023 Accepted: 19 December 2023

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