Perceptual & Motor Skills: Learning & Memory 2013, 116, 3, 707-723. © Perceptual & Motor Skills 2013
MENSTRUAL CYCLE MODULATION OF THE LATE POSITIVE POTENTIAL EVOKED BY EMOTIONAL FACES1, 2, 3 WENJUAN ZHANG Beijing Key Lab of Applied Experimental Psychology Beijing Normal University RENLAI ZHOU Beijing Key Lab of Applied Experimental Psychology and State Key Laboratory of Cognitive Neurosciences and Learning Beijing Normal University MAOLIN YE Management School Jinan University Summary.—The objective of the present study was to elucidate the time course and neural basis of facial expression recognition as modulated by the menstrual cycle in women. Event-related potentials (ERPs) associated with recognition of different emotional faces were assessed in 29 healthy women during the premenstrual, postmenstrual, and periovulation phases of the menstrual cycle. Accuracy in recognizing different facial expressions was not affected by the menstrual cycle phase. ERP data indicated that only the late positive potential (LPP) was affected by the menstrual cycle phase for all facial expressions: during the periovulation phase, the amplitude of the LPP (750 to 1,000 msec. post-stimulus) was larger than that during the premenstrual phase. A positive correlation between the amplitude of the LPP and facial expression recognition performance was observed only during the periovulation phase. The present study provides electrophysiological evidence that the LPP evoked by emotional faces is modulated by the menstrual cycle, which may be correlated with fluctuations of ovarian hormones.
Changes in emotion are associated with the menstrual cycle (Farage, Osborn, & MacLean, 2008). In general, females report more negative moods, including depression, anxiety and anger, during the premenstrual (or luteal) phase (Ivey & Bardwick, 1968; May, 1976; Landén & Eriksson, 2003). Studies also found increased negative moods during the luteal Address correspondence to Renlai Zhou, School of Psychology, Beijing Normal University, Xinjiekouwai Street 19, Beijing, China or e-mail ([email protected]). 2 This research is supported by National Basic Research Program of China (No2011CB505101) and Beijing Key Laboratory Of TCM Syndrome and Formula Fund Open Topics of Beijing University of Chinese Medicine (2011-SYSKFKT03). 3 The authors are grateful to Mr. Senqi Hu and Mr. Yansong Li for their advice during preparation of this manuscript, to Suya Zhou and Yan Li for their help in data collection, and to Shannon Davidson for checking the English. The authors also thank the anonymous reviewers for their suggestions to improve this article. 1
DOI 10.2466/22.27.PMS.116.3.707-723
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phase compared to the follicular phase (Reed, Levin, & Evans, 2008; Allen, Allen, & Pomerleau, 2009). Emotion Recognition The ability to recognize facial expressions, an important emotional perception, is closely related to females’ mood states. Patients with premenstrual dysphoric disorder (PMDD) have been found to display a tendency to judge neutral faces as more negative (sad) during the luteal phase, which may be associated with a negative mood state, premenstrually (Rubinow, Smith, Schenkel, Schmidt, & Dancer, 2007). Behavioral studies have demonstrated differences in the ability to recognize facial expressions among healthy females during different phases of the menstrual cycle: females in the follicular phase recognized fearful faces more accurately than females in the menstrual phase (Pearson & Lewis, 2005). However, a later behavioral study found that accuracy in recognizing six facial expressions was better in the follicular group than that in the luteal group. Furthermore, analysis of error patterns in this study revealed that females in the luteal group mistook other negative emotional faces as angry or disgusted more frequently (Derntl, Kryspin-Exner, Fernbach, Moser, & Habel, 2008). Increased amygdala activation was observed in response to emotions, especially disgust and happy expressions, in females during the follicular phase, compared to females during the luteal phase in a functional magnetic resonance imaging (fMRI) study (Derntl, Windischberger, Robinson, Lamplmayr, Kryspin-Exner, Gur, et al., 2008). The menstrual cycle has also been implicated in observed sex differences in the identification of emotion from facial expressions. Females are more adept at identifying emotions than males (Montagne, Kessels, Frigerio, de Haan, & Perrett, 2005). A menstrual cycle study with men as the control group found the early follicular group correctly identified angry faces with higher accuracy than all other groups. Sadness was more accurately recognized by the early follicular group, compared to the luteal group. Regarding the recognition of fearful faces, similar performance but significantly higher accuracy was observed in the early follicular group and the ovulatory group, respectively, compared to men (Guapo, Graeff, Zani, Labate, dos Reis, & Del-Ben, 2009). Facial Expressions and ERPs Although behavioral studies have found differences in facial expression recognition during the menstrual cycle, relatively few studies have investigated the time course during the menstrual cycle of facial expression recognition processing, with event-related potentials (ERPs) as indicators of the perceptual process. In relation to visual processing of faces, a distinction can be made between early (N1/P1), middle (N2/P2,
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N170), and late (P3) ERP components, which are thought to reflect separable stages of encoding (Staugaard, 2010). As an early ERP component, P1 may reflect visual attention to facial expressions (Kolassa & Miltner, 2006; Eimer & Holmes, 2007). The amplitude of N1 was greater to fearful faces than happy or neutral faces (Luo, Feng, He, Wang, & Luo, 2010), but another study found no emotion-related N1 modulation (Rossignol, Philippot, Douilliez, Crommelinck, & Campanella, 2005). P2 may signal a recognition or decision-making process (Rousselet, Husk, Bennett, & Sekuler, 2008). N2, occurring in the anterior brain area, was found to be largest for threatening faces (Kiss & Eimer, 2008). P3 seems to reflect attention and memory processes in the frontal and temporal/parietal cortices (Polich, 2007). The late positive potential or component (LPP or LPC), occurring between 300 and 1,200 msec. after stimulus onset, was larger in amplitude to emotional than to neutral stimuli, especially arousing emotional stimuli (Olofsson, Nordin, Sequeira, & Polich, 2008). Positive and negative facial expressions can be distinguished in the occipital area at approximately 100 msec. post-stimulus (Pourtois, Grandjean, Sander, & Vuilleumier, 2004). However, a majority of ERP studies have reported that the affective expression of faces are processed at relatively later stages in the recognition system, during a time window subsequent to the occipito-temporal N170 component, a specific face-recognition component (Vuilleumier & Pourtois, 2007). The late ERP responses to emotional faces, such as LPP or LPC, can be sustained for a long period, up to 1000 msec. or more (Krolak-Salmon, Fischer, Vighetto, & Mauguière, 2001), but these effects do not seem specific to particular expressions. Menstrual Cycle and ERPs Previous ERP studies have found that only the late ERP components were modulated by the menstrual cycle. P3 was largest in response to babies and male models when progesterone levels were high, and may be an adaptive association to high progesterone levels (Johnston & Wang, 1991). Furthermore, the LPC (peaking 550–600 msec., post-stimulus) showed a larger peak (ε = 0.58) during the ovulatory phase for sexual stimuli (i.e., nude men), which suggested increased sexual desire and deeper emotional processing (Krug, Plihal, Fehm, & Born, 2000). Research Objective and Hypotheses The goal was to measure ERPs as an index of the effect of the menstrual cycle on recognition of facial expressions in healthy females, with regular menstrual cycles assessed in three phases (premenstrual, postmenstrual, and periovulation phases). On the basis of females recognizing face expressions more accurately during the follicular phase in the above studies, it was predicted that:
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Hypothesis 1. Recognition performance during the periovulation and postmenstrual phases would be better than that during the premenstrual phase. Hypothesis 2. Late ERP components, such as P3 and LPP, would increase during the periovulation and postmenstrual phases. It was also of interest whether the early and middle ERP components (e.g., N1, P2) would be affected by the menstrual cycle. METHOD Participants Screening.—To minimize individual differences, potential participants were screened from a large sample of 117 female undergraduates and graduate students according to the scores obtained on the following questionnaires: basic information on the women’s menstrual cycles (Appendix A); Premenstrual Syndrome (PMS; Bancroft, 1993); Trait Anxiety Inventory (TAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983); Beck Depression Inventory (BDI; Beck & Steer, 1993; Hautzinger, Bailer, Worall, & Keller, 1994); Eysenck Personality Questionnaire–Revised, Short Scale for Chinese (EPQ–RSC; Qian, Wu, Zhu, & Zhang, 2000); and Symptom Checklist–90 (SCL–90; Kass, Charles, Klein, & Cohen, 1983). Respondents were considered eligible if they did not have clinical scores in the following selfreported medical conditions: anxiety, depression, premenstrual syndrome, psychiatric history, personality disorders, pregnancy or lactating within the past 12 mo., or use of oral contraceptives within the past 4-mo. Sample.—Twenty-nine women voluntarily participated in the study. They were all right-handed, as determined by Chapman and Chapman’s scale (Chapman & Chapman, 1987). All participants had normal or corrected-tonormal vision. Three participants were excluded, one due to a high error rate and the other two due to excessive head motion. In sum, data from 26 participants (M age = 20.6 yr., SD = 1.5) were included in analyses. The mean age at menarche was 12.7 yr. (SD = 1.5), the mean duration of the menstrual cycle was 29.0 days (SD = 1.4), and the mean menses duration was 5 days (SD = 1.1). Seven women reported dysmenorrheal, two reported menorrhagia, seven reported mild menses, and 17 reported normal menses. Most participants (n = 23) believed that menstruation was a “natural phenomenon,” two reported that menstruation was “a troubling phenomenon,” and one responded “other,” respectively. The participants were not aware of the purpose of the study. The experiment was approved by the local ethical committee and informed consent was obtained from all participants before the experiment. Measures Each woman participated in three experimental sessions (premenstrual, postmenstrual, and periovulation phases). Test sessions were sepa-
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rated by at least seven days. Thus, three test occasions extended across one or two successive menstrual cycles. The time of testing for periovulation was determined using an ovulation test kit. Participants were instructed in how to use the kit and they used it throughout the middle of their menstrual cycle, that was, between days 11 and 16. They were asked to return to the laboratory when a distinct increase in the urinary concentration of luteinizing hormone (LH) occurred. The actual day of menstrual onset was confirmed with each participant by e-mails or telephone calls. The testing dates were as follows: the premenstrual phase was 2.3 days before the next menses (range = 0–6 days), the postmenstrual phase was 2.0 days after the menses (range = 1–4 days) and the range of periovulation phase was 11–18 days. Materials and Equipment Photos of faces obtained from the Chinese Facial Affective Picture System (CFAPS; Wang & Luo, 2005) were used. The CFAPS includes 600 pictures of fearful, happy, sad, angry, disgusted, surprised, and neutral faces of oriental female and male subjects. These images were rated by both Chinese college students (gender-matched) with respect to the valence category (unpleasant to pleasant) and arousal level (low to high) of the images on a nine-point scale (Luo, et al., 2010). We selected the 15 pictures with highest arousal indexes for each facial expression (“disgusted,” “sad,” “angry,” “fearful,” “neutral,” and “happy”). Arousal indexes for each facial expression were compared using analysis of variance (ANOVA). When statistically significant differences were found between mean arousal indexes for the different facial expression picture sets, pictures were exchanged to ensure that the mean arousal indexes across facial expressions were balanced. We selected 30 face pictures (15 male and 15 female) depicting each of the following facial expressions, for a total of 180 pictures: “disgusted,” “sad,” “angry,” “fearful,” “neutral,” and “happy” (Fig. 1). The visual stimuli were matched with respect to brightness and contrast and presented on a gray background at a distance of 60 cm from the participants, using a 19-inch computer screen with a resolution of 1024 × 768 and a refresh rate of 75 Hz. The photos were 11.5 cm × 13.5 cm in size. A fixation cross (0.7 cm × 0.7 cm) was presented at the center of the screen. Procedure Sessions took place in an electronically shielded, air-conditioned, and dimly lit room with the participants sitting in a reclining chair. Participants completed a Self-rating Anxiety Scale as well as a Self-rating Depression Scale before each test session to evaluate mood states during the menstrual cycle. Next, they were asked to complete the recognition tasks. The
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FIG. 1. Representative examples of the 180 faces used in the study, expressing six basic emotions (“disgusted,” “sad,” “fearful,” “neutral,” angry,” and “happy.”)
experiment entailed the following sequence of steps: fixation (200 msec.), blank screen (600–1,000 msec.), photo of facial expression (500 msec.), blank screen (1,000 msec.), reaction screen (5,000 msec.), and finally, a blank screen (1,000 msec.). The participants were instructed to recognize the emotion being depicted and to click the corresponding choices on the reaction screen (all 6 categories were present: “angry,” “disgusted,” “fearful,” “happy,” “sad,” and “neutral”). The experiment had three blocks of 60 unique trials and each photo was shown twice, so that each emotion was presented a total of 60 times (for a sum total of 360 trials). Within each block, the photos were divided into pseudo-random sequences to ensure a unique presentation sequence for each run. After each block (120 trials), the participants were allowed to take a short break. Each session lasted 30 min. The participants were asked to continue looking at the fixation and to keep their head and body still. To control order effects, a counterbalanced repeated-measure design was employed, with about one-third of the sample completing the premenstrual phase (n = 7), postmenstrual phase (n = 11) or periovulation phase (n = 8) measurement first. Compared to a between-subjects comparison (Pearson & Lewis, 2005; Derntl, KryspinExner, et al., 2008; Derntl, Windischberger, et al., 2008), this within-subjects comparison design ameliorates inter-subject variations and thus yields a higher statistical power (Compton, Costello, & Diepold, 2004).
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ERP Data Acquisition Ag–AgCl electrodes of the NeuroScan ERP recording and analysis system were used to record the ERPs from 64 scalp positions according to the International 10–20 System. Data were referenced to the left mastoids online and were re-referenced offline to the average of both mastoids. The ground electrode was placed along the midline between FPz and Fz. Vertical eye movements were registered with two electrodes positioned above and below the left eye. The horizontal electrooculograms were recorded with lateral electrodes from both eyes. Impedances were below 5 kΩ for all recordings. The sampling rate was 1,000 Hz. Data were filtered online with a band-pass filter between 0.05 Hz and 100 Hz. Ocular artifact reduction was performed offline using a regression method (Gratton, Coles, & Donchin, 1983). Epochs with voltages exceeding 100 μV at any electrode except the HEOG and VEOG channels were excluded from further analysis. Trials with eye blinks and lateral eye movements were excluded (no more than 5% of the number of trials in one block). The low-pass offline filter was 30 Hz (24 dB/oct). Epochs of 1,000 msec. after stimulus onset were computed with an additional 200 msec. pre-stimulus baseline. Trials were averaged for each condition and participant, as well as for each condition across participants (grand average). The average number of trials considered for the analyses of each facial expression was as follows: M = 37.03 (SD = 13.44) for disgust, M = 46.86 (SD = 8.52) for sadness, M = 38.42 (SD = 11.49) for anger, M = 48.78 (SD = 8.29) for fear, M = 50.85 (SD = 8.98) for neutral, and M = 51.16 (SD = 8.12) for happiness. Early components (N1, P2, N2) were measured at the electrodes Fz, Cz and Pz. where they were most prominent. Latencies were taken at the electrode where amplitude was maximal. Late components (P3, LPP) were measured at the electrodes Pz and Oz. Six time windows were chosen between 110 and 1000 msec. for quantification at N1 (110–130 msec.), P2 (150-190 msec.), N2 (230–300 msec.), P3 (300–500 msec.) and Late Positive Potential (500–750 msec. and 750–1,000 msec.). After visual inspection, ERPs during the time windows 500–750 msec. and 750–1,000 msec. were found to differ. To differentiate between them, LPP was designated as LPP1 during the 500 to 750 msec. time window and LPP2 during the 750 to 1,000 msec. time window. Statistical Analysis Data were analyzed using SPSS Version 16.0. One-way within-subjects ANOVAs were run on Self-rating Anxiety and Depression scores with menstrual cycle phases as factors (premenstrual phase, postmenstrual phase, and periovulation phase). Behavioral response accuracy was analyzed by ANOVA with repeated measures, considering the factors menstrual cycle phase (premenstrual phase, postmenstrual phase, and periovulation phase)
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and facial expression (“disgusted,” “sad,” “angry,” “fearful,”“neutral,” and “happy”). ERP data analysis also included the factor electrode sites. Statistically significant main effects and interactions were followed up by post hoc comparisons and simple effects analyses. Greenhouse-Geisser corrected p values were used for the ANOVAs and post hoc results were Bonferroni corrected. As a measure of effect size, partial η2p was reported. The associations between performance in the task and amplitude of ERP components were analyzed using Pearson-correlation coefficients (two-tailed). Values of p < .05 were considered statistically significant. RESULTS Behavioral Data & Self-rating Anxiety and Depression Scores For accuracy of facial expression recognition, no statistically significant main effect of menstrual cycle phase was found (F2,50 = 0.66, p = .52). There was a statistically significant main effect of facial expression (F5,125 = 41.62, p < .001, partial η2 = 0.63), with “happy” being the facial expression that was most accurately recognized and “disgust” the least, according to the following sequence: happy > neutral > fearful> sad > angry > disgust (disgust vs anger: p = .43, sad vs fearful: p = .02, neutral vs happy: p = .02, all other p < .001). However, the interaction was not statistically significant (F10,250 = 1.60, p = .11; see Table 1). Results showed no statistically significant differences in Self-rating Anxiety and Depression scores between each of the menstrual cycle phases (Anxiety: F2,50 = 0.48, p = .62; Depression: F2,50 = 0.01, p = .99). ERP Data The values of F and partial η2 of repeated-measures ANOVAs on the ERP components were shown in Table 2. N1 & P2 (110–130 msec. and 150–190 msec.).—N1 showed maximum amplitudes over frontal and central cortical areas, electrode site (p < .001), TABLE 1 RECOGNITION ACCURACY FOR THE SEPARATE EMOTIONS AND ACROSS ALL (TOTAL) FOR ALL PARTICIPANTS IN THREE MENSTRUAL CYCLE PHASES (N = 26) Menstrual Cycle Phase Expression Disgust Sadness Anger Fear Neutral Happiness Total
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Pre-menstrual M SD 0.67 0.19 0.87 0.18 0.74 0.19 0.92 0.25 0.96 0.23 0.97 0.19 0.86 0.16
Post-menstrual M SD 0.73 0.19 0.89 0.19 0.70 0.19 0.92 0.26 0.96 0.23 0.98 0.19 0.86 0.17
Peri-ovulation M SD 0.67 0.21 0.88 0.23 0.73 0.25 0.92 0.29 0.97 0.25 0.97 0.24 0.85 0.17
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TABLE 2 RESULTS OF MENSTRUAL CYCLE PHASES (M) × FACIAL EXPRESSIONS (F) × ELECTRODE SITES (ES) ANOVAS FOR N1, P2, N2, P3, LPP1, AND LPP2 AMPLITUDES (N = 26) Source
df
N1 F
P2 η
2 p
F
N2 η
2 p
F
P3 η
2 p
F
LPP1 η
2 p
F
η
LPP2 2 p
F
η2p
M 2,50 5.12 0.17 F 5,125 3.07 0.11 3.14 0.11 4.37 0.15 3.81 0.13 17.50 0.41 ES 1,25 34.77 0.58 55.03 0.69 8.79 0.26 16.78 0.40 139.94 0.85 M × ES 2,50 3.72 0.13 F × ES 5,125 2.52 7.40 0.23 2.64 0.10 8.52 0.25 Note.—Only statistically significant (p < .05) results are indicated in the table. Interaction between menstrual cycle phases and facial expressions and three-way interaction were not statistically significant. df = degree of freedom; η2p = partial eta square effect size.
with the amplitudes of Fz and Cz statistically significantly larger than those of Pz (ps < .001). Regarding facial expression (p = .01), the mean amplitude of N1 to “fearful” face stimuli was larger than that to “disgusted” (p = .02). With regard to the latency of P2, the main effect of facial expression was statistically significant (p = .001), with “happy” being the expression that was most quickly recognized, and “fearful” the least, according to the following sequence: happy < neutral < angry < disgusted < sad < fearful (happy vs fearful : p < .01, happy vs angry : p = .09, all other p < .05). No main effects of menstrual cycle phase or facial expression were found on the latency of N1 and mean amplitude of P2. N2 (230–300 msec.).—N2 amplitude was largest in the recording from the Fz electrode site (p < .001). The main effect of facial expression was also statistically significant (p = .02). Furthermore, a statistically significant interaction of facial expression and electrode sites was observed (p = .03), with the difference between N2 amplitudes to “sad” (–3.11 μV) and “fearful” (–2.07 μV) face stimuli being pronounced at the Fz (p = 0.011), Cz (p = 0.013) and Pz (p = 0.012). P3 (300–500 msec.).—The main effects of facial expression and electrode site on P3 amplitude were statistically significant (p = .004; p = .007). The interaction of the menstrual cycle phases and electrode sites was statistically significant (p = .04). Further analysis showed that the amplitude of P3 during different phases at Oz was marginal significant (p = .06), with periovulation phase (6.96 μV) > postmenstrual phase (6.45 μV) > premenstrual phase (6.25 μV). The interaction of facial expressions and electrode sites was also statistically significant (p < .001), with the mean amplitude of P3 to “fearful” (7.72 μV) face stimuli being larger than those to “neutral” (7.40 μV) and “happy” (6.73 μV) face stimuli, pronounced at Pz (p = .002) and Oz (p < .001). LPP1 (500–750 msec.).—The main effect of facial expression and electrode site on LPP1 amplitude was statistically significant (p = .003; p < .001).
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Pz
postmenstrual phase periovulation phase
–5.0
–200
0 5.0
12.5
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750 msec 200
400
850 msec 600
1,000 msec 800
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+12.5 +10.9 +9.4 +7.8 +6.3 +4.7 +3.1 +1.6 0 –1.6 –3.1 –4.7 –6.3 –7.8 –9.4 –10.9 –12.5
FIG. 2. Grand averaged ERPs by menstrual cycle phases at Pz with topographic maps at 750 msec., 850 msec., and 1,000 msec. post-stimulus; ANOVA indicated a statistically significant main effect of phase on mean amplitude between 750 to 1,000 msec. (p < .05), with the diminished amplitude during the premenstrual phase than that during the periovulation phase.
Interactions between facial expressions and electrode sites were also statistically significant (p = .03) with the difference between fear (7.02 μV) and disgust (6.05 μV) being pronounced at Pz (p = .016) and Oz (p < .001). LPP2 (750–1,000 msec.).—As shown in Fig. 2, a statistically significant main effect of menstrual cycle phase was observed on the mean amplitude of LPP2 (p = .01). Post hoc comparisons showed that the amplitude during the periovulation phase (5.70 μV) was larger than that during the premenstrual phase (4.24 μV, p = .01). The main effects of facial expressions and electrode sites were also statistically significant (ps < .001). There were also statistically significant interactions between facial expressions and electrode sites (p < .001), with LPP2 responses to the “angry” (9.28 μV) face stimuli having the largest amplitude and to “happy” (5.17 μV) stimuli the smallest amplitude; this difference was pronounced both at the Pz than Oz (ps < .001). Correlations: LPP2 at Electrode Pz and Accuracy Diminished amplitude of the LPP2 was found during the premenstrual phase, relative to that during periovulation phase. To characterize the relation between the amplitude of LPP2 and accuracy, further correlational analysis was performed over all facial expressions. The results showed a statistically significant positive relationship between the amplitude of LPP2 and accuracy of facial expression recognition only during the periovulation phase (r = .42, p = .03, 95%CI = 0.04, 0.70), but not during the premenstrual phase (r = .33, p = .11, 95%CI = 0.07, 0.64) or the postmenstrual phase (r = .34, p = .09, 95%CI = 0.06, 0.64).
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DISCUSSION In this study, the effect of the menstrual cycle on the time course of facial expression recognition was investigated by means of ERPs. Behavioral results revealed only statistically significant differences in accuracy for different facial expressions, with the highest accuracy for “happy” and the lowest for “disgusted” expressions. The primary finding was that the late ERP component evoked by facial expressions was affected by the phase of the menstrual cycle: the amplitude of the LPP2 (750 to 1,000 msec. poststimulus) during the periovulation phase was larger than that during the premenstrual phase. This effect did not appear specific to particular expressions. Furthermore, a positive correlation between LPP2 amplitude and facial expression recognition accuracy was found during the periovulation phase; while the ERP components before 750 msec. post-stimuli, including N1, P2, N2, P3, and LPP1, were not sensitive to the menstrual cycle phase. Statistically significant menstrual cycle phase differences in facial expression recognition accuracy were not observed. This may be related to the presentation time of the stimuli. In previous behavioral studies, the visual stimuli disappeared once the participants made a keyboard response (Pearson & Lewis, 2005; Derntl, Kryspin-Exner, et al., 2008; Guapo, et al., 2009). Recognition in these studies were as follows: fear ACC: 58%, 74%, 72%, and 66% (Pearson & Lewis, 2005), total ACC: 90%, 85% (Derntl, Kryspin-Exner et al., 2008), and each emotion and total ACC are all lower than 70%(Guapo, et al., 2009), but the presentation time was fixed at 500 msec. and following a 1,000 msec. blank screen, thus the participants had enough time to choose a correct answer, which may lead to a reduction in the variance of the recognition accuracy(ACC) in different phases of the menstrual cycle (fear ACC: 92%,92%,92%, total ACC: 86%,86%, and 85%) in this study. Additionally, the fixed presentation time and the following blank screen may result in the participants’ expectations before they made the behavioral responses, which can also influence the behavioral results. Menstrual Cycle Modulation of the LPP The most important result from this study is that only the late ERP component LPP2 was affected by the menstrual cycle, supporting previous findings (Johnston & Wang, 1991; Krug, et al., 2000). However, in the present study, this effect was independent of the specific facial expression. The amplitude of the LPP was associated with the allocation of attention processes in previous studies (Hajcak & Foti, 2009; Gable & Harmon-Jones, 2010). Here, the larger LPP2 during the periovulation phase suggested that women may expend more attentional resources in emotion recognition than during the premenstrual phase. Ferrari, Codispoti, Cardinale, and Bradley (2008) found that both emotion and task-relevant stimuli additively determined the amplitude of the LPP. This study
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expands this view by demonstrating that the amplitude of the LPP is also determined by the menstrual cycle. Processing of the emotion information contained in faces are key to social and mating behaviors. The statistically significant, positive correlation between behavioral performance and the LPP2 during the periovulation phase indicates the more accurately the women recognized the facial expressions, the larger the amplitude of LPP2—although only about 17% of the variance was explained by menstrual cycle phases. The underlying endocrine mechanism may be related with the luteinizing hormone, which peaks in the ovulation phase but is low in other phases. This idea is consistent with an earlier behavioral study in which ovulating women were found to be more accurate in their social cognitive judgments and interpersonal perception while in this hormonal state, compared to women during menstruation (Macrae, Alnwick, Milne, & Schloerscheidt, 2002). During the periovulation phase, the maximal potential for successful reproduction is enhanced due to increased accuracy in the ability to identify facial expressions correctly, which forms an important basis for successful social interactions. However, N1, P2, N2, P3, and LPP1 (before 750 msec. post-stimulus) were not affected by the menstrual cycle, consistent with an early auditory ERP study (Fleck & Polich, 1988). These results indicated that the menstrual cycle modulation of the facial expressions recognition occurred in the late stage, not the early and middle stage. However, the modulation of menstrual cycle on the N2 latency (ε = 0.75) was found in another auditory study (Walpurger, Pietrowsky, Kirschbaum, & Wolf, 2004). As the menstrual cycle modulation on the ERP components is complex, further menstrual cycle ERP studies are needed to clarify the mechanism. LPP2 and Ovarian hormones It is possible that the mechanism behind the difference in LPP2 can be explained in terms of ovarian hormone differences during the menstrual cycle. Previous behavioral studies have demonstrated that the modulation of the ability to recognize facial expressions by the menstrual cycle may be related to the accompanying fluctuation of ovarian hormones, especially estrogen and progesterone (Pearson & Lewis, 2005; Derntl, Kryspin-Exner, et al., 2008; Guapo, et al., 2009). According to a previous review (Farage, et al., 2008), we can have a general idea as to the approximate level of estrogen and progesterone during the test phases in this study: the postmenstrual phase is characterized by low levels of both estrogen and progesterone; estrogen is at the highest level and progesterone rises during the periovulation phase; the premenstrual phase is characterized by declines in both estrogen and progesterone that reach baseline shortly before the onset of menstruation. Since the
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progesterone is low in both postmenstrual and periovulation phase, and high in the premenstrual phase, the diminished LPP2 amplitude in the premenstrual phase may be caused by the high progesterone level. As there were no direct measures of ovarian hormones in our study, this corollary awaits further confirmation. Differences in ERPs to Facial Expressions All the ERP components in this study were affected by the facial expression category. Differences in amplitude of the early ERP components (N1, P2, and N2) were observed for different emotions distributed mainly over the anterior area, but for the late ERP components (P3, LPP1 and LPP2), the differences distributed mainly over the posterior area. The largest peak amplitudes were observed for different negative expressions in different time courses: fear (110–130 msec., 150–190 msec. and 300–500 msec.), sadness (230–300 msec.), and anger (750–1,000 msec.). The recognition of “fearful” face stimuli, as the representative negative emotion, is quicker than that of other expressions during 110–500 msec. post-stimulus. Due to its importance to survival and evolution, fearful faces may be allocated more processing resources and subsequently evoke the largest ERP amplitudes (Eimer & Holmes, 2007). Additionally, after “fearful,” “angry” occupies the advantage in processing, as anger has a greater importance for social survival (Pearson & Lewis, 2005). However, recognition of “sad” faces predominated from 230 to 300 msec. post-stimulus. These negative emotional expressions with different meanings apparently captured more cognitive resources than neutral and positive emotional faces and occupy superior positions at different processing times. Therefore, any exploration of negative emotional processing mechanisms needs to consider specific differences within subcategories of negative emotions. Finally, several limitations in the present study should be mentioned. Firstly, the ovarian hormones were not obtained, so the causality of hormonal changes in emotional recognition needs confirmation in further research. Secondly, the behavioral results might have been be influenced by the fixed presentation time and the participants’ expectations. Thirdly, no differences in mean self-reported Anxiety and Depression scores were found across phases of the menstrual cycle, as the participants were screened and included only healthy females without premenstrual syndrome, whose moods remain relatively stable. Besides, mood changes during the menstrual cycle are relatively weak and may be counteracted by life events, and of course cyclic variance may be smaller than other individual differences and not stable. It could be difficult to detect statistically significant differences based on subjective self-reports.
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Conclusion The present study indicates that the LPP component of facial emotion recognition is affected by menstrual cycle phase, with diminished LPP amplitude in the premenstrual phase. In addition, the correlation between LPP amplitude and accuracy in identification of facial expressions during periovulation suggests that changes in LPP amplitude may reflect changes in emotion processing performance. Findings imply that the effect of the menstrual cycle phase should be taken into consideration in future ERP studies of emotion. REFERENCES
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QIAN, M., WU, G., ZHU, R., & ZHANG, S. (2000) Develoment of the Revised Eysenck Personality Questionaire Short Scale for Chinese (EPQ–RSC). Journal of Chinese Psychology Acta Psychologica Sinica, 32(3), 317-323. [in Chinese] REED, S. C., LEVIN, F. R., & EVANS, S. M. (2008) Changes in mood, cognitive performance and appetite in the late luteal and follicular phases of the menstrual cycle in women with and without PMDD (premenstrual dysphoric disorder). Hormones and Behavior, 54(1), 185-193. ROSSIGNOL, M., PHILIPPOT, P., DOUILLIEZ, C., CROMMELINCK, M., & CAMPANELLA, S. (2005) The perception of fearful and happy facial expression is modulated by anxiety: an event-related potential study. Neuroscience Letters, 377(2), 115-120. ROUSSELET, G. A., HUSK, J. S., BENNETT, P. J., & SEKULER, A. B. (2008) Time course and robustness of ERP object and face differences. Journal of Vision, 8(12), 1-18. RUBINOW, D. R., SMITH, M. J., SCHENKEL, L. A., SCHMIDT, P. J., & DANCER, K. (2007) Facial emotion discrimination across the menstrual cycle in women with premenstrual dysphoric disorder (PMDD) and controls. Journal of Affective Disorders, 104(1-3), 37-44. RUPP, H. A., JAMES, T. W., KETTERSON, E. D., SENGELAUB, D. R., JANSSEN, E., & HEIMAN, J. R. (2009) Neural activation in the orbitofrontal cortex in response to male faces increases during the follicular phase. Hormones and Behavior, 56(1), 66-72. SPIELBERGER, C. D., GORSUCH, R. L., LUSHENE, R., VAGG, P. R., & JACOBS, G. A. (1983) Manual for the State–trait Anxiety Inventory. Consulting Psychology Press: Mountain View, CA. STAUGAARD, S. R. (2010) Threatening faces and social anxiety: a literature review. Clinical Psychology Review, 30(6), 669-690. VUILLEUMIER, P., & POURTOIS, G. (2007) Distributed and interactive brain mechanisms during emotion face perception: evidence from functional neuroimaging. Neuropsychologia, 45(1), 174-194. WALPURGER, V., PIETROWSKY, R., KIRSCHBAUM, C., & WOLF, O. T. (2004) Effects of the menstrual cycle on auditory event-related potentials. Hormones and Behavior, 46(5), 600-606. WANG, Y., & LUO, Y. (2005) Standardization and assessment of college students’ facial expression of emotion. Chinese Journal of Clinical Psychology, 13(4), 396-398. [in Chinese] Accepted April 17, 2013.
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APPENDIX A Basic Information on the Menstrual Cycle Please complete the following information and select the multiple choice answer that most accurately applies to you. 1. Age at menarche_____ 2. Date of onset of last menstrual cycle____(month) _____(day) 3. Menstrual regularity A. Yes B. No; approximate cycle length _______________________ Other circumstances (please specify) _____________________ 4. Menstrual period duration: ______ days 5. Menstrual quantity A. Less B. Normal C. More 6. (a) Dysmenorrhea A. Yes B. No (b) If dysmenorrhea, how serious? _______________________ 7. Your attitude about the menstrual cycle________ A. Troubling phenomenon B. Natural phenomenon C. Divine phenomenon D. Does not affect behavior E. Other (please specify) _______________________
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MENSTRUAL CYCLE MODULATION OF THE LATE POSITIVE POTENTIAL EVOKED BY EMOTIONAL FACES1, 2, 3 WENJUAN ZHANG Beijing Key Lab of Applied Experimental Psychology Beijing Normal University RENLAI ZHOU Beijing Key Lab of Applied Experimental Psychology and State Key Laboratory of Cognitive Neurosciences and Learning Beijing Normal University MAOLIN YE Management School Jinan University Summary.—The objective of the present study was to elucidate the time course and neural basis of facial expression recognition as modulated by the menstrual cycle in women. Event-related potentials (ERPs) associated with recognition of different emotional faces were assessed in 29 healthy women during the premenstrual, postmenstrual, and periovulation phases of the menstrual cycle. Accuracy in recognizing different facial expressions was not affected by the menstrual cycle phase. ERP data indicated that only the late positive potential (LPP) was affected by the menstrual cycle phase for all facial expressions: during the periovulation phase, the amplitude of the LPP (750 to 1,000 msec. post-stimulus) was larger than that during the premenstrual phase. A positive correlation between the amplitude of the LPP and facial expression recognition performance was observed only during the periovulation phase. The present study provides electrophysiological evidence that the LPP evoked by emotional faces is modulated by the menstrual cycle, which may be correlated with fluctuations of ovarian hormones.
Changes in emotion are associated with the menstrual cycle (Farage, Osborn, & MacLean, 2008). In general, females report more negative moods, including depression, anxiety and anger, during the premenstrual (or luteal) phase (Ivey & Bardwick, 1968; May, 1976; Landén & Eriksson, 2003). Studies also found increased negative moods during the luteal Address correspondence to Renlai Zhou, School of Psychology, Beijing Normal University, Xinjiekouwai Street 19, Beijing, China or e-mail ([email protected]). 2 This research is supported by National Basic Research Program of China (No2011CB505101) and Beijing Key Laboratory Of TCM Syndrome and Formula Fund Open Topics of Beijing University of Chinese Medicine (2011-SYSKFKT03). 3 The authors are grateful to Mr. Senqi Hu and Mr. Yansong Li for their advice during preparation of this manuscript, to Suya Zhou and Yan Li for their help in data collection, and to Shannon Davidson for checking the English. The authors also thank the anonymous reviewers for their suggestions to improve this article. 1
DOI 10.2466/22.27.PMS.116.3.707-723
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phase compared to the follicular phase (Reed, Levin, & Evans, 2008; Allen, Allen, & Pomerleau, 2009). Emotion Recognition The ability to recognize facial expressions, an important emotional perception, is closely related to females’ mood states. Patients with premenstrual dysphoric disorder (PMDD) have been found to display a tendency to judge neutral faces as more negative (sad) during the luteal phase, which may be associated with a negative mood state, premenstrually (Rubinow, Smith, Schenkel, Schmidt, & Dancer, 2007). Behavioral studies have demonstrated differences in the ability to recognize facial expressions among healthy females during different phases of the menstrual cycle: females in the follicular phase recognized fearful faces more accurately than females in the menstrual phase (Pearson & Lewis, 2005). However, a later behavioral study found that accuracy in recognizing six facial expressions was better in the follicular group than that in the luteal group. Furthermore, analysis of error patterns in this study revealed that females in the luteal group mistook other negative emotional faces as angry or disgusted more frequently (Derntl, Kryspin-Exner, Fernbach, Moser, & Habel, 2008). Increased amygdala activation was observed in response to emotions, especially disgust and happy expressions, in females during the follicular phase, compared to females during the luteal phase in a functional magnetic resonance imaging (fMRI) study (Derntl, Windischberger, Robinson, Lamplmayr, Kryspin-Exner, Gur, et al., 2008). The menstrual cycle has also been implicated in observed sex differences in the identification of emotion from facial expressions. Females are more adept at identifying emotions than males (Montagne, Kessels, Frigerio, de Haan, & Perrett, 2005). A menstrual cycle study with men as the control group found the early follicular group correctly identified angry faces with higher accuracy than all other groups. Sadness was more accurately recognized by the early follicular group, compared to the luteal group. Regarding the recognition of fearful faces, similar performance but significantly higher accuracy was observed in the early follicular group and the ovulatory group, respectively, compared to men (Guapo, Graeff, Zani, Labate, dos Reis, & Del-Ben, 2009). Facial Expressions and ERPs Although behavioral studies have found differences in facial expression recognition during the menstrual cycle, relatively few studies have investigated the time course during the menstrual cycle of facial expression recognition processing, with event-related potentials (ERPs) as indicators of the perceptual process. In relation to visual processing of faces, a distinction can be made between early (N1/P1), middle (N2/P2,
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N170), and late (P3) ERP components, which are thought to reflect separable stages of encoding (Staugaard, 2010). As an early ERP component, P1 may reflect visual attention to facial expressions (Kolassa & Miltner, 2006; Eimer & Holmes, 2007). The amplitude of N1 was greater to fearful faces than happy or neutral faces (Luo, Feng, He, Wang, & Luo, 2010), but another study found no emotion-related N1 modulation (Rossignol, Philippot, Douilliez, Crommelinck, & Campanella, 2005). P2 may signal a recognition or decision-making process (Rousselet, Husk, Bennett, & Sekuler, 2008). N2, occurring in the anterior brain area, was found to be largest for threatening faces (Kiss & Eimer, 2008). P3 seems to reflect attention and memory processes in the frontal and temporal/parietal cortices (Polich, 2007). The late positive potential or component (LPP or LPC), occurring between 300 and 1,200 msec. after stimulus onset, was larger in amplitude to emotional than to neutral stimuli, especially arousing emotional stimuli (Olofsson, Nordin, Sequeira, & Polich, 2008). Positive and negative facial expressions can be distinguished in the occipital area at approximately 100 msec. post-stimulus (Pourtois, Grandjean, Sander, & Vuilleumier, 2004). However, a majority of ERP studies have reported that the affective expression of faces are processed at relatively later stages in the recognition system, during a time window subsequent to the occipito-temporal N170 component, a specific face-recognition component (Vuilleumier & Pourtois, 2007). The late ERP responses to emotional faces, such as LPP or LPC, can be sustained for a long period, up to 1000 msec. or more (Krolak-Salmon, Fischer, Vighetto, & Mauguière, 2001), but these effects do not seem specific to particular expressions. Menstrual Cycle and ERPs Previous ERP studies have found that only the late ERP components were modulated by the menstrual cycle. P3 was largest in response to babies and male models when progesterone levels were high, and may be an adaptive association to high progesterone levels (Johnston & Wang, 1991). Furthermore, the LPC (peaking 550–600 msec., post-stimulus) showed a larger peak (ε = 0.58) during the ovulatory phase for sexual stimuli (i.e., nude men), which suggested increased sexual desire and deeper emotional processing (Krug, Plihal, Fehm, & Born, 2000). Research Objective and Hypotheses The goal was to measure ERPs as an index of the effect of the menstrual cycle on recognition of facial expressions in healthy females, with regular menstrual cycles assessed in three phases (premenstrual, postmenstrual, and periovulation phases). On the basis of females recognizing face expressions more accurately during the follicular phase in the above studies, it was predicted that:
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Hypothesis 1. Recognition performance during the periovulation and postmenstrual phases would be better than that during the premenstrual phase. Hypothesis 2. Late ERP components, such as P3 and LPP, would increase during the periovulation and postmenstrual phases. It was also of interest whether the early and middle ERP components (e.g., N1, P2) would be affected by the menstrual cycle. METHOD Participants Screening.—To minimize individual differences, potential participants were screened from a large sample of 117 female undergraduates and graduate students according to the scores obtained on the following questionnaires: basic information on the women’s menstrual cycles (Appendix A); Premenstrual Syndrome (PMS; Bancroft, 1993); Trait Anxiety Inventory (TAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983); Beck Depression Inventory (BDI; Beck & Steer, 1993; Hautzinger, Bailer, Worall, & Keller, 1994); Eysenck Personality Questionnaire–Revised, Short Scale for Chinese (EPQ–RSC; Qian, Wu, Zhu, & Zhang, 2000); and Symptom Checklist–90 (SCL–90; Kass, Charles, Klein, & Cohen, 1983). Respondents were considered eligible if they did not have clinical scores in the following selfreported medical conditions: anxiety, depression, premenstrual syndrome, psychiatric history, personality disorders, pregnancy or lactating within the past 12 mo., or use of oral contraceptives within the past 4-mo. Sample.—Twenty-nine women voluntarily participated in the study. They were all right-handed, as determined by Chapman and Chapman’s scale (Chapman & Chapman, 1987). All participants had normal or corrected-tonormal vision. Three participants were excluded, one due to a high error rate and the other two due to excessive head motion. In sum, data from 26 participants (M age = 20.6 yr., SD = 1.5) were included in analyses. The mean age at menarche was 12.7 yr. (SD = 1.5), the mean duration of the menstrual cycle was 29.0 days (SD = 1.4), and the mean menses duration was 5 days (SD = 1.1). Seven women reported dysmenorrheal, two reported menorrhagia, seven reported mild menses, and 17 reported normal menses. Most participants (n = 23) believed that menstruation was a “natural phenomenon,” two reported that menstruation was “a troubling phenomenon,” and one responded “other,” respectively. The participants were not aware of the purpose of the study. The experiment was approved by the local ethical committee and informed consent was obtained from all participants before the experiment. Measures Each woman participated in three experimental sessions (premenstrual, postmenstrual, and periovulation phases). Test sessions were sepa-
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rated by at least seven days. Thus, three test occasions extended across one or two successive menstrual cycles. The time of testing for periovulation was determined using an ovulation test kit. Participants were instructed in how to use the kit and they used it throughout the middle of their menstrual cycle, that was, between days 11 and 16. They were asked to return to the laboratory when a distinct increase in the urinary concentration of luteinizing hormone (LH) occurred. The actual day of menstrual onset was confirmed with each participant by e-mails or telephone calls. The testing dates were as follows: the premenstrual phase was 2.3 days before the next menses (range = 0–6 days), the postmenstrual phase was 2.0 days after the menses (range = 1–4 days) and the range of periovulation phase was 11–18 days. Materials and Equipment Photos of faces obtained from the Chinese Facial Affective Picture System (CFAPS; Wang & Luo, 2005) were used. The CFAPS includes 600 pictures of fearful, happy, sad, angry, disgusted, surprised, and neutral faces of oriental female and male subjects. These images were rated by both Chinese college students (gender-matched) with respect to the valence category (unpleasant to pleasant) and arousal level (low to high) of the images on a nine-point scale (Luo, et al., 2010). We selected the 15 pictures with highest arousal indexes for each facial expression (“disgusted,” “sad,” “angry,” “fearful,” “neutral,” and “happy”). Arousal indexes for each facial expression were compared using analysis of variance (ANOVA). When statistically significant differences were found between mean arousal indexes for the different facial expression picture sets, pictures were exchanged to ensure that the mean arousal indexes across facial expressions were balanced. We selected 30 face pictures (15 male and 15 female) depicting each of the following facial expressions, for a total of 180 pictures: “disgusted,” “sad,” “angry,” “fearful,” “neutral,” and “happy” (Fig. 1). The visual stimuli were matched with respect to brightness and contrast and presented on a gray background at a distance of 60 cm from the participants, using a 19-inch computer screen with a resolution of 1024 × 768 and a refresh rate of 75 Hz. The photos were 11.5 cm × 13.5 cm in size. A fixation cross (0.7 cm × 0.7 cm) was presented at the center of the screen. Procedure Sessions took place in an electronically shielded, air-conditioned, and dimly lit room with the participants sitting in a reclining chair. Participants completed a Self-rating Anxiety Scale as well as a Self-rating Depression Scale before each test session to evaluate mood states during the menstrual cycle. Next, they were asked to complete the recognition tasks. The
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FIG. 1. Representative examples of the 180 faces used in the study, expressing six basic emotions (“disgusted,” “sad,” “fearful,” “neutral,” angry,” and “happy.”)
experiment entailed the following sequence of steps: fixation (200 msec.), blank screen (600–1,000 msec.), photo of facial expression (500 msec.), blank screen (1,000 msec.), reaction screen (5,000 msec.), and finally, a blank screen (1,000 msec.). The participants were instructed to recognize the emotion being depicted and to click the corresponding choices on the reaction screen (all 6 categories were present: “angry,” “disgusted,” “fearful,” “happy,” “sad,” and “neutral”). The experiment had three blocks of 60 unique trials and each photo was shown twice, so that each emotion was presented a total of 60 times (for a sum total of 360 trials). Within each block, the photos were divided into pseudo-random sequences to ensure a unique presentation sequence for each run. After each block (120 trials), the participants were allowed to take a short break. Each session lasted 30 min. The participants were asked to continue looking at the fixation and to keep their head and body still. To control order effects, a counterbalanced repeated-measure design was employed, with about one-third of the sample completing the premenstrual phase (n = 7), postmenstrual phase (n = 11) or periovulation phase (n = 8) measurement first. Compared to a between-subjects comparison (Pearson & Lewis, 2005; Derntl, KryspinExner, et al., 2008; Derntl, Windischberger, et al., 2008), this within-subjects comparison design ameliorates inter-subject variations and thus yields a higher statistical power (Compton, Costello, & Diepold, 2004).
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ERP Data Acquisition Ag–AgCl electrodes of the NeuroScan ERP recording and analysis system were used to record the ERPs from 64 scalp positions according to the International 10–20 System. Data were referenced to the left mastoids online and were re-referenced offline to the average of both mastoids. The ground electrode was placed along the midline between FPz and Fz. Vertical eye movements were registered with two electrodes positioned above and below the left eye. The horizontal electrooculograms were recorded with lateral electrodes from both eyes. Impedances were below 5 kΩ for all recordings. The sampling rate was 1,000 Hz. Data were filtered online with a band-pass filter between 0.05 Hz and 100 Hz. Ocular artifact reduction was performed offline using a regression method (Gratton, Coles, & Donchin, 1983). Epochs with voltages exceeding 100 μV at any electrode except the HEOG and VEOG channels were excluded from further analysis. Trials with eye blinks and lateral eye movements were excluded (no more than 5% of the number of trials in one block). The low-pass offline filter was 30 Hz (24 dB/oct). Epochs of 1,000 msec. after stimulus onset were computed with an additional 200 msec. pre-stimulus baseline. Trials were averaged for each condition and participant, as well as for each condition across participants (grand average). The average number of trials considered for the analyses of each facial expression was as follows: M = 37.03 (SD = 13.44) for disgust, M = 46.86 (SD = 8.52) for sadness, M = 38.42 (SD = 11.49) for anger, M = 48.78 (SD = 8.29) for fear, M = 50.85 (SD = 8.98) for neutral, and M = 51.16 (SD = 8.12) for happiness. Early components (N1, P2, N2) were measured at the electrodes Fz, Cz and Pz. where they were most prominent. Latencies were taken at the electrode where amplitude was maximal. Late components (P3, LPP) were measured at the electrodes Pz and Oz. Six time windows were chosen between 110 and 1000 msec. for quantification at N1 (110–130 msec.), P2 (150-190 msec.), N2 (230–300 msec.), P3 (300–500 msec.) and Late Positive Potential (500–750 msec. and 750–1,000 msec.). After visual inspection, ERPs during the time windows 500–750 msec. and 750–1,000 msec. were found to differ. To differentiate between them, LPP was designated as LPP1 during the 500 to 750 msec. time window and LPP2 during the 750 to 1,000 msec. time window. Statistical Analysis Data were analyzed using SPSS Version 16.0. One-way within-subjects ANOVAs were run on Self-rating Anxiety and Depression scores with menstrual cycle phases as factors (premenstrual phase, postmenstrual phase, and periovulation phase). Behavioral response accuracy was analyzed by ANOVA with repeated measures, considering the factors menstrual cycle phase (premenstrual phase, postmenstrual phase, and periovulation phase)
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and facial expression (“disgusted,” “sad,” “angry,” “fearful,”“neutral,” and “happy”). ERP data analysis also included the factor electrode sites. Statistically significant main effects and interactions were followed up by post hoc comparisons and simple effects analyses. Greenhouse-Geisser corrected p values were used for the ANOVAs and post hoc results were Bonferroni corrected. As a measure of effect size, partial η2p was reported. The associations between performance in the task and amplitude of ERP components were analyzed using Pearson-correlation coefficients (two-tailed). Values of p < .05 were considered statistically significant. RESULTS Behavioral Data & Self-rating Anxiety and Depression Scores For accuracy of facial expression recognition, no statistically significant main effect of menstrual cycle phase was found (F2,50 = 0.66, p = .52). There was a statistically significant main effect of facial expression (F5,125 = 41.62, p < .001, partial η2 = 0.63), with “happy” being the facial expression that was most accurately recognized and “disgust” the least, according to the following sequence: happy > neutral > fearful> sad > angry > disgust (disgust vs anger: p = .43, sad vs fearful: p = .02, neutral vs happy: p = .02, all other p < .001). However, the interaction was not statistically significant (F10,250 = 1.60, p = .11; see Table 1). Results showed no statistically significant differences in Self-rating Anxiety and Depression scores between each of the menstrual cycle phases (Anxiety: F2,50 = 0.48, p = .62; Depression: F2,50 = 0.01, p = .99). ERP Data The values of F and partial η2 of repeated-measures ANOVAs on the ERP components were shown in Table 2. N1 & P2 (110–130 msec. and 150–190 msec.).—N1 showed maximum amplitudes over frontal and central cortical areas, electrode site (p < .001), TABLE 1 RECOGNITION ACCURACY FOR THE SEPARATE EMOTIONS AND ACROSS ALL (TOTAL) FOR ALL PARTICIPANTS IN THREE MENSTRUAL CYCLE PHASES (N = 26) Menstrual Cycle Phase Expression Disgust Sadness Anger Fear Neutral Happiness Total
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Pre-menstrual M SD 0.67 0.19 0.87 0.18 0.74 0.19 0.92 0.25 0.96 0.23 0.97 0.19 0.86 0.16
Post-menstrual M SD 0.73 0.19 0.89 0.19 0.70 0.19 0.92 0.26 0.96 0.23 0.98 0.19 0.86 0.17
Peri-ovulation M SD 0.67 0.21 0.88 0.23 0.73 0.25 0.92 0.29 0.97 0.25 0.97 0.24 0.85 0.17
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TABLE 2 RESULTS OF MENSTRUAL CYCLE PHASES (M) × FACIAL EXPRESSIONS (F) × ELECTRODE SITES (ES) ANOVAS FOR N1, P2, N2, P3, LPP1, AND LPP2 AMPLITUDES (N = 26) Source
df
N1 F
P2 η
2 p
F
N2 η
2 p
F
P3 η
2 p
F
LPP1 η
2 p
F
η
LPP2 2 p
F
η2p
M 2,50 5.12 0.17 F 5,125 3.07 0.11 3.14 0.11 4.37 0.15 3.81 0.13 17.50 0.41 ES 1,25 34.77 0.58 55.03 0.69 8.79 0.26 16.78 0.40 139.94 0.85 M × ES 2,50 3.72 0.13 F × ES 5,125 2.52 7.40 0.23 2.64 0.10 8.52 0.25 Note.—Only statistically significant (p < .05) results are indicated in the table. Interaction between menstrual cycle phases and facial expressions and three-way interaction were not statistically significant. df = degree of freedom; η2p = partial eta square effect size.
with the amplitudes of Fz and Cz statistically significantly larger than those of Pz (ps < .001). Regarding facial expression (p = .01), the mean amplitude of N1 to “fearful” face stimuli was larger than that to “disgusted” (p = .02). With regard to the latency of P2, the main effect of facial expression was statistically significant (p = .001), with “happy” being the expression that was most quickly recognized, and “fearful” the least, according to the following sequence: happy < neutral < angry < disgusted < sad < fearful (happy vs fearful : p < .01, happy vs angry : p = .09, all other p < .05). No main effects of menstrual cycle phase or facial expression were found on the latency of N1 and mean amplitude of P2. N2 (230–300 msec.).—N2 amplitude was largest in the recording from the Fz electrode site (p < .001). The main effect of facial expression was also statistically significant (p = .02). Furthermore, a statistically significant interaction of facial expression and electrode sites was observed (p = .03), with the difference between N2 amplitudes to “sad” (–3.11 μV) and “fearful” (–2.07 μV) face stimuli being pronounced at the Fz (p = 0.011), Cz (p = 0.013) and Pz (p = 0.012). P3 (300–500 msec.).—The main effects of facial expression and electrode site on P3 amplitude were statistically significant (p = .004; p = .007). The interaction of the menstrual cycle phases and electrode sites was statistically significant (p = .04). Further analysis showed that the amplitude of P3 during different phases at Oz was marginal significant (p = .06), with periovulation phase (6.96 μV) > postmenstrual phase (6.45 μV) > premenstrual phase (6.25 μV). The interaction of facial expressions and electrode sites was also statistically significant (p < .001), with the mean amplitude of P3 to “fearful” (7.72 μV) face stimuli being larger than those to “neutral” (7.40 μV) and “happy” (6.73 μV) face stimuli, pronounced at Pz (p = .002) and Oz (p < .001). LPP1 (500–750 msec.).—The main effect of facial expression and electrode site on LPP1 amplitude was statistically significant (p = .003; p < .001).
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Pz
postmenstrual phase periovulation phase
–5.0
–200
0 5.0
12.5
µV
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+12.5 +10.9 +9.4 +7.8 +6.3 +4.7 +3.1 +1.6 0 –1.6 –3.1 –4.7 –6.3 –7.8 –9.4 –10.9 –12.5
FIG. 2. Grand averaged ERPs by menstrual cycle phases at Pz with topographic maps at 750 msec., 850 msec., and 1,000 msec. post-stimulus; ANOVA indicated a statistically significant main effect of phase on mean amplitude between 750 to 1,000 msec. (p < .05), with the diminished amplitude during the premenstrual phase than that during the periovulation phase.
Interactions between facial expressions and electrode sites were also statistically significant (p = .03) with the difference between fear (7.02 μV) and disgust (6.05 μV) being pronounced at Pz (p = .016) and Oz (p < .001). LPP2 (750–1,000 msec.).—As shown in Fig. 2, a statistically significant main effect of menstrual cycle phase was observed on the mean amplitude of LPP2 (p = .01). Post hoc comparisons showed that the amplitude during the periovulation phase (5.70 μV) was larger than that during the premenstrual phase (4.24 μV, p = .01). The main effects of facial expressions and electrode sites were also statistically significant (ps < .001). There were also statistically significant interactions between facial expressions and electrode sites (p < .001), with LPP2 responses to the “angry” (9.28 μV) face stimuli having the largest amplitude and to “happy” (5.17 μV) stimuli the smallest amplitude; this difference was pronounced both at the Pz than Oz (ps < .001). Correlations: LPP2 at Electrode Pz and Accuracy Diminished amplitude of the LPP2 was found during the premenstrual phase, relative to that during periovulation phase. To characterize the relation between the amplitude of LPP2 and accuracy, further correlational analysis was performed over all facial expressions. The results showed a statistically significant positive relationship between the amplitude of LPP2 and accuracy of facial expression recognition only during the periovulation phase (r = .42, p = .03, 95%CI = 0.04, 0.70), but not during the premenstrual phase (r = .33, p = .11, 95%CI = 0.07, 0.64) or the postmenstrual phase (r = .34, p = .09, 95%CI = 0.06, 0.64).
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DISCUSSION In this study, the effect of the menstrual cycle on the time course of facial expression recognition was investigated by means of ERPs. Behavioral results revealed only statistically significant differences in accuracy for different facial expressions, with the highest accuracy for “happy” and the lowest for “disgusted” expressions. The primary finding was that the late ERP component evoked by facial expressions was affected by the phase of the menstrual cycle: the amplitude of the LPP2 (750 to 1,000 msec. poststimulus) during the periovulation phase was larger than that during the premenstrual phase. This effect did not appear specific to particular expressions. Furthermore, a positive correlation between LPP2 amplitude and facial expression recognition accuracy was found during the periovulation phase; while the ERP components before 750 msec. post-stimuli, including N1, P2, N2, P3, and LPP1, were not sensitive to the menstrual cycle phase. Statistically significant menstrual cycle phase differences in facial expression recognition accuracy were not observed. This may be related to the presentation time of the stimuli. In previous behavioral studies, the visual stimuli disappeared once the participants made a keyboard response (Pearson & Lewis, 2005; Derntl, Kryspin-Exner, et al., 2008; Guapo, et al., 2009). Recognition in these studies were as follows: fear ACC: 58%, 74%, 72%, and 66% (Pearson & Lewis, 2005), total ACC: 90%, 85% (Derntl, Kryspin-Exner et al., 2008), and each emotion and total ACC are all lower than 70%(Guapo, et al., 2009), but the presentation time was fixed at 500 msec. and following a 1,000 msec. blank screen, thus the participants had enough time to choose a correct answer, which may lead to a reduction in the variance of the recognition accuracy(ACC) in different phases of the menstrual cycle (fear ACC: 92%,92%,92%, total ACC: 86%,86%, and 85%) in this study. Additionally, the fixed presentation time and the following blank screen may result in the participants’ expectations before they made the behavioral responses, which can also influence the behavioral results. Menstrual Cycle Modulation of the LPP The most important result from this study is that only the late ERP component LPP2 was affected by the menstrual cycle, supporting previous findings (Johnston & Wang, 1991; Krug, et al., 2000). However, in the present study, this effect was independent of the specific facial expression. The amplitude of the LPP was associated with the allocation of attention processes in previous studies (Hajcak & Foti, 2009; Gable & Harmon-Jones, 2010). Here, the larger LPP2 during the periovulation phase suggested that women may expend more attentional resources in emotion recognition than during the premenstrual phase. Ferrari, Codispoti, Cardinale, and Bradley (2008) found that both emotion and task-relevant stimuli additively determined the amplitude of the LPP. This study
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expands this view by demonstrating that the amplitude of the LPP is also determined by the menstrual cycle. Processing of the emotion information contained in faces are key to social and mating behaviors. The statistically significant, positive correlation between behavioral performance and the LPP2 during the periovulation phase indicates the more accurately the women recognized the facial expressions, the larger the amplitude of LPP2—although only about 17% of the variance was explained by menstrual cycle phases. The underlying endocrine mechanism may be related with the luteinizing hormone, which peaks in the ovulation phase but is low in other phases. This idea is consistent with an earlier behavioral study in which ovulating women were found to be more accurate in their social cognitive judgments and interpersonal perception while in this hormonal state, compared to women during menstruation (Macrae, Alnwick, Milne, & Schloerscheidt, 2002). During the periovulation phase, the maximal potential for successful reproduction is enhanced due to increased accuracy in the ability to identify facial expressions correctly, which forms an important basis for successful social interactions. However, N1, P2, N2, P3, and LPP1 (before 750 msec. post-stimulus) were not affected by the menstrual cycle, consistent with an early auditory ERP study (Fleck & Polich, 1988). These results indicated that the menstrual cycle modulation of the facial expressions recognition occurred in the late stage, not the early and middle stage. However, the modulation of menstrual cycle on the N2 latency (ε = 0.75) was found in another auditory study (Walpurger, Pietrowsky, Kirschbaum, & Wolf, 2004). As the menstrual cycle modulation on the ERP components is complex, further menstrual cycle ERP studies are needed to clarify the mechanism. LPP2 and Ovarian hormones It is possible that the mechanism behind the difference in LPP2 can be explained in terms of ovarian hormone differences during the menstrual cycle. Previous behavioral studies have demonstrated that the modulation of the ability to recognize facial expressions by the menstrual cycle may be related to the accompanying fluctuation of ovarian hormones, especially estrogen and progesterone (Pearson & Lewis, 2005; Derntl, Kryspin-Exner, et al., 2008; Guapo, et al., 2009). According to a previous review (Farage, et al., 2008), we can have a general idea as to the approximate level of estrogen and progesterone during the test phases in this study: the postmenstrual phase is characterized by low levels of both estrogen and progesterone; estrogen is at the highest level and progesterone rises during the periovulation phase; the premenstrual phase is characterized by declines in both estrogen and progesterone that reach baseline shortly before the onset of menstruation. Since the
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progesterone is low in both postmenstrual and periovulation phase, and high in the premenstrual phase, the diminished LPP2 amplitude in the premenstrual phase may be caused by the high progesterone level. As there were no direct measures of ovarian hormones in our study, this corollary awaits further confirmation. Differences in ERPs to Facial Expressions All the ERP components in this study were affected by the facial expression category. Differences in amplitude of the early ERP components (N1, P2, and N2) were observed for different emotions distributed mainly over the anterior area, but for the late ERP components (P3, LPP1 and LPP2), the differences distributed mainly over the posterior area. The largest peak amplitudes were observed for different negative expressions in different time courses: fear (110–130 msec., 150–190 msec. and 300–500 msec.), sadness (230–300 msec.), and anger (750–1,000 msec.). The recognition of “fearful” face stimuli, as the representative negative emotion, is quicker than that of other expressions during 110–500 msec. post-stimulus. Due to its importance to survival and evolution, fearful faces may be allocated more processing resources and subsequently evoke the largest ERP amplitudes (Eimer & Holmes, 2007). Additionally, after “fearful,” “angry” occupies the advantage in processing, as anger has a greater importance for social survival (Pearson & Lewis, 2005). However, recognition of “sad” faces predominated from 230 to 300 msec. post-stimulus. These negative emotional expressions with different meanings apparently captured more cognitive resources than neutral and positive emotional faces and occupy superior positions at different processing times. Therefore, any exploration of negative emotional processing mechanisms needs to consider specific differences within subcategories of negative emotions. Finally, several limitations in the present study should be mentioned. Firstly, the ovarian hormones were not obtained, so the causality of hormonal changes in emotional recognition needs confirmation in further research. Secondly, the behavioral results might have been be influenced by the fixed presentation time and the participants’ expectations. Thirdly, no differences in mean self-reported Anxiety and Depression scores were found across phases of the menstrual cycle, as the participants were screened and included only healthy females without premenstrual syndrome, whose moods remain relatively stable. Besides, mood changes during the menstrual cycle are relatively weak and may be counteracted by life events, and of course cyclic variance may be smaller than other individual differences and not stable. It could be difficult to detect statistically significant differences based on subjective self-reports.
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Conclusion The present study indicates that the LPP component of facial emotion recognition is affected by menstrual cycle phase, with diminished LPP amplitude in the premenstrual phase. In addition, the correlation between LPP amplitude and accuracy in identification of facial expressions during periovulation suggests that changes in LPP amplitude may reflect changes in emotion processing performance. Findings imply that the effect of the menstrual cycle phase should be taken into consideration in future ERP studies of emotion. REFERENCES
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APPENDIX A Basic Information on the Menstrual Cycle Please complete the following information and select the multiple choice answer that most accurately applies to you. 1. Age at menarche_____ 2. Date of onset of last menstrual cycle____(month) _____(day) 3. Menstrual regularity A. Yes B. No; approximate cycle length _______________________ Other circumstances (please specify) _____________________ 4. Menstrual period duration: ______ days 5. Menstrual quantity A. Less B. Normal C. More 6. (a) Dysmenorrhea A. Yes B. No (b) If dysmenorrhea, how serious? _______________________ 7. Your attitude about the menstrual cycle________ A. Troubling phenomenon B. Natural phenomenon C. Divine phenomenon D. Does not affect behavior E. Other (please specify) _______________________
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