Skip to main content
Download PDF

Association between resting-state EEG oscillation and psychometric properties in perimenopausal women

BMC Women's Health volume 22, Article number: 149 (2022) Cite this article

A Correction to this article was published on 16 June 2022

This article has been updated

Abstract

Background

The perimenopausal period is associated with a higher risk of various mood disorders. Similarly, although resting-state electroencephalogram (rsEEG) brain oscillatory activity has been associated with various neuropsychological disorders and behaviours, these issues have not been assessed in perimenopausal women. This study aimed to evaluate quantitative relationships between psychometric properties and rsEEG rhythms (delta, theta, alpha, beta and gamma powers) in perimenopausal women.

Methods

A cross-sectional correlational descriptive study was conducted to quantitatively analyze the correlations between rsEEG low-to-high band activities (delta, theta, alpha, beta, and gamma powers) and psychometric properties in 14 perimenopausal women. Participants completed a psychological inventory comprising the State Anxiety Inventory (SAI), Depression Inventory (DI), Behavioural Inhibition Scale (BIS) and short-form UPPS Impulsive Behaviour Scale (IS) before EEG recording.

Results

Results showed that impulsivity was positively related to the beta power, symmetrical at most channels (frontal, temporal, central, parietal and occipital regions; p < .05); but did not related to the delta, theta, alpha and gamma powers. The brainwave low-to-high bands, delta, theta, alpha, beta and gamma power were not associated with DI, SAI or BIS scores.

Conclusions

This study’s findings propose that significantly enhanced resting-state beta activity is a trait of impulsivity in perimenopausal women. Therefore, results have potential implications for the preclinical or clinical evaluation of these issues in perimenopausal women.

Peer Review reports

Background

Perimenopause is a part of the processes of a woman’s transition into menopause that encompasses the final years of her reproductive life [1]. Stages of Reproductive Ageing Workshop (STRAW) [2, 3] divided the adult female life into three broad phases: the reproductive, menopausal transition and postmenopause, with each of these phases presenting their issues. In particular, reproductive hormone fluctuations characterise perimenopause, leading to mood disorders and other psychiatric problems [4]. Therefore, research has suggested that the stages of hormonal fluctuations during a woman’s lifespan run parallel with an increased risk of mood disturbances [5, 6]. Women between 45 and 55 years of age experience a phase of life known as “the window of vulnerability,” during which mental health problems that occur, such as anxiety and depressive symptoms, have been extensively explored [5]. Furthermore, women in the perimenopause stage frequently experience emotional changes, such as irritability, sadness, lack of motivation, fatigue and mood swings [7, 8], and mild cognitive decline associated with perimenopause has also been reported [9]. These emotions significantly influence social cognitive processes, which in turn, influence life outcomes [10, 11]. Thus, a better understanding of this life period, which every woman passes through, can be gained through the efforts of neuroscientists, including those in the biological and psychological sciences, to detect early-stage biomarkers of cognitive decline and work on the implications of brain oscillations. However, limited evidence on such oscillations has been gathered from preclinical, perimenopausal women.

Apart from the anxiety and depression experienced during perimenopause [12], negative affective behaviours, such as impulsive behaviour strength (IS) and behavioural inhibition sensitivity (BIS), have also been reported in relation to hormonal imbalance [13,14,15,16]. Studies have shown that increasing oestrogen levels lead to less impulsive decision making [12, 15,16,17]. Moreover, the hormonal imbalance influences impulsivity strength, relating to aggressive behaviour, which is relevant to the serotonergic modulation in neural circuits [16,17,18]. Similarly, diminished brain serotonin synthesis is further linked with lack of impulse control and various psychiatric disorders [18]. Besides, ovarian hormones mediate the behavioural inhibition hypothesis that potential mechanisms occur through the dopamine pathway across the cortical limbic network [19]. The behavioural inhibition system is activated through aversive stimulation, responding to conditioned stimuli of punishment, novel stimuli and innate fear stimuli. It also elicits the affective state of anxiety and leads to behavioural inhibition [20, 21]. Therefore, the reproductive hormone is correlated with psychological alterations and symptoms that can negatively impact emotion and mood. However, to the best of our knowledge, no study has yet surveyed the resting EEG in perimenopause women. Furthermore, although resting EEG signals have been widely used in classifying different emotional states [22,23,24], no such signals exist for this middle age group of perimenopause women. The present study findings underline the importance of the selected psychometric properties of this study.

The characterisation of rsEEG brain oscillatory activity is a practical methodology used to study human behaviour. Clinically, EEG signals have been used as valuable indicators of psychological characteristics. rsEEG signals are composed of multiple waves cycling at different frequencies: delta (δ: 0.5–4 Hz), theta (θ: 4–8 Hz), alpha (α: 8–13 Hz), beta (β: 13–30 Hz) and gamma (γ: 31–40 Hz) [25]. Essentially, human behaviour begins in the brain. Therefore, quantitative EEG (qEEG) signals can be interpreted and clinically applied to evaluate brain function. Furthermore, a network of brain regions known as the ‘default mode network’ shows increased brain activity even in the resting state, reflecting spontaneous cognitive processes [26]. Hence, spontaneous activities and their various rhythms correlate with psychometric properties relevant to psychological factors in physical health and illness that are known to influence neuropsychiatric diseases [27, 28]. The quantitative spectral analysis of rsEEG provides an efficient, convenient and relatively inexpensive way to study the relationships between brain activity and behaviour [29]. Through observations of brain rhythms and considering their tagged functional roles, an attempt was made to reveal how activity across rsEEG frequency bands is associated with the psychological characteristics of perimenopausal women. Such research is necessary, as this potential neural entrainment is not completely covered in previous literature. Thus, examining resting-state EEG data is proposed to enhance our understanding of basic brain functions.

Self-report inventories have become the most prevalent method used to assess psychological characteristics. The psychological inventories used in this study were chosen according to common psychological factors experienced during perimenopause and the early stages of ageing [30,31,32,33]. These scales have been widely used in psychological research or clinical practise, and their validity and reliability have been well-established. These following scales are included: (1) the Center for Epidemiologic Studies-Depression scale (CES-D; Depression Inventory [DI]) was developed by the National Institute of Mental Health, measuring depressive symptoms in the general population, (2) the State-Trait Anxiety Inventory (SAI), a reflective psychological self-report inventory relating to anxiety, which was assessed across two dimensions (state and trait) [34]. State anxiety denotes negative mood, reflecting a ‘transitory emotional state or condition of the human organism’. (3) the BIS assessment attributes sensitivity to signals of punishments [35]. Heightened BIS sensitivity is proposed to increase one’s risk of developing anxiety or depressive disorders [36]. Finally, (4) the modified short version of the UPPS IS assesses impulsive behaviour, commonly related to a sense of urgency, sensation seeking and lack of premeditation and perseverance [32, 37]. Thus, four inventories—the DI, SAI, BIS and IS—were included in our study.

Taken together, this study investigated the relationship between neuropsychological assessments and quantitative rsEEG band power in perimenopause women. Subsequently, the cross-correlation between EEG frequency bands and psychometric properties examined whether potential neurophysiological trait markers existed for specific psychological characteristics in non-clinical perimenopausal women.

Methods

Participants and ethical considerations

The present research was a cross-sectional correlational study, and all procedures were conducted following the Institutional Review Board of CGMH, where 14 right-handed individuals aged 46 and 54 years (Mage = 51 years) participated. We recruited participants through advertisements among the community of Tao Yuan, Taiwan. All subjects were within the menopausal transition phase, characterised using the Stages of Reproductive Ageing Workshop (STRAW) [3, 14, 17]. Participants were screened via standardised interviews. Participants reported irregular menstrual cycles but were not menopausal and reported as not experiencing menstruation or blood discharge in the past year. Subsequently, participants were excluded if they (1) had used oral contraceptive pills or hormone therapy in the past year (2) had a history of neurological, psychiatric, or personality disorders, including premenstrual dysphoric disorder, as evaluated using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition or (3) had auditory-visual impairment. Before the experiments, all participants were asked to refrain from consuming alcohol for at least 48 h, caffeine and tobacco for 12 h, and engaging in physical activity for 6 h. Furthermore, written informed consent was obtained from each participant before the experiment.

EEG recording and preprocessing

Participants were tested in a light-controlled and sound-attenuated room. First, spontaneous brain activity was recorded continuously for three minutes while participants relaxed and stayed awake with their eyes closed. The study was conducted using an Encerphalan-EEGR-19/26 computerised EEG (Version 5.4–16–2.0, Medicom MTD). Then, EEG activity was recorded from 19 sites—Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1 and O2, within the standard 10–20 system, covering symmetrical brain areas: the anterofrontal, frontal, anterotemporal, temporal, posterior temporal, central, parietal and occipital regions (Fig. 1). EEG and electrooculography were recorded at a sampling rate of 256 Hz/s, and the high-and low-frequency filters were set at 70 Hz and 0.5 Hz, respectively.

Fig. 1
figure 1

Electrode placement based on the International 10–20 System. The 19 sites: Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1, and O2 within the standard 10–20 system, covering symmetrical brain areas; the anterofrontal, frontal, anterotemporal, temporal, posterior temporal, central, parietal, and occipital regions, are shown

Full size image

Each recording was visually screened to remove epochs with head movements and eye blinks, after which a computer-based rejection algorithm discarded any epochs with an activity greater than ± 75 μV in amplitude. Subsequently, the absolute power value (APV, μV2) and relative power value (RPV, %) were recorded using power spectrum analysis from non-artefact four-second signals of approximately 20–45 epochs. Then, the baseline-correction was applied to all accepted epochs, and the fast Fourier transforms were averaged in five selected frequency bands: delta (δ; 0.5–4 Hz), theta (θ; 4–8 Hz), alpha (α; 8–13 Hz), beta (β; 13–30 Hz) and gamma (γ; 31–40 Hz). Next, to minimise the effects of inter-individual variability in power, relative power (%) was obtained by computing the fraction of power in each frequency band, divided by the sum of power measurements across 0.5–40 Hz for each frequency band in each of the 19 channels. Finally, relative power was calculated as the total power ratio of a band to all bands from delta to gamma (0.5–40 Hz), following the relative power calculation of each band and the ratios of each region. Thus, whether the rsEEG oscillation of relative power (%) relates to the psychometric properties was determined in this study.

Neuropsychological measures

A questionnaire was administered before EEG recording which comprised four inventories. (1) The DI, which contains 20 items. Scores were weighted from 1 to 4, with a rating of 4 indicating the highest level of depression. Total scores ranged from 20 to 80. (2) The SAI also contained 20 items. Scores were weighted from 1 to 4, where the rating of 4 indicates the highest level of anxiety. Total scores ranged from 20 to 80. (3) The BIS, comprising seven items, probed the degree of anxiety felt when confronted with punishment cues [35]. It was rated on a 4-point Likert scale (range 1–4). A high BIS score was associated with highly negative effects in response to punishments. (4) The IS [38], which contains 16 items. Scores were weighted from 1 to 4, with a rating of 4 indicating the highest level of impulsivity. Total scores ranged from 16 to 64. These inventories were well estimated using the Cronbach’s alpha value and were used for clinical settings to detect and measure the functional manifest.

Analysis

Descriptive statistical analyses were used for neuropsychological assessments (SAI, DI, BIS and IS) and resting-state band power analysis of delta, theta, alpha, beta and gamma activities (RPV %) at each channel. Consequently, we evaluated the quantitative relationships between psychometric properties and rsEEG rhythms (delta, theta, alpha, beta and gamma powers). Then, the Kolmogorov–Smirnov test (KS test) was applied to determine whether sample data are normally distributed, including frequency bands of the whole brain (%) and the scores of four inventories. Once data revealed a normal distribution, Pearson’s approach was used to evaluate whether correlations existed between the power of the five frequency bands and participants’ neuropsychological scores (SAI, DI, BIS and IS) at the 19 sites accordingly. The significance-level threshold was set at p < 0.05. Then, we separately calculated the average correlation coefficients for the 19 sites in each brain wave. Afterwards, average correlation coefficients were used to explore whether any brain wave activity was highly correlated with psychometric properties in perimenopausal women.

This study aimed to understand the association between frequency relative power (delta, theta, alpha, beta and gamma; %) and inventory scores due to previous relevant issues that have not been regarded. First, adequately simple correlations were conducted in the continuous variable using step-by-step reports obtained from five bands and four psychometric inventories at 19 regions. For the purpose of the study, these serial data obtained had no requirement of being spread over multiple comparisons in the elucidated group (or category) difference at this time. However, if inconsistent or particular findings were observed after Pearson’s analysis, we considered additional statistical examinations. Subsequently, a widespread significant relationship was shown in the beta power and impulse strength at most channels, which were inconsistent in others. Therefore, a simple linear regression was conducted to test and illustrate the predictive function of the entire beta oscillation power (%) in four inventories. All statistical analyses were conducted using SPSS version 26.0.

Results

Descriptive statistics

Means and standard deviations (mean ± SD) of participants’ scores for the DI AI, BIS and IS were 35.21 ± 5.81, 31.57 ± 8.37, 20.57 ± 3.16 and 34.71 ± 5.03, respectively. The mean and standard error (SE) of the resting band powers (delta, theta, alpha, beta and gamma activity; %) at each channel are shown in Table 1 accordingly. Results showed entire band powers in 19 regions, including five frequencies and four inventory scores, thus revealing normal distribution (p > 0.05, KS test).

Table 1 The RPV (Relative Power Value, %) at each channel from rsEEG rhythms in perimenopause women
Full size table

Relationship between brain activity and behaviour

Results showed that the resting brain oscillation for delta, theta, alpha, beta and gamma presented different findings using Pearson’s correlation analysis based on the four inventory scores (Table 2). As observed, the IS was positively related to the beta power activity of the symmetrical results for almost all the brain regions (frontal, temporal, central, parietal and occipital regions; r = 0.58–0.84, all p < 0.05). Contrastingly, DI, AI and BIS scores did not show evident correlations with the delta, theta, alpha, beta and gamma powers (Table 1). Figure 2 shows that the whole-brain resting-beta power may have a specific function in predicting the impulsivity strength in perimenopausal women (p < 0.01). We observed that the whole-brain resting activity for delta, theta, alpha, beta and gamma showed no significant correlation with DI, SAI and BIS scores (p > 0.05; Fig. 2).

Table 2 The Pearson r value between the psychological score and band power at each channel
Full size table
Fig. 2
figure 2

Bands showing the r value for the psychometric scores (r values were derived from the average of 19 channels). The IS is positively related to the beta power in the cross–correlation analysis. IS, Impulsivity Score; DI, Depression Inventory; SAI, State Anxiety Inventory; BIS, Behavioural inhibition sensitivity scale; **significant at a p level of 0.01

Full size image

After obtaining the entire beta oscillation power, we observed that the Impulse score (IS) was 68% of the total variance in the beta oscillation power (%) using regression analysis (adjusted R2 currently = 0.679; unadjusted R2 = 0.786). Alternatively, the IS had the maximum variance of all the four scales and it was found to be 68% of the total variance.

Discussion

Research has not yet clarified the association between spontaneous neural oscillations and neuropsychological evaluations for perimenopausal women. Therefore, this study conducted a quantitative investigation of the associations between rsEEG low-to-high band activities and psychological assessments of perimenopausal women. Results indicated that the rsEEG beta power can be used to specifically predict the degree of impulsivity (as measured by the IS) in perimenopausal women, unlike other psychological characteristics. It has been reported that as mild cognitive decline exists in middle-aged populations, emotion interacts with and influences cognition or behaviour [39]. Furthermore, the beta band expressed a strong relationship with predicted impulses. These findings provide a potential implication for the preclinical or clinical evaluations of perimenopausal women.

Our results indicated a significant positive correlation between the rsEEG beta power and impulsivity (p < 0.01; Fig. 2). No such consistent relationship has been observed among the delta, theta, alpha and gamma powers. Previous studies have reported that beta activity was related to symptoms of brain over-arousal, such as anxiety, obsessiveness, sleeping difficulties and hyperactivity in adults [40,41,42,43]. Similarly, the IS questionnaire assessed a sense of urgency, premeditation, perseverance and sensation seeking, all of which are factors for impulsive behaviour [44, 45]. Moreover, impulsivity is defined as an individual’s tendency to behave without forethought or regard for the consequences. Studies have revealed that impulsivity is associated with spindling beta activity in clinical conditions, such as attention-deficit/hyperactivity disorder, epilepsy or psychosis and gambling disorders, including during hallucinations [46,47,48]. Studies providing this evidence have considered employing task-based methods, such as Go/NoGo and stop-signal tasks. However, the brain is constantly active, even at rest without external stimuli [49, 50]. No report has discussed the association between the degree of impulsivity and rsEEG in perimenopausal women. Here, the association between four self-reported psychology inventories across broad frequency bands was evaluated. Subsequently, we highlighted that the intrinsic beta power signature had an important correlation with the degree of impulsivity, making it a potential biomarker for impulsivity in perimenopausal women.

The main finding regarding the relation between spontaneous brain activity and scores of psychological tests was that the default beta power was positively related to IS. This phenomenon was symmetrically exhibited at 16 channels (r = 0.58 to 0.84, all p < 0.05), with a maximum correlation coefficient (r value) at the T4 channel (r = 0.84). T4 mainly reveals activity in the middle temporal gyrus [51], connected by regional circuits with the amygdala, hippocampus, superior temporal gyrus, the occipitobasal cortex and orbital gyrus. Additionally, the anatomical and functional organisation of the temporal lobe involves impulse inhibition functions underlying trait impulsivity [52]. Moreover, the temporal lobe communicates with the hippocampus, playing a key role in forming impulsivity. These features are proposed to explain our finding that the maxima correlation coefficient of beta power, with the degree of impulsivity in perimenopausal women, was at the T4 region.

In this study, although other age groups were not included in the comparison of the power of different spectrums, extensive correlation comparisons were made for different psychometric scales. From this preliminary result, it is evident that the indicators of IS represented 68% of the total variance in the entire brain beta oscillation power (%) using regression analysis (adjusted R2). Such information has not been reported previously for perimenopausal women, using the non-invasive brain wave device. Impulsiveness is a personality trait whose force can be modulated through hormone fluctuations. These hormonal alterations involve prefrontal-subcortical neurocircuitry modulatory activities that result in an imbalanced contribution of the sub-thalamic nucleus associated with irritability and impulse control disorders [16, 45, 53]. Furthermore, emotions shape behaviour. Inappropriate impulsiveness relates to the anger emotion, which may lead to adverse behaviour such as suicide or violence and interrupt social function. Exploring a reliable indicator for reflecting the intensity of impulses in middle-aged older women is important. Notably, the precise reappraisal of IS or other psychological matters for perimenopausal women also accounts for the advancement in research requirements for clinical or neuroscience researchers. The present study presents a novel academic view on impulsive-EEG biomarkers in the course of menopause transition. For evaluating perimenopause emotional vulnerability, tracking band power modulation through various interventions—such as neurofeedback or meditation for psychological well-being across the lifespan—should be considered in improving women’s health.

We analysed the RPV of frequency bands in each channel using whole-brain averages, not conducting derived analyses of these spectrum bands, such as their spatial coherence or asymmetry. The strength of our approach was that 20–45 epochs were obtained for each band in the statistical analysis, which helped determine the effective size and resulted in a high-reliability estimate. Furthermore, apart from the correlation between the beta power and IS score, the cross-correlational findings of this study identified no neurophysiological biomarker for psychological characteristics in perimenopausal women. These observations further indicate that the quantitative EEG methodology has discriminative power and offers objective and biological insight into cognitive processes, as our study results revealed no significant relationship between delta, theta, alpha, and gamma powers and the SAI, DI, and BIS scores in perimenopausal women. Such a result could encourage clinical care professionals to work on a brain evidence-based manner in the health promotion during the early ageing stages of perimenopausal women.

This study has several limitations. First, we did not address the power spectral density of each band or conduct functional connectivity analysis. Furthermore, we did not apply multiple comparisons due to the fact that the dataset collected was continuous variable, and we had no means of converting these values to categories. Finally, changes in ovarian hormone production affected various neuropsychological health outcomes. Conceivably, however, this study provides more advanced analysis perspectives for future investigations.

Conclusion

This pilot study shows that the rsEEG beta power specifically predicts impulsivity in perimenopausal women, proposing it as a potential biomarker to assist future research on neurobehavioural estimations in perimenopausal women.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Change history

Abbreviations

BIS:

Behavioural Inhibition Scale

DI:

Depression Inventory

IS:

Impulsive Behaviour Scale

RPV:

Relative power value

SAI:

State Anxiety Inventory

References

  1. Santoro N. Perimenopause: from research to practice. J Womens Health (Larchmt). 2016;25(4):332–9.

    Article  Google Scholar 

  2. Harlow SD, Gass M, Hall JE, et al. Executive summary of the Stages of Reproductive Aging Workshop +10: addressing the unfinished agenda of staging reproductive aging. Climacteric. 2012;15(2):105–14.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Harlow SD, Gass M, Hall JE, et al. Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging. Menopause. 2012;19(4):387–95.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lin HL, Hsiao MC, Liu YT, Chang CM. Perimenopause and incidence of depression in midlife women: a population-based study in Taiwan. Climacteric. 2013;16(3):381–6.

    Article  PubMed  Google Scholar 

  5. Freeman EW. Associations of depression with the transition to menopause. Menopause. 2010;17(4):823–7.

    Article  PubMed  Google Scholar 

  6. Novaes C, Almeida OP, de Melo NR. Mental health among perimenopausal women attending a menopause clinic: possible association with premenstrual syndrome? Climacteric. 1998;1(4):264–70.

    Article  CAS  PubMed  Google Scholar 

  7. Alexander JL, Dennerstein L, Woods NF, et al. Neurobehavioral impact of menopause on mood. Expert Rev Neurother. 2007;7(11 Suppl):S81-91.

    Article  PubMed  Google Scholar 

  8. Koike K, Yamamoto Y, Suzuki N, et al. Efficacy of porcine placental extract on climacteric symptoms in peri- and postmenopausal women. Climacteric. 2013;16(1):28–35.

    Article  CAS  PubMed  Google Scholar 

  9. Duka T, Tasker R, McGowan JF. The effects of 3-week estrogen hormone replacement on cognition in elderly healthy females. Psychopharmacology. 2000;149(2):129–39.

    Article  CAS  PubMed  Google Scholar 

  10. Koban L, Pourtois G. Brain systems underlying the affective and social monitoring of actions: an integrative review. Neurosci Biobehav Rev. 2014;46(Pt 1):71–84.

    Article  PubMed  Google Scholar 

  11. do Nascimento EB, Dierschnabel AL, de Macedo Medeiros A, Suchecki D, Silva RH, Ribeiro AM. Memory impairment induced by different types of prolonged stress is dependent on the phase of the estrous cycle in female rats. Hormones Behav. 2019;115:104563.

    Article  CAS  Google Scholar 

  12. Soares CN. Can depression be a menopause-associated risk? BMC Med. 2010;8:79.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hwang RJ, Chen LF, Yeh TC, Tu PC, Tu CH, Hsieh JC. The resting frontal alpha asymmetry across the menstrual cycle: a magnetoencephalographic study. Hormones Behav. 2008;54(1):28–33.

    Article  Google Scholar 

  14. Stone EA, Lin Y, Sarfraz Y, Quartermain D. The role of the central noradrenergic system in behavioral inhibition. Brain Res Rev. 2011;67(1–2):193–208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Smith CT, Sierra Y, Oppler SH, Boettiger CA. Ovarian cycle effects on immediate reward selection bias in humans: a role for estradiol. J Neurosci. 2014;34(16):5468–76.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Trifu SC, Tudor A, Radulescu I. Aggressive behavior in psychiatric patients in relation to hormonal imbalance (Review). Exp Ther Med. 2020;20(4):3483–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Pavlov KA, Chistiakov DA, Chekhonin VP. Genetic determinants of aggression and impulsivity in humans. J Appl Genet. 2012;53(1):61–82.

    Article  CAS  PubMed  Google Scholar 

  18. Helmbold K, Zvyagintsev M, Dahmen B, et al. Serotonergic modulation of resting state default mode network connectivity in healthy women. Amino Acids. 2016;48(4):1109–20.

    Article  CAS  PubMed  Google Scholar 

  19. Wang JX, Zhuang JY, Fu L, Lei Q, Fan M, Zhang W. How ovarian hormones influence the behavioral activation and inhibition system through the dopamine pathway. PLoS ONE. 2020;15(8):e0237032.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hirsh JB, Kang SK. Mechanisms of identity conflict: uncertainty, anxiety, and the behavioral inhibition system. Person Soc Psychol Rev. 2016;20(3):223–44.

    Article  Google Scholar 

  21. Barros-Loscertales A, Meseguer V, Sanjuan A, et al. Behavioral inhibition system activity is associated with increased amygdala and hippocampal gray matter volume: a voxel-based morphometry study. Neuroimage. 2006;33(3):1011–5.

    Article  CAS  PubMed  Google Scholar 

  22. Xing M, Tadayonnejad R, MacNamara A, et al. Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder. NeuroImage Clin. 2017;13:24–32.

    Article  PubMed  Google Scholar 

  23. Knyazev GG, Savostyanov AN, Bocharov AV, Bazovkina DV, Proshina EA. Resting state networks mediate the effect of genotype by environment interaction on mental health. Neuroscience. 2018;369:139–51.

    Article  CAS  PubMed  Google Scholar 

  24. Balconi M, Vanutelli ME, Grippa E. Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues. Brain Behav. 2017;7(5): e00686.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Crespel A, Gélisse P. Atlas of electroencephalography: activation procedures and artifacts, awake and sleep EEG. John Libbey Eurotext; 2005.

    Google Scholar 

  26. Suss H, Ehlert U. Psychological resilience during the perimenopause. Maturitas. 2020;131:48–56.

    Article  PubMed  Google Scholar 

  27. Rojas GM, Alvarez C, Montoya CE, de la Iglesia-Vaya M, Cisternas JE, Galvez M. Study of resting-state functional connectivity networks using EEG electrodes position as seed. Front Neurosci. 2018;12:235.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lindquist KA, Wager TD, Kober H, Bliss-Moreau E, Barrett LF. The brain basis of emotion: a meta-analytic review. Behav Brain Sci. 2012;35(3):121–43.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rogala J, Kublik E, Krauz R, Wrobel A. Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance. Sci Rep. 2020;10(1):5064.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yu J, Rawtaer I, Fam J, et al. Sleep correlates of depression and anxiety in an elderly Asian population. Psychogeriatrics. 2016;16(3):191–5.

    Article  PubMed  Google Scholar 

  31. Laredo-Aguilera JA, Carmona-Torres JM, Garcia-Pinillos F, Latorre-Roman PA. Effects of a 10-week functional training programme on pain, mood state, depression, and sleep in healthy older adults. Psychogeriatrics. 2018;18(4):292–8.

    Article  PubMed  Google Scholar 

  32. Rochat L, Delbeuck X, Billieux J, d’Acremont M, Van der Linden AC, Van der Linden M. Assessing impulsivity changes in Alzheimer disease. Alzheimer Dis Assoc Disord. 2008;22(3):278–83.

    Article  PubMed  Google Scholar 

  33. Porcino J. Psychological aspects of aging in women. Women Health. 1985;10(2–3):115–22.

    CAS  PubMed  Google Scholar 

  34. Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Manual for the State-Trait Anxiety Inventory. Palo Alto: Consulting Psychologists Press; 1983.

    Google Scholar 

  35. Carver CS, White TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. J Pers Soc Psychol. 1994;67(2):319–33.

    Article  Google Scholar 

  36. Harmon-Jones E, Allen JJ. Behavioral activation sensitivity and resting frontal EEG asymmetry: covariation of putative indicators related to risk for mood disorders. J Abnorm Psychol. 1997;106(1):159–63.

    Article  CAS  PubMed  Google Scholar 

  37. Rochat L, Beni C, Billieux J, Azouvi P, Annoni JM, Van der Linden M. Assessment of impulsivity after moderate to severe traumatic brain injury. Neuropsychol Rehabil. 2010;20(5):778–97.

    Article  PubMed  Google Scholar 

  38. Es B. Impulsiveness subtraits: Arousal and information processing. In: Spence JT, Itard CE, editors. Motivation, emotion, and personality. Amsterdam: Elsevier; 1985. p. 137–46.

    Google Scholar 

  39. Dolan RJ. Emotion, cognition, and behavior. Science. 2002;298(5596):1191–4.

    Article  CAS  PubMed  Google Scholar 

  40. Koelsch S, Sammler D, Jentschke S, Siebel WA. EEG correlates of moderate intermittent explosive disorder. Clin Neurophysiol. 2008;119(1):151–62.

    Article  PubMed  Google Scholar 

  41. Herrera-Diaz A, Mendoza-Quinones R, Melie-Garcia L, et al. Functional connectivity and quantitative EEG in women with alcohol use disorders: a resting-state study. Brain Topogr. 2016;29(3):368–81.

    Article  PubMed  Google Scholar 

  42. Lee JY, Park SM, Kim YJ, et al. Resting-state EEG activity related to impulsivity in gambling disorder. J Behav Addict. 2017;6(3):387–95.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ieong HF, Yuan Z. Resting-state neuroimaging and neuropsychological findings in opioid use disorder during abstinence: a review. Front Hum Neurosci. 2017;11:169.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Sediyama CYN, Moura R, Garcia MS, et al. Factor analysis of the Brazilian Version of UPPS Impulsive Behavior Scale. Front Psychol. 2017;8:622.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Whiteside SP, Lynam DR. Understanding the role of impulsivity and externalizing psychopathology in alcohol abuse: application of the UPPS impulsive behavior scale. Exp Clin Psychopharmacol. 2003;11(3):210–7.

    Article  PubMed  Google Scholar 

  46. Li H, Zhao Q, Huang F, et al. Increased beta activity links to impaired emotional control in ADHD adults with high IQ. J Attent Disord. 2019;23(7):754–64.

    Article  Google Scholar 

  47. Glushchenko VV. Psychophysiological disturbances in attention-deficit hyperactivity disorder in adolescents. Zh Nevrol Psikhiatr Im S S Korsakova. 2010;110(12):9–12.

    CAS  PubMed  Google Scholar 

  48. Tyng CM, Amin HU, Saad MNM, Malik AS. The influences of emotion on learning and memory. Front Psychol. 2017;8:1454.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Yuvaraj R, Murugappan M, Mohamed Ibrahim N, et al. On the analysis of EEG power, frequency and asymmetry in Parkinson’s disease during emotion processing. Behav Brain Funct. 2014;10:12.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Yuvaraj R, Murugappan M, Ibrahim NM, et al. Emotion classification in Parkinson’s disease by higher-order spectra and power spectrum features using EEG signals: a comparative study. J Integr Neurosci. 2014;13(1):89–120.

    Article  CAS  PubMed  Google Scholar 

  51. Chiang CT, Ouyang CS, Yang RC, Wu RC, Lin LC. Increased temporal lobe beta activity in boys with attention-deficit hyperactivity disorder by LORETA analysis. Front Behav Neurosci. 2020;14:85.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ding WN, Sun JH, Sun YW, et al. Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study. Behav Brain Funct. 2014;10:20.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Lee V, Hoaken PN. Cognition, emotion, and neurobiological development: mediating the relation between maltreatment and aggression. Child Maltreat. 2007;12(3):281–98.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We sincerely appreciate all participants for their kind cooperation. I am also grateful for the insightful comments offered by the anonymous peer reviewers at manuscript. They improved this study in innumerable ways and saved me from many errors; those that inevitably remain are entirely my own responsibility.

Funding

The project findings were supported by the National Science Council (NSC 100-2410-H-255-005-MY2) and CGUST (NMRPF3B0132T).

Author information

Authors and Affiliations

  1. Department of Nursing, Chang Gung University of Science and Technology, Taoyuan City, 33303, Taiwan, ROC

    Ren-Jen Hwang, Lee-Fen Ni, Hsin-Ju Chen & Yu-Sheun Lee

  2. Department of Nursing, Chang Gung Memorial Hospital, Linkou, Taoyuan City, 33303, Taiwan, ROC

    Ren-Jen Hwang, Lee-Fen Ni & Yueh-O. Chuang

  3. Graduate Institute of Gerontology and Health Care Management, Chang Gung University of Science and Technology, Taoyuan City, 33303, Taiwan, ROC

    Hsiu-Chin Hsu

  4. Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan City, 33303, Taiwan, ROC

    Hsiu-Chin Hsu

Authors
  1. Ren-Jen Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsiu-Chin Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lee-Fen Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hsin-Ju Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu-Sheun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yueh-O. Chuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed in designing the study, planning the analysis, and interpreting the results. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ren-Jen Hwang.

Ethics declarations

Ethics approval and consent to participate

Study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (CGMH/ID 99-4099B), Taiwan. Participants’ informed consent was obtained before the experiment and aware of the study purpose, risks and benefits. All research procedures were performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: the incorrect version of figure 1 and table 2 was corrected.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hwang, RJ., Hsu, HC., Ni, LF. et al. Association between resting-state EEG oscillation and psychometric properties in perimenopausal women. BMC Women's Health 22, 149 (2022). https://doi.org/10.1186/s12905-022-01729-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12905-022-01729-7

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us