Processing emotions: Effects of menstrual cycle phase and premenstrual symptoms on the startle reflex, facial EMG and heart rate

Armbruster, Diana; Grage, Tobias; Kirschbaum, Clemens; Strobel, Alexander

doi:10.1016/j.bbr.2018.05.030

Cited by 20 publications

(17 citation statements)

References 58 publications

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“…A total of 45 studies published between the year 1995 and October 2018 were included in the systematic review [40,42,43,44,45,49,50,51,52,53,54,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90]. Only one of the 45 studies was published in Russian [43]; all others were written in English.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity across the Menstrual Cycle: Implications for Female Health and Future Studies

Schmalenberger

Eisenlohr‐Moul

Würth

et al. 2019

JCM

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Interest in cardiac vagal activity (CVA; e.g., parasympathetically-mediated heart rate variability) as a biomarker of physical and mental health has increased exponentially in recent years. However, the understanding of sources of within-person change (i.e., intra-individual variance) in CVA is lagging behind. This systematic review and meta-analysis summarizes and quantifies current empirical evidence of within-person changes in measures of CVA across the menstrual cycle in naturally-cycling premenopausal females. We conducted an extensive literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement in five databases to identify observational studies with repeated measures of CVA in at least two menstrual cycle phases. A broad meta-analysis (nstudies = 37; nindividuals = 1,004) revealed a significant CVA decrease from the follicular to luteal phase (d = −0.39, 95% CI (−0.67, −0.11)). Furthermore, 21 studies allowed for finer-grained comparisons between each of two cycle phases (menstrual, mid-to-late follicular, ovulatory, early-to-mid luteal, and premenstrual). Significant decreases in CVA were observed from the menstrual to premenstrual (nstudies = 5; nindividuals = 200; d = −1.17, 95% CI (−2.18, −0.17)) and from the mid-to-late follicular to premenstrual phases (nstudies = 8; nindividuals = 280; d = −1.32, 95% CI (−2.35, −0.29)). In conclusion, meta-analyses indicate the presence of CVA fluctuations across the menstrual cycle. Future studies involving CVA should control for cycle phase. Recommendations for covarying or selecting cycle phase are provided.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity across the Menstrual Cycle: Implications for Female Health and Future Studies

Schmalenberger

Eisenlohr‐Moul

Würth

et al. 2019

JCM

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“…To date, only seven studies (of roughly 49 currently available studies on cardiac vagal activity across the menstrual cycle as suggested by our meta-analysis [28] and a recent review [30]) have reported associations between ovarian hormone levels and cardiac vagal activity, with mixed results [27,[31][32][33][34][35][36]. However, four major methodological and statistical limitations in these studies prevent clear conclusions.…”

Section: Introductionmentioning

confidence: 80%

“…After a primary peak of P4 and a secondary peak of E2 levels, both E2 and P4 withdraw premenstrually and a new cycle begins. Longitudinal and experimental work shows that the menstrual cycle can modulate affective and behavioral outcomes (e.g., [21][22][23][24]) as well as physiological functions, like nutritional metabolism (e.g., [25,26]) and vagally-mediated HRV (e.g., [27]). Intra-individual variations in vagally-mediated HRV across the menstrual cycle with their potential implications for female daily functioning and well-being have therefore been the subject of various empirical studies.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the use of a repeated-measures dataset in a simple correlation analysis violates the statistical assumption that observations are independent from one another (since repeated observations are nested within participants). Third, some studies appear to subset their repeated measures datasets by cycle phase, then perform correlations between absolute hormone levels and cardiac vagal activity (in a between-person manner); this fails to capture the predictor variance of interest, which is that of within-person changes in hormones across the cycle [27,34]. Finally, each of these studies appeared to examine E2 and P4 as predictors of cardiac vagal activity in separate models, which fails to test both the unique effects of changes in each hormone and the potential interactive effects of these hormones [27,[31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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Menstrual Cycle Changes in Vagally-Mediated Heart Rate Variability Are Associated with Progesterone: Evidence from Two Within-Person Studies

Schmalenberger

Eisenlohr‐Moul

Jarczok

et al. 2020

JCM

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A recent meta-analysis revealed that cardiac vagal activity (mostly indicated by vagally-mediated heart rate variability; HRV) decreases significantly from the follicular to luteal menstrual cycle phase in naturally-cycling participants. However, the question remains as to whether cyclical changes in estradiol (E2), progesterone (P4), or both are responsible for HRV fluctuations. We present the first studies to use repeated measures of E2, P4, and HRV across the cycle to model both the unique and interactive effects of person-centered E2 and P4 on HRV in multilevel models. In study one, 40 naturally-cycling participants were assessed weekly across four weeks, and were blind to the cycle focus of the study. In study two, 50 naturally-cycling participants were examined in three precisely defined cycle phases via ovulation testing. Both studies revealed that only P4 was correlated with HRV, such that higher-than-usual P4 significantly predicted lower-than-usual HRV within a given participant. In line with this, cycle phase comparisons revealed lower HRV in the mid-luteal phase (characterized by elevated P4) than in other phases. No significant main or interactive effects of E2 on HRV were found. Future female health studies should investigate individual differences in these effects and potential consequences of cyclical HRV changes on daily functioning.

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“…Since the cortical activities directly and objectively reflect changes in emotion states, the measurement of their electrophysiological signals is often used as an analytical signal resource of emotion classification, such as EEG [16,17,18], electrocardiogram (ECG) [19,20], electromyogrphy (EMG) [21,22,23], etc.…”

Section: Introductionmentioning

confidence: 99%

EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Zeng

Zhang

et al. 2019

Brain Sciences

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Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

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Processing emotions: Effects of menstrual cycle phase and premenstrual symptoms on the startle reflex, facial EMG and heart rate

Cited by 20 publications

References 58 publications

A Systematic Review and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity across the Menstrual Cycle: Implications for Female Health and Future Studies

A Systematic Review and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity across the Menstrual Cycle: Implications for Female Health and Future Studies

Menstrual Cycle Changes in Vagally-Mediated Heart Rate Variability Are Associated with Progesterone: Evidence from Two Within-Person Studies

EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

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