Hippocampal volume and affect in response to fluctuating estrogens in menstrual cycle irregularity: a longitudinal single-subject study

Abstract

Irregular menstrual cycles, affecting approximately 30% of women in their reproductive years, are often overlooked in research, limiting our insights into the broader spectrum of hormonal interactions. Understanding the relationship between endogenous hormone fluctuations and brain function across the menstrual cycle, particularly beyond regular menstrual cycles, is essential for comprehending mental disorders prevalent in women. To this aim, a healthy female with an irregular menstrual cycle underwent dense sampling for 5 consecutive weeks, primarily covering the follicular phase and ovulation. Blood draws provided measurements of estradiol, estrone, and progesterone. T 1-weighted MRI scans assessed bilateral hippocampal volumes. Positive and negative affect were collected at each session. Statistical analyses included cubic regression curves, Spearman correlations, and mediation regression models to explore hormonal associations with hippocampal morphology and affect. Significant fluctuations in hormonal concentrations, hippocampal volume, and affect were observed across the 25 testing days. Estradiol and estrone significantly correlated with hippocampal volume, while progesterone showed no significant association. Increasing concentrations of estrogens were linked to decreasing positive affect, mediated by hippocampal volume fluctuations. Increasing concentrations of estrogens were further associated with increasing negative affect, however, independently of hippocampal changes. Our findings suggest potential roles of estrogens in affect regulation and brain function in a participant with an irregular menstrual cycle. This research serves as a blueprint for future investigations into the complex interplay between sex hormones and structural brain dynamics beyond regular menstrual cycles and establishes a fundamental framework for the advancement of sex-specific precision medicine.

Full text

npj | women's health Article
https://doi.org/10.1038/s44294-024-00023-1
Hippocampal volume and affect in
response to fluctuating estrogens in
menstrual cycle irregularity: a longitudinal
single-subject study
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Carina Heller1,2,3,4,5 , Daniel Güllmar6, Carina J. Koeppel1,7, Philine Rojczyk5,8, Heidemarie Stein1,
Caitlin M. Taylor9, Emily G. Jacobs9,10, Birgit Derntl11,12,13,ZoraKikinis
5, Martin Walter2,3,4 & Ilona Croy1,3,4
Irregular menstrual cycles, affecting approximately 30% of women in their reproductive years, are
often overlooked in research, limiting our insights into the broader spectrum of hormonal interactions.
Understanding the relationship between endogenous hormone fluctuations and brain function across
the menstrual cycle, particularly beyond regular menstrual cycles, is essential for comprehending
mental disorders prevalent in women. To this aim, a healthy female with an irregular menstrual cycle
underwent dense sampling for 5 consecutive weeks, primarily covering the follicular phase and
ovulation. Blood draws provided measurements of estradiol, estrone, and progesterone.
T
1
-weighted MRI scans assessed bilateral hippocampal volumes. Positive and negative affect were
collected at each session. Statistical analyses included cubic regression curves, Spearman
correlations, and mediation regression models to explore hormonal associations with hippocampal
morphology and affect. Significant fluctuations in hormonal concentrations, hippocampal volume,
and affect were observed across the 25 testing days. Estradiol and estrone significan tly correlated with
hippocampal volume, while progesterone showed no significant association. Increasing
concentrations of estrogens were linked to decreasing positive affect, mediated by hippocampal
volume fluctuations. Increasing concentrations of estrogens were further associated with increasing
negative affect, however, independently of hippocampal changes. Our findings suggest potential roles
of estrogens in affect regulation and brain function in a participant with an irregular menstrual cycle.
This research serves as a blueprint for future investigations into the complex interplay between sex
hormones and structural brain dynamics beyond regular menstrual cycles and establishes a
fundamental framework for the advancement of sex-specific precision medicine.
1Department of Clinical Psychology, Friedrich Schiller University Jena, Jena, Germany. 2Department of Psychiatry and Psychotherapy, Jena University Hospital,
Jena, Germany. 3German Center for Mental Health (DZPG), Partner Site Jena-Magdeburg-Halle, Jena, Germany. 4Center for Intervention and Research on adaptive
and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany. 5Psychiatry Neuroimaging Laboratory, Department of
Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA. 6Medical Physics Group, Institute of Diagnostic and Interventional
Radiology, Jena University Hospital, Jena, Germany. 7Department of Psychology, University of Toronto, Toronto, Canada. 8cBRAIN, Department of Child and
Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. 9Department of Psychological and Brain Sci-
ences, University of California, Santa Barbara, USA. 10Neuroscience Research Institute, University of California, Santa Barbara, USA. 11Department of Psychiatry
and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany. 12Lead Graduate School,
University of Tübingen, Tübingen, Germany. 13German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany.
e-mail: carina.heller@uni-jena.de
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The regular menstrual cycle is an important indicator for women’srepro-
ductive, physical, as well as mental health, serving as an independent proxy
fortheoverallhealthstatusamongwomenofreproductiveage
1.Yet,5to
36% of women are affected by irregular menstrual cycles, depending on age,
country of residence, or occupation2. These irregularities in the menstrual
cycle can be attributed to various underlying causes, such as a dysfunction of
the hypothalamic-pituitary-ovarian (HPO) axis, which influences the pro-
duction of estradiol. Research findings suggest that prolonged and irregular
cycle length are associated with decreased exposure to estradiol3.Itshouldbe
noted that 69% of the variance in total menstrual cycle length is due to the
variance of the follicular phase length when estradiol is the dominating
hormone4,5.
Estrogens, including estradiol, estrone, and estriol, in conjunction with
progesterone represent a major class of neuromodulatory sex steroid hor-
mones with the brain serving as an important target for their actions6,7.
Estradiol is the most biologically active estrogen in humans8. It is approxi-
mately 10 times more active than estrone and 80 times more active than
estriol9. Both estrone and estriol are a precursors and metabolites of
estradiol10,11. While cells in many brain regions express estrogen and pro-
gesterone receptors, an increased presence of receptors is found in the
hippocampus ex vivo in humans12 and in rodents13. It is well known that the
hippocampus plays a major role in memory and control of attention.
Investigating the effect of endogenous estrogens and progesterone on hip-
pocampal neuroplasticity in humans in vivo is a relatively recent scientific
effort. Traditionally, data collection relies predominantly on cross-sectional
designs that involve simultaneous data collection from multiple individuals,
followed by mean comparisons to establish hormone-brain connections
based on this aggregated data (e.g.14–18). Nevertheless, this cross-sectional
methodology tends to disregard the rhythmic nature of hormone produc-
tion in the human body. Recent years have marked a transformation in
neuroimaging studies, embracing an alternative approach that entails the
longitudinal monitoring of individuals over extended periods spanning
weeks and months19,20. Following this, a series of recent neuroimaging
studies have densely sampled women across the full menstrual cycle21–28 to
enrich our understanding of hormone action in the human brain. Barth
et al.21 showed in a longitudinal study of a natural cycling single woman21 a
positive association between endogenous estrogen concentrations and
bilateral white matter hippocampal fractional anisotropy, an indicator of the
microstructural properties in white matter29. The interplay between regional
brain volume and hormonal levels was also demonstrated in another
longitudinal study of one woman, demonstrating no effect of estradiol, but a
relation between endogenous progesterone concentrations and gray matter
volume in the hippocampal subfields of CA2/3 and paralimbic structures of
the parahippocampal gyrus, perirhinal and entorhinal cortex. Subsequent
pharmacological suppression of progesterone eliminated these effects22.
However, these studies were performed in women with a regular menstrual
cycle, and it is unknown whether the relationship is present in those with
irregular menstrual cycles.
The hippocampus is implicated in mental health disorders such as
depression30. Women are approximately twice as likely to be diagnosed with
major depressive disorder (MDD) compared to men31,andthereisa
recognized connection between fluctuations in ovarian hormones and
depression susceptibility in women32,33. Particularly noteworthy is the
association between menstrual cycle irregularities and mental disorders,
including depression34,35. While the majority of individuals with regular
menstrual cycles are unlikely to undergo impairing changes in affect asso-
ciated with cycling hormones, a minority of individuals experience such
changes36,37. An example of those who experience changes in affect with
cycling hormones are individuals with premenstrual dysphoric disorder
(PMDD), a severe type of premenstrual syndrome, which is observed in 2 to
8% of women in their reproductive years. PMDD is characterized by cyclic
mood alterations leading to clinically marked distress and functional
impairment38,39. There is a growing body of literature showing that hor-
monal fluctuations, rather than stable hormone levels, impact affect40–42,
identifying mood sensitivity to fluctuating hormones43–45. Notably, current
literature excludes individuals with irregular menstrual cycles. Despite
progress in assessing menstrual cycles, there remains a dearth of approaches
specifically tailored to study irregular cycles, pointing to a critical gap in the
existing literature. Since menstrual cycle length varies between individuals
(the average is between 21 and 37 days) and within individuals cycle-to-
cycle46–48, self-report assessments of menstrual cycle stages are often mis-
leading. Further, saliva-based assessments are subpar49. Recognizing the
menstrual cycle as a within-person process, repeated measure designs,
which are considered the state-of-the-art approach5, can capture a com-
prehensive picture of menstrual cycle irregularities. However, within-
subject designs are challenging in terms of the demands placed on the
participant. Therefore, these dense-sampling studies, as described above, are
still scarce.
This study seeks to narrow the gap in the current literature by focusing
on menstrual cycle irregularities, aiming to further our understanding of the
complex interplay between hormone fluctuations, hippocampal morphol-
ogy, and effects beyond the regular menstrual cycle pattern. In a dense-
sampling study of one female participant with an irregular menstrual cycle,
we explored whether endogenous fluctuations in sex hormone concentra-
tions impact hippocampal morphology and affect. First, we investigated
associations of endogenous concentrations of estradiol, estrone, and pro-
gesterone with hippocampal morphology across five consecutive weeks
(n= 25 testing days), covering mostly the follicular phase and ovulation.
Second, we determined the association between the concentrations of cir-
culating hormones, hippocampal morphology, and affect. This investigation
aims to provide insights into brain-hormones-behavior interactions which
are usually studied in regular menstrual cycles but often overlooked in cases
of menstrual cycle irregularities.
Methods
Participant
A healthy female (30 years of age) participated in this dense-sampling,
longitudinal study. The participant underwent testing mostly from Monday
to Friday for five consecutive weeks (August 2nd–September 2nd, 2022)
while freely cycling, resulting in n= 25 test sessions. The female participant
was free from hormonal medication for 55 months (mean menstrual cycle
length = 38.3 days, SD = 6.68 days during that time) before the assessment.
Thefemaleparticipanthadnohistoryof psychiatric, neurological, and
endocrine diagnoses, breastfeeding or pregnancy, and no history of smok-
ing, alcohol, or drug abuse. The participant gave written informed consent,
and the Friedrich Schiller University Jena Ethics Committee approved
the study.
Image acquisition and postprocessing
The imaging data were acquired on a 3T Siemens PrismaFit scanner
(Siemens Medical Solutions, Erlangen, Germany) with a 64-channel head
coil. Structural MRI were acquired with T
1
-weighted (T1w) MPRAGE
sequence with GRAPPA acceleration. Scan parameters were: echo time
(TE) = 2.22 ms, repetition time (TR) = 2400 ms, inversion time
(TI) = 1000 ms, matrix size = 320 × 320, field of view (FOV) = 256 mm, flip
angle = 8°, scan orientation = sagittal, phase encoding direction = A >> P,
bandwidth = 220 Hz/pixel, number of slices = 208, slice thickness =
0.80 mm, voxel size = 0.80 × 0.80 × 0.80 mm. Scans were collected every day
at 7.30 am local time. The parameters used to acquire the images (e.g., sizes,
space directions, space origin), and the quality of the images (e.g., motion
artifacts, ringing, ghosting of the skull or eyeballs, cut-offs, signal drops, and
other artifacts) were visually checked. Sequence Adaptive Multimodal
SEGmentation (SAMSEG)50 was used to segment both hemispheres’total
hippocampal volumes. Initially, a subject-specific template was created by
spatially co-registering all 3D T1w MRPAGE volumes through an iterative
process51. The co-registered 3D volumes were then employed to implement
longitudinal SAMSEG52.Thefinal segmentations were visually quality
checked and then used to extract the volume of the hippocampus structure
directly for each measurement day. The numerical values of the left and right
hippocampal volumes were demeaned by subtracting the hemisphere mean
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 2

from each individual value, for both hemispheres respectively. Demeaning
the hippocampal volume serves to center it around zero, allowing us to focus
on variations around the average, which is crucial for identifying patterns in
the data related to the actual variation attributed to the day of the menstrual
cycle. This preprocessing step is essential for isolating variations associated
with hormonal fluctuations while removing potential sources of systematic
bias or shift. It helps eliminate variations caused by external factors or
measurement errors. By removing these sources of variation, we can focus
more precisely on changes in hippocampal volumes linked to hormonal
fluctuations. As a result, this process enhances the sensitivity and specificity
of our measurements. The demeaned hippocampal volumes were then
added and divided by two to obtain an average demeaned hippocampal
volume.
Endocrine procedure
The blood was drawn at 9.00 am. One 7.5 ml blood sample was collected in a
S-Monovette®Serum-GEL (Sarstedt) with clotting activator/gel each test
session. The sample clotted at room temperature and was stored at 5°
Celsius until centrifugated (2500 x g for 10 minutes). Estradiol (pg/ml),
progesterone (ng/ml), and luteinizing hormone (LH) serum concentrations
(IU/l) were determined at the Bioscientia Laboratory in Jena, Germany.
Estrone serum concentrations (pg/ml) were determined at the Bioscientia
Laboratory in Ingelheim, Germany. Estradiol was assessed with the elec-
trochemiluminescence immunoassay (ECLIA) Elecsys®Estradiol III Assay.
Assay antibodies, measuring ranges (defined by the limit of detection and
the maximum of the master curve), and intra-assay coefficients of variation
for estradiol were the following: antibodies, two biotinylated monoclonal
anti-estradiol antibodies (rabbit), 2.5 ng/ml and4.5 ng/ml; measuring range,
18.4–11,010 pmol/l (5–3000 pg/ml), < 5% relative SD. Radioimmunoassay
(RIA) was used to determine concentrations of estrone.
Progesterone was assessed with the ECLIA Elecsys®Progesterone III
Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of
variation for progesterone were the following: antibodies, biotinylated
monoclonal anti-progesterone antibody (recombinant sheep), 30 ng/ml;
measuring range, 0.159–191 nmol/l (0.05–60 ng/ml), <5% relative SD.
Ovulation was confirmed through ovulation tests and LH blood con-
centrations. LH was assessed with the ECLIA Elecsys®LH Assay. Assay
antibodies, measuring ranges, and intra-assay coefficients of variation for
LH were the following: antibodies, biotinylated monoclonal anti-LH anti-
body (mouse), 2.0 mg/l; measuring range, 0.3–200 mIU/ml (0.3–200 IU/l);
intra-assay precision, ≤2.2% variation coefficient.
All assays were determined on the cobas®e 801 analyzer (Roche
Diagnostics GmbH, Mannheim, Germany) and were used according to the
manufacturer’s instructions.
To evaluate hormonal fluctuations over the five-week testing period,
we used a centering approach for absolute hormonal concentrations of
estradiol, estrone, and progesterone. This involved subtracting the overall
mean hormonal concentration from each individual hormonal value (e.g.
hormonal concentration on test day 1 minus the mean overall hormonal
concentration). This method allows us to account for hormonal fluctuations
relative to the average hormonal level across the five-week duration.
Psychological measures
Positive and negative affect was assessed for each test session separately
using the Positive and Negative Affect Schedule (PANAS)53.Itisawidely
used and well-established instrument for assessing affective states which can
be employed in daily assessments. The PANAS is a 20 item self-reporting
questionnaire assessing positive emotions such as joy, interest, and alertness,
and negative emotions such as sadness, distress, and irritability. Each item
on the PANAS is rated on a 5-point scale, ranging between 1 and 5, with 1
indicating low agreement to the specificitem(notatall)and5indicatinga
high agreement (very much). The positive affect score was calculated as the
average of the 10 positive items. The negative affect score was calculated as
the average of the 10 negative items. Hence, positive and negative affect
scores can range from 1 to 5. Lower scores represent lower levels of positive
and negative affect, whereas higher scores represent higher levels of positive
and negative affect, respectively.
Statistical approach
Statistical analyses were performed using R software (https://www.
r-project.org), Statistical Package for Social Sciences (SPSS) version 27,
and GraphPad Prism 8. First, cubic regression curve estimations were used
as the data followed a cubic curve to checkwhether hormones, hippocampal
volume, and affect changed significantly across the 25 testing sessions.
Second, Shapiro-Wilk’s test was used to check for the normal dis-
tribution of the variables. As hormonal concentrations were not normally
distributed, Spearman correlations were performed between hippocampal
volumes, hormones, and positive and negative affect. False Discovery Rate
(FDR) correction was used to correct for multiple comparisons in all
analyses54.
Third, in case of a significant correlation, we used post-hoc mediation
regression analyses to investigate whether changes in positive and negative
affect were a direct effect of fluctuations in hormonal concentrations across
the 25 test sessions or an indirect effect mediated by fluctuations in left and
right hippocampal volume. Post-hoc mediation regression analyses were
only performed in case prior Spearman correlations were significant,
ensuring a more targeted exploration of the relationship between variables.
Mediation regression models were calculated with positive and negative
affect as dependent variables and hormonal levels as independent variables.
Hippocampal volumes were added as a mediator variable to the model. Due
to high multicollinearity among the independent variables the analyses were
conducted separately for each hormone, hippocampal hemisphere, and
positive and negative affect. In our mediation regression models, path ais
the linear effect of the hormonal levels (independent variable) on hippo-
campal volume. Path bis the effect of hippocampal volume (mediator) on
positive and negative affect (outcome variables). The indirect effect a*b
measures the amount of mediation, and the direct effect c’is the effect of the
hormonal levels on positive and negative affect after controlling for hip-
pocampal volume. The total effect cis the sum of direct and indirect effects.
Results were based on 5000 bootstrapped samples. Residuals of the
regressions were normally distributed.
Results
Analysis I: Fluctuations across the 25 test sessions
Absolute hormonal concentrations (estradiol: F(3,21) = 6.698, p= 0.002;
estrone: F(3,21) = 14.728, p< 0.001; progesterone: F(3,21) = 46.306,
p< 0.001), hippocampal volume (F(3,21) = 5.574, p= 0.006), and affect
(positive affect: F(3,21) = 17.604, p< 0.001; negative affect: F(3,21) = 13.986,
p< 0.001) changed significantly across the 25 testing days covering mainly
the follicular phase and ovulation using cubic regression curve estimations
(see Fig. 1).Themenstrualcycleatthetimeofthescanlasted53days,which
represented a longer irregular menstrual cycle than usual (M=38.3 days,
SD = 6.36 days during the 55 hormone-medication-free months prior to the
study). Ovulation occurred on testing days 21 and 22, representing men-
strual cycle days 37 and 38. Following this, the study covered 20 days of the
follicular phase, 2 days of ovulation, and 3 days of the luteal phase. The luteal
phase of this menstrual cycle covered 15 to 16 days in total.
Analysis II: Fluctuating hormonal concentrations in association
with hippocampal volume and affect
Next, we tested whether fluctuations in hormonal concentrations, derived
from the five-week period average, were associated with both hippocampal
volume and affect using Spearman correlations. Bilateral hippocampal
volume correlated significantly with fluctuating estradiol (r= 0.637,
p= 0.001, p
FDR
= 0.002) and fluctuating estrone (r= 0.745, p< 0.001,
p
FDR
< 0.001) but not with fluctuating progesterone (r=−0.036, p= 0.863,
p
FDR
= 0.919). Both fluctuating estradiol and estrone correlated significantly
with positive (estradiol: r=−0.469, p= 0.018, p
FDR
= 0.026; estrone:
r=−0.427, p= 0.033, p
FDR
= 0.040) as well as negative affect (estradiol:
r= 0.773, p< 0.001, p
FDR
< 0.001; estrone: r= 0.661, p< 0.001,
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 3

p
FDR
< 0.001), suggesting that increasing concentrations of estrogens were
associated with decreasing positive but with increasing negative affect.
Fluctuating progesterone correlated significantly with positive (r= 0.464,
p= 0.019, p
FDR
= 0.026) but not with negative affect (r=−0.021, p= 0.919,
p
FDR
= 0.919), suggesting that increasing progesterone concentrations were
associated with increasing positive affect.
Furthermore, positive affect was significantly inversely associated with
bilateral hippocampal volume (r=−0.681, p< 0.001, p
FDR
<0.001),
whereas negative affect was significantly associated with bilateral hippo-
campal volume (r= 0.485, p= 0.014, p
FDR
=0.026).
Analysis III:Estrogens are linked to negative affect, hippocampal
volume is linked to positive affect
We used post-hoc mediation regression analyses to investigate whether
positive and negative effects were a direct effect of hormonal concentrations
across the 25 test sessions, or an indirect effect mediated by fluctuations in
hippocampal volume. These analyses were selectively performed in case
prior Spearman correlations were significant, ensuring a focused explora-
tion of the relationship between variables. Consequently, post-hoc media-
tion regression analyses were specifically applied to fluctuations in estradiol
and estrone across the five-week duration.
For the outcome variable positive affect, model 1.a included estradiol as
the independent variable and hippocampal volume as mediator. For model
1.b, estradiol was replaced by estrone as independent variable. Neither
estradiol nor estrone were identified as significant predictors since total
effects cand direct effects c’were insignificant. The indirect effect a*bfor
hippocampal volume was significant in both models, suggesting that
bilateral hippocampal volumes were related to positive affect rather than
estrogens. Figure 2shows the detailed results for mediation analysis model
1.a and 1.b.
For negative affect, models 2.a included estradiol as the independent
variable and hippocampal volume as mediators. Since total effects cand
direct effects c’were significant, estradiol was identified as a significant
predictor for negative affect. Model 2.b included estrone as the independent
variable, negative affect as the outcome variable, and hippocampal volume
as mediator. Similar to estradiol, the total effects cand direct effects c’of
estrone predicted negative affect. The indirect effect a*bfor hippocampal
volume was not significant in both models, suggesting that negative affect
was directly related to fluctuating concentrations of estrogens and was not
mediated by bilateral hippocampal volumes. Figure 3shows the detailed
results for mediation analysis 2.a and 2.b.
Discussion
In our series of dense-sampling venipuncture and brain imaging, fluctuating
concentrations of estrogens were positively associated with bilateral hip-
pocampal volume during a prolonged follicular phase. The results are
consistent with Barth et al.21 reporting increased volumetric fractional
anisotropy (FA) in the bilateral hippocampus associated with increased
estradiol across the full menstrual cycle21. Moreover, our results are con-
sistent with cross-sectional data reporting lower hippocampal volumes in
the early follicular phase and increased hippocampal volume during the late
follicular phase of the menstrual cycle55,56. As was the case for our study,
Barth et al.21 did not report hippocampal volume associations with
progesterone21. This is inconsistent with the reported results of associations
between hippocampal volume and progesterone by Taylor et al.22.One
explanation for the missing associations between progesterone and hippo-
campal volume in our study could be that the 25 test sessions in the current
study only covered three days of the luteal phase of the menstrual cycle when
progesterone concentrations are dominant. The majority of test sessions
covered the follicular phase as well as ovulation when progesterone is,
overall, low. Another explanation would be that additional brain regions
than the hippocampus may underly relationships with hormones and affect.
Taylor et al.22 performed correlations between progesterone concentrations
and volumes of hippocampal subfields, such as CA1, CA2/3, dentate gyrus,
and the medial temporal gyrus. The hippocampus has been investigated in
more details by Barth et al.21 who performed whole-volume hippocampal
correlations20. The dentate gyrus of the hippocampal formation is one of the
few brain areas that may exhibit adult neurogenesis. Therefore, the back-
ground of the observed structural changes in the hippocampal formation
might be linked to alterations in dendritic branching or neuronal cell
growth57. The underlying mechanisms by which sex hormones and hip-
pocampal morphology are linked still need to be elaborated. Published
results to date indicate that hormonal fluctuations across the menstrual
cycle as well as during an irregular prolonged follicular phase impact hip-
pocampal morphology. While regular menstrual cycles of ~28 days are
associated with an increased estradiol exposure, longer irregular menstrual
cycles are associated with an decreased estradiol exposure as estradiol
concentrations remain lower for a longer period of time3. In the context of
Fig. 1 | Hormonal concentrations, hippocampal volume, positive and negative
affect across 25 testing days. Estradiol, estrone, and progesterone concentrations are
displayed across the 25-day experiment. Changesin hippocampal volume, positive and
negativeaffect acrossthe experiment aredisplayed. Notethat ‘Test Day 1’refers to cycle
day 10. Ovulation occurred on testing days 21 and 22 which refer to cycle days 37 and
38. Hormone icon pictogram, source: iStock. Licensed under the standard license.
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 4

the current study, this implies that decreased hippocampal volumes are
associated with prolonged irregular cycles.
In addition to variations in hormonal patterns and bilateral hippo-
campal volumes, we report significant fluctuations in positive and negative
affect across the 25 test sessions. Both positive and negative affect were
significantly associated with estrogen levels and hippocampal volume.
Decreasing positive emotions were significantly associated with increasing
concentrations of estrogens, suggesting that positive emotions were low
during phases when estrogens were high, such as during the late follicular
phase and ovulation. However, the mediation analysis revealed that hip-
pocampal volume indirectly influenced this relationship, suggesting that
positive affect fluctuations are more closely tied to hippocampal morphol-
ogy than directly to estrogen levels. On the other hand, increased negative
emotions, such as sadness and irritability, were associated with increasing
concentrations of estrogens, indicating elevated negative affect during
estrogen peaks. Unlike positive affect, the association between increased
negative affect and estrogens was not mediated by hippocampal volume.
The findings suggest that fluctuations in negative emotions were better
explained by estrogens than by fluctuations in hippocampal morphology. It
should be noted that, in our study, negative affect scores were non-
pathological and generally low, given the healthy participant involved.
Contrary to common assumptions that negative affect is more
prevalent during the premenstrual phase and positive affect is more
common around ovulation58–61, our results may suggest an opposing
mechanism. Furthermore, there is also evidence that ovarian hor-
mones make little or no contribution to daily mood and affective
variability in naturally cycling women62–66. However, the peripubertal
phase and the onset of menopause, characterized by irregular men-
strual cycles, appear to be a time of mood sensitivity to hormone
changes, indicating an increased susceptibility to mood variations43 –45.
Specifically, the mood sensitivity to estradiol predicts risk to perime-
nopausal depression, particularly in women who are otherwise con-
sidered at low risk45. Additionally, irregular menstrual cycle variability
before pregnancy was reported to be associated with depression during
pregnancy67. Focusing on menstrual cycle irregularities in an otherwise
healthy individual, our study may provide insight into the endocrine
factors that underlie increased susceptibility and prevalence of
depression in women32,33. Women are twice as likely to be diagnosed
with depression compared to men31 and this increased susceptibility is
seen only during the reproductive years while the prevalence of
depression in prepuberty and after the age of 55 is almost the same in
men68. The menstrual cycle serves as an important indicator for
reproductive, general, and mental well-being in women69.Notably,it
plays a pivotal role in ensuring fertility and functioning of the female
reproductive system70. Additionally, the hormonal fluctuations of
estrogens and progesterone during the menstrual cycle influence var-
ious bodily functions beyond reproduction, impacting aspects such as
bone health71,72 and cardiovascular function73,74,withimplicationsthat
extend into later stages of life. Menstrual cycle irregularities have been
associated with a greater risk of premature mortality1and mental
health conditions34.Thefindings of our study highlight the role of
estradiol and estrone in affect sensitivity in an irregular menstrual
cycle. Moreover, the results suggest that short-term changes in hip-
pocampal volume within 25 test sessions influence the perception of
less positive emotions. Prior research has linked a decrease in positive
affect with depression75,aswellaschangesinhippocampalvolume
with depressive symptoms57. We cannot provide conclusive inter-
pretations for these associations given that our findings are based on
Fig. 2 | Mediation analysis of hormonal levels,
positive affect, and hippocampal volumes. Path a
is the linear effect of the hormonal levels (indepen-
dent variable) on hippocampal volume. Path bis the
effect of hippocampal volume (mediator) on posi-
tive affect (outcome variable). The indirect effect a*b
measures the amount of mediation, and the direct
effect c’is the effect of the hormonal levels on
positive affect after controlling for hippocampal
volume. The total effect cis the sum of direct and
indirect effects. All paths’estimates are depicted as
regression coefficients, respective p-values and 95%
confidence interval (95%CI). Significant results are
indicated in bold. n.s. = non-significant. Hormone
icon pictogram, source: iStock. Licensed under the
standard license.
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 5

one healthy participant with menstrual cycle irregularities. However,
the question arises as to what function these short-term changes serve
across the menstrual cycle and whether fluctuations in estrogens and
hippocampal volume act as a protective factor or risk factor for
developing depressive disorders in some women. The results of this
study suggest that we need to continue to investigate the influence of
irregular menstrual cycles on the brain and mental health.
It should be noted that estrone is a precursor to estradiol. Our results
indicate its potential role, besides estradiol, on affect during the (irregular)
menstrual cycle. It remains unclear whether the role of estrone in hippo-
campal morphology and affect is uniquely in women with an irregular cycle
or whether it has the same impact in women with a regular menstrual cycle
of ~28 days.
Several limitations to our study must be noted. First, although no
endocrine condition or diagnosis was known before scanning, the partici-
pant had a 53-day menstrual cycle during the 25 testing sessions. A local
gynecologist ruled out a diagnosis of Polycystic Ovarian Syndrome. Further
hormonal analyses, ordered by the gynecologist, revealed increased pro-
lactin levels of unknown origin after scanning. Hyperprolactinemia is the
most common pituitary hormone hypersecretion syndrome, which most
commonly affects women between the ages of 25 and 3476,withwomen
often reporting irregular menstrual cycles77. Given that stress influences
prolactin secretion in humans78, it is possible that prolactin serum con-
centrations were elevated due to the stressful procedures of daily MRI scans
and blood draw. Following this, the results could also be explained by
increased or fluctuating prolactin concentration, which was not assessed
during this study. It is, furthermore, noteworthy to consider potential
interactions with the hypothalamic-pituitary-adrenal (HPA) axis. The HPA
axis regulates cortisol release in response to stress and hormonal variations.
Hormonal fluctuations, particularly estradiol and estrone, can influence the
sensitivity of the HPA axis79, potentially impacting cortisol levels80.Our
findings, which include associations between affective states and hormonal
changes, align with the complex relationship between the HPA axis, emo-
tional well-being, and stress-related conditions, including depression80.
While our study did not measure the HPA axis or cortisol, these results
highlight the importance of their inclusion in future research.
Second, due to a longer menstrual cycle of overall 53 days during the
scanning sessions, we were not able to scan the participant across a complete
(irregular) menstrual cycle which usually consists of menses, follicular
phase, ovulation, and luteal phase. Future studies should continue dense-
sampling studies with complete menstrual cycles. However, when con-
ducting in vivo research with humans, unexpected events like a prolonged
menstrual cycle are not always predictable or avoidable. Despite striking
differences in cycle length between densely-sampling participants, the
consistency of our findings with previous reports underscores the robust-
ness of these associations.
Third, one of the authors was the participant in this study and it was not
possible to conduct a blinded study. As a result, responses to the questions
on the positive and negative affect scores may have been influenced by the
knowledge that individuals, for example, might experience premenstrual
symptoms such as stress anxiety, fatigue, mood swings, anxiety, or
depression shortly before the end of the menstrual cycle81. However, this is
unlikely given that the participant experienced an irregular menstrual cycle
with a total length of 53 days at the time of scanning. Therefore, the parti-
cipant was not fully aware of which phase she was in, since hormonal levels
were not revealed until after completion of the test sessions.
Fourth, we made rigorous efforts to minimize systematic bias or shift
in the neuroimaging data. This was accomplished by consistently using
Fig. 3 | Mediation analysis of hormonal levels,
negative affect, and hippocampal volumes. Path a
is the linear effect of the hormonal levels (indepen-
dent variable) on hippocampal volume. Path bis the
effect of hippocampal volume (mediator) on nega-
tive affect (outcome variable). The indirect effect a*b
measures the amount of mediation, and the direct
effect c’is the effect of the hormonal levels on
negative affect after controlling for hippocampal
volume. The total effect cis the sum of direct and
indirect effects. All paths’estimates are depicted as
regression coefficients, respective p-values and 95%
confidence interval (95%CI). Significant results are
indicated in bold. n.s. = non-significant. Hormone
icon pictogram, source: iStock. Licensed under the
standard license.
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 6

the same scanner at the same time of the day for all scans, using above-
standard spatial resolution, employing a longitudinal segmentation
pipeline, and applying volume normalization techniques, resulting in a
highly sensitive measurement. The observed hippocampal volume
changes, which are approximately 1%, reflect a sufficient level of bias
reduction. It is worth noting that due to the high sensitivity of the mea-
surement, even small variations can be meaningful. In contrast, a cross-
sectional study reported a test-retestreliability of hippocampal volumes at
a magnitude of 3% in both men and women not controlling for the
menstrual cycle phase82.
Lastly, since this study is a longitudinal study with a dense single-
subject design, interpretations and explanations of the reported relations
should be made with caution as no causal effects can be generalized to
larger populations. Given that our study is the first to report the interplay
of estrogen fluctuations with hippocampal volumes and positive and
negative affect across a dense-sampling study of five weeks in an irregular
menstrual cycle, further studies are needed to replicate and extend these
results. Furthermore, scores on positive and negative affect were non-
pathological since the study was conducted with a healthy participant
without a history of mental health diagnoses. Future studies could include
female participants diagnosed with MDD to clarify whether the influence
of the menstrual cycle and its hormonal fluctuations is different in indi-
viduals with and without clinical depression.
The strength of this study was its dense daily measurement time reso-
lution over a total of five weeks to investigate macrostructural changes in
hippocampal volume in the brain under the influenceofhormonesofthe
female menstrual cycle (irregularities). Compared to this study, Barth et al.21
acquired MRI scans every second or third day in two separate scanning
sessions covering two full menstrual cycles. Because our study revealed
changes in the hippocampus across female menstrual cycle irregularities and
Taylor et al.22 reported changes across a regular menstrual cycle, it would be
beneficial to examine male participants over five weeks and determine sex
hormones, such as estradiol, progesterone, and testosterone,to clarify whether
thesechangesareuniquetowomen.Furthermore,investigatingfemalepar-
ticipants with a diagnosis of MDD would shed light on whether fluctuating
hormones and hippocampal volumes are associated with an increased sus-
ceptibility to depressive symptoms in women. Lastly, while our study focused
on general affect, future investigations could benefit from a more nuanced
examination using instruments like the Daily Record of Severity of Problems
(DRSP)83, which may provide a more detailed understanding of specificand
more severe affective disturbances associated with the menstrual cycle.
In conclusion, this dense-sampling study provides valuable insights
into the complex interplay between endogenous hormone fluctuations,
hippocampal morphology, and affect in a participant with an irregular
menstrual cycle. The findings highlight the significant associations of
estradiol and estrone with bilateral hippocampal volume, suggesting
potential hormonal contributions to brain structure. Moreover, fluctuating
concentrations of estrogens were linked to affect, revealing their role in
influencing positive and negative emotions. The study’s focus on an irre-
gular menstrual cycle emphasizes the importance of investigating hormone-
brain relationships beyond regular cycles, shedding light on potential
implications for mental health disorders prevalent in women. However,
these results are based on a single participant, warranting caution in gen-
eralizing findings to the broader population. Further research with larger
and diverse samples is necessary to validate and expand these findings,
elucidating the mechanisms underlying hormonal influences on brain
health and affect regulation in women.
Data availability
The dataset generated and analyzed during the current study is available
from the corresponding author on reasonable request.
Received: 22 August 2023; Accepted: 11 May 2024;
References
1. Wang, Y. X. et al. Menstrual cycle regularity and length across the
reproductive lifespan and risk of premature mortality: prospective
cohort study. BMJ 371, m3464 (2020).
2. Kwak, Y., Kim, Y. & Baek, K. A. Prevalence of irregular menstruation
according to socioeconomic status: a population-based nationwide
cross-sectional study. PLoS ONE 14,1–12 (2019).
3. Mumford, S. L. et al. The utility of menstrual cycle length as an
indicator of cumulative hormonal exposure. J. Clin. Endocrinol.
Metab. 97, E1871 (2012).
4. Fehring, R. J., Schneider, M. & Raviele, K. Variability in the phases of the
menstrual cycle. J. Obstet. Gynecol. Neonatal Nurs. 35, 376–384 (2006).
5. Schmalenberger, K. M. et al. How to study the menstrual cycle:
practical tools and recommendations. Psychoneuroendocrinology
123, 104895 (2021).
6. Juraska, J. M., Sisk, C. L. & DonCarlos, L. L. Sexual differentiation of
the adolescent rodent brain: hormonal influences and developmental
mechanisms. Horm. Behav. 64, 203–210 (2013).
7. Rehbein, E., Hornung, J., Sundström Poromaa, I. & Derntl, B. Shaping
of the female human brain by sex hormones: a review.
Neuroendocrinology 111, 183–206 (2021).
8. Jeyakumar, M., Carlson, K. E., Gunther, J. R. & Katzenellenbogen, J.
A. Exploration of dimensions of estrogen potency: parsing ligand
binding and coactivator binding affinities. J. Biol. Chem. 286,
12971–12982 (2011).
9. Guyton, A. C., Emeritus, P., John Hall, M. E., Barrett, K. E. & Barman S.
M. Textbook of Medical Physiology (Saunders Elsevier, 1996).
10. MacDonald, P. C., Edman, C. D., Kerber, I. J. & Siiteri, P. K. Plasma
precursors of estrogen. Gynecol. Obstet. Invest. 7, 165–175 (1976).
11. Diczfalusy, E. The early history of estriol. J. Steroid Biochem. 20,
945–953 (1984).
12. Österlund, M. K., Gustafsson, J. Å., Keller, E. & Hurd, Y. L. Estrogen
receptor β(ERβ) messenger ribonucleic acid (mRNA) expression
within the human forebrain: distinct distribution pattern to ERαmRNA.
J. Clin. Endocrinol. Metab. 85, 3840–3846 (2000).
13. Brinton, R. D. et al. Progesterone receptors: form and function in brain.
Front. Neuroendocrinol. 29, 313 (2008).
14. Pletzer, B. et al. Menstrual cycle and hormonal contraceptive use
modulate human brain structure. Brain Res. 1348,55–62 (2010).
15. Hagemann, G. et al. Changes in brain size during the menstrual cycle.
PLoS ONE 6,1–7 (2011).
16. De Bondt, T. et al. Regional gray matter volume differences and sex-
hormone correlations as a function of menstrual cycle phase and
hormonal contraceptives use. Brain Res. 1530,22–31 (2013).
17. De Bondt, T., Pullens, P., Van Hecke, W., Jacquemyn, Y. & Parizel, P.
M. Reproducibility of hormone-driven regional grey matter volume
changes in women using SPM8 and SPM12. Brain Struct. Funct. 221,
4631–4641 (2016).
18. Zsido, R. G. et al. Ultra-high-field 7T MRI reveals changes in human
medial temporal lobe volume in female adults during menstrual cycle.
Nat. Ment. Heal 1, 761–771 (2023).
19. Poldrack, R. A. et al. Long-term neural and physiological phenotyping
of a single human. Nat Commun. 6(2015).
20. Gordon, E. M. et al. Precision functional mapping of individual human
brains. Neuron 95, 791–807.e7 (2017).
21. Barth, C. et al. In-vivo dynamics of the human hippocampus across
the menstrual cycle. Sci Rep 6,1–9 (2016).
22. Taylor, C. M. et al. Progesterone shapes medial temporal lobe volume
across the human menstrual cycle. Neuroimage 220, 117125 (2020).
23. Greenwell, S. et al. High-amplitude network co-fluctuations linked to
variation in hormone concentrations over the menstrual cycle. Netw
Neurosci. 7, 1181–1205 (2023).
24. Pritschet, L., Taylor, C. M., Santander, T. & Jacobs, E. G. Applying
dense-sampling methods to reveal dynamic endocrine modulation of
the nervous system. Curr. Opin. Behav. Sci. 40,72–78 (2021).
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 7

25. De Filippi, E. et al. The menstrual cycle modulates whole-brain
turbulent dynamics. Front. Neurosci. 15 (2021).
26. Mueller, J. M. et al. Dynamic community detection reveals transient
reorganization of functional brain networks across a female menstrual
cycle. Netw. Neurosci. (Cambridge, Mass) 5, 125–144 (2021).
27. Pritschet, L. et al. Functional reorganization of brain networks across
the human menstrual cycle. Neuroimage 220, 117091 (2020).
28. Fitzgerald, M., Pritschet, L., Santander, T., Grafton, S. T. & Jacobs, E.
G. Cerebellar network organization across the human menstrual
cycle. Sci. Rep. 10, 20732 (2020).
29. Beaulieu, C. The basis of anisotropic water diffusion in the nervous
system—a technical review. NMR Biomed. 15, 435–455 (2002).
30. Campbell, S. & MacQueen, G. The role of the hippocampus in the
pathophysiology of major depression. J. Psychiatry Neurosci. 29,
417 (2004).
31. Seedat, S. et al. Cross-national associations between gender and
mental disorders in the World Health Organization World Mental
Health Surveys. Arch. Gen. Psychiatry 66, 785–795 (2009).
32. Kuehner, C. Why is depression more common among women than
among men? Lancet Psychiatry 4, 146–158 (2017).
33. Kundakovic, M. & Rocks, D. Sex hormone fluctuation and increased
female risk for depressionand anxiety disorders: from clinicalevidence
to molecular mechanisms. Front. Neuroendocrinol.66, 101010 (2022).
34. Padda, J. et al. Depression and its effect on the menstrual cycle.
Cureus 13, e16532 (2021).
35. Klusmann, H. et al. Higher depressive symptoms in irregular
menstrual cycles: converging evidence from cross-sectional and
prospective assessments. Psychopathology 9,1–8 (2024).
36. Eisenlohr-Moul, T. Premenstrual disorders: a primer and research
agenda forpsychologists. Clin Psychol 72, 5 (2019).
37. Gehlert, S., Song, I. H., Chang, C. H. & Hartlage, S. A. The prevalence
of premenstrual dysphoric disorder in a randomly selected group of
urban and rural women. Psychol Med. 39, 129 (2009).
38. Hofmeister, S. & Bodden, S. Premenstrual syndrome and premenstrual
dysphoric disorder. Am. Fam. Physician 94, 236–240 (2016).
39. Matsumoto, T., Asakura, H. & Hayashi, T. Biopsychosocial aspects of
premenstrual syndrome and premenstrual dysphoric disorder.
Gynecol. Endocrinol. 29,67–73 (2013).
40. Schiller, C. E., Johnson, S. L., Abate, A. C., Schmidt, P. J. & Rubinow,
D. R. Reproductive steroid regulation of mood and behavior. Compr.
Physiol. 6, 1135–1160 (2016).
41. Wei, S. M., Schiller, C. E., Schmidt, P. J. & Rubinow, D. R. The role of
ovarian steroids in affective disorders. Curr. Opin. Behav. Sci. 23,
103–112 (2018).
42. Schmidt, P. J. et al. Premenstrual dysphoric disorder symptoms
following ovarian suppression: triggered by change in ovarian steroid
levels but not continuous stable levels. Am. J. Psychiatry 174,
980–989 (2017).
43. Andersen, E. et al. Methods for characterizing ovarian and adrenal
hormone variability and mood relationships in peripubertal females.
Psychoneuroendocrinology 141, 105747 (2022).
44. Eisenlohr-Moul, T. A. et al. Are there temporal subtypes of
premenstrual dysphoric disorder?: using group-based trajectory
modeling to identify individual differences in symptom change.
Psychol. Med. 50, 964–972 (2020).
45. Gordon,J.L.,Sander,B.,Eisenlohr-Moul,T.A.&Sykes
Tottenham, L. Mood sensitivity to estradiol predicts depressive
symptoms in the menopause transition. Psychol. Med. 51,
1733–1741 (2021).
46. Münster, K., Schmidt, L. & Helm, P. Length and variation in the
menstrual cycle-a cross-sectional study from a Danish county. Br. J.
Obstet. Gynaecol. 99, 422–429 (1992).
47. Bakos, O., Lundkvist, Ö., Wide, L. & Bergh, T. Ultrasonographical and
hormonal description of the normal ovulatory menstrual cycle. Acta
Obstet. Gynecol. Scand. 73, 790–796 (1994).
48. Harlow, S. D. & Ephross, S. A. Epidemiology of menstruation and
its relevance to women’s health. Epidemiol. Rev. 17,265–286
(1995).
49. Arslan, R. C. et al. Not within spitting distance: Salivary
immunoassays of estradiol have subpar validity for predicting cycle
phase. Psychoneuroendocrinology 149, 105994 (2023).
50. Puonti, O., Iglesias, J. E. & Van Leemput, K. Fast and sequence-
adaptive whole-brain segmentation using parametric Bayesian
modeling. Neuroimage 143, 235–249 (2016).
51. Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject
template estimation for unbiased longitudinal image analysis.
Neuroimage 61, 1402–1418 (2012).
52. Cerri, S., Hoopes, A., Greve, D. N., Mühlau, M. & Van Leemput, K. A
longitudinal method for simultaneous whole-brain and lesion
segmentation in multiple sclerosis. Lecture Notes in Computer
Science (including Subser Lect Notes Artif Intell Lect Notes
Bioinformatics). 2020;12449 LNCS:119–128.
53. Watson, D., Clark, L. A. & Tellegen, A. Development and validation of
brief measures of positive and negative affect: the PANAS scales. J.
Pers. Soc. Psychol. 54, 1063–1070 (1988).
54. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a
practical and powerful approach to multiple testing. J. Roy. Statistical
Soc. Ser. B (Methodological) 57, 289–300 (1995).
55. Lisofsky, N. et al. Hippocampal volume and functional connectivity
changes during the female menstrual cycle. Neuroimage 118,
154–162 (2015).
56. Protopopescu, X. et al. Hippocampal structural changes across the
menstrual cycle. Hippocampus 18, 985–988 (2008).
57. Toda, T., Parylak, S. L., Linker, S. B. & Gage, F. H. The role of adult
hippocampal neurogenesis in brain health and disease. Mol.
Psychiatry 24, 67 (2019).
58. Gonda,X. et al.Patterns of mood changesthroughoutthe reproductive
cycle in healthy women without premenstrual dysphoric disorders.
Prog Neuro-Psychopharmacol. Biol. Psychiatry 32, 1782–1788 (2008).
59. Hromatko, I. & Mikac, U. A mid-cycle rise in positive and drop in
negative moods among healthy young women: a pilot study. Brain
Sci. 13, 105 (2023).
60. Ocampo Rebollar, A., Menéndez Balaña, F. J. & Conde Pastor, M.
Comparison of affect changes during the ovulatory phase in women
with and without hormonal contraceptives. Heliyon 3, e00282 (2017).
61. Guevarra, D. A. et al. Examining a window of vulnerability for affective
symptoms in the mid-luteal phase of the menstrual cycle.
Psychoneuroendocrinology 147, 105958 (2023).
62. Schwartz, D. H., Romans, S. E., Meiyappan, S., De Souza, M. J. &
Einstein, G. The role of ovarian steroid hormones in mood. Horm.
Behav. 62, 448–454 (2012).
63. Weigard, A., Loviska, A. M. & Beltz, A. M. Little evidence for sex or
ovarian hormone influences on affective variability. Sci. Rep. 11,
1–12 (2021).
64. Hengartner, M. P. et al. Negative affect is unrelated to fluctuations in
hormone levels across the menstrual cycle: Evidence from a multisite
observational study across two successive cycles. J. Psychosom.
Res. 99,21–27 (2017).
65. Krüger, T. H. C. et al. The androgen system across the menstrual
cycle: Hormonal, (epi-)genetic and psychometric alterations. Physiol.
Behav. 259, 114034 (2023).
66. Romans, S., Clarkson, R., Einstein, G., Petrovic, M. & Stewart, D.
Mood and the menstrual cycle: a review of prospective data studies.
Gend. Med. 9, 361–384 (2012).
67. Sasaki, N., Akiyama, H., Kawakami, N. & Nishi, D. Preconception
menstrual cycle disorder and antenatal depression: a cross-sectional
study with prerecorded information. J. Psycosom. Obstet. Gyanaecol.
43, 411–418 (2021).
68. Harris, T. Depression in women and its sequelae. J. Psychosom. Res.
54, 103–112 (2003).
https://doi.org/10.1038/s44294-024-00023-1 Article
npj Women's Health | (2024) 2:19 8

69. Popat, V. B., Prodanov, T., Calis, K. A. & Nelson, L. M. The menstrual
cycle a biological marker of general health in adolescents. Ann. N. Y.
Acad. Sci. 1135, 43 (2008).
70. Small, C. M. et al. Menstrual cycle characteristics: associations with
fertility and spontaneous abortion. Epidemiology 17,52–60 (2006).
71. Väänänen, H. K. & Härkönen, P. L. Estrogen and bone metabolism.
Maturitas 23 (1996).
72. Seifert-Klauss, V. & Prior, J. C. Progesterone and bone: actions
promoting bone health in women. JOsteoporos2010,1–18 (2010).
73. Okoth, K., Smith, W. P., Thomas, G. N., Nirantharakumar, K. &
Adderley, N. J. The association between menstrual cycle
characteristics and cardiometabolic outcomes in later life: a
retrospective matched cohort study of 704,743 women from the UK.
BMC Med. 21,1–14 (2023).
74. Okoth, K. et al. Association between the reproductive health of young
women and cardiovascular disease in later life: umbrella review. BMJ
371, m3502 (2020).
75. Feldman, L. A. Distinguishing depression and anxiety in self-report:
Evidence from confirmatory factor analysis on nonclinical and clinical
samples. J Consult Clin. Psychol. 61, 631–638 (1993).
76. Thapa, S. & Bhusal, K. Hyperprolactinemia. Gynakologe 52, 529–537
(2022).
77. Melmed, S. et al. Diagnosis and treatment of hyperprolactinemia: an
endocrine society clinical practice guideline. J. Clin. Endocrinol.
Metab. 96, 273–288 (2011).
78. Fava, M. & Guaraldi, G. P. Prolactin and stress. Stress Med. 3,
211–216 (1987).
79. Weiser, M. J. & Handa, R. J. Estrogen impairs glucocorticoid
dependent negative feedback on the hypothalamic-pituitary-adrenal
axis via estrogen receptor alpha within the hypothalamus.
Neuroscience 159, 883–895 (2009).
80. Walf, A. A. & Frye, C. A. A review and update of mechanisms of estrogen
in the hippocampusand amygdalafor anxiety and depression behavior.
Neuropsychopharmacology 31,1097–1111 (2006).
81. Schmidt, P. J., Nieman, L. K., Danaceau, M. A., Adams, L. F. &
Rubinow, D. R. Differential behavioral effects of gonadal steroids in
women with and in those without premenstrual syndrome. N. Engl. J.
Med. 338, 209–216 (1998).
82. Brown, E. M. et al. Test-retest reliability of FreeSurfer automated
hippocampal subfield segmentation within and across scanners.
Neuroimage 210, 116563 (2020).
83. Endicott, J., Nee, J. & Harrison, W. Daily Record of Severity of
Problems (DRSP): reliability and validity. Arch. Womens Ment. Health
9,41–49 (2006).
Acknowledgements
This work was supported by theFriedrich Schiller University Jena (IMPULSE
Project) to CH.The funder played norole in study design,data collection,
analysis and interpretation of data, or the writing of this manuscript. We
thank Gregor Anslinger,MD, for his skilled blood sample collection,
enhancing the precision of our study’s biological data.
Author contributions
C.H. was responsible for the study concept and des ign, acquired the MRI
data, blood samples, and psychological questionnaires, processed and
analyzed the data, performed the statistical analysis, and wrote the
manuscript. D.G. acquired, processed and analyzed the MRI data,
supervised the inspection of the anatomical data, and wasinvolved in the
critical revision of the final manuscript. C.J.K. and H.S. collected the
blood samples and were involved in the critical revision of the final
manuscript. P.R., C.M.T., E.G.J., and B.D. assisted with the
interpretation of the findings and were involved in the critical revision of
the final manuscript. Z.K., M.W., and I.C. assisted with thestudy concept
and design and were involved in the critical revision of the final
manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s44294-024-00023-1.
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Carina Heller.
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