HHS Public Access: Estrogen-And Progesterone-Mediated Structural Neuroplasticity in Women: Evidence From Neuroimaging

HHS Public Access
Author manuscript
Brain Struct Funct. Author manuscript; available in PMC 2017 November 09.
Author Manuscript
Published in final edited form as:

Brain Struct Funct. 2016 November ; 221(8): 3845–3867. doi:10.1007/s00429-016-1197-x.
Estrogen- and progesterone-mediated structural neuroplasticity

in women: evidence from neuroimaging
Eva Catenaccio1, Weiya Mu1, and Michael L. Lipton1,2,3,4,5
1The Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, 1300
Morris Park Avenue, Bronx, NY 10461, USA
2Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx,
Author Manuscript
NY, USA
3Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx,
NY, USA
4Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, USA
5Department of Radiology, Montefiore Medical Center, Bronx, NY, USA
Abstract
There is substantial evidence that the ovarian sex hormones, estrogen and progesterone, which
vary considerably over the course of the human female lifetime, contribute to changes in brain
structure and function. This structured, quantitative literature reviews aims to summarize
Author Manuscript
neuroimaging literature addressing physiological variation in brain macro- and microstructure

across an array of hormonal transitions including the menstrual cycle, use of hormonal
contraceptives, pregnancy, and menopause. Twenty-five studies reporting structural neuroimaging
of women, addressing variation across hormonal states, were identified from a structured search of
PUBMED and were systematically reviewed. Although the studies are heterogenous with regard to
methodology, overall the results point to overlapping areas of hormone related effects on brain
structure particularly affecting the structures of the limbic system. These findings are in keeping
with functional data that point to a role for estrogen and progesterone in mediating emotional
processing.
Keywords
Author Manuscript
Hormones; Neuroimaging; Sex differences; Women; Menstrual cycle; Neuroplasticity; Voxel-

based morphometry; Estrogen; Progesterone; Neuroendocrinology
Introduction
Across the lifespan, women transition sequentially through states modulated by relative
levels of the ovarian sex hormones, estrogen and progesterone. These include the pre-
pubertal years, menarche, menstruation, pregnancy, lactation, and menopause. Additionally,
Correspondence to: Michael L. Lipton.

Catenaccio et al. Page 2
exogenous sex hormones, including hormonal contraceptives and hormone replacement

Author Manuscript
therapy (HRT), may further modify normal hormonal states. Time courses of hormone level
fluctuation include higher frequency effects such as the menstrual cycle or oral contraceptive
pill (OCP) use, as well as lower frequency effects that span many years, such as menopause
or long-lasting hormonal birth control methods such as progesterone implants (e.g.
Nexplanon) (Fig. 1).
It is important to characterize and account for natural physiological variation to distinguish it

from pathology. The impact of cyclical variation of endogenous or exogenous hormones on
brain structure and function may also introduce bias to studies comparing men and women
or different groups of women. This effect may be direct, such as hormone-mediated
modulation of gene transcription in central nervous system (CNS) cells, or indirect, through
the enhancement or mitigation of systemic processes that affect brain structure and function,
such as inflammation. The impact of ovarian sex hormones on the female brain is an
Author Manuscript
important area of ongoing investigation.
Functional neuroimaging has been employed to assess the impact of ovarian sex hormones
on cortical function in healthy female animals (Chen et al. 2009), healthy women (Dreher et
al. 2007; Schoning et al. 2007), and in human disease states such as premenstrual dysphoric
disorder (PMDD) (Poromaa 2014; Protopopescu et al. 2008b). Functional, but not structural,
imaging studies of women across hormonal transitions have recently been extensively
reviewed (Peper et al. 2011; Sacher et al. 2013; Toffoletto et al. 2014), and will therefore
provide context, but not be the focus of this review. Brain areas exhibiting hormone-related
functional effects are diverse, but tend to show a concentration of putatively hormone-
mediated functional changes in areas related to emotional processing, memory, and
cognition.
Author Manuscript
Given the short time course over which ovarian sex hormones fluctuate, it might seem that
the changes detectable by neuroimaging would be purely functional in nature. Since brain
networks underlying behavior depend on structural network connections, functional
neuroimaging effects related to changes in behavior implicate underlying changes in the
structural components of brain networks: network nodes (grey matter, GM) and network
connections (white matter, WM). In other contexts, neuroimaging has demonstrated rapid
changes in both brain function and structure in response to extrinsic stimuli. For example,
both brain macro- and microstructure are enhanced following motor training. Novice
jugglers exhibit increases in GM volume and in WM anisotropy over 3 months of training,
which regress to baseline after cessation of practice (Draganski et al. 2004; Scholz et al.
2009).
Author Manuscript
Multiple studies, in fact, support the notion that structural foundations underlay the
functional alterations associated with hormonal fluctuations in healthy women. However,
these studies of structural plasticity have not been comprehensively synthesized or integrated
with knowledge from functional imaging studies to identify salient insights regarding the
impact of the ovarian sex hormones on the brain. This structured literature review aims to
summarize macro- and microstructural changes associated with different hormonal states
and transitions across the female lifespan.
Background
Author Manuscript
Endocrinology of endogenous and exogenous ovarian sex hormones

Estrogen and progesterone are steroid hormones derived from enzymatic modifications of
cholesterol. In women, production of estrogen and progesterone occurs primarily in the
ovary, but also in the adrenal gland, and at other locations, such as adipose tissue. The
gonadotropins, follicle stimulating hormone (FSH) and luteinizing hormone (LH), modulate
ovarian sex hormone synthesis and secretion. FSH and LH are thus released from the
pituitary gland under CNS control, mediated predominantly by the hypothalamus, which is
in turn extensively connected to other CNS areas.
Different stages of the female lifespan may be characterized according to the relative levels
of the ovarian sex hormones (Fig. 1). Estrogen and progesterone levels are low throughout
childhood, but increase dramatically at the onset of puberty under the influence of pulsatile
Author Manuscript
gonadotropin release from the pituitary (Gillies and McArthur 2010). This process typically
leads to the onset of regular menstrual cycling, divided into two phases: the follicular phase
when serum levels of estrogen are high and progesterone low, and the luteal phase, during
which the progesterone level is high relative to the estrogen level (Abraham et al. 1972). The
late luteal phase is associated with a spectrum of premenstrual symptoms including
headaches, abdominal bloating, cramping, breast tenderness, weight changes, irritability,
decreased concentration, depression, and anxiety (Biggs and Demuth 2011). The two major
biologically active estrogens in non-pregnant women are estrone and estradiol, while
pregnant women also produce significant quantities estriol (Torrealday et al. 2000). Estradiol
circulates at higher concentrations and has greater biological potency than estrone (Blaustein
2008; Gillies and McArthur 2010).
Author Manuscript
Combined estrogen/progesterone OCP most commonly create daily spikes in estrogen and
progesterone levels over a three-week period (active pill phase), followed by a period of low
estrogen and progesterone levels during a week when no hormones are administered
(inactive pill phase). This “placebo week” is comparable to the early follicular phase of the
normal menstrual cycle in terms of serum hormone levels (De Bondt et al. 2013a). The
predominant form of estrogen comprising OCP is ethinyl estradiol (EE), rather than the
endogenously produced estradiol (Stanczyk et al. 2013). Combination birth control pills—
those that contain both EE as well as a progestin—may be administered with different
cyclical patterns, termed monophasic, biphasic, triphasic, or multiphasic, based on the
degree of variation in the magnitude of the estrogen and progesterone dose across the cycle.
Other hormonal methods of birth control alternate modes of delivery for a combination of
estrogen and progesterone (transdermal patch, intravaginal ring), progesterone-only methods
Author Manuscript
(pills, injections, intradermal implants) and the hormone eluting intra-uterine device (IUD).
During pregnancy, both estrogen and progesterone increase steadily across the three
trimesters and then return rapidly to baseline following parturition (Torrealday et al. 2000).
During the postpartum phase, the estrogen level is relatively low, a fact that has been
implicated in the pathophysiology of postpartum depression (Skalkidou et al. 2012). The
typically high prolactin level present during lactation suppresses the release of
gonadotropin-releasing hormone (GnRH), the synthesis of estrogen and progesterone, and

Author Manuscript
ovulation (Heinrichs et al. 2001).
During the years preceding menopause, the progesterone level declines more quickly than
estrogen. Finally, menopause ultimately results in a permanent decline in the levels of both
estrogen and progesterone. Symptoms of menopause are associated with low levels of
estrogen. Hence, estrogen supplementation (HRT) has been used to treat symptoms and
physiological consequences of menopause including vasomotor symptoms (hot flashes) and
decreased bone mineral density (Soares and Frey 2010).
Neurosteroids
Various types of steroid hormones are synthesized in multiple organs as well as within the
CNS, either from serum precursors or de novo. Due to their lipophilic properties both
estrogen and progesterone, cross the blood brain barrier (Banks 2012). They may either exert
Author Manuscript
their effects directly or undergo further modifications. Within the CNS, a potent neuroactive
metabolite of progesterone is 3-alpha, 5-alpha-TH-progesterone, also called
allopregnanolone (ALLO), which directly modulates gamma-aminobutyric acid type A
(GABA-A) receptors affecting neuronal and glial excitability (Pluchino et al. 2006).
Additionally, levels of peripheral ovarian sex hormones are known to affect de novo
neurosteroidgenesis within the CNS (Pluchino et al. 2013). ALLO concentrations, for
example, vary with estrus phase in rodents (Palumbo et al. 1995). Synthetic progestins, such
as those used in hormonal birth control, frequently are not metabolized to ALLO (Herson et
al. 2009) and may therefore be less neuroactive (Pluchino et al. 2006).
The limbic system

Studies of the effects of ovarian sex hormones on brain structure and function have
Author Manuscript
frequently focused on components of the limbic system (Fig. 2). In rodents, limbic structures
are crucial for sexual behavior. For example, the male rat amygdala mediates sexual arousal
(Kondo et al. 1997), while the female rat hypothalamus coordinates lordosis (back-arching)
behavior which indicates sexual receptivity (Cooke and Woolley 2005). In humans, the
limbic system comprises an extended set of structures including both subcortical regions
(olfactory bulb, thalamus, hypothalamus, amygdala, mammillary bodies, nucleus
accumbens, and septum) and cortical regions (hippocampal formation, parahippocampal
gyrus, insula, orbitofrontal cortex (OFC), medial prefrontal cortex (PFC) and cingulate
gyrus) (Braun 2011). These areas mediate reproductive function and neuroendocrine
homeostasis (hypothalamus), as well as a diverse set of additional functions, including
memory (hippocampal formation) and emotional processing (amygdala, nucleus
accumbens). The OFC and other prefrontal regions provide top-down modulation of limbic
Author Manuscript
system function and are involved in emotional learning and decision-making (Protopopescu
et al. 2005; Rolls 2000). Many of the symptoms associated with menstrual cycling represent
impairments of these functions including irritability, impulsivity, decreased concentration,
anger, and anxiety (Biggs and Demuth 2011).
Several white matter tracts link the diverse structures comprising the limbic system, which
can be considered as nodes on a “limbic network”. The fornix projects from the
hippocampus, to the mammillary bodies, anterior thalamic nuclei, nucleus accumbens,

Author Manuscript
septum, and hypothalamus; the uncinate fasciculus projects from the anterior temporal lobe
to the OFC;1 the cingulum projects from the amgydala and the parahippocampal gyrus to the
OFC; and the anterior thalamic radiations project from the thalamus to the OFC and anterior
cingulate cortex (ACC) (Catani et al. 2013). Through these complex connections the limbic
system components modulate hypothalamic and pituitary function and hormonal state
(Harris 1970; Herman et al. 2005).
CNS receptors for progesterone

The classic nuclear receptor for progesterone has been localized in the rat brain to the frontal
cortex, hypothalamus, thalamus, amygdala, hippocampus and cerebellum as well as other
areas (Brinton et al. 2008; Pluchino et al. 2006). Additionally, a transmembrane G-protein-
coupled receptor (GPCR) for progesterone is widely distributed in the CNS (Brinton et al.
Author Manuscript
2008). Progesterone receptor distribution is sexually dimorphic and is responsive to

variations in estrogen and progesterone levels in female and, to a lesser extent, male rats
(Guerra-Araiza et al. 2002).
CNS receptors for estrogen

Estrogen receptors (ERs) in the CNS include the classic nuclear ER-alpha and ER-beta
nuclear receptors as well as a GPCR that inserts into the plasma membrane (Srivastava et al.
2013). Areas in the rat brain showing the highest concentrations of ER messenger
ribonucleic acid (mRNA) expression include the amygdala, septum, thalamus,
hypothalamus, and the dentate nucleus of the cerebellum (Simerly et al. 1990). Studies in
both animals and humans demonstrate expression of ER-alpha in the ventromedial nucleus
of the hypothalamus and amygdala, and expression of both ER-alpha and ER–beta at high
Author Manuscript
concentrations in the hippocampus, with ER-beta expression dominating in the subiculum

[for review see (Gillies and McArthur 2010)]. Although there are sex differences in ER
concentration in the hypothalamus (Kruijver et al. 2002), overall both classic nuclear ERs
are similarly widely distributed throughout the rest of the brain in adult males and females
(Gillies and McArthur 2010). Aromatase, the enzyme responsible for aromatizing androgens
to estrogens in estrogen biosynthesis, is expressed both pre- and post-synaptically in CNS
neurons suggesting that neuroactive forms of estrogen may act as neuromodulators at the
synapse (Srivastava et al. 2013).
Given the range of sites at which ovarian sex hormones may be neuroactive, both
progesterone and estrogen are poised to induce and modulate neuroplasticity, thereby
influencing neuroendocrine states, emotional processing, learning, and memory, among
other domains. These effects, described below, lead to modification of CNS structure
Author Manuscript
through both classic genomic and non-genomic (membrane-based, synaptic) signaling and
across a spectrum of time scales ranging from seconds to days.
1Despite the relatively late maturation of the uncinate fasciculus, a review of DTI imaging examining the uncinate fasciculus across
the lifespan did not identify sex differences in white matter microstructure (Hasan et al. 2009). This is at least suggestive that there
may not be a robust relationship between myelination of this structure and the sex hormones.
Mechanisms of ovarian sex hormone modulation of structural neuroplasticity

Author Manuscript
Changes in brain structure occur through a variety of mechanisms and across a range of
scales that can affect GM, WM, and/or extra-neuronal tissue. Volumetric changes in GM
may represent axon sprouting, dendritic branching, synaptogenesis, neurogenesis,
myelination or changes in neural morphology (Zatorre et al. 2012). The relative
contributions of these different mechanisms to volumetric changes will vary with
developmental stage and brain region—e.g. neurogenesis is well characterized in the
hippocampus (Eriksson et al. 1998), but its contribution in other areas remains uncertain and
controversial. In WM, changes may occur in axon fiber number, density, diameter,
branching, and myelination (Zatorre et al. 2012). Uniquely to humans compared to other
mammals, volume changes in WM may also reflect plasticity of interstitial neuronal cell
bodies located in cerebral WM that persist beyond the completion of embryonic brain
development (Meyer et al. 1992). Inferences drawn from pre-clinical or animal models and
Author Manuscript
applied to non-invasive imaging techniques in humans may thus be challenging. Changes in

structures that are composed of a mixture of GM and WM, such as the thalamus and basal
ganglia, will be driven by both sets of mechanisms. Extra-neuronal tissue changes may
represent angiogenesis or glial cell proliferation or growth. Each of these mechanisms has
differential effects on magnetic resonance imaging (MRI) signal intensity—for example,
myelination affects lipid content and thus relaxation times (Laule et al. 2006) and increases
fractional anisotropy (FA) by creating barriers to water diffusion perpendicular to the fiber
orientation.
Historically, the effects of sex steroids on brain development and function were divided into
permanent organizational/structural effects early in development and transient activational/
functional effects that continued after brain maturation (Arnold and Gorski 1984; Phoenix et
al. 1959). A wealth of animal studies in the 1960s and 1970s investigated the effects of
Author Manuscript
prenatal testosterone and estradiol exposure on adult CNS structure and sexual behaviors
and characterized the locations, timing, and molecular mechanisms of steroid hormone
effects (Breedlove and Arnold 1980; Lieberburg and McEwen 1975; McEwen et al. 1975)
[for review see (Arnold and Gorski 1984; McEwen et al. 2015)]. Eventually, evidence
accumulated supporting the presence of ongoing organizational/structural effects of sex
steroid hormones long into adult life.
In mammalian adulthood, the ovarian sex hormones are thought to induce neuroplasticity in
the CNS primarily by modulating dendritic spine and synapse density in areas such as the
hippocampus, hypothalamus, nucleus accumbens, and amygdala (Cooke and Woolley 2005;
Micevych and Christensen 2012). This plasticity may also have important consequences for
WM volume, such as enhancement of myelination. Over the course of the five-day estrous
Author Manuscript
cycle in rats, lower serum levels of estradiol are correlated with lower hippocampal synapse
density and higher serum estradiol levels with higher hippocampal synapse density (Woolley
and McEwen 1992). Significant changes in synapse density may occur over as little as a 24-
h period. These fluctuations may correspond to hippocampal volume variation over the
estrous cycle visualized by MRI (Qiu et al. 2013). In the rat hypothalamus, specifically the
ventromedial nucleus, estrogen positively modulates dendritic spine density of hypothalamic
neurons (Madeira et al. 2001). In the ventrolateral subdivision of the ventromedial nucleus,
dendritic spine density doubles during the proestrus phase (high estrogen) compared to
Author Manuscript
diestrus (low estrogen). Changes in dendritic spine density in this region were accompanied
by increases in dendrite length and terminal branch number. The effects of estradiol on spine
density in the hypothalamus are mediated by a GPCR, the metabotropic glutamate receptor
1a (Christensen et al. 2011). In the nucleus accumbens estradiol exposure decreases
dendritic spine density, similarly via a GPCR, the metabotropic glutamate receptor subtype
five (Peterson et al. 2015).
Although the literature on sex steroid mediated structural plasticity in the rat amygdala has
focused on male rats, it is worth noting that, at least in the amygdala, the effects of
androgens are mediated by ERs; androgens are converted to estradiol by aromatase
expressed by amygdala neurons (Cooke et al. 2003; Cooke and Woolley 2005). Additionally,
exposure to both estradiol and testosterone increase cell proliferation in the medial portion
of the amygdala of castrated meadow voles, but exposure to a non-aromatizable androgen
Author Manuscript
does not (Fowler et al. 2003). In the adult rat brain, androgens have a trophic effect on cell
size rather than cell number (Cooke et al. 1999).
The mechanisms of progesterone-mediated neuroplasticity have been less extensively

characterized, but experimental treatment of rat cortical neurons with progesterone and
synthetic progestin has similarly been shown to increase dendritic spine number and density
(Sanchez et al. 2013). Additionally, progesterone has been implicated as a potent
neuroprotectant that reduces inflammation and restores blood–brain-barrier function in
response to a wide variety of insults including traumatic brain injury and cerebral ischemia
(Herson et al. 2009).
Structural neuroimaging
Author Manuscript
MRI and computed tomography (CT) both non-invasively delineate brain structure and have
been used to quantify brain volume in both normal individuals and disease states (Dastidar et
al. 1999; Lai 2013). Neuroimaging methods for quantifying regional brain volumes can be
divided into three broad categories: manual, semi-automated, and fully automated
segmentation/parcellation methods. Manual parcellation requires segmentation of brain
regions, based on anatomic landmarks, by an expert rater. This approach is, in expert hands,
considered the gold standard, but is extremely labor intensive, demands specific
neuroanatomical knowledge, and requires validation to demonstrate adequate intra- and
inter-rater reliability (Bergouignan et al. 2009). Semi-automated and fully automated
segmentation methods can reduce the burden of manual parcellation, but are subject to
limitations of the tissue segmentation and image registration techniques they depend on.
Additionally, these techniques are computationally intensive and statistically complex.
Author Manuscript
Voxel-based morphometry (VBM) is one fully automated technique designed to support

inferences regarding group-level variation of brain structure. Measurements made by VBM
correlate well with manual parcellation (Davies et al. 2009). VBM, however, is not a truly
quantitative technique, as it does not directly measure volume. In VBM, a series of statistical
tests are performed, comparing signal intensity across all image voxels of the T1-weighted
MRI volumes of multiple subjects. Because GM, WM, and cerebrospinal fluid (CSF) exhibit
distinct signal intensities, contrast differences between groups of subjects can be leveraged
to infer, but not to actually quantify, the location of volume differences (Ashburner and
Author Manuscript
Friston 2000). Images from all subjects are spatially normalized (or registered) either to a
published or custom template brain volume, segmented by tissue class (GM, WM, CSF), and
scaled to correct for changes in tissue volume during spatial normalization (modulation).
Finally, smoothing is performed, a process that replaces the intensity of a voxel with an
average of the intensities of a neighborhood of voxels, thereby improving the signal-to-noise
ratio and moderating variance induced by the spatial transformation procedures. Global
VBM is useful in that it is unbiased and operator-independent. However, hypothesis-driven
region of interest (ROI) analyses are more sensitive than whole brain-comparisons and many
studies using VBM take both approaches (Bergouignan et al. 2009). ROIs may be delineated
in an operator-independent fashion, based on published atlases of brain structures.
Interpreting the results of VBM across multiple studies may be challenging because of
potential variability in the image processing steps and in statistical analyses including
Author Manuscript
correction for multiple comparisons (Whitwell 2009).
The issue of type I error is particularly important in whole brain analyses because of the
sheer number of statistical tests performed and appropriate correction for multiple
comparisons is necessary. Some potential approaches include the Bonferroni correction,
false discovery rate (FDR) estimation and family-wise error rate correction. However,
because VBM is frequently performed on small samples, many studies report results both
before (uncorrected) and after accounting for multiple comparisons (Ridgway et al. 2008).
Additionally, while VBM provides information about alterations of tissue volume, its results
do not address underlying mechanisms of volume change.
Methods
Author Manuscript
We developed a search strategy in consultation with a research librarian and utilized both
medical subject headings (MeSH) terms and keywords in article titles/abstracts in
PUBMED. Terms and keywords searched included “menstrual cycle”, “hormonal
contraception”, “pregnancy”, “menopause”, “neuroimaging”, “magnetic resonance
imaging”, “diffusion weighted imaging”, “diffusion tensor imaging”, “brain”, and relevant
variations and abbreviations. Functional MRI (fMRI) studies have been reviewed recently
and were not included (Sacher et al. 2013; Toffoletto et al. 2014). A total of 532 unique
citations were identified and reviewed. Off-target studies were eliminated on the basis of the
following exclusion criteria: papers with use of non-structural imaging techniques (fMRI,
electroencephalography, magnetoencephalography, positron emission tomography), papers
with a focus on HRT or a focus on diseases other than PMDD, premenstrual-syndrome
(PMS) or primary dysmenorrhea (PD), reviews, case reports, case series, editorials,
Author Manuscript
abstracts, or posters. This process yielded 20 relevant papers. Five additional papers were
identified from the cited articles’ references and using the “cited by” feature in the Web of
Science database. We included studies that examined the pituitary gland because, although it
is not part of CNS, it acts a key hormonal bridge between the brain and the body and directly
modulates sex hormone production under the influence of both centrally and peripherally
originating signals.
A structured review of each study was performed to extract methodological details across a
Author Manuscript
predefined set of parameters including imaging modality, experimental design, control group
assignment, method for assessing menstrual cycle phase, image analysis method, and areas
of brain examined (Table 1). Where criteria were not clearly reported (1 study), attempts
were made to reach authors for clarification, but were not answered.
Results and discussion

Papers included
Of the 25 papers we identified: 14 papers examined structural brain changes associated with
natural menstrual cycling and use of hormonal birth control (Baroncini et al. 2010; De Bondt
et al. 2013a, b; Grant et al. 1988; Hagemann et al. 2011; Lisofsky et al. 2015b; Ossewaarde
et al. 2013; Pletzer et al. 2010, 2015; Protopopescu et al. 2008a; Teasdale et al. 1988; Tu et
al. 2010, 2013; Witte et al. 2010), seven examined structural brain changes associated with
Author Manuscript
pregnancy and the postpartum period (Dinc et al. 1998; Elster et al. 1991; Gonzalez et al.
1988; Hinshaw et al. 1984; Kim et al. 2010; Miki et al. 2005; Oatridge et al. 2002), 3 papers
examined structural brain changes associated with the menopause transition (Goto et al.
2011a, b; Sullivan et al. 2005), and 1 paper examined structural pituitary changes across
multiple categories (Grams et al. 2010). We identified instances where study subjects were
included across two publications: Teasdale et al. (1988) and Grant et al. (1988), Goto et al.
(2011a, b), and De Bondt et al. (2013a, b). Reporting of subject overlap was unclear between
two studies by Tu et al. (2010), (2013), but the authors did not respond to requests for
clarification. The total number of unique subjects across all studies was approximately 1321,
with 383 male subjects and 938 female subjects. Subjects’ ages ranged from 18 to 85.
Studies’ sample, measurement and design characteristics

Author Manuscript
Results of the structured assessment of study methods are presented in Table 1. Timing of
MRI scans during the menstrual cycle or OCP cycle varied considerably across studies and
is depicted along with estimated relative serum hormone levels in Fig. 1. Additionally, use of
menstrual cycle terminology was inconsistent across studies. For clarity, the following terms
will be used to describe an idealized 28 day menstrual cycle with menses beginning on day
one: follicular (days 1–14), luteal (days 15–28). The following phases are also included
because they are frequently used comparatively in the reviewed studies: menstrual (days 1–
7), periovulatory (days 12–16) and premenstrual (days 24–28). For OCP, the first 3 weeks
during which hormonally active pills are administered will be referred to as the “active pill
phase” while the remaining week of placebo pills or skipped doses will be referred to as the
“inactive pill phase”. These phase designations are indicated in Fig. 1.
Author Manuscript
Of those studies including menstruating women, two assessed patients with PD (Tu et al.
2010, 2013), and one study enrolled both healthy women and patients with PMDD so as to
include a range of menstrual symptoms (Protopopescu et al. 2008a). Menopause status, for
papers addressing this transition, was usually assigned based on participant age (probable
menopause status) or self-report; no studies determined menopause based on hormone
levels. Sullivan et al. (2005) did not report the proportion of pre- vs. post-menopausal
women in their sample. Of the four papers that enrolled pregnant patients, only one
described how patients were assigned to gestational age groups, employing a consensus
Author Manuscript
approach based on menstrual dating and ultrasound (Gonzalez et al. 1988).
Almost all studies employed volumetric structural MRI and compared the same subjects
across two hormones states (e.g. follicular phase versus luteal phase or menstrual phase
versus peri-ovulatory phase). Findings of these studies are summarized separately for each
hormonal phase comparison (Tables 2, 3, 4, 5) and discussed in detail below. Only three
studies employed any type of diffusion MRI (Baroncini et al. 2010; De Bondt et al. 2013b;
Lisofsky et al. 2015b). Four studies reported associations of imaging measures with serum
hormone levels (De Bondt et al. 2013a, b; Hagemann et al. 2011; Lisofsky et al. 2015b;
Witte et al. 2010). These findings are summarized in Table 6. Seven studies examined the
relationship between GM volume and either cognitive or affective changes in participants
(Kim et al. 2010; Lisofsky et al. 2015b; Ossewaarde et al. 2013; Pletzer et al. 2015;
Protopopescu et al. 2008a; Tu et al. 2010, 2013). These findings are discussed in the section
Author Manuscript
“(Structure–function relationships in brain areas affected by ovarian sex hormones)”.
Menstrual cycle
Global brain changes identified over the course of the menstrual cycle include variation in
brain size, total CSF volume, and total GM volume. Hagemann et al. (2011) used automated
segmentation to partition images into GM, WM, and CSF and then summed the number of
voxels for each tissue compartment to calculate total tissue volumes at three different points
in the menstrual cycle (menstrual, peri-ovulatory, and luteal). No differences were found in
total GM volume between the menstrual and luteal phases, a result supported by those of
Pletzer et al. (2010). Lisofsky et al. (2015b), who focused on hippocampal volume changes,
did not report on total GM, WM, or CSF volume changes across the four time points at
which they scanned subjects. Additionally, Hagemann et al. did not find differences between
Author Manuscript
menstrual and luteal phases in WM or CSF volume. However, there was a statistically
significant 1.81 % increase in GM volume and a corresponding 4.4 % decrease in CSF
volume between the luteal and peri-ovulatory phases with no change in WM volume. The
increase in brain volume is generally consistent with the results of Grant et al. (1988) and
Teasdale et al. (1988) who similarly found lesser CSF volume (mean decrease of 11.3 %)
during the peri-ovulatory phase compared to the pre-menstrual phase. Since Grant and
Teasdale did not report on changes in GM or WM, however, it is not possible to infer
whether the implicit change in tissue volume is exclusively due to GM volume increase as
found by Hagemann et al. A follow up analysis by the same authors as Hagemann et al.
demonstrated that the peri-ovulatory increase in total GM could have a significant impact on
estimates of brain age that model normal age related brain atrophy with women appearing to
be younger at time of ovulation (Franke et al. 2015).
Author Manuscript
Structural plasticity has also been localized to specific brain regions. Areas that exhibit
follicular/luteal structural plasticity include hippocampus, the parahippocampal gyrus, the
fusiform gyrus, the cingulate cortex (in particular, ACC), the insula, the middle frontal
gyrus, the thalamus and the cerebellum. Overall, estrogen seems to have a trophic effect on
the hippocampus. Protopopescu et al. (2008a) identified lower volume in the right anterior
hippocampus during the pre-menstrual (luteal) phase. The association of lesser hippocampal
volume with the low-estrogen luteal phase is consistent with findings in rodents that identify
Author Manuscript
decreases in hippocampal spine density associated with declining estrogen levels during the
low estrogen estrus phase (Woolley and McEwen 1992). Lisofsky et al. specifically
examined the relationship between serum estrogen and hippocampal volume by comparing
volume during the late follicular phase (high estrogen) to the menstrual phase (low
estrogen), thereby minimizing the potentially confounding effects of progesterone. Lisofsky
et al. found regions of significantly increased volume in the posterior portions of both right
and left hippocampi during the late follicular phase. Furthermore, in these regions, greater
GM volume was associated with lower mean diffusivity (MD) in the same region—lower
MD is associated with higher GM density that restricts the free diffusion of water. As
discussed above, higher GM density may be the result of increases in dendritic spines and
synapses.
Both Pletzer et al. and Protopopescu et al. identified lower volume in the right
Author Manuscript
parahippocampal/fusiform gyrus during the progesterone dominated pre-menstrual (luteal)

phase compared with the estrogen dominated follicular phase. De Bondt et al. (2013a) found
that fusiform gyrus volume during the luteal phase was smaller in women with higher serum
progesterone levels, an apparent extension of the effect observed by Pletzer et al. and
Protopopescu et al. During the follicular phase, however, fusiform volume trended positively
with serum progesterone, but negatively with serum estrogen levels. Lisofsky et al. identified
a follicular increase in parahippocampal volume on the left side and a positive correlation
between left hippocampal/parahippocampal volume and serum estrogen levels.
Three studies identified changes to the middle frontal gyrus—De Bondt et al. on the left, and
Protopopescu et al., and Lisofsky et al. on the right. Both De Bondt et al. and Protopopescu
et al. identified greater GM volume during the follicular phase compared with the luteal
Author Manuscript
phase. Lisofsky et al. identified a positive correlation between serum estradiol levels and left
middle frontal gyrus volume. Two studies—De Bondt et al. and Lisofsky et al.—discovered
increased GM volume of the insula during the follicular phase but in opposite hemispheres.
Other areas including the cingulate gyrus, ACC, amygdala, thalamus, cerebellum, and
parietal and temporal cortical areas also exhibit follicular vs. luteal structural differences, but
these locations have each been reported in individual studies only. De Bondt et al. identified
decreased ACC volume in the mid-luteal phase compared with the early follicular phase.
Moreover, across subjects ACC volume was negatively correlated with serum estradiol
concentration during the luteal phase. Ossewaarde et al. (2013) identified luteal phase
increases in left amygdala volume, a finding in keeping with evidence from animals (Cooke
2006; Cooke et al. 2003; Cooke and Woolley 2005; Fan et al. 2008). Witte et al. (2010),
Author Manuscript
studying women in the mid-follicular phase, found that higher serum estradiol is associated
with greater GM volume in the left superior parietal lobule and that higher serum
progesterone is associated with lower GM volume in the right temporal pole. Ossewaarde et
al. found increased GM in the left superior parietal lobule during the luteal phase—as Witte
et al. did not image subjects during the luteal phase, it cannot be determined if the results of
these two studies are in conflict. De Bondt et al. also investigated the association of GM
volume with serum hormone levels, but did not detect similar structural variations.
Inconsistencies between the results reported by Witte et al. and De Bondt et al. may be
attributed to slight differences in the timing of imaging during the follicular phase; De Bondt
Author Manuscript
et al. imaged subjects during the early follicular phase, when both estrogen and progesterone
levels are low, while Witte et al. imaged subjects during the mid-follicular phase when
estrogen rises rapidly. Alternatively, results of the studies may differ because the design of
Witte et al. included comparison to men, whereas De Bondt et al. did not.
Two papers by Tu et al. (2010, 2013) report on peri-ovulatory vs. menstrual phase effects
and specifically examine the trait- and state-dependent effects of PD. The first paper
compares women with PD to healthy controls in the pain-free peri-ovulatory phase. Women
with PD exhibit higher GM volume in areas related to endocrine function (hypothalamus)
and emotional processing (parahippocampus and ACC), and lower GM volume in areas
related to pain transmission and sensory processing (insula) and in areas that regulate
affective responses to negative stimuli (medial PFC). Unlike most changes associated with
the menstrual cycle, which revert to baseline over the course of the cycle, the signature brain
Author Manuscript
volumetric changes of PD persist throughout the pain-free peri-ovulatory period. The

enduring nature of the structural changes associated with PD distinguishes the disorder as an
entity beyond the normal spectrum of PMS and menstrual symptoms. Although the
volumetric changes could be attributed to the effects of chronic cyclic pain rather than purely
to the effects of hormone variability, they are likely mediated at least in part by estrogen and
progesterone. As discussed by the authors, PD has been associated with increased estrogen
levels during the later stages of the menstrual cycle (Ylikorkala et al. 1979), and with
menstrual cramping due to increased estrogen-mediated prostaglandin synthesis (Ham et al.
1975).
Tu et al. next examined state-dependent structural changes within the same ROIs identified
above, but comparing the pain-free peri-ovulatory phase with the symptomatic menstrual
Author Manuscript
phase. During menses, PD patients exhibit increased GM in the medial OFC and
hypothalamus and decreased GM in the secondary somatosensory cortex and ACC,
compared to the period surrounding ovulation. In the control subjects, no changes were
found in global GM volume or in regional volume of the ROIs. This is in contrast to the
study by Hagemann et al. that found total GM volume to increase in the peri-ovulatory
compared to the menstrual phase and to the study by Lisofsky et al. which identified
regional changes between the peri-ovulatory and menstrual phases in the right insula,
bilateral inferior parietal lobes, bilateral posterior hippocampi, bilateral thalami, and right
cerebellum. Notwithstanding the divergent nature of some findings, the unique structural
changes associated with PD support the phenomenon of rapid neuroplasticity in association
with a time-limited stimulus (1–3 days of menstrual cramping pain).
Author Manuscript
No menstrual cycle dependent effects were found on pituitary size (Grams et al. 2010). As
pituitary size did not change in response to menstrual fluctuations in estrogen, the degree of
hormone variation may need to be quite large in order for pituitary size to be affected in a
detectable way. During puberty, for example, when there is a rapid and substantial increase
in ovarian sex hormones and testosterone, the pituitary gland also experiences a
physiological volume increase (Wong et al. 2014).
Hormonal contraceptives
Author Manuscript
Global brain changes have not been identified between menstrual cycling women and OCP
users, despite assessments of CSF volume (Grant et al. 1988; Teasdale et al. 1988) and total
GM volume (De Bondt et al. 2013a; Pletzer et al. 2010, 2015). Even when different
formulations of OCPs have been considered (i.e. androgenic vs. anti-androgenic), no total
GM volume differences between menstrual cycling women and OCP users were detected
(Pletzer et al. 2015).
Despite the lack of global differences, structural differences have been identified in specific
brain regions between menstrual cycling women and OCP users. Pletzer et al. (2010)
identified greater GM volume in the PFC, ACC, parahippocampal and fusiform gyri, and
cerebellum in OCP users compared to menstrual cycling women. De Bondt et al. (2013a)
similarly identified GM volume effects in the fusiform gyri and ACC and an additional
Author Manuscript
finding in the superior frontal gyrus, but no cerebellar findings. Pletzer et al. (2015) found
that compared with menstrual cycling women, users of anti-androgenic OCPs had relatively
larger GM volumes in the bilateral fusiform gyri, parahippocapmus, and cerebellum, while
users of androgenic OCPs had relatively smaller volumes in the bilateral middle and superior
frontal gyri.
De Bondt et al. (2013b), one of the few diffusion-MRI studies, also found increased MD in
the fornix, a WM tract connecting the hippocampus and mammillary body, key elements of
the limbic system, in the OCP group compared to the naturally cycling group. The authors
hypothesize that increased MD may represent lower synapse number in the OCP group
compared to the naturally cycling group. In a study correlating histological findings with
diffusion imaging, rats who had undergone Morris Water Maze training showed histological
evidence of increased astrocyte processes and synaptic markers and a decreased apparent
Author Manuscript
diffusion coefficient (ADC; equivalent to MD measured with diffusion tensor imaging),

representing greater tissue density, on imaging (Blumenfeld-Katzir et al. 2011).
Additionally, in De Bondt et al., MD in the fornix was significantly negatively correlated
with serum LH and estradiol concentrations, implying that LH and estradiol may up-regulate
synaptogenesis or preserve existing synapses as has also been demonstrated in animal
literature (Naftolin et al. 2007).
Both De Bondt et al. (2013a, b) and Pletzer et al. (2015) compared the active and inactive
pill phases, but did not have overlapping findings. De Bondt et al., however, did not
characterize OCP type. Baroncini et al. (2010) focused on changes in the hypothalamus
between the active and inactive phases using a ROI-based diffusion-weighted MRI analysis.
The ADC within the hypothalamus was higher during the inactive pill phase than during the
Author Manuscript
active pill phase. Again, this may represent higher tissue density during the active pill phase
as the result of synaptogenesis associated with higher hormone levels. No changes were
observed in a control ROI placed in the thalamus, a region chosen as unrelated to
reproductive neuroendocrine function. Structural changes within the hypothalamus as a
result of exogenous hormone administration suggest a possible mechanism for hormonal
birth control-mediated ovulation suppression through effects on synapse and astrocyte
morphology.
Additionally, women taking OCPs were found to have slightly smaller pituitary volumes
Author Manuscript
than naturally cycling women (Grams et al. 2010).
Pregnancy and the postpartum period

Almost all the studies examining pregnant women report exclusively on changes of the
pituitary (Dinc et al. 1998; Elster et al. 1991; Gonzalez et al. 1988; Hinshaw et al. 1984;
Miki et al. 2005) even though this period, during which progesterone significantly dominates
over estrogen, would provide an exceptional window into the unique contributions of
progesterone relative to estrogen. Overall the findings from these papers showed
considerable overlap—pituitary gland volume increased throughout pregnancy up until
delivery and the first postpartum week and then declined throughout the postpartum period
until reaching normal size by about 6 months after delivery. Pituitary volume changes
associated with pregnancy result from physiologic hyperplasia of lactotrophic cells under the
Author Manuscript
influence of placental estrogen (Karaca et al. 2010).
Oatridge et al. (2002) investigated changes in total maternal brain volume from pre-
conception to postpartum. Total brain volume decreased leading up to delivery and then
increased during the postpartum phase. This change was complemented by an increase in
ventricular size during pregnancy and subsequent decrease postpartum. All pregnancy-
related changes returned to pre-conception baseline by 24 weeks postpartum. Kim et al.
(2010) used VBM and demonstrated that GM volume of a number of regions increased
significantly from the early to the late postpartum period, including the PFC, pre- and post-
central gyri, superior/inferior parietal lobule, insula, and thalamus.
These findings are in agreement with evidence of maternal brain structural plasticity in
animals. Virgin rats exposed to an artificial approximation of the hormone milieu of
Author Manuscript
parturition will begin to respond to pups in a similar manner as pregnant/maternal rats

(Siegel and Rosenblatt 1975). Parturient rats exhibit a greater number of hypothalamic
astrocytes in proportion to the number of interactions they have with their pups
(Featherstone et al. 2000). It is likely that many of these postpartum functional and structural
changes are related to other hormones, including oxytocin and vasopressin, as well as
estrogen and progesterone. In lactating rats, glial processes in the hypothalamus withdraw
allowing direct neuronal contact (neuro-juxtaposition) between oxytocinergic neurons
(Montagnese et al. 1987). These changes return to baseline after lactation ends.
Menopause
Most studies we identified that compared pre-menopausal and post-menopausal women
focused on the effects of HRT and thus are beyond the scope of this review. We identified
Author Manuscript
two studies that reported structural brain changes associated with physiologic menopause,
both of which specifically focused on the effect of menopause on the hippocampus. Sullivan
et al. (2005) used a semi-automated segmentation method and manual ROI placement to
look at age- and menopause-related hippocampal volume changes and failed to find evidence
of either. It should be noted that Sullivan et al. did include some women who took HRT, but
also failed to find an effect of HRT on hippocampal volume. Goto et al. (2011a, b) made two
separate comparisons, women in their fourth decade vs. women in their fifth decade and pre-
menopausal vs. post-menopausal women. Both comparisons showed age- and menopause-
Author Manuscript
related decline in hippocampal volume. In a follow-up investigation, the authors performed

an atlas-based ROI analysis to determine absolute changes in hippocampal volume. This
study confirmed the prior results and demonstrated a greater decrement in hippocampal
volume when comparing women in their fifties to women in their forties than in similar age
groups of men.
The results from these two studies investigating the hippocampus during menopause are in
direct conflict. This may be attributable to differences in sample size—Sullivan et al.
enrolled 17 premenopausal women, 16 post-menopausal women not receiving HRT, and 11
post-menopausal women receiving HRT, while Goto et al. enrolled 54 pre- and 54 post-
menopausal women not receiving HRT. Thus, Sullivan et al., who reported no effect of
menopause, may not have been powered to detect change in hippocampal volume. However,
another study of 210 post-menopausal women that measured serum estradiol levels and used
Author Manuscript
manual tracing to quantify hippocampal volume also failed to find an association between
estrogen and hippocampal size or memory performance (den Heijer et al. 2003). Overall,
despite multiple studies, the effects of aging, menopause, and HRT on cognition and
memory remain controversial (Fischer et al. 2014). In part this may be due to the particular
difficulty of disentangling the confounding factors of menopausal status and age.
Additionally, although pituitary volumes decreased with age, no effect of menopause

duration on pituitary size was found (Grams et al. 2010).
Hormone mediated structural neuroplasticity in humans

We identify a salient theme of GM volume changes associated with variation in the ovarian
sex hormones in women. These effects are prominent among many components of the
Author Manuscript
limbic system, including the hippocampus, parahippocampal gyrus, fusiform gyrus,

cingulate gyrus, insula, amygdala, thalamus, and hypothalamus, as well as in the middle
frontal gyrus, basal ganglia, and cerebellum (Fig. 2). Although much fewer in number,
studies of WM microstructure have also revealed plasticity of the limbic system structures,
such as the fornix, related to ovarian sex hormone transitions. Notably, the structures
exhibiting volumetric change include those exhibiting high concentrations of estrogen and
progesterone receptors including the hypothalamus, amygdala, and hippocampus.
Variations in brain structure among women may represent a segment of the larger spectrum
of structural variability among humans; the findings we have reviewed in fact do overlap
known male–female dimorphisms of brain structure. A meta-analysis of studies examining
sex differences of brain structure found larger volume and higher density of GM in the
Author Manuscript
amygdala, hippocampus, parahippocampus, insula, and putamen in men compared with

women (Ruigrok et al. 2014). It is unclear, however, if the meta-analysis controlled for
overall brain size in these comparisons; evidence supports that, as a fraction of brain size,
the hippocampus may in fact be larger in women (Filipek et al. 1994). Other areas identified
in the meta-analysis overlapped with areas associated with hormonal transitions, including
anterior and posterior cingulate cortices, OFC, left temporal pole, right frontal pole, right
and left middle frontal gyri, and right and left thalamus. Notably, many of the studies
included in the meta-analysis did not characterize menstrual cycle phase of their female
participants. Another analysis of sexually dimorphic brain structures in humans found that
Author Manuscript
the magnitude of the dimorphism was proportional to the expression of sex hormone
receptors during development in homologous structures in other animals (Andreano and
Cahill 2009; Goldstein et al. 2001). Additionally, as demonstrated by Witte et al. (2010),
serum hormone levels correlate with GM volumes in sexually dimorphic areas across both
male and female adult subjects.
Effects of estrogen and progesterone are of course not limited to the limbic system. A
second concentration of structural changes associated with variation in ovarian sex
hormones may be found in somatosensory processing areas. The “pain network” as
identified by neuroimaging and neurophysiology investigations includes the primary and
secondary somatosensory cortices as well as the insula, ACC, and thalamus (Nakata et al.
2014). Menstrual cycle disorders, such as PD, have been reported in association with both
trait- and state-dependent changes in limbic areas (hippocampus/parahippocampal gyrus) as
Author Manuscript
well as somatosensory and pain pathway areas (post-central gyrus, secondary somatosensory
cortex, and insula) (Tu et al. 2010, 2013). These areas as well as the thalamus (Lisofsky et
al. 2015b) and ACC (De Bondt et al. 2013a) may play a role in mediation of somatic
menstrual cycle complaints including headaches, fatigue, nausea, menstrual cramps, and
increased pain sensitivity. OCPs also affect volume of the insula (De Bondt et al. 2013a), as
does the transition from the early to late postpartum period (Kim et al. 2010).
Structural variability also plays a role in the patho-physiology of PMDD. A VBM study
comparing PMDD patients with healthy controls found higher GM density in the
hippocampus and lower GM density in the parahippocampal gyrus (Jeong et al. 2012).
PMDD is thought to be a disorder of hypersensitivity to estrogen and progesterone; serum
levels of these hormones in PMDD patients are normal (Backstrom et al. 2003). In this
Author Manuscript
regard, the volume of GM in ER-rich structures could be a structural modulator of estrogen

sensitivity.
Findings regarding the effect of OCPs do not represent a mere extension of the effects of
endogenous sex hormones. The impact of OCPs was stronger in frontal areas including the
PFC and weaker, though still present, in the hippocampus and parahippocampus.
Additionally, differences between OCP users and menstrual cycling women were for the
most part much larger than the differences within either group, in terms of cluster size.
Moreover, OCP use enhanced differences found between the sexes, in the PFC in particular
(Pletzer et al. 2010). Variable cerebral uptake of endogenous ovarian sex hormones
compared with exogenous artificial hormones may explain structural and functional
divergence between naturally cycling women and users of hormonal contraception.
Author Manuscript
Moreover, as demonstrated by Pletzer et al. (2015), variable androgenic activity of different

progestins used in OCPs may also explain the range of effects.
Structure–function relationships in brain areas affected by ovarian sex hormones

Functional domains that display variation in behavior and in activation patterns on functional
neuroimaging in association with ovarian sex hormone transitions, such as emotion—e.g.
viewing erotic images (Abler et al. 2013), emotionally valenced pictures (Goldstein et al.
2005), or facial expressions (Ossewaarde et al. 2010)—and cognition—affective response
inhibition (Amin et al. 2006) and working memory (2-back task) (Konrad et al. 2008)—are
Author Manuscript
extensively linked to limbic regions. Limbic structures play a central role in mood and
emotion, functional domains that underpin menstrual symptoms such as irritability,
depression, and anxiety (Biggs and Demuth 2011). Although a detailed summary of the
functional effects of estrogen and progesterone is beyond the scope of this review,2
preliminary structure–function correlations support the concept that structural alteration of
limbic components represents a key substrate of this functional variability.
The hippocampus was the region that most consistently exhibited neuroplasticity in
association with variation in hormonal factors and these findings were present across
multiple hormone transitions, including menstrual cycle, OCP use, and menopause. As
suggested by animal studies, estrogen exhibits a trophic effect on the hippocampus in
humans. We found, with relative consistency across the reviewed studies, that the
hippocampus was larger during estrogen dominated phases (i.e. the follicular phase of the
Author Manuscript
menstrual cycle and before the onset of menopause). We would therefore expect verbal and
visuospatial memory performance would improve during the follicular phase compared with
the luteal phase; there is some evidence from functional and cognitive studies that this is the
case (Rosenberg and Park 2002; Sundstrom Poromaa and Gingnell 2014). Contrary to this
expectation, however, Lisofsky et al. (2015b) did not find any reliable variation in cognitive
tasks across the menstrual cycle and so did not correlate hippopcampal structural changes
with cognitive outcomes. They did, however, identify increased functional connectivity
between the hippocampi and bilateral superior parietal lobes during the late follicular phase.
The right hippocampus has traditionally been implicated in visuospatial memory formation
(Smith and Milner 1981) while the left has been associated with verbal or narrative
memories in connection with left sided language structures (Burgess et al. 2002; Frisk and
Author Manuscript
Milner 1990). However, Protopopescu et al. found that improved verbal memory during the
follicular phase (versus the luteal phase) positively correlated with increased GM volume in
the right anterior hippocampus (Protopopescu et al. 2008a). A systematic review of studies
examining cognitive effects of combined oral contraceptives found inconclusive and
contradictory effects overall, though improved verbal memory in OCP users has been
demonstrated (Warren et al. 2014). The positive effect of estrogen on the hippocampus and
memory within women is also in keeping with the well-established finding of superior
verbal memory performance in women compared with men (Andreano and Cahill 2009).
Volume changes associated with menstrual cycling and OCP use were found in the
parahippocampal and fusiform gyri. The parahippocampal gyrus has been ascribed a diverse
set of functions, but it is most prominently related to episodic memory and visuospatial
Author Manuscript
processing (Aminoff et al. 2013). The robust response of the parahippocampus to viewing
spatial scenes, but not objects or faces, led to the designation of the parahippocampal place
area (Aminoff et al. 2013; Epstein and Kanwisher 1998). The fusiform gyrus plays a role in
the processing of both faces (Kanwisher et al. 1997) and words (Harris et al. 2015). Pletzer
2For further review of functional neuroimaging studies investigating the menstrual cycle and OCP use see Sacher et al. (2013) and
Toffoletto et al. (2014). For further review of the putative roles of neurosteroids in symptomatology of PMS, pregnancy, the
postpartum period, and menopause, see Pluchino et al. (2013).
et al. (2015) found that users of anti-androgenic OCPs had greater GM volumes in both the
Author Manuscript
parahippocampal place area and the fusiform face area compared to menstrual cycling
women as well as better performance on a facial recognition task that was correlated with
GM volume.
Surprisingly, the amygdala, which has a well-established sexual dimorphism, with respect to
its size, and has been found to be functionally variable across the menstrual cycle across
multiple functional imaging studies (Lisofsky et al. 2015a; Toffoletto et al. 2014), only
demonstrated menstrual cycle related volume change in a single ROI analysis (Ossewaarde
et al. 2013). Ossewaarde et al. found that, during the pre-menstrual period, increasing GM
volume in the amygdala correlates with heightened negative affect in the context of stressful
visual stimuli. The amygdala is involved in generating affective and behavioral responses to
both aversive and rewarding stimuli (Janak and Tye 2015). Frequently, amygdala
hyperactivity is invoked as an example of failure of top-down modulation of limbic activity
Author Manuscript
by frontal areas. This is exemplified in PMDD patients who demonstrate decreased OFC and
increased amgydala activity in the context of negatively valenced emotional stimuli during
the pre-menstrual (luteal) phase (Protopopescu et al. 2008b). Additionally, during the
postpartum period, greater GM volume in the maternal amygdala and hypothalamus was
associated with mothers endorsing a more positive perception of their babies (Kim et al.
2010)—this effect may reflect changes in a subpopulation of valence-selective neurons
within the amygdala that respond to reward rather than to fear (Paton et al. 2006).
Tu et al. (2010), (2013) identified correlations between the experience of menstrual pain
measured by the McGill Pain Questionnaire and GM volume. During the pain-free peri-
ovulatory phase, patients’ ratings of menstrual pain were negatively correlated with GM
volume in the bilateral PFC and positively correlated with GM volume in the bilateral OFC
Author Manuscript
and ACC (Tu et al. 2010). As with Protopopescu et al. (2008b), this result suggests a failure
of top-down modulation of limbic structures. During menses, in women with dysmenorrhea,
greater GM volume in the right caudate nucleus and the hypothalamus was positively
correlated with a higher pain score, while GM volume in the left thalamus was negatively
correlated with pain score. As suggested by Tu et al., both the caudate and the hypothalamus
(via the bulbospinal loop) play a role in the regulation of both pain processing and the
negative emotional processing associated with the experience of pain (Scott et al. 2006;
Suzuki et al. 2004); changes in these areas, therefore, may represent maladaptive plasticity
underlying the hyperalgesia associated with PD (Tu et al. 2013).
Although speculating on the evolutionary advantages or disadvantages of hormone mediated

neuroplasticity in detail is beyond the scope of this review, various theories regarding the
Author Manuscript
origins of PMS and other menstrual cycle related cognitive changes have been put forth
(Gillings 2014). One theory suggests that PMS symptoms, such as irritability, functioned to
dissolve sexual partnerships that did not result in pregnancy. However, given that for much
of human history women may have had far fewer cycles than modern women, due to earlier
and more frequent pregnancy and prolonged lactation-induced amenorrhea, menstrual cycle
related changes and symptoms may instead represent an evolutionary byproduct. If frequent
menstrual cycling is, in fact, a relatively recent physiologic phenomenon, it may only now
begin to be exposed to natural and sexual selection pressures.
Methodological considerations
Author Manuscript
Limitations of the individual structural neuroimaging studies reviewed here are similar to
those affecting fMRI studies on this topic and include variable timing of scans during
hormone cycles, small individual study sample sizes and lack of a standard method for
menstrual cycle phase determination (Sacher et al. 2013; Toffoletto et al. 2014).
The majority of the studies we have reviewed utilized VBM to assess structural changes.
However, due to small sample sizes, many studies were insufficiently powered to identify
effects across the whole brain and many reported whole brain findings did not survive
correction for multiple comparisons. When hypothesis-driven atlas-based ROI approaches
were used to supplement the whole brain VBM analyses, as in Protopopescu et al., De Bondt
et al., and Pletzer et al., areas that were identified in whole brain analysis at an uncorrected
significance level, were found to be significant. Most of these hypothesis-driven ROI
Author Manuscript
analyses specifically targeted limbic structures implicated by the underpowered whole brain
analyses. While this approach suggests that a specific subset of brain regions demonstrate
structural plasticity related to ovarian sex hormone levels, it may also be the case that the
whole-brain approaches, which drove the analyses, selectively captured regions exhibiting
the most dramatic degree of variability (i.e. limbic structures). Studies employing larger
sample sizes might detect a greater diversity of effects of ovarian sex hormones in other
brain regions.
A critical question for researchers preparing structural neuroimaging studies examining

hormonal effects or that include women with variable hormonal status is the magnitude of
detected changes in volume that are attributable to hormone-based neuroplasticity, in light of
the variance across the sample. This effect size (e.g. Cohen’s d) was not reported in a
systematic way across the studies reviewed here. However, for studies where the means and
Author Manuscript
standard deviations of volume measurements or the degrees of freedom from t tests are
provided, it may be possible to calculate the effect size as the mean difference between two
conditions (e.g. menstrual phases, pregnant vs. non-pregnant) divided by the pooled standard
deviation or from the t-statistic (Rosenthal and DiMatteo 2001). Sufficient information was
only available, however, for four of the 25 papers included in our review.
Although effect size calculations were possible in only a minority of the studies, these few
examples suggest that hormone associated volume changes have a broad range of effect
sizes.3 Goto et al. (2011b) demonstrated moderate to large effect sizes (d = 0.90 for the
right, d = 0.61 for the left) for hippocampal volume differences observed between pre and
post-menopausal women using atlas-based morphometry. For the difference in pituitary
volume between menstrual cycling women and women taking OCPs, the effect size was
Author Manuscript
small (d = 0.34) (Grams et al. 2010). There was only one instance in which effect sizes could
be reasonably compared between two studies. In their comparison of the luteal and follicular
phases in menstrually cycling women, Protopopescu et al. (2008a) demonstrated a small
effect size (d = 0.12) for GM volume increase in the right anterior hippocampus and for GM
volume decrease in the right dorsal basal ganglia (d = 0.24). However, Lisofsky et al.
3Cohen’s d is commonly used to classify the effect size as small (<00.4), medium (0.4–0.7), or large (>0.7) (Rose and Donohoe 2013).
(2015b) found a large effect size (d = 0.99) for average hippocampal GM volume increase
Author Manuscript
from the menstrual to the peri-ovulatory phase. One possible explanation for this
discrepancy in the size of the effect is that the relative difference in serum estrogen between
the luteal and early follicular (menstrual) phases (Protopopescu et al.) is much smaller than
the difference between the early follicular (menstrual) and peri-ovulatory phases (Lisofsky et
al.). Given the theoretical trophic effect of estrogen on the hippocampus, we might expect a
greater degree of hippocampal volume change in response to the peri-ovulatory estrogen
peak (Lisofsky et al.). Future studies that examine hormone-associated structural plasticity
should report effect sizes in a standardized manner to facilitate comparison across studies
and ultimately rigorous meta-analysis.
Additionally, VBM does not provide information regarding the degree of volume change—it
will be important to confirm these intriguing results with more detailed assessments of
cortical thickness, as was done by Goto et al. in their atlas-based analysis. Only two studies
Author Manuscript
employed diffusion MRI to assess microstructural features; the relationship between

hormones and brain microstructure remains an opportune area for deeper investigation.
Finally, as functional network and connectivity fMRI investigations become increasingly
popular, the underlying structural framework for connectivity between disparate brain
regions will be an important focus area.
In light of the temporal variation of both endogenous and exogenous hormone exposure
among women and the evidence that these variations impact brain structure and function,
researchers planning neuroimaging studies that will enroll women should consider
characterizing female subjects’ hormonal status. Variation in menstrual cycle effects could
be addressed in future studies by imaging female participants at a specific menstrual cycle
phase (Witte et al. 2010), or by exclusively enrolling either women who are or those who are
Author Manuscript
not taking oral contraceptives. Alternatively, counterbalanced, longitudinal crossover

designs, where subjects serve as their own controls, could be employed to address this issue
(Poromaa 2014). Most practical, however, might be determination of menstrual cycle phase
at the time of imaging, which can then be considered as a regressor during data analysis.
Confirmation of menstrual cycle phase must be balanced between practical and logistical
concerns associated with more accurate approaches such as daily measurement of serum
progesterone, estrogen, LH, or FSH levels or transvaginal ultrasound, and less accurate
ascertainment when relying on subject reporting of menstrual cycle pattern. One study
comparing women’s self report of menstrual cycle phase, urine LH kits (which approximate
ovulation by the LH surge), and serum progesterone levels found that when counting
forward from the first day of menses to determine ovulation, only 35–42 % had a positive
Author Manuscript
urinary test between 10 and 14 days and only 18 % had a serum progesterone >2.0 ng/mL,
an indication that ovulation has occurred (Wideman et al. 2013). A combined approach
using urine ovulation kits that measure luteinizing hormone levels and subject self-report
may be an effective and less invasive option.
Variation due to birth control type should also be considered; different OCP formulations
confer different estrogen and progesterone levels and some formulations (biphasic, triphasic,
or multiphasic) may vary the relative doses over the course of a 1-month cycle. However,
one study reported that while patients were taking different formulations of monophasic
Author Manuscript
combined (estrogen and progesterone) OCPs there were no significant differences in

measurements of hormonal concentrations of serum samples (De Bondt et al. 2013b).
Alternative methods of birth control such as the estrogen/progesterone patch or vaginal ring
are associated with lower, but more constant serum levels of estrogen and progesterone, (van
den Heuvel et al. 2005) and both the copper and Mirena (levonorgestrel-releasing)
intrauterine devices may leave normal ovarian hormone cycles intact (Faundes et al. 1980;
Xiao et al. 1995).
In studies involving older women, establishing reproductive senescence may be more

amenable to subject self-report measures since, in a clinical setting, menopause is diagnosed
after 12 months of amenorrhea in the absence of other causes. Alternatively, ascertaining
elevation of FSH may be confirmatory and could be correlated with the degree of structural
change.
Author Manuscript
Conclusions
Multiple studies in young healthy women demonstrate the impact of menstrual cycle phase
and hormonal contraceptives on brain macrostructure as well as similar structural effects
related to menopause in older healthy women. Generalizing the findings of the studies
reviewed here is constrained by sample and methodological heterogeneity across studies, but
overall they support the notion that ovarian sex hormones drive neuroplasticity. Cyclical and
gradual hormonal variation throughout the female lifespan may also be important in a
myriad of disorders including traumatic brain injury, stroke, multiple sclerosis, and
migraine. Neuroimaging is an appropriate and informative tool for exploring hormone-
related structural plasticity and pathology. The interaction of hormone exposure and
Author Manuscript
neuroimaging findings can also serve to develop hypotheses regarding structure–function

relationships, especially when considering menstrual cycle specific disorders such as PD,
PMS, and PMDD.
Abbreviations
ACC Anterior cingulate cortex
ADC Apparent diffusion coefficient
ALLO Allopregnanolone
CNS Central nervous system

Author Manuscript
CSF Cerebrospinal fluid
CT Computed tomography
EE Ethinyl estradiol
ER Estrogen receptor
FDR False discovery rate
fMRI Functional magnetic resonance imaging

Author Manuscript
FSH Follicle stimulating hormone
GABA Gamma-aminobutryic acid
GM Grey matter
GnRH Gonadotropin releasing hormone
GPCR G-protein coupled receptor
HRT Hormone replacement therapy
IUD Intrauterine device
LH Luteinizing hormone
Author Manuscript
MD Mean diffusivity
MNI Montreal Neurological Institute
MRI Magnetic resonance imaging
mRNA Messenger ribonucleic acid
OCP Oral contraceptive pills
OFC Orbitofrontal cortex
PD Primary dysmenorrhea
Author Manuscript
PFC Prefrontal cortex
PMDD Premenstrual dysphoric disorder
PMS Premenstrual syndrome
ROI Region of interest
VBM Voxel based morphometry
WM White matter
References
Abler B, Kumpfmuller D, Gron G, Walter M, Stingl J, Seeringer A. Neural correlates of erotic
Author Manuscript
stimulation under different levels of female sexual hormones. PloS one. 2013; 8:e54447.doi:
10.1371/journal.pone.0054447 [PubMed: 23418428]
Abraham GE, Odell WD, Swerdloff RS, Hopper K. Simultaneous radioimmunoassay of plasma FSH,
LH, progesterone, 17-hydroxyprogesterone, and estradiol-17 beta during the menstrual cycle. J Clin
Endocrinol Metabol. 1972; 34:312–318. DOI: 10.1210/jcem-34-2-312
Amin Z, Epperson CN, Constable RT, Canli T. Effects of estrogen variation on neural correlates of
emotional response inhibition. NeuroImage. 2006; 32:457–464. DOI: 10.1016/j.neuroimage.
2006.03.013 [PubMed: 16644236]
Aminoff EM, Kveraga K, Bar M. The role of the parahippocampal cortex in cognition. Trends in
cognitive sciences. 2013; 17:379–390. DOI: 10.1016/j.tics.2013.06.009 [PubMed: 23850264]
Author Manuscript
Andreano JM, Cahill L. Sex influences on the neurobiology of learning and memory. Learn Memory.
2009; 16:248–266. DOI: 10.1101/lm.918309
Arnold AP, Gorski RA. Gonadal steroid induction of structural sex differences in the central nervous
system. Annu Rev Neurosci. 1984; 7:413–442. DOI: 10.1146/annurev.ne.07.030184.002213
[PubMed: 6370082]
Ashburner J, Friston KJ. Voxel-based morphometry—the methods. NeuroImage. 2000; 11:805–821.
DOI: 10.1006/nimg.2000.0582 [PubMed: 10860804]
Backstrom T, et al. Pathogenesis in menstrual cycle-linked CNS disorders. Ann N Y Acad Sci. 2003;
1007:42–53. [PubMed: 14993039]
Banks WA. Brain meets body: the blood-brain barrier as an endocrine interface. Endocrinology. 2012;
153:4111–4119. DOI: 10.1210/en.2012-1435 [PubMed: 22778219]
Baroncini M, Jissendi P, Catteau-Jonard S, Dewailly D, Pruvo JP, Francke JP, Prevot V. Sex steroid
hormones-related structural plasticity in the human hypothalamus. NeuroImage. 2010; 50:428–
433. DOI: 10.1016/j.neuroimage.2009.11.074 [PubMed: 19969095]
Author Manuscript
Bergouignan L, et al. Can voxel based morphometry, manual segmentation and automated
segmentation equally detect hippocampal volume differences in acute depression? NeuroImage.
2009; 45:29–37. DOI: 10.1016/j.neuroimage.2008.11.006 [PubMed: 19071222]
Biggs WS, Demuth RH. Premenstrual syndrome and premenstrual dysphoric disorder. Am Fam
Physician. 2011; 84:918–924. [PubMed: 22010771]
Blaustein JD. An estrogen by any other name. Endocrinology. 2008; 149:2697–2698. DOI:
10.1210/en.2008-0396 [PubMed: 18493023]
Blumenfeld-Katzir T, Pasternak O, Dagan M, Assaf Y. Diffusion MRI of structural brain plasticity
induced by a learning and memory task. PloS One. 2011; 6:e20678.doi: 10.1371/journal.pone.
0020678 [PubMed: 21701690]
Braun K. The prefrontal-limbic system: development, neuroanatomy, function, and implications for
socioemotional development. Clin Perinatol. 2011; 38:685–702. DOI: 10.1016/j.clp.2011.08.013
[PubMed: 22107898]
Breedlove SM, Arnold AP. Hormone accumulation in a sexually dimorphic motor nucleus of the rat
Author Manuscript
spinal cord. Science. 1980; 210:564–566. [PubMed: 7423210]

Brinton RD, et al. Progesterone receptors: form and function in brain. Front Neuroendocrinol. 2008;
29:313–339. DOI: 10.1016/j.yfrne.2008.02.001 [PubMed: 18374402]
Burgess N, Maguire EA, O’Keefe J. The human hippocampus and spatial and episodic memory.
Neuron. 2002; 35:625–641. [PubMed: 12194864]
Catani M, Dell’acqua F, Thiebaut de Schotten M. A revised limbic system model for memory, emotion
and behaviour. Neurosci Biobehav Rev. 2013; 37:1724–1737. DOI: 10.1016/j.neubiorev.
2013.07.001 [PubMed: 23850593]
Chen W, Shields J, Huang W, King JA. Female fear: influence of estrus cycle on behavioral response
and neuronal activation. Behav Brain Res. 2009; 201:8–13. DOI: 10.1016/j.bbr.2009.01.019
[PubMed: 19428610]
Christensen A, Dewing P, Micevych P. Membrane-initiated estradiol signaling induces spinogenesis
required for female sexual receptivity. J Neurosci Off J Soc Neurosci. 2011; 31:17583–17589.
DOI: 10.1523/JNEUROSCI.3030-11.2011
Cooke BM. Steroid-dependent plasticity in the medial amygdala. Neuroscience. 2006; 138:997–1005.
Author Manuscript
DOI: 10.1016/j.neuroscience.2005.06.018 [PubMed: 16330154]

Cooke BM, Woolley CS. Gonadal hormone modulation of dendrites in the mammalian CNS. J
Neurobiol. 2005; 64:34–46. DOI: 10.1002/neu.20143 [PubMed: 15884004]
Cooke BM, Tabibnia G, Breedlove SM. A brain sexual dimorphism controlled by adult circulating
androgens. Proc Nat Acad Sci USA. 1999; 96:7538–7540. [PubMed: 10377450]
Cooke BM, Breedlove SM, Jordan CL. Both estrogen receptors and androgen receptors contribute to
testosterone-induced changes in the morphology of the medial amygdala and sexual arousal in
male rats. Horm Behav. 2003; 43:336–346. [PubMed: 12694644]
Dastidar P, et al. Volumes of brain atrophy and plaques correlated with neurological disability in
secondary progressive multiple sclerosis. J Neurol Sci. 1999; 165:36–42. [PubMed: 10426145]
Author Manuscript
Davies RR, Scahill VL, Graham A, Williams GB, Graham KS, Hodges JR. Development of an MRI
rating scale for multiple brain regions: comparison with volumetrics and with voxel-based
morphometry. Neuroradiology. 2009; 51:491–503. DOI: 10.1007/s00234-009-0521-z [PubMed:
19308367]
De Bondt T, Jacquemyn Y, Van Hecke W, Sijbers J, Sunaert S, Parizel PM. Regional gray matter
volume differences and sex-hormone correlations as a function of menstrual cycle phase and
hormonal contraceptives use. Brain Res. 2013a; 1530:22–31. DOI: 10.1016/j.brainres.2013.07.034
[PubMed: 23892107]
De Bondt T, et al. Does the use of hormonal contraceptives cause microstructural changes in cerebral
white matter? Prelim Results DTI Tract Study. Eur Radiol. 2013b; 23:57–64. DOI: 10.1007/
s00330-012-2572-5 [PubMed: 22814829]
den Heijer T, Geerlings MI, Hofman A, de Jong FH, Launer LJ, Pols HA, Breteler MM. Higher
estrogen levels are not associated with larger hippocampi and better memory performance. Arch
Neurol. 2003; 60:213–220. [PubMed: 12580706]
Author Manuscript
Dinc H, Esen F, Demirci A, Sari A, Resit Gumele H. Pituitary dimensions and volume measurements
in pregnancy and post partum. MR Assess Acta (Radiol Stockholm Sweden, 1987). 1998; 39:64–
69.
Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Neuroplasticity: changes in grey
matter induced by training. Nature. 2004; 427:311–312. DOI: 10.1038/427311a [PubMed:
14737157]
Dreher JC, Schmidt PJ, Kohn P, Furman D, Rubinow D, Berman KF. Menstrual cycle phase modulates
reward-related neural function in women. Proc Natl Acad Sci USA. 2007; 104:2465–2470. DOI:
10.1073/pnas.0605569104 [PubMed: 17267613]
Elster AD, Sanders TG, Vines FS, Chen MY. Size and shape of the pituitary gland during pregnancy
and post partum: measurement with MR imaging. Radiology. 1991; 181:531–535. DOI: 10.1148/
radiology.181.2.1924800 [PubMed: 1924800]
Epstein R, Kanwisher N. A cortical representation of the local visual environment. Nature. 1998;
392:598–601. DOI: 10.1038/33402 [PubMed: 9560155]
Eriksson PS, Perfilieva E, Bjork-Eriksson T, Alborn AM, Nordborg C, Peterson DA, Gage FH.
Author Manuscript
Neurogenesis in the adult human hippocampus. Nat Med. 1998; 4:1313–1317. DOI: 10.1038/3305
[PubMed: 9809557]
Fan L, Hanbury R, Pandey SC, Cohen RS. Dose and time effects of estrogen on expression of neuron-
specific protein and cyclic AMP response element-binding protein and brain region volume in the
medial amygdala of ovariectomized rats. Neuroendocrinology. 2008; 88:111–126. DOI:
10.1159/000129498 [PubMed: 18446018]
Faundes A, Segal SJ, Adejuwon CA, Brache V, Leon P, Alvarez-Sanchez F. The menstrual cycle in
women using an intrauterine device. Fertil Steril. 1980; 34:427–430. [PubMed: 7439408]
Featherstone RE, Fleming AS, Ivy GO. Plasticity in the maternal circuit: effects of experience and
partum condition on brain astrocyte number in female rats. Behav Neurosci. 2000; 114:158–172.
[PubMed: 10718271]
Filipek PA, Richelme C, Kennedy DN, Caviness VS. The young adult human brain: an MRI-based
morphometric analysis. Cereb Cortex (New York NY 1991). 1994; 4:344–360.
Fischer B, Gleason C, Asthana S. Effects of hormone therapy on cognition and mood. Fertil Steril.
Author Manuscript
2014; 101:898–904. DOI: 10.1016/j.fertnstert.2014.02.025 [PubMed: 24680649]

Fowler CD, Freeman ME, Wang Z. Newly proliferated cells in the adult male amygdala are affected by
gonadal steroid hormones. J Neurobiol. 2003; 57:257–269. DOI: 10.1002/neu.10273 [PubMed:
14608662]
Franke K, Hagemann G, Schleussner E, Gaser C. Changes of individual BrainAGE during the course
of the menstrual cycle. NeuroImage. 2015; doi: 10.1016/j.neuroimage.2015.04.036
Frisk V, Milner B. The role of the left hippocampal region in the acquisition and retention of story
content. Neuropsychologia. 1990; 28:349–359. [PubMed: 2111523]
Gillies GE, McArthur S. Estrogen actions in the brain and the basis for differential action in men and
women: a case for sex-specific medicines. Pharmacol Rev. 2010; 62:155–198. DOI: 10.1124/pr.
Author Manuscript
109.002071 [PubMed: 20392807]

Gillings MR. Were there evolutionary advantages to premenstrual syndrome? Evol Appl. 2014; 7:897–
904. DOI: 10.1111/eva.12190 [PubMed: 25469168]
Goldstein JM, et al. Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic
resonance imaging. Cereb Cortex (New York NY 1991). 2001; 11:490–497.
Goldstein JM, Jerram M, Poldrack R, Ahern T, Kennedy DN, Seidman LJ, Makris N. Hormonal cycle
modulates arousal circuitry in women using functional magnetic resonance imaging. J Neurosci
Off J Soc Neurosci. 2005; 25:9309–9316. DOI: 10.1523/JNEUROSCI.2239-05.2005
Gonzalez JG, Elizondo G, Saldivar D, Nanez H, Todd LE, Villarreal JZ. Pituitary gland growth during
normal pregnancy: an in vivo study using magnetic resonance imaging. Am J Med. 1988; 85:217–
220. [PubMed: 3400697]
Goto M, et al. 3 Tesla MRI detects accelerated hippocampal volume reduction in postmenopausal
women. J Magn Reson Imaging JMRI. 2011a; 33:48–53. DOI: 10.1002/jmri.22328 [PubMed:
21182120]
Author Manuscript
Goto M, et al. Accelerated hippocampal volume reduction in post-menopausal women: an additional

study with Atlas-based method. Radiol Phys Technol. 2011b; 4:185–188. DOI: 10.1007/
s12194-011-0120-7 [PubMed: 21604214]
Grams AE, Gempt J, Stahl A, Forschler A. Female pituitary size in relation to age and hormonal
factors. Neuroendocrinology. 2010; 92:128–132. DOI: 10.1159/000314196 [PubMed: 20523033]
Grant R, Condon B, Lawrence A, Hadley DM, Patterson J, Bone I, Teasdale GM. Is cranial CSF
volume under hormonal influence? An MR study. J Comput Assist Tomogr. 1988; 12(1):36–39.
[PubMed: 3335669]
Guerra-Araiza C, Coyoy-Salgado A, Camacho-Arroyo I. Sex differences in the regulation of
progesterone receptor isoforms expression in the rat brain. Brain Res Bull. 2002; 59:105–109.
[PubMed: 12379440]
Hagemann G, Ugur T, Schleussner E, Mentzel HJ, Fitzek C, Witte OW, Gaser C. Changes in brain size
during the menstrual cycle. PloS One. 2011; 6:e14655.doi: 10.1371/journal.pone.0014655
[PubMed: 21326603]
Author Manuscript
Ham EA, Cirillo VJ, Zanetti ME, Kuehl FA Jr. Estrogen-directed synthesis of specific prostaglandins
in uterus. Proc Natl Acad Sci USA. 1975; 72:1420–1424. [PubMed: 165507]
Harris GW. Effects of the nervous system on the pituitary-adrenal activity. Prog Brain Res. 1970;
32:86–88. [PubMed: 4321532]
Harris RJ, Rice GE, Young AW, Andrews TJ. Distinct but overlapping patterns of response to words
and faces in the fusiform gyrus. Cereb Cortex (New York, NY 1991). 2015; doi: 10.1093/cercor/
bhv147
Hasan KM, et al. Development and aging of the healthy human brain uncinate fasciculus across the
lifespan using diffusion tensor tractography. Brain Res. 2009; 1276:67–76. DOI: 10.1016/
j.brainres.2009.04.025 [PubMed: 19393229]
Heinrichs M, Meinlschmidt G, Neumann I, Wagner S, Kirschbaum C, Ehlert U, Hellhammer DH.
Effects of suckling on hypothalamic-pituitary-adrenal axis responses to psychosocial stress in
postpartum lactating women. J Clin Endocrinol Metabol. 2001; 86:4798–4804. DOI: 10.1210/
jcem.86.10.7919
Herman JP, Ostrander MM, Mueller NK, Figueiredo H. Limbic system mechanisms of stress
Author Manuscript
regulation: hypothalamopituitary-adrenocortical axis. Prog Neuropsychopharmacol Biol

Psychiatry. 2005; 29:1201–1213. DOI: 10.1016/j.pnpbp.2005.08.006 [PubMed: 16271821]
Herson PS, Koerner IP, Hurn PD. Sex, sex steroids, and brain injury. Sem Reprod Med. 2009; 27:229–
239.
Hinshaw DB Jr, Hasso AN, Thompson JR, Davidson BJ. High resolution computed tomography of the
post partum pituitary gland. Neuroradiology. 1984; 26:299–301. [PubMed: 6462437]
Janak PH, Tye KM. From circuits to behaviour in the amygdala. Nature. 2015; 517:284–292. DOI:
10.1038/nature14188 [PubMed: 25592533]
Jeong HG, Ham BJ, Yeo HB, Jung IK, Joe SH. Gray matter abnormalities in patients with
premenstrual dysphoric disorder: an optimized voxel-based morphometry. J Affect Disord. 2012;
Author Manuscript
140:260–267. DOI: 10.1016/j.jad.2012.02.010 [PubMed: 22381950]

Kanwisher N, McDermott J, Chun MM. The fusiform face area: a module in human extrastriate cortex
specialized for face perception. J Neurosci Off J Soc Neurosci. 1997; 17:4302–4311.
Karaca Z, Tanriverdi F, Unluhizarci K, Kelestimur F. Pregnancy and pituitary disorders. Eur J
Endocrinol Eur Fed Endocr Soc. 2010; 162:453–475. DOI: 10.1530/EJE-09-0923
Kim P, Leckman JF, Mayes LC, Feldman R, Wang X, Swain JE. The plasticity of human maternal
brain: longitudinal changes in brain anatomy during the early postpartum period. Behav Neurosci.
2010; 124:695–700. DOI: 10.1037/a0020884 [PubMed: 20939669]
Kondo Y, Sachs BD, Sakuma Y. Importance of the medial amygdala in rat penile erection evoked by
remote stimuli from estrous females. Behav Brain Res. 1997; 88:153–160. [PubMed: 9404624]
Konrad C, et al. The functional anatomy of semantic retrieval is influenced by gender, menstrual cycle,
and sex hormones. J Neural Transm. 2008; 115:1327–1337. DOI: 10.1007/s00702-008-0073-0
[PubMed: 18548194]
Kruijver FP, Balesar R, Espila AM, Unmehopa UA, Swaab DF. Estrogen receptor-alpha distribution in
Author Manuscript
the human hypothalamus in relation to sex and endocrine status. J Comp Neurol. 2002; 454:115–
139. DOI: 10.1002/cne.10416 [PubMed: 12412138]
Lai CH. Gray matter volume in major depressive disorder: a meta-analysis of voxel-based
morphometry studies. Psychiatry Res. 2013; 211:37–46. DOI: 10.1016/j.pscychresns.2012.06.006
[PubMed: 23146253]
Laule C, Leung E, Lis DK, Traboulsee AL, Paty DW, MacKay AL, Moore GR. Myelin water imaging
in multiple sclerosis: quantitative correlations with histopathology. Mult Scler. 2006; 12:747–753.
[PubMed: 17263002]
Lieberburg I, McEwen BS. Estradiol-17beta: a metabolite of testosterone recovered in cell nuclei from
limbic areas of adult male rat brains. Brain Res. 1975; 91:171–174. [PubMed: 1131699]
Lisofsky N, Lindenberger U, Kuhn S. Amygdala/hippocampal activation during the menstrual cycle:
evidence for lateralization of effects across different tasks. Neuropsychologia. 2015a; 67:55–62.
DOI: 10.1016/j.neuropsychologia.2014.12.005 [PubMed: 25496966]
Lisofsky N, Martensson J, Eckert A, Lindenberger U, Gallinat J, Kuhn S. Hippocampal volume and
Author Manuscript
functional connectivity changes during the female menstrual cycle. NeuroImage. 2015b; 118:154–
162. DOI: 10.1016/j.neuroimage.2015.06.012 [PubMed: 26057590]
Madeira MD, Ferreira-Silva L, Paula-Barbosa MM. Influence of sex and estrus cycle on the sexual
dimorphisms of the hypothalamic ventromedial nucleus: stereological evaluation and Golgi study.
J Comp Neurol. 2001; 432:329–345. [PubMed: 11246211]
McEwen BS, Pfaff DW, Chaptal C, Luine VN. Brain cell nuclear retention of [3H]estradiol doses able
to promote lordosis: temporal and regional aspects. Brain Res. 1975; 86:155–161. [PubMed:
163665]
McEwen BS, Gray JD, Nasca C. 60 years of neuroendocrinology: redefining neuroendocrinology:
stress, sex and cognitive and emotional regulation. J Endocrinol. 2015; 226:T67–T83. DOI:
10.1530/JOE-15-0121 [PubMed: 25934706]
Meyer G, Wahle P, Castaneyra-Perdomo A, Ferres-Torres R. Morphology of neurons in the white
matter of the adult human neocortex. Exp Brain Res. 1992; 88:204–212. [PubMed: 1541357]
Micevych P, Christensen A. Membrane-initiated estradiol actions mediate structural plasticity and
reproduction. Front Neuroendocrinol. 2012; 33:331–341. DOI: 10.1016/j.yfrne.2012.07.003
Author Manuscript
[PubMed: 22828999]
Miki Y, et al. The pituitary gland: changes on MR images during the 1st year after delivery. Radiology.
2005; 235:999–1004. DOI: 10.1148/radiol.2353040243 [PubMed: 15833983]
Montagnese CM, Poulain DA, Vincent JD, Theodosis DT. Structural plasticity in the rat supraoptic
nucleus during gestation, post-partum lactation and suckling-induced pseudogestation and
lactation. J Endocrinol. 1987; 115:97–105. [PubMed: 3668451]
Naftolin F, et al. Estrogen-induced hypothalamic synaptic plasticity and pituitary sensitization in the
control of the estrogen-induced gonadotrophin surge. Reprod Sci. 2007; 14:101–116. DOI:
10.1177/1933719107301059 [PubMed: 17636222]
Nakata H, Sakamoto K, Kakigi R. Meditation reduces pain-related neural activity in the anterior
cingulate cortex, insula, secondary somatosensory cortex, and thalamus. Front Psychol. 2014;
Author Manuscript
5:1489.doi: 10.3389/fpsyg.2014.01489 [PubMed: 25566158]

Oatridge A, Holdcroft A, Saeed N, Hajnal JV, Puri BK, Fusi L, Bydder GM. Change in brain size
during and after pregnancy: study in healthy women and women with preeclampsia. AJNR Am J
Neuroradiol. 2002; 23:19–26. [PubMed: 11827871]
Ossewaarde L, Hermans EJ, van Wingen GA, Kooijman SC, Johansson IM, Backstrom T, Fernandez
G. Neural mechanisms underlying changes in stress-sensitivity across the menstrual cycle.
Psychoneuroendocrinology. 2010; 35:47–55. DOI: 10.1016/j.psyneuen.2009.08.011 [PubMed:
19758762]
Ossewaarde L, van Wingen GA, Rijpkema M, Backstrom T, Hermans EJ, Fernandez G. Menstrual
cycle-related changes in amygdala morphology are associated with changes in stress sensitivity.
Hum Brain Mapp. 2013; 34:1187–1193. DOI: 10.1002/hbm.21502 [PubMed: 22162177]
Palumbo MA, et al. Allopregnanolone concentration in hippocampus of prepubertal rats and female
rats throughout estrous cycle. J Endocrinol Invest. 1995; 18:853–856. [PubMed: 8778157]
Paton JJ, Belova MA, Morrison SE, Salzman CD. The primate amygdala represents the positive and
Author Manuscript
negative value of visual stimuli during learning. Nature. 2006; 439:865–870. DOI: 10.1038/
nature04490 [PubMed: 16482160]
Peper JS, van den Heuvel MP, Mandl RC, Hulshoff Pol HE, van Honk J. Sex steroids and connectivity
in the human brain: a review of neuroimaging studies. Psychoneuroendocrinology. 2011; 36:1101–
1113. DOI: 10.1016/j.psyneuen.2011.05.004 [PubMed: 21641727]
Peterson BM, Mermelstein PG, Meisel RL. Estradiol mediates dendritic spine plasticity in the nucleus
accumbens core through activation of mGluR5. Brain Struct Funct. 2015; 220:2415–2422. DOI:
10.1007/s00429-014-0794-9 [PubMed: 24878822]
Phoenix CH, Goy RW, Gerall AA, Young WC. Organizing action of prenatally administered
testosterone propionate on the tissues mediating mating behavior in the female guinea pig.
Endocrinology. 1959; 65:369–382. DOI: 10.1210/endo-65-3-369 [PubMed: 14432658]
Pletzer B, Kronbichler M, Aichhorn M, Bergmann J, Ladurner G, Kerschbaum HH. Menstrual cycle
and hormonal contraceptive use modulate human brain structure. Brain Res. 2010; 1348:55–62.
DOI: 10.1016/j.brainres.2010.06.019 [PubMed: 20550945]
Pletzer B, Kronbichler M, Kerschbaum H. Differential effects of androgenic and anti-androgenic
Author Manuscript
progestins on fusiform and frontal gray matter volume and face recognition performance. Brain
Res. 2015; 1596:108–115. DOI: 10.1016/j.brainres.2014.11.025 [PubMed: 25446458]
Pluchino N, et al. Progesterone and progestins: effects on brain, allopregnanolone and beta-endorphin.
J Steroid Biochem Mol Biol. 2006; 102:205–213. DOI: 10.1016/j.jsbmb.2006.09.023 [PubMed:
17052903]
Pluchino N, Santoro A, Casarosa E, Wenger JM, Genazzani AD, Petignat P, Genazzani AR. Advances
in neurosteroids: role in clinical practice. Climact J Int Menopause Soc. 2013; 16(Suppl 1):8–17.
Poromaa IS. Physiological correlates of premenstrual dysphoric disorder (PMDD). Curr Topics Behav
Neurosci. 2014; doi: 10.1007/7854_2014_296
Protopopescu X, et al. Orbitofrontal cortex activity related to emotional processing changes across the
menstrual cycle. Proc Natl Acad Sci USA. 2005; 102:16060–16065. DOI: 10.1073/pnas.
0502818102 [PubMed: 16247013]
Protopopescu X, et al. Hippocampal structural changes across the menstrual cycle. Hippocampus.
2008a; 18:985–988. DOI: 10.1002/hipo.20468 [PubMed: 18767068]
Author Manuscript
Protopopescu X, et al. Toward a functional neuroanatomy of premenstrual dysphoric disorder. J Affect

Disord. 2008b; 108:87–94. DOI: 10.1016/j.jad.2007.09.015 [PubMed: 18031826]
Qiu LR, Germann J, Spring S, Alm C, Vousden DA, Palmert MR, Lerch JP. Hippocampal volumes
differ across the mouse estrous cycle, can change within 24 hours, and associate with cognitive
strategies. NeuroImage. 2013; 83:593–598. DOI: 10.1016/j.neuroimage.2013.06.074 [PubMed:
23831531]
Ridgway GR, Henley SM, Rohrer JD, Scahill RI, Warren JD, Fox NC. Ten simple rules for reporting
voxel-based morphometry studies. NeuroImage. 2008; 40:1429–1435. DOI: 10.1016/
j.neuroimage.2008.01.003 [PubMed: 18314353]
Rolls ET. The orbitofrontal cortex and reward. Cereb cortex (New York, NY: 1991). 2000; 10:284–294.
Rose EJ, Donohoe G. Brain vs. behavior: an effect size comparison of neuroimaging and cognitive
Author Manuscript
studies of genetic risk for schizophrenia. Schizophr Bull. 2013; 39:518–526. DOI: 10.1093/
schbul/sbs056 [PubMed: 22499782]
Rosenberg L, Park S. Verbal and spatial functions across the menstrual cycle in healthy young women.
Psychoneuroen-docrinology. 2002; 27:835–841.
Rosenthal R, DiMatteo MR. Meta-analysis: recent developments in quantitative methods for literature
reviews. Annu Rev Psychol. 2001; 52:59–82. DOI: 10.1146/annurev.psych.52.1.59 [PubMed:
11148299]
Ruigrok AN, Salimi-Khorshidi G, Lai MC, Baron-Cohen S, Lombardo MV, Tait RJ, Suckling J. A
meta-analysis of sex differences in human brain structure. Neurosci Biobehav Rev. 2014; 39:34–
50. DOI: 10.1016/j.neubiorev.2013.12.004 [PubMed: 24374381]
Sacher J, Okon-Singer H, Villringer A. Evidence from neuroimaging for the role of the menstrual
cycle in the interplay of emotion and cognition. Front Human Neurosci. 2013; 7:374.doi:
10.3389/fnhum.2013.00374
Sanchez AM, Flamini MI, Genazzani AR, Simoncini T. Effects of progesterone and
Author Manuscript
medroxyprogesterone on actin remodeling and neuronal spine formation. Mol Endocrinol. 2013;
27:693–702. DOI: 10.1210/me.2012-1278 [PubMed: 23487486]
Scholz J, Klein MC, Behrens TE, Johansen-Berg H. Training induces changes in white-matter
architecture. Nat Neurosci. 2009; 12:1370–1371. DOI: 10.1038/nn.2412 [PubMed: 19820707]
Schoning S, et al. Functional anatomy of visuo-spatial working memory during mental rotation is
influenced by sex, menstrual cycle, and sex steroid hormones. Neuropsychologia. 2007;
45:3203–3214. DOI: 10.1016/j.neuropsychologia.2007.06.011 [PubMed: 17689571]
Scott DJ, Heitzeg MM, Koeppe RA, Stohler CS, Zubieta JK. Variations in the human pain stress
experience mediated by ventral and dorsal basal ganglia dopamine activity. J Neurosci Off J Soc
Neurosci. 2006; 26:10789–10795. DOI: 10.1523/JNEUROSCI.2577-06.2006
Siegel HI, Rosenblatt JS. Estrogen-induced maternal behavior in hysterectomized-overiectomized
virgin rats. Physiol Behav. 1975; 14:465–471. [PubMed: 1169780]
Simerly RB, Chang C, Muramatsu M, Swanson LW. Distribution of androgen and estrogen receptor
mRNA-containing cells in the rat brain: an in situ hybridization study. J Comp Neurol. 1990;
Author Manuscript
294:76–95. DOI: 10.1002/cne.902940107 [PubMed: 2324335]

Skalkidou A, Hellgren C, Comasco E, Sylven S, Sundstrom Poromaa I. Biological aspects of
postpartum depression. Women’s Health. 2012; 8:659–672. DOI: 10.2217/whe.12.55
Smith ML, Milner B. The role of the right hippocampus in the recall of spatial location.
Neuropsychologia. 1981; 19:781–793. [PubMed: 7329524]
Soares CN, Frey BN. Challenges and opportunities to manage depression during the menopausal
transition and beyond. Psychiatr Clin N Am. 2010; 33:295–308. DOI: 10.1016/j.psc.2010.01.007
Srivastava DP, Woolfrey KM, Penzes P. Insights into rapid modulation of neuroplasticity by brain
estrogens. Pharmacol Rev. 2013; 65:1318–1350. DOI: 10.1124/pr.111.005272 [PubMed:
24076546]
Stanczyk FZ, Archer DF, Bhavnani BR. Ethinyl estradiol and 17beta-estradiol in combined oral
contraceptives: pharmacokinetics, pharmacodynamics and risk assessment. Contraception. 2013;
87:706–727. DOI: 10.1016/j.contraception.2012.12.011 [PubMed: 23375353]
Sullivan EV, Marsh L, Pfefferbaum A. Preservation of hippocampal volume throughout adulthood in
healthy men and women. Neurobiol Aging. 2005; 26:1093–1098. DOI: 10.1016/j.neurobiolaging.
Author Manuscript
2004.09.015 [PubMed: 15748789]

Sundstrom Poromaa I, Gingnell M. Menstrual cycle influence on cognitive function and emotion
processing-from a reproductive perspective. Front Neurosci. 2014; 8:380.doi: 10.3389/fnins.
2014.00380 [PubMed: 25505380]
Suzuki R, Rygh LJ, Dickenson AH. Bad news from the brain: descending 5-HT pathways that control
spinal pain processing. Trends Pharmacol Sci. 2004; 25:613–617. DOI: 10.1016/j.tips.
2004.10.002 [PubMed: 15530638]
Teasdale GM, Grant R, Condon B, Patterson J, Lawrence A, Hadley DM, Wyper D. Intracranial CSF
volumes: natural variations and physiological changes measured by MRI. Acta neurochir Suppl
Author Manuscript
(Wien). 1988; 42:230–235. [PubMed: 3189015]

Toffoletto S, Lanzenberger R, Gingnell M, Sundstrom-Poromaa I, Comasco E. Emotional and
cognitive functional imaging of estrogen and progesterone effects in the female human brain: a
systematic review. Psychoneuroendocrinology. 2014; 50:28–52. DOI: 10.1016/j.psyneuen.
2014.07.025 [PubMed: 25222701]
Torrealday, S., Taylor, HS., Burney, RO., Mooney, SB., Giudice, LC. Endocrinology of Pregnancy. In:
De Groot, LJ., et al., editors. Endotext. South Dartmouth, MA: 2000.
Tu CH, et al. Brain morphological changes associated with cyclic menstrual pain. Pain. 2010;
150:462–468. DOI: 10.1016/j.pain.2010.05.026 [PubMed: 20705214]
Tu CH, et al. Menstrual pain is associated with rapid structural alterations in the brain. Pain. 2013;
154:1718–1724. DOI: 10.1016/j.pain.2013.05.022 [PubMed: 23693160]
van den Heuvel MW, van Bragt AJ, Alnabawy AK, Kaptein MC. Comparison of ethinylestradiol
pharmacokinetics in three hormonal contraceptive formulations: the vaginal ring, the transdermal
patch and an oral contraceptive. Contraception. 2005; 72:168–174. DOI: 10.1016/j.contraception.
Author Manuscript
2005.03.005 [PubMed: 16102549]

Warren AM, Gurvich C, Worsley R, Kulkarni J. A systematic review of the impact of oral
contraceptives on cognition. Contraception. 2014; 90:111–116. DOI: 10.1016/j.contraception.
2014.03.015 [PubMed: 24856205]
Whitwell JL. Voxel-based morphometry: an automated technique for assessing structural changes in
the brain. J Neurosci Off J Soc Neurosci. 2009; 29:9661–9664. DOI: 10.1523/JNEUROSCI.
2160-09.2009
Wideman L, Montgomery MM, Levine BJ, Beynnon BD, Shultz SJ. Accuracy of calendar-based
methods for assigning menstrual cycle phase in women. Sports Health. 2013; 5:143–149. DOI:
10.1177/1941738112469930 [PubMed: 24427382]
Witte AV, Savli M, Holik A, Kasper S, Lanzenberger R. Regional sex differences in grey matter
volume are associated with sex hormones in the young adult human brain. NeuroImage. 2010;
49:1205–1212. DOI: 10.1016/j.neuroimage.2009.09.046 [PubMed: 19796695]
Wong AP, et al. Estimating volumes of the pituitary gland from T1-weighted magnetic-resonance
images: effects of age, puberty, testosterone, and estradiol. NeuroImage. 2014; 94:216–221. DOI:
Author Manuscript
10.1016/j.neuroimage.2014.02.030 [PubMed: 24632090]

Woolley CS, McEwen BS. Estradiol mediates fluctuation in hippocampal synapse density during the
estrous cycle in the adult rat. J Neurosci Off J Soc Neurosci. 1992; 12:2549–2554.
Xiao B, Zeng T, Wu S, Sun H, Xiao N. Effect of levonorgestrel-releasing intrauterine device on
hormonal profile and menstrual pattern after long-term use. Contraception. 1995; 51:359–365.
[PubMed: 7554977]
Ylikorkala O, Puolakka J, Kauppila A. Serum gonadotrophins, prolactin and ovarian steroids in
primary dysmenorrhoea. Br J Obstet Gynaecol. 1979; 86:648–653. [PubMed: 497135]
Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain
structure during learning. Nat Neurosci. 2012; 15:528–536. DOI: 10.1038/nn.3045 [PubMed:
22426254]
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 1.
Variation of relative levels of estrogen (pink) and progesterone (blue) levels across a the
Author Manuscript
menstrual cycle, b the oral contraceptive pill cycle, c pregnancy and the postpartum period,
and d the menopause transition. Timing of structural MRI scans of reviewed studies shown
based on an idealized 28-days cycle menstrual cycle, gestation length of 40 weeks, and
menopause at 50 years. Menstrual cycle length may vary physiologically from 21 to 35
days. †Study resulted in more than 1 publication. ‡Specific day ranges of scan timing was
not reported, so ranges are estimated based on text descriptions. *Subjects were scanned
beyond 6 months postpartum. Oatridge et al. (2002) also scanned two subjects before
Author Manuscript
conception. **Women were scanned up to age 77

Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Author Manuscript
Fig. 2.
Areas of the limbic system that show structural variation with predominance of either
estrogen (pink) or progesterone (blue). Circles are based on approximate locations of peak
MNI coordinates of clusters identified from the reviewed studies. Anterior cingulate cortex
ACC; orbitofrontal cortex OFC
Author Manuscript
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Table 1
Methodological characteristics of the reviewed studies group by hormonal transition assessed
Study Imaging modality Design Subject group(s) Ages Hormone status determination Analysis method Areas examined
Menstrual cycle and OCPs
Catenaccio et al.
Baroncini et al. dMRI LO 10 OCP ♀, 10 ♂ 19–25 N/A Manual ROI ROI

(2010)
De Bondt et al. sMRI, DTI LO 15 MC ♀, 15 OCP ♀ 18–28 Serum VBM, tractography WB, ROI
(2013a, b)
Grant et al. 1988/ sMRI LO 10 MC ♀, 10 OCP ♀, 10 post- 18–64 Self-report Manual ROI WB
Teasdale (1988) menopause ♀, 14 ♂
Hagemann et al. sMRI LO 8 MC ♀, 8 ♂ NR Serum, TVUS Automated segmentation volumtery WB
(2011)
Lisofsky et al. sMRI, DTI LO 21 MC ♀ 22–31 Serum, urine VBM, tractography WB, ROI
(2015b)
Ossewaarde et al. sMRI LO 28 MC ♀ 18–38 Saliva, urine VBM, atlas-based ROI WB, ROI
(2013)
Pletzer et al. (2010) sMRI LO 14 MC ♀, 14 OCP ♀, 14 ♂ 19–31 Urine VBM, atlas-based ROI WB, ROI
Pletzer et al. (2015) sMRI CS, LO 20 MC ♀, 40 OCP ♀ 20–33 NR VBM, atlas-based ROI WB, ROI
Protopopescu et al. sMRI LO 21 MC ♀ 22–35 Urine VBM, atlas-based ROI WB, ROI
(2008a)
Tu et al. (2010) sMRI CS 32 MC♀ with PD, 32 MC ♀ 20–27 Urine VBM, atlas-based ROI WB, ROI
Tu et al. (2013) sMRI LO 32MC ♀ with PD, 32 MC ♀ 21–28 Urine VBM, atlas-based ROI WB, ROI
Witte et al. (2010) sMRI CS 17 MC ♀, 17 ♂ 21–36 Serum VBM WB
Pregnancy and postpartum
Dinc et al. (1998) sMRI CS 78 pregnant/postpartum ♀, 18 non- 20–38 NR Manual ROI Pituitary
pregnant ♀
Elster et al. (1991) sMRI CS, LO 68 pregnant/postpartum ♀, non- NR NR Manual ROI Pituitary
pregnant ♀
Gonzalez et al. sMRI CS 32 pregnant ♀, 20 nulliparous ♀ 16–30 Menstrual dating, TVUS Manual ROI Pituitary
(1988)
Hinshaw et al. CT CS, LO 8 pregnant/postpartum ♀ NR NR Manual ROI Pituitary
(1984)
Kim et al. (2010) sMRI LO 19 pregnant/postpartum ♀ 27–40 NR VBM WB
Miki et al. (2005) sMRI LO 13 pregnant/postpartum ♀ 26–32 NR Manual ROI Pituitary
Oatridge et al. sMRI LO 9 pregnant/postpartum ♀ 20–38 NR Automated segmentation volumetry WB
(2002)
Menopause
Page 33
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Study Imaging modality Design Subject group(s) Ages Hormone status determination Analysis method Areas examined
Goto et al. sMRI CS 59 pre-menopause ♀, 46 post- 40–70 Self-report VBM, atlas-based ROI WB, ROI
(2011a, b) menopause ♀
Sullivan et al. sMRI CS 17 pre-menopause ♀, 27 post- 20–85 Self-report Manual ROI ROI
(2005) menopause ♀, 84 ♂
Multiple categories
Catenaccio et al.
Grams et al. (2010) sMRI CS Menstrual: 47 MC ♀ 18–80 Self-report Semi-automated segmentation volumetry Pituitary
OCP: 18 OCP ♀, 31 MC ♀
Menopause: 49 pre, 45 post ♀
Scan timing is reported in Fig. 1. For hormone status determination—”serum” refers to measurement of serum estrogen and/or progesterone, “urine” refers to the serial measurement of luteinizing hormone
in the urine, and self-report refers to women providing confirmation about the beginning or ending of menses
♀ female, ♂ male
CS cross sectional, dMRI diffusion magnetic resonance imaging, DTI diffusion tensor imaging, LO longitudinal, MC menstrual cycling, N/A not applicable, NR not reported, OCP oral contraceptive pills,
PD primary dysmenorrhea, ROI region of interest, sMRI structural magnetic resonance imaging, TVUS transvaginal ultrasound, VBM voxel-based morphometry, WB whole brain
Page 34
Table 2
Volumetric changes associated with the menstrual cycle

Author Manuscript
Menstrual cycle—volumetric changes
Study Analysis Regions

Follicular > luteal
Protopopescu et al. (2008a)a VBM L lingual g., R hippocampus/parahippocampal g., L mid frontal g.
ROI R hippocampus
Pletzer et al. (2010) VBM R fusiform/parahippocampal g.
De Bondt et al. (2013a) VBM R mid frontal g., b/l BA 6, L cingulate g., R ACC, L mid temporal g., L insula
Lisofsky et al. (2015b) VBM L hippocampus/parahippocampal g., b/l cerebellum
Luteal > follicular
Grant et al. 1988/Teasdale et al. (1988) ROI CSF volume
Protopopescu et al. (2008a)a VBM L sup parietal lobule, R dorsal basal ganglia, R medial frontal g./ACC, R thalamus
Author Manuscript
ROI R dorsal basal ganglia

Pletzer et al. (2010) VBM None
Ossewaarde et al. (2013) ROI L amygdala
De Bondt et al. (2013a) VBM R sup temporal g.
Lisofsky et al. (2015b) VBM R cerebellum
Follicular = luteal
Pletzer et al. (2010) VBM Total GM
Grams et al. (2010) ROI Pituitary gland
Hagemann et al. (2011) Volumetry Total GM, total WM, total CSF
Peri-ovulatory > menstrual
Hagemann et al. (2011) Volumetry Total GM
Tu et al. (2013)a ROI (PD patients): L secondary somatosensory cortex, L ACC/dPCC

Author Manuscript
(Healthy controls): none

Lisofsky et al. (2015b) VBM R cerebellum, R insula, L inf parietal lobe, b/l post hippocampib, b/l thalamib
Menstrual > peri-ovulatory
Hagemann et al. (2011) Volumetry CSF volume
Tu et al. (2013)a ROI (PD patients): L medial OFC, L precentral g., L inf temporal g., R hypothalamus
(Healthy controls): none
Lisofsky et al. (2015b) VBM R inf parietal lobe
Peri-ovulatory = menstrual
Tu et al. (2013)a VBM Total GM
Grams et al. (2010) ROI Pituitary Gland

Hagemann et al. (2011) Volumetry Total WM
Primary dysmenorrhea patients > healthy controls
Author Manuscript
Tu et al. (2010)a ROI R post hippocampus/parahippocampal g., ACC/dPCC, periaqueductal grey,

hypothalamus, L ventral precuneus, L sup/mid temporal g., R cerebellar tonsil
Healthy controls > primary dysmenorrhea patients
Tu et al. (2010)a ROI R medial frontal g./PFC, R central and ventral precuneus, b/l secondary somatosensory
cortices, insula, R culmen, L cerebellar tonsil
Primary dysmenorrhea patients = healthy controls
Menstrual cycle—volumetric changes

Author Manuscript
Tu et al. (2010)a VBM Total GM
ACC anterior cingulate cortex, CSF cerebrospinal fluid, dPCC dorsal posterior cingulate cortex, GM grey matter, g gyrus, inf inferior, L left, med
medial, mid middle, NA not applicable, NR not reported, OCP oral contraceptive pills, OFC orbitofrontal cortex, PFC prefrontal cortex, post
posterior, R right, ROI region of interest, sup superior, VBM voxel-based morphometry
a
Includes or focuses on patients with menstrual cycle related symptoms (PMDD or PD)
b
These two regions were found in a comparison of the early follicular (menstrual) vs. late follicular phases, but the late follicular phase as defined
by Lisofsky et al. (2015b) (days 10–13) shows considerable overlap with our designation of the peri-ovulatory phase (days 12–16) and thus is
grouped with the other menstrual vs. peri-ovulatory results for simplicity
Author Manuscript
Author Manuscript
Author Manuscript
Table 3
Volumetric changes associated with hormonal contraceptive use

Author Manuscript
Hormonal contraceptive use—volumetric changes

Menstrual cycling women > OCP users
Pletzer et al. (2010) VBM None
Grams et al. (2010) ROI Pituitary volume
De Bondt et al. (2013a) VBM L Fusiform gyrus
Pletzer et al. (2015) ROI (vs. androgenic OCP users): b/l mid frontal g., L sup frontal g.
OCP users > menstrual cycling women
Pletzer et al. (2010) VBM B/l PFC, b/l ACC, b/l pre/post-central g., b/l SMA, R fusiform g., R parahippocampal g., R
lingual g., R sup/inf temporal g., b/l cerebellum
De Bondt et al. (2013a) VBM R BA 6, R sup frontal g., b/l fusiform g., R cingulum
Author Manuscript
Pletzer et al. (2015) ROI (vs. anti-androgenic OCP users): b/l fusiform g., b/l fusiform face areas, b/l parahippocampal
place area, cerebellum
Menstrual cycling women = OCP users
Grant et al. 1988/Teasdale (1988) ROI Total CSF
De Bondt et al. (2013a) VBM Total GM
Active pill > inactive pill
De Bondt et al. (2013a) VBM L ACC, L insula
Inactive pill > active pill
De Bondt et al. (2013a) VBM L BA 6, R post-central g., L caudate (ACC)
Pletzer et al. (2015) ROI L fusiform g., b/l fusiform face areas, L parahippocampal place area, R cerebellum
Author Manuscript
ACC anterior cingulate cortex, b/l bilateral, CSF cerebrospinal fluid, dPCC dorsal posterior cingulate cortex, g gyrus, GM grey matter, inf inferior,
L left, mid middle, OCP oral contraceptive pills, PFC prefrontal cortex, post posterior, R right, ROI region of interest, sup superior, SMA
supplementary motor area, VBM voxel-based morphometry
Author Manuscript
Table 4
Volumetric changes associated with pregnancy and the postpartum period

Author Manuscript
Pregnancy and postpartum—volumetric changes

Pregnant > not pregnant
Gonzalez et al. (1988), Elsteret al. (1991), Dinc et al. (1998) ROI Pituitary gland volume
Pregnant > postpartum
Oatridge (2002) Volumetry Whole brain volume (corresponding decrease in CSF)
Gonzalez et al. (1988), Elsteret al. (1991), Dinc et al. (1998) ROI Pituitary volumea
Late postpartum > early postpartum
Oatridge et al. (2002) Volumetry Whole brain volume (corresponding decrease in CSF)
Kim et al. (2010) VBM PFC, pre/post-central g., sup/inf parietal lobe, insula, thalamus
Author Manuscript
CSF cerebrospinal fluid, g gyrus, inf inferior, PFC prefrontal cortex, ROI region of interest, sup superior, VBM voxel-based morphometry
a
Pituitary gland volume peaked 0–6 days postpartum and then decreased in size
Author Manuscript
Author Manuscript
Table 5
Volumetric changes associated with menopause

Author Manuscript
Menopause—volumetric changes

Pre-menopausal > post-menopausal
Goto et al. (2011a, b) VBM/ROI B/l hippocampus
Pre-menopausal = post-menopausal
Sullivan et al. (2005) ROI B/l hippocampus
Grams et al. (2010) ROI Pituitary
Author Manuscript
Author Manuscript
Author Manuscript
Table 6
Structural variation correlated with serum hormone levels

Author Manuscript
Study Serum hormones—imaging correlations

Estrogen—positive correlations
Hagemann et al. (2011) None
Witte et al. (2010) Follicular: L sup parietal g.
De Bondt et al. (2013a) Follicular: BA 8, cingulum, post-central g., insula Luteal: none
Lisofsky et al. (2015b) L parahippocampal g., L mid frontal g., R cerebellum
Estrogen—negative correlations
Hagemann et al. (2011) None
Witte et al. (2010) Follicular: none
De Bondt et al. (2013a) Follicular: Fusiform g.
Luteal: ACC, sup frontal g., mid temporal g.
De Bondt et al. (2013b) MD in the fornix
Author Manuscript
Lisofsky et al. (2015b) None

Progesterone—positive correlations
Hagemann et al. (2011) Luteal (vs. menstrual): Total CSF
Witte et al. (2010) Follicular: R mid temporal pole
De Bondt et al. (2013a) Follicular: Fusiform g., lingual/parahippocampal g., pre-central g.
Luteal: BA 8, SMA
Progesterone—negative correlations
Hagemann et al. (2011) Luteal (vs. menstrual): Total GMa
Witte et al. (2010) Follicular: none
De Bondt et al. (2013a) Follicular: none
Luteal: Fusiform g.
Follicle-stimulating hormone—positive correlations
Author Manuscript
De Bondt et al. (2013a) Follicular/Luteal: none

Follicle-stimulating hormone—negative correlations
Luteinizing hormone—positive correlations
Luteinizing hormone—negative correlations
De Bondt et al. (2013a) MD in the fornix
ACC anterior cingulate cortex, CSF cerebrospinal fluid, FSH follicle stimulating hormone, g gyrus, GM grey matter, L left, LH luteinizing
hormone, MD mean diffusivity, mid middle, R right, ROI region of interest, sup superior, SMA supplementary motor area, VBM voxel-based
morphometry
a
Excluding one outlier subject (n = 6)
Author Manuscript

HHS Public Access: Estrogen-And Progesterone-Mediated Structural Neuroplasticity in Women: Evidence From Neuroimaging

Uploaded by

Copyright:

Available Formats

HHS Public Access: Estrogen-And Progesterone-Mediated Structural Neuroplasticity in Women: Evidence From Neuroimaging

Uploaded by

Document Information

Original Title

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

HHS Public Access: Estrogen-And Progesterone-Mediated Structural Neuroplasticity in Women: Evidence From Neuroimaging

Uploaded by

Copyright:

Available Formats

HHS Public Access

Published in final edited form as:

Estrogen- and progesterone-mediated structural neuroplasticity

neuroimaging literature addressing physiological variation in brain macro- and microstructure

Hormones; Neuroimaging; Sex differences; Women; Menstrual cycle; Neuroplasticity; Voxel-

Correspondence to: Michael L. Lipton.

exogenous sex hormones, including hormonal contraceptives and hormone replacement

It is important to characterize and account for natural physiological variation to distinguish it

important area of ongoing investigation.

Endocrinology of endogenous and exogenous ovarian sex hormones

gonadotropin-releasing hormone (GnRH), the synthesis of estrogen and progesterone, and

ovulation (Heinrichs et al. 2001).

The limbic system

hippocampus, to the mammillary bodies, anterior thalamic nuclei, nucleus accumbens,

CNS receptors for progesterone

2008). Progesterone receptor distribution is sexually dimorphic and is responsive to

CNS receptors for estrogen

concentrations in the hippocampus, with ER-beta expression dominating in the subiculum

Mechanisms of ovarian sex hormone modulation of structural neuroplasticity

applied to non-invasive imaging techniques in humans may thus be challenging. Changes in

The mechanisms of progesterone-mediated neuroplasticity have been less extensively

Voxel-based morphometry (VBM) is one fully automated technique designed to support

correction for multiple comparisons (Whitwell 2009).

Results and discussion

Studies’ sample, measurement and design characteristics

approach based on menstrual dating and ultrasound (Gonzalez et al. 1988).

“(Structure–function relationships in brain areas affected by ovarian sex hormones)”.

parahippocampal/fusiform gyrus during the progesterone dominated pre-menstrual (luteal)

volumetric changes of PD persist throughout the pain-free peri-ovulatory period. The

diffusion coefficient (ADC; equivalent to MD measured with diffusion tensor imaging),

than naturally cycling women (Grams et al. 2010).

Pregnancy and the postpartum period

influence of placental estrogen (Karaca et al. 2010).

parturition will begin to respond to pups in a similar manner as pregnant/maternal rats

related decline in hippocampal volume. In a follow-up investigation, the authors performed

Additionally, although pituitary volumes decreased with age, no effect of menopause

Hormone mediated structural neuroplasticity in humans

limbic system, including the hippocampus, parahippocampal gyrus, fusiform gyrus,

amygdala, hippocampus, parahippocampus, insula, and putamen in men compared with

regard, the volume of GM in ER-rich structures could be a structural modulator of estrogen

Moreover, as demonstrated by Pletzer et al. (2015), variable androgenic activity of different

Structure–function relationships in brain areas affected by ovarian sex hormones

Although speculating on the evolutionary advantages or disadvantages of hormone mediated

A critical question for researchers preparing structural neuroimaging studies examining

employed diffusion MRI to assess microstructural features; the relationship between

not taking oral contraceptives. Alternatively, counterbalanced, longitudinal crossover

combined (estrogen and progesterone) OCPs there were no significant differences in

In studies involving older women, establishing reproductive senescence may be more

neuroimaging findings can also serve to develop hypotheses regarding structure–function

ADC Apparent diffusion coefficient

CNS Central nervous system

CSF Cerebrospinal fluid

FDR False discovery rate

fMRI Functional magnetic resonance imaging

FSH Follicle stimulating hormone

GABA Gamma-aminobutryic acid

GnRH Gonadotropin releasing hormone

GPCR G-protein coupled receptor

HRT Hormone replacement therapy