Why and How to Account for Sex and Gender in Brain and Behavioral Research

Abstract

Long overlooked in neuroscience research, sex and gender are increasingly included as key variables potentially impacting all levels of neurobehavioral analysis. Still, many neuroscientists do not understand the difference between the terms “sex” and “gender,” the complexity and nuance of each, or how to best include them as variables in research designs. This TechSights article outlines rationales for considering the influence of sex and gender across taxa, and provides technical guidance for strengthening the rigor and reproducibility of such analyses. This guidance includes the use of appropriate statistical methods for comparing groups as well as controls for key covariates of sex (e.g., total intracranial volume) and gender (e.g., income, caregiver stress, bias). We also recommend approaches for interpreting and communicating sex- and gender-related findings about the brain, which have often been misconstrued by neuroscientists and the lay public alike.

Full text

TechSights
WhyandHowtoAccountforSexandGenderinBrainand
Behavioral Research
Lise Eliot,1Annaliese K. Beery,2Emily G. Jacobs,3Hannah F. LeBlanc,4Donna L. Maney,5and
Margaret M. McCarthy6
1
Stanson Toshok Center for Brain Function and Repair, Chicago Medical School, Rosalind Franklin University of Medicine & Science, North
Chicago, Illinois 60064,
2
Department of Integrative Biology, University of California-Berkeley, Berkeley, California 94720,
3
Department of
Psychological & Brain Sciences, University of California-Santa Barbara, Santa Barbara, California 93106,
4
Division of the Humanities & Social
Sciences, California Institute of Technology, Pasadena, California 91125,
5
Department of Psychology, Emory University, Atlanta, Georgia 30322, and
6
Department of Pharmacology, University of Maryland School of Medicine, Baltimore, Maryland 21201
Long overlooked in neuroscience research, sex and gender are increasingly included as key variables potentially impacting all
levels of neurobehavioral analysis. Still, many neuroscientists do not understand the difference between the terms “sex”and
“gender,”the complexity and nuance of each, or how to best include them as variables in research designs. This TechSights
article outlines rationales for considering the influence of sex and gender across taxa, and provides technical guidance for
strengthening the rigor and reproducibility of such analyses. This guidance includes the use of appropriate statistical methods
for comparing groups as well as controls for key covariates of sex (e.g., total intracranial volume) and gender (e.g., income,
caregiver stress, bias). We also recommend approaches for interpreting and communicating sex- and gender-related findings
about the brain, which have often been misconstrued by neuroscientists and the lay public alike.
Key words: sexual dimorphism; gender disparity; testosterone; female inclusion; statistical analysis; anti-sexism
Introduction: Why Account for Sex and Gender?
Sex and gender differences in the brain and behavior are of great
interest to society and have the potential to impact the diagnosis
and treatment of neuropsychiatric and neurologic disorders. But
there remains considerable misunderstanding about the differ-
ence in meaning between the two terms, the complexity that
arises by treating each as a research variable, and thus, uncer-
tainty about how to detect and interpret sex and gender influen-
ces when conducting neuroscientific research.
A consensus report from the National Academies of Sciences,
Engineering, and Medicine (2022) has defined “sex”as:
A multidimensional construct based on a cluster of anatomical
and physiological traits, that include external genitalia, second-
ary sex characteristics, gonads, chromosomes, and hormones.
And the report has defined “gender”as:
A multidimensional construct that links gender identity,
which is a core element of a person’s individual identity;
gender expression, which is how a person signals their
gender to others through their behavior and appearance;
and cultural expectations about social status, characteris-
tics, and behavior that are associated with sex traits.
Neither variable is strictly binary and each is composed of
multiple dimensions that may or may not “match”within a sin-
gle individual. Further, the two constructs are inextricably linked
with each other such that it is almost never possible, particularly
in studies of humans, to experimentally separate them. For that
reason, in this article we will sometimes refer to them together as
“sex/gender”to acknowledge their entanglement.
Many human psychological traits show gender differences, albeit
most are minor (Hyde, 2005;Zell et al., 2015). Each of these could
be shaped by sex-related factors (e.g., presence or absence of the Y
chromosome in mammals), gender-related factors (e.g., occupational
experience), or both. This interplay is even more critical when neu-
roscientists seek to explain gender disparities in mental health and
neurologic disorders; diagnoses such as depression, anxiety, eating
disorders, and dementia are at least 50% more common in women,
whereas ADHD, substance use disorders, dyslexia, and autism spec-
trum disorders are at least twice as common in men (Fig. 1).
Motivated in part by such disparities, many research funding
agencies have begun mandating the consideration of sex and
gender in biomedical research. In the United States, this process
began with the 1993 NIH Revitalization Act, which required that
all clinical trials funded through the agency include women and
minority groups when clinically relevant. In neuroscience, gen-
der parity has largely been achieved in imaging studies of the
human brain (Eliot et al., 2021) but women continue to be
Received Apr. 18, 2023; revised July 14, 2023; accepted July 18, 2023.
This work was supported by National Institutes of Health Grants R01MH52716 and R01DA039062 to
M.M.M., R01AG063843 to E.G.J., and U54AG062334 to D.L.M.; National Science Foundation CAREER 2239635 to A.K.B.;
the Fred B. Snite Foundation to L.E.; and the Ann S. Bowers Women’s Brain Health Initiative to E.G.J. This article
originated from a professional development workshop at the 2022 Society for Neuroscience annual meeting. We thank
Vlera Kojcini for her excellent support coordinating this session.
The authors declare no competing financial interests.
Correspondence should be addressed to Lise Eliot at lise.eliot@rosalindfranklin.edu.
https://doi.org/10.1523/JNEUROSCI.0020-23.2023
Copyright © 2023 the authors
6344 •The Journal of Neuroscience, September 13, 2023 •43(37):6344–6356

under-represented in research on certain disorders, including
stroke (Carcel et al., 2021). A greater problem is the failure of
researchers to report data disaggregated by sex, even when
women and men are included in comparable numbers (Geller et
al., 2018). Considering the large sample size of many human
studies, this omission amounts to a lost opportunity and short-
changes systematic reviews and meta-analyses.
With regard to preclinical research involving animals and cells, it
was not until 2016 that the U.S. National Institutes of Health (NIH)
mandated that sex be considered, through its “Sex as a Biological
Variable”(SABV) policy. In many fields, including neuroscience,
female nonhuman animals had been systematically excluded under
the mistaken impression that the estrus cycle adds unacceptable vari-
ability (Beery and Zucker, 2011). However, meta-analyses of trait
variability in male versus unstaged female mice (Prendergastetal.,
2014)andrats(Becker et al., 2016) have demonstrated that, while
certain traits can be more variable in one sex than another, females
of these species are no more variable than males overall and are sig-
nificantly less variable in spontaneous behavior, both within and
across individuals (Levy et al., 2023). Similarly among humans, the
coefficient of variation for cognitive, mental, and physical health
does not differ between men and women during the reproductively
active years (Smarr et al., 2021;Pritschet, 2022). Imaging studies of
the brain demonstrate that structural volumes and surface area are
modestly more variable in males (Forde et al., 2020;Wierenga et al.,
2022). Thus, while pregnancy remains a concern in certain clinical
trials (Blehar et al., 2013), in most cases there is no reason to exclude
females from basic science or clinical research.
Single-sex studies still have an important place in addressing
certain research questions, but for most biomedical topics, includ-
ing neuroscience, there are ample reasons to include equal num-
bers of females and males. In addition to exploring the neural
correlates of gendered behavioral differences and health dispar-
ities, such inclusion can uncover mechanisms of CNS function
that may not be evident at the behavioral or even cellular level.
Differences in underlying cellular and molecular mechanisms,
known as “latent sex differences”(Jain et al., 2019)couldpoten-
tially explain earlier inconsistencies in the scientific literature
when sex was not specified, and theoretically lead to different
treatments or diagnostic criteria for boys versus girls or men ver-
sus women. Some more prominent examples of latent sex differ-
ences in animal studies include the cellular and molecular
mediators of pain (Sorge and Strath, 2018), social attachment (De
Vries, 2004), reward (Becker and Koob, 2016), and hippocampal
endocannabinoid signaling (Tabatadze et al., 2015).
By contrast, other sex effects can be divergent, where a spe-
cific context or environmental perturbation invokes a sex dif-
ference in behavior from an otherwise similar neurobiological
profile. In rodents, for example, stress can have opposite effects
on learning and memory in males and females (Shors et al.,
2001) and behavioral strategies in response to foot shock can
diverge between the sexes (Gruene et al., 2015). In such cases,
studies that exclude one sex might provide a distorted view of
the phenomenon, and pooling male and female animals may
even cancel out an effect of the treatment or exposure (Beery,
2018). The only way to uncover such mechanisms is to include
sufficient numbers of male and female animals and to include
sex as a factor in statistical analyses.
For United States-based researchers, implementation of the
SABV policy and growing awareness of the importance of female
inclusion have produced a substantial increase in the proportion
of studies including both male and female animals. Over the
decade from 2009 to 2019, studies using males and females
increased across biological disciplines from 23% to 49% (Beery
and Zucker, 2011;Woitowich et al., 2020)(Fig. 2A). But while
inclusion has improved, other aspects of reporting and statisti-
cal analysis remain problematic. Across 10 domains of preclini-
cal biology using animal subjects, only 50% of mixed-sex
studies in 2009 considered sex in their analysis; 10 years later,
this number fell to 42% of mixed-sex studies reporting sex-
based analyses (Woitowich et al., 2020). In neuroscience, these
numbers were 22% and 18%, respectively (Fig. 2B), and only a
fraction of those used appropriate analyses to detect sex differ-
ences (Garcia-Sifuentes and Maney, 2021). An in-depth analy-
sis of neuroscience and psychiatry papers over the same time
interval replicated the finding that most studies did not use an
optimal design for discovering sex differences (Rechlin et al.,
2022). The authors documented frequent omission of sample
size, imbalanced use of males and females, inappropriate statis-
tical approaches, and use of males and females in some but not
all parts of a study, revealing considerable room for improve-
ment. Thus, researchers would benefit from greater awareness
of the context and components of sex-based analysis to fully
achieve the aims of the SABV policy.
For studies of humans, the influence of environmental fac-
tors is multiplied many-fold because of the contribution of gen-
der. In human societies, gender roles and other gender-related
categorizations can have profound effects on the brain and
behavior throughout the lifespan. Thus, in addition to account-
ing for sex, human neuroscience research must take account of
gender; that is, gender norms, power relations, economic secu-
rity, life experiences, and other factors that may contribute to
disparities favoring men or women. Such approaches are now
advocated by public funding agencies including the Canadian
Institutes of Health and the European Commission (White et
al., 2021). Gender analysis is recommended, though not yet
required, by the U.S. NIH (U.S. Department of Health and
Human Services, 2023).
Given the potential importance of sex and gender to brain
and behavioral function, and the opportunities they provide to
uncover novel neurobiological processes and foster new avenues
for translational research, neuroscientists should invest greater
effort in understanding and accounting for their effects. Doing
so, however, requires more than simply including equal numbers
of male and female subjects. In the remainder of this article, we
will address the “how”of studying sex- and gender-related influ-
ences on the brain and behavior, including considerations of
sample size and statistical comparisons, key covariates of both
Figure 1. Gender prevalence ratios for common neuropsychiatric disorders. Modified from Eliot
et al. (2021).Sources:autism(Loomes et al., 2017); ADHD (Polanczyk et al., 2007); alcohol use disor-
der (Grant et al., 2004); dyslexia (Rutter et al., 2004); dementia (Buckley et al., 2019); anxiety disor-
ders (McLean et al., 2011); depression (Salk et al., 2017); and eating disorders (Hudson et al., 2007).
Eliot et al. ·Sex and Gender in Brain and Behavioral Research J. Neurosci., September 13, 2023 •43(37):6344–6356 •6345

sex and gender, strategies for analyzing mechanisms underlying
male/female difference, and methods for operationalizing “gen-
der”in human studies. We end with a focus on how sex- and
gender-related findings are interpreted and communicated to
the nonscientific public, an often-fraught enterprise in need of
greater thoughtfulness on the part of researchers (Eliot, 2011;
Maney, 2014;Rippon et al., 2021). After each main section, we
offer specific recommendations to aid neuroscientists in their
reporting and analyses of sex and/or gender effects.
Understanding Sex Effects
Operationalization of sex
Although we often think of sex as a single binary variable, it
is actually a complex phenotype composed of many physio-
logical elements that can change dramatically at different
stages of development and in different environmental con-
texts. Thus, because it is not a unitary variable, the first step
in accounting for sex is to operationalize it using a stated
measure (e.g., karyotype, anogenital distance, self-report) on
which data are subsequently collected and published. The
measure chosen to represent sex does not have to be the
same across laboratories or even across studies within a labo-
ratory; for reasons of practicality, sex is typically operational-
ized using different measures for cells in culture versus mice
ordered from a vendor versus patients in a clinical trial. In
any of these cases, the measure used should be transparently
reported and consistent within a study (Richardson, 2022).
Once it is decided which variable will represent sex in a study,
it is equally important to recognize variables that covary with
that one; these additional variables have potential to confound
the interpretation of results. In the sections below, we review
some of the mechanisms that are typically included in the con-
struct of sex, plus others not typically included that can nonethe-
less give rise to brain and bodily sex differences. Our goal in
these sections is to raise researchers’awareness of proximal
causes of sex differences as well as covariates of sex that could be
addressed. Sex differences in the brain and behavior are never a
purely direct effect of genes, gonads, or any other variable used
to operationalize sex; rather, they involve intermediary steps. In
rodents, for example, sex differences can result from differential
transcription of genes, apoptosis of cells in specific brain regions,
and differential responses of a parent to male and female off-
spring (McCarthy and Arnold, 2011). So it is typically these
underlying, sex-related variables or processes that are informa-
tive for elucidating the neural basis of male-female difference,
along with phenotypic variance within sex. Sometimes, sex-
related variables, such as body size or longevity, may provide
simple explanations for apparent neural or clinical differences
between males and females; in other situations, cellular and mo-
lecular processes may differ, opening up unique opportunities
for advances in basic and clinical science.
Effects of genes
The determination of gonadal phenotype in mammals is con-
trolled by a single gene on the Y chromosome, the Sry gene (sex-
determining region of the Y chromosome), which codes for a
transcription factor that initiates a gene expression cascade that
drives the undifferentiated gonad toward a testis. If Sry is not
present, the gonad develops into an ovary (Goodfellow and
Lovell-Badge, 1993). The presence of testes and subsequent pre-
natal testosterone production is the key initiator of masculiniza-
tion of the brain and behavior. For these reasons, the presence of
the Y chromosome is a common sex-related factor used to opera-
tionalize sex. But are there genes on the X or Y chromosome that
could directly differentiate the brain and/or impact sex differen-
ces in behavior? The advent of a genetically modified mouse in
which the Sry gene is relocated to an autosome allowed for this
question to be asked definitively, by permitting the comparison
of WT animals to XX males (i.e., with testis) and XY females
(i.e., with ovaries). Known as the 4-core-genotype (Arnold and
Burgoyne, 2004), this model is being used to dissect out the rela-
tive influence of gonadal steroid hormones and X or Y chromo-
some genes. For example, certain features of aggressive and
parental behaviors in mice, along with vasopressin immunoreac-
tivity in the lateral septum, were found to be influenced by X or
Y chromosomes, independent of gonadal sex (Gatewood et al.,
2006). Similarly, using this model, susceptibility to an experi-
mental form of autoimmune encephalomyelitis was found to be
greater in XX than XY males (Smith-Bouvier et al., 2008), which
may be pertinent to the gender difference in multiple sclerosis
prevalence. Importantly, effects of X or Y complement are not
necessarily directly attributable to genes on one of those chromo-
somes, as both contain genes with broad epigenetic effects that
can alter expression on the autosomes (Arnold and Chen, 2009).
Expression of genes throughout the genome can now be
quantified using deep sequencing of the transcriptome from tis-
sue samples or even single cells, revealing unprecedented levels
of specification within what were once considered simple phe-
notypes, such as inhibitory versus excitatory neurons. The im-
portance of transcriptional networks, as opposed to single
genes, further expands the complexity of gene regulatory con-
trol and modifiability by hormones and other factors, which
may or may not be sex-specific (McCarthy, 2020). Such
approaches are uncovering highly specific nodes of specialized
cells involved in the control of reproductive and other social
behaviors (Raam and Hong, 2021;Knoedler et al., 2022). From
AB
Figure 2. Adoption and analysis of mixed-sex studies show improvement and room for growth. A, Across biological subdisciplines and in neuroscience (shown here), studies including males
and females in at least one part of the study became significantly more common over a decade. B, Despite progress in female inclusion, there was no increase in the percent of studies that
conducted sex-based analysis. A substantial proportion of studies that did conduct sex-based analyses used inappropriate statistical approaches (Garcia-Sifuentes and Maney, 2021). A,B,Based
on data from Woitowich et al. (2020).
6346 •J. Neurosci., September 13, 2023 •43(37):6344–6356 Eliot et al. ·Sex and Gender in Brain and Behavioral Research

a disease perspective, transcriptomics has revealed vast and
unpredicted patterns of sex-specific gene expression associated
with the same diagnostic phenotype in males and females
(Labonté et al., 2017;Massey et al., 2021). However, a word of
caution is warranted when unbiased transcriptomic analysis is
benchmarked against databases that themselves may be sex-bi-
ased. The five most commonly used omics resources are Gene
Ontology, KEGG, Reactome, Wiki Pathways, and Panther,
from which are derived third-party tools, such as DAVID and
Profiler. The omics resources all provide the citations from
which the original data were drawn, but none of them anno-
tates the data by sex. Given that the bulk of biomedical research
over the last few decades has been conducted on male subjects,
there is a strong potential for embedded bias in the databases
(Bond et al., 2021). Such bias could manifest as greater accuracy
in one sex, missed components in one sex, or frank inaccuracy
in one sex. Independent verification, by PCR or other quantita-
tive means, is the only way at present to be assured that latent
or hidden biases are not the true origin of a sex difference found
using omics databases.
Effects of hormones
Hormones are so closely tied with our conceptualization of sex
that they are included in the very definition (e.g., National
Academies of Sciences, Engineering, and Medicine, 2022). The
hormones most relevant in this context are those produced by
the ovary and testis, such as testosterone and estradiol. In the
past, these hormones have been referred to as “sex steroids”or
“gonadal steroids.”These terms are falling out of use, first
because there is no hormone that itself defines a sex; testoster-
one and estradiol are produced and active in female and male
bodies alike. Further, tissues other than the gonads, such as the
brain, can synthesize these steroids de novo (Azcoitia et al.,
2018). Here, we will call these hormones “steroid hormones”
with the caveat that this label can also be used to refer to corti-
costeroids, sometimes known as stress hormones. We recom-
mend using the specific name of the hormone when possible
and relevant (e.g., “testosterone”) or the more general terms
“androgens”or “estrogens.”
Steroid hormones are powerful modulators of biological
processes at all levels, often independently of other markers of an
animal’ssex(
McCarthy and Konkle, 2005). Nonetheless, because
plasma levels of androgens and estrogens can differ dramatically
between males and females, they are often the default hypothe-
sized mechanism for findings of sex differences (Maney et al.,
2023), particularly when the hypothesizing is done post hoc.
However, steroid hormones are just one of many potential con-
tributors to sex differences. Care should be taken to consider less
obvious factors, some of which are discussed below in “Effects of
age,”“Effects of body size,”and “Effects of environment.”
Prenatal and neonatal hormones
In rodents, many sex differences in the brain and subsequent
behavior arise from differences in prenatal or perinatal exposure
to testosterone and its major metabolite, estradiol (Fig. 3). In both
rats and mice, the fetal testis begins copious androgen production
during late gestation, 3-5d before birth, and continuing through
birth but dropping precipitously thereafter and staying low until
puberty. In females, the ovaries remain quiescent, and there is far
less exposure to steroids developmentally. Much of what we
understand about steroid-mediated sexual differentiation of the
brain comes from the fortuitous fact that newborn female rodents
Figure 3. Unique endocrine exposures in male and female rodents over the lifespan. Directly comparing male and female rodents at a given life stage must take into account underlying dif-
ferences in past or current hormonal milieu and brain maturation. (1) Before birth, males are exposed to relatively high testosterone of their own making, whereas in females, brain exposure
to testosterone, estrogens, and progestins remains low during this time. (2) Females enter puberty sooner than males; and (3) during the reproductive years, females may be cycling, pregnant,
or lactating, all of which are physiological states distinct from the relatively stable and high levels of testosterone in males. (4) Later in life, females spend considerable time in a post-reproduc-
tive state with very low steroid hormone levels. This figure illustrates general hormonal changes across the rodent lifespan, not precise levels, and omits the exposure of males to estrogens
and progestins and females to androgens. Colored ovals in the brains represent hypothetical regions exhibiting sex-specific characteristics during particular life stages or hormonal conditions.
Observations of sex differences are appropriately framed when all of these variables are considered.
Eliot et al. ·Sex and Gender in Brain and Behavioral Research J. Neurosci., September 13, 2023 •43(37):6344–6356 •6347

remain sensitive to the actions of testosterone; if it is administered
exogenously, the process of masculinization is initiated and the
endpoints that are normally achieved in males prenatally can now
be triggered in females postnatally, making them an interesting
model in which to test the effects of androgens across the lifespan
(McCarthy et al., 2018). In humans and nonhuman primates, ste-
roid-mediated brain sexual differentiation begins during the sec-
ond trimester, and this surge is largely complete by birth, greatly
challenging the ability to interrogate mechanisms. Regardless,
some behaviors, along with the sensitivity to steroid hormones in
adulthood, appear to be influenced by this prenatal exposure in
primates (Breedlove, 1994;Wallen and Baum, 2002), making it
an important period in brain sexual differentiation.
Rhythmic changes in hormone production
A central feature of the mammalian endocrine system is that
hormone production varies over time. Many of these endoge-
nous endocrine rhythms occur independently of sex. For exam-
ple, gonadotropin and steroid hormones exhibit circadian
rhythms tied to the sleep-wake cycle in males and females.
Testosterone and cortisol production peak in the A.M. and
decline in the P.M., a circadian pattern that is common to males
and females. In other instances, the frequency, duration, and in-
tensity of these endocrine rhythms can differ by sex. For exam-
ple, ovarian cycles, such as the 4- to 5-d estrous cycle in
rodents and ;28 d menstrual cycle in humans, produce cyclic
changes in circulating estrogens and progesterone. During
pregnancy, serum estrogens and progesterone concentrations
increase ;100-fold and then decline precipitously at parturi-
tion. Each of these neuroendocrine transitions drives physio-
logical changes throughout the body, in some cases including
the brain. For example, in a series of foundational studies in
rats, Woolley et al. (1990) identified changes in hippocampal
spine density in CA1 neurons that are tied to stage of the
estrous cycle, with a ;30% increase in spines during proestrus
(the day of ovulation with estradiol peaks) relative to estrus 24
h later. Similarly rapid effects of steroid hormones on hippo-
campal morphology and the functional connectome have been
observed over the human menstrual cycle (Pritschet et al.,
2020,2021;Taylor et al., 2020), with ultra high-field imaging
recently revealing volumetric increases in hippocampal CA1
during high estradiol/low progesterone phases (Zsido et al.,
2023). During pregnancy, organizational effects of steroid hor-
mones are likely responsible for brain morphologic changes that
have been observed between prepartum and postpartum phases in
women (Hoekzema et al., 2017). Neuroanatomical adaptations are
also evident in new fathers during the transition to parenthood
(Martínez-García et al., 2023), although their endocrine drivers
and time course likely differ from the neuronal changes observed
in mothers.
Effects of age
Depending on species, males and females may develop, mature,
and age at different rates. In humans, the sex difference in lon-
gevity is one of the more robust findings, not seen in many other
mammals (Austad and Fischer, 2016). Although its basis is not
well understood, this longevity gap in humans has a major
impact on neurodegenerative disorders, notably Alzheimer’s dis-
ease, where it largely explains the nearly double number of
women as men (Fig. 1) living with the disease (Fiest et al., 2016;
Buckley et al., 2019). Maturational sex differences are also impor-
tant earlier in life, as girls undergo puberty and reach their peak
brain volume, gray matter density, and cortical surface area 1-
2 years earlier than boys (Kaczkurkin et al., 2019). Such asyn-
chrony is pertinent when comparing adolescent brain or behav-
ioral measures between sexes, since what looks like a sex
difference at a particular age may be better explained by a differ-
ence in pubertal timing. Conversely, when comparing boys and
girls at similar pubertal stages, their mismatch in age may explain
any brain or behavioral differences better than differences in ste-
roid hormone levels (Vijayakumar et al., 2018).
Indeed, age is often an important variable when considering
the effects of hormones. In rodents, marked differences in hor-
monal profiles of males and females occur across the lifespan, be-
ginning before birth, diverging at puberty, and then again after
reproductive senescence or menopause in females. Moreover,
females experience unique hormonal fluctuations (e.g., the estrus
cycle, pregnancy), in addition to the diurnal changes in hormone
production experienced by both males and females. This varia-
tion presents a challenge when one is directly comparing males
and females as they may differ by both maturational status and
hormonal profile (Fig. 3). There is no simple answer to this co-
nundrum, but awareness of what is actually being compared is
essential. For example, an insult to the nervous system in a 35-d-
old mouse or rat will coincide with a prepubertal window in
males but a postpubertal window in females, because of differen-
ces in the timing of the onset of puberty. Likewise, aged males
are likely to have circulating testosterone similar to that of a
younger adult, whereas aged females will have minimal circulat-
ing estrogens or progestins.
In humans, reproductive aging (defined as changes in steroid
hormone production that occur after mid-life) is a major con-
tributor to physiological changes in both men and women. For
men, age-related changes in steroid hormone production are lin-
ear and protracted, gradually declining beginning in the mid-30s.
In contrast, women undergo a more complex transition at mid-
life, marked by high fluctuations in steroid hormone production
during the perimenopausal phase, and culminating in ovarian se-
nescence at the end of menopause, with the median age of com-
plete reproductive senescence at 52.4 years in the United States
(Gold et al., 2013). The menopausal transition results in a sub-
stantial decline in ovarian hormone production: up to 90% for
both estradiol and progesterone. Human studies of the aging
brain should therefore distinguish between the effects of chrono-
logical versus endocrine aging, although many do not. One
approach is to capitalize on variation in the timing of the meno-
pausal transition, matching participants on chronological age
while allowing reproductive stage to vary (Jacobs et al., 2016).
Another approach is to study the effects of pharmacological
manipulations that temporarily and reversibly induce a meno-
pause-like state (Berman et al., 1997).
Effects of body size
In many of the commonly studied mammals, males have larger
bodies than females. While there are many sex differences that
size cannot account for, body weight makes an important con-
tribution to many traits (Wilson et al., 2022). This size differ-
ence is reflected in brain volumes, with differences ranging
from 2.5% larger male brain volume in C57BL/6J mice (Spring
et al., 2007) to 10% larger in Fischer 344 rats (Goerzen et al.,
2020)and11%largerinhumans(Williams et al., 2021). For
studies exploring regional brain differences between males
and females, it is therefore necessary to control for total brain
volume (TBV) or intracranial volume (ICV), which largely
eliminates regional volume differences in humans (Jäncke et
al., 2015;Ritchie et al., 2018). The small (1%-2%) volumetric
6348 •J. Neurosci., September 13, 2023 •43(37):6344–6356 Eliot et al. ·Sex and Gender in Brain and Behavioral Research

sex differences that remain after controlling for TBV or ICV
have proven quite variable and dependent on image process-
ing pipeline and the method of size normalization (Eliot et al.,
2021). Absolute brain size also affects other features of brain
architecture, such as the ratio of white:gray matter and the ratio
of interhemispheric to intrahemispheric connections. Larger
brains need more or thicker white matter pathways to intercon-
nect more distant regions, and interhemispheric traffic grows
inefficient with increases in brain size (Zhang and Sejnowski,
2000). Hence, men’s brains have ;6% higher white:gray matter
ratio (Pintzka et al., 2015;Ritchie et al., 2018) and a lower ratio
of interhemispheric to intrahemispheric connections (Ingalhalikar
et al., 2014)thanwomen’s brains, both of which are attributable
to brain size, not sex per se (Lewis et al., 2009;Hänggi et al.,
2014). In other words, such measures differ between large- and
small-headed men as much as between men and women, so are
unlikely relevant to behavioral or clinical gender differences.
These findings highlight the importance of controlling for indi-
vidual ICV or TBV when conducting any study of individual
brain structural difference, gender-related or otherwise (Jäncke
et al., 2015), using correction factors that may differ for differ-
ent brain compartments (Jäncke, 2018;Williams et al., 2021).
Effects of environment
Still other important, but minimally studied, contributors to
brain sex differences are social and environmental factors.
Although the term “gender”is usually restricted to humans, male
and female animals often inhabit different social spaces and are
faced with different experiences that can alter their brains and
behavior. Such differential experience can begin even before
birth; for example, females’exposure to a male co-twin or adja-
cent male littermate may shift certain features of physiology and
behavior in a male-typical direction, presumably as a result of
prenatal testosterone exposure (Ryan and Vandenbergh, 2002;
Tapp et al., 2011).
After birth, the opportunity for sex- or gender-differentiated
experience is magnified many-fold. Although we tend to focus
on such differential experiences primarily in humans, the social
environment can be highly differentiating in other animals as
well. For example, rat dams treat male and female pups differ-
ently from the first days of life, licking and grooming males
more than females, a difference that acts to shape the neural cir-
cuit underlying male copulatory behavior (Moore, 1992). Since
maternal licking-and-grooming, acting through epigenetic
mechanisms, is also known to influence pups’stress regula-
tion, exploratory behavior, and spatial learning (Champagne
and Curley, 2009), such sex-specific rearing differences could
contribute to brain and behavioral sex differences. In nonhu-
man primates, too, the complex social environment is known
to foster sex differences in behavior; for example, wild chim-
panzees dig for termites with flexible sticks, a useful skill that
females in some locales learn earlier than males, because
young females spend more time watching their mothers “fish”
in this way (Lonsdorf et al., 2004).
One environmental factor that has been widely studied is
stress, which can produce effects that sometimes, but not always,
depend on sex (McCarthy, 2016). For example, Bohaceketal.
(2015) found sex differences in stress-induced hippocampal gene
expression in response to swim stress, but not restraint stress.
Research in humans and nonhuman animals has now docu-
mented the impact of a variety of stressors, occurring at various
times across the lifespan, on the emergence of brain and behav-
ioral sex differences, especially in psychopathology (Bale and
Epperson, 2015). Such findings illustrate the importance of envi-
ronmental context in generating neurobehavioral sex differences,
and may be especially impactful given the different stressors that
are often exerted on male versus female bodies (Hyde and
Mezulis, 2020).
Recommendations for studying sex effects
An updated understanding of sex as a multidimensional con-
struct leads to several recommendations for analyzing its influ-
ence in brain and behavioral studies:
•Clearly operationalize sex using a stated measure and collect
data on that measure.
•Recognize that factors contributing to sex differences
include genes, hormones, developmental stage, and body
size.
•When conducting transcriptomic analyses using so-called
“unbiased”approaches,suchasRNA-Seq,beawareof
potential hidden bias in the publicly available omics data-
bases and independently verify differences you consider
important.
•Avoid the term “sex hormones”since the actions of these
agents are not limited by sex.
•Acknowledge and assess, when possible, the broad environ-
mental factors that can lead to differences between and
among males and females, including intrauterine exposures,
parental care, social grouping, stressors, and other experien-
ces across the lifespan.
Understanding Gender Effects
This brings us to a preeminent part of human experience that
has thus far been largely ignored in our effort to understand
male-female brain differences: the impact of gender. As defined
above, gender is a complex psychosocial construct that can have
profound effects on experiences and, therefore, the brain. In
some cases, gender acts independently of sex, and in others, sex
and gender interact over the lifespan (Krieger, 2003;Springer et
al., 2012;Hyde et al., 2019).
Researchers outside of neuroscience have found that sex
and gender can have independent effects on health and can
do so even in cases where there is no obvious difference
between women and men. For example, among younger patients
diagnosed with acute coronary syndrome, Pelletier et al. (2016)
observed no difference in the risk of recurrence between women
and men, but instead found that patients with a higher “feminin-
ity score”were more likely to experience a recurrence of acute
coronary syndrome, regardless of their sex.
Operationalizing gender as separate from sex shows promise
in brain imaging studies as well. In a recent review, Rauch and
Eliot (2022) identified eight neuroimaging studies that have
assessed brain parameters using gender as an independent vari-
able. While still preliminary, this work provides evidence that
gender attributes can explain some of the individual variance in
regional brain structure and functional circuitry, apart from or
in addition to binary sex effects.
Despite these encouraging efforts, research using gender as a
variable of interest is still in its infancy, in part because of the
inadequacy of existing instruments for its operationalization.
Existing measures largely fail to capture gender’s nonbinary,
multidimensional nature (Horstmann et al., 2022). Research
that operationalizes gender as either one-dimensional (a bipolar
continuum that ranges from “masculine”to “feminine”)or
two-dimensional (with masculinity and femininity as distinct
Eliot et al. ·Sex and Gender in Brain and Behavioral Research J. Neurosci., September 13, 2023 •43(37):6344–6356 •6349

spectra) cannot identify the specific gendered factors that affect
health or the brain (Nielsen et al., 2021;Horstmann et al.,
2022). Moreover, such polar conceptualizations of gender often
exclude the experiences of trans, nonbinary, and genderqueer
individuals (Hyde et al., 2019;Miani et al., 2021).
In addition to being nonbinary, gender operates on many
scales: from individuals’gender identities, to the structure of
labor markets, to expectations around caregiving work, to expe-
riences of discrimination (Bolte et al., 2021;Miani et al., 2021).
While these different dimensions often correlate with one
another, any single dimension (e.g., gender identity) may be a
poor proxy for any other dimension (e.g., the number of hours
spent caring for a family member). Gender remains a primary cat-
egory of social organization, but any individual may exhibit a
unique constellation of such factors, which also intersect with
other axes of social inequality in ways that can affect brain health.
To address these problems and create a better tool for opera-
tionalizing gender, an international team recently developed a
new instrument, called the Stanford Gender-related Variables for
Health Research (GVHR) (Nielsen et al., 2021). The GVHR con-
ceptualizes gender across three domains: gender norms, gender-
related traits, and gender relations. Gender norms are spoken
and unspoken rules enforced through social institutions, such as
the workplace, family, and broader culture. Gender-related traits
refer to individuals’behaviors and tendencies, which may or may
not conform to their gender norms. Gender relations refer to the
social relationships between individuals, whose own gender iden-
tities and related traits operate within a system of gender norms.
Gender norms, traits, and relations are culture- and context-spe-
cific and change over time.
The GVHR is a questionnaire of 25 items that measure seven
gender-related variables across these three domains. The varia-
bles are as follows: caregiver strain and work strain (gender
norms); independence, risk-taking, and emotional intelligence
(gender-related traits); and social support and discrimination
(gender relations). The questionnaire was validated in three
online samples totaling .4000 diverse respondents.
The GVHR has several advantages for neuroscience researchers.
First, the scales make no assumptions about whether traits are
“masculine”or “feminine,”as such labels are often normative and
imprecise. Second, each of the variables is scored separately, rather
than being collapsed into a single gender metric. This scoring sys-
tem improves the precision of the measure, gives researchers the op-
portunity to select variables that are most relevant to their scientific
question, and makes it appropriate for use with nonbinary and trans
populations. The GVHR does not replace the independent variable
of gender identity (e.g., woman, man, genderqueer, genderfluid,
etc.), but may be used in combination with it to better characterize
participants within the gender space. Using the GVHR or similar
instruments in combination with large-scale neuroimaging should
enhance our understanding of the neural basis of gender-related
behaviors and psychopathologies.
Recommendations for studying gender effects
Growing awareness of gender as a multidimensional variable
with biomedical impact should broaden the way neuroscientists
study the human brain and behavior, including the following:
•Whenever possible in human studies, operationalize gender
as a variable separate from sex.
•Appreciate the key components of gender beyond identity
that may impact brain health and incorporate such measures
as appropriate.
Analytic Approaches
Whether one is studying animals or humans, accounting for sex
or gender adds complexity to statistical analyses. In this section,
we address issues of sample size and analytical approaches. In
alignment with the existing SABV policy, we focus on analyses of
binary group differences, recognizing that more complex statisti-
cal models are needed for analyses that use continuous measures
of sex or gender, such as the GVHR, as the independent variable.
Design considerations
Mere inclusion of females and males is not enough to provide
insight into the impact of sex/gender on neurobehavioral phe-
nomena. Having rejected concerns that females are more vari-
able than males (Prendergast et al., 2014;Becker et al., 2016), we
must still consider the possibility of differences between males
and females and their potential influence on other findings.
When sex-inclusive data are analyzed without sex as a factor,
sex differences have the potential to increase the variability of the
sample as a whole. Fortunately, inclusion of sex as a factor in
analysis recovers statistical power in many instances, whether sex
differences are present or absent (Fig. 4)(seealsoPhillips et al.,
2023). Thus, to harvest the “low hanging fruit,”researchers
should analyze data from males and females with sex (formally
operationalized) as a factor in the analysis and appropriately bal-
ance sample sizes. Of note, some studies treat sex/gender as a
nuisance variable in their models, accounting or “controlling”
for it without noting possible effects (Beltz et al., 2019). Whether
or not sex contributes to the final results, the presence or absence
of sex differences and interactions between sex and treatment
should be reported, descriptive data should be disaggregated and
reported by sex, and consideration should be given to the statisti-
cal power of the dataset to detect sex differences.
Exploratory versus confirmatory studies
In their guidance on how to incorporate SABV into
research designs and analysis, NIH distinguishes between
studies “intended”and “not intended”to detect sex differ-
ences (National Institutes of Health, 2015). In the former
scenario, testing for sex differences is an a priori, stated goal of
the study and included among its hypotheses. This sort of
research, which we will call “confirmatory”(as opposed to “ex-
ploratory,”see below), must be powered to test hypotheses
about sex. Depending on the question, the sexes might simply
be compared with each and no other factors, such as a drug
treatment, need be considered. In most cases, however, authors
may be testing the extent to which males and females respond
differently to a treatment or exposure. For such “factorial”
designs, one can test for effects of one factor (e.g., treatment)
and effects of another factor (e.g., sex) in the same statistical
model. These “main effects”can indicate whether the treatment
had an effect overall, regardless of sex (Fig. 4A) and whether
there was a sex difference in the outcome measure regardless of
treatment (Fig. 4B). However, a main effect of sex does not
indicate that females and males responded differently to the
treatment. For example, in a study examining the effect of an
intervention on stress hormone levels, a main effect of sex
would indicate a significant sex difference in stress hormone
levels but does not provide evidence that males and females
responded differently to the intervention.
For studies with factorial designs, testing whether males and
females responded differently to a drug, intervention, or exposure
canbedonebyincludinganinteractionterminthestatistical
6350 •J. Neurosci., September 13, 2023 •43(37):6344–6356 Eliot et al. ·Sex and Gender in Brain and Behavioral Research

model, for example, sex treatment (Fig. 4C). A significant inter-
action would indicate that males and females did indeed respond
differently to the treatment. Such interactions can require substan-
tial power to detect. Thus, studies intended to reveal “sex-specific”
effects of another factor typically require a larger sample size than
is required to detect the factor’s effect when the sexes are pooled
into a single sample. Other authors have provided more detailed
guidance on determining sample sizes when the goal is to detect a
sex difference in the effect of another factor (e.g., Buch et al., 2019;
Galea et al., 2020;Phillips et al., 2023).
Sometimes, researchers are not focused on sex differences:
they are including males and females to increase the generaliz-
ability of their findings, or to make their work more inclusive, or
simply to comply with the SABV policy. In these “exploratory”
studies, per NIH guidance, researchers need not increase the
sample size beyond that necessary to detect the main effects of
other variables of interest, such as exposure, time, or treatment
(National Institutes of Health, 2020). Most main effects that
can be detected with a certain number of men or males, for
example, should theoretically be detectable using a similarly
sized sample of mixed sex or gender (Buch et al., 2019;
National Institutes of Health, 2020;Phillips et al., 2023).
Notably, NIH expects researchers to consider sex even in ex-
ploratory studies that are not powered to compare the sexes
(National Institutes of Health 2015,2020;Clayton and
Tannenbaum, 2016;Cornelison and Clayton, 2017;Clayton,
2018). However, the latter practice can potentially lead to inac-
curate pronouncements of sex or gender differences (false
Baseline Treated
48
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Females
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t-test
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t-test
12 males
t-test
6f, 6m
2-way ANOVA
6f, 6m (trx effect)
distribution of p−values
2-way ANOVA
sex*trx interaction
2-way ANOVA
sex*trx interaction
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t-test
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distribution of p−values
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t-test
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t-test
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t-test
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sex*trx interaction
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Figure 4. Example data scenarios and impact of analysis as single-sex samples (only females or only males tested), pooled mixed-sex samples, or factorial analysis of mixed-sex samples (two-way
ANOVA assessing effects of sex, treatment, and their interaction). Left panels, Mean and SD of each group (12 females or males in single-sex studies, or 6 males and 6 females in mixed-sex groups).
Effect sizes in each sex were matched (Cohen’sd¼1.0) and 10,000 simulated groups of each type were generated for comparisons. Right panels, Statistical consequences of different analysis paths as vio-
lin plots of pvalues generated for each test across 10,000 samples. Distributions weighted closer to zero (e.g., red distributions) yielded “significant”treatment differences in more simulated comparisons.
Red represents effect of treatment across all-female comparisons. Blue represents effect of treatment in all-male comparisons. Purple represents effect of treatment when males and females are pooled
and sample size is not increased. Gray represents treatment and sex interaction effects in a two-way ANOVA. A, Pooling has no impact in the absence of sex differences, but (B) obscures the treatment
effect in the presence of a main effect of sex, or (C) when there is an interaction between sex and treatment (i.e., the treatment affected the sexes differently). Factorial analysis rescues the treatment sig-
nal when there is a main effect of sex (B), and reveals a potential sex treatment interaction (C). If the sample size is not increased for mixed-sex exploratory studies, a finding such as Cwould call for
a future follow-up with a larger sample of males and females, and potential future studies of mechanisms in each sex. Redrawn from and based on simulations performed by Beery (2018).
Eliot et al. ·Sex and Gender in Brain and Behavioral Research J. Neurosci., September 13, 2023 •43(37):6344–6356 •6351

positives); and conversely, real sex or gender differences may
not be detectable with small sample sizes, leading to the report-
ing of false negatives. In the next sections, we discuss some
common mistakes and how to avoid them.
How to test for a sex-specific effect
NIH recommends that researchers analyze and report data
separately for males and females (Clayton and Tannenbaum,
2016;Cornelison and Clayton, 2017;Clayton, 2018;National
Institutes of Health, 2020). Many journals offer similar guid-
ance (Heidari et al., 2016). Although this reporting is important
to promote transparency and future meta-analyses, the analysis
of disaggregated data can lead to a well-known analytical error.
If two independent samples are analyzed separately and a sig-
nificant effect is found in one but not the other (e.g., p¼0.02
vs. 0.20), such a result does not constitute evidence that the
effect in one sample is reliably larger or smaller than in the
other (Gelman and Stern, 2006;Sainani, 2010;Nieuwenhuis et
al., 2011;Makin and de Xivry, 2019). The problem is that this
approach does not test whether an effect in one group is stronger
than in another. To show that an effect is significantly different
between two sexes, we must directly compare the two effects by
testing for an interaction between the factor in question and sex
(Gelman and Stern, 2006;Sainani, 2010;Nieuwenhuis et al.,
2011;Bland and Altman, 2015;George et al., 2016;Makin and de
Xivry, 2019;Vorland, 2021;Vorland et al., 2021).
Failing to test formally for sex-specific effects is so rampant an
error that it has earned its own acronym: the Difference in Sex-
Specific Significance (DISS) error (Maney and Rich-Edwards,
2023). DISS errors most often result in Type I errors (Bland and
Altman, 2015), biasing researchers toward positive findings. But
DISS approaches can also lead to Type II errors, or false negatives,
where sex-specific effects are missed (Vorland et al., 2023). Both
types of errors hamper reproducibility.
When assessing sex-specific effects, it is therefore critical to test
for interactions between sex and other factors of interest (e.g., Fig. 4,
gray plots) and not simply compare the statistical significance of that
effect within each sex. Testing for interactions will increase power to
detect effects of the other factors (because all of the samples can be
considered), while at the same time ensuring that exploratory testing
for potential sex differences is done in a statistically valid way.
This rigorous approach is especially important in studies not pow-
ered to detect small- or medium-sized interactions, since the false
positive rate of the DISS error can reach 50% in such cases (Allison
et al., 2016). If the interaction with sex is significant, then one can
follow-up with post hoc tests within each sex. If the interaction is not
significant, however, the post hoc tests are not warranted and should
not be performed; authors should instead report that there is no evi-
dence that the sexes responded differently, with the caveat that the
study may be underpowered. In these cases, it may be appropriate to
call for follow-up studies to test for sex-specific effects of the treat-
ment or exposure.
Before statistically comparing the sexes, it is important
to discern whether the sexes are actually in a comparable
state. In the “Effects of age”sectionabove,wepointedout
that same-age males and females can be at very different
stages of pubertal development or reproductive senescence.
In such cases, when sex is strongly confounded with
another relevant variable, it may be appropriate to test
hypotheses only within-sex rather than including males and
females in the same statistical model. This approach does
not compare the sexes with each other; thus, no claim can
be made about sex difference per se.
Regardless of the analytical approach, whenever mixed-sex
studies are performed, it is imperative to make all raw data, dis-
aggregated by sex, available for future investigations. This can be
done either in a main paper or in its Supplemental Material, and
will facilitate subsequent meta-analyses and help avoid “file
drawer”omissions (Rosenthal, 1979).
Recommendations
To comply with sex/gender inclusion guidelines and avoid com-
mon statistical errors, we offer the following recommendations:
•Balance female and male sample sizes as appropriate for the
research question (except in cases when single-sex studies
can be justified).
•Analyze results with sex/gender as an explicit factor in the
statistical model.
•Report all statistics relevant to sex comparisons, including
main effects and interactions.
•For exploratory, underpowered studies, acknowledge the
limitations of the approach without relaxing statistical rigor.
•Regardless of the outcome of sex comparison, publish the
sex-disaggregated data for each sample either in the main
paper or supplemental material.
Interpretation and Communication
Because the framing of male/female brain differences has broad
impact on issues related to gender equity, health disparities, and
the rights of sexual and gender minorities, we end with guidance
on the interpretation and communication of such differences.
Neuroscientific findings hold great appeal in the public imagina-
tion, particularly as they relate to differences between men and
women or boys and girls. But too often, complex and unrepli-
cated findings about male/female brain differences are simplified
and overhyped by popular media, propagating stereotypes and
distorting the real science they are purporting to convey (Eliot,
2011;Maney, 2014,2016;Rippon et al., 2021). When it comes to
research on sex and gender difference, neuroscientists should
therefore pay closer attention to: (1) negative findings, which of-
ten do not get mentioned in studies including both males and
females; (2) the actual magnitude (both raw measure and effect
size) of group-level sex/gender differences, as opposed to mere
statistical significance; (3) the practical relevance of such findings
to biomedical treatment or policy-making; and (4) the multifac-
torial determinants (e.g., genetic, morphologic, psychological,
social) that may underlie such differences, before attempting
to speculate on their clinical or behavioral importance. Just
as the National Academies of Sciences, Engineering, and
Medicine (2023) recently warned against “typological think-
ing”about race and ancestry in the dissemination of genetics
and genomics research, sex/gender difference researchers
should be cautious about interpreting group-level differences
as fixed and attributable to “biological”processes, especially
when the underlying mechanisms are largely unknown.
As a baseline, neuroscientists are advised to avoid the term
“dimorphism”to describe statistical differences between male
and female brains (McCarthy et al., 2012), which rarely satisfy
the proper definition of “two forms.”The term “dimorphism”
refers to two distinct types, or distinct structures present in
only one, but not the other sex. To date, dimorphic structures
have not been found in the central nervous systems of verte-
brates, with the possible exceptions of striatal Area X in some
songbirds (Nottebohm and Arnold, 1976) and the bulbocaver-
nosus spinal motor nucleus in some mammals (Breedlove and
6352 •J. Neurosci., September 13, 2023 •43(37):6344–6356 Eliot et al. ·Sex and Gender in Brain and Behavioral Research

Arnold, 1981). Like racial categorization, the term “dimor-
phism”promotes typological or essentialist thinking, whereas
most brain features are more appropriately described as
“monomorphic”across the sexes of humans and common ex-
perimental animals (Eliot et al., 2021). Indeed, for most meas-
ures of mammalian brains and behavior, there is considerable
overlap between groups, such that the sex of any individual
cannot be identified on the basis of that measure alone.
Similarly, a sex difference requiring hundreds of individuals to
detect cannot be used to predict the best course of treatment for
any single individual (Fig. 5).
As the foregoing discussion makes clear, sex and gender are
pertinent to many issues concerning the brain and behavior.
However, sex and gender themselves comprise many compo-
nents and covariates that account for the differences between
groups of males and females or variance across all genders
(Richardson, 2022). Thus, precise operationalization and atten-
tion to confounds is key, and as researchers increasingly incorpo-
rate comparisons of males and females, it is important to note
this nuance and evaluate these confounds accordingly. For
example, if a certain drug induces more side effects in women
than men, should the dosing be adjusted by sex or by body
weight, fat-free mass, circulating steroid hormone levels, or some
other continuous physiological variable that may better explain
variation (Özdemir et al., 2022)? Similarly, when differences are
found in neuropsychiatric disease risk, might they be explained
by covariates of gender or sex, such as caregiver stress or occupa-
tional attainment (Mielke et al., 2018)? These questions are chal-
lenging to answer, but vital to address for any drug, treatment, or
condition that shows group-level differences between men and
women, or boys and girls.
Finally, it is important for anyone commenting on brain
sex/gender differences to be mindful of the sexist history that
hasprefacedresearchonthistopic.Acrossthemanycentu-
ries that philosophers and scientists have been addressing
brain sex/gender differences, their search has largely focused
on the neural basis of women’s presumed intellectual and creative
inferiority (Schiebinger, 1993;Saini, 2017). Although recent peer-
reviewed literature may contain less overt misogyny, the question of
brain sex/gender differences is often still posed within a stereotyped
frame, for example, referring to men’spropensityfor“action”versus
women’s“intuition”(Ingalhalikar et al., 2014). These assumptions
demonstrably influence public understanding of gender and rein-
force benevolent sexism (O’Connor and Joffe, 2014). Most of this
concern is focused on human studies, but as preclinical studies are
funded on the basis of their relevance to human health, such consid-
erations are also important when translating findings from nonhu-
man animals.
As part of an antisexist framework, there is also a renewed
call to look beyond sex differences, to study key aspects of the
human condition largely ignored by prior generations of
research. Neuroscientists know little of how menopause, preg-
nancy, the menstrual cycle, and hormone-based medications
influence the brain (Taylor et al., 2021). Even less of the research
on these topics has addressed people across diverse ethnic, socio-
economic, and geographic origins (Petersen et al., 2023). These
intersectional blind spots have limited progress on human health
generally, and brain health in particular (Taylor et al., 2019).
Thus, special caution applies to the interpretation of neuro-
scientific findings based on the predominantly White and (pre-
sumed) cisgender populations that comprise most of the large and
highly used brain/behavioral databases. Available bodies of knowl-
edge largely neglect trans and gender-diverse populations, for
example. As we seek to understand the causes of sex/gender behav-
ioral and mental health disparities, it is important to ask, “which
men?”and “which women?”and to appreciate that sex and gender
lie within an intersectional framework of social identities, all of
which interact to shape neural function and behavior (Duchesne
and Kaiser Trujillo, 2021). Merely averaging brain measures in one
population, no matter how large, may not lead to a better under-
standing of average male-female behavioral differences and risks
endorsing essentialist interpretations that are already the
default among many clinicians and the lay public. Little atten-
tion has been paid thus far to cross-cultural or ethnic variation
in gender expression, to understanding the development and
environmental modulation of gender expression, or—more
broadly—to understanding the neural basis of gender in any
nonbinary way (Rauch and Eliot, 2022). The danger is espe-
cially great at present, as legislative bodies increasingly co-opt
biomedical findings to define individual rights based on “genet-
ics,”a“person’s chromosomes,”or “anatomy”as assessed at
birth. By embracing, rather than oversimplifying, the nuance in
sex- and gender-related neurobehavioral data, neuroscientists
Figure 5. Importance of considering variance within sex/gender and overlap between
them. A, Hypothetical raw data for the clearance of a drug. B, Means and SEM in men and
women. A t-test produces a pvalue of 0.01, meaning that this is a significant sex difference.
The effect size, depicted as Cohen’sd(C), is 0.60, a medium-sized effect that warrants atten-
tion (Klein et al., 2015). But despite the low pvalue and compelling effect size, closer inspec-
tion reveals that there are about the same number of men and women above and below
the mean (22 men and 18 women above the dotted line, and 18 men and 22 women
below). The overlap between the sexes, depicted as Weitzman’s
d
,is76%.Ifwewereto
guess the sex of one of these participants knowing only their clearance rate, our accuracy
would be little better than 50%. Further, (D) one-third of the men have a clearance rate
lower than the average woman and 30% of the women are clearing the drug faster than
the average man (E). Sex-specific dosing of this drug, therefore, might benefit patients in
the tails of the distribution, but most patients could get a suboptimal dose. For the dataset
and guidance on how to create graphs like C,seeManey (2016).
Eliot et al. ·Sex and Gender in Brain and Behavioral Research J. Neurosci., September 13, 2023 •43(37):6344–6356 •6353

have the opportunity to challenge such essentialist interpreta-
tions that are presently aimed at restricting the rights of sexual
and gender minorities (Sudai et al., 2022).
Recommendations
As general guidelines for the reporting and interpretation of sex-
and gender-related neurobehavioral data, we recommend that
neuroscientists:
•Report negative as well as positive findings.
•Consider effect sizes and practical relevance of sex/gender
differences.
•Consider environmental contributions to sex/gender differences,
even when the outcome measures are physiological in nature.
•Remain mindful of researcher bias and the sexist history of
sex/gender difference research.
•Address the degree to which the study population is repre-
sentative of human diversity.
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