Are There Sex Differences in the Brain?
This essay explores how sex differences in the brain manifest in unexpected ways, helping readers to approach future literature with a nuanced perspective.
Illustration by Cynthia (@PTElephant).
For decades now, scientists have been fascinated with understanding how sex, and its physiological correlates – namely hormones and gene expression, exert their effects on both brain and behaviour.
A rudimentary search on PubMed with the key terms “sex differences” and “brain” returns a staggering 32,476 results, spanning all the way back from the 1950s through to the present day. Notably, a peak was achieved in 2021 with 2,590 papers gracing the scientific literature (see Figure 1). Even by 2017, with a total of 1,772 papers released, the Journal of Neuroscience Research dedicated an entire issue to sex differences, titled “An Issue Whose Time Has Come: Sex/Gender Influences on Nervous System Function” (the first of its kind).
This large body of research highlights the ever-growing recognition of the influence that biological sex exerts on the brain.
Figure 1. The number of publication per year with the key terms “sex differences” and “brain.”
While the growing recognition of sex differences is embraced by many scientists, it remains a topic of considerable controversy (and probably will for a long time). For some, the concern is not merely whether disparities exist (on average) between men and women, but rather the potential harm these differences pose to women – the major fear being the use of these differences to justify sexist stereotypes. Others focus on the origins of these differences, questioning whether they are primarily biological in origin or if they arise from societal pressures shaping individuals to conform to behavioural patterns expected from one’s sex. Some individuals go so far as to outright deny the existence of sex differences, asserting that we are born as “blank slates.” It might be assumed that most of these disparities, especially within the general population, stem from political or personal held beliefs. Nevertheless, similar discrepancies and arguments persist within academia.
Numerous large-scale reviews, journal articles, blog posts, and opinion pieces offer varying conclusions, ostensibly grounded in “the data”, regarding the existence of sex differences in the human brain. However, a significant number of these contributions appear to lack nuance, or, in some cases, misrepresent and misunderstand the existing literature. This brief essay aims to inject a different and nuanced perspective into the on-going discussion and seeks to identify a middle ground that addresses the fundamental question: Are there sex differences in the human brain? Specifically, this essay tries to consider what causes variation within the literature and how we can interpret this variation in understanding whether biological sex influences the brain.
Many studies that investigate human sex differences in vivo often rely on the analysis of the brains grey matter (the outer-most layer of brain tissue consisting of neuronal cell bodies responsible for mental functions) [Luders et al. 2005; Allen et al. 2003; Nopoulos et al. 2000]. This is commonly done by comparing regional grey matter volume (GMV) between men and women, a composite measure that includes key structural parameters such as cortical thickness (CTh) and surface area (SA). CTh is tied to the number of neurons, synapses, and glial cells, whereas SA is predominantly shaped by the folding and convolutions of the cerebral cortex, reflecting its degree of gyrification (see Figure 2). While CTh and SA can be influenced by overall brain volume, they are largely independent of each other (Winkler et al. 2010). Importantly, only GMV can be used to compare sex differences in sub-cortical structures.
Figure 2. Schematic diagram of the 3 major parameters of cerebral grey matter taken from Bethlehem et al., 2022.
While regional GMV is a valuable metric for understanding overall grey matter differences, it is crucial to recognise that a lack of sex difference in GMV for a particular region does not imply an absence of sex differences in that area.
To illustrate, consider the following scenario: a brain region may show female-bias in CTh but male-bias in SA. This seemingly cancels out at the broader level of GMV, suggesting no apparent sex difference. An example of this phenomenon can be observed in an analysis of the parietal lobe (Salinas et al. 2012), a structure involved in sensory processing, body awareness, self-related processes, and visuospatial abilities (Cavanna & Trimble, 2006; Spreng, Mar, & Kim 2009; Davey, Pujol, & Harrison 2016; Harris et al. 2000; Klingberg 2006; Culham & Kanwisher 2001). In the examined sample (n=1,733) [Salinas et al. 2012], no discernible sex difference in regional GMV was evident in any parietal lobe structure during adolescence. However, a noteworthy finding emerged in adulthood, revealing significantly greater SA in the parietal lobe in males (Salinas et al. 2012). Conversely, a distinct study unveiled a consistent female-bias in CTh across the entire parietal lobe, persisting even after accounting for inter-individual variation in brain size (Luders et al. 2006). In fact, many studies have converged on greater relative grey matter in the parietal lobe in women compared to men (on average) [Nopoulos et al. 2000; Luders et al. 2006; Im et al. 2006; Sowell et al. 2007; Liu et al. 2020].
One of the visuospatial abilities linked to the parietal lobe is mental rotation (Alivisatos & Petrides 1997; Roberts & Bell 2000; Seurinck et al. 2004), in which increasing task difficulty leads to a significant increase in parietal lobe activation (Tagaris et al. 1996). Sex disparities in mental rotation abilities are well-documented, typically revealing males to outperform females (on average) [Collins & Kimura 1997; Maeda & Yoon 2013]. This trend emerges early in infants and is influenced by both prenatal and environmental factors (Linn & Petersen 1985; Moore & Johnson 2008; Falter, Arroyo, & Davis 2006; Puts et al. 2010; Baenninger & Newcombe 1989; Gold et al. 2018). Interestingly, it has been reported that greater GMV in the parietal lobe in females is disadvantageous in terms of mental rotation performance (r = -0.36), whereas greater SA in males is correlated a performance advantage (r = 0.42) [Koscik et al. 2009]. Further, activation of the left parietal lobe, assessed via an electroencephalogram (EEG), is greater in males than females undergoing a 2D mental rotation task (Roberts & Bell 2000). Sex differences in parietal activation have even been demonstrated in males and females who do not differ in mental rotation abilities (Jordan et al. 2002), perhaps reflecting differences in the strategies employed by men and women to perform the same spatial activities (Pezaris & Casey 1991). The above data demonstrate how sex differences in brain structure/function may contribute to sex differences in behaviour/cognition (on average).
One important variable to remember when comparing studies on sex differences in the human brain is age. The brain undergoes many dynamic/temporal changes throughout life that will undoubtedly impact sex differences, such as those in adolescent development and even the rate of atrophy in older life (Guo et al. 2016; Lenroot et al. 2007; Kurth, Gaser, & Luders 2021).
As a demonstration of the dynamic and temporal relationship between sex and brain structure, consider data from Raznahan and colleagues (2010) [Raznahan et al. 2010]. This study involved extensive brain scans of both male and female participants, commencing at the age of 9 and extending through to 24 years. At the initial age of 9, males exhibited greater CTh all over the frontal lobe (a brain region involved in higher order executive functions) compared to females. Interestingly, this pattern underwent a reversal over the ensuing years, with older females demonstrating increased CTh in those same frontal areas. How this temporal change in directional sex-bias of the frontal lobe contributes to behavioural or cognitive differences currently remains unknown.
Another essential aspect to consider when trying to understand whether sex differences exist within the brain is the inherent limitations of human research. Unlike rodents, we cannot image the human brain in the same detailed manner, adding complexity to our interpretation. Looking at studies on the hippocampus in rats reveals intriguing insights. Although no sex difference in hippocampal GMV is observed between male and female rodents, a stark sexual dimorphism emerges in some of its intracellular signalling pathways (Huang & Woolley 2012; Tabatadze et al. 2015). In females, estrogen (E2), acting through E2 receptor alpha (ERα), increases the synthesis of endocannabinoids, subsequently reducing inhibitory signalling from pre-synaptic neurons (Huang & Woolley 2012; Tabatadze et al. 2015). Strikingly, this effect is entirely absent in male rodents (Huang & Woolley 2012; Tabatadze et al. 2015).
While the applicability of these finding to humans remains uncertain, it underscores the importance of contemplating when an apparent lack of sex difference in GMV may indeed manifest sexual dimorphism. For instance, a recent meta-analysis investigating the impact of sex on GMV of the hippocampus revealed no sex difference (Tan et al. 2016), but does that really suggest no sex difference in other domains? Aside from what has been mentioned thus far, you could also consider sex differences in hippocampal functional connectivity, its sub-regions (CA1, CA3, dentate gyrus, etc), electrical activity, structural connections with other brain regions, lateralization, and even task-related performance.
The Lise Eliot Review
In a now-famous meta-synthesis review of structural magnetic resonance imaging (sMRI) studies, titled - Dump the dimorphism: Comprehensive synthesis of human brain studies reveals few male-female differences beyond size, Lise Eliot and colleagues (2021) suggested that sex contributes to less than 1% of the variance in neuroanatomy between men and women, attributing any observed difference to the overall brain size disparity (with males having an average 10-13% larger brain). They examined essential neurological parameters, including GMV, SA, CTh, and sub-cortical volumes, ultimately concluding that excessive heterogeneity prevails across the literature (600+ studies).
While the magnitude of data amassed in their meta-synthesis review is impressive, their employed methodology itself is liable to other confounding sources of variance. Critical factors such as variation in study design, discrepancies in imaging technique (including type of scanner), participant age, sample size, pipeline pre-processing steps, brain size-correction method, and overall workflow are frequently overlooked (DeCasien et al. 2022), perhaps misleading the authors to conclude that the large heterogeneity in the literature means there are no apparent sex differences in the human brain.
The analysis of sMRI data often involves Voxel-based Morphometry (VBM) – a technique that subdivides brain images into three-dimensional units known as voxels, facilitating the comparison of regional GMV between groups (Ashburner & Friston 2000; Whitwell 2009). However, researchers often leverage distinct software packages to automate and streamline their analysis (Zhou et al. 2022). These pipeline procedures encompass essential steps such as pre-processing sMRI images to correct for distortions, normalisation to spatially align individual images into standardised anatomical space and smoothing to enhance the signal-to-noise ratio (Zhou et al. 2022). Commonly used packages include CAT, FSLVBM, and FSLANAT. In the context of variability, an analysis of the exact same sMRI scans from a sample of men and women (n=200; females=100) revealed a neuroanatomical spatial overlap of just 10.98% between the aforementioned packages (when using parametric tests) (Zhou et al. 2022). Even within the two FSL pipelines, the spatial overlap does not exceed 13.16%. It is important to note that this variance may be more pronounced in smaller, and thus underpowered studies.
While not all pipeline procedures were analysed in the above study, this discrepancy highlights the substantial impact of software choice on neuroimaging outcomes. It emphasizes the need to consider such methodological nuances in sex differences research, a consideration that Lise Eliot’s review fails to address (instead insisting that the variance is the result of a lack of sex differences).
Given the significant variability stemming from differences in pipeline procedures and the acknowledged heterogeneity in reported sex differences by Lise Eliot, a pertinent question arises: If we adopt a consistent procedure with a sufficiently large sample, are there consistent spatial patterns between men and women (no matter how small)?
Addressing this inquiry, Lui et al. (2020) utilised sMRI data from the Human Connectome Project (HCP) to explore regional GMV differences between males (n=488) and females (n=488). The analysis controlled for age, total GMV (indicative of brain size), and employed adjustments for multiple comparisons. The findings revealed regions where GMV in females exceeded that of males (F>M), encompassing frontal, parietal, and temporal areas. This replicated previous studies that found more grey matter and a higher density of neurons in language-associated areas of temporal lobe (superior temporal cortex) in females (Witelson, Glezer, & Kigar 1995; Harasty et al. 1997), in agreement with behavioural findings of a female advantage in verbal fluency/memory (Hirnstein et al. 2023). Conversely, regions with greater GMV in males (M>F) were predominantly sub-cortical, involving the hypothalamus, bed nucleus of the stria terminalis (BNST), and amygdala.
To assess spatial reproducibility, the authors conducted a rigorous analysis on 1,000 independent split-halves of the HCP, yielding a mean spatial correlation of r=0.86 (ranging from 0.75-0.9). Furthermore, for additional validation, they examined the spatial distribution of sex differences in a non-overlapping cohort from a separate database, the UK-Biobank (560 males, 560 females), resulting in a between-dataset spatial correlation of r=0.85. This paper, characterised by its clever and meaningful methodology, emphasizes the presence of small (Cohens d = 0.1-0.3), but highly reproducible sex differences in the human brain on the population level. Notably, while Lise Eliot cited this study in her review, the methodology and its significance were not explicitly addressed, raising concerns about equal weighting of results without considering the nuances or giving due weight to studies, a notable critique of Eliot’s review.
Other recent largescale neuroimaging studies have come to similar conclusions (Ritchie et al. 2018; Lotze et al. 2019). The first, by Ritchie and colleagues (2018), examined data from a large cohort from the UK-Biobank (female = 2750, male = 2466) and investigated various neurological parameters.
Raw GMV’s and SA were greater in males in the majority of the cortex, whereas females showed greater raw CTh. After adjustment for total brain volume, females showed slightly greater overall GMV compared to males in 13 regions, with the largest difference (Cohens d = 0.21) in the right superior parietal gyrus (mean effect size = 0.15). Males instead showed greater GMV in 11 regions, the largest being in the left isthmus cingulate (d = 0.22). In relation to CTh, a similar effect was observed with greater CTh in females in 24/68 regions analysed, with the left inferior parietal regions (among other) showing the most substantial difference (Cohens d = 0.32). In contrast, 25 regions demonstrated male bias, with the right insula demonstrating the largest difference (Cohens d = -0.34). The second paper, by Lotze et al., 2019 (also cited by Lise Eliot) examined a sample of 2,838 participants from the Study of Health in Pomerania (SHIP) who found that, after brain size correction, females had greater GMV in the inferior/middle frontal gyri, frontal pole, dorsolateral prefrontal cortex, among others. In contrast, males demonstrated greater GMV in the parahippocampal gyrus and hippocampus, amygdala, putamen, fusiform gyrus, among others.
In a similar fashion to the Lui et al. (2020) study, DeCasien and colleagues (2022) published a paper in direct response to Lise Eliot’s paper Dump the dimorphism, wherein they compared the spatial sex differences between the HCP, UK-Biobank, and SHIP datasets from the 3 previously mentioned studies. In Figure 3 (from DeCasien et al., 2022), it can be observed that the 3 datasets show a high level of similarity in the reported neuroanatomical regions and their direction of sex-bias.
Figure 3. Spatial distribution of sex differences across 3 independent datasets.
Another significant source of variability stems from the strategy employed to adjust for brain size, aiming to mitigate the influence of the larger brain in males.
As previously mentioned, males exhibit a 10-13% greater brain size compared to females (Bethlehem et al. 2022), resulting in an average of 19 billion cortical neurons in females and 23 billion in males (a 16% difference) [Pakkenberg & Gundersen 1997]. Underscoring this difference, total brain volume demonstrates a substantial effect size (Cohens d) of 1.41 (Seurinck et al. 2004) (refer to Figure 2). This implies a mere 48.1% overlap between the two normal distributions (male and female), with 92.1% of males surpassing the mean brain size of females. As a result, if you were to randomly select a male from the population, he would have a larger brain than a randomly selected female 84.1% of the time. Hence, researchers often claim that brain size needs to be accounted for when investigating sex differences.
Interestingly, sex differences in brain size are not solely due to sex differences in body size, as males still have a slightly (albeit reduced) larger brain volume compared to women of the same height and weight (Williams et al. 2021). Height only contributes to 39% of the variance when it comes to sex differences in brain size, whereas body weight contributes very little (Williams et al. 2021).
Figure 4. Cohens d effect-size sex differences in brain size.
While it is commonly asserted that sex differences in the brain vanish after brain size-correction, the challenge lies in the diverse methods employed for this correction, akin to the complexities seen in pipeline procedures.
Notably, the choice of what to control for plays a pivotal role, with two prevalent factors being intracranial volume (ICV) and total brain volume (TBV). ICV encompasses all the content within the skull, such as the brain, cerebrospinal fluid, blood vessels, and meninges (three membrane layers covering the brain). In contrast, TBV specifically accounts for the volume occupied by the brain itself. Both ICV and TBV correction methods are commonly employed by researchers, but the specific one chosen may depend on their specific research question. However, adjusting for brain size using either ICV or TBV introduces an influence on the observed sex differences in regional GMV (Kijonka et al. 2020). Notably, in Lise Eliot’s review, numerous tables incorporated studies reporting sex differences that employed either TBV or ICV as brain size correction adjustments. This inclusion of studies utilizing different size-correction methods may have contributed to the overall variance in the findings.
Additionally, the statistical methodology employed for brain size correction holds significance. It must consider the non-linear scaling properties of the brain, a concept encapsulated by allometry. Allometry comprises isometry, indicating proportional scaling, along with hyper- and hypo-allometry, signifying accelerated and decelerated scaling, respectively, in response to changes in brain size.
In a study by Williams et al., 2021, they employed brain size corrections using the log-log covariate method on >40,000 sMRI scans that accounted for the brains non-linear scaling. The log-log covariate method log10 transforms the data (TBV and regional GMV measures) then performs a linear regression analysis. The idea is that by taking the logarithm of variables, the relationship between them becomes linear on the log scale, which can often be a better fit for certain types of data that do not exhibit a linear relationship, much like the brain and allometry. Figure 5 shows which regions of the brain demonstrate these different allometric properties in relation to regional GMV, CTh, and SA. It becomes clear that since sex difference researchers need to control for brain size (perhaps more so than in other fields), an understanding of the brain under these conditions is necessary.
Figure 5. Brain regions showing allometry.
After applying the log-log covariate method on over 40,000 scans of men and women (see Figure 6), it was revealed that there were sex differences in 66% of regional volumes, 57% of surface areas, and 66% of cortical thicknesses. In terms of cortical volume, 42% were larger in males and 35% larger in females. For CTh, 34% of regions were greater in males, and 32% in females. In relation to SA, males showed greater SA in 34% of regions, and females showed greater SA in 23% of regions. The effect sizes here ranged from d = 0.06 to d = -0.39 (small-medium). Interestingly, the data from Williams et al (2021) replicated the majority of sex differences (93%) from the data reported by Ritchie et al (2018).
Figure 6. Data from Williams et al (2021) showing sex differences (n=40,000) post brain-size correction with the non-linear log-log covariate method. Warm colours (positive effects) represent greater female than male volumes (F>M), whereas cooler colours (negative effects) represent greater male than female volumes (M>F).
Whilst it is doubtful that any serious sex difference researcher would dispute Lise Eliot’s assertation that there is no clear-cut “male and female brain”, akin to the distinct sexual dimorphism in genitalia, the main contention lies in her inference that these subtle differences are inconsequential – a sentiment I, among others, vehemently oppose (Hirnstein & Hausmann 2021).
In her paper, Eliot presents multiple tables displaying inconsistent sex-biases in both directions, countering the notion of absolute sexual dimorphism of the brain. However, it is crucial to note that this does not negate the existence of sex differences, or that sex influences brain structure at some level. As highlighted earlier, merely aggregating data in a meta-synthesis review falls short. Various factors, encompassing socioeconomic status, education levels, sexual orientation, the timing of pubertal exposure to gonadal hormones, androgen receptor efficiency, and genetic disparities, all contribute to sex differences in the brain. While Lise Eliot acknowledged some of these variables, they were glossed over when it came to summarizing her data, instead simply giving equal weight to all studies in the literature and focusing on the end result.
Further, the relationship between brain structure/function and behaviour/cognition remains a complex puzzle. Despite our inclination towards linear thinking, it is not obvious that small structural differences translate equally to minor behavioural disparities or if substantial sex differences result in correspondingly significant behavioural variations. Enter the concept of “compensation”, a hypothesis suggesting that sex differences in the brain might function to align us behaviourally, offsetting disparities in genetics, hormone exposure, and brain size (De Vries 2004). For instance, while brain size loosely correlates with general intelligence (IQ), the overall larger brain in males does not manifest as a sex difference in IQ (Colom et al. 2002). Thus, the interplay between sex differences in the brain might defy a simplistic linear logic.
Conclusions
This essay embarked on a journey to present an alternative viewpoint regarding the discrepancies found in the literature on sex differences in the human brain. Instead of delving into the specific neuroanatomical regions demonstrating sex differences, the primary objective was to emphasize the challenge of concluding that variation necessarily implies no impact of sex on the brain.
The diverse employment of study designs, brain-size correction methods, and pipeline procedures across studies may contribute to the observed variation. Notably, the quality of studies varies, a crucial aspect often overlooked in many reviews. The primary goal of this article is to encourage readers to recognise the potential non-linear manifestations of sex differences, urging them to approach future literature with a nuanced perspective.
While the Paradox Institute, including myself, rejects the notion of a strict male-female brain dichotomy, we firmly assert that, despite the observed variation, biological sex remains a significant variable influencing the brain. The existence of variation in precise neuroanatomical regions, as elucidated earlier, can be attributed to many factors, many rooted in the intrinsic influence of biological sex.
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