Sex on the brain – a tale of two studies
The issue of whether there are biological differences between male and female brains is a fraught one and an area where political positions or prior expectations seem to have a strong influence on the interpretation of scientific data. These trends are illustrated by two papers published in the last couple years, which, despite fairly comparable findings, were interpreted in almost polar opposite fashions.
Both studies found strong group differences between male and female brains, one in volume of brain areas, the other in structural connectivity. But the authors of one study went on to (over)interpret these group differences as the basis for sex differences in cognition, while the other downplayed them entirely and instead emphasised the inherent variability within genders to conclude that there was no such thing as a “male brain” or a “female brain”. Both received extensive coverage in the media, fuelled by the associated press releases, resulting in headlines making hilariously contradictory claims, even in the same newspaper!
The 2013 study was described with these headlines:
Brain Connectivity Study Reveals Striking Differences Between Men and Women
The hardwired difference between male and female brains could explain why men are 'better at map reading'
Male and female brains wired differently, scans reveal (The Guardian)
The 2015 study with these:
The brains of men and women aren’t really that different, study finds
Men are from Mars, women are from Venus? New brain study says not (The Guardian again!)
Let’s look at the more recent one first, to see what the data actually show and how they were analysed and interpreted. Daphna Joel and colleagues analysed MRI scans of 169 females and 112 males, and segmented them into 116 regions using a standard brain atlas. By analysing how much warping was required to map each brain onto a reference template, it was possible to compare the relative grey matter volume of all these regions across the two sexes. From this group comparison the 10 regions showing the largest sex differences were chosen for subsequent analyses.
So far, so good: the primary finding is that there are statistically significant group differences between males and females in grey matter volume across many brain regions. That’s nothing new – a recent meta-analysis of 167 studies confirms consistent group sex differences in many brain areas between men and women
The authors went on, however, to ask what could have been a more interesting question: across those 10 regions, how “male” or “female” were the structures of individual brains? This is where the subjectivity comes in – there are many ways to analyse these data and the authors chose arguably the most simplistic and extreme one, which enabled them to draw the conclusion that male and female brains are not categorically different.
They report that: “35% percent of brains showed substantial variability, and only 6% of brains were internally consistent”. Importantly they chose to classify only those subjects showing extreme male or female values for all 10 regions as “internally consistent”. A quick look at panel E of the figure below shows that while such brains may indeed be rare, most of the female brains showed a mostly female pattern (lots of pink) while most of the male brains showed a mostly male pattern (lots of blue – don’t blame me, I didn’t pick the colours!).
There is, in fact, nothing at all surprising in their finding of substantial variability within individuals. To explain why, consider the distributions for height for males and females.These distributions are very wide and mostly overlapping but there is a strong and consistent group difference in the mean – the distribution for males is shifted to the right. For any individual, however, knowing their sex gives almost no predictive power for how tall they are. What the group difference does suggest is the following: if I know how tall a particular woman is, I can say that if she had been a man (but was genetically otherwise identical) she would probably have been a little taller than that. She may happen to fall at the low or high end of the overall spectrum for other reasons, but that prediction remains the same. The existence of the group difference does not suggest that all males should be at the extreme “male” end of the height distribution or they’re not really very manly at all. That would be true if all other things were equal, but they’re not equal, and those other variations, which have nothing to do with sex, have a much bigger effect on final height than the sex effect does.
Now, consider what will happen if we have ten different variables, each showing that same sort of wide distribution with an even smaller group sex effect. If the volumes of different brain regions vary independently within individuals (taking overall brain volume out of the equation - as shown here, for example), then we should expect some of these values to fall more towards the male end and others more towards the female end in any individual simply due to that underlying variation, which has nothing to do with sex. It would be extremely unlikely to end up at the extreme end for all ten regions, by chance, and such individuals should thus be extremely rare, as observed.
So, the fact that each individual shows this kind of pattern does not mean that each of us has a “mosaic brain” that is partly male and partly female, as claimed by the authors. It is simply exactly what is expected given that sex is only one of the factors affecting the size of each of these regions. We can’t know for each individual what the size of each region would have been if their sex were different (which is really what we’d like to know) – we can only deduce from the group average effects that there would likely have been some effect.
The headlines suggesting that male and female brains are not that different are thus not well supported by these findings at all. The group differences are clear and highly significant. And even if very few of the males or females are at the extreme end of the distribution for all ten of these regions, the overall pattern suggests that you could build a very good classifier from the volumes of these ten regions taken together, which would be quite successful at predicting whether a given brain scan came from a male or a female. Indeed, this would have been a far more objective test of whether MRI volumetric differences between male and female brains are categorical or dimensional.
Given this, it is interesting to ask why the authors chose to analyse and present their data in the way they did. This is what they say in the introduction to the paper:
"Documented sex/gender* differences in the brain are often taken as support of a sexually dimorphic view of human brains (“female brain” vs. “male brain”), and consequently, of a sexually dimorphic view of human behavior, cognition, personality, attitudes, and other gender characteristics (3). Joel (4, 5) has recently argued that the existence of sex/gender differences in the brain is not sufficient to conclude that human brains belong to two distinct categories. Rather, such a distinction requires the fulfillment of two conditions: one, the form of the elements that show sex/gender differences should be dimorphic, that is, with little overlap between the forms of the elements in males and females. Two, there should be a high degree of internal consistency in the form of the different elements of a single brain (e.g., all elements have the “male” form)."
It seems pretty clear from that that the authors set out to show that male and female brains are not that different, or at least not dimorphic. In particular, they take aim at a paper by Madura Ingalhalikar and colleagues (their reference 3, above), which is the second paper I wish to discuss. These authors found comparable group difference results as Joel et al (using a different measure of brain structure), yet reached almost opposite conclusions.
They used diffusion tensor imaging to define the structural connectivity networks across the brains of 949 youths (428 males and 521 females). They then analysed these networks using a variety of statistical measures of regional and global connectivity and compared these between males and females. They found that females had greater connectivity between hemispheres than males, on average, while males had greater connectivity within each hemisphere. Males also showed greater local connectivity and concomitantly increased modularity in the network (again, on average).
(In this figure from the paper, the top panel shows connections that are stronger in males, the bottom those that are stronger in females; blue are intrahemispheric, orange are interhemispheric).
Once again, so far, so good – the results look significant and interesting. (It would have been nice to see the analyses done with a discovery and replication sample, instead of one big group but at least it is a large sample). Where these authors got onto shakier ground was in extrapolating their findings as explanations for a variety of group differences in cognition between men and women. The participants in the structural connectivity analysis were part of a larger sample for which cognitive data had already been obtained, showing sex differences in a variety of domains. Such differences have been widely documented and range from quite small to fairly large (see here for a meta-analysis).
However, the idea that the structural connectivity network differences observed are the cause of such cognitive differences is entirely speculative. I have nothing against speculation, per se, and the discussion section of a paper is a perfect place to explore the possible implications of one’s results. Where this got a bit out of hand was in the associated press release and the consequent media coverage. This is from the press release itself:
For instance, on average, men are more likely better at learning and performing a single task at hand, like cycling or navigating directions, whereas women have superior memory and social cognition skills, making them more equipped for multitasking and creating solutions that work for a group. They have a mentalistic approach, so to speak. "
Those kinds of assertive generalisations, and especially the idea that the connectivity findings provide a neural basis for them, are not at all supported by the data and rightly provoked howls of protest from the scientific community. This included commentary by Joel and colleagues , to which Ingalhalikar and colleagues responded. The unfortunate outcome was that the authors’ over-extrapolation ended up undermining trust in their primary findings, which actually look quite solid in themselves.
To my mind, both these studies over-reached in the interpretation of their results, ironically drawing opposite conclusions from what are broadly comparable primary findings. More generally, it also seems that a little more humility is in order in drawing sweeping conclusions from these kinds of studies, given the crudeness of group-wise volumetric and tractography analyses and the very low resolution of MRI scans. Even if such scans showed no consistent group differences between male and female brains, this would not imply that male and female brains are not different. It would only imply such differences could not be detected by MRI. We know there are many differences in the numbers of neurons in small brain regions or numbers of connections between regions in male and female brains that are invisible to MRI, not to mention sex differences in densities of synaptic spines or other subcellular parameters that have also been demonstrated (as in this recent example).
A final note: why should we care? Why should we investigate sex differences in the brain? And if we find them, what are their implications for public policies? Many people are rightly concerned that demonstrations of biological differences in brain structure between males and females will be used to reinforce the idea of systematic differences in cognitive abilities and justify sexism. Of course, even if such differences were large and consistent across individuals, it would not imply one version is better than the other. But more importantly, the distributions for cognitive domains are so overlapping and the sex effects typically so small that inferring anything about the cognitive profiles of individuals on the basis of these group differences is, simply put, a very bad bet. Sex differences for interests are a little bit bigger, but still by no means categorical and there is likely a strong cultural reinforcement of gender norms in this area.
There are, however, other areas where there are more robust sex differences. The most obvious but also the most commonly over-looked of these is sexual preference – something in the brains of males makes the vast majority of them sexually attracted to females, and vice versa. This is by far the strongest genetic effect on behaviour that we know of in humans (mediated by the SRY gene on the Y chromosome). It would therefore be interesting to find out how that preference is wired into the brain, as an exemplar for how genes can influence innate behaviour. Sex differences in physical aggression are also large and another important topic to understand (as are differences in idiotic behaviour as measured by the Darwin awards!).
Finally, though, a main reason we should care is due to the large sex differences in prevalence of psychiatric conditions, which range from autism, ADHD and Tourette syndrome (much more common in males), to schizophrenia and dyslexia (more common in males), to depression (more common in females) and eating disorders (much more common in females). There is strong and consistent evidence, for example, that females are somewhat protected against the effects of mutations that typically cause autism in males. Females may carry such mutations with relatively little clinical effect; conversely, females who do have autistic symptoms tend to have larger or more severe mutations than affected males (suggesting that it takes a more drastic insult at the genetic level to push a female brain into a clinically autistic state). Understanding how sex influences vulnerability to these conditions is thus a hugely important question.
Too important to let politics, bias or spin affect our interpretation of scientific findings.