This is the rather more interesting part of the article; not just claiming some magic task AI can do, but explaining how it does it, and what it bases its decisions on. This means that in theory, someone can validate whether this approach is actually true, or if (as others have pointed out) the AI is just reading something else off those images that we're not aware of.
> Until recently, a model like the one Menon’s team employed would help researchers sort brains into different groups but wouldn’t provide information about how the sorting happened. Today, however, researchers have access to a tool called “explainable AI,” which can sift through vast amounts of data to explain how a model’s decisions are made.
> Using explainable AI, Menon and his team identified the brain networks that were most important to the model’s judgment of whether a brain scan came from a man or a woman. They found the model was most often looking to the default mode network, striatum, and the limbic network to make the call.
This (feature or attention maps) is BS. A real explanation would show what factors were being used to make the determination. Claiming the model is "looking at" something has no explanatory power. We already knew it was looking at the picture. These heat maps are great for showing people what they want to see and making wishy washy claims like this but they really don't explain anything in the commonly understood sense of telling us what is different.
I disagree. It clearly tells us _what_ is different, it just doesn't tell us _how_ it differs from case to case. So it's not a full explanation (which would, as you note, require a model of how it differs and, optimally, _why_) but it is a step towards a explanation, and not to be sneered at.
I might get decapitated for suggesting this here, but it would be interesting to see the results on people with gender dysphoria. Do the hormones change the brain or does it remain the same? Test it both on patients starting as teens and as adults and study over time.
If I can derail conversation for a moment, what is the idea behind 'valid' or 'invalid' transition? It's not a phrase I've heard before sorry. I'm not in any kind of other forum where I think I could ask this without getting very emotionally-charged responses.
There have been studies that have shown that a persons preference for things over interactions were affected by testosterone levels from a young age (1 or 2) to teenage years. That would also affect the brain development etc..
That being the case, unless you changed it at a young age I'm not sure that this could be changed later on in life.
Many people suggest that trans people have brains more inline with their desired gender, and this causes feelings of gender incongruence.
However, if we take a closer look, we see some important underlying issues.
In a famous study, the authors compared prepubertal and adolescent children, then suggest sex atypical cerebral differentiation occurs within these individuals [1].
The authors found sex atypical differences in the adolescent cohort, but the majority of that cohort is homosexual:
- Homosexuality = 23% of trans boys + 44% of trans girls (prepubertal cohort)
- Homosexuality = 100% of trans boys + 78% of trans girls (adolescent cohort)
The only non-sex typical finding which was specific to gender dysphoria was in visual network-1 (VN-1; via fMRI). It was suggested that alterations in this network may disrupt body perception in gender dysphoric individuals.
In another study by some of the same authors, they tested whether transgender people (with gender dysphoria) would have sex atypical hypothalamic activation to androstenedione, a steroid hormone in human sweat that causes sex-specific olfactory responses [2].
But similar to the previous study, sexual orientation was not accounted for. Why is this important? Well, the same sex atypical hypothalamic response is observed in homosexual men [3] and in homosexual women [4].
It's becoming increasingly clear that the only people with any sort of sex atypical cerebral differentiation occurs in homosexual individuals (on average).
This is further supported by functional connectivity studies of sex atypical amygdala co-variance. You can see this in Figure 1 of this paper: [5]. Notice the high similarity in amygdala activity (at rest) between heterosexual females and homosexual males (and vice versa).
Interestingly, in an effort to bring all of this together, this study examined the brains of heterosexual transgender people in order to control for sexual orientation: [6]. As Figure 1 of this paper shows, the authors found sexual dimorphism in various gray matter parameters in control male and females. However, these findings were not found in the heterosexual transgender population.
So rather than sex atypical brain structure/function, what is specific to gender dysphoria itself?
It's been shown that individuals with gender dysphoria show weaker structural and functional connectivity within the default mode network (DMN) of the brain, which is vital for body perception/image and self-referential processing [7]. Findings which have been replicated in [8] and [9].
The DMN consists of cerebral midline structures, including the medial prefrontal cortex (mPFC) and the posterior cingulate cortex (PCC). Interestingly, it's been shown that trans individuals (with GD) show a stronger activation pattern within these DMN structures when viewing pictures of their body morphed to the opposite sex: [10]. You can see the results in Figure 5 of this paper.
It's important to note that correlation is not causation. Just because we observe a different pattern in gender dysphoric subjects compared to neuro-typical controls, does not suggest it's innate (born with) or a product of post-natal experience. We simply do not know.
Also, transgender people tend to have lots of co-morbidities from depression, anxiety, anorexia, autism, and a homosexual orientation. All of this needs to be considered when looking at neuroscience studies on this population.
[1] Nota, N. M., Kreukels, B. P. C., den Heijer, M., Veltman, D. J., Cohen-Kettenis, P. T., Burke, S. M., & Bakker, J. (2017). Brain functional connectivity patterns in children and adolescents with gender dysphoria: Sex-atypical or not? https://pubmed.ncbi.nlm.nih.gov/28972892/
[2] Burke, S. M., Cohen-Kettenis, P. T., Veltman, D. J., Klink, D. T., & Bakker, J. (2014). Hypothalamic response to the chemo-signal androstadienone in gender dysphoric children and adolescents. https://pubmed.ncbi.nlm.nih.gov/24904525/
[5] Savic, I., & Lindström, P. (2008). PET and MRI show differences in cerebral asymmetry and functional connectivity between homo- and heterosexual subjects. https://pubmed.ncbi.nlm.nih.gov/18559854/
[7] Burke, S. M., Manzouri, A. H., & Savic, I. (2017). Structural connections in the brain in relation to gender identity and sexual orientation. https://pubmed.ncbi.nlm.nih.gov/29263327/
[8] Uribe, C., Junque, C., Gómez-Gil, E., Abos, A., Mueller, S. C., & Guillamon, A. (2020). Brain network interactions in transgender individuals with gender incongruence. https://pubmed.ncbi.nlm.nih.gov/32057995/
[9] Feusner, J. D., Lidström, A., Moody, T. D., Dhejne, C., Bookheimer, S. Y., & Savic, I. (2017). Intrinsic network connectivity and own body perception in gender dysphoria. https://pubmed.ncbi.nlm.nih.gov/27444730/
[10] Majid, D. S. A., Burke, S. M., Manzouri, A., Moody, T. D., Dhejne, C., Feusner, J. D., & Savic, I. (2020). Neural Systems for Own-body Processing Align with Gender Identity Rather Than Birth-assigned Sex. https://pubmed.ncbi.nlm.nih.gov/31813993/
> This breakthrough supports the theory that significant sex differences in brain organization exist, challenging long-standing controversies.
It doesn't, because there is no explanation for how it determines the difference. It's just as likely some confounding thing that's not causally based on the brain, like all these other "AI can tell" studies that don't identify an actual mechanism.
That said I'm surprised that we can't tell men and women apart from the brain. I would have assumed there would be some differences that would make it obvious at least for the average brain. It's more surprising to find they are indistinguishable.
>It doesn't, because there is no explanation for how it determines the difference.
You don't need to know the mechanism to prove that it "supports the theory that there is a difference in brain organization". If there wasn't a difference, it wouldn't be able to find it.
>It's just as likely some confounding thing that's not causally based on the brain
Given that it does it's identification by looking at the brain, whatever the cause is, it is ALSO manifested as a "difference in brain organization".
>That said I'm surprised that we can't tell men and women apart from the brain.
Who said we can't? The article points to that we can.
And there are other brain attributes besides organization we can use to tell them apart, the biggest being size, including size of certain structures.
> Given that it does it's identification by looking at the brain, whatever the cause is, it is ALSO manifested as a "difference in brain organization".
I think the person you’re responding to is talking about confounding factors _outside_ the brain scan, like when a model that could identify skin cancer used the presence of a ruler as its most weighted input: https://venturebeat.com/business/when-ai-flags-the-ruler-not...
This is a huge problem in medical use for AI - you have non-technical medical professionals influencing the data used to train the model, and introducing biases in the data that the model is learning from.
Some silly examples of stuff it might find that aren't actually brain differences:
1. There's an M/F letter or other identifying text on the scans
2. Men and women get put into the machine slightly differently, and the ai sees a slight difference in the pose of the scan
3. It sees something else that's identifying and pretty good at splitting, like ear holes for ear rings
I’m reminded of the tale of a similar breakthrough, and upon ablation, it was found to be indexing on a particular artifact found on one X-ray machine. Or maybe the patient name?
Anyways, would recommend some caution, there’s no reason to believe there’s some obvious fundamental difference in brain organization that we can’t see ourselves, until the research is a bit further along :)
Unfortunately, these things can be extremely subtle. Far too often it boils down to sampling bias where the differences are actually differences in the variance estimation that results in a significant result. For example suppose that "male" dice are biased to role a 5 slightly more often than "female" dice. You will find a statistically significant difference related to the 5-side of the dice if you roll them enough. It doesn't mean that rolling a 5 indicates anything about the "gender" of a particular dice.
> It doesn't, because there is no explanation for how it determines the difference
I don’t think an explanation is required for this.
I should be able to state that two species have a different lifespan based on a difference in mean age of death - without needing to provide any specific biological mechanism in my study.
Substitute that mean for a linear regression / neural network and the species lifespan for some vector of brain organisation to get the results in the article.
> I should be able to state that two species have a different lifespan based on a difference in mean age of death - without needing to provide any specific biological mechanism in my study.
You need to control for confounders. For example, if I told you:
- Mean lifespan of species 1 is: 3 years
- Mean lifespan of species 2 is: 10 years
Does that mean that species 2 has a longer lifespan than species 1?
What happens when you find out that species 1 is domesticated cattle, which has (almost) all of its males slaughtered at age 2?
You could then say "I meant for two species that are in controlled conditions", but now you need to define what conditions you're controlling, which implies what casual mechanisms you're controlling for.
> Until recently, a model like the one Menon’s team employed would help researchers sort brains into different groups but wouldn’t provide information about how the sorting happened. Today, however, researchers have access to a tool called “explainable AI,” which can sift through vast amounts of data to explain how a model’s decisions are made.
> Using explainable AI, Menon and his team identified the brain networks that were most important to the model’s judgment of whether a brain scan came from a man or a woman. They found the model was most often looking to the default mode network, striatum, and the limbic network to make the call.
What's more fascinating it that it's not just a parlor trick and actually can be "validated" in some sense by doing a cognition test for just that part of the brain and observing the differences. So the model said "check here and you'll see a difference," and apparently, they did.
> They developed sex-specific models of cognitive abilities: One model effectively predicted cognitive performance in men but not women, and another in women but not men. The findings indicate that functional brain characteristics varying between sexes have significant behavioral implications.
> “These models worked really well because we successfully separated brain patterns between sexes,” Menon said. “That tells me that overlooking sex differences in brain organization could lead us to miss key factors underlying neuropsychiatric disorders.”
FWIW, feature maps are not really reliable (to put it mildly). Knowing what area the model is "looking at" is virtually meaningless if you don't know what it's looking at. We already knew it was looking at the brain so is it turtles all the way down? Those maps are great for confirming what people want to see "oh look it tells a cat from a dog by the eyes" but have no explanatory power. If they did then the paper would be about AI finding a physiological difference and would tell us what it is, not some handwaving about "here's where it looked".
> “These models worked really well because we successfully separated brain patterns between sexes,” Menon said. “That tells me that overlooking sex differences in brain organization could lead us to miss key factors underlying neuropsychiatric disorders.”
Although never demonstrated, it's always been most intuitive to me that sex-related brain differences underlie gender dysphoria. I would feel exceptionally relieved and vindicated to finally have some objective, falsifiable observation to point at that suggests the reality of dysphoria.
There’s some linear and obvious differences like the brain volume. Don’t need ai for that lol. I think I remember that it’s a 20% average difference. Downvote me please
This is comparable to trying to determine sex based on secondary sex characteristics.
Sex is binary, based on gametes which are binary. Anything with a bimodal distribution will by necessity never be the right tool for determining something which is truly binary.
The next best determinant of sex other than gametes is the presence or absence of the SRY gene.
I don’t like comments like this. I’m not picking on you in particular because I see them all the time, especially as drive-by comments. What I like a lot about HN is that typically these drive-by comments are not highly rewarded.
There is a kind of cocktail-party level expertise that is behind comments that scientific results are obvious. First of all, even if they were, it’s likely there’s a novel angle the researchers are targeting. We should ask ourselves why a researcher would spend time on such “obvious” results.
Conveniently, the article author asked the researchers why they would study this. It’s not just a question of whether their brains are dissimilar, but how.
Second, whether male and female brains are structurally similar is controversial. As the article states, brain structures tend to look mostly the same in men and women.
Forensic investigators can reconstruct the face from a skull, not only race/ethnicity/gender. It is probably just a question, how to measure improvement of AI, added to existing method. Let's say you find the skull of the alleged "new Saddam Hussein", the AI reconstructs the face, the face looks "similar", but are you willing to go along with the AI? Maybe the neural network just got good at drawing the face of the average Arab.
This is going to turn out to be the new phrenology, completely flawed, but it won't stop phobic types from using it to "prove" their antisocial theories.
I think the implication is it doesn't actually work well. Running hard-to-explain models on brain scans has a history of producing papers which fail to reproduce, there were a bunch of bunk results in fMRI research a few years ago.
Readit News
> Until recently, a model like the one Menon’s team employed would help researchers sort brains into different groups but wouldn’t provide information about how the sorting happened. Today, however, researchers have access to a tool called “explainable AI,” which can sift through vast amounts of data to explain how a model’s decisions are made.
> Using explainable AI, Menon and his team identified the brain networks that were most important to the model’s judgment of whether a brain scan came from a man or a woman. They found the model was most often looking to the default mode network, striatum, and the limbic network to make the call.
I don't know if I want to stake my legal rights on an MRI machine. Even if I have a "guy" brain I'd still rather stay on E.
That being the case, unless you changed it at a young age I'm not sure that this could be changed later on in life.
Deleted Comment
However, if we take a closer look, we see some important underlying issues.
In a famous study, the authors compared prepubertal and adolescent children, then suggest sex atypical cerebral differentiation occurs within these individuals [1].
The authors found sex atypical differences in the adolescent cohort, but the majority of that cohort is homosexual:
- Homosexuality = 23% of trans boys + 44% of trans girls (prepubertal cohort)
- Homosexuality = 100% of trans boys + 78% of trans girls (adolescent cohort)
The only non-sex typical finding which was specific to gender dysphoria was in visual network-1 (VN-1; via fMRI). It was suggested that alterations in this network may disrupt body perception in gender dysphoric individuals.
In another study by some of the same authors, they tested whether transgender people (with gender dysphoria) would have sex atypical hypothalamic activation to androstenedione, a steroid hormone in human sweat that causes sex-specific olfactory responses [2].
But similar to the previous study, sexual orientation was not accounted for. Why is this important? Well, the same sex atypical hypothalamic response is observed in homosexual men [3] and in homosexual women [4].
It's becoming increasingly clear that the only people with any sort of sex atypical cerebral differentiation occurs in homosexual individuals (on average).
This is further supported by functional connectivity studies of sex atypical amygdala co-variance. You can see this in Figure 1 of this paper: [5]. Notice the high similarity in amygdala activity (at rest) between heterosexual females and homosexual males (and vice versa).
Interestingly, in an effort to bring all of this together, this study examined the brains of heterosexual transgender people in order to control for sexual orientation: [6]. As Figure 1 of this paper shows, the authors found sexual dimorphism in various gray matter parameters in control male and females. However, these findings were not found in the heterosexual transgender population.
So rather than sex atypical brain structure/function, what is specific to gender dysphoria itself?
It's been shown that individuals with gender dysphoria show weaker structural and functional connectivity within the default mode network (DMN) of the brain, which is vital for body perception/image and self-referential processing [7]. Findings which have been replicated in [8] and [9].
The DMN consists of cerebral midline structures, including the medial prefrontal cortex (mPFC) and the posterior cingulate cortex (PCC). Interestingly, it's been shown that trans individuals (with GD) show a stronger activation pattern within these DMN structures when viewing pictures of their body morphed to the opposite sex: [10]. You can see the results in Figure 5 of this paper.
It's important to note that correlation is not causation. Just because we observe a different pattern in gender dysphoric subjects compared to neuro-typical controls, does not suggest it's innate (born with) or a product of post-natal experience. We simply do not know.
Also, transgender people tend to have lots of co-morbidities from depression, anxiety, anorexia, autism, and a homosexual orientation. All of this needs to be considered when looking at neuroscience studies on this population.
[1] Nota, N. M., Kreukels, B. P. C., den Heijer, M., Veltman, D. J., Cohen-Kettenis, P. T., Burke, S. M., & Bakker, J. (2017). Brain functional connectivity patterns in children and adolescents with gender dysphoria: Sex-atypical or not? https://pubmed.ncbi.nlm.nih.gov/28972892/
[2] Burke, S. M., Cohen-Kettenis, P. T., Veltman, D. J., Klink, D. T., & Bakker, J. (2014). Hypothalamic response to the chemo-signal androstadienone in gender dysphoric children and adolescents. https://pubmed.ncbi.nlm.nih.gov/24904525/
[3] Savic, I., Berglund, H., & Lindström, P. (2005). Brain response to putative pheromones in homosexual men. https://pubmed.ncbi.nlm.nih.gov/15883379/
[4] Berglund, H., Lindström, P., & Savic, I. (2006). Brain response to putative pheromones in lesbian women. https://pubmed.ncbi.nlm.nih.gov/16705035/
[5] Savic, I., & Lindström, P. (2008). PET and MRI show differences in cerebral asymmetry and functional connectivity between homo- and heterosexual subjects. https://pubmed.ncbi.nlm.nih.gov/18559854/
[6] Savic, I., & Arver, S. (2011). Sex dimorphism of the brain in male-to-female transsexuals. https://pubmed.ncbi.nlm.nih.gov/21467211/
[7] Burke, S. M., Manzouri, A. H., & Savic, I. (2017). Structural connections in the brain in relation to gender identity and sexual orientation. https://pubmed.ncbi.nlm.nih.gov/29263327/
[8] Uribe, C., Junque, C., Gómez-Gil, E., Abos, A., Mueller, S. C., & Guillamon, A. (2020). Brain network interactions in transgender individuals with gender incongruence. https://pubmed.ncbi.nlm.nih.gov/32057995/
[9] Feusner, J. D., Lidström, A., Moody, T. D., Dhejne, C., Bookheimer, S. Y., & Savic, I. (2017). Intrinsic network connectivity and own body perception in gender dysphoria. https://pubmed.ncbi.nlm.nih.gov/27444730/
[10] Majid, D. S. A., Burke, S. M., Manzouri, A., Moody, T. D., Dhejne, C., Feusner, J. D., & Savic, I. (2020). Neural Systems for Own-body Processing Align with Gender Identity Rather Than Birth-assigned Sex. https://pubmed.ncbi.nlm.nih.gov/31813993/
It doesn't, because there is no explanation for how it determines the difference. It's just as likely some confounding thing that's not causally based on the brain, like all these other "AI can tell" studies that don't identify an actual mechanism.
That said I'm surprised that we can't tell men and women apart from the brain. I would have assumed there would be some differences that would make it obvious at least for the average brain. It's more surprising to find they are indistinguishable.
You don't need to know the mechanism to prove that it "supports the theory that there is a difference in brain organization". If there wasn't a difference, it wouldn't be able to find it.
>It's just as likely some confounding thing that's not causally based on the brain
Given that it does it's identification by looking at the brain, whatever the cause is, it is ALSO manifested as a "difference in brain organization".
>That said I'm surprised that we can't tell men and women apart from the brain.
Who said we can't? The article points to that we can.
And there are other brain attributes besides organization we can use to tell them apart, the biggest being size, including size of certain structures.
I think the person you’re responding to is talking about confounding factors _outside_ the brain scan, like when a model that could identify skin cancer used the presence of a ruler as its most weighted input: https://venturebeat.com/business/when-ai-flags-the-ruler-not...
This is a huge problem in medical use for AI - you have non-technical medical professionals influencing the data used to train the model, and introducing biases in the data that the model is learning from.
1. There's an M/F letter or other identifying text on the scans 2. Men and women get put into the machine slightly differently, and the ai sees a slight difference in the pose of the scan 3. It sees something else that's identifying and pretty good at splitting, like ear holes for ear rings
Anyways, would recommend some caution, there’s no reason to believe there’s some obvious fundamental difference in brain organization that we can’t see ourselves, until the research is a bit further along :)
I don’t think an explanation is required for this.
I should be able to state that two species have a different lifespan based on a difference in mean age of death - without needing to provide any specific biological mechanism in my study.
Substitute that mean for a linear regression / neural network and the species lifespan for some vector of brain organisation to get the results in the article.
You need to control for confounders. For example, if I told you:
- Mean lifespan of species 1 is: 3 years - Mean lifespan of species 2 is: 10 years
Does that mean that species 2 has a longer lifespan than species 1?
What happens when you find out that species 1 is domesticated cattle, which has (almost) all of its males slaughtered at age 2?
You could then say "I meant for two species that are in controlled conditions", but now you need to define what conditions you're controlling, which implies what casual mechanisms you're controlling for.
> Until recently, a model like the one Menon’s team employed would help researchers sort brains into different groups but wouldn’t provide information about how the sorting happened. Today, however, researchers have access to a tool called “explainable AI,” which can sift through vast amounts of data to explain how a model’s decisions are made.
> Using explainable AI, Menon and his team identified the brain networks that were most important to the model’s judgment of whether a brain scan came from a man or a woman. They found the model was most often looking to the default mode network, striatum, and the limbic network to make the call.
What's more fascinating it that it's not just a parlor trick and actually can be "validated" in some sense by doing a cognition test for just that part of the brain and observing the differences. So the model said "check here and you'll see a difference," and apparently, they did.
> They developed sex-specific models of cognitive abilities: One model effectively predicted cognitive performance in men but not women, and another in women but not men. The findings indicate that functional brain characteristics varying between sexes have significant behavioral implications.
> “These models worked really well because we successfully separated brain patterns between sexes,” Menon said. “That tells me that overlooking sex differences in brain organization could lead us to miss key factors underlying neuropsychiatric disorders.”
Although never demonstrated, it's always been most intuitive to me that sex-related brain differences underlie gender dysphoria. I would feel exceptionally relieved and vindicated to finally have some objective, falsifiable observation to point at that suggests the reality of dysphoria.
According to the article, we can.
On the other hand, welcome to the machine...
Sex is binary, based on gametes which are binary. Anything with a bimodal distribution will by necessity never be the right tool for determining something which is truly binary.
The next best determinant of sex other than gametes is the presence or absence of the SRY gene.
There is a kind of cocktail-party level expertise that is behind comments that scientific results are obvious. First of all, even if they were, it’s likely there’s a novel angle the researchers are targeting. We should ask ourselves why a researcher would spend time on such “obvious” results.
Conveniently, the article author asked the researchers why they would study this. It’s not just a question of whether their brains are dissimilar, but how.
Second, whether male and female brains are structurally similar is controversial. As the article states, brain structures tend to look mostly the same in men and women.