Susan Greenfield Causes Autism

British neuroscientist Susan Greenfield has caused a storm with her suggestion that the recent rise in the use of the internet and social media may be related to the recent rise in autism.

I point to the increase in autism and I point to internet use. That's all. Establishing a causal relationship is very hard but there are trends out there that we must think about.

This has led to fellow Oxford neuroscientist Dorothy Bishop of BishopBlog writing an Open Letter asking her to "please, please, stop talking about autism". Twitter has been enlivened by #greenfieldism's such as "I point to the rise of Rebecca Black and the Greek sovereign debt crisis, that is all."



However, in a Neuroskeptic exclusive, I can reveal that the situation is far worse than anyone feared. Greenfield is not merely spreading unwarranted speculations about the recent rise in autism diagnoses.



She caused that rise.



The graph above shows the total number of scientific citations for Susan Greenfield's papers, over time. This is as good a measure as any of the influence Greenfield has had over our culture.



The trend is obvious, the growth is dramatic, and the correlation with the modern autism epidemic is undeniable.

Susan Greenfield Causes Autism

British neuroscientist Susan Greenfield has caused a storm with her suggestion that the recent rise in the use of the internet and social media may be related to the recent rise in autism.

I point to the increase in autism and I point to internet use. That's all. Establishing a causal relationship is very hard but there are trends out there that we must think about.

This has led to fellow Oxford neuroscientist Dorothy Bishop of BishopBlog writing an Open Letter asking her to "please, please, stop talking about autism". Twitter has been enlivened by #greenfieldism's such as "I point to the rise of Rebecca Black and the Greek sovereign debt crisis, that is all."



However, in a Neuroskeptic exclusive, I can reveal that the situation is far worse than anyone feared. Greenfield is not merely spreading unwarranted speculations about the recent rise in autism diagnoses.



She caused that rise.



The graph above shows the total number of scientific citations for Susan Greenfield's papers, over time. This is as good a measure as any of the influence Greenfield has had over our culture.



The trend is obvious, the growth is dramatic, and the correlation with the modern autism epidemic is undeniable.

Vaccines Cause Autism, Until You Look At The Data

According to a much-discussed new paper, vaccines may cause autism after all: A Positive Association found between Autism Prevalence and Childhood Vaccination uptake across the U.S. Population.

The author is Gayle DeLong, who "teaches international finance at Baruch College, City University of New York", according to her profile as a board member of anti-vaccine group SafeMinds. She correlated rates of coverage of the government recommended full set of vaccines in the 51 US states including Washington D.C., with registered rates of autism in those states six years later.

Uh-oh - there was a correlation between vaccination in two year kids, and the rate of autism in the state six years later, when those kids were eight. As the abstract says:

The higher the proportion of children receiving recommended vaccinations, the higher was the prevalence of AUT... The results suggest that although mercury has been removed from many vaccines, other culprits may link vaccines to autism. Further study into the relationship between vaccines and autism is warranted.

Sounds rather scary. Until you look at the data, helpfully provided in the paper. First up, here's the scatterplot of all of the vaccination rates and all of the autism-six-years-later rates:

There's more than 51 data points as you can see: there's actually 355 because each state had seven different datapoints (1995 vaccines vs 2001 autism though to 2001 vs 2007). This scatterplot shows no correlation. You can tell just from looking at it, but the correlation coefficient confirms this, as it's a tiny r 0.012 (from a possible range of 0 to 1).

To be fair, that's a very noisy measure, because each state has unique characteristics, so the effect of vaccines will be diluted. However, it's still a useful sanity check, and shows that there can't be a major effect, otherwise it would be too big to get diluted.

To get around this I next looked at the change in the rates of vaccination from one year to the next, and correlated that with the corresponding change in future rates of autism, within each state. A "change" of 1 means no change, 0.5 means it halved and 2 means it doubled, etc.

Zilch. Correlation coeffiencent r is 0.034.

Maybe the changes year-to-year were too small? So I checked the changes between the last year, and the first year.

This made the changes bigger, because more tends to change over six years than in just one. And, to be fair, this does produces a slightly stronger vaccine-autism effect... but it's still tiny. The correlation coefficient here, r, is 0.18 which means that vaccination changes accounts for 3% of the variability in autism changes (r^2 = 0.034.) The p value is 0.20, statistically insignificant.

My conclusion is that this dataset shows no evidence of any association. The author nonetheless found one. How? By doing some statistical wizardry.

The statistical model used took into consideration the unique characteristics of each state. For example, each state had a unique mixture of pollution, which may have affected the prevalence of autism, yet such an effect was not included in this study. A fixed-effects, within-group panel regression (Hall and Cummins 2005) controlled for these unique yet undefined characteristics by deriving a different starting point (intercept) for each state.

The 51 different intercepts - one for each state - reflected the base level of autism or speech disorders occurring in that state that were not explained by the other independent variables (vaccination rates, income, or ethnicity). The model then produced a single relationship between the independent variables and the prevalence of autism or speech disorders.

OK, that's all very fancy, but when the raw data shows zilch and you can only find a signal by "controlling for" stuff, alarm bells start ringing. Given sufficient statistical analysis you can make any data say anything you want.

If the author had given details of the methods, and explained why she chose to control for the variables she did, and not others, that might be different. But she didn't. Nor did she justify only looking at the effects six years later, when five or seven or ten would be just as sensible... and so on.

(Note: whenever I've said "autism", that's my shorthand for autism + SLI, which is what the paper looked at; autism alone data are not presented. Note also that by "vaccination %" I mean "% who got the full vaccine schedule"; the other kids may have got vaccines, just not all of them.)

Delong G (2011). A Positive Association found between Autism Prevalence and Childhood Vaccination uptake across the U.S. Population. Journal of toxicology and environmental health. Part A, 74 (14), 903-16 PMID: 21623535

Vaccines Cause Autism, Until You Look At The Data

According to a much-discussed new paper, vaccines may cause autism after all: A Positive Association found between Autism Prevalence and Childhood Vaccination uptake across the U.S. Population.

The author is Gayle DeLong, who "teaches international finance at Baruch College, City University of New York", according to her profile as a board member of anti-vaccine group SafeMinds. She correlated rates of coverage of the government recommended full set of vaccines in the 51 US states including Washington D.C., with registered rates of autism in those states six years later.

Uh-oh - there was a correlation between vaccination in two year kids, and the rate of autism in the state six years later, when those kids were eight. As the abstract says:

The higher the proportion of children receiving recommended vaccinations, the higher was the prevalence of AUT... The results suggest that although mercury has been removed from many vaccines, other culprits may link vaccines to autism. Further study into the relationship between vaccines and autism is warranted.

Sounds rather scary. Until you look at the data, helpfully provided in the paper. First up, here's the scatterplot of all of the vaccination rates and all of the autism-six-years-later rates:

There's more than 51 data points as you can see: there's actually 355 because each state had seven different datapoints (1995 vaccines vs 2001 autism though to 2001 vs 2007). This scatterplot shows no correlation. You can tell just from looking at it, but the correlation coefficient confirms this, as it's a tiny r 0.012 (from a possible range of 0 to 1).

To be fair, that's a very noisy measure, because each state has unique characteristics, so the effect of vaccines will be diluted. However, it's still a useful sanity check, and shows that there can't be a major effect, otherwise it would be too big to get diluted.

To get around this I next looked at the change in the rates of vaccination from one year to the next, and correlated that with the corresponding change in future rates of autism, within each state. A "change" of 1 means no change, 0.5 means it halved and 2 means it doubled, etc.

Zilch. Correlation coeffiencent r is 0.034.

Maybe the changes year-to-year were too small? So I checked the changes between the last year, and the first year.

This made the changes bigger, because more tends to change over six years than in just one. And, to be fair, this does produces a slightly stronger vaccine-autism effect... but it's still tiny. The correlation coefficient here, r, is 0.18 which means that vaccination changes accounts for 3% of the variability in autism changes (r^2 = 0.034.) The p value is 0.20, statistically insignificant.

My conclusion is that this dataset shows no evidence of any association. The author nonetheless found one. How? By doing some statistical wizardry.

The statistical model used took into consideration the unique characteristics of each state. For example, each state had a unique mixture of pollution, which may have affected the prevalence of autism, yet such an effect was not included in this study. A fixed-effects, within-group panel regression (Hall and Cummins 2005) controlled for these unique yet undefined characteristics by deriving a different starting point (intercept) for each state.

The 51 different intercepts - one for each state - reflected the base level of autism or speech disorders occurring in that state that were not explained by the other independent variables (vaccination rates, income, or ethnicity). The model then produced a single relationship between the independent variables and the prevalence of autism or speech disorders.

OK, that's all very fancy, but when the raw data shows zilch and you can only find a signal by "controlling for" stuff, alarm bells start ringing. Given sufficient statistical analysis you can make any data say anything you want.

If the author had given details of the methods, and explained why she chose to control for the variables she did, and not others, that might be different. But she didn't. Nor did she justify only looking at the effects six years later, when five or seven or ten would be just as sensible... and so on.

(Note: whenever I've said "autism", that's my shorthand for autism + SLI, which is what the paper looked at; autism alone data are not presented. Note also that by "vaccination %" I mean "% who got the full vaccine schedule"; the other kids may have got vaccines, just not all of them.)

Delong G (2011). A Positive Association found between Autism Prevalence and Childhood Vaccination uptake across the U.S. Population. Journal of toxicology and environmental health. Part A, 74 (14), 903-16 PMID: 21623535

Neurology vs Psychiatry

Neurology and psychiatry are related fields - if for no other reason, because neurological disorders can often manifest as, and get misdiagnosed as, psychiatric ones.

But what's the borderline between neurology and psychiatry? What makes one disease "neurological" and another "mental"? Are some psychiatric disorders more "neurological" than others?

It's a rather philosophical question and you could discuss it for as long as you wanted. Rather than doing that I thought I'd have a look to see which disorders are, at the moment, considered to fall into each category.

To do this I did a quick search the archives of two journals, Neurology which the world's leading journal of... well, guess, and the American Journal of Psychiatry. I looked to see how many papers from the past 20 years had either a Title or an Abstract which referred to various different diseases. You can see the results above. Note that the total number of papers varied, obviously, and I've only plotted the proportion.

Some interesting results. Schizophrenia, which is probably considered "the most neurological" psychiatric disorder, is in fact the least talked about in Neurology. Depression is top amongst the "core" psychiatric ones.

Autism occupies a middle ground, discussed by psychiatrists at 70% and neurologists at 30%. That didn't surprise me, but what did was that ADHD is almost as neurological as autism. Mental retardation is also intermediate, though it's 30:70 in favour of neurology. Whether autism is really less neurological than mental retardation, is a good question.

Then out of the disorders with a known neuropathology, Alzheimer's disease, Huntington's disease and "dementia" (which overlaps with Alzheimer's) are a bit psychiatric while stuff like headache and epilepsy is almost 100% neurological. Why this is, is not entirely clear, since both dementia and epilepsy are caused by neurological damage, and they can both cause "psychiatric" symptoms.

I suspect the difference is that it's just much harder to treat Alzheimer's, Huntington's and dementia. With epilepsy or meningitis, neurologists have a very good chance of controlling the symptoms and few patients will be left with ongoing psychiatric problems. But with the neurodegenerative disorders, neurologists can't really do much, leaving a large pool of people for psychiatrists to study.

Someone once said that neurologists take all of the curable diseases and leave psychiatrists with the ones they can't help. These figures suggest that there may be some truth in this.

Neurology vs Psychiatry

Neurology and psychiatry are related fields - if for no other reason, because neurological disorders can often manifest as, and get misdiagnosed as, psychiatric ones.

But what's the borderline between neurology and psychiatry? What makes one disease "neurological" and another "mental"? Are some psychiatric disorders more "neurological" than others?

It's a rather philosophical question and you could discuss it for as long as you wanted. Rather than doing that I thought I'd have a look to see which disorders are, at the moment, considered to fall into each category.

To do this I did a quick search the archives of two journals, Neurology which the world's leading journal of... well, guess, and the American Journal of Psychiatry. I looked to see how many papers from the past 20 years had either a Title or an Abstract which referred to various different diseases. You can see the results above. Note that the total number of papers varied, obviously, and I've only plotted the proportion.

Some interesting results. Schizophrenia, which is probably considered "the most neurological" psychiatric disorder, is in fact the least talked about in Neurology. Depression is top amongst the "core" psychiatric ones.

Autism occupies a middle ground, discussed by psychiatrists at 70% and neurologists at 30%. That didn't surprise me, but what did was that ADHD is almost as neurological as autism. Mental retardation is also intermediate, though it's 30:70 in favour of neurology. Whether autism is really less neurological than mental retardation, is a good question.

Then out of the disorders with a known neuropathology, Alzheimer's disease, Huntington's disease and "dementia" (which overlaps with Alzheimer's) are a bit psychiatric while stuff like headache and epilepsy is almost 100% neurological. Why this is, is not entirely clear, since both dementia and epilepsy are caused by neurological damage, and they can both cause "psychiatric" symptoms.

I suspect the difference is that it's just much harder to treat Alzheimer's, Huntington's and dementia. With epilepsy or meningitis, neurologists have a very good chance of controlling the symptoms and few patients will be left with ongoing psychiatric problems. But with the neurodegenerative disorders, neurologists can't really do much, leaving a large pool of people for psychiatrists to study.

Someone once said that neurologists take all of the curable diseases and leave psychiatrists with the ones they can't help. These figures suggest that there may be some truth in this.

British Government Fails Maths, Economics

The British government has decided to change the way English universities are funded. They say that this will improve teaching quality; I doubt it. Worse, however, is that the new scheme, the stated justification for which was to save money, is now seriously at risk of being too expensive. From next year universities will be allowed to charge up to £9,000 per year for undergraduate courses, up from the current limit of just over £3,000. However the government is also cutting their basic funding allowance by 80% to compensate. So government pays less and students pay more - eventually; the government will pay all the money up front in the form of a loan to the students.

The government's cost projections assumed that the mean fee at English universities, and hence the mean size of their loans, would be £7,500 per year. Why, no-one seems to know. Sources are unanimous that this was what they assumed, but no-one links to any kind of report explaining why. Maybe they gazed into a magic crystal ball. Parliament, performing a separate analysis, also worked under the assumption of £7,500, and their reason was that

we have assumed that... this fee covers the 80% reduction in [central government funding]. The average fee... is assumed to be £7,500 per annum for an undergraduate degree.

However, this is just silly. For averagefees to be £7,500, anyone charging some amount more than that, would have to be balanced out by someone charging the same amount less. That's what an average is.

However, no-one can afford to charge less, even if they wanted to, because they need to charge £7,500 to pay for their teaching and break even. £7,500 is the minimum not the average. But plenty will want to charge more. Oxford and Cambridge, for instance, were blatantly going to charge the top amount, because they're "top" universities. As a result, every other university which aspires to be elite will have to charge £9k, to keep up with Oxbridge.

Hence a domino effect goes down the line: every university will want to charge as much as the ones immediately ahead of them, so as not to look cheap. (The alternative, that they'd try to undercut them in price, makes no sense when you consider the amounts of money involved; the savings to the students would be minimal but the message - "we are cheap, therefore not very good" - would be loud and clear.)

I've whipped up a little plot showing all the universities which have currently announced their fees along with their position in the latest university rankings. A few small institutions are unranked and so don't appear.

The rankings go up to 115 so if the universities ranked over 58 charge over £7.5k, the others would have to charge less to cancel them out. I'll try to update this chart when fees are announced, but I think it's a forgone conclusion that this won't happen. Last updated 06/04/2011 10 am. See also here for a frequently-updated expert analysis.

The government is now seriously talking about having to cut what little direct university funding remains, in order to avoid losing money - from a policy which was supposed to save money. Yet this was always going to happen given what I said above. Indeed this policy, which was sold to the country as a cost-cutting measure, was always going to, at best, break even until the graduates repay their loans, and they won't even start doing that until the first batch graduate, in 2015 which is the next election year.

So there seem to be only two possible options. Maybe they knew it wouldn't save money, but in that case, why did they do it? It's not winning them any votes, so there must be a long-term plan, but what? The other possibility is that they genuinely thought it would save money. So it's a question of bungling incompetence vs. mysterious scheme. I'm not sure which is worse.

British Government Fails Maths, Economics

The British government has decided to change the way English universities are funded. They say that this will improve teaching quality; I doubt it. Worse, however, is that the new scheme, the stated justification for which was to save money, is now seriously at risk of being too expensive. From next year universities will be allowed to charge up to £9,000 per year for undergraduate courses, up from the current limit of just over £3,000. However the government is also cutting their basic funding allowance by 80% to compensate. So government pays less and students pay more - eventually; the government will pay all the money up front in the form of a loan to the students.

The government's cost projections assumed that the mean fee at English universities, and hence the mean size of their loans, would be £7,500 per year. Why, no-one seems to know. Sources are unanimous that this was what they assumed, but no-one links to any kind of report explaining why. Maybe they gazed into a magic crystal ball. Parliament, performing a separate analysis, also worked under the assumption of £7,500, and their reason was that

we have assumed that... this fee covers the 80% reduction in [central government funding]. The average fee... is assumed to be £7,500 per annum for an undergraduate degree.

However, this is just silly. For averagefees to be £7,500, anyone charging some amount more than that, would have to be balanced out by someone charging the same amount less. That's what an average is.

However, no-one can afford to charge less, even if they wanted to, because they need to charge £7,500 to pay for their teaching and break even. £7,500 is the minimum not the average. But plenty will want to charge more. Oxford and Cambridge, for instance, were blatantly going to charge the top amount, because they're "top" universities. As a result, every other university which aspires to be elite will have to charge £9k, to keep up with Oxbridge.

Hence a domino effect goes down the line: every university will want to charge as much as the ones immediately ahead of them, so as not to look cheap. (The alternative, that they'd try to undercut them in price, makes no sense when you consider the amounts of money involved; the savings to the students would be minimal but the message - "we are cheap, therefore not very good" - would be loud and clear.)

I've whipped up a little plot showing all the universities which have currently announced their fees along with their position in the latest university rankings. A few small institutions are unranked and so don't appear.

The rankings go up to 115 so if the universities ranked over 58 charge over £7.5k, the others would have to charge less to cancel them out. I'll try to update this chart when fees are announced, but I think it's a forgone conclusion that this won't happen. Last updated 06/04/2011 10 am. See also here for a frequently-updated expert analysis.

The government is now seriously talking about having to cut what little direct university funding remains, in order to avoid losing money - from a policy which was supposed to save money. Yet this was always going to happen given what I said above. Indeed this policy, which was sold to the country as a cost-cutting measure, was always going to, at best, break even until the graduates repay their loans, and they won't even start doing that until the first batch graduate, in 2015 which is the next election year.

So there seem to be only two possible options. Maybe they knew it wouldn't save money, but in that case, why did they do it? It's not winning them any votes, so there must be a long-term plan, but what? The other possibility is that they genuinely thought it would save money. So it's a question of bungling incompetence vs. mysterious scheme. I'm not sure which is worse.

What Diseases Get Researched?

Dorothy Bishop (of BishopBlog) has a nice PLoS paper looking at: Which neurodevelopmental disorders get researched and why?.

She took 35 "neurodevelopmental" disorders, ranging from rare genetic syndromes like Rett's, up to autism, ADHD and specific language impairment (SLI), and compared their prevalence stated in a textbook, to the number of scientific papers published about them over the past 15 years.

Note that with something like Rett's, there's no question that they're problems with brain development. With autism, some people would contest that but not many nowadays. With ADHD and some others, however, it's pretty controversial. Bishop includes them on the grounds that they're generally treated as neurodevelopmental in the scientific literature.

The graph above - which I should stress is mine; Bishop's are much less messy - shows the basic results.

First up, there's a correlation between prevalence and the number of research publications, but as you can see, it's pretty weak. Within the rare genetic disorders (pretty much everything below 0.1% prevalence) there does seem to be a relationship. When you get to the more common disorders, which are also the ones which are more controversial, there's no correlation at all.

Some points stand out:

• Autism is very popular; it gets the same amount of research as intellectual disability aka mental retardation (ID/MR), even though ID/MR is 9 times more common (0.65% vs. 5.5%)
• Down's Syndrome gets a huge amount of research despite being rare. It gets much more than Cerebral Palsy and Fragile X despite them all being severe and roughly as common.
• Tourette's is much less studied than any other disorder with a similar prevalence.
  
• In the bottom left you'll see a bunch of apparantly very common disorders like dyslexia, dyscalculia, and specific language impairment, which are extremely under-studied... if you accept those prevalence figures.

Bishop also notes that both the number of publications, and the amount of US government funding, for ADHD and autism research have skyrocketed in recent years. Especially ADHD which had just 356 publications in 1985–1989 but this increased nearly twenty-fold to 6158 in 2005–2009!

As for why all these figures are they way they are, it's less clear. Bishop discusses various factors like severity and the availability of funding in the paper, but this can't explain everything. It seems likely that some things are just more scientifically fashionable than others, for whatever reason...

Link: See also Bishop's Guardian piece about the paper.

Bishop DV (2010). Which neurodevelopmental disorders get researched and why? PloS one, 5 (11) PMID: 21152085

What Diseases Get Researched?

Dorothy Bishop (of BishopBlog) has a nice PLoS paper looking at: Which neurodevelopmental disorders get researched and why?.

She took 35 "neurodevelopmental" disorders, ranging from rare genetic syndromes like Rett's, up to autism, ADHD and specific language impairment (SLI), and compared their prevalence stated in a textbook, to the number of scientific papers published about them over the past 15 years.

Note that with something like Rett's, there's no question that they're problems with brain development. With autism, some people would contest that but not many nowadays. With ADHD and some others, however, it's pretty controversial. Bishop includes them on the grounds that they're generally treated as neurodevelopmental in the scientific literature.

The graph above - which I should stress is mine; Bishop's are much less messy - shows the basic results.

First up, there's a correlation between prevalence and the number of research publications, but as you can see, it's pretty weak. Within the rare genetic disorders (pretty much everything below 0.1% prevalence) there does seem to be a relationship. When you get to the more common disorders, which are also the ones which are more controversial, there's no correlation at all.

Some points stand out:

• Autism is very popular; it gets the same amount of research as intellectual disability aka mental retardation (ID/MR), even though ID/MR is 9 times more common (0.65% vs. 5.5%)
• Down's Syndrome gets a huge amount of research despite being rare. It gets much more than Cerebral Palsy and Fragile X despite them all being severe and roughly as common.
• Tourette's is much less studied than any other disorder with a similar prevalence.
  
• In the bottom left you'll see a bunch of apparantly very common disorders like dyslexia, dyscalculia, and specific language impairment, which are extremely under-studied... if you accept those prevalence figures.

Bishop also notes that both the number of publications, and the amount of US government funding, for ADHD and autism research have skyrocketed in recent years. Especially ADHD which had just 356 publications in 1985–1989 but this increased nearly twenty-fold to 6158 in 2005–2009!

As for why all these figures are they way they are, it's less clear. Bishop discusses various factors like severity and the availability of funding in the paper, but this can't explain everything. It seems likely that some things are just more scientifically fashionable than others, for whatever reason...

Link: See also Bishop's Guardian piece about the paper.

Bishop DV (2010). Which neurodevelopmental disorders get researched and why? PloS one, 5 (11) PMID: 21152085

The Rise of the Mouse

Everyone knows that scientists experiment on rats, and guinea-pigs. That's why we have "lab rats" and why, if you're trying out something new, you're a "human guinea-pig".

But this is all out of date. Nowadays, mice are the most popular lab animals. Here's a graph showing the number of scientific papers published each year, mentioning each kind of critter (data gathered with this script):

Rats were on top until about 10 years ago, when mice overtook them. Why? No-one wants to study mice if they can help it: they are horrible to work with compared to rats, and rats are more similar to humans physiologically. This is why rats were more popular for a long time. (Contrary to popular belief, guinea pigs were never used all that much, and they've become even less popular with the rise of mice.)

Non-scientists tend to think of rats as just big mice. They're not: mice are less intelligent, harder to handle (they bite... a lot), and they smell bad. The fact that they're smaller makes surgery, and even simple stuff like taking blood samples, much harder. On the plus side, you can fit more of them in any given space, making them cheaper, but that's about it.

So why did mice suddenly claim the crown? One word - knockout. Mice are the only mammal in which it's easy to perform genetic knockout, i.e. eliminating the function of a single gene. It's extremely difficult in rats, because, for reasons no-one really understands, it is harder to get rat stem cells to grow in vitro.

Knockout mice were "invented" in 1989, and the inexorable rise in the number of mouse papers began a few years later. Recently, there have been reports that knockout rats may now be easy; whether this will lead to a rat renaissance remains to be seen.

Knockouts have revolutionized biology, because they make it easy to investigate what each gene does. Just knock it out, and see what's wrong with your mouse. This is why there are mouse models of so many genetic diseases, while rat and monkey models are only available for a few disorders.

The Rise of the Mouse

Everyone knows that scientists experiment on rats, and guinea-pigs. That's why we have "lab rats" and why, if you're trying out something new, you're a "human guinea-pig".

But this is all out of date. Nowadays, mice are the most popular lab animals. Here's a graph showing the number of scientific papers published each year, mentioning each kind of critter (data gathered with this script):

Rats were on top until about 10 years ago, when mice overtook them. Why? No-one wants to study mice if they can help it: they are horrible to work with compared to rats, and rats are more similar to humans physiologically. This is why rats were more popular for a long time. (Contrary to popular belief, guinea pigs were never used all that much, and they've become even less popular with the rise of mice.)

Non-scientists tend to think of rats as just big mice. They're not: mice are less intelligent, harder to handle (they bite... a lot), and they smell bad. The fact that they're smaller makes surgery, and even simple stuff like taking blood samples, much harder. On the plus side, you can fit more of them in any given space, making them cheaper, but that's about it.

So why did mice suddenly claim the crown? One word - knockout. Mice are the only mammal in which it's easy to perform genetic knockout, i.e. eliminating the function of a single gene. It's extremely difficult in rats, because, for reasons no-one really understands, it is harder to get rat stem cells to grow in vitro.

Knockout mice were "invented" in 1989, and the inexorable rise in the number of mouse papers began a few years later. Recently, there have been reports that knockout rats may now be easy; whether this will lead to a rat renaissance remains to be seen.

Knockouts have revolutionized biology, because they make it easy to investigate what each gene does. Just knock it out, and see what's wrong with your mouse. This is why there are mouse models of so many genetic diseases, while rat and monkey models are only available for a few disorders.

Are "Antipsychotics" Antipsychotics?

This is the question asked by Tilman Steinert & Martin Jandl in a letter to the journal Psychopharmacology.

They point out that in the past 20 years, the word "antipsychotic" has exploded in popularity. Less than 100 academic papers were published with that word in the title in 1990, but now it's over 600 per year.

The older term for the same drugs was "neuroleptics". This terminology, however, has slowly but surely fallen into disuse over the same time period.

To illustrate this they have a nice graph of PubMed hits. Neuroskeptic readers will be familiar with these as I have often posted my own and I recently wrote a bash script to harvest this data automatically. Now you too can be a historian of medicine from the comfort of your own home...

Why does it matter what we call them? A name is just a name, right? No, that's the problem. Actually, neuroleptic is just a name, because it doesn't mean anything. The term derives from the Greek "neuron", meaning... neuron, and "lambanō" meaning "to take hold of". However, no-one knows that unless they look it up on Wikipedia because it's just a name.

Antipsychotic, on the other hand, means something: it means they treat psychosis. But whether or not this is an accurate description of what "antipsychotics" actually do, is controversial. For one thing, these drugs are also used to treat many non-psychotic illnesses, like depression, and PTSD.

More fundamentally, it's not universally accepted that they have a direct anti-psychotic effect. All antipsychotics are powerful sedatives. There's a school of thought that says that this is in fact all they are, and rather than treating psychosis, they just sedate people until they stop being obviously psychotic.

Personally, I don't believe that, but that's not really the point: the point is that it's controversial, and calling them antipsychotics makes it hard to think about that controversy in a sensible way. To say that antipsychotics aren't actually antipsychotic is a contradiction in terms. To say they are antipsychotic is a tautology. Names shouldn't dictate the terms of a debate in that way. A name should just be a name.

The same point applies to more than just antipsychotics - I mean neuroleptics - of course. Perhaps the worst example is "antidepressants". Prozac, for example, is called an antidepressant. Implying that it treats depression.

But according to clinical trials, Prozac and other SSRIs are a lot more effective, relative to placebo, in obsessive-compulsive disorders (OCD) than they are in depression (though this is not necessarily true of all "antidepressants", yet more evidence that the word is unhelpful.)

So, as I asked in a previous post: "Are SSRIs actually antiobsessives that happen to be helpful in some cases of depression?" Personally, I think the only name for them which doesn't make any questionable assumptions, is simply 'SSRIs'.

Tilman Steinert and Martin Jandl (2010). Are antipsychotics antipsychotics? Psychopharmacology DOI: 10.1007/s00213-010-1927-3

Are "Antipsychotics" Antipsychotics?

This is the question asked by Tilman Steinert & Martin Jandl in a letter to the journal Psychopharmacology.

They point out that in the past 20 years, the word "antipsychotic" has exploded in popularity. Less than 100 academic papers were published with that word in the title in 1990, but now it's over 600 per year.

The older term for the same drugs was "neuroleptics". This terminology, however, has slowly but surely fallen into disuse over the same time period.

To illustrate this they have a nice graph of PubMed hits. Neuroskeptic readers will be familiar with these as I have often posted my own and I recently wrote a bash script to harvest this data automatically. Now you too can be a historian of medicine from the comfort of your own home...

Why does it matter what we call them? A name is just a name, right? No, that's the problem. Actually, neuroleptic is just a name, because it doesn't mean anything. The term derives from the Greek "neuron", meaning... neuron, and "lambanō" meaning "to take hold of". However, no-one knows that unless they look it up on Wikipedia because it's just a name.

Antipsychotic, on the other hand, means something: it means they treat psychosis. But whether or not this is an accurate description of what "antipsychotics" actually do, is controversial. For one thing, these drugs are also used to treat many non-psychotic illnesses, like depression, and PTSD.

More fundamentally, it's not universally accepted that they have a direct anti-psychotic effect. All antipsychotics are powerful sedatives. There's a school of thought that says that this is in fact all they are, and rather than treating psychosis, they just sedate people until they stop being obviously psychotic.

Personally, I don't believe that, but that's not really the point: the point is that it's controversial, and calling them antipsychotics makes it hard to think about that controversy in a sensible way. To say that antipsychotics aren't actually antipsychotic is a contradiction in terms. To say they are antipsychotic is a tautology. Names shouldn't dictate the terms of a debate in that way. A name should just be a name.

The same point applies to more than just antipsychotics - I mean neuroleptics - of course. Perhaps the worst example is "antidepressants". Prozac, for example, is called an antidepressant. Implying that it treats depression.

But according to clinical trials, Prozac and other SSRIs are a lot more effective, relative to placebo, in obsessive-compulsive disorders (OCD) than they are in depression (though this is not necessarily true of all "antidepressants", yet more evidence that the word is unhelpful.)

So, as I asked in a previous post: "Are SSRIs actually antiobsessives that happen to be helpful in some cases of depression?" Personally, I think the only name for them which doesn't make any questionable assumptions, is simply 'SSRIs'.

Tilman Steinert and Martin Jandl (2010). Are antipsychotics antipsychotics? Psychopharmacology DOI: 10.1007/s00213-010-1927-3

The Fall of Freud

The works of Sigmund Freud were enormously influential in 20th century psychiatry, but they've now been reduced to little more than a fringe belief system. Armed with the latest version of my PubMed history script, and inspired by this classic gnxp post on the death of Marxism, postmodernism, and other stupid academic fads I decided to see how this happened.

As you can see, the number of published scientific papers related to Freud-y search terms like psychoanalytic has flat-lined for the past 50 years. That represents a serious collapse of influence, given the enormous expansion in the amount of research being published over this time.

Since 1960 the number of papers on schizophrenia has risen by a factor of 10 and anxiety by a factor of 80 (sic). The peak of Freud's fame was 1968, when almost as many papers referenced psychoanalytic (721) as did schizophrenia (989), and it was more than half as popular as antidepressants (1372). Today it's just 10% of either. Proportionally speaking, psychoanalysis has gone out with a whimper, though not a bang.

The rise of Cognitive Behavioral Therapy (CBT), however, is even more dramatic. From being almost unheard until the late 80's, it overtook psychoanalytic in 1993, and it's now more popular than antipsychotics and close on the heels of antidepressants.

What's going to happen in the future? If there is to be a struggle for influence it looks set to be fought between CBT and biological psychiatry, if only because they're pretty much the only games left in town. Yet one of the reasons behind CBT's widespread appeal is that it hasn't thus far overtly challenged biology, has adopted the methods of medicine (clinical trials etc.), and has presented itself as being useful as well as medication rather than instead of it.

One of the few exceptions was Richard Bentall's book Madness Explained (2003) in which he criticized psychiatry and presented a cognitive-behavioural alternative to orthodox biological theories of schizophrenia and bipolar disorder. Bentall remains on the radical wing of the CBT community but in the coming decades this kind of thing may become more common. Only time will tell...

The Fall of Freud

The works of Sigmund Freud were enormously influential in 20th century psychiatry, but they've now been reduced to little more than a fringe belief system. Armed with the latest version of my PubMed history script, and inspired by this classic gnxp post on the death of Marxism, postmodernism, and other stupid academic fads I decided to see how this happened.

As you can see, the number of published scientific papers related to Freud-y search terms like psychoanalytic has flat-lined for the past 50 years. That represents a serious collapse of influence, given the enormous expansion in the amount of research being published over this time.

Since 1960 the number of papers on schizophrenia has risen by a factor of 10 and anxiety by a factor of 80 (sic). The peak of Freud's fame was 1968, when almost as many papers referenced psychoanalytic (721) as did schizophrenia (989), and it was more than half as popular as antidepressants (1372). Today it's just 10% of either. Proportionally speaking, psychoanalysis has gone out with a whimper, though not a bang.

The rise of Cognitive Behavioral Therapy (CBT), however, is even more dramatic. From being almost unheard until the late 80's, it overtook psychoanalytic in 1993, and it's now more popular than antipsychotics and close on the heels of antidepressants.

What's going to happen in the future? If there is to be a struggle for influence it looks set to be fought between CBT and biological psychiatry, if only because they're pretty much the only games left in town. Yet one of the reasons behind CBT's widespread appeal is that it hasn't thus far overtly challenged biology, has adopted the methods of medicine (clinical trials etc.), and has presented itself as being useful as well as medication rather than instead of it.

One of the few exceptions was Richard Bentall's book Madness Explained (2003) in which he criticized psychiatry and presented a cognitive-behavioural alternative to orthodox biological theories of schizophrenia and bipolar disorder. Bentall remains on the radical wing of the CBT community but in the coming decades this kind of thing may become more common. Only time will tell...

How to Be A PubMed Historian

Quite a lot of people seem to like those graphs I sometimes make showing the number of papers published about a certain topic in any given year, based on the number of PubMed hits.

But how do I do it? Surely I don't sit there manually searching PubMed for each term, for each year, right? That would mean dozens, maybe hundreds, of manual searches. Well, unfortunately, that is exactly how I've done it in the past. I really am that cool, see.


Actually it doesn't take very long once you get into the swing of it, but I've now worked out a better way. See below for a bash script which repeatedly searches PubMed for a given sequence of years, downloads the first page of the results, picks out the bit where it tells you how many hits you got, and puts it all into a single output text file ready to be pasted into Excel or whatever. This comes with no guarantees whatsoever, but it seems to work. Enjoy...

Edit 29/06/2010: Vastly improved version that searches for multiple different terms sequentially, accepts terms that include spaces, and outputs the data into a sensible format. The search term text file should be a plain text file containing one search term per line. e.g:

serotonin depression
dopamine depression
GABA depression

Would search for each of those terms and output the data for each year into a single text file - with three data columns in this case - good for comparing the relative popularity of many different terms across time.

---
#! /bin/bash
# 29 . 06 . 2010
#PubMedHistory script by Neuroskeptic http://neuroskeptic.blogspot.com
# script to find out how many PubMed hits for a certain string in a given year range.

# usage: script (search term text file) (start year) (end year) (output file)
# e.g script list_of_terms.txt 2000 2005 dope.txt
#first, print the HEADER line of the output file.

printf "YEAR\t" > $4
cat $1 | while read subject
do
#pre-format the subject to remove spaces
ffa=${subject/' '/%20}
echo -n "$ffa" >> $4
printf "\t" >> $4
done
#and a newline
printf "\n" >> $4

#Now the real thing. The main loop is a YEAR loop:

for (( yearz=$2; yearz<=$3; yearz++ )) do #For each year, create a temporary file t.txt containing the output for this line.
#First, the year, then a tab.

printf "$yearz\t" > t.txt

#now, a second loop to go through the list of searches
cat $1 | while read subject
do
one=${subject/' '/%20}
wget -O $yearz.txt http://www.ncbi.nlm.nih.gov/sites/entrez?term="$one"+"$yearz"'[Publication Date]'

#find the line in the output with what we're interested in
output=`cat $yearz.txt | grep ncbi_resultcount | awk '{print}'`
#now, change it to get rid of the bit containing the search term
#as this will screw up the next step if it contains spaces!
output=${output/content*publication/LOL}
#print to a temp file
echo $output > temp$one$2$3$4.txt
#find the bit we want using awk
output=`awk '{ print $22 }' temp$one$2$3$4.txt`
rm temp$one$2$3$4.txt
rm $yearz.txt
#trim output
trimmedout=${output#content\=\"}
trimmedoutB=${trimmedout%\"}
#replace "false" with 0 because that's what "false" means
trimmedoutC=${trimmedoutB/'false'/0}
echo in year $yearz , I got $trimmedoutC. Saving to temp file t.txt
#write the result, and a tab, to the TEMPORARY output file
printf "$trimmedoutC\t" >> t.txt
done
#Now we've done all the search terms for this YEAR, so send the temporary data to the final file
cat t.txt >> $4
#and give it a newline
printf "\n" >> $4
done
rm t.txt

How to Be A PubMed Historian

Quite a lot of people seem to like those graphs I sometimes make showing the number of papers published about a certain topic in any given year, based on the number of PubMed hits.

But how do I do it? Surely I don't sit there manually searching PubMed for each term, for each year, right? That would mean dozens, maybe hundreds, of manual searches. Well, unfortunately, that is exactly how I've done it in the past. I really am that cool, see.


Actually it doesn't take very long once you get into the swing of it, but I've now worked out a better way. See below for a bash script which repeatedly searches PubMed for a given sequence of years, downloads the first page of the results, picks out the bit where it tells you how many hits you got, and puts it all into a single output text file ready to be pasted into Excel or whatever. This comes with no guarantees whatsoever, but it seems to work. Enjoy...

Edit 29/06/2010: Vastly improved version that searches for multiple different terms sequentially, accepts terms that include spaces, and outputs the data into a sensible format. The search term text file should be a plain text file containing one search term per line. e.g:

serotonin depression
dopamine depression
GABA depression

Would search for each of those terms and output the data for each year into a single text file - with three data columns in this case - good for comparing the relative popularity of many different terms across time.

---
#! /bin/bash
# 29 . 06 . 2010
#PubMedHistory script by Neuroskeptic http://neuroskeptic.blogspot.com
# script to find out how many PubMed hits for a certain string in a given year range.

# usage: script (search term text file) (start year) (end year) (output file)
# e.g script list_of_terms.txt 2000 2005 dope.txt
#first, print the HEADER line of the output file.

printf "YEAR\t" > $4
cat $1 | while read subject
do
#pre-format the subject to remove spaces
ffa=${subject/' '/%20}
echo -n "$ffa" >> $4
printf "\t" >> $4
done
#and a newline
printf "\n" >> $4

#Now the real thing. The main loop is a YEAR loop:

for (( yearz=$2; yearz<=$3; yearz++ )) do #For each year, create a temporary file t.txt containing the output for this line.
#First, the year, then a tab.

printf "$yearz\t" > t.txt

#now, a second loop to go through the list of searches
cat $1 | while read subject
do
one=${subject/' '/%20}
wget -O $yearz.txt http://www.ncbi.nlm.nih.gov/sites/entrez?term="$one"+"$yearz"'[Publication Date]'

#find the line in the output with what we're interested in
output=`cat $yearz.txt | grep ncbi_resultcount | awk '{print}'`
#now, change it to get rid of the bit containing the search term
#as this will screw up the next step if it contains spaces!
output=${output/content*publication/LOL}
#print to a temp file
echo $output > temp$one$2$3$4.txt
#find the bit we want using awk
output=`awk '{ print $22 }' temp$one$2$3$4.txt`
rm temp$one$2$3$4.txt
rm $yearz.txt
#trim output
trimmedout=${output#content\=\"}
trimmedoutB=${trimmedout%\"}
#replace "false" with 0 because that's what "false" means
trimmedoutC=${trimmedoutB/'false'/0}
echo in year $yearz , I got $trimmedoutC. Saving to temp file t.txt
#write the result, and a tab, to the TEMPORARY output file
printf "$trimmedoutC\t" >> t.txt
done
#Now we've done all the search terms for this YEAR, so send the temporary data to the final file
cat t.txt >> $4
#and give it a newline
printf "\n" >> $4
done
rm t.txt

This Season's Hottest Brain Regions

Are you a budding neuroscientist who's not sure which part of the brain to specialize in? Or perhaps you're a purveyor of media neuro-nonsense who's wondering which area to namedrop as being the key to sex / intelligence / politics next?

Well, wonder no more, because Neuroskeptic can now exclusively reveal which parts of the brain are hot, and which are not, right now (thanks to the high-tech method of searching PubMed and counting the papers published referring to eight major brain regions, each year from 1985 to 2009.)

The hippocampus stands out as an extremely hot region with both a huge number of papers and rapid growth over 25 years. So it's probably a good place to build a career... but on the other hand, the market may be saturated already, and it shows some signs of flatlining in the past few years. The cerebellum has long been popular, but growth has been extremely slow lately.

To better highlight the growth curves here's the same data but normalized to the year 2000 (so "2" means twice as many papers as in 2000, etc.)

This shows major differences. The orbitofrontal cortex and cingulate cortex are both undergoing massive growth at the moment. The amygdala and parietal cortex are pretty hot too. By contrast, the cerebellum and the caudate are stuck in the scientific doldrums.

Why are the patterns so different for different parts of the brain? That's a big question which hopefully will get discussed in the Comments. I suspect that the recent rise of the cingulate cortex and the orbitofrontal cortex, however, has much to do with the rise of fMRI (i.e. within the last 10 years, mostly), which allows them to be easily studied in humans for the first time.

Both of these areas are quite difficult to study with older technologies like EEG, because of their location within the head. That said, the same problem applies to plenty of other regions, but the orbitofrontal and cingulate cortex are also difficult to study in lab rats and mice, because it's not clear which parts of the rodent brain map onto which parts of the human brain in these regions. By contrast, things like the cerebellum and caudate nucleus have exact rodent equivalents, perhaps making them more attractive to early researchers.

This Season's Hottest Brain Regions

Are you a budding neuroscientist who's not sure which part of the brain to specialize in? Or perhaps you're a purveyor of media neuro-nonsense who's wondering which area to namedrop as being the key to sex / intelligence / politics next?

Well, wonder no more, because Neuroskeptic can now exclusively reveal which parts of the brain are hot, and which are not, right now (thanks to the high-tech method of searching PubMed and counting the papers published referring to eight major brain regions, each year from 1985 to 2009.)

The hippocampus stands out as an extremely hot region with both a huge number of papers and rapid growth over 25 years. So it's probably a good place to build a career... but on the other hand, the market may be saturated already, and it shows some signs of flatlining in the past few years. The cerebellum has long been popular, but growth has been extremely slow lately.

To better highlight the growth curves here's the same data but normalized to the year 2000 (so "2" means twice as many papers as in 2000, etc.)

This shows major differences. The orbitofrontal cortex and cingulate cortex are both undergoing massive growth at the moment. The amygdala and parietal cortex are pretty hot too. By contrast, the cerebellum and the caudate are stuck in the scientific doldrums.

Why are the patterns so different for different parts of the brain? That's a big question which hopefully will get discussed in the Comments. I suspect that the recent rise of the cingulate cortex and the orbitofrontal cortex, however, has much to do with the rise of fMRI (i.e. within the last 10 years, mostly), which allows them to be easily studied in humans for the first time.

Both of these areas are quite difficult to study with older technologies like EEG, because of their location within the head. That said, the same problem applies to plenty of other regions, but the orbitofrontal and cingulate cortex are also difficult to study in lab rats and mice, because it's not clear which parts of the rodent brain map onto which parts of the human brain in these regions. By contrast, things like the cerebellum and caudate nucleus have exact rodent equivalents, perhaps making them more attractive to early researchers.