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

Do Antidepressants Work? The Internet Says...

..."yes and no". A while back I blogged about some researchers who analysed internet discussions of antidepressants to work out what users thought about them. Now a new paper's just come out, doing much the same thing but focussed on a single comment thread: Miracle Drug, Poison, or Placebo.

Back in 2008, MSNBC ran this article, prompted by the recent publication of the famous Kirsch paper. The article itself was short but the ensuing discussion in the comments rapidly grew to epic proportions. By the end of it there were a total of 1,629 posts by a total of over 1,200 people.

In the new paper, author Michael Montagne presents an analysis of the thread. He read through all of the postings and focussed on the ones written by people who had personally taken antidepressants. After excluding obvious spammers and other undesirables (see the picture...), there were still 960 antidepressant users who wrote 1,231 posts.

He first looked to see how many people thought antidepressants were "miracle drugs, poisons, or placebos", which was the title of the original article. However, only a handful of people used those terms in their comments. Almost everyone agreed that antidepressants were not just placebos.

Users employed a range of metaphors to describe the experience. 45 people described them as "livesaving" and 8 said they were a "Godsend". But 21 accused them of turning them into "a zombie".

Down at the bottom of the list were some more unique phrases that only one person used such as "Unleashes a 100 blind monkeys in your brain with instructions to rewire", "Uberpositive girl" and "Robot-zombie wrapped in 4 inches of insulation". That last one could be quite a good horror movie actually.

While there were a small number of absolutely negative comments like "evil" and "Devil's drug", the most consistent theme in the metaphors was that of emotional numbing, with the idea that these drugs remove the symptoms by removing the ability to feel (see e.g. "zombie", "robot", "disconnected", "in a bubble", "band aid".) which seems rather ambivalent. However, only about 10% of the users used any metaphors at all, so take that with a pinch of salt.

Even more salt is required for this graph I made from the table showing the number of positive, negative and mixed judgements on each antidepressant. I've not shown the data from drugs like tricyclics where there were less than 20 total responses. It's interesting, though, that people tended to be more positive about specific drugs than they were when talking about "antidepressants" in general.

There were various other themes in the comments including an ongoing debate between people who said that depressed people ought to seek help from God (who tended to be non-users) vs those who disagreed (who tended to be users). Overall it's an interesting read, but I think it's one of those papers that's more interesting than it really deserves to be. At the end of the day, it's one comment thread on one article on one site.

Montagne M (2011). Miracle drug, poison, or placebo: patients' experiences with antidepressant medications as described in postings on an online message board. Substance use & misuse, 46 (7), 922-30 PMID: 21599508

Autistic Brains 'Genes Differ'

The BBC say:

The brains of people with autism are chemically different to those without autism, according to researchers. A study, published in the journal Nature, showed the unique characters of the frontal and temporal lobes had disappeared.

It's not a bad summary, although it doesn't explain quite how interesting the new results are. Here's the paper, from a joint US/British team: Transcriptomic analysis of autistic brain reveals convergent molecular pathology

The authors took 19 brains from people with autism and 17 healthy ones. These came from people who donated their brains to science and then died. The study involved taking samples from three areas of the brain, the superior temporal gyrus, the prefrontal cortex, and the cerebellum. These are regions that have been implicated in autism, although to be honest, so has everywhere else in the brain.

They then looked at gene expression: mRNA levels. This measures the degree to which different genes are "activated" and being used to make proteins. Bear in mind that a gene itself could be completely normal, and yet be abnormally expressed: this was not a study of DNA mutations. So the BBC's headline is a bit misleading. The genes themselves were not the focus of this study.

Anyway, comparing the autistic and control brains, they found 444 genes that were statistically significantly either over- or under-expressed in the cerebral cortex samples from the autistic group. However, in the cerebellum, there were just 2 differences: so the cerebellum was ruled out from further analysis.

They then replicated the study in a different cortical area in 6 new cases and 5 new controls. They found extremely strong overlap with the original cohort, with the same genes being altered in the same direction in almost all cases. This makes me confident that there is something going on here. This scatterplot shows that almost all of the genes that were significantly different in the first batch were also different in same direction in the second one (although not always significantly, as you'd expect.)

However, the authors didn't stop there, and this is where it gets interesting. First, they used a pattern classification algorithm to try to distinguish patients and controls on the basis of gene expression. This is very much like the paper from last year showing that pattern classification could predict autism on the basis of brain structure.

Interestingly, the algorithm correctly " diagnosed" a case of autism who turned out to have a 15q duplication mutation. 15q duplication is a genetic disorder which causes autism, amongst other things, and it may explain up to 1% of cases of autism. This is only one case but it's important because it suggests that "15q autism" is not all that different to other kinds of autism on the neural level.

The authors then looked at what the over- and under- expressed genes actually were. They found that the "up" genes tended to be genes relating to immune and glial function, while the "down" genes tended to be involved in the formation and function of synapses between cells.

Very interestingly, one of the major clusters of genes , "M12", showed strong overlap with genes previously known to be expressed in a type of cell called PV+ GABA interneurons. In mouse models of autism, these are known to be deficient. M12 was underexpressed in autism, and it contains many genes which have previously been found to be mutated in some people with autism, such as CNTNAP2.

Another cluster, "M16", was overexpressed; it contains genes involved in immune and microglial function (microglia are specialized immune system cells inside the brain). However, M16 did not contain overrepresentation of suspected asd genes.

So this all points to something like this: autism is caused by disruption to the function of certain gene networks in the brain involved in synaptic function. This network is a delicate balance and it can be thrown off course by many different mutations and/or environmental factors.

There's no one gene for autism, but all of the genes for autism might be related, or rather, they might form a team that works together. If you want to look at it this way, you could say that autism is a bit like blindness. People can go blind for lots of different reasons: it could be damage to the surface of the eye, or the retina, or the optic nerve which carries information to the brain, or the brain itself. All of these parts depend on all the others to work, and if one of them goes wrong, the whole system suffers.

Also, whatever the abnormality in autism is, it seems to trigger a secondary change in the brain which is immune and/or glial related. By "secondary" I don't mean that it's less important. It might be what causes the symptoms of autism. But it's not the root cause (because if it were, mutations in this network would cause autism, and they don't seem to.)

This study raises many more questions than it answers, but in a good way. It certainly doesn't explain autism, but it's pointed the way towards more focussed research in the future - gene cluster M12.

Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, & Geschwind DH (2011). Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature PMID: 21614001

How To Set Someone On Fire

I've just come across a deeply disturbing paper: Attempted ignition of petrol vapour by lit cigarettes and lit cannabis resin joints

The authors set out to discover whether you could set petrol on fire by dropping a lit cigarette or hash joint onto it. It turns out, surprisingly, that you can't.

Thirty nine (39) ignition attempts that involved exposing lit commercial cigarettes, hand-rolled cigarettes and cannabis resin joints to petrol vapour were undertaken; ignition was not achieved in any of the scenarios. In addition, a single attempt to ignite petrol vapour emanating from a pool of liquid fuel was effected with a smouldering piece of cannabis resin; no ignition occurred. In all cases the petrol was clearly present within the limits of flammability since ignition was subsequently effected using a naked flame.

It's just not hot enough. Apparantly, for the "hot surface" ignition of petrol vapor to occur, you need a surface with a peak temperature of about 1000 degrees C. Puffing on a cigarette can raise the temperature at the tip to maybe 950 degrees, and while you're not puffing, it's more like 700. A cigarette lighter, however, will do it.

Well, you learn something new every day. Although I'm still not going to be lighting up in the garage any time soon. Don't try this at home.

Anyway, what makes this paper so sinister is the context. This research was done as part of a murder enquiry:

...the consideration of hot surface ignition of petrol vapour was of legitimate interest since the defence proposed the ignition of petrol vapour by “bombers” (small pieces of hot cannabis resin) falling from a lit cannabis resin joint when the smoker puffed on the joint.

The details of the murder aren't specified but reading between the lines, I guess someone burned someone else to death by covering them in petrol and lighting them, and their defence was that it was an accident caused by their joint.

It gets worse. There are pictures.

"The second set of experiments was designed to recreate circumstances specific to the murder case under investigation."

What was the murder case in question? I think I've found it, and if so, the good news is that the guy was found guilty. Stewart Blackburn, 18, was convicted of the murder of his girlfriend Jessica McCagh, in her bedroom, using petrol. The dates fit, as does the fact that the murder took place in Scotland. The paper comes from an English forensic company, but it acknowledges the help of various Scottish legal and police experts.

Jewell RS, Thomas JD, & Dodds RA (2011). Attempted ignition of petrol vapour by lit cigarettes and lit cannabis resin joints. Science & justice : journal of the Forensic Science Society, 51 (2), 72-6 PMID: 21605828

How To Fix Science

Over at Bad Science, Ben Goldacre discusses a big problem with modern science - the published literature is all very well and good, but we don't know what people are finding that goes unpublished:

The scale of the academic universe is dizzying, after all. Our most recent estimate is that there are over 24,000 academic journals in existence, 1.3 million academic papers published every year, and over 50 million papers published since scholarship began.

And for every one of these 50 million papers there will be unknowable quantities of blind alleys, abandoned experiments, conference presentations, work in progress seminars, and more. Look at the vast number of undergraduate and masters dissertations that had an interesting finding, and got turned into finished academic papers: and then think about the even vaster number that don’t...

We are living in the age of information, and vast tracts of data are being generated around the world on every continent and every question. A £200 laptop will let you run endless statistical analyses. The most interesting questions aren’t around individual nuggets of data, but rather how we can corral it to create an information architecture which serves up the whole picture.

I agree with all of this. It is a problem. In fact I'd say it's the single biggest problem with science today. Scientists are required to publish ever-increasing numbers of high-impact papers, in order to get grants and promotions, with the "best" papers, usually meaning the ones with the most interesting positive results, being favored.

Findings that show that nothing especially interesting is going on here all too often get swept under the carpet or re-re-analyzed until a positive result falls out. If you do a study of a certain gene and its association to brain function, say, and find it has no association: that's bad news for you. That will make a low-impact paper, if it makes a paper at all. But maybe it has an association with brain structure? Or personality?

Anyway, that's the problem. What to do about it? Goldacre notes that in medicine, there are mechanisms in place to deal with this:

In medicine, where the stakes are tangible, systems have grown up to try and cope with this problem: trials are supposed registered before they begin, so we can notice the results that get left unpublished. But even here, the systems are imperfect; and pre-registration is very rarely done, even in medical research, for anything other than trials.

Clinical trial pre-registration is a fantastic idea. The systems are certainly imperfect, but they're getting better, and they're much better than nothing. Back in 2008 I proposed that all scientific studies, not just clinical trials, should be publically pre-registered. That way everyone could know what science was going unpublished and could tell when authors were doing analyzes they hadn't originally planned to do (which is fine, so long as you admit to it.)

I still think that would be a good idea. But how would it work in practice? Here's what I've come up with:

Scientific papers should be submitted to journals for publication before the research has started. The Introduction and the Methods section, detailing what you plan to do and why, would then get peer reviewed. The rest of the paper would obviously be a blank at this stage. Anonymous experts would have a chance to critique the methods and rationale.

If the paper's accepted, you then do the research, get the results, and write the Results and Discussion section of the paper. The journal is then required to publish the final paper, assuming that you kept to the original plan. The Introducion and primary Methods would be fixed - you can't change them once the data come in.

You can do additional stuff and run additional analyses all you like, but they'll be marked as secondary, which of course is what they are. Publication would therefore be based on the scientific merits of the experiment, the importance of the question and the quality of the methods, not the "interestingness" of the results. If you want a paper in Nature, it needs to be a great idea, not a lucky shot.

This would be a radical change from the current system. Too radical, almost certainly, to ever happen in one go. So here's another idea as to a kind of stepping-stone on the way:

Already, scientists have to spell out their original rationale and original methods before they do any work - when they apply for funding from a grant awarding body. These grant applications are often very detailed, but at the moment, they're private. And people don't always stick to them.

Why not make the full publication of the grant application a condition of being awarded the money? This would be rather like preregistration of the Introduction and Methods, although less elegant, but it would do the job. And given that most grants consist of public cash, the public really have a right to know this. These applications are usually just PDF files. It would be trivial to put them online - after redacting personal information like applicant résumés, if desired.

How Your Brain Gets In The Game

You're running down a corridor in a castle that's under attack by terrorists.

Why would terrorists want to blow up a castle, you start to wonder, but your musings are cut short. As you round the corner, you bump into not one but three of the fortress-hating fiends. No problem - you're carrying an AK-47 which you picked up from the corpse of one of their buddies. You open fire, and two go down, but the third turns on you with his handgun.

Luckily, this castle is full of huge wooden crates, and you manage to duck behind one. He's out of ammo - but so are you. You desperately try to reload. Just before your new clip is in place... the experiment ends, and you're taken out of the MRI scanner.

This was what it must have been like to take part in an fMRI study just out from a group of German neuroscientists: Neural contributions to flow experience during video game playing. The authors measured brain activity in response to playing a computer game in order to work out the neural correlates of "flow": the feeling of being in the game world, not merely playing it.

Video game brain scanning studies aren't new but previous ones have tended to use simplistic "games" designed specifically for research. This study used a real one, a first-person shooted called Tactical Ops. It's 9 years old and it was not bad, but it was basically a copy of Counter Strike. Which for the younger gamers amongst you was pretty much Call of Duty: Black Ops. Which, for the non-gamers amongst you, is a game where you run around and shoot people.

Anyway, 13 male volunteers took part. They all played quite a lot of games, several hours per week. The experiment consisted of 5 rounds of Tactical Ops, each round lasting at least 12 minutes.

The authors were specifically interested in the experience of flow which was defined by the famous Mihály "That Flow Guy...Sizzenzy...You Know Who I Mean Right" Csíkszentmihályi as consisting of several things including "direct and unambiguous feedback of action results" and "clear goals of the activity" amongst others.

But when during the game were players most strongly experiencing these factors, specifically? This is where things get a bit weird. Rather than asking the players, they watched the replays of what happened during the rounds and noted when certain events happened, using these as a proxy for 5 "flow factors". Clear goals, for example, were rated as present during active fighting and so on, but absent when players were just wandering around the same room for 10 or more seconds.

That makes a lot of sense. However in order to assess "direct feedback of action results", they decided that

High feedback was coded if the player further interacted with the dead body of the victim after the kill, whereas low feedback was coded when the latter was not observable. Success events were only considered if they ended the combat situation and the player did not have to subsequently face other opponents.

In other words, if you just killed a guy and ran off that was not evidence of direct feedback, but if you subsequently "interacted" with his body it was. I'm not sure what this interaction involved... traditionally in these games it means doing something involving tea and bagging. Hmm. To be fair, the authors did verify that the flow factors were happening at these times, in a seperate set of 15 players. That one, however, didn't work.

Anyway, they found various areas of different activation for each of the 5 flow factors, except the aforementioned teabagging one, where they found no effect. The areas involved in each were rather different in each case (see above) but when they pooled across all four of the factors that did product activations, they found an overall effect in two areas: the cerebellum and the left somatosensory cortex.

The idea of using actual videogames in an fMRI experiment is a very nice one, and this study shows that it's feasible (it's not easy, due to the limitations of the equipment you can put inside a scanner.) However, I'm not sure that these are the areas I'd have expected to be flow-y. Especially not the cerebellum. But as the authors say:

Flow is a highly subjective experience...we investigated situations with an enhanced probability of flow experience. Obviously, this is not equivalent to measuring flow experiences directly.

Klasen M, Weber R, Kircher TT, Mathiak KA, & Mathiak K (2011). Neural contributions to flow experience during video game playing. Social cognitive and affective neuroscience PMID: 21596764

Kanazawa's Black Day

Satoshi "The Scientific Fundamentalist" Kanazawa has suddenly become the most talked-about neuro/psychology blogger in the world, getting a BBC News article all to himself.


There's no need for the rest of us to feel jealous though. There may be no such thing as bad publicity, but I think that being accused of being a racist and a sexist who should be sacked, for something you wrote on your blog, something that swiftly got pulled, must come pretty close.

You can read the controversial article here, and in other places, because it was helpfully archived. It used to be here.

Kanazawa based his argument on the Add Health project which was a massive observational study of American adolescents and young adults.

Add Health is huge. It's produced over 3,000 scientific papers, presentations and other documents. That's because it collected a wealth of data on everything from genetics to blood chemistry to social relationships and emotional issues.

Kanazawa looked at the data on physical attractiveness. Attractiveness was rated by an interviewer. Each subject got interviewed for a couple of hours by one interviewer and at the end the interviewer rated how hot they found them.

The fateful post claimed that, according to the Add Health data, black women were rated less attractive, on average, than white, Asian and Native American ones. Let's assume that he's done his sums correctly and that this is true of the data.

The obvious problem is that maybe the interviewers were biased against black women, and rated them lower for that reason. Kanazawa didn't consider this in his post, which is unquestionably an oversight, but he did go on to speculate as to the biological reasons why they might be less attractive.

However, looking at the original Add Health data, can we check whether this bias was at play or not?

Short answer: I found no evidence either way.

Long answer: I first looked over the Add Health website but it doesn't seem to mention anything about who the interviewers were. It doesn't mention their own ethnicity, which would be helpful, although even if they were all black themselves, they might have internalized racism, so that wouldn't be conclusive. They were trained, but then, you can't train someone to not be a racist.

Then I decided to look at the publications. I searched Google Scholar for "Add Health" + attractiveness. This reveals a number of articles, including a 2007 one by Kanazawa ironically, but only one seemed really relevant: Weight Preoccupation as a Function of Observed Physical Attractiveness. (There are other hits, but I skimmed the most likely looking ones and they didn't address bias.)

The details are unimportant, but it involved race and attractiveness, so the authors had to deal with the question of potential rater bias. Unlike Kanazawa they didn't just brush this under the carpet:

Although the interviewers were different races and ethnicities, there is no information about the race or ethnicity of the interviewer for any one respondent to examine systematic bias.

However, post hoc cluster analyses that controlled for an interviewer effect yielded similar results; thus, it is unlikely that interviewers had any substantial biases against any one ethnic group or that they rated attractiveness significantly differently from each other.

The point about "post-hoc cluster analysis" is the key here. To try to control for rater effects (not just racial ones) they analyzed the data covarying for which interviewer rated each girl. They didn't know what races the interviewers were, but they did know which girls got rated by the same interviewer. They found that controlling for the rater did not affect their results.

So does that mean there was no bias? No. Because - this only applies to their results, which were not about attractiveness per se, but about the interaction of attractiveness with other factors to predict an outcome variable (dieting and concern about weight) within a given race.

Even supposing that half of the raters were KKK members who cruelly subtracted, say, a million points from the rated attractiveness of any given black subject, so long as they still rated some black subjects as more attractive than others, all of the comparisons within the black subjects would still work fine: the millions would all cancel out.

So in my judgement, we just can't tell. Unless I've missed something, in which case, please tell us about it in the comments.

Free Will Is In The Brain

Warning: this post may change your brain.


Well, all of my posts change your brain, because everything changes your brain. But this one might make a rather bigger impact than usual.

According to a new paper in Psychological Science, reading a short article which argues that free will is an illusion causes measurable changes in brain function: Inducing Disbelief in Free Will Alters Brain Correlates of Preconscious Motor Preparation.

The authors took 30 people and randomly assigned them to read one of two passages from this book. One of the quotes was a fairly forceful attack on the concept of free will, saying that all of our actions are determined by our genes and environment. The other, placebo extract, was the same length and talked about conciousness but made no reference to free will.

After that, all the volunteers were given EEG while performing the Libet Task. This was invented by the neuroscientist Benjamin Libet, and it's famous as evidence against free will. Basically, the task just involves pushing a button, and you can make an entirely free choice as to when to push it. You then report, with the help of a clock, the moment at which you decided to push it.

What Libet found, using EEG recording, was that there's an electrical change in the brain, a negative voltage called the readiness potential, which starts about 2 seconds before you move. However, most people report "deciding" to move just 200 milliseconds before the actual button click - long after "their brain decided to move", in terms of the readiness potential. Maybe.

Anyway, in the current study they found that reading about determinism reduced the size of the readiness potential, although it still happened:

So ironically, reading an argument against free will reduces the size of a phenomenon which is itself used as an argument against free will... it's enough to make your head spin. The authors say that this fits with earlier work showing that "The early RP...is restricted to movements that are executed with the 'introspective feelings of the willful realization of the intention to move at a particular time'."

This is interesting, but there's a few caveats. The result was nicely significant with a p value of 0.011, but we're not shown the data from individual participants, only the group averages so the effect might be driven by one or two outliers with huge or absent readiness potentials.

Also, it's possible that the effect wasn't about belief in free will as such, but just some kind of distraction. Maybe being confronted with the idea that free will is an illusion just shook the participants up and got them thinking hard, distracting them from the task. To their credit the authors did try to control for this by also measuring EEG responses to simple visual stimuli, finding no effect, but ideally I'd want to see a control consisting of a very controversial, non-free-will article.

In case you were wondering, here's the start of the readiness-potential-reducing passage:

“You,” your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells and their associated molecules. Who you are is nothing but a pack of neurons.

Most religions hold that some kind of spirit exists that persists after one’s bodily death and, to some degree, embodies the essence of that human being. Religions may not have all the same beliefs, but they do have a broad agreement that people have souls. Yet the common belief of today has a totally different view. It is inclined to believe that the idea of a soul, distinct from the body and not subject to our known scientific laws, is a myth.

It is quite understandable how this myth arose without today’s scientific knowledge of nature of matter and radiation, and of biological evolution. Such myths, of having a soul, seem only too plausible. For example, four thousand years ago almost everyone believed the earth was flat. Only with modern science has it occurred to us that in fact the earth is round.

From modern science we now know that all living things, from bacteria to ourselves, are closely related at the biochemical level. We now know that many species of plants and animals have evolved over time. We can watch the basic processes of evolution happening today, both in the field and in our test tubes and therefore, there is no need for the religious concept of a soul to explain the behavior of humans and other animals...

It goes on, but I'll stop there... for the sake of your brain.

Rigoni D, Kühn S, Sartori G, & Brass M (2011). Inducing disbelief in free will alters brain correlates of preconscious motor preparation: the brain minds whether we believe in free will or not. Psychological science : a journal of the American Psychological Society / APS, 22 (5), 613-8 PMID: 21515737

Writing Emotions

A handy guide to some of the emotional states that you may feel in the process of writing.

DELation - The relief and sense of freedom that comes from deleting a passage you've been endlessly trying to get right, as you realize that you don't need it there at all.

Obsessive Compul-Save Disorder - A state of chronic dread based on the fear that your computer will crash and all your work will be lost, leading to ritualistic clicking of the Save button at least six times after changing each sentence. Often seen in people who have suffered a catastrophic experience (when it is called Post Traumatic Save Disorder).

Keyophobia - A state in which you will do anything to avoid actually typing some words. e.g. choosing the best font, bolding and unbolding the title to see which looks better, browsing for some illustrations, or tidying up your references.

Character Count Down - The sense of crushing disappointment when you realize that the huge number you're looking at is the character count, not the word count, and you actually have 5,000 words to go.

Paste Haste - The seductive sense of accomplishment that comes from boosting your word-count by copying-and-pasting something you've previously written into your current project "just to get provide a backbone". This often ends up slowing you down in the long run, as you have to adapt and update the old stuff and this can take longer than just rewriting it. More paste, less speed.

Writer's Blaaah-k - Writer's block is when you can't think of anything to write. Writer's blaaah-k is when you're wondering why you should. This all so boring! Why am I doing this, again? This can be a sign that what you're writing really isn't interesting, but it's often just a response to the fact that you've been working on it for so long. Of course it will seem boring to you, but the readers will come to it with fresh eyes. To rekindle your enthusiasm, try re-reading your original pitch or notes.

Antivirals and Suicide

A case report from India describes a man who became suicidally depressed while being given drugs to treat a viral infection:

A 43-year-old man diagnosed with chronic hepatitis C viral infection ... was started on therapy with interferon -α-2a and ribavirin ... Screening tests for hepatitis B virus, hepatitis A virus, and HIV were negative.

In initial 3 months of start of therapy with IFN-α-2a and ribavirin, the patient experienced adverse effects in the form of high-grade fever, malaise, myalgia, and fatigue which were relieved by paracetamol. After 16 weeks of therapy, the patient reported to experience feeling of guilt, anxiety, fear, and sadness.

He wanted to keep himself isolated from family and friends. He started blaming himself for financial crises he was facing that time. He was unable to perform his job as school teacher. Hamilton Depression Rating Scale (HDRS-17) revealed the patient to be suffering from moderate to severe depression with score of 15.

He was given psychotherapy for the same. Paroxetine and zolpidem were started [but] he did not respond significantly to antidepressants over 3 weeks. After 25 days of starting antidepressants, the patient attempted suicide but was rescued in time.

IFN-α-2a and ribavirin were withheld for 1 month and antidepressants were continued. Patient's condition normalized and he started taking interest in self and surroundings. He started following his normal routine.

He was then put back on the drugs for 3 weeks, but he got depressed again. So treatment was aborted and he was back to feeling fine within a week.

Interferons are powerful antivirals but they have the dubious honor of being one of the few medical drugs clearly implicated in causing depression. Others include reserpine, an anti-hypertensive and rimonabant, a weight-loss drug (it got banned for this reason).

The anti-malarial mefloquine can cause a range of neuropsychiatric symptoms including depression but also hallucinations and nightmares, as can the HIV drug efavirenz which I covered recently.

Most people who take each of these drugs don't experience problems but in a non-trivial minority it happens. It obviously poses a serious problem for doctors, but it's also very interesting for people researching mood and depression. Work out why these drugs cause depression, and it might help work out why people get "normal" clinical depression.

For example, just recently it was shown that mefloquine has a unique and unusual effect on cells in the dopamine system of the brain, responsible for motivation and pleasure. Whether this explains the side-effects is an open question but without mefloquine we wouldn't even be able to ask it.

As for interferons, which are actually not drugs as such but rather molecules produced by the immune system during infections, it's given rise to the inflammation theory of depression. There's always a risk, though, that by focussing too much on just one class of depressing drug, you'll end up with a narrow theory that can't account for the others.

Inder D, Rehan HS, Yadav M, Manak S, & Kumar P (2011). IFN-α-2a (Interferon) and ribavirin induced suicidal attempt in a patient of chronic HCV: A rare case report. Indian journal of pharmacology, 43 (2), 210-1 PMID: 21572662

Secondhand Smoke Goes To Your Head

Secondhand smoking. It's bad for you. But does it get you high?

According to UCLA researchers Arthur Brody et al, it might do, because exposure to secondhand cigarette smoke can cause you to absorb enough nicotine that it has measurable effects in the brain.

That's quite interesting, but the best thing about this study is the methodology. This is the first neuroimaging study I've seen which involved a car. Not a picture of a car. An actual car.

They used PET scanning to measure the binding of nicotine to brain nicotinic acetylcholine receptors (nAChRs), the major target of the drug. They first injected people with a radioactive tracer compound, in this case a nicotine-like molecule which binds to nAChRs. Because nicotine binds to the same target, it displaces the tracer and reduces the radioactive signal from the brain.

Where did the car come in? Well, volunteers were scanned before and after sitting in a car for one hour, next to a smoker who smoked cigarettes over the course of the hour. An average of 3.7 cigarettes to be precise. The windows were closed to keep the car nice and smoky.

The scene was made even more remarkable by the fact that the subject was still being injected with the tracer compound during this period: they were attached to a drip which went through a little gap in the window and outside. Sadly, they don't show us any pictures...


Anyway, they found that secondhand smoking did cause modest but significant binding to the receptors. The graph shows tracer binding in four areas of the brain - the lower the line, the more nicotine. After secondhand smoke, the lines go down.

After sitting in a "placebo car" in which no-one was smoking, however, there was no effect (the empty circles.) Then later on, the participants were able to smoke some cigarettes first-hand: this had a much stronger effect as you'd expect.

The effect of secondhand smoke was pretty large, though. Actual smoking led to nicotine receptor occupancy of about 50%. Secondhand smoke weighed in at about 20%. Interestingly, in the participants who were regular smokers themselves, the secondhand smoke made them report increased cravings for a cigarette - and this correlated with secondhand smoke nicotine binding (though only in one area of the brain.)

Is this a realistic study? I can't imagine many smokers sit smoking inside their car with the windows up for hours on end. If nothing else because it make their car smell all smoky. (Did they have to pay for the car? If so, this might be the only legitimate example of someone buying themselves a new car using their research grant money...) But the authors say that being in a room with multiple smokers would lead to even higher levels of smoke.

Brody AL, Mandelkern MA, London ED, Khan A, Kozman D, Costello MR, Vellios EE, Archie MM, Bascom R, & Mukhin AG (2011). Effect of Secondhand Smoke on Occupancy of Nicotinic Acetylcholine Receptors in Brain. Archives of general psychiatry PMID: 21536968

Filters

At TED, Eli Pariser, author of the The Filter Bubble, talks about how:

As web companies strive to tailor their services (including news and search results) to our personal tastes, there's a dangerous unintended consequence: We get trapped in a "filter bubble" and don't get exposed to information that could challenge or broaden our world-view. Eli Pariser argues powerfully that this will ultimately prove to be bad for us and bad for democracy.

His point is that the web is, technologically, a fantastic system of giving the consumers of information (i.e. you) exactly what they want, when they want it. It's enabled a degree of personalization which old media could never come close to. But this isn't necessarily a good thing, because people tend to pick and choose information that fits with their existing views and interests, and filters out everything else.

The problem is not entirely new. Back in the days when everyone read their daily newspaper, the newspaper editor was your filter. And because there were maybe a dozen newspapers in your region that you could buy, you'd choose the one that best fitted with your world-view.


Indeed, in the UK, what newspaper you read says considerably more about you than what party you vote for. There are only 3 main political parties, but there are about 10 main newspapers, and in my experience people are more likely to change their vote than to change what they read.

But the internet allows people to cherry-pick far more effectively. The Guardian, for example, regularly prints articles that annoy, or at least challenge, many Guardian readers. That's inevitable, because no two people have exactly the same tastes: what one reader loves will have another reader tearing up his paper in frustration.

Nowadays, it's quite possible to get all of your news and views from blogs. Blogs are specialized: they cover a particular kind of stories, with a particular slant. Many of them do that extremely well. If you don't quite agree with a given blog, there's plenty of others with a slightly different approach to pick from. And you can pick as many blogs as you like until you've got a full set - exactly how you want it. Clearly, the potential to only find out about what you already want to hear is much greater.

New or not, it's certainly a problem. The good thing is that the internet makes it extremely easy to snap out of the filter bubble. A completely different perspective is just a click away: that's new, as well. All you need is to want to do that.

Why should you? Always reading stuff that you already agree with isn't the best way to get informed about something. Actually, it's just about the worst way to do that. If you're serious about wanting to learn the truth about something, you need to (critically) read different sources. But beyond that, it's just boring to always do the same things. There are a lot of cool things going on that you've never heard of.

Finally, if you're a blogger, remember that you're not just telling readers your opinions, you're helping them to filter out other people's. You don't have to feel bad about that, it's inevitable, but remember: if you really want to help your readers understand something, you need to tell them about the areas of disagreement.

I don't just mean linking to stupid people and then explaining why they're stupid. That's fun, but if you're serious, you need to link to the best examples of alternative views and give them a fair hearing. This is something that I feel I could do more of on this blog, and I hope to do it more in future.

Duck or Rabbit?

Ambigous figures are drawings that seem to flip from being one thing to another.

Psychologists Melissa Allen and Alison Chambers recently showed these images to teenagers with autism in an attempt to find out whether they were able to perceive the effect normally: Implicit and explicit understanding of ambiguous figures by adolescents with autism spectrum disorder

A leading theory of autism is weak central coherence - the idea that autistic people tend to be focussed on details, rather than the "big picture". This might predict that autism would interfere with the perception of these figures because the ambiguity is all about the global, gestalt meaning: the details are fixed, but you can see them as adding up to two different things.

The autistic teens and a control group were showed the images and asked to copy them using a pen and paper. Then their drawings were rated for "duckness" or "rabbitness", or equivalent, by a rater who wasn't told which diagnosis the drawer had.

The results showed that the autistic group were able to perceive both interpretations of the figures, and were equally likely to report experiencing the "reversal" phenomena in which the image seems to flip. However, when it came to the drawings, they were less biased by being told which interpretation to use. When the instructions said "Draw this rabbit" as opposed to "Draw this picture", controls tended to make their copy more rabbity, but autistic people copied it faithfully.


Beyond their relevance to autism, these kinds of pictures are interesting because they tell us something important about perception.

You can't see these images for what they really are. They really are ambiguous - they're neither duck, nor rabbit. They're both. However, our brains insist that they are one the other, at any one time. They're duck, rabbit, duck, rabbit. But they never seem to be a "duckrabbit". Not for me anyway. Even though I know, in an abstract sense, that this is what they really are.

Both "duck" and "rabbit" are things we've encountered a thousand times before. So we seem to be drawn to see them in those familiar terms. "Duckrabbits" are unheard of, outside psychology. Rather than sit on the fence, our perceptions fall into the well-worn grooves of our preexisting categories.

Allen ML, & Chambers A (2011). Implicit and explicit understanding of ambiguous figures by adolescents with autism spectrum disorder. Autism : the international journal of research and practice PMID: 21486897

There's no DNA in "Disease"

Back when I was a mere first year biology student, the first thing we were taught was this:

DNA makes RNA makes Protein.

This is the Central Dogma of Molecular Biology, and it describes the intricate and beautiful process by which genes influence living things. The whole thing really is remarkable.

Unfortunately, some people in psychiatry seem to have forgotten this. Reading some of the literature, you would think that:

DNA makes DSM Diagnoses

Or if you're feeling especially adventurous and concious of the fact that diagnoses are not necessarily real entities

DNA makes Symptoms (which add up to make DSM Diagnoses)

In fact, DNA has nothing to do with symptoms either, not directly. DNA makes proteins. Proteins interact with each other, and with all kinds of hormones and other signalling molecules, to control the growth and function of cells. Cells don't get symptoms. People get symptoms - and people are very complex systems made of billions of cells.

So it would be extremely weird if a particular genetic variant only ever caused one specific disease. That would mean that, whenever you have that variant, and regardless of any other variants or environmental factors, it will always mess up cell function such that it causes the same ultimate symptoms.

That does happen. There are lots of single-gene disorders - or to put it another way, single-disorder genes. But they may well be the exception. Rather, as Matthew State says in a short paper just out in Biological Psychiatry, the latest research suggests that genes that are linked to one psychiatric disorder are usually linked to lots of them, sometimes ones with quite different symptoms.

I previously wrote about the case of "The ADHD Gene" that's actually a gene for lots of stuff including, sometimes, ADHD. State focusses on the example of the gene CNTNAP2, variants in which have been linked to (deep breath): epilepsy, mental retardation, autism, social anxiety, schizophrenia and Tourette's. Sometimes the same variant causes multiple different disorders in different people. Sometimes one variant causes one thing and protects against another, related, thing. Hmm.

As State says, one possibility is that any given mutation always causes the same symptoms, it's just that our diagnostic categories are imperfect so the same symptoms get labelled as many different things. That's certainly true but as he points out, there's a more radical possibility: the same variant might cause genuinely different symptoms.

mutations at single gene or locus may carry significant risks for truly divergent neurodevelopmental outcomes, neither demonstrating specificity for a clinically observable phenomenon nor conferring any reliable overlap among disparate behavioral phenotypes.

How? Well, suppose there was a variant, "pinker", that codes for a fluorescent protein that makes half of your brain cells glow bright pink. By itself, that wouldn't cause symptoms. No-one would even know.

Yet imagine another variant, "pinkophobe", that made cells refuse to communicate with pink cell. That wouldn't cause any symptoms either, by itself. But in conjunction with "pinker", where it would cause serious problems: half of your cells would be effectively out of action.

But suppose you carried "pinker" and yet another variant, "welovepink", that made your cells respond much more strongly to pink cells. Then, you would have the opposite problem. Half of your cells would be super-responsivie to the other half, and that would probably cause epilepsy, amongst other things. You'd get symptoms, but they would be completely different symptoms from people who had "pinker" and "pinkophobe".

So what symptoms does "pinker" cause? It doesn't cause symptoms. It's just a gene. The symptoms come much later. "pinker" would be associated with all kinds of stuff, even though it has a very specific role. It just codes for one protein. Genes are pretty simple folk. The complexity comes later.

This is a silly example, but maybe not so far fetched after all. Neurons don't glow pink, but they do release neurotransmitters, and they don't have color preferences, but they do have receptors that respond to transmitters.

State MW (2011). The Erosion of Phenotypic Specificity in Psychiatric Genetics: Emerging Lessons from CNTNAP2. Biological psychiatry, 69 (9), 816-7 PMID: 21497679

Bin Laden's Smile

So they got him.


Why was he so "popular"? I think it was his smile.

Bin Laden always smiled. This was his unique selling point. Most photos of extremists show either a hateful scowl, emotionless resolve, or at best a forced, unfriendly smile.

Bin Laden smiled, but it wasn't an evil smile. It looked perfectly genuine. He wasn't smiling because he'd just killed lots of enemies. He was just calm and content with being a killer. At peace. His videos illustrate this most dramatically. He was collected, quiet, almost shy. I've seen more passionate performances by college chemistry lecturers.

That was surely his appeal. No-one joins a movement like Al Qaeda unless they're angry, but Bin Laden seemed to be living proof that you didn't have to stay angry to stay a member. Al Qaeda was the way out of that. Al Qaeda could bring you inner peace. Whether Bin Laden was really like that, I have no idea. He might have been tormented by inner doubts, and just good at acting for the cameras. The point is, it doesn't matter. The images were out there, and that was the message.

His calm was also the reason why he was hated and feared more than the other members of his organization, including the ones who had a more direct role in 9/11. Osama was the one man to whom the image of the ranting, delusional extremist couldn't apply. Someone who planned terrorist attacks out of insane rage: that would be bad enough, but at least it would be understandable. That someone could do it with an agreeable smile on their face, was something else.

Given which, it's no surprise that the U.S. reported that Osama died a coward, hiding behind his wife. Nothing could have shattered the Osama image better than that. He wasn't beyond human emotion after all, he was scared just like anyone else. Again, whether or not that actually happened, is not the point. It's the message that went out, and I suspect that's the message that will stick.

Revenge Of The Depression Gene

Last year, the world of psychiatric genetics was rocked by the news that a highly-studied gene, believed to be associated with depression, wasn't in fact linked to depression at all.

The genetic variant was 5-HTTLPR. It's a length variant in the gene coding for the serotonin transporter protein (5HTT) which the target of antidepressants like Prozac. There are two flavors of this variant, short and long.

Many studies have shown that the short ("s") variant is associated with a high risk of getting depression in response to stress - but then last year a large meta-analysis of all the evidence concluded that there was in reality no link. Bummer.

Now another team of researchers have done a new analysis of the 5-HTTLPR & stress & depression data and they claim that there is a link after all: hooray! So who's right? I'm not sure, but the new paper raises many questions.

The new paper puts together the results of all 54 studies which have looked at this gene in the context of depression, caused by any kind of stress. The authors were intentionally liberal in their inclusion criteria: studies in any population were OK, for example they included people with Parkinson's disease or heart disease.

They say that this is the main difference between the present work and earlier meta-analyses that found no link. The famous 2010 paper, for example, only included 14 studies because they only considered certain kinds of stress.

Anyway, the short variant is associated with depression after all, across all of the studies. They extracted the p values from the results of all previous studies, and took the average of those, weighted by the sample size. They found a very significant association: P=.00002.

Here's all the results. Each square is a study, the further to the left, the more strongly they found an association. Bigger squares mean larger studies. As you can see, most studies found a link but the three largest studies - which were much larger than the others - found none. Hmm.

In terms of specific kinds of stress, they found strong evidence that "specific stressors" (like medical illness), and childhood trauma, were associated with more depression in s-allele carriers. However, in the studies on "Stressful Life Events", which is a broad category meaning pretty much anything bad that happens, the evidence was weaker. The previous meta-analyses only considered these studies.

Ultimately, I think this analysis should remind us that the issue of 5HTTLPR is still "open", but I have concerns about the dataset. The fact that larger studies seem less likely to be positive is a classic warning sign of publication bias.

The authors do consider this and say that they calculate that there would have to be over 700 unpublished, negative studies out there, in order to make the overall data negative. They also find that you could ignore the smallest 45 studies and still find a result. But still. Something doesn't feel right. Maybe I just have the wrong 5HTTLPR variant.

Karg K, Burmeister M, Shedden K, & Sen S (2011). The Serotonin Transporter Promoter Variant (5-HTTLPR), Stress, and Depression Meta-analysis Revisited: Evidence of Genetic Moderation. Archives of general psychiatry, 68 (5), 444-54 PMID: 21199959

Psychiatry and Phrenology

The notorious John P. "Most Published Research Findings Are False" Ioannidis has turned his baleful statistical gaze upon the literature on brain volume abnormalities in psychiatric disorders.


Reports of regional volume differences in the brains of people with mental illness compared to healthy people have appeared in increasing numbers in recent years. Such studies have given plenty of positive results. People with depression have smaller hippocampi. The amygdala is bigger in people with autism. And so on.

Last month, Ioannidis took a comprehensive look at this literature and he argues that it suffers from a fairly serious case of "excess significance bias" - essentially, that scientists are somehow biased towards reporting differences between patients and controls, and are not telling people about the times when there wasn't a difference. This could be because of publication bias, p-value fishing or other scientific sins.

Scientists tend to call a difference between two groups significant if it has a p value of less than 0.05. This means that if there were no real difference, just some random noise, this result would be less than 5% likely to occur.

However, there's many ways you could end up with a low (i.e. good) p value. You would get a significant result, even if the true difference was very small, if you do a big enough study. Even a small difference will be detected if you study enough people. On the other hand, when the true difference is huge, you might only need a small study to get the same p value.

A power calculation is a way of specifying how likely a given study would be to detect a difference of a given size, based on the size of the study. These are usually used ahead of time to work out how big your upcoming study needs to be, assuming you can guess roughly how big the real effect you're interested in is going to be.

Ioannidis turned this on its head and asked: assuming that the true difference in the brain volume is what the average of all the published studies says it is, how many of the published studies were big enough that they ought to have succesfully detected it?

He found 41 seperate meta-analyses for different brain regions in various disorders. These were published in 8 papers - because each paper reported on multiple regions. He only looked at meta-analyses published in the past 4 years, but these analyses will themselves have included older work. This means that this paper is a kind of meta-meta-analysis. He didn't directly consider the raw brain scans at all.

The meta-analyses found many significant volume differences - but in 29 of those 41, there was an excess of significant papers. In other words, the papers were too small to have a good chance to detect the effect that they themselves found - suggesting that something funny was going on. Although, strangely, in 10/41 there were too few, and only in 2 were there the "right" number.


For what it's worth, studies on schizophrenia and on relatives-of-people-with-schizophrenia showed the least evidence of this problem, while autism was terrible, with 4 times as many significant papers as expected by chance. I'm not sure this is worth much, though. We don't know if this tells us more about schizophrenia vs autism, or more about the researchers that study them.

Anyway, this is an important study, and the inverse power calculation approach is certainly a useful one. It's not new, but it's not used as widely as it ought to be. It does make the assumption that the meta-analyses are "right" about the effect size, and then paradoxically concludes that they are biased. However, this means that the true bias is probably even bigger than this suggests (because if the analyses as biased, the true effect size is smaller than assumed, and the studies should have been even less likely to find it.)

Unfortunately, this doesn't tell us which of the studies are wrong, so it's not directly useful for people researching mental illness. It tells us that there is something wrong with scientific publishing, however. Truth be told, I suspect that a similar picture would emerge if you did this kind of thing in many other fields of science. The only real solution, in my book, would be to require the pre-registration of scientific studies. Ioannidis actually advocates this at the end of the paper.

Ioannidis JP (2011). Excess Significance Bias in the Literature on Brain Volume Abnormalities. Archives of general psychiatry PMID: 21464342