In This Is Spin̈al Tap, British heavy metal god Nigel Tufnel says, in reference to one of his band's less succesful creations:

It's such a fine line between stupid and...uh, clever.

A team of Japanese scientists have found the most sarcastic part of the brain known to date. They also found the metaphor centre of the brain and, well, it's kind of like a pair of glasses.

The paper is Distinction between the literal and intended meanings of sentences and it's brought to you by Uchiyama et al. They took 20 people and used fMRI to record neural activity while the volunteers read 4 kinds of statements:

• Literally true
• Nonsensical
• Sarcastic
• Metaphorical

Does being heavier make you happier?

An interesting new paper from a British/Danish collaboration uses a clever trick based on genetics to untangle the messy correlation between obesity and mental health.

They had a huge (53,221) sample of people from Copenhagen, Denmark. It measured people's height and weight to calculate their BMI, and asked them some simple questions about their mood, such as "Do you often feel nervous or stressed?"

Many previous studies have found that being overweight is correlated with poor mental health, or at least with unhappiness ("psychological distress"). And this was exactly what the authors found in this study, as well.

Being very underweight was also correlated with distress; perhaps these were people with eating disorders or serious medical illnesses. But if you set those small number of people aside, there was a nice linear correlation between BMI and unhappiness. When they controlled for various other variables like income, age, and smoking, the effect of BMI became smaller but it was still significant.

But that's just a correlation, and as we all know, "correlation doesn't imply causation". Actually, it does; something must be causing the correlation, it didn't just magically appear out of nowhere. The point is that shouldn't make simplistic assumptions about what the causal direction is.

It would be easy to make these assumptions. Maybe being miserable makes you fat, due to comfort eating. Or maybe being fat makes you miserable, because overweight is considered bad in our society. Or both. Or neither. We don't know.

Finding this kind of correlation and then speculating about it is where a lot of papers finish, but for these authors, it was just the start. They genotyped everyone for two different genetic variants known, from lots of earlier work, to consistently affect body weight (FTO rs9939609 and MC4R rs17782313).

They confirmed that they were indeed associated with BMI; no surprise there. But here's the surprising bit: the "fat" variants of each gene were associated with less psychological distress. The effects were very modest, but then again, their effects on weight are small too (see the graph above; the effects are in terms of z scores and anything below 0.3 is considered "small".)

The picture was very similar for the other gene.

This allows us to narrow down the possibilities about causation. Being depressed clearly can't change your genotype. Nothing short of falling into a nuclear reactor can change your genotype. It also seems unlikely that genotype was correlated with something else which protects against depression. That's not impossible; it's the problem of population stratification, and it's a serious issue with multi-ethnic samples, but this paper only included white Danish people.

So the author's conclusion is that being slightly heavier causes you to be slightly happier, even though overall, weight is strongly correlated with being less happy. This seems paradoxical, but that's what the data show.

That conclusion would fall apart, though, if these genes directly effect mood, and also, separately, make you fatter. The authors argue that this is unlikely, but I wonder. Both FTO and MC4R are active in the brain: they influence weight by making you eat more. If they can affect appetite, they might also affect mood. A quick PubMed search only turns up a couple of rather speculative papers about MC4R and its possible links to mood, so there's no direct evidence for this, but we can't rule it out.

But this paper is still an innovative and interesting attempt to use genetics to help get beneath the surface of complex correlations. It doesn't explain the observed correlation between BMI and unhappiness - it actually makes it more mysterious. But that's a whole lot better than just speculating about it.

Lawlor DA, Harbord RM, Tybjaerg-Hansen A, Palmer TM, Zacho J, Benn M, Timpson NJ, Smith GD, & Nordestgaard BG (2011). Using genetic loci to understand the relationship between adiposity and psychological distress: a Mendelian Randomization study in the Copenhagen General Population Study of 53,221 adults. Journal of internal medicine PMID: 21210875

MRI scanners have revolutionized medicine and provided neuroscientists with some incredible tools for exploring the brain.

But that doesn't mean they're fun to use. They can be annoying, unpredictable beings, and you never know whether they're going to bless you with nice results or curse you with cancelled scans and noisy data.

So for the benefit of everyone who has to work with MRI, here is a devotional litany which might just keep your scanner from getting wrathful at the crucial moment. Say this before each scan. Just remember, the magnet is always on and it can read your mind, so make sure you really mean it, and refrain from scientific sins...

Yes, England has finally won something. After a poor showing in the 2010 World Cup, the Eurovision Song Contest, and the global economic crisis, we're officially #1 in neuroscience. Which clearly is the most important measure of a nation's success.

According to data collated by ScienceWatch.com and released recently, each English neuroscience paper from the past 10 years has been cited, on average, 24.53 times, making us the most cited country in the world relative to the total number of papers published (source here). We're second only to the USA in terms of overall citations.

(In this table, "Rank" refers to total number of citations).

Why is this? I suspect it owes a lot to the fact that England has produced many of the technical papers which everyone refers to (although few people have ever read). Take the paper Dynamic Causal Modelling by Karl Friston et al from London. It's been cited 649 times since 2003, because it's the standard reference for the increasingly popular fMRI technique of the same name.

Or take Ashburner and Friston's Voxel-Based Morphometry—The Methods, cited over 2000 times in the past 10 years, which introduced a method for measuring the size of different brain regions. Or take...most of Karl Friston's papers, actually. He's the single biggest contributor to the way in which modern neuroimaging is done.

How do you know whether a scientific idea is a good one or not?


The only sure way is to study it in detail and know all the technical ins and outs. But good ideas and bad ideas behave differently over time, and this can provide clues as to which ones are solid; useful if you're a non-expert trying to evaluate a field, or a junior researcher looking for a career.

Today's ideas are the basis for tomorrow's experiments. A good idea will lead to experiments which provide interesting results, generating new ideas, which will lead to more experiments, and so on.

Before long, it will be taken as granted that it's true, because so many successful studies assumed it was. The mark of a really good idea is not that it's always being tested and found to be true; it's that it's an unstated assumption of studies which could only work if it were true. Good ideas grow onwards and upwards, in an expanding tree, with each exciting new discovery becoming the boring background of the next generation.

Astronomers don't go around testing whether light travels at a finite speed as opposed to an infinite one; rather, if it were infinite, their whole set-up would fail.

Bad ideas generate experiments too, but they don't work out. The assumptions are wrong. You try to explain why something happens, and you find that it doesn't happen at all. Or you come up with an "explanation", but next time, someone comes along and finds evidence suggesting the "true" explanation is the exact opposite.

Unfortunately, some bad ideas stick around, for political or historical reasons or just because people are lazy. What tends to happen is that these ideas are, ironically, more "productive" than good ideas: they are always giving rise to new hypotheses. It's just that these lines of research peter out eventually, meaning that new ones have to take their place.

As an example of a bad idea, take the theory that "vaccines cause autism". This hypothesis is, in itself, impossible to test: it's too vague. Which vaccines? How do they cause autism? What kind of autism? In which people? How often?

The basic idea that some vaccines, somewhere, somehow, cause some autism, has been very productive. It's given rise to a great many, testable, ideas. But every one which has been tested has proven false.

First there was the idea that the MMR vaccine causes autism, linked to a "leaky gut" or "autistic enterocolitis". It doesn't, and it's not linked to that. Then along came the idea that actually it's mercury preservatives in vaccines that cause autism. It doesn't. No problem - maybe it's aluminium? Or maybe it's just the Hep B vaccine? And so on.

At every turn, it's back to square one after a few years, and a new idea is proposed. "We know this is true; now we just need to work out why and how...". Except that turns out to be tricky. Hmm. Maybe, if you keep ending up back at square one, you ought to find a new square to start from.

Reboxetine is an antidepressant. Except it's not, because it doesn't treat depression.

This is the conclusion of a much-publicized article just out in the BMJ: Reboxetine for acute treatment of major depression: systematic review and meta-analysis of published and unpublished placebo and SSRI controlled trials.

Reboxetine was introduced to some fanfare, because its mechanism of action is unique - it's a selective norepinephrine reuptake inhibitor (NRI), which has no effect on serotonin, unlike Prozac and other newer antidepressants. Several older tricyclic antidepressants were NRIs, but they weren't selective because they also blocked a shed-load of receptors.

So in theory reboxetine treats depression while avoiding the side effects of other drugs, but last year, Cipriani et al in a headline-grabbing meta-analysis concluded that in fact it's the exact opposite: reboxetine was the least effective new antidepressant, and was also one of the worst in terms of side effects. Oh dear.

And that was only based on the published data. It turns out that Pfizer, the manufacturers of reboxetine, had chosen to not publish the results of most of their clinical trials of the drug, because the data showed that it was crap.

The new BMJ paper includes these unpublished results - it took an inordinate amount of time and pressure to make Pfizer agree to share them, but they eventually did - and we learn that reboxetine is:

• no more effective than a placebo at treating depression.
• less effective than SSRIs, which incidentally are better than placebo in this dataset (a bit).
• worse tolerated than most SSRIs, and much worse tolerated than placebo.
  

The dust is starting to settle after the Hauser-gate scandal which rocked psychology a couple of weeks back.

Harvard Professor Marc Hauser has been investigated by a faculty committee and the verdict was released on the 20th August: Hauser was "found solely responsible... for eight instances of scientific misconduct." He's taking a year's "leave", his future uncertain.

Unfortunately, there has been no official news on what exactly the misconduct was, and how much of Hauser's work is suspect. According to Harvard, only three publications were affected: a 2002 paper in Cognition, which has been retracted; a 2007 paper which has been "corrected" (see below), and another 2007 Science paper, which is still under discussion.

But what happened? Cognition editor Gerry Altmann writes that he was given access to some of the Harvard internal investigation. He concludes that Hauser simply invented some of the crucial data in the retracted 2002 paper.

Essentially, some monkeys were supposed to have been tested on two conditions, X and Y, and their responses were videotaped. The difference in the monkey's behaviour between the two conditions was the scientifically interesting outcome.

In fact, the videos of the experiment showed them being tested only on condition X. There was no video evidence that condition Y was even tested. The "data" from condition Y, and by extension the differences, were, apparently, simply made up.

If this is true, it is, in Altmann's words, "the worst form of academic misconduct." As he says, it's not quite a smoking gun: maybe tapes of Y did exist, but they got lost somehow. However, this seems implausible. If so, Hauser would presumably have told Harvard so in his defence. Yet they found him guilty - and Hauser retracted the paper.

So it seems that either Hauser never tested the monkeys on condition B at all, and just made up the data, or he did test them, saw that they weren't behaving the "right" way, deleted the videos... and just made up the data. Either way it's fraud.

Was this a one-off? The Cognition paper is the only one that's been retracted. But another 2007 paper was "replicated", with Hauser & a colleague recently writing:

In the original [2007] study by Hauser et al., we reported videotaped experiments on action perception with free ranging rhesus macaques living on the island of Cayo Santiago, Puerto Rico. It has been discovered that the video records and field notes collected by the researcher who performed the experiments (D. Glynn) are incomplete for two of the conditions.

Glynn was not an author on the only paper which has actually been retracted [the Cognition 2002 paper that Altmann refers to]... according to his resume, he didn't arrive in Hauser's lab until 2005.

Update: Lots of stuff has happened since I wrote this post: see here for more.

A major scandal looks to be in progress involving Harvard Professor Marc Hauser, a psychologist and popular author whose research on the minds of chimpanzees and other primates is well-known and highly respected. The Boston Globe has the scoop and it's well worth a read (though you should avoid reading the comments if you react badly to stupid.)

Hauser's built his career on detailed studies of the cognitive abilities of non-human primates. He's generally argued that our closest relatives are smarter than people had previously believed, with major implications for evolutionary psychology. Now one of his papers has been retracted, another has been "corrected" and a third is under scrutiny. Hauser has also announced that he's taking a year off from his position at Harvard.

It's not clear what exactly is going on, but the problems seem to centre around videotapes of the monkeys that took part in Hauser's experiments. The story begins with a 2007 paper published in Proceedings of the Royal Society B. That paper has just been amended in a statement that appeared in the same journal last month:

In the original study by Hauser et al., we reported videotaped experiments on action perception with free ranging rhesus macaques living on the island of Cayo Santiago, Puerto Rico. It has been discovered that the video records and field notes collected by the researcher who performed the experiments (D. Glynn) are incomplete for two of the conditions.

So the draft of DSM-V is out.

If, as everyone says, the Diagnostic and Statistical Manual is the Bible of Psychiatry, I'm not sure why it gets heavily edited once every ten years or so. Perhaps the previous versions are a kind of Old Testament, and only the current one represents the New Revelation from the gods of the mind?

Mind Hacks has an excellent summary of the proposed changes. Bear in mind that the book won't be released until 2013. Some of the headlines:

• Asperger's Syndrome is out - everyone's going to have an "autistic spectrum disorder" now.
  
• Personality Disorders are out - kind of. In their place, there's 5 Personality Disorder Types, each of which you can have to varying degrees, and also 6 Personality Traits, each of which you can have to varying degrees.
• Hypoactive Sexual Desire Disorder - the disease which failed-antidepressant-turned-aphrodisiac flibanserin is supposed to treat - is out, to be replaced by Sexual Interest and Arousal Disorder.
• Binge Eating Disorder, Hypersexuality Disorder, and Gambling Addiction are in. Having Fun is not a disorder yet, but that's on the agenda for DSM-VI.

A. A behavioral or psychological syndrome or pattern that occurs in an individual

B. The consequences of which are clinically significant distress (e.g., a painful symptom) or disability (i.e., impairment in one or more important areas of functioning)

C. Must not be merely an expectable response to common stressors and losses...

D. That reflects an underlying psychobiological dysfunction

E. That is not primarily a result of social deviance or conflicts with society

J. When considering whether to add a mental/psychiatric condition to the nomenclature, or delete a mental/psychiatric condition from the nomenclature, potential benefits (for example, provide better patient care, stimulate new research) should outweigh potential harms (for example, hurt particular individuals, be subject to misuse)

The diagnosis of bipolar [in children] "is being given, we believe, too frequently," said Dr. David Shaffer, a member of the work group on disorders in childhood and adolescence. In reality, when such children are tracked into adulthood, very few of them turn out to be bipolar, he said.

Last month Wired, announced that Placebos Are Getting More Effective. Drugmakers Are Desperate to Know Why.

The article's a good read, and the basic story is true, at least in the case of psychiatric drugs. In clinical trials, people taking placebos do seem to get better more often now than in the past (paper). This is a big problem for Big Pharma, because it means that experimental new drugs often fail to perform better than placebo, i.e. they don't work. Wired have just noticed this, but it's been being discussed in the academic literature for several years.

Why is this? No-one knows. There have been many suggestions - maybe people "believe in" the benefits of drugs more nowadays, so the placebo effect is greater; maybe clinical trials are recruiting people with milder illnesses that respond better to placebo, or just get better on their own. But we really don't have any clear idea.

What if the confusion is because of the very concept of the "placebo"? Earlier this year, the BMJ ran a short opinion piece called It’s time to put the placebo out of our misery. Robin Nunn wants us to "stop thinking in terms of placebo...The placebo construct conceals more than it clarifies."

A reader drew my attention to this gem from Craig Bennett, who blogs at prefrontal.org:

Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction

This is a poster presented by Bennett and colleagues at this year's Human Brain Mapping conference. It's about fMRI scanning on a dead fish, specifically a salmon. They put the salmon in an MRI scanner and "the salmon was shown a series of photographs depicting human individuals in social situations. The salmon was asked to determine what emotion the individual in the photo must have been experiencing."

I'd say that this research was justified on comedic grounds alone, but they were also making an important scientific point. The (fish-)bone of contention here is multiple comparisons correction. The "multiple comparisons problem" is simply the fact that if you do a lot of different statistical tests, some of them will, just by chance, give interesting results.

In fMRI, the problem is particularly severe. An MRI scan divides the brain up into cubic units called voxels. There are over 40,000 in a typical scan. Most fMRI analysis treats every voxel independently, and tests to see if each voxel is "activated" by a certain stimulus or task. So that's at least 40,000 separate comparisons going on - potentially many more, depending upon the details of the experiment.

Luckily, during the 1990s, fMRI pioneers developed techniques for dealing with the problem: multiple comparisons correction. The most popular method uses Gaussian Random Field Theory to calculate the probability of falsely "finding" activated areas just by chance, and to keep this acceptably low (details), although there are other alternatives.

But not everyone uses multiple comparisons correction. This is where the fish comes in - Bennett et al show that if you don't use it, you can find "neural activation" even in the tiny brain of dead fish. Of course, with the appropriate correction, you don't. There's nothing original about this, except the colourful nature of the example - but many fMRI publications still report "uncorrected" results (here's just the last one I read).

Bennett concludes that "the vast majority of fMRI studies should be utilizing multiple comparisons correction as standard practice". But he says on his blog that he's encountered some difficulty getting the results published as a paper, because not everyone agrees. Some say that multiple comparisons correction is too conservative, and could lead to genuine activations being overlooked - throwing the baby salmon out with the bathwater, as it were. This is a legitimate point, but as Bennett says, in this case we should report both corrected and uncorrected results, to make it clear to the readers what is going on.

Last week, I wrote about a paper finding that the mosquito repellent chemical, DEET, inhibits an important enzyme, cholinesterase. If DEET were toxic to humans, this finding might explain why.
But it isn't - tens of millions of people use DEET safely every year, and there's no reason to think that it is dangerous unless it's used completely inappropriately. That didn't stop this laboratory finding being widely reported as a cause for concern about the safety of DEET.

This is putting the cart before the horse. If you know that something happens, then it's appropriate to search for an explanation for it. If you have a phenemonon, then there must be a mechanism by which it occurs.

But this doesn't work in reverse: just because you have a plausible mechanism by which something could happen, doesn't mean that it does in fact happen. This is because there are always other mechanisms at work which you may not know about. And the effect of your mechanism may be trivial by comparison.

Caffeine can damage DNA under some conditions. Other things which damage DNA, like radiation, can cause cancer. But the clinical evidence is that, if anything, drinking coffee may protect against some kinds of cancer (previous post). There's a plausible mechanism by which coffee could cause cancer, but it doesn't.

Medicine has learned the hard way that while understanding mechanisms is important, it's no substitute for clinical trials. The whole philosophy of evidence-based medicine is that treatments should only be used when there is clinical evidence that they do in fact work.

Unfortunately, in other fields, the horse routinely finds itself behind the cart. An awful lot - perhaps most - of political debate consists of saying that if you do X, Y will happen, through some mechanism. If you legalize heroin, people will take more of it, because it'll be more available and cheaper. If you privatize public services, they'll improve, because competition will ensure that only the most efficient services survive. If you topple this dictator, the country will become a peaceful democracy, because people like peace and democracy. And so on.

These kinds of arguments sound good. And they invite opponents to respond in kind: actually, legalizing heroin is a good idea, because it will make taking it much safer by eliminating impurities and infections... And so the debate becomes a case of fantasizing about things that might happen, with the winner being the person whose fantasy sounds best.

If you want to know what will happen when you implement some policy, the only way of knowing is to look at other countries or other places which have already done it. If no-one else has ever done it, you are making a leap into the unknown. This is not necessarily a bad thing - there's a first time for everything. But it means that "We don't know" should be heard much more often in politics.

Who's more likely to start digging prematurely: one guy with a metal-detector looking for an old nail, or a field full of people with metal-detectors searching for buried treasure?

In any area of science, there will be some things which are more popular than others - maybe a certain gene, a protein, or a part of the brain. It's only natural and proper that some things get of lot of attention if they seem to be scientifically important. But Thomas Pfeiffer and Robert Hoffmann warn in a PLoS One paper that popularity can lead to inaccuracy - Large-Scale Assessment of the Effect of Popularity on the Reliability of Research.

They note two reasons for this. Firstly, popular topics tend to attract interest and money. This means that scientists have much to gain by publishing "positive results" as this allows them to get in on the action -

The second effect results from multiple independent testing of the same hypotheses by competing research groups. The more often a hypothesis is tested, the more likely a positive result is obtained and published even if the hypothesis is false. ... We refer to this mechanism as “multiple testing effect”.

You may well have already heard about neuro images, a new blog from Neurophilosophy's Mo. As the name suggests, it's all about pictures of the brain. All of them are very pretty. Some are also pretty gruesome.

But images are, of course, more than decoration. There are dozens of ways of picturing the brain, each illuminating different aspects of neural function. Neuropathologists diagnose diseases by examining tissue under the microscope; using various stains you can visualize normal and abnormal cell types -

FDG-PET scans reveal metabolic activity in different areas, which can be used to diagnose tumors amongst much else -

Egaz Moniz, better known as the inventor of "psychosurgery", pioneered cerebral angiography, a technique for visualizing the blood vessels of the brain using x-rays (this is the view from below) -

And so on. However, for all too many cognitive neuroscientists - e.g. fMRI researchers - the only kind of brain images that matter are MRI scans, traditionally black-and-white with "activity" depicted on top in colour -

fMRI is a powerful technique. But there is much more to the brain than that. Even a casual glance down a microscope reveals that brain tissue is composed of a rich variety of cells, the most numerous of which, glia, do not transmit neural signals - they are not "brain cells" at all. And there are many different types of brain cells, which inhabit distinct layers of the cerebral cortex - the cortex has at least six layers in most places, and different things happen in each one.

The brain, in other words, is a living organ, not a grey canvas across which activity patterns occasionally flash. Of course, no-one denies this, but all too many neuroscientists forget it because in their day-to-day work all they see of the brain is what an MRI scan reveals. This is especially true for those scientists who came to fMRI from a psychology background, many of whom have never studied neurobiology.

Maybe researchers should have to spend a week with a scalpel cutting up an actual brain before they get allowed to use fMRI - this might help to guard against the kind of simplistic "Region X does Y" thinking that plagues the field.