The Neuro-Recession

Everyone's favourite British psychopharmacologist David "Ecstasy Vs Horseriding" Nutt joins four other leading neuroscientists to discuss the impact of the financial crisis on neuroscience, in an article over at NR:N: Neuroscience in recession?

It's interesting to get an international perspective. Susan Amara, President of the Society for Neuroscience, says that American scientists were encouraged by the surprise $10bn boost to NIH funds that made it into the 2009 economic stimulus package. But these funds are due to run out in 2012.

Meanwhile, in Europe, some countries have slashed funding as part of their austerity programmes - Greece most of all - while the larger and richer nations like France and Germany have protected science. Japan has also opted against major cuts, so far, but with a massive deficit, researchers fear that the axe will fall in coming years.

A repeated complaint is that biomedical research has faced a rate of inflation much higher than the rate experienced by the economy as a whole. Nutt says that if the overall inflation rate is 4% per year, the rate paid by scientists is more like 10%. As a result, even if nominal budgets are protected, the real budget will fall. The current British government has decided to keep nominal science funding flat, while cutting pretty much everything else, which is nice, but it still means falling real investment.

So everyone pretty much agrees that there are cuts, and cuts are bad. OK. Where things get more interesting is in the debate over what this means for individual scientists. Susan Amara says that she fears that investigator-initiated "R01" grants are in danger. These are when a scientist gets an idea, writes it up as a proposal and says "Isn't this cool? Can we have some money to do it?"

Amara warns that this kind of thing seems to be getting harder, while established, ongoing research programmes are being protected. But Tom Insel, head of the NIMH and, therefore, the guy with ultimate responsibility for these R01 grants, says the exact opposite. Insel claims that R01s are being protected in favour of the big programmes! "Where have we cut back in order to preserve R01 grants? ... We have reduced the budget of our intramural research programme."

Who's right on this point? I'm not sure. Maybe US readers might be able to comment.

The authors express particular worry that young neuroscientists (postdocs and PhD students) will suffer, either directly, as a result of not being able to find money, or indirectly in terms of poor morale and a sense that their talents might be better rewarded outside of science - leading to long-term harm to the next generation of neuroscientists.

They offer some words of encouragement, though, saying that the pendulum will swing back towards more investment in the future. Until then, hang on as best you can, even if it means being willing to move to find work with a supervisor, or in a country, which does have good funding prospects...

Amara SG, Grillner S, Insel T, Nutt D, & Tsumoto T (2011). Neuroscience in recession? Nature reviews. Neuroscience, 12 (5), 297-302 PMID: 21505517

The Schizophrenic Computer

All over the world, inanimate objects are getting schizophrenia. Last week, it was a dish (full of neurons).

Before that, it was a computer program. That's according to a paper, which appeared in Biological Psychiatry last month, although it involved no biology, called Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia.

The authors set up a neural network model, called DISCERN, and trained it to "read" stories. The nuts and bolts are, we're reassured, not something that readers of Biological Psychiatry need to worry about: "Its details, many of which are not essential in understanding this study..."

Anyway, it's basically a series of connectionist models. These are computer simulations of a large number of simple units, or nodes, which can have "activations" of varying strengths, and which have "connections" to other nodes. The model "learns" by modifying the strength of these connections according to some kind of simple learning rule.

Connectionist models are a bit like brains, in other words. A bit. They're several orders of magnitude simpler than a real brain, in several different respects. Still, they can "learn" to do some quite complicated things. You can train them to recognise faces and stuff, which is not trivial.


Anyway, DISCERN is a connectionist model of language, but it's not necessary a model of how the human brain actually learns language. Because we just have no idea how the human brain does that. We don't even know if our brain acts as a connectionist network at all, above the cellular level. Some cognitive scientists think it is, but others think that those guys are talking out of an orifice connected to their mouth, but not their mouth. Not in so many words you understand.

So they set up this system and got it to learn 28 stories, each of which consisted of multiple sentences. Some of the stories were the autobiography of a doctor - "I was a doctor. I worked in New York. I liked my job. I was good doctor" - he was not a great communicator, clearly. Others were a story about gangster ("Tony was a gangster. Tony worked in Chicago..." etc.) The network had to read these stories and then recall them.

The core of the study was that they tested to see what happened when they interfered with the program by introducing certain bugs - interfering with the activations or connections of nodes in particular parts of the model. They tried 8.

They compared the computer's performance to that of 37 actual patients with schizophrenia (or the related schizoaffective disorder) who were tested on a similar task, compared to 20 healthy controls. When the human patients came to recall the stories they'd read, they tended to make more errors of particular kinds: mixing up who did what ("agent switching"), and adding stuff that wasn't in the story ("derailment").

What they found was that DISCERN made the same kinds of errors when it was given 2 particular deficits, "working memory disconnection" and "hyperlearning". The other 6 deficits didn't cause the same pattern of findings. Hyperlearning was the best match.

They comment that

A majority of three-parameter best-fit hyperlearning simulations also recurrently confused specific agents in personal stories (including the self-representation) with specific agents in crime stories (and vice versa) in a highly nonrandom fashion.

Noteworthy was the high frequency of agent-slotting exchanges between the hospital boss, Joe, and the Mafia boss, Vito, and parallel confusions between the “I” self-reference and underling Mafia members, suggesting generalization of boss/underling relationships.

Insofar as story scripts provide templates for assigning intentions to agents, a consequence of recurrent agent-slotting confusions could be assignment of intentions and roles to autobiographical characters (possibly including the self) that borrow from impersonal stories derived from culture or the media.

Confusion between agent representations in autobiographical stories and those in culturally determined narratives could account for the bizarreness of fixed, self-referential delusions, e.g., a patient insisting that her father-in-law is Saddam Hussein or that she herself is the Virgin Mary.

So if you believe it, they've just made a program that experiences schizophrenic-type paranoid delusions.

It's fair to say that this is speculative. On the other hand, it's an interesting approach, and at least it's theory-based, rather than just an attempt to use ever more powerful genetic, neuroimaging and biological techniques to find differences between a patient group and a control group.

Hoffman RE, Grasemann U, Gueorguieva R, Quinlan D, Lane D, & Miikkulainen R (2011). Using computational patients to evaluate illness mechanisms in schizophrenia. Biological psychiatry, 69 (10), 997-1005 PMID: 21397213

The Media and Numbers: "It's Complicated".

According to everyone in the British media, 25% of young men are worried about the amount of porn they watch online and men watch an average of 2 hours per week.


Says who? The BBC apparently "teamed up with doctors from the Portman Clinic", a London specialist mental health hospital, to do the study. The actual survey was done online by a certain market research company, which I am not going to name, because they've already got free advertising in every newspaper.

What does this tell us about pornography? Nothing. Dr Petra Boynton explains why in a long and excellent deconstruction. In order to properly interpret these results, we'd need to know lots of details about the study design, which we weren't told. Of course this doesn't stop us from going ahead and interpreting them improperly. 25%! 2 hours. Ooh, that's a lot. Is it? This online porn, eh. Tut tut.

So, sure, 25% could be The True Proportion Of Men Who Worry About Online Porn. Or it might not be. Or the whole question might be so fraught as to be meaningless. The point is, we don't know, we cannot possibly know from the limited amount of information we were given, and we weren't meant to know, because numbers like these are essentially pornographic themselves - they're just for show.

Numbers very rarely make a good news story. When you look into it, the vast majority of them only make sense to people who know all of the background, and by definition, if you have to spend a few pages explaining the background, it's not a good news story. A good news story is one which anyone who can read can immediately understand, and get angry/scared/amused by.

Yet journalists also love numbers because everyone knows, on some level, that numbers matter. The very fact that a story has numbers in it, makes that story better. Indeed, very often, there would be no story without them. Someone doing a survey and finding some numbers can make a news story out of nothing. "Modern online pornography worries some people and is a complicated issue" isn't news; "25% of men..." is news.

So what we end up with is lots of news stories which have numbers in them, but which don't, actually, tell us anything about the world, which is what numbers are supposed to do. Numbers to most of the media are like an attractive trophy wife. They like to be seen with them in public. But deep down they're not all that attached.

Slipping Through Time In Autism

Have you ever felt like you're reliving the past?


Have you ever felt like you're reliving the past? A curious paper from Japan: ‘Time slip’ phenomenon in adolescents and adults with autism spectrum disorders. Have you ever felt like you're...OK, sorry. I'll stop that.

The paper describes the cases of two young men with autism, who suffered from an unusual affliction - very vivid memories of a single past event. These recollections were so unpleasant that they led to outbursts of violence. In the first case, the event was somewhat traumatic in itself:

Case 1, a male patient, was 16 years old at the time of his first visit to our hospital. He had not shown any delay in language development but had been isolated and unable to make friends since his infancy... He had been bullied by a classmate when he was in the 8th grade; thereafter he refused to go to school and began to stay indoors.

One day, he clearly recalled the bullying incident that had occurred a few years earlier and re-experienced the feelings of fear and frustration as if he were once again experiencing that event. Thereafter, he often had similar experiences, even though he did not purposely intend to recall the event, and he became strongly distressed.

He and his family stated that the recalled content was always the same. He thought that the distress could only be relieved by obtaining revenge on the boy who had bullied him, and he visited the boy’s house with a knife. He was subsequently admitted to the emergency ward of our hospital.

This is not, perhaps, very surprising and sounds a bit like post-traumatic stress disorder. The second case, however, is more mysterious because the event that was remembered was, in itself, completely trivial - someone throwing away a cigarette end:

Case 2, a male patient, was 27 years old at the time of his first visit. Since an early age, he had exhibited disturbed reciprocal sociality and did not have any close friendships. His interest was limited to collecting figures of comic characters. He began to be bullied during junior high school. He entered senior high school but quit during the second year. Thereafter, he tended to seclude himself at home.

One day, he watched his neighbor discarding a cigarette butt in front of his home. Thereafter, he began to be annoyed by that memory. Almost every time he heard the voice of that neighbor or saw that man, he would leave his home and curse at the neighbor. His behavior became more violent and he eventually threatened the neighbor with a wooden sword.

The authors end by saying that out of seven autistic patients who presented to their psychiatric emergency ward, no less than four of them experienced "time slips", though it's not clear how this was diagnosed and patients presenting to the emergency ward are a highly selected population - mostly people who have suddenly become violent or aggressive.

The "time slip" phenomenon seems unknown outside of Japan. Google reveals that the only papers discussing it are Japanese. Is it something that only happens in Japan, like buru-sera? Are people with autism elsewhere experiencing this, and going unnoticed?

Tochimoto S, Kurata K, & Munesue T (2011). 'Time slip' phenomenon in adolescents and adults with autism spectrum disorders: Case series. Psychiatry and clinical neurosciences PMID: 21489047

Language Is General?

So according to the authors of a paper in Nature:

It suggests rather that language is part of not a specialised module distinct from the rest of cognition, but more part of broad human cognitive skills.

The paper is Evolved structure of language shows lineage-specific trends in word-order universals. They found that the various grammatical rules governing the proper order of different words in a sentence changed over time, and crucially that there were no fixed associations between them: no correlations such that when one rule changed, another rule had to change at the same time.


This, they say, is inconsistent with the currently dominant linguistic theory of "language universals" fixed by the structure of the human brain/mind. One of the authors has written an excellent explanation here and languagelog has a nice discussion here.

Yet I'm not convinced that "broad human cognitive skills" can explain language. I'm not qualified to comment on the details of this study, but, I do know that the average 7 year old kid has effortlessly learned how to use at least one language, with the appropriate grammar, syntax, and a vocabulary of thousands of words.

On the other hand, take my phone. My phone can't do that. It can, just about, take my voice and convert it into text. It gets it right most of the time. It has absolutely no idea what those words mean. All it can do is send them to Google and search for them.

Speaking of Google, Google Translate is what you get when roomfuls of computers try to "do language". It's useful, it's cool, and it gets it more-or-less right most of the time. But the output it produces is stilted, often ungrammatical, and generally sounds nothing like a native speaker would ever produce.

Let me repeat myself:

On the other hand, take my phone. My phone is that you can not do it. It just converts the text to voice can take me. Most of the time it gets to the right. What is the meaning of the word that has absolutely no idea. That it can, Google, is to send them to find them. Speaking of Google, Google translator you use your computer's roomfuls said, "do language" and attempt to, are obtained. It's cool, then great, but it is more or less right, gets most of the time. However, the output it generates is often exaggerated ungrammatical It sounds more like a native speaker so far generated in general.

That's my last paragraph Google Translated to Japanese and right back. Hmm.

On the other hand my phone can perform millions of arithmetical operations per second. The 7 year old probably takes a minute or two of hard effort to multiply two digits together. So who's got more "general cognitive ability"?

To say that language is a manifestation of human "general" or "broad" cognition is to say that human general cognition is better at learning languages than it is at doing arithmetic: which rather begs the question of how "general" it is.

This doesn't mean that language is a special module of the brain, or that there are "language universals" beyond the fact that they're all languages, though that seems like a pretty big one. But it would take very, very strong evidence to make me doubt that the existence of language is somehow built into the human brain.

Dunn M, Greenhill SJ, Levinson SC, & Gray RD (2011). Evolved structure of language shows lineage-specific trends in word-order universals. Nature PMID: 21490599

Schizophrenia In A Dish...?

...or a storm in a teacup?


According to a new paper just out in Nature from the prestigious Salk Institute, schizophrenia may be associated with differences in neural wiring which can be observed in cells grown in the lab, thus offering a window into the normally inaccessible development of the human brain.

The paper is here, and here's an open-access Nature news bit discussing it: Schizophrenia 'in a dish'. It's certainly an incredible piece of biology. They took fibroblasts, a cell found in the skin, from 4 patients with schizophrenia and 6 healthy controls.

Using genetically modified viruses, they turned these cells into human induced pluripotent stem cells (hiPSCs), which have the ability to become any other type of cell in the human body. Then, they made those hiPSCs turn into neurons by putting them in a dish with various brain-related chemicals and culturing them for three months. Not entirely unlike those brains-in-a-vat that philosophers like to talk about...

To test the connectivity of these cells, they then infected them with a modified rabies virus, after first infecting them yet another modified virus to make that work. Rabies can only spread from cell to cell via synapses between cells; they could spot the infected cells because the rabies was modified to carry a special fluorescent protein. So they could tell how many connections the neurons made.

What they found was that cultures derived from schizophrenia patients made fewer connections:


The distinct lack of red in the schizophrenia patient's dish shows that the rabies virus was less able to travel from cell to cell; the normal amount of green, yellow and blue shows that this wasn't just because it couldn't get into the cells in the first place.

OK, that's extremely cool. But then it gets a bit tricky. They tried adding five different antipsychotic drugs to the dishes for 3 weeks. Four did nothing; one, loxapine, made the cells form more connections. But it's odd that it was loxapine, a drug with unremarkable efficacy, which did this; they also tried clozapine, the only antipsychotic which is verifiably more effective than any others, and it didn't.

Loxapine is similar to (and metabolized to) amoxapine, an antidepressant; that's an issue, I would say, because we already know that antidepressants cause cells to sprout new connections. It would have been good to have used some antidepressants and some other medications as a control.

They did a lot of other work, but the data are hard to interpret. The cells "mis-expressed" about 600 genes, but we're not hold how many genes they tested. 25% of them had been previously linked to schizophrenia, but you could say that of lots of genes: is that more than would be expected by chance alone?

The patients were also unusual. Patient 1 suffered an onset of schizophrenia at age 6, and died by suicide aged 22; childhood-onset schizophrenia is extremely rare. Patients 2 and 3 were brother and sister; this means their data may not be independent, so there are (being conservative) only really 3 patients here.

Overall it's a great idea, a technical tour-de-force, and I'm sure we'll be seeing much more work along these lines on schizophrenia and other neurological and psychiatric disorders. However, as it stands, schizophrenia remains mysterious.

Brennand KJ, Simone A, Jou J, Gelboin-Burkhart C, Tran N, Sangar S, Li Y, Mu Y, Chen G, Yu D, McCarthy S, Sebat J, & Gage FH (2011). Modelling schizophrenia using human induced pluripotent stem cells. Nature PMID: 21490598

Callaway, E. (2011). Schizophrenia 'in a dish' Nature DOI: 10.1038/news.2011.232

Where Papers Come From

The Scientific Paper (Publishus orperishus) is one of the most sought-after, yet elusive, creatures on earth. Though they're a common sight in journals around the world, many dedicate their lives to the art of tracking down these rare beasts and trying to convince them to reproduce.

So, here's a handy guide to the life-cycle of this creature.

1. Conception

Every Paper begins its life as a tiny seed, an idea, which worms itself deep into the brain of a host scientist, producing a pleasurable sensation. Ideas can appear anywhere, but certain places and environmental conditions are optimal. Coffee, sugar and alcohol are known to stimulate the germination of ideas.

2. Incubation

The most dangerous stage in the life of a young Paper. Once the initial buzz of the germinating idea has worn off, the infected scientist may well forget all about it, and before long it will wither away. Others become buried under the bulk of older Papers - even though, given a chance, they might have ended up as a much better specimen.

3. Birth

This can be the most painful part of the process. The scientist suddenly finds that instead of the fully-formed Paper they dreamed of, they have on their hands a messy Experiment which requires their full-time support and care. Crying and tantrums are very common. Some scientists - especially older ones - find the whole process so taxing that they routinely put their Papers up for foster care.

4. Adolescence

By this stage the Paper is growing rapidly; tables of results start to expand, faster than anyone ever imagined. This can be exciting, but as the Paper's owner starts to consider sending it out into the wild with all the other papers, doubts and anxieties arise. Is the Paper ready for this? Will other Papers make fun of it? Some keep their Papers cooped up indoors for years, but this is rarely conductive to their growth and maturity. Others resort to "doping" with performance-enhancing practices.

5. Peer review

In order to be accepted into the community, each juvenile must undergo the stressful and sometimes vicious ritual known as Peer Review. Breeders say that this ensures that only fit and healthy Papers can pass on their genes to future generations. However, some argue that it is all too often a random and arbitrary process which favours external plumage over true strength.

6. Adulthood

The paper is finally finished, though it rarely looks anything like anyone imagined all those years ago. Now it must interact with all the other Papers, and so the grand cycle begins anew. Every Paper-fancier's dream is that their Paper will go on to breed and raise many offspring of its own ("citations").

Who Gets Autism?

According to a major new report from Australia, social and family factors associated with autism are associated with a lower risk of intellectual disability - and vice versa. But why?


The paper is from Leonard et al and it's published in PLoS ONE, so it's open access if you want to take a peek. The authors used a database system in the state of Western Australia which allowed them to find out what happened to all of the babies born between 1984 and 1999 who were still alive as of 2005. There were 400,000 of them.

The records included information on children diagnosed with either an autism spectrum disorder (ASD), intellectual disability aka mental retardation (ID), or both. They decided to only look at singleton births i.e. not twins or triplets.

In total, 1,179 of the kids had a diagnosis of ASD. That's 0.3% or about 1 in 350, much lower than more recent estimates, but these more recent studies used very different methods. Just over 60% of these also had ID, which corresponds well to previous estimates.

There were about 4,500 cases of ID without ASD in the sample, a rate of just over 1%; the great majority of these (90%) had mild-to-moderate ID. They excluded an additional 800 kids with ID associated with a "known biomedical condition" like Down's Syndrome.

So what did they find? Well, a whole bunch, and it's all interesting. Bullet point time.

• Between 1984 to 1999, rates of ID without ASD fell and rates of ASD rose, although there was a curious sudden fall in the rates of ASD without ID just before the end of the study. In 1984, "mild-moderate ID" without autism was by far the most common diagnosis, with 10 times the rate of anything else. By 1999, it was exactly level with ASD+ID, and ASD without ID was close behind. Here's the graph; note the logarithmic scale:
  

• Boys had a much higher rate of autism than girls, especially when it came to autism without ID. This has been known for a long time.
  
• Second- and third- born children had a higher rate of ID, and a lower rate of ASD, compared to firstborns.
  
• Older mothers had children with more autism - both autism with and without ID, but the trend was bigger for autism with ID. But they had less ID. For fathers, the trend was the same and the effect was even bigger. Older parents are more likely to have autistic children but less likely to have kids with ID.
  

• Richer parents had a strongly reduced liklihood of ID. Rates of ASD with ID were completely flat, but rates of ASD without ID were raised in the richer groups, though it was not linear (the middle groups were highest. - and effect was small.)
  

To summarize: the risk factors for autism were in most cases the exact opposite of those for ID. The more “advantaged” parental traits like being richer, and being older, were associated with more autism, but less ID. And as time went on, diagnosed rates of ASD rose while rates of ID fell (though only slightly for severe ID).

Why is this? The simplest explanation would be that there are many children out there for whom it's not easy to determine whether they have ASD or ID. Which diagnosis any such child gets would then depend on cultural and sociological factors - broadly speaking, whether clinicians are willing to give (and parents willing to accept) one or the other.

The authors note that autism has become a less stigmatized condition in Australia recently. Nowdays, they say, a diagnosis of ASD may be preferable to a diagnosis of "just" "plain old" ID, in terms of access to financial support amongst other things. However, it is also harder to get a diagnosis of ASD, as it requires you to go through a more extensive and complex series of assessments.

Clearly some parents will be better able to achieve this than others. In other countries, like South Korea, autism is still one of the most stigmatized conditions of childhood, and we'd expect that there, the trend would be reversed.

The authors also note the theory that autism rates are rising because of some kind of environmental toxin causing brain damage, like mercury or vaccinations. However, as they point out, this would probably cause more of all neurological/behavioural disorders, including ID; at the least it wouldn't reduce the rates of any.

These data clearly show that rates of ID fell almost exactly in parallel with rates of ASD rising, in Western Australia over this 15 year period. What will the vaccine-vexed folks over at Age of Autism make of this study, one wonders?

Leonard H, Glasson E, Nassar N, Whitehouse A, Bebbington A, Bourke J, Jacoby P, Dixon G, Malacova E, Bower C, & Stanley F (2011). Autism and intellectual disability are differentially related to sociodemographic background at birth. PloS one, 6 (3) PMID: 21479223

First Fish, Now Cheese, Get Scanned

Here at Neuroskeptic we have closely followed the development of fMRI scanning on fish.


But a new study has taken it to the next level by scanning... some cheese.

OK, this is not quite true. The study used NMR spectroscopy to analyze the chemistry of some cheeses, in order to measure the effects of different kinds of probiotic bacteria on the composition of the cheese. NMR is the same technology as MRI, and indeed you can use an MRI scanner to gather NMR spectra.

In fact, NMR is Nuclear Magnetic Resonance and MRI is Magnetic Resonance Imaging; it was originally called NMRI, but they dropped the "N" because people didn't like the idea of being scanned by a "nuclear" machine. However, this study didn't actually involve putting cheese into an MRI scanner.

But the important point is that they could have done it by doing that. And if you did that, what with the salmon and now the cheese, you could get a nice MRI-based meal going. All we need is for someone to scan some vegetables, some herbs, and a slice of lemon, and we'd have a delicious dataset. Mmm.

How to cook it? Well, it's actually possible to heat stuff up with an MRI scanner. When scanning people, you set it up to make sure this doesn't happen, but the average fMRI experiment still causes mild heating. It's unavoidable.

I'm not sure what the maximum possible heating effect of an average MRI scanner would be. I doubt anyone has gone out of their way to try and maximize it, but maybe someone ought to look into it. Think of the possibilites.

You've just finished a hard day's scanning and you're really hungry, but the microwave at the MRI building is broken. Not to worry! Just pop your fillet of salmon in probiotic cheese sauce in the magnet, and scan it 'till it's done. You could inspect the images and the chemical composition of the meal before you eat it, to make sure it's just right.

Just make sure you don't use a steel saucepan...



Rodrigues D, Santos CH, Rocha-Santos TA, Gomes AM, Goodfellow BJ, & Freitas AC (2011). Metabolic Profiling of Potential Probiotic or Synbiotic Cheeses by Nuclear Magnetic Resonance (NMR) Spectroscopy. Journal of agricultural and food chemistry PMID: 21443163

BBC: Something Happened, For Some Reason

According to the BBC, the British recession and spending cuts are making us all depressed.


They found that between 2006 and 2010, prescriptions for SSRI antidepressants rose by 43%. They attribute this to a rise in the rates of depression caused by the financial crisis. OK there are a few caveats, but this is the clear message of an article titled Money woes 'linked to rise in depression'. To get this data they used the Freedom of Information Act.

What they don't do is to provide any of the raw data. So we just have to take their word for it. Maybe someone ought to use the Freedom of Information Act to make them tell us? This is important, because while I'll take the BBC's word about the SSRI rise of 43%, they also say that rates of other antidepressants rose - but they don't say which ones, by how much, or anything else. They don't say how many fell, or stayed flat.

Given which it's impossible to know what to make of this. Here are some alternative explanations:

• This just represents the continuation of the well-known trend, seen in the USA and Europe as well as the UK, for increasing antidepressant use. This is my personal best guess and Ben Goldacre points out that rates rose 36% during the boom years of 2000-2005.
  
• Depression has not got more common, it's just that it's more likely to be treated. This overlaps with the first theory. Support for this comes from the fact that suicide rates haven't risen - at least not by anywhere near 40%.
  
• Mental illness is no more likely to be treated, but it's more likely to be treated with antidepressants, as opposed to other drugs. There was, and is, a move to get people off drugs like benzodiazepines, and onto antidepressants. However I suspect this process is largely complete now.
• Total antidepressant use isn't rising but SSRI use is because doctors increasingly prescribe SSRIs over opposed to other drugs. This was another Ben Goldacre suggestion and it is surely a factor although again, I suspect that this process was largely complete by 2007.
  
• People are more likely to be taking multiple different antidepressants, which would manifest as a rise in prescriptions, even if the total number of users stayed constant. Add-on treatment with mirtazapine and others is becoming more popular.
• People are staying on antidepressants for longer meaning more prescriptions. This might not even mean that they're staying ill for longer, it might just mean that doctors are getting better at convincing people to keep taking them by e.g. prescribing drugs with milder side effects, or by referring people for psychotherapy which could increase use by keeping people "in the system" and taking their medication. This is very likely. I previously blogged about a paper showing that in 1993 to 2005, antidepressant prescriptions rose although rates of depression fell, because of a small rise in the number of people taking them for very long periods.
  
• Mental illness rates are rising, but it's not depression: it's anxiety, or something else. Entirely plausible since we know that many people taking antidepressants, in the USA, have no diagnosable depression and even no diagnosable psychiatric disorder at all.
• People are relying on the NHS to prescribe them drugs, as opposed to private doctors, because they can't afford to go private. Private medicine in the UK is only a small sector so this is unlikely to account for much but it's the kind of thing you need to think about.
• Rates of depression have risen, but it's nothing to do with the economy, it's something else which happened between 2007 and 2010: the Premiership of Gordon Brown? The assassination of Benazir Bhutto? The discovery of a 2,100 year old Japanese melon?
  

Personally, my money's on the melon.

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.

The Tufnel Effect

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.

This is all too true when it comes to science. You can design a breathtakingly clever experiment, using state of the art methods to address a really interesting and important question. And then at the end you realize that you forgot to type one word when writing the 1,000 lines of software code that runs this whole thing, and as a result, the whole thing's a bust.

It happens all too often. It has happened to me, let me think, three times in my scientific career and, I know of several colleagues who had similar problems and I'm currently struggling to deal with the consequences of someone else's stupid mistake.

Here's my cautionary tale. I once ran an experiment involving giving people a drug or placebo and when I crunched the numbers I found, or thought I'd found, a really interesting effect which was consistent with a lot of previous work giving this drug to animals. How cool is that?

So I set about writing it up and told my supervisor and all my colleagues. Awesome.

About two or three months later, for some reason I decided to reopen the data file, which was in Microsoft Excel, to look something up. I happened to notice something rather odd - one of the experimental subjects, who I remembered by name, was listed with a date-of-birth which seemed wrong: they weren't nearly that old.

Slightly confused - but not worried yet - I looked at all the other names and dates of birth and, oh dear, they were all wrong. But why?

Then it dawned on me and now I was worried: the dates were all correct but they were lined up with the wrong names. In an instant I saw the horrible possibility: m ixed up names would be harmless in themselves but what if the group assignments (1 = drug, 0 = placebo) were lined up with the wrong results? That would render the whole analysis invalid... and oh dear. They were.

As the temperature of my blood plummeted I got up and lurched over to my filing cabinet where the raw data was stored on paper. It was deceptively easy to correct the mix-up and put the data back together. I re-ran the analysis.

No drug effect.

I checked it over and over. Everything was completely watertight - now. I went home. I didn't eat and I didn't sleep much. The next morning I broke the news to my supervisor. Writing that email was one of the hardest things I've ever done.

What happened? As mentioned I had been doing all the analysis in Excel. Excel is not a bad stats package and it's very easy to use but the problem is that it's too easy: it just does whatever you tell it to do, even if this is stupid.

In my data as in most people's, each row was one sample (i.e. a person) and each column was a piece of info. What happened was that I'd tried to take all the data, which was in no particular order, and reorder the rows alphabetically by subject name to make it easier to read.

How could I screw that up? Well, by trying to select "all the data" but actually only selecting a few of the columns. Then I reordered them, but not the others, so all the rows became mixed up. And the crucial column, drug=1 placebo=0, was one of the ones I reordered.

The immediate lesson I learned from this was: don't use Excel, use SPSS, which simply does not allow you to reorder only some of the data. Actually, I still use Excel for making graphs and figures but every time I use it, I think back to that terrible day.

The broader lesson though is that if you're doing something which involves 100 steps, it only takes 1 mistake to render the other 99 irrelevant. This is true in all fields but I think it's especially bad in science, because mistakes can so easily go unnoticed due to the complexity of the data, and the consequences are severe because of the long time-scale of scientific projects.


Here's what I've learned: Look at your data, every step of the way, and look at your methods, every time you use them. If you're doing a neuroimaging study, the first thing you do after you collect the brain scans is to open them up and just look at them. Do they look sensible?

Analyze your data as you go along. Every time some new results come in, put it into your data table and just look at it. Make a graph which just shows absolutely every number all on one massive, meaningless line from Age to Cigarette's Smoked Per Week to EEG Alpha Frequency At Time 58. For every subject. Get to know the data. That way if something weird happens to it, you'll know. Don't wait to the end of the study to do the analysis. And don't rely on just your own judgement - show your data to other experts.

Check and recheck your methods as you go along. If you're running, say, a psychological experiment involving showing people pictures and getting them to push buttons, put yourself in the hot seat and try it on yourself. Not just once, but over and over. Some of the most insidious problems with these kinds of studies will go unnoticed if you only look at the task once - such as the old "randomized"-stimuli-that-aren't-random issue.

Trust no-one. This sounds bad, but it's not. Don't rely on their work, in experimental design or data analysis, until you've checked it yourself. This doesn't mean you're assuming they're stupid, because everyone makes these mistakes. It just means you're assuming they're human like you.

Finally, if the worst happens and you discover a stupid mistake in your own work: admit it. It feels like the end of the world when this happens, but it's not. However, if you don't admit it, or even worse, start fiddling other results to cover it up - that's misconduct, and if you get caught doing that, it is the end of the world, or your career, at any rate.

"1 Boring Old Man" Blog Isn't

Just wanted to let everyone know about a blog called 1 boring old man, which is a very poor name as it isn't boring at all.


I don't know if it's written by an old man or not, one can only assume so, but whoever writes it, it has got a lot of extremely good stuff about psychiatry and psychiatric drugs. Fans of Daniel Carlat's blog or even former readers of the now seemingly defuct Furious Seasons will find it extremely interesting.

It's actually been going since 2005, but for some reason I've only just found out about it (many thanks to regular Neuroskeptic commentator Bernard Carroll).

Herbs Are Not Your Friends

Rather than nasty artificial drugs, wouldn't it be nice if we could just take some herbs and get better? A lot of people think so. Indeed, a large proportion of our drugs and medicines come from plants, or are closely related to plant chemicals. There's aspirin, morphine, caffeine, cocaine, quinine, and many more. It's as if plants were going out of their way to help us.

In fact, it's more like the opposite. Most of these drugs are poisons, produced by the plant to stop animals (that means you) from eating them. As a plant, you don't want to get eaten, but being, well, rooted to the spot, you can't exactly run away. All you can do is to make animals not want to eat you. So you fill yourself with noxious, or at least nasty-tasting, chemicals.

By contrast, many plants do want their seeds to get swallowed (but not chewed) by animals and birds, because this ensures that they are spread over a wide area. So they wrap them in delicious, colourful packages. This is why, with only a few exceptions, fruit are sweet and safe while while plant leaves, roots and stems are unpleasant, and often toxic.

In fact, this is quite possibly why the taste of bitter is so unpleasant. Plant toxins are usually alkaloids. Animals must have evolved to find alkaloids nasty, because many of them are poisonous and you survive longer if you don't enjoy eating poison.

Caffeine, for example, is found in the seeds ("beans") of the coffee plant, and it makes them taste bitter, to deter herbivores. But those seeds are themselves wrapped in a fruit called the coffee cherry, which is apparently sweet and tasty, although most of them get thrown away in the production of coffee. Coffee wants you to eat the fruit, but swallow the seeds whole, and thereby help spread its DNA. Quinine is one of the bitterest substances on earth, and it's there to protect the bark of the tree. Nicotine is a bitter insecticide. And so on.

There are some plant chemicals which have medicinal effects which are entirely coincidental: St John's Wort for example contains some molecules with interesting effects on animals, which are probably quite unrelated to its role in the plant (it absorbs light). It's also true that plants contain lots of nutrients and the non-toxic ones are, by and large, "healthy" foods, compared to animal products. I say this as a vegetarian. But that doesn't mean that they cure anything.

So the idea that herbal medicines are "natural", and thereby safe, is completely backwards. They are natural; that doesn't make them safe; nature is red in tooth and claw and even the plants are out to get you.

Women Are Better Connected... Neurally

The search for differences between the brains of men and women has a long and rather confusing history. Any structural differences are small, and their significance is controversial. The one rock-solid finding is that men's brains are slightly bigger on average. Then again, men are slightly bigger on average in general.

A new paper just out from Tomasi and Volkow (of cell-phones-affect-brain fame) offers, on the face of it, extremely strong evidence for a gender difference in the brain, not in structure but in function: Gender Differences in Brain Functional Connectivity Density.

Here's the headline pic:
They used resting-state "functional connectivity" (though see here for why this term may be misleading) fMRI in men and women. This essentially means that they put people in the MRI scanner, told them to just lie there and relax, and measured the degree to which activity in different parts of the brain was correlated to activity in every other part. They had a whopping 561 brains in total, though they didn't scan everyone themselves: they downloaded the data from here.

As you can see the results were highly consistent around the world. In both men and women, the main "connectivity hub" was an area called the ventral precuneus. This is interesting in itself although not a new finding as the precuneus has long been known to be involved in resting-state networks. However, the degree of connectivity was higher in women than in men 14% higher, in fact.

The method they used, which they've dubbed "Local Functional Connectivity Density Mapping", is apparantly a fast way of calculating the degree to which each part of the brain is functionally related to each other part.

You could do this by taking every single voxel and correlating it with every other voxel, for every single person, but this would take forever unless you had a supercomputer. LFCDM is, they say, a short-cut. I'm not really qualified to judge whether it's a valid one, but it looks solid.

Also, men's brains were on average bigger, but interestingly they show that women had, relative to brain size, more grey matter than men. Here's the data (I'm not sure about the color scheme...)

So what does the functional connectivity finding mean? It could mean anything, or nothing. You could interpret the highly interconnected female brain as an explanation for why women are more holistic, better at multi-tasking, and more in touch with their emotions than men with their fragmented faculties. Or whatever.

Or you could say, that that's sexist rubbish, and all this means is that men and women on average are thinking about different things when they lie in MRI scanners. We already know that resting-state functional connectivity centred on the precuneus is suppressed whenever your attention is directed towards an external "task".

That's not a fault of this research, which is excellent as far as it goes and certainly raises lots of interesting questions about functional connectivity. But we don't know what it means quite yet.

Tomasi D, & Volkow ND (2011). Gender differences in brain functional connectivity density. Human brain mapping PMID: 21425398