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?

ResearchBlogging.orgLeonard 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

Peripheral Nervous System (PNS)

The peripheral nervous system connects the central nervous system with the rest of the body. All motor, sensory and autonomic nerve cells and fibers outside the CNS are generally considered part of the PNS. Specifically, the PNS comprises the ventral (motor) nerve roots, dorsal (sensory) nerve roots, spinal ganglia, and spinal and peripheral nerves, and their endings, as well as a major portion of the autonomic nervous system (sympathetic trunk). The first two cranial nerves (the olfactory and optic
nerves) belong to the CNS, but the remainder belong to the PNS.

Peripheral nerves may be purely motor or sensory but are usually mixed, containing variable fractions of motor, sensory, and autonomic nerve fibers (axons). A peripheral nerve is made up of multiple bundles of axons, called fascicles, each of which is covered by a connective tissue sheath (perineurium). The connective tissue lying between axons within a fascicle is called endoneurium, and that between fascicles is called epineurium. Fascicles contain myelinated and unmyelinated axons, endoneurium, and capillaries. Individual axons are surrounded by supportive cells called Schwann cells. A single Schwann cell surrounds several axons of unmyelinated type. Tight winding of the Schwann cell membrane around the axon produces the myelin sheath that covers myelinated axons. The Schwann cells of a myelinated axon are spaced a small distance from one another; the intervals between them are called nodes of Ranvier. The nerve conduction velocity increases with the thickness of the myelin sheath. The specialized contact zone between a motor nerve fiber and the muscle it supplies is called the neuromuscular junction or motor end plate. Impulses arising in the sensory receptors of the skin, fascia, muscles, joints, internal organs, and other parts
of the body travel centrally through the sensory (afferent) nerve fibers. These fibers have their cell bodies in the dorsal root ganglia (pseudounipolar cells) and reach the spinal cord by way of the dorsal roots.

Which regions of the brain lack a significant blood-brain barrier?

Brain regions that lack a significant blood-brain barrier tend to be midline structures located
near ventricular spaces. They include the area postrema, median eminence of the
hypothalamus, and neurohypophysis.

Blood-brain Barrier Components?

The blood-brain barrier is not a single barrier, but a composite of many systems that act to
control the entry of substances from the blood to the brain:
1. Capillary endothelial cells linked by tight junctions and expressing specialized uptake
systems for particular metabolic substrates (e.g., glucose, amino acids)
2. A prominent basement membrane between endothelia and adjacent cells
3. Pericapillary astrocytes with end-feet adjacent to capillaries
A similar system exists for the choroidal epithelium (blood-cerebrospinal fluid [CSF]

Understanding the molecular and cellular mechanisms, it's so important, why?

1. Enhancement of diagnostic possibilities and treatment options
2. More appropriate selection of diagnostic tests and interpretation of test results
3. Prediction of drug side effects and interactions
4. Selection of optimal drug regimens
5. Aid to critical review of novel concepts and therapies
6. Understanding of the rationale for current clinical trials
7. Provision of a background for communicating information to patients and families

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...

ResearchBlogging.orgRodrigues 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).

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