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