New, Voodoo-Free fMRI Technique

MIT brain scanners Fedorenko et al present A new method for fMRI investigations of language: Defining ROIs functionally in individual subjects. Also on the list of authors is Nancy Kanwisher, one of the feared fMRI voodoo correlations posse.

The paper describes a technique for mapping out the "language areas" of the brain in individual people, not for their own sake, but as a way of improving other fMRI studies of language. That's important because while everyone's brain is organized roughly the same way, there are always individual differences in the shape, size and location of the different regions.

This is a problem for fMRI researchers. Suppose you scan 10 people and show them pictures of apples and pictures of pears. And suppose that apples activate the brain's Fruit Cortex much more strongly than pears. But unfortunately, the Fruit Cortex is a small area, and its location varies between people. In fact, in your 10 subjects, no-one's Fruit Cortex overlaps with anyone else's, even though everyone has one and they all work exactly the same way.

If you did this experiment you'd fail to find the effect of apples vs. pears, even though it's a strong effect, because there will be no one place in the brain where apples reliably cause more activation. What you need is a way of finding the Fruit Cortex in each person beforehand. What you'd need to do is a functional localization scan - say, showing people a big bowl of fruit - as a preliminary step.

Fedorenko et al scanned a bunch of people while doing a simple reading task, and compared that to a control condition, reading a random list of nonsense which makes no linguistic sense. As you can see, there's a lot of variation between people, but there's also clearly a basic pattern of activation: it looks a bit like a tilted "V" on the left side of the brain:

These are the language areas of each person. (Incidentally, this is why fMRI, despite its limitations, is an amazing technology. There is no better way of measuring this activation. EEG is cheaper but nowhere near as good at localizing activity; PET is close, but it's slow, expensive and involves injecting people with radioactivity.)

Fedorenko et al then overlapped all the individual images to produce of map of the brain showing how many people got activation in each part:

The most robust activations were on the left side of the brain, and they formed a nice "V" shape again. These are the areas which have long been known to be involved in language, so this is not surprising in itself.

Here's the clever bit: they then took the areas activated in a large % of people, and automatically divided them up into sub-regions; each of the "peaks" where an especially large proportion of subjects showed activation became a separate region.

This is on the assumption that these peaks represent parts of the brain with distinct functions - separate "language modules" as it were. But each module will be in a slightly different place in each person (see the first picture). So they overlapped the subdivisions with the individual activation blobs to get a set of individual functional zones they call Group-constrained Subject-Specific functional Regions of Interest, or GcSSfROIs to their friends.

Fedorenko et al claim various advantages to this technique, and present data showing that it produces nice results in independent subjects (i.e. not the ones they used to make the group map in the first place.)

In particular, they argue that it should allow future fMRI studies to have a better chance of finding the specific functions of each region. So far, experiments using fMRI to investigate language have largely failed to find activations specific to particular aspects of language like grammar, word meaning, etc. which is unexpected because patients suffering lesions to specific areas often do show very selective language problems.

Does this relate to the voodoo correlations issue? Indirectly, yes. The voodoo (non-independence error) problem arises when you do a large number of comparisons, and then focus on the "best" results, because these are likely to be wholly, or partially, only that good by chance.

Fedorenko et al's method allows you to avoid doing lots of comparisons in the first place. Instead of looking all over the whole brain for something interesting, you can first do a preliminary scan to map out where in each person's brain interesting stuff is likely to happen, and then focus on those bits in the real experiment.

There's still a multiple-comparisons problem: Fedorenko et al identified 16 candidate language areas per brain, and future studies could well provide more. But that's nothing compared to the 40,000 voxels in a typical whole-brain analysis. We'll have to wait and see if this technique proves useful in the real world, but it's an interesting idea...

Fedorenko, E., Hsieh, P., Nieto Castanon, A., Whitfield-Gabrieli, S., & Kanwisher, N. (2010). A new method for fMRI investigations of language: Defining ROIs functionally in individual subjects Journal of Neurophysiology DOI: 10.1152/jn.00032.2010

Head Trip

A quick post to recommend the 2007 book Head Trip, by Jeff Warren.

Head Trip is about "24 hours in the life of your brain": sleeping, waking, and everything in-between, from lucid dreaming to daydreams and hypnosis.

Warren gives a nice overview of current research and theory along with the story of his personal quest to experience the full spectrum of conciousness.

The book's most interesting chapter is called "The Watch". It's about that hour or two of wakefulness which occurs in the middle of the night, between the first sleep and the second sleep. You know the one...right? Neither did I, but apparently, this makes us a bit weird, historically speaking.

Warren says that until the era of artificial lighting and alarm clocks, sleep was segmented. It was common for people to sleep twice each night, with a bout of awakeness in the middle. This nocturnal alertness wasn't quite like daytime waking, though: it was more relaxed, less focussed, carefree. Our modern sleep pattern, then, is kind of compressed, with the two sleeps pushed together until they merge into one.

There are two lines of evidence for this. Writings from the pre-modern era routinely make reference to "first sleep" and "second sleep", and in many languages, although not modern English, there were special words for these periods and the wakefulness between. This is according to historian A. Roger Ekirch in his history of night-time, At Day's Close (review, Wiki), a book I really want to read now.

On the other hand, there's the findings of sleep psychiatrist Thomas Wehr, in particular his classic 1992 study called In short photoperiods, human sleep is biphasic. Wehr took healthy American volunteers and put them in an artificial environment with a controlled light cycle, such that there were only 10 hours of brightness per day. (That's 6 hours less than we get on average, even in winter, due to artificial light.) Within a few weeks "their sleep episodes expanded and usually divided into two symmetrical bouts, several hours in duration, with a 1-3 h waking interval between them."

This is pretty freaky. Sleeping all night seems natural, normal and healthy: if we wake up before we need to get up, we're dismayed and we call it insomnia. Maybe this is a modern invention like electric lighting. There's something amazing and also a bit disturbing about this idea. As Warren says, it's like finding out that your house "is really the exposed bell-tower of a vast underground cathedral".

I'm Bipolar, You're a Schizophrenic

Over at Comment is Free, Beatrice Bray takes issue with this cartoon (for those who don't follow British politics, the guy on the right is trying to win an election at the moment.)

The use of the word "psychotic" was offensive. You may think this political correctness gone mad, but if you are ill, or have been, you need words to describe your experience to yourself and to others. If for you these words are negative, you will hate yourself. Language can make or break your happiness. That is why mental health activists do not like psychiatric terms being used as abuse...

Hmm. Fair enough... but why would a sick person care if people insulted their illness? Cancer patients don't seem to be offended when things are called "a cancer on our society" or whatever, because not many cancer patients like cancer.

Maybe the clue is later on:

And please allow individuals an identity apart from their illness, so always say "a person with schizophrenia" rather than "a schizophrenic".

So the problem is that unlike cancer patients, the mentally ill aren't seen as people separate from their illness. That is a serious issue - but getting offended by someone using "psychotic" as a term of abuse surely only reinforces the idea that sufferers identify with it?

In fact, a lot of people with psychiatric illnesses don't follow Bray's advice when talking about themselves. "Bipolar", for example, is commonly used to describe people, rather than their illness - and many bipolars do this... bipolar people... people with bipolar disorder. Whatever.

On Google, "I'm bipolar" gets 247,000 hits and "I am bipolar" gets 235k, so that's about 500k in total. "I have bipolar" gets 576k - so "having" and "being" are about equally popular.

Likewise for schizophrenia, "I have schizophrenia" gets 174k, but "I'm schizophrenic" gets 136k, and "I am schizophrenic" 31k - almost equal again. "I'm a schizophrenic" gets 465k, mainly because of a movie, however if you exclude those you still get over 100k.

So if mental health activists want to reform the way we talk about mental illness, it's not just the "them" of the general public who need bringing into line. But I've never been convinced that changing what words people use about things like this is a good way of changing minds: it's an easy way to create the appearance of doing so, but actually changing minds is hard, and I don't think language reform is even a good first step.

You don't change minds by telling people to please change, you make them change by showing them examples of why they're wrong. If your aim is to convince that schizophrenia happens to people and doesn't define them, a movie like A Beautiful Mind (or more recently perhaps Shutter Island, although it takes a lot of artistic license with the symptoms of psychosis) is worth a thousand word-changes.

Of Yeast and Men

Nature reports on the Dissection of genetically complex traits with extremely large pools of yeast segregants.


Ehrenreich et al have a new way of mapping the genetic basis of complex traits in yeast, "complex" being what geneticists call anything which isn't controlled by one single gene. They dub their approach "Extreme QTL mapping". This suggests images of geneticists running experiments atop Everest, or perhaps collecting blood samples from lions with their bare hands, but actually

Extreme QTL mapping (X-QTL) has three key steps. The first is the generation of segregating populations of very large size. The second is selection-based phenotyping of these populations to recover large numbers of progeny with extreme trait values. This can be accomplished, for example, by selection for drug resistance or by cell sorting. The final step is quantitative measurement of pooled allele frequencies across the genome.

The basic idea is to cross breed two strains of yeast to generate lots of different hybrid strains each with a random selection of DNA from each "parent". Then, you put all the hybrids under some kind of selective pressure - for example, by adding the toxin 4-NQO to their dish.

Some yeast are more or less resistant to 4-NQO, and this trait is largely determined by genetics. So after a while, the vulnerable hybrids will die out and only the most highly resistant strains will be left in the 4-NQO dish to reproduce. It's a quick and dirty form of selective breeding. Finally, you can compare the genetics of the 4-NQO resistant hybrids to a control group of hybrids who didn't get any toxins, using a GWAS. Any genetic differences are likely to represent 4-NQO resistance genes.

Using this method, Ehrenreich et al found no less than 14 4-NQO resistance variants. That includes two replications of previous findings, and 12 new ones. Collectively, the genes explained

59% of the phenotypic variance in 4-NQO sensitivity in an additive model. Because we measured the heritability of this trait to be 0.84, the loci explained 70% of the genetic variance, indicating that we have explained most of the genetic basis of this trait with the loci detected by X-QTL.

In other words, they've found most of the genes with a substantial effect on 4-NOR resistance, but not all of them. (They then did the same thing for several other toxins). About 30% of the heritability is "missing". Compare that to most human complex traits, where the missing heritability is more like 95%-99% at the moment. For example, twin studies and similar find human height to have a heritability of about 0.8, and more than 40 genetic variants have been associated with height, but together they only explain 5% of the heritability.

Why is Neuroskeptic posting about yeast? Well, partly because we live in a yeast-based society. Without yeast, we would have no alcoholic drinks. I think it's important to acknowledge their contribution to our lives. But mainly because there's a lesson here for people interested in the genetics of complex traits in humans, like, say, personality, IQ, and mental illness.

Yeast resistance to toxins is about the most straightforwardly "biological" trait you could imagine. Finding its genetic basis ought to be easy. But it wasn't. It was...extreme. Ehrenreich et al had to breed and select yeast with extreme traits (e.g. extremely high resistance to toxins), and compare them to control yeast of the same ancestry, to find the genes, and they still had a good deal of missing variance.

If they'd had to work on a random bunch of yeast from the wild, they'd have had a lot more trouble. That's why previous yeast GWAS studies didn't get results as good as these. Yet when it comes to humans, we're indeed forced to use a random bunch of people from the wild. You can't selectively breed people.

You can breed, say, mice, but it takes a lot longer than with yeast. I think there have been a few studies breeding mice for a certain trait and then looking at their genetics but not with a great degree of success, even though the first thing every mouse researcher learns is that different strains of mice are very different (C57BL/6 mice, for example, are notoriously hard to handle and love biting people.)

This is bad news for human genetics, where the interesting traits are clearly a lot more complex, ill-defined, and hard to measure than in yeast. On the other hand, though, it's perhaps also rather reassuring, as it suggests that our failure to explain more than a few % of the heritability so far reflects technical limitations rather than because these traits just aren't as genetic as we think after all...

Ehrenreich IM, Torabi N, Jia Y, Kent J, Martis S, Shapiro JA, Gresham D, Caudy AA, & Kruglyak L (2010). Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature, 464 (7291), 1039-42 PMID: 20393561

Neural Correlates of Being a Total Bad-Ass

A new fMRI study in PLoS reports Differential Brain Activation to Angry Faces by Elite Warfighters, the elite warfighters being US Navy SEALs.

SEALs are indeed pretty elite. This being a British blog, I wouldn't want to say that they're the world's elitest naval special forces unit. That's the British Special Boat Service. But they could still kill you ten times before you knew they were there (unless you're in the Special Boat Service.)

Anyway, San Diego researchers Paulus et al scanned 11 SEALs and 23 healthy civilian men during an emotional face matching (originally developed by Hariri et al) that involved seeing happy, angry, and fearful faces.

Such tasks are very popular in neuroimaging at the moment because looking at faces of people expressing strong emotions reliably activates emotion-related brain areas, without needing to actually induce emotions in your volunteers which can cause practical problems, i.e. people getting scared and maybe panicking in the MRI scanner. Whether studying emotional-face-induced activation is a valid substitute for studying emotion-induced activation is an open question.


What happened? fMRI being a sensitive way of measuring human brain activation, they found some differences between the two groups in neural responses to seeing the faces:

elite warfighters relative to comparison subjects showed relatively greater right-sided insula, but attenuated left-sided insula, activation. Second, these individuals showed selectively greater activation to angry target faces relative to fearful or happy target faces bilaterally in the insula.

OK. So what does that mean?

These findings support the notion that elite warfighters... deploy greater neural processing resources toward potential threat-related facial expressions and reduced processing resources to non-threat-related facial expressions. This finding suggests that rather than expending more effort in general, elite warfighters show more focused neural and performance tuning, such that greater neural processing resources are directed toward threat stimuli and processing resources are conserved when facing a nonthreat stimulus situation.

So the message is that SEALs are better at focusing on threats and don't get distracted by benign background stuff. Although apparently this is only true of their insula, not an area known for its role in attention, and the threat was an angry face on a screen. But that aside, this is not very surprising given that they're highly-trained soldiers.

But the unsurprisingness of this result is a problem. We don't need neuroscience to tell us that elite soldiers are good at detecting and responding to threats. That's rather obvious. I'd guess that most of them were pretty good at it before they got selected, and then they got even better with training. This must have something to do with the brain, because your brain is what allows you to learn stuff.

What we don't understand very well yet is how training (or other forms of learning) works, on a neural level, i.e. what the molecular and cellular mechanisms are. It would be really nice to find out. Unfortunately, fMRI studies like this are unable to tell us that, because they only study the very last stage in the process, the final product.

This is in no way a problem with this paper alone, and it's no worse than many other articles. The same issue applies to many neuroimaging studies of abnormal states like depression or, as I've posted about previously, psychological trauma. Such results can form the basis for investigations into mechanisms, and as ways of testing theories, but on their own, finding that abnormal brains react in abnormal ways is not, in itself, very useful.

Paulus, M., Simmons, A., Fitzpatrick, S., Potterat, E., Van Orden, K., Bauman, J., & Swain, J. (2010). Differential Brain Activation to Angry Faces by Elite Warfighters: Neural Processing Evidence for Enhanced Threat Detection PLoS ONE, 5 (4) DOI: 10.1371/journal.pone.0010096