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

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.)

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

One of the difficulties doctors face when prescribing antidepressants is that they're unpredictable.

One person might do well on a certain drug, but the next person might get no benefit from the exact same pills. Finding the right drug for each patient is often a matter of trying different ones until one works.

So a genetic test to work out whether a certain drug will help a particular person would be really useful. Not to mention really profitable for whoever patented it. Three recent papers, published in three major journals, all claim to have found genes that predict antidepressant response. Great! The problem is, they were different genes.

First up, American team Binder et al looked at about 200 variants in 10 genes involved in the corticosteroid stress response pathway. They found one, in a gene called CRHBP, that was significantly associated with poor response to the popular SSRI antidepressant citalopram (Celexa), using the large STAR*D project data set. But this was only true of African-Americans and Latinos, not whites.

Garriock et al used the exact same dataset, but they did a genome-wide association study (GWAS), which looks at variants across the whole genome, unlike Binder et al who focussed on a small number of specific candidate genes. Sadly no variants were statistically significantly correlated with response to citalopram, although in a GWAS, the threshold for genome-wide significance is very high due to multiple comparisons correction. Some were close to being significant, but they weren't obviously related to CRHBP, and most weren't anything to do with the brain.

Uher et al did another GWAS of response to escitalopram and nortriptyline in a different sample, the European GENDEP study. Escitalopram is extremely similar to citalopram, the drug in the STAR*D studies; nortriptyline however is very different. They found one genome-wide significant hit. A variant in a gene called UST was associated with response to nortriptyline, but not escitalopram. No variants were associated with response to escitalopram, although one in the gene IL11 was close. There were some other nearly-significant results, but they didn't overlap with either of the STAR*D studies.

Finally, one of the STAR*D studies found a variant significantly linked to tolerability (side effects) of citalopram. GENDEP didn't look at this.

Frankenstein op saved me from suicide!!!

That's the front page headline on today's News of The World, a popular British Sunday tabloid which would probably be the worst newspaper in most other countries, but which by British standards is only slightly below average.

TV actress Bev Callard tells how she suffered an episode of severe clinical depression, and was given ECT or as the News put it "terrifying electric shock treatment" which "plunged her into a scene of horror beyond anything TV scriptwriters could have imagined." Although it's never made clear what the horror was: according to Callard her twelve sessions of shock therapy was the only thing which worked to help lift her out of her suicidal state and, as the headline says, it may even have saved her life.

She was worried about the possible side effects, including memory problems, and describes experiencing difficulty learning her lines initially, but she says, she always managed to do so successfully.

The print version includes some sensible comments from a doctor who points out that ECT is effective as a last resort in cases of depression that don't respond to drugs alone. He also comments that "Bev is so brave to talk about this".

She is, but she shouldn't have to be: talking about depression shouldn't be a matter of being brave, any more than talking about any other illness. The reason it takes courage is that unlike with other diseases, admitting to suffering from a mental illness is liable to land you on the front page of the papers.

Ever wondered what the British public think about mental illness?

Well, the British government has, and the results of the 2010 Attitudes to Mental Illness Survey are out. I'm never sure how much faith to put in such data because what people are willing to say they think, and what they really feel, are not the same.

So while it's encouraging that only 20% of people say they agree with the statement that "Anyone with a history of mental illness should be excluded from taking public office", it would be naive to think that the other 80% would really be equally likely to vote for someone with a psychiatric history when push came to shove. We've moved on since McGovern, but maybe not all that much.

Worse, a lot of the questions are dubious. One asks whether you agree that "Mental hospitals are an outdated means of treating people with mental illness", the 'right' answer, that gets counted as a nice positive attitude, being to agree. I disagree, not least because inpatient treatment for depression helped my grandfather hugely when he was a young man. If that means I have a bad attitude to the mentally ill, so be it. I don't think it does.