Neurology Center

Allegedly, British Prime Minister Gordon Brown takes a monoamine oxidase inhibitor (MAOi) antidepressant.

That's the rumor, based on the rumored fact that he is unable to eat certain things, notably cheese and Chianti wine. These are foods rich in tyramine, a chemical that's normally harmless, but can be toxic in people taking MAOis. So, if Brown is indeed on a Chianti-and-cheeseless regime, he almost certainly is taking one of the several MAOis on the market today.

The original source for this idea is this blogger, who claims to have heard it from an unnamed Brown aide. Is he to be believed? A glance over his website shows he is hardly an impartial commentator, and he goes on to demonstrate his psychological insight with statements like

"Obsessive Compulsive Disorder (OCD) is relatively common. Most of us display some obsessive features in everyday life, but under stress a minority of people become borderline or actual OCD in their behaviour, and need medication to control both this and the depression which almost always presents soon afterwards. ... Gordon Brown's symptoms are obvious when viewed in this light: the constant repetition of phrases, and an almost embarrassing (for his Party) need to spray every Parliamentary answer with statistics... they - and the constant speech repetition - represent Brown's unconscious means of controlling the severe anxiety that accompanies depression with OCD."

• are not "powerful", "heavy duty" antidepressants. In terms of effectiveness, they are no better, on average, than Prozac. In fact, no antidepressant is much better than any other one. They differ in terms of side effects, but not "strength". For what it's worth, current opinion is that if there is a best antidepressant, it is escitalopram, a modern Prozac-like SSRI with very mild side effects, which is just about as unlike a MAOi as you can imagine.
  
• do not "impair" or "affect judgment". Antidepressants don't. Except that they treat depression, and someone who's happy might make different judgments to someone who's depressed. But these drugs do not affect judgment in the way that intoxicants like alcohol or cocaine do. You don't get high on them. This is why they have no street value. Most drugs which impair judgment get used recreationally, because having your judgment impaired can be fun. Antidepressants aren't.
  
• are not exclusively used in "severe depression". They are usually reserved for when a patient has not responded to other drugs. This is because of their troublesome side effects, including high blood pressure, and the fact that you can't eat cheese. But "treatment-resistant" depression is not the same as "severe" depression. In fact, the more severe the depression, the more likely it is to respond to treatment with conventional drugs. If Brown is on MAOis, he has probably tried at least two or three other drugs, but this is by no means uncommon because antidepressants just don't work especially well. According to the largest trial in a real-world setting, the STAR*D project, only 30% of people fully recover on their first antidepressant and only 30% of the rest respond to the second one.
  
• are not especially effective in OCD, as the source of the rumor claimed - "this older class of drugs has one huge advantage: for severe depression and obsessive compulsive disorder it remains very effective", emphasis in the original. This is just flat-out wrong. Other antidepressants are more useful in OCD. Here's a recent review of drug therapy for OCD. MAOis get a mention... right at the end, after (deep breath) SSRIs, clomipramine, atypical antipsychotics, SNRIs, pregabalin, tricyclic antidepressants, and benzodiazepines. Here's the only published trial comparing a monoamine oxidase inhibitor to another drug, Prozac, for OCD. The MAOi didn't work, Prozac did.
• were the first class of antidepressants to be discovered; the very first, iproniazid, was discovered in 1952. Others followed, such as tranylcypromine, phenelzine, and selegiline. Today, there are a handful of MAOis on the market. These include some newer drugs such as moclobemide (which has milder side effects) and the selegiline transdermal patch (which carries fewer dietary restrictions). MAOis are primarily used to treat depression, but are also used in Parkinson's disease.

Continuing the theme of interesting single case reports, I was pleased to see a paper about brain activity in someone who suffered a panic attack in the middle of an fMRI brain scan experiment.

The unfortunate volunteer, a 46 year old woman, was taking part in an experiment looking at restless-leg syndrome. The scan lasted 40 minutes, and everything was going smoothly until quite near the end, when out of the blue, she had a panic attack.

Obviously, the scan had to be abandoned - as soon as the volunteer pressed the emergency "panic button", they stopped the scan and got her out of the MRI. (This kind of thing is why we have such buttons!) However, they decided to see what happened in the woman's brain as the panic started using the data they acquired up to that point.

Here's what they found: the top graph here shows her heart rate. It starts increasing a bit and then spikes, which shows exactly when the attack occurred. What about the brain? Well, amygdala and left insula activity sort of increase around this time. A bit. If you stare at the lines hard enough.

If you believe they did, it makes sense because the amygdala is known to be involved in anxiety (amongst other things) while the insula is responsible for the perception of the body's internal state, which is rather out of whack during a panic attack.

What doesn't make sense is the middle temporal gyrus bit, which was statistically the only part of the brain where activity was significantly correlated with heart rate (in whole-brain analysis). That region is not believed to have anything to do with panic, and to be honest, it's probably just a fluke.

This is only the second published report about panic during fMRI. There was one previous paper from 2006 about an attack in someone with a history of panic, which also found amygdala activation. But there are sure to be others out there which haven't made it into print - anxiety and panic during scans is not unheard of (the scanner is rather claustrophobic). It would be interesting to get more data on this, because it's obviously rather hard to research real-life panic attacks, on account of them being unpredictable.

Spiegelhalder, K., Hornyak, M., Kyle, S., Paul, D., Blechert, J., Seifritz, E., Hennig, J., Tebartz van Elst, L., Riemann, D., & Feige, B. (2009). Cerebral correlates of heart rate variations during a spontaneous panic attack in the fMRI scanner Neurocase, 1-8 DOI: 10.1080/13554790903066909

A reader drew my attention to this gem from Craig Bennett, who blogs at prefrontal.org:

Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction

This is a poster presented by Bennett and colleagues at this year's Human Brain Mapping conference. It's about fMRI scanning on a dead fish, specifically a salmon. They put the salmon in an MRI scanner and "the salmon was shown a series of photographs depicting human individuals in social situations. The salmon was asked to determine what emotion the individual in the photo must have been experiencing."

I'd say that this research was justified on comedic grounds alone, but they were also making an important scientific point. The (fish-)bone of contention here is multiple comparisons correction. The "multiple comparisons problem" is simply the fact that if you do a lot of different statistical tests, some of them will, just by chance, give interesting results.

In fMRI, the problem is particularly severe. An MRI scan divides the brain up into cubic units called voxels. There are over 40,000 in a typical scan. Most fMRI analysis treats every voxel independently, and tests to see if each voxel is "activated" by a certain stimulus or task. So that's at least 40,000 separate comparisons going on - potentially many more, depending upon the details of the experiment.

Luckily, during the 1990s, fMRI pioneers developed techniques for dealing with the problem: multiple comparisons correction. The most popular method uses Gaussian Random Field Theory to calculate the probability of falsely "finding" activated areas just by chance, and to keep this acceptably low (details), although there are other alternatives.

But not everyone uses multiple comparisons correction. This is where the fish comes in - Bennett et al show that if you don't use it, you can find "neural activation" even in the tiny brain of dead fish. Of course, with the appropriate correction, you don't. There's nothing original about this, except the colourful nature of the example - but many fMRI publications still report "uncorrected" results (here's just the last one I read).

Bennett concludes that "the vast majority of fMRI studies should be utilizing multiple comparisons correction as standard practice". But he says on his blog that he's encountered some difficulty getting the results published as a paper, because not everyone agrees. Some say that multiple comparisons correction is too conservative, and could lead to genuine activations being overlooked - throwing the baby salmon out with the bathwater, as it were. This is a legitimate point, but as Bennett says, in this case we should report both corrected and uncorrected results, to make it clear to the readers what is going on.

Mental illness: how common is it? A popular answer is one in four - 25% of people will experience it at least once in their lives. In fact, most published research suggests that the lifetime rate is higher, around 30-50%, in Western nations.

Moffitt, T., Caspi, A., Taylor, A., Kokaua, J., Milne, B., Polanczyk, G., & Poulton, R. (2009). How common are common mental disorders? Evidence that lifetime prevalence rates are doubled by prospective versus retrospective ascertainment Psychological Medicine DOI: 10.1017/S0033291709991036

According to a new paper in the prestigous journal PNAS, High-field MRI reveals an acute impact on brain function in survivors of the magnitude 8.0 earthquake in China.

The earthquake, you'll remember, happened on 12th May last year in central China. Over 60,000 people died. The authors of this paper took 44 earthquake survivors, and 32 control volunteers who had not experienced the disaster.

The volunteers underwent a "resting state" fMRI scan; survivors were scanned between 13 and 25 days after the earthquake. Resting state fMRI is simply a scan conducted while lying in the scanner, not doing anything in particular. Previous work has shown that fMRI can be used to measure resting state neural activity in the form of low-frequency oscillations.

The authors found differences in the resting state low-frequency activity (ALFF) between the trauma survivors and the controls. In survivors, resting state activity was increased in several areas:

"The whole-brain analysis indicated that, vs. controls, survivors showed significantly increased ALFF in the left prefrontal cortex and the left precentral gyrus, extending medially to the left presupplementary motor area... [and] region of interest (ROI) analyses revealed significantly increased ALFF in bilateral insula and caudate and the left putamen in the survivor group..."

One of the limitations of antidepressants is that they don't always work. Worse, they don't work in an unpredictable way. Some people benefit from some drugs, and others don't, but there's no way of knowing in advance what will happen in any particular case - or of telling which pill is right for which person.

As a result, drug treatment for depression generally involves starting with a cheap medication with relatively mild side-effects, and if that fails, moving onto a series of other drugs until one helps. But since it can take several weeks for any new drug to work, this can be a frustrating process for patients and doctors alike.

Some means of predicting the antidepressant response would thus be very useful. Many have been proposed, but none have entered widespread clinical use. Now, a pair of papers(1,2) from UCLA's Andrew Leuchter et al make the case for prediction using quantitative EEG (QEEG).

EEG, electroencephalography, is a crude but effective way of recording electrical activity in the brain via electrodes attached to the head. "Quantitative" EEG just means using EEG to precisely measure the level of certain kinds of activity in the brain.

Leuchter et al's system is straightforward: it uses six electrodes on the front of the head. The patient simply relaxes with their eyes closed for a few minutes while neural activity is recorded.

This procedure is performed twice, once just before antidepressant treatment begins and then again a week later. The claim is that by examining the changes in the EEG signal after one week of drug treatment, the eventual benefit of the drug can be predicted. It's not an implausible idea, and if it did work, it would be rather helpful. But does it?

Leuchter et al say: yes! The first paper reports that in 73 depressed patients who were given the antidepressant escitalopram 10mg/day, QEEG changes after one week predicted clinical improvement six weeks later. Specifically, people who got substantially better at seven weeks had a higher "Antidepressant Treatment Response Index" (ATR) at one week than people who didn't: 59.0 ± 10.2 vs 49.8 ± 7.8, which is highly significant (p less than 0.001).

In the companion paper, the authors examined patients who started on escitalopram and then either kept taking it or switched to a different antidepressant, bupropion. They found that patients who had a high ATR after a week of escitalopram tended to do well if they stayed on it, while patients who had a low ATR to escitalopram did better when they switched to the other drug.

These are interesting results, and they follow from ten years of previous work (mostly, but not exclusively, from the same group) on the topic. Because the current study didn't include a placebo group, we can't say that the QEEG predicts antidepressant response as such, only that it predicts improvement in depression symptoms. But even this is pretty exciting, if it really works.

In order to verify that it does, other researchers need to replicate this experiment. But they may find this a little difficult. What is the Antidepressant Treatment Response Index use in this study? It's derived from an analysis of the EEG signal, and we're told that you get it from this formula:

Some of the terms here are common parameters that any EEG expert will understand. But "A", "B", and "C" are not. They're constants, which are not given in the paper. They're secret numbers. Without knowing what those numbers are, no-one can calculate the "ATR" even if they have an EEG machine.

Why keep them secret? Well...

"Financial support of this project was provided by Aspect Medical Systems. Aspect participated in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation and review of the manuscript."

"To facilitate independent replication of the work reported here, Aspect intends to make available a limited number of investigational systems for academic researchers. Please contact Scott Greenwald, Ph.D... for further information."

[BPSDB]

Leuchter AF, Cook IA, Gilmer WS, Marangell LB, Burgoyne KS, Howland RH, Trivedi MH, Zisook S, Jain R, Fava M, Iosifescu D, & Greenwald S (2009). Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry research PMID: 19709754

Leuchter AF, Cook IA, Marangell LB, Gilmer WS, Burgoyne KS, Howland RH, Trivedi MH, Zisook S, Jain R, McCracken JT, Fava M, Iosifescu D, & Greenwald S (2009). Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: Results of the BRITE-MD study. Psychiatry research PMID: 19712979