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...



Rodrigues 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

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.

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

A team of Japanese scientists have found the most sarcastic part of the brain known to date. They also found the metaphor centre of the brain and, well, it's kind of like a pair of glasses.

The paper is Distinction between the literal and intended meanings of sentences and it's brought to you by Uchiyama et al. They took 20 people and used fMRI to record neural activity while the volunteers read 4 kinds of statements:

• Literally true
• Nonsensical
• Sarcastic
• Metaphorical

Here at Neuroskeptic fMRI scanning and antidepressants are both big topics.


As I discussed lask week, fish - specifically salmon - are the next big thing in fMRI and the number of salmon brains being scanned is growing at a remarkable rate. But fish haven't made much of an entrance into the world of antidepressants...until now.

Swedish scientists Holmberg et al have just published a paper asking: Does waterborne citalopram affect the aggressive and sexual behaviour of rainbow trout and guppy?

SSRI antidepressants, of which citalopram is one, are very popular. So popular, in fact, that non-trivial levels of SSRIs have been found in sewage and there's a concern that they might make their way into lakes and rivers and thereby affect the behaviour of the animals living there.

Holmberg et al set out to see what citalopram did to some fish in an attempt to find out whether this is likely to be a major problem. So they put some citalopram in the fish's water supplies and then tested their aggressiveness and also their sex drives. It turns out that one of the main ways of measure fish aggression is to put a mirror in their tank and see if they try to fight their own reflection. Fish are not very bright, really.

Anyway, the good news for fish everywhere was that seven days of citalopram exposure had no effect at all, even at doses much higher than those reported as a pollutant (the maximum dose was 0.1 mg/l). And the authors had no conflicts of interest: Big Pharma had nothing to do with this research, although Big Fish Farmer did because they bought the fish from one.

However, this may not be the end of the story, because it turned out that citalopram was very poorly absorbed into the fish's bloodstreams. But other antidepressants have been reported to accumulate in fish. Clearly, the only way to find out for sure what's going on would be to use fMRI...

Holmberg A, Fogel J, Albertsson E, Fick J, Brown JN, Paxéus N, Förlin L, Johnsson JI, & Larsson DG (2011). Does waterborne citalopram affect the aggressive and sexual behaviour of rainbow trout and guppy? Journal of hazardous materials PMID: 21300431

Back in 2009, a crack team of neuroscientists led by Craig Bennett (blog) famously put a dead fish into an MRI scanner and showed it some pictures.



They found some blobs of activation - when they used an inappropriately lenient statistical method. Their point, of course, was to draw attention to the fact that you really shouldn't use that method for fMRI. You can read the whole paper here. The Atlantic Salmon who heroically volunteered for the study was no more than a prop. In fact, I believe he ended up getting eaten.

But now, a Japanese team have just published a serious paper which actually used fMRI to measure brain activity in some salmon: Olfactory Responses to Natal Stream Water in Sockeye Salmon by BOLD fMRI.

How do you scan a fish? Well, like this:

A total of 6 fish were scanned. The salmon were immobilized by adding an anaesthetic (eugenol) and a muscle relaxant (gallamine) to their tank of water. Then, they were carefully clamped into place to make sure they really wouldn't move, while a stream of oxygenated water was pumped through their tank.

Apart from that, it was pretty much a routine fMRI scan.

Why would you want to scan a fish? This is where the serious science comes in. Salmon are born in rivers but they swim out to live in the ocean once they reach maturity. However, they return to the river to breed. What's amazing is that salmon will return to the same river that they were born in - even if they have to travel thousands of miles to get there.

How they manage this is unclear, but the smell (or maybe taste) of the water from their birth river has long been known to be crucial at least once they've reached the right general area (see here for a good overview). Every river contains a unique mixture of chemicals, both natural and artificial (pollutants). Salmon seem to be attracted to whatever chemicals were present in the water when they were young.

In this study, the fMRI revealed that relative to pure water, home-stream water activated a part of the salmon's telencephalon - the most "advanced" part (in humans, it constitutes the vast majority of the brain; in fish, it's tiny). By contrast, a control scent (the amino acid L-serine) did not activate this area, even though the concentration of L-serine was far higher than that of anything in the home-stream water. How this happens is unclear, but further studies of the identified telencephalon area ought to shed more light on it.

So fishMRI is clearly a fast-developing area of neuroscience. In fact, as this graph shows, it's enjoying exponential growth and, if current trends continue, could become almost as popular as scanning people...

Link: Also blogged at NeuroDojo.

There's a lot of talk, much of it rather speculative, about "neuroethics" nowadays.

But there's one all too real ethical dilemma, a direct consequence of modern neuroscience, that gets very little attention. This is the problem of incidental findings on MRI scans.

An "incidental finding" is when you scan someone's brain for research purposes, and, unexpectedly, notice that something looks wrong with it. This is surprisingly common: estimates range from 2–8% of the general population. It will happen to you if you regularly use MRI or fMRI for research purposes, and when it does, it's a shock. Especially when the brain in question belongs to someone you know. Friends, family and colleagues are often the first to be recruited for MRI studies.

This is why it's vital to have a system in place for dealing with incidental findings. Any responsible MRI scanning centre will have one, and as a researcher you ought to be familiar with it. But what system is best?

Broadly speaking there are two extreme positions:

1. Research scans are not designed for diagnosis, and 99% of MRI researchers are not qualified to make a diagnosis. What looks "abnormal" to Joe Neuroscientist BSc or even Dr Bob Psychiatrist is rarely a sign of illness, and likewise they can easily miss real diseases. So, we should ignore incidental findings, pretend the scan never happened, because for all clinical purposes, it didn't.
2. You have to do whatever you can with an incidental finding. You have the scans, like it or not, and if you ignore them, you're putting lives at risk. No, they're not clinical scans, they can still detect many diseases. So all scans should be examined by a qualified neuroradiologist, and any abnormalities which are possibly pathological should be followed-up.

MRI scanners have revolutionized medicine and provided neuroscientists with some incredible tools for exploring the brain.

But that doesn't mean they're fun to use. They can be annoying, unpredictable beings, and you never know whether they're going to bless you with nice results or curse you with cancelled scans and noisy data.

So for the benefit of everyone who has to work with MRI, here is a devotional litany which might just keep your scanner from getting wrathful at the crucial moment. Say this before each scan. Just remember, the magnet is always on and it can read your mind, so make sure you really mean it, and refrain from scientific sins...

What's the difference between walking down the street yesterday, and walking down the street tomorrow?

It's nothing to do with the walking, or the street: that's the same. When seems to be something external to the what, how, and where of the situation. But this creates a problem for neuroscientists.

We think we know how the fact that the brain could store the concept of "walking down the street" (or "walking" and "street"). Very roughly, simple sensory impressions are thought to get built up into more and more complex combinations, and this happens as you move away from the brain's primary visual cortex (V1) and down the so-called ventral visual stream.

In area V1, cells respond mostly to nothing more complex than position and the orientations of straight lines: / or \ or _ , etc. Whereas once you get to the temporal lobe, far down the stream, you have cells that respond to Jennifer Aniston. In between are progressively more complex collections of features.

Even if the details are wrong, the fact that complex objects are composed of simpler parts and ultimately raw sensations, means that our ability to process complex scenes doesn't seem too mysterious, given that we have senses.

But the fact that we can take any given scene, and effortlessly think of it as either "past", "present", or "future", is puzzling under this view because, as I said, the scene itself is the same in all cases. And it's not as if we have a sense devoted to time: the only time we're ever directly aware of, is "right now".

Swedish neuroscientists Nyberg et al used fMRI to measure brain activity associated with "mental time travel": Consciousness of subjective time in the brain. They scanned volunteers and asked them imagine walking between two points, in 4 different situations: past, present, future, or remembered (as opposed to imagined in the past). This short walk was one which they'd really done, many times.

What happened?
Compared to a control task of doing mental arithmetic, both remembering and imagining the walk activated numerous brain areas and there was very strong overlap between the two conditions. No big surprise there.

The crucial contrast was between remembering, past imagining and future imagining, vs. imagining in the present. This revealed a rather cute little blob:

This small nugget of the left parietal cortex represents an area where the brain is more active when thinking about times other than the present, relative to thinking about the same thing, but right now. They note that this area "partly overlaps a left angular region shown to be recruited during both past and future thinking and with parietal regions implicated in self-projection in past, present, or future time."

So what? This is a nice study, but like most fMRI it doesn't tell us what this area is actually doing. To know that, we'd need to know what would happen to someone if that area were damaged. Would they be unable to imagine any time except the present? Would they think their memories were happening right now? Maybe you could use rTMS could temporarily inactivate it - if you could find volunteers willing to lose their sense of time for a while...

Nyberg L, Kim AS, Habib R, Levine B, & Tulving E (2010). Consciousness of subjective time in the brain. Proceedings of the National Academy of Sciences of the United States of America PMID: 21135219

Can fMRI scans be used to detect deception?

It would be nice, although a little scary, if they could. And there have been several reports of succesful trials under laboratory conditions. However, a new paper in Neuroimage reveals an easy way of tricking the technology: Lying In The Scanner.

The authors used a variant of the "guilty knowledge test" which was originally developed for use with EEG. Essentially, you show the subject a series of pictures or other stimui, one of which is somehow special; maybe it's a picture of the murder weapon or something else which a guilty person would recognise, but the innocent would not.

You then try to work out whether the subject's brain responds differently to the special target stimulus as opposed to all the other irrelevant ones. In this study, the stimuli were dates, and for the "guilty" volunteers, the "murder weapon" was their own birthday, a date which obviously has a lot of significance for them. For the "innocent" people, all the dates were random.

What happened? The scans were extremely good at telling the "guilty" from the "innocent" people - it managed a 100% accuracy with no false positive or false negatives. The image above shows the activation associated with the target stimulus (birthdays) over and above the control stimuli. In two seperate groups of volunteers, the blobs were extremely similar. So the technique does work in principle, which is nice.

But the countermeasures fooled it entirely, reducing accuracy to well below random chance. And the countermeasures were very simple: before the scan, subjects were taught to associate an action, a tiny movement of one of their fingers or toes, with some of the "irrelevant" dates. This, of course, made these dates personally relevant, just like the really relevant stimuli, so there was no difference between them, making the "guilty" appear "innocent".

Maybe it'll be possible in the future to tell the difference between brain responses to really significant stimuli as opposed to artifical ones, or at least, to work out whether or not someone is using this trick. Presumably, if there's a neural signiture for guilty knowledge, there's also one for trying to game the system. But as it stands, this is yet more evidence that lie detection using fMRI is by no means ready for use in the real world just yet...

Ganis G, Rosenfeld JP, Meixner J, Kievit RA, & Schendan HE (2010). Lying in the scanner: Covert countermeasures disrupt deception detection by functional magnetic resonance imaging. NeuroImage PMID: 21111834

Yes, England has finally won something. After a poor showing in the 2010 World Cup, the Eurovision Song Contest, and the global economic crisis, we're officially #1 in neuroscience. Which clearly is the most important measure of a nation's success.

According to data collated by ScienceWatch.com and released recently, each English neuroscience paper from the past 10 years has been cited, on average, 24.53 times, making us the most cited country in the world relative to the total number of papers published (source here). We're second only to the USA in terms of overall citations.

(In this table, "Rank" refers to total number of citations).

Why is this? I suspect it owes a lot to the fact that England has produced many of the technical papers which everyone refers to (although few people have ever read). Take the paper Dynamic Causal Modelling by Karl Friston et al from London. It's been cited 649 times since 2003, because it's the standard reference for the increasingly popular fMRI technique of the same name.

Or take Ashburner and Friston's Voxel-Based Morphometry—The Methods, cited over 2000 times in the past 10 years, which introduced a method for measuring the size of different brain regions. Or take...most of Karl Friston's papers, actually. He's the single biggest contributor to the way in which modern neuroimaging is done.

According to the BBC (and many others)...

Libido problems 'brain not mind'

Scans appear to show differences in brain functioning in women with persistently low sex drives, claim researchers.

The US scientists behind the study suggest it provides solid evidence that the problem can have a physical origin.

Back in 1991, Mark Knopfler sang

"Sex and money are my major kicks
Get me in a fight I like the dirty tricks"

Two categories (high and low intensity) of erotic pictures and monetary gains were used. Nudity being the main criteria driving the reward value of erotic stimuli, we separated them into a “low intensity” group displaying women in underwear or bathing suits and a “high intensity” group displaying naked women in an inviting posture. Each erotic picture was presented only once to avoid habituation. A similar element of surprise was introduced for the monetary rewards by randomly varying the amounts at stake: the low amounts were €1-3 and the high amounts were €10-12.

As hypothesized, monetary rewards specifically recruited the anterior lateral OFC ... In contrast, erotic rewards elicited activity specifically in the posterior part of the lateral OFC straddling [fnaar fnaar] the posterior and lateral orbital gyri. These results demonstrate a double dissociation between monetary/erotic rewards and the anterior/posterior OFC ... Among erotic-specific areas, a large cluster was also present in the medial OFC, encompassing the medial orbital gyrus, the straight [how appropriate] gyrus, and the most ventral part of the superior frontal gyrus. [immature emphasis mine]

How mature are you? Have you ever wanted to find out, with a 5 minute brain scan? Of course you have. And now you can, thanks to a new Science paper, Prediction of Individual Brain Maturity Using fMRI.

This is another clever application of the support vector machine (SVM) method, which I've written about previously, most recently regarding "the brain scan to diagnose autism". An SVM is a machine learning algorithm: give it a bunch of data, and it'll find patterns in it.

In this case, the input data was brain scans from children, teenagers and adults, and the corresponding ages of each brain. The pattern the SVM was asked to find was the relationship between age and some complex set of parameters about the brain.

The scan was resting state functional connectivity fMRI. This measures the degree to which different areas of the brain tend to activate or deactivate together while you're just lying there (hence "resting"). A high connectivity between two regions means that they're probably "talking to each other", although not necessarily directly.

It worked fairly well:

Out of 238 people aged 7 to 30, the SVM was able to "predict" age pretty nicely on the basis of the resting state scan. This graph shows chronological age against predicted brain age (or "fcMI" as they call it). The correlation is strong: r2=0.55.

The authors then tested it on two other large datasets: one was resting state, but conducted on a less powerful scanner (1.5T vs 3.0T) (n=195), and the other was not designed as a resting state scan at all, but did happen to include some resting state-like data (n=186). Despite the fact that these data were, therefore, very different to the original dataset, the SVM was able to predict age with r2 over 0.5 as well.

Following on from fMRI in 1000 words, which seemed to go down well, here's the next step: how to analyze the data.

There are many software packages available for fMRI analysis, such as FSL, SPM, AFNI, and BrainVoyager. The following principles, however, apply to most. The first step is pre-processing, which involves:

• Motion Correction aka Realignment – during the course of the experiment subjects often move their heads slightly; during realignment, all of the volumes are automatically adjusted to eliminate motion.
• Smoothing – all MRI signals contain some degree of random noise. During smoothing, the image of the whole brain is blurred. This tends to smooth out random fluctuations. The degree of smoothing is given by the “Full Width to Half Maximum” (FWHM) of the smoother. Between 5 and 8 mm is most common.
• Spatial Normalization aka Warping – Everyone’s brain has a unique shape and size. In order to compare activations between two or more people, you need to eliminate these differences. Each subject’s brain is warped so that it fits with a standard template (the Montreal Neurological Institute or MNI template is most popular.)