3D fMRI Promises Deeper Neuroscience

A new approach to fMRI scanning offers a three-dimensional look at brain activation.

fMRI is already a 3D technique, of course, but in the case of the cerebral cortex - which is what the great majority of neuroscientists are most interested in - the 3D data are effectively just 2D images folded up in space.

The cortex can be thought of a big sheet crumpled up into the shape of a brain, and it's possible to use software to 'unfold' the cortex into a 2D map for the purposes of fMRI data visualization. It's more informative because it shows you which areas are closest to each other.

But the cortex isn't really a sheet. It's more like six sheets stacked up - the cortex is formed of six layers, each with distinct cell types, connections, and functions. The difference between Layer III and Layer V of a particular cortical area is, in some ways, as important as the difference between two adjacent areas, but fMRI can't distinguish them because they're too close together.

Until now. In a new paper, Minnesota neuroscientists Olman et al say that they've given fMRI a  third dimension - Layer-specific FMRI reflects different neuronal computations at different depths in human v1.

They used a powerful 7 Tesla MRI scanner and a T2-weighted 3D GRASE pulse sequence that provides extremely high spatial resolution (0.7 mm - whereas 3 mm is the fMRI standard). The trade-off was that they were only able to scan a small chunk of the brain, namely the primary visual cortex. However, this is a good place to start, because it has a very well-understood layering system.

Does it work?

Probably, although the data they present are a little messy. By showing volunteers various kinds of pictures, they tried to find evidence of layer-specific visual cortex activation. However, most of the stimuli they used activated all layers equally. In my view the best evidence for layer-specific results was this, from two people -

Showing that the upper layers of the cortex were more activated by colourful stimuli that activate "P cells" compared to rapidly changing stimuli that act on "M cells".

We'll need more data to be sure that this technique works, but if it does, it promises some awesome science in the future. Still, it's not all good news for us neuroscientists. We'll have to relearn all the facts about cortical layers that most of us studied in Neuroscience 101 and then promptly forgot about.

Someone remind me, is Layer I or VI the top one...?

Olman CA, Harel N, Feinberg DA, He S, Zhang P, Ugurbil K, and Yacoub E (2012). Layer-specific FMRI reflects different neuronal computations at different depths in human v1. PloS one, 7 (3) PMID: 22448223

Brain Scanning - Just the Tip of the Iceberg?

Neuroimaging studies may be giving us a misleading picture of the brain, according to two big papers just out.

By big, I don't just mean important. Both studies made use of a much larger set of data than is usual in neuroimaging studies. Thyreau et al scanned 1,326 people. For comparison, a lot of fMRI studies have more like n=13. Gonzalez-Castillo et al, on the other hand, only had 3 people - but each one was scanned while performing the same task 500 times over.

Both studies found that pretty much the whole brain "lit up" when people are doing simple tasks. In one case it was seeing videos of people's faces, in the other it was deciding whether stimuli on the screen were letters or numbers.

With all that data, the authors could detect effects too small to be noticed in most fMRI experiments, and it turned out that pretty much everywhere was activated. The signal was stronger in some areas than others, but it wasn't limited to particular "blobs".

So conventional fMRI experiments may just be showing us the tip of the iceberg of brain activity. In a small study, only the strongest activations pass the statistical threshold to show up as blobs, but that doesn't mean the rest of the brain is inactive. It just means it's less active. The idea that only small parts of the brain are 'involved' in any particular task may be a statistical artefact.

In fact, I wonder if the whole idea of treating statistically significant blobs as different from nearly-significant areas is itself a form of the error of interacting effects?

As if that wasn't enough, Gonzalez-Castillo further show that there are lots of activations in the brain - even to very simple stimuli - that might go undetected in conventional studies, because they don't follow the time-course predicted by the usual models.

Have a look -

This shows the average neural activation from various regions of the brain during a letter-number task. The two areas I've highlighted in red are the primary visual cortex, and they do follow the expected 'boxcar' pattern - the brain is active when the stimuli are on the screen, inactive when they're not. But you can see that all kinds of other brain areas are also responding to the stimuli - just in different ways.

For example, the left primary motor cortex was activated during the task. That area controls the right hand, and that makes sense, as people responded by pressing buttons with the right hand. But interestingly, the same area on the other side of the brain was deactivated at exactly the same time, even though people weren't doing anything with their left hand.

These papers illustrate the fact that conventional fMRI is a blunt instrument that often only tells us about the most straightforward events that happen in the brain. A bit like how we only hear the shouts and screams from through our neighbor's walls, not their normal conversations, which aren't loud enough to reach our ears.

That's the bad news, but every blob has a silver lining. fMRI is clearly more powerful than most neuroscientists have realized, and this holds out hope for cracking some of the trickiest questions. As Gonzalez-Castillo et al put it

This result helps narrow the gap between thousands of fMRI manuscripts showing limited activation in response to tasks and cognition theories that defend that cognition—understood as the process of “configuring the way in which sensory information becomes linked to adaptive responses and meaningful experiences”—can only result from the distributed collaboration of primary sensory, upstream and downstream unimodal, heteromodal, paralimbic, and limbic regions... [we were able to] switch from a regime where activity detection relates primary to sensory processing to a more sensitive regime, where activity detection includes also cognitive processes with subtler BOLD signatures.

Link: See also the interesting discussion here: Surely, God loves the .06 (blob) nearly as much as the .05.


Thyreau, B., Schwartz, Y., Thirion, B., Frouin, V., Loth, E., Vollstädt-Klein, S., Paus, T., Artiges, E., Conrod, P., Schumann, G., Whelan, R., and Poline, J. (2012). Very large fMRI study using the IMAGEN database: Sensitivity–specificity and population effect modeling in relation to the underlying anatomy NeuroImage DOI: 10.1016/j.neuroimage.2012.02.083

Gonzalez-Castillo, J., Saad, Z., Handwerker, D., Inati, S., Brenowitz, N., and Bandettini, P. (2012). Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1121049109

The Blinking Brain - A Problem For fMRI?

Every time we blink, a wave of activity sweeps through our brain - and this could be a serious problem for some fMRI researchers.

French neuroscientists Hupé et al report on A BOLD signature of eyeblinks in the visual cortex. They found that spontaneous blinks are associated with a neural activation pattern over the occipital cortex areas responsible for processing vision.

In many ways this is not surprising - when you blink, everything goes dark, and then lights up again, all within a fraction of second, which means that blinks are a kind of very dramatic visual stimulus, equivalent to a big black object suddenly appearing and then vanishing again. However, it's long been believed that blink suppression mechanisms in the eye and brain somehow block out the responses that would otherwise happen during a blink.

Don't be so sure, say Hupé et al. In an elegant experiment, they showed volunteers a standard set of visual stimuli during fMRI scanning, while recording blinks using an eye tracking camera. Then they simply treated the blinks as events, and used standard analysis methods to find neural activation associated with them.

Blinks caused a significant BOLD response over a number of "visual" areas.

Compared to the "real" visual stimuli in the task, the blink signal was less extensive, but no less strong.

So what? The great majority of fMRI experiments don't use eyetracking to measure blinks, so this study raises the scary possibility that blinks could lie behind some of the "stimulus-related" activations that we all know and love. It would be a problem if subject blinks were correlated with the stimuli or tasks, which they might be, because blink rate may vary with our psychological state.

I don't think we should be too worried yet. The blink blobs were essentially confined to parts of the visual cortex. So any study that's not focussed on vision is probably in the clear (although that's just the average response: in some individual subjects, the activations were a lot wider.)

However, as the authors point out, there is a risk that alterations in blink rate, caused, perhaps, by emotional or cognitive stress, might be wrongly "found" to be causing visual cortex activation, which might call into question claims of "top-down" influences on early visual cortex... oh dear.

Hupé, J., Bordier, C., and Dojat, M. (2012). A BOLD signature of eyeblinks in the visual cortex NeuroImage DOI: 10.1016/j.neuroimage.2012.03.001

Bringing the Real World into Brain Scanning

Canadian Neuroscientists Jacqueline Snow et al propose a new method of making brain scanning studies a bit more realistic.

Typically, in an fMRI or other neuroimaging study, any visual stimuli shown to the volunteer are just pictures on a screen. Sometimes videos will be used, but in almost all cases they're just 2D images. Is that adaquate? People have hoped so.

Snow et al's data suggest that it might not be.

They created a contraption for presenting subjects with real objects during a scan. See above. Now, to the uninitiated this might not seem like a big deal, but those with MRI experience will appreciate how impressive this is.

Everything from the angle of the volunteer's head to the LED lighting is an achivement, given the nature of MRI. The stimuli were controlled by one of the researchers, who had to sit next to the scanner, in total darkness, and operate the turntable with the help of some glow-in-the-dark stickers.

Having built this device, they then used it to compare the brain's responses to real objects vs photos of those same objects. The experiment was designed to test fMRI adaptation - the phenemenon whereby if you present the same stimuli repeatedly, the neural responses are reduced.

fMRI adaptation has been found to happen in many studies using 2D pictures, but Snow et al show that the effect was much smaller, maybe entirely absent, when people were repeatedly shown real objects: this graph shows the BOLD neural response in the lateral occipital complex. Seeing the same pictures over and over led to a weaker response, as expected; but seeing the same 3D objects didn't:

This is a good study and an important result, which suggests that the much-studied fMRI adaptation might not be a universal phenemonon. And the potential implications are big, as the authors write:

Finally, our preliminary fMRI results raise the provocative suggestion that the presence of real-world objects (i.e., as indicated initially via stereoscopic cues) invokes qualitatively different computations to those elicited by 2D images. Researchers in the field of behavioral psychophysics have expressed long-standing concern about the extent to which pictures of objects capture the properties of their real-world counterparts (i.e., their ecological validity), with reservations as to their appropriateness as stimuli with which to examine the nature of human object perception...

Snow, J., Pettypiece, C., McAdam, T., McLean, A., Stroman, P., Goodale, M., and Culham, J. (2011). Bringing the real world into the fMRI scanner: Repetition effects for pictures versus real objects Scientific Reports, 1 DOI: 10.1038/srep00130

Visualizing The Connected Brain

So it seems as though the "connectome" is the latest big thing in neuroscience. This is the brain's wiring diagram, in terms of the connections between neurons and on a larger scale, between brain regions.

We certainly won't understand the brain without getting to grips with the connections but equally, it's not the whole story. I previously emphasised that the brain is not made of soup; it's not made of spaghetti, either.

Connectomics does however unquestionably provide some of the prettiest images in neuroscience. And they just got prettier, with a new technique for visualizing connections, just revealed in Neuroimage: Circular representation of human cortical networks for subject and population-level connectomic visualization.

See above. It's a rather lovely vista (for which the authors Irimia et al share credit with the folks behind the Circos visualization tool they used).

All you need are some MRI scans, and a lot of image processing, and you can produce one of these "Connectograms". But what does it mean? Here's the authors' description:

The outermost ring shows the various brain regions arranged by lobe (fr — frontal; ins — insula; lim — limbic; tem — temporal; par — parietal; occ — occipital; nc — non-cortical; bs — brain stem; CeB — cerebellum) and further ordered anterior-to-posterior. The color map of each region is lobe-specific and maps to the color of each regional parcellation.

In other words, the outer ring is just a list of brain regions, each with an assigned colour. The inner rings tell us about those regions:

Proceeding inward towards the center of the circle, these measures are: total GM volume, total area of the surface associated with the GM–WM interface (at the base of the cortical ribbon), mean cortical thickness, mean curvature and connectivity per unit volume. For non-cortical regions, only average regional volume is shown.

So each of the five inner rings displays data about one aspect of brain anatomy, for each of the regions. The colors are a heat map of the numbers.

Finally, the lines between regions represent the degrees of connectivity between regions via white matter tracts, as measured with diffusion tensor imaging:

The links represent the computed degrees of connectivity between segmented brain regions. Links shaded in blue represent DTI tractography pathways in the lower third of the distribution of FA, green lines the middle third, and red lines the top third (see text for details).

You can also make a pooled connectogram of the average neuroanatomy across a group of people. Still, it remains to be seen whether these are as useful as they are beautiful.

Irimia A, Chambers MC, Torgerson CM, and Van Horn JD (2012). Circular representation of human cortical networks for subject and population-level connectomic visualization. NeuroImage PMID: 22305988

Mystery Joker Parodies Neuroscience

Someone has created a hilarious spoof paper poking fun at neuropsychoanalysis (but all of fMRI takes some hits too): A Triple Dissociation of Neural Systems Supporting ID, EGO, and SUPEREGO.

Featuring gems such as

• Authors "Steven Z. Fisher and Stephen T. Student" with contact details "mother@amaliastate.edu".
• "Twenty-four healthy participants (all 19-year-old white, male undergraduates who sat near each other in an Introductory Psychology course and were raised in upper middle class suburban New
  England neighborhoods) were scanned but 17 were excluded for not following instructions or falling asleep in the scanner."
• "If you’re like us, you’ve probably been thinking that Social Neuroscience, Neuroeconomics, and Developmental Social Cognitive Affective Clinical Neuroscience are just not cutting edge enough
  anymore. Do not despair. This study represents the first of what is likely to be a productive and active new field of Psychoanalytic Neuroscience."

It really is very funny, but it's also deadly accurate in its highlighting serious problems that plague a certain genre of neuroimaging papers. Who made it? The PDF appeared on Dropbox a couple of weeks ago and, while a few people have Tweeted about it, no-one has claimed ownership, yet.

For the record, it wasn't me.

Voodoo Neuroscience Revisited

Two years ago, neuroscientists were shaken by the appearance of a draft paper showing that half of the published work in a particular field had fallen prey to a major statistical error.

Originally called "Voodoo Correlations in Social Neuroscience", it ended up with the less snappy name of Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. I prefer the old title.

The error in question is now known variously as the "circular analysis problem", "non-independence problem" or "double-dipping" although I still call it the "voodoo problem". In a nutshell it arises whenever you take a large set of data, search for data points which are statistically significantly different from some baseline (null hypothesis), and then go on to perform further statistics only on those significant data points.

The problem is that when you picked out the statistically significant observations, you selected the data points that were especially "good", so if you then do some more analyses only on those data, you are almost guaranteed to find something "good". To avoid this you need to make sure that your second analysis is truly independent of your first one.

Anyway, Vul and Pashler, the main authors of the original voodoo article, have just written a short piece in NeuroImage offering some reflections on the paper and the aftermath. They don't make any major new arguments but it's a good read. Particularly fun is their explanation of what inspired them to look into the voodoo problem:

In early 2005 a speaker in our department reported that BOLD activity in a small region of the brain can account for the great majority of the variance in speed with which subjects walk out of the experiment several hours later (this finding was never published as far as we know). The implications of this result struck us as puzzling, to say the least: Are walking speeds really so reliable that most of their variability can be predicted? Does a focal cortical region determine walking speeds? Are walking speeds largely predetermined hours in advance? These implications all struck us as far-fetched...

But they reveal that it was one paper in particular that set them off voodoo-hunting

Our interest in probing the matter was further whetted by an episode occurring a short while later: Grill-Spector et al. (2006) reported that individual voxels in face selective regions have a variety of stable stimulus preferences; in a critical commentary, Baker et al. (2007) found that the analysis used to ascertain this fact implicitly built these conclusions into the method, such that the same analysis applied to noise data (voxels from the nasal cavity) revealed a similar variety of stable preferences. It occurred to us that a similar circularity might underlie the puzzlingly high correlations.

To their credit, Grill-Spector et al quickly accepted Baker et al's criticism and admitted that some of their original conclusions had been wrong.

Vul, E., and Pashler, H. (2012). Voodoo and circularity errors NeuroImage DOI: 10.1016/j.neuroimage.2012.01.027

Do Brain Scans Sway Juries?

Does seeing a criminal's brain affect jury decisions?

Edith Greene and Brian Cahill ask this question in a new study which put volunteers in the position of jurors in a murder trial. The 'defendant' was guilty, but the question was: should they get life in prison, or death?

It turned out that seeing brain scans didn't have much of an effect - but it's not clear how far the results would generalize.

208 mock-jurors were randomly assigned to get different kinds of mitigationinformation about the accused. Sometimes, all they were told was that he had been diagnosed with schizophrenia, depression and a substance misuse disorder. Others were also given neuropsychological test scores showing that he did poorly on various tests of reasoning and cognition. Finally, some were shown brain scans on top of all that, scans which were described as showing left frontal lobe damage.

All these materials were based on a real 2007 court case.

What happened? When the defendent was said to have been assessed as probably "dangerous" in future, people who were only told his diagnosis of schizophrenia usually sent him to the chair. But when they were given his psychological test scores - showing that he suffered from cognitive impairments - they were far more lenient. Seeing the neuroimages had no effect on top of that.

If the guy was described as posing a low risk of future violence, the verdicts were lenient, no matter what else they were told about him. In the real case, by the way, he got life.

This suggests that brain scans don't exert a seductive allure on jury decisions, at least not over-and-above psych test scores. But I'm not sure how representative the results are. The 'jurors' were all psychology undergrads. Most were Hispanic (63%) females (67%). Are psychology students especially resistant to the allure of brain scans - and/or especially vulnerable to the allure of psychological test scores? No-one knows, but it's surely plausible.

On some level, neuroimaging evidence clearly can influence people's decisions, like any other evidence; lawyers wouldn't bother presenting it otherwise. The question is how much of an impact it has, but that is surely going to depend on the details of the case as well as the juror's background; I'm not sure how much a study like this one, focussing on one example, will be able to tell us.


Greene E, and Cahill BS (2011). Effects of Neuroimaging Evidence on Mock Juror Decision Making. Behavioral Sciences and the Law PMID: 22213023

How Realistic is fMRI?

How representative are fMRI experiments? Is "the brain" that we investigate with fMRI the same brain that we use outside the MRI scanner?

A new paper from Bernhard Hommel and colleagues of Leiden in the Netherlands offers some important caveats. They looked to see what effect playing some recorded MRI scanner sounds had on people's ability to perform some simple cognitive tasks, while sitting outside the scanner.

MRI is notoriously noisy. When you have an MRI scan you have to wear earplugs to protect against the sound but they only block out some of it. Opinions differ on whether the sound is pleasant or not. Personally I find the repetitive tick-tock rather soothing now, but then I've heard it many times over the years. First-timers can find it quite overwhelming.

Anyway, Hommel et al found that while scanner noise had no overall effects on reaction time or accuracy, it actually improved performance on three measures of "cognitive control".

For instance in a task in which participants had to respond to the colour of a circle by pressing the left or the right arrow key, they were slower to react when the circle appeared on the "wrong" side of the screen, i.e. on the left when the correct answer was the right arrow. This slowing of responses caused by a stimulus-response clash is called the Simon effect.

The results showed that the Simon effect was reduced by noise. The same thing happened in two other studies: noise meant better performance.

All of the noise effects were modest and the sample sizes were also quite small (14-18 per task, with everyone studied twice, noisy vs silent) but this paper joins a number of others raising questions about the representativeness of fMRI, with evidence that fMRI activates the brain and maybe even improves mood (although I doubt that last one).

The authors' interpretation is that the noise made people pay more attention to the tasks, to compensate for the distraction, and that this means that fMRI studies may be biased in their measurements of cognitive control:

Generalizing from fMRI findings to behavioral observations and vice versa seems to be more problematic than commonly thought, at least as far as control  processes are concerned. In a sense, then, investigating cognitive processes by means of  fMRI... is inevitably facing Heisenberg’s (1927) uncertainty principle, according to which the act of measurement can change what is being measured.

To my mind the biggest weakness of this is that it only looked at noise. While scanners are noisy, that's not the only distracting thing about them: during an fMRI study you also have to lie down, in a small confined tube, and your only way to see the "screen" on which experimental stimuli are shown is indirectly via a small mirror which often doesn't give a good view.

So ironically, I'm not sure how realistic this study is...


Hommel, B., Fischer, R., Colzato, L., van den Wildenberg, W. and Cellini, C. (2011). The effect of fMRI (noise) on cognitive control. Journal of Experimental Psychology: Human Perception and Performance DOI: 10.1037/a0026353

Scanning The Brain While Looking At Scans

A new study investigated what goes on in the brain when doctors make a diagnosis.

Radiologists use X-rays and other imaging techniques to diagnose diseases - but in this study, they went into the scanner themselves. Brazilian researchers Marcio Melo et al used fMRI to record neural activity while the radiologists were shown an array of chest X-rays.

Some of the scans showed evidence of disease, which the doctors were required to diagnose. There were also two control conditions, in which the stimuli were still X-rays but with little pictures of either animals or letters embedded in them, instead of diseases.

The image above shows how it worked. As well as pneumonia, one patient has a severe case of Alligator Lung, while the other looks like they've got the Influenza 'B' virus.

Now, the point of all this was to compare the mental process of making a diagnosis to that of seeing an object. The idea is that a trained radiologist sees particular diseases in the scans, in the same way that anyone can see an alligator.

Activity during diagnosis, object-recognition and letter naming was very similar (compared to doing nothing); this presumably represents the visual and language areas involved in looking at the image, recognizing what it is, and saying it out loud:

There were some slight differences, with the left inferior frontal cortex and the posterior cingulate cortex being more activated by diagnosis than animals. But this difference disappeared after controlling for the number of different possible descriptions the radiologists reported thinking about for each image.

The authors conclude that

These results support the hypothesis that medical diagnoses based on prompt visual recognition of clinical signs and naming in everyday life are supported by similar brain systems.

Which seems fair enough, although it's important to remember that the diagnoses in this study were quite easy ones. The mean response time was just 1.3 seconds and only 6% of those split-second diagnoses were wrong. Unfortunately diagnosis is not always that easy.

Anyway, this study is all very well, but why stop at chest X-rays? Last year I speculated on the fun neuroscientists could have with a real-time fMRI machine:

You could lie there in the scanner and watch your brain light up. Then you could watch your brain light up some more in response to seeing your brain light up...

We really need to scan people while they're looking at brain scans. Only then will we be able to understand the neurological basis of being a neurologist, and find the brain's looking-at-a-blob blob.

Melo M, Scarpin DJ, Amaro E Jr, Passos RB, Sato JR, Friston KJ, and Price CJ (2011). How doctors generate diagnostic hypotheses: a study of radiological diagnosis with functional magnetic resonance imaging. PloS ONE, 6 (12) PMID: 22194902

Beware Dead Fish Statistics

An editorial in the Journal of Physiology offers some important notes on statistics.

But even more importantly, it refers to a certain blog in the process:

The Student’s t-test merely quantifies the ‘Lack of support’ for no effect. It is left to the user of the test to decide how convincing this lack might be. A further difficulty is evident in the repeated samples we show in Figure 2: one of those samples was quite improbable because the P-value was 0.03, which suggests a substantial lack of support, but that’s chance for you! A parody of this effect of multiple sampling, taken to extremes, can be found at http://neuroskeptic.blogspot.com/2009/09/fmri-gets-slap-in-face-with-dead-fish.html

This makes it the second academic paper to refer to this blog as far. Although I feel rather bad about this one, since the citation ought to have been to the original dead salmon brain scanning study by Craig Bennett. I just wrote about it.

Actually, though, this editorial was published in five separate journals: The Journal of Physiology, Experimental Physiology, the British Journal of Pharmacology, Advances in Physiology Education, Microcirculation, and Clinical and Experimental Pharmacology and Physiology. Phew.

In fact, you could say that this makes not two but six citations for Neuroskeptic now. Yes. Let's go with that.

Anyway, after discussing the history of the ubiquitous Student's t-test - which was invented in a brewery - it reminds us that the p value you get from such a t-test doesn't tell you how likely it is that your results are "real".

Rather, it tells you how often you'd get the result you did, if there was no effect and it was just random chance. That's a big difference. A p value of 0.01 doesn't mean your results are 99% likely to be real. It means that there's a 1% chance that you'd get them, by chance. But if you did say 100 experiments, or more likely, 100 statistical tests on the same data, then you'd expect to get at least one result with a p value of 0.01 purely by chance.

In that case it would be silly to think that the finding was only 1% likely to be a fluke. Of course it could be true. But we'd have no particular reason to think so until we get some more data.

This is what the dead salmon study was all about. This multiple comparisons issue is very old, but very important. Arguably the biggest problem in science today is that we're doing too many comparisons and only reporting the significant ones.

Drummond GB, & Tom BD (2011). Statistics, probability, significance, likelihood: words mean what we define them to mean. British journal of pharmacology, 164 (6), 1573-6 PMID: 22022804

Does MRI Make You Happy?

A startling new paper from Tehran claims Antidepressant effects of magnetic resonance imaging-based stimulation on major depressive disorder.

Yes, this study says that having an MRI scan has a powerful antidepressant effect.

They took 51 depressed patients, and gave them all either an MRI scan or a placebo sham scan. The sham was a "scan" in a decommissioned scanner. The magnet was off but they played recorded scannerish sounds to make it believable. Patients were blinded to group.

They found that people in the scanner group improved much more than those in the sham group over two weeks. Actually there were two different kinds of scans, T1 structural MRI and EPI functional MRI, but they were the same:

Now, if this is true, it's huge. Obviously. For one thing, it would undermine the whole premise of functional MRI, which is that it's a method of recording brain activity. If it's also stimulating the brain in some way at the same time, then it would make it hard to interpret those activations. In particular it would cast all the studies using fMRI in depression into doubt.

So is it true? I can't see any obvious flaws in the design. Assuming that the authors are right when they say that "patients could not distinguish the difference between the actual and sham MRI scan", i.e. assuming that the blind was truly blind, then the methodology was sound.

But let's look at the statistics. The paper is full of very impressive p values less than 0.001 but those turn out to all be referring to the changes within each group, and those changes are fairly meaningless. What matters is the differences in the groups and

Changes in BDI scores (between baseline and day 14) were significantly different among the three studied groups (F=5.48, p=0.007 overall) using ANOVA, and between the DWI group vs. Sham and T1 vs. Sham (p<0.05) using post hoc tests. Changes in HAMD24 scores (between baseline and day 14) were also compared among the 3 groups using ANOVA but the level of significance was slightly above the significance threshold (F=2.89, p=0.06).

Which is rather less convincing. There was a close-to-significant group difference in the HAMD24, and a significant but only just effect on the BDI. Remember that there were only 17 people in each group.

I'm inclined to think that this is one of the 5% of experiments which will produce a nominally significant result even assuming everything goes to plan and there are no confounds. My suspicion is that everyone in the trial got better (they were all on antidepressants, plus there's the placebo effect and the effect of time) - except a small number of people who didn't improve. And by chance they were all in the sham group.

The reason I'm skeptical is that I just can't see a plausible mechanism. The authors suggest that MRI scans might stimulate the brain in a similar way to TMS and that this could have antidepressant effects.

But there's a lot of problems with this: 1) the evidence is questionable whether TMS even works for depression 2) the magnetic stimulation of the brain generated during MRI is much weaker than in the case of TMS and 3) if MRI really stimulated the brain like TMS, then, like TMS, it would have a risk of triggering seizures in people with epilepsy. But it doesn't.

Vaziri-Bozorg SM, et al (2011). Antidepressant effects of magnetic resonance imaging-based stimulation on major depressive disorder: a double-blind randomized clinical trial. Brain imaging and behavior PMID: 22069111

Modern War-fMRI : Graphics Cards for Science

Videogames and neuroscience have a rocky relationship.

On the one hand you have Susan Greenfield and her games-hurt-the-brain theory. But she's not representative of neuroscientists as a whole: games have also helped neuroscience, for example, in this study of the neural correlates of "flow" experiences.

Now neuroscientists have another reason to be thankful for games, according to a new paper. It turns out that modern 3D graphics cards - which mostly exist in order to render videogame visuals - can be used to do fMRI data analysis.

According to Sweden's Eklund et al, a graphics card can perform intensive fMRI analysis hundreds of times faster than a regular processor of the equivalent speed, because graphics processors make use of parallel computing optimized for 3D images and that's ultimately what all brain scans are.

They developed a way to run non-parametric statistical analyses of brain imaging data. Proponents say that non-parametric stats have many advantages over conventional parametric ones - and they're certainly becoming increasingly popular. But they involve doing far more calculations. Thousands of times more, in some cases.

It turns out though that armed with 2.5 GHz CPU and three NVidia GTX 480s, and making use of NVidia's graphics programming language, they were able to cut the time to analyse one person's brain with 100,000 permutations, from 24 hrs to just 9 minutes. The whole setup cost $4000, so it's not cheap, but they say it's "a fraction of the price for a PC cluster with equivalent computational performance" i.e. one relying on lots of general purpose processors, rather than graphics cards. Even on GTX480 did the job very well.

Best of all, this gives neuroscientists an excuse to spend their grant money on awesome gaming rigs. Why do I want the latest GForce on my work computer? To do non-parametric data analysis, obviously. Sure, it would also allow me to run Modern Warfare 3 at the highest settings... but that's not why I want it.

Eklund A, Andersson M, Knutsson H (2011). Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis. International journal of biomedical imaging, 2011 PMID: 22046176

Dream Action, Real Brain Activation

A neat little study has brought Inception one step closer to reality. The authors used fMRI to show that dreaming about doing something causes similar brain activation to actually doing it.

The authors took four guys who were all experienced lucid dreamers - able to become aware that they're dreaming, in the middle of a dream. They got them to go to sleep in an fMRI scanner. Their mission was to enter a lucid dream and move their hands in it - first their left, then their right, and so on. They also moved their eyes to signal when they were about to move their hands.

Unfortunately, only one of the intrepid dream-o-nauts succeeded, even though each was scanned more than once. Lucid dreaming isn't easy you know. Two didn't manage to enter a lucid dream. One thought he'd managed it, but the data suggested he might have actually been awake.

But one guy made it and the headline result was that his sensorimotor cortex was activated in a similar way to when he made the same movements in real life, during the lucid dream -  although less strongly. Depending on which hand he was moving in the dream, the corresponding side of the brain lit up:

EEG confirmed that he was in REM sleep and electromyography confirmed that his muscles were not in fact being activated. (During REM sleep, an inhibitory mechanism in the brain prevents muscle movement. If the EMG shows activity this is a sign that you're actually partially awake).

They also repeated the experiment with another way of measuring brain activation, NIRS. Out of five dudes, one made it. Interesting this showed the same pattern of results - weak sensorimotor cortex activation during movement - but it also showed stronger than normal supplementary motor area activation, which is responsible for planning movements.

This is rather cool but in many ways not surprising. After all, if you think about it, dreaming presumably involves all of the neural structures that are involved in really perceiving or doing whatever it is you're dreaming about. Otherwise, why would we experience it so clearly as being a dream about that thing?

It may be, however, that lucid dreaming is different, and that the motor cortex isn't activated in this way in normal dreams. I suppose it depends what the dream was about.

That raises the interesting question of what someone with brain damage would dream about. On the theory that dream experiences come from the same structures as normal experiences, you shouldn't be able to dream about something that you couldn't do in real life... I wonder if there's any data on that?

Dresler M, Koch SP, Wehrle R, Spoormaker VI, Holsboer F, Steiger A, Sämann PG, Obrig H, & Czisch M (2011). Dreamed Movement Elicits Activation in the Sensorimotor Cortex. Current biology : CB PMID: 22036177

What Is Brain "Activation" on fMRI?

Functional MRI is one of the most popular ways of measuring human brain activity. But what is "activity"?

Fundamentally, neural activity is electical potentials and chemical signals. fMRI doesn't measure these directly. Rather, it measures changes in the oxygen content of blood in different parts of the brain.

The more the brain cells are firing, the more oxygen they use up, although oxygenation actually increases as a kind of compensation for the activity and this increase is what gets measured. The oxygenation changes associated with neural firing is called the BOLD response.

Using fMRI you can measure BOLD and end up with some pretty blobs of activation. But what does it mean for a region of the brain to be activated? Just as no man is an island, no brain region can do anything on its own. Every area gets inputs from other areas, and sends outputs as well.

So if an area gets more active, that could mean one or more of three things:

1. It's sending more outputs
2. It's getting more inputs
3. It's doing more "internal" processing within that area - "talking to itself".

Which of these contributes to BOLD? It's known that number 1 - output from the area in question - is not a major contributor to the fMRI signal, but what about 2 and 3? A 2010 paper that I just came across argues that 80% of the BOLD signal is caused by internal processing, and only 20% is due to input.

They took some rats, and stimulated their whiskers. Using electrodes, they measured blood oxygenation changes in an area called the barrel cortex, which is known to deal with whisker-based sensations (they didn't actually use fMRI, but this would be seen as a BOLD signal if they had.)

But they then added a drug called muscimol to the barrel cortex. Muscimol reduces neuronal firing, but it doesn't affect synaptic input. They show that muscimol strongly reduced the blood oxygenation response, by about 80%. This suggests that 80% of the signal was not caused directly by sensory input to the cortex, but was generated within the cortex.

In many ways this is not surprising: it would be weird if the cortex were just picking up signals and doing nothing with them. However, it's good to be able to put a figure on just how much intra-cortical processing contributes to the fMRI signal. In rats, at any rate.

Harris S, Jones M, Zheng Y, & Berwick J (2010). Does neural input or processing play a greater role in the magnitude of neuroimaging signals? Frontiers in neuroenergetics, 2 PMID: 20740075

You Use Your Partner To Phone And Play Angry Birds. Literally.

WITH lots of weddings expected on Tuesday, people in love across the world are getting ready for their latest fix.

But should we really characterize the intense devotion shown by people in love, as love? A recent experiment that I carried out using neuroimaging technology suggests that love-related terms like “romance” and “soulmates” aren’t scientifically accurate - not compared to a word we use to describe our relationships with our smartphones. That word is “owning an iPhone.”

As a branding consultant, why am I even writing this article for the NYT? Never mind. Earlier this year, I carried out an fMRI experiment to find out whether iPhones were really, truly addictive, no less so than alcohol, cocaine, shopping or video games (sic)... wait, are those last two actually addictive? Whatever, let's just say they are.

In conjunction with the San Diego-based firm MindSign Neuromarketing (kerching! Wait, did I write that, or just think it?), I enlisted eight men and eight women between the ages of 18 and 25. Our 16 subjects were exposed separately to audio and to video of a wife or husband.

In each instance, the results showed activation in both the audio and visual cortices of the subjects’ brains. In other words, when they were exposed to the video, our subjects’ brains didn’t just see their partner, they “heard” them, too. This powerful cross-sensory phenomenon is known as "the brain storing information about people and objects, and retrieving it in response to related stimuli", or "memory" to use the technical term.

But most striking of all was the flurry of activation in the insular cortex of the brain, which has also been associated with seeing an iPhone. The subjects’ brains responded to the sound of their partner as they would respond to the presence or proximity of a top of the range smartphone (with free WiFi in thousands of locations!)

In short, the subjects didn’t demonstrate the classic brain-based signs of addiction when they were shown pictures of their lovers. Instead, they made calls and played Angry Birds on them.

---

The silliness of equating insula activation on fMRI with love and using this to argue that we love our iPhones as a recent crap OpEd in the New York Times did, has been excellently covered over at [Citation Needed], Neurocritic and many others. I'm sure you've heard plenty about this story already.

But let's set aside the fact that loads of other things, by no means limited to disgust and drug addiction, are known to involve the insula in fMRI. Let's assume (as the NYT piece did) that the only two things that had ever been shown to activate the insula were seeing an iPhone and seeing someone you love.

This study still wouldn't show that people love their phones. You could equally well turn the whole thing on its head and argue that it shows that we think of people we love as something to make phone calls with. Hey, the brain activity is the same as when you look at an iPhone.

This might strike you as implausible, but given the fMRI data alone, you have no grounds for saying one interpretation is more or less plausible than the other.

There are countless other interpretations, each equally plausible given the imaging data. Maybe the insula is only about love, and the activation to the iPhone is due to conditioned association (you call people you love on it). Maybe it's about objects you see every day, which includes your phone and people you love. Maybe...

The only reason to prefer any particular interpretation would be because you have evidence from outside neuroimaging - from other areas of neuroscience or psychology. So if you discovered that insula lesions cause people to be unable to fall in love (they don't, as far as I know) then you could make a case for the love interpretation. But only then.

Neuroimaging, on its own, can't tell us anything about the brain. It's like a peek under the hood of your car. If you already know how a car works, you can look under the hood and work out what's going on, and what's gone wrong. But only if you have that prior knowledge. Otherwise, it's just a big set of metal pipes.

To Catch A Predator... With A Brain Scanner?

With the help of an MRI scanner and some child pornography, a new study claims to be able to tell whether someone is a paedophile: Assessment of Pedophilia Using Hemodynamic Brain Response to Sexual Stimuli.

It was an fMRI study of 24 self-identified paedophiles (recruited through a clinic offering anonymous treatment) and 32 male controls. Everyone was shown a series of images of naked men, women, boys and girls. The neural response to child vs. adult images was the main outcome measure.

Respect to the authors for getting that past the ethics committee.

The blob-o-grams above show that the paedophile's brains reacted differently to the control brains, when shown images of naked children, which is not surprising because the brain is what makes you a paedophile (and everything else.)

However, what's more interesting is that by comparing each individual's brain activity to the average activity of the paedophile group and the control group, it was possible to diagnose people as paedophiles or not with high accuracy (90+%).

Plotting the "typical paedophile"-ness of the neural response to girls vs women and boys vs men, the paedophiles (triangles) form a clear cluster. There were also some differences between homosexual and heterosexuals in both groups.

The statistics seem kosher: they used leave-one-out cross-validation to avoid the error of double dipping.

What's not clear is whether this was measuring sexual attraction as such. All it's measuring is how much each person's activity correlated with the paedophile group average. Maybe it's picking up on the shame paedophiles feel over being reminded of what they've done. Maybe the controls were just averting their eyes when the child porn came on.

However, you could say that if you're just interested in the practical business of catching paedophiles, that's academic. More concerning is the question of whether it would be possible to fool the technique. A recent study showed that it's easy to fool a brain scan designed to detect lying.

But let's suppose it does work out. Would that be a good thing? What is "a paedophile", anyway? Is it someone's who's attracted to children, or someone who acts on that attraction?

For example, there are people who are caught with child porn, and who admit they downloaded it, but who deny being attracted to children. The Who shredder Pete Townsend and comedian Chris Langham being two British examples. Both admit downloading illegal images, but say it was for 'research purposes'.

Now it might be possible, using fMRI, to find out if they're telling the truth. Let's suppose it was doable.

So what? Downloading child pornography is a crime - whatever your motivation. Being attracted to children is legal, in itself. So from a legal perspective it should make no difference at all in cases like this.

Of course, we don't in fact go around seeing things from a purely legal perspective. We care whether someone is attracted to children or not. But should we care? Is that fair? You don't choose your sexual orientation. What you choose is whether to break the law by commiting the crime.

There are surely people out there - no-one knows how many - who are attracted the children, and never act on it. Do we want to be able to "catch" them?

Edit: The original version of this post linked to the wrong paper, an older paper by the same authors. This has been fixed now.

Ponseti, J., Granert, O., Jansen, O., Wolff, S., Beier, K., Neutze, J., Deuschl, G., Mehdorn, H., Siebner, H., & Bosinski, H. (2011). Assessment of Pedophilia Using Hemodynamic Brain Response to Sexual Stimuli Archives of General Psychiatry DOI: 10.1001/archgenpsychiatry.2011.130

Why Brain Scanners Make Your Head Spin

Here at Neuroskeptic we see a lot of dizzyingly bad (and sometimes even good) neuroscience, but did you know that brain scanners can literally send your head into a spin? A new paper explains why, with implications for all MRI researchers.

MRI scanners rely on extremely powerful magnetic fields. This is why you can't take metal objects into the scanner room, as they'd be pulled into it. Yet the fields can also exert other kinds of effects on the body.

I'd always been told that static, unchanging magnetic fields are biologically inert. But moving through the field too quickly can cause side effects. When an object moves through a magnetic field, induction happens - electrical currents are produced.

In the case of the human body, these small currents can activate nerve cells. Depending on which cells they hit this can cause you to feel dizzy, see flashes of light, experience tingling sensations, and so on. Or so I thought.

However, a new paper from Dale Roberts et al of Johns Hopkins shows that just being in a powerful magnetic field can cause dizziness and vertigo - with no movement required. They noticed that lying still in or near an MRI scanner causes nystagmus, abnormal horizontal eye movements, and that the amount of eye movement is directly correlated with the angle at which the head is positioned relative to the field.

The nystagmus was caused by an automatic reflex in response to effects in the vestibular ("balance") system of the ear. Roberts et al realized that the static magnetic field causes electrical currents that activate vestibular cells, even when the head is perfectly still. It happens because there's a natural flow of electrically charged ions into these cells in a part of the ear called the semicircular canal. The magnetic field interacts with this ion current, in what's called a Lorentz force.

The semicircular canals normally allow us to sense when our head is moving. Our eyes automatically compensate for head movement to keep us looking in the same direction. The MRI magnet fooled the ear into thinking the head was rotating, and the eyes produced nystagmus as a result.

Two patients who had suffered damage to their semicircular canals were immune to the effect.

This has important implications for functional MRI studies of brain function. Many people are interesting in measuring eye movements during MRI scans. This finding suggests that these movements may be unusual, compared to normal eye movements outside the scanner. Worst, the vestibular stimulation could alter brain activity:

Vestibular stimulation induced by the magnetic field in healthy subjects simply lying in the bore could activate many brain areas related to vision, eye movements, and the perception of the position and motion of the body.

Roberts, D., Marcelli, V., Gillen, J., Carey, J., Della Santina, C., & Zee, D. (2011). MRI Magnetic Field Stimulates Rotational Sensors of the Brain Current Biology DOI: 10.1016/j.cub.2011.08.029