A Blog by Ed Yong

The Distributed Brainpower of Social Insects

Attenborough-brainsHere’s David Attenborough, chilling out on a rock in the middle of Africa, with four lumps of plasticine. The smallest one on the far left represents the brain of a bushbaby, a small primate that lives on its own. The next one is the brain of a colobus monkey, which lives in groups of 15 or so. The one after that is a guenon, another monkey; group size: 25. And on the far right: a baboon that lives in groups of 50. “Were you to give a skull to a researcher who works on monkeys, even though they didn’t know what kind of monkey it belonged to, they would be able to accurately predict the size of group in which it lived,” says Attenborough.

That sequence, from The Life of Mammals, is a wonderful demonstration of the social brain hypothesis—a bold idea, proposed in the 1980s, which suggests that living in groups drove the evolution of large brains. Social animals face mental challenges that solitary animals do not: they have to recognise the other members of their cliques, cope with fluid and shifting alliances, manage conflicts, and manipulate or deceive their peers. So as social groups get bigger, so should brains. This idea has been repeatedly tested and confirmed in many groups of animals, including hoofed mammals, carnivores, primates, and birds.

What about insects? Ants, termites, bees, and wasps, also live in large societies, and many of them have unusually big brains—at least, for insects. But in 2010, Sarah Farris from West Virginia University and Susanne Schulmeister from the American Museum of Natural History showed that in these groups, large brains evolved some 90 million years before big social groups. If anything, they correlated with parasitic body-snatching rather than group-living.

“That got people thinking,” says Sean O’Donnell from Drexel University. “In recent years, there’s been a growing rumbling, almost subterranean movement arguing that social brain ideas may not apply to the social insects.” His new study is the latest addition to that movement.

O’Donnell’s team studied potter wasps, which lead solitary lives, and the closely related paper wasps, which live in colonies of varying sizes and complexities. They collected queens and workers from 29 species of these wasps, carefully dissected their brains, and measured the size of their mushroom bodies—a pair of structures in insect brains that control higher mental abilities like learning and memory.

And to their surprise, they found that as the wasp colonies got bigger, their mushroom bodies got smaller. Even within the social paper wasps, the team found that species with distinct queens and workers—a sign of a more complex society—have similarly sized mushroom bodies to those with no such castes.

“The pattern is so clear,” says O’Donnell. “Sociality may actually decrease demands on individual cognition rather than increasing it.”

“Here we have the first concerted evidence that costly brains aren’t needed to allow sociality, when you can do it other ways,” says Robin Dunbar, who first proposed the social brain hypothesis. “There are many routes to sociality.”

What other routes? O’Donnell notes that insects and (most) mammals build their societies in fundamentally different ways. Large mammal societies typically include individuals who are distantly related or even unrelated. Insect societies, by contrast, are basically gigantic families, where all the members are either queens (which reproduce) or their descendants (which do not). You could view these colonies less as groups of individuals and more as extensions of the queens.

As such, their members don’t particularly need to keep track of shifting relationships, or manage conflicts, or manipulate their peers, or any of the other social challenges that, say, a baboon or a human faces. They have less of a need for bigger and more sophisticated brains.

Social insects also benefit from swarm intelligence, where individuals can achieve astonishing feats of behaviour by following incredibly simple rules. They can build living buildings, raise crops, vaccinate themselves, and make decisions about where to live. In some cases, they make decisions in a way that’s uncannily similar to neurons—a colony behaves like a giant brain, and in more than a merely metaphorical way. They have a kind of ‘distributed cognition’, where many of the mental feats that other animals carry out using a single brain happen at the level of the colony.

Entomologist Seirian Sumner from Bristol University says that there are mammals, like meerkats and banded mongooses, which live in simple societies where adults cooperatively raise their young. These are often compared to primitively social insects, like paper wasps. “They share very similar family structures, group sizes and plasticity in behavioural roles,” Sumner says. It would be very interesting to see if the brains of these mammals follow the same patterns as those of O’Donnell’s wasps.

O’Donnell is all in favour of more studies. He wants to see if the same patterns hold in other insect groups that include both social and solitary species, including bees and cockroaches. And he’s intrigued by the naked mole rats—colonial mammals that have queen and worker castes, much like ants and wasps. “If our ideas are correct, we’d expect to see mole rats following a similar pattern to insects,” he says.

Reference: O’Donnell, Bulova, DeLeon, Khodak, Miller & Sulger. 2015. Distributed cognition and social brains: reductions in mushroom body investment accompanied the origins of sociality in wasps (Hymenoptera: Vespidae). Proc Roy Soc B. Citation tbc.

PS: The size of brain regions isn’t always the best indicator of intelligence. I asked O’Donnell about this, and he stands by his decision to focus on the mushroom bodies. “It’s definitely a blunt tool for studying brain evolution,” he says. “Brain tissue is metabolically very expensive, and even if it was just filler, tissue weight is a big deal, especially for a flying insect. We expect there to be really strong constrains on the size of the [mushroom bodies].”

A Blog by Carl Zimmer

Genius and the Brain

The 92nd St. Y in New York is presenting “Seven Days of Genius” this week. As part of the festivities, the video site Big Think invited me to film a conversation with neuroscientist Heather Berlin about the nature of genius and the origin of creativity in the brain.

Here’s the video, which we taped at YouTube headquarters:


A Blog by Carl Zimmer

We Are Instant Number Crunchers

If you have ever struggled through a math class, you may not think of numbers as natural. They may seem more like a tool that you have learn how to use, like Excel or a nail gun. And it’s certainly true that numbers pop in the archaeological record just a few thousand years ago, with the abruptness you’d expect from an invention. People then improved the number system after that, with the addition of zero and other upgrades.

But scientists have found that we are actually born with a deep instinct for numbers. And a new study suggests that our number sense operates much faster than previously thought. It might be better called our number reflex.

Some of the most compelling evidence for the number sense comes from studies on babies. In a 2010 study, for example, Elizabeth Brannon of Duke and her colleagues showed 6-month-old babies pictures of dots. As they switched between different pictures, they tracked how long the babies looked at each one. In some cases, the pictures were identical. In others, the dots differed in size or spacing. And in still other cases, Brannon and her colleagues added extra dots to the pictures.

When Brannon and her colleagues looked over their data, they found that the attention of the babies tended to be grabbed when they switched the number of dots. What’s more, the babies looked longer at a picture when the difference in the number was bigger.

The number sense in infants is the raw material for math aptitude later in life, as Brannon documented when she followed up on the infants three years later. Brannon found that their sensitivity to numbers as six-month-olds predicted how well they scored on math tests as three-year-olds. Other scientists have also found that a link between number sense and math skills in fourteen-year-olds.

Having discovered our number sense, Brannon and other researchers have begun probing our brains to see how it works biologically. It’s not easy to tease out the number sense from all the other things our brains do when they take in a visual scene. There’s a huge amount of information to decipher in an instant of vision, and our brains use a complex network of regions to get the job done.

When light hits our eyes, the retina takes the first pass at processing the image and then fires signals down the optic nerve to the back of the head. The visual cortex then teases out some basic features, such as brightness, edges, color, and so on. The regions where this processing takes place then send signals to other parts of the brain, which detect more complex things, like body movements and faces.

Some researchers have proposed that our awareness of numbers only emerges late in this pathway. We may first have to detect other features of a scene, and then analyze them in order to figure out how many objects there are in a group. If we look at three lemons on a counter, for example, we might first have to calculate the total area of yellow in our field of vision, determine how much yellow is in each lemon, and then divide the former by the latter.

To probe where our number sense lies on the path of thought, Brannon and her colleagues placed EEG caps on people’s heads. Then they showed their volunteers pictures of dots. As in Brannon’s earlier experiments, they varied the pictures with extra dots, as well as changing the size or spacing. Each time, the scientists recorded the electricity produced by people’s brains as they processed what they saw.

Analyzing the different responses, the scientists noticed one fascinating spike of electrical activity that emerged from the back of the brain. The strength of the spike varied with the number of dots people saw. The more dots, the bigger the spike.

The size and spacing of the dots, by contrast, had no effect on the spike. If we sensed numbers only by analyzing other features of objects, then you might expect to see an influence. But Brannon and her colleagues could find none. They conclude that this spike represents our direct detection of numbers.

What makes this spike even more intriguing is how fast it occurs: just 75 millisecond after the scientists present a picture. At that stage in visual perception, the visual cortex is just starting to process signals from the eye. Numbers, the new research suggests, are so important that we start sensing them before we’re even aware of what we’re seeing.

(For more on our number sense and other discoveries about the brain, see my ebook anthology, Brain Cuttings.)

A Blog by Ed Yong

Fast-Evolving Human DNA Leads to Bigger-Brained Mice

Between 5 and 7 million years of evolution separate us humans from our closest relatives—chimpanzees. During that time, our bodies have diverged to an obvious degree, as have our mental skills. We have created spoken language, writing, mathematics, and advanced technology—including machines that can sequence our genomes. Those machines reveal that the genetic differences that separate us and chimps are subtler: we share between 96 and 99 percent of our DNA.

Some parts of our genome have evolved at particularly high speed, quickly accumulating mutations that distinguish them from their counterparts in chimps. You can find these regions by comparing different mammals and searching for stretches of DNA that are always the same, except in humans. Scientists started identifying these “human-accelerated regions” or HARs about a decade ago. Many turned out to be enhancers—sequences that are not part of genes but that control the activity of genes, telling them when and where to deploy. They’re more like coaches than players.

It’s tempting to think these fast-evolving enhancers, by deploying our genes in new formations, drove the evolution of our most distinguishing traits, like our opposable thumbs or our exceptionally large brains. There’s some evidence for this. One HAR controls the activity of genes in the part of the hand that gives rise to the thumb. Many others are found near genes involved in brain development, and at least two are active in the growing brain. So far, so compelling—but what are these sequences actually doing?

To find out, J. Lomax Boyd from Duke University searched a list of HARs for those that are probably enhancers. One jumped out—HARE5. It had been identified but never properly studied, and it seemed to control the activity of genes involved in brain development. The human version differs from the chimp version by just 16 DNA ‘letters’. But those 16 changes, it turned out, make a lot of difference.

Boyd’s team introduced the human and chimp versions of HARE5 into two separate groups of mice. They also put these enhancers in charge of a gene that makes a blue chemical. As the team watched the embryos of their mice, they would see different body parts turning blue. Those were the bits where HARE5 was active—the areas where the enhancer was enhancing.

Embryonic mice start building their brains on their ninth day of life, and HARE5 becomes active shortly after. The team saw that the human version is more strongly active than the chimp one, over a larger swath of the brain, and from a slightly earlier start.

HARE5 seems to be particularly active in stem cells that produce neurons in the brain. The human version of the enhancer makes these stem cells divide faster—they take just 9 hours to split in two, compared to the usual 12. So in a given amount of time, the mice with human HARE5 developed more neural stem cells than those with the chimp version. As such, they accumulated more neurons.

And they developed bigger brains. On average, their brains were 12 percent bigger than those of their counterparts. “We weren’t expecting to get anything that dramatic,” says Debra Silver, who led the study.

“Ours stands as among the first studies to demonstrate any functional impact of one of these HARs,” she adds. “It shows that just having a few changes to our DNA can have a big impact on how the brain is built. We’ve only tested this in a mouse so we can’t say if it’s relevant to humans, but there’s strong evidence for a connection.”

“I’m really excited that people are following up [on these HARs] and finding out what they do,” says Katherine Pollard from the Gladstones Institutes, who was one of the scientists who first identified these sequences. “It’s been really daunting to figure out what the heck these things do. Each one takes years. These guys went the extra mile beyond what everyone else has been doing, by showing changes in the cell cycle and in brain size.”

“It’s a very clever use of mice as readouts for human-chimp differences,” says Arnold Kriegstein from the University of California, San Francisco. “The [brain] size difference isn’t terribly big, but it’s certainly in the correct direction.”

Eddy Rubin from the Joint Genome Institute is less convinced. His concern is that the team’s methods could have saddled the mice with multiple copies of HARE5 in various parts of their genome. As such, it’s not clear if the differences between the two groups are due to these factors, rather than to the 16 sequence differences between the human and chimp enhancers. “[That] casts major shadow on the conclusions,” says Rubin. “This is an interesting study pursuing an important issue, but the results should be taken with a grain of salt.”

Regardless, Silver’s team are now continuing to study HARE5. Now that their mice have grown up, they are designing tests to see if the adults behave differently thanks to their larger brains. This is important—bigger brains don’t necessarily mean smarter animals. They’re also looking into a few other enhancers. One of them, for example, seems to a control a gene that affects the growth of neurons.

“I think HARE5 is just the tip of the iceberg,” says Silver. “It is probably one of many regions that explain why our brains are bigger than those of chimps. Now that we have an experimental paradigm in place, we can start asking about these other enhancers.”

Reference: Boyd, Skove, Rouanet, Pilaz, Bepler, Gordan, Wray & Silver. 2015. Human-Chimpanzee Differences in a FZD8 Enhancer Alter Cell-Cycle Dynamics in the Developing Neocortex. Current Biology http://dx.doi.org/10.1016/j.cub.2015.01.041

More on enhancers:

Did a gene enhancer humanise our thumbs?

RNA gene separates human brains from chimpanzees

A Blog by Carl Zimmer

Wi-Fi Brain Implants For Robot Arms

For many paralyzed people, their problem is a communication gap. They can generate the signals in their brain require to control their muscles–to walk, to wash dishes, to weed a garden. But damage to their nervous system prevents those signals from reaching their destination.

Last year, in a feature I wrote for National Geographic about the brain, I recounted the work of scientists and engineers who are trying to bridge that gap. Their dream is to create a technology that reads signals from people’s brains and uses them to control machines. The machines might be robot arms that people could use to feed themselves, or computers to compose emails, or perhaps even exoskeletons that could enable people to walk.

Scientists have been investigating these brain-machine interfaces for decades, and in recent years they’ve made some impressive advances–some of which I described in my story. But it would be wrong to giddily declare that scientists have reached their goal. You need only look at this picture below to get a sense of how far we are from science-fiction dreams.


This woman, Jan Scheuermann, is at the forefront of brain-machine interface research. She volunteered to have an electrodes injected into the surface of her brain. Researchers at the University of Pittsburgh connected the electrodes to pedestals on top of her scalp. Cables can be attached to the pedestals; they connect to a computer and a power source.

Scheuermann and the scientists worked together to train the computer to recognize signals from her brain and use them to control a robot arm. In December 2012, Scheuermann made news by controlling the robot arm so well she could feed herself a bar of chocolate.

But this system was hardly ready for prime time. The electrode apparatus has to pass through a hole in a patient’s skull, creating the risk of infection. The cables tether the patient to bulky machines, which would make the whole system cumbersome rather than liberating.

In addition, the robot arm had plenty of room for improvement. It had seven degrees of freedom. Scheuermann could control its shoulder, elbow, and wrist joints. The hand, however, could only open and close. So Scheuermann had the same kind of dexterity as if she wore a mitten.

A 1958 pacemaker--wired to a cart of machines. Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232561/
A 1958 pacemaker–wired to a cart of machines. Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232561/

None of this was any reason to dismiss brain-machine interfaces as having reached a dead end. The history of pacemakers started out in much the same place. Today, people can walk around with pacemakers implanted in their chests without anyone around them having the slightest awareness that a device is regulating their heartbeat. Sixty years ago, however, the first pacemakers were enormous, cumbersome affairs. Implanted electrodes were tethered to wires that ran to big machines. Patients either had to lay next to the machines or trundle them around on a cart. The pacemakers were also relatively simple, delivering fixed patterns of electricity to the heart. Too often, they failed to keep the heart working.

In the 1960s, pacemakers became portable and battery-powered. They still needed external wires, but the wires now ran to a small box that a patient could carry on a belt. Finally, pacemakers disappeared into the body completely. In 2009, doctors began implanting pacemakers that not only had their own power supply but could also communicate medical information to doctors with a Wi-Fi connection to the Internet. Pacemakers also deliver more sophisticated signals to the heart, using algorithms to adjust their rhythms. If someone looked at the ungainly state of pacemakers in 1960 and declared them hopeless, they would have been profoundly wrong.

Two new studies are pushing brain-machine interfaces forward in the same way.

Yin et al Neuron 2014 http://dx.doi.org/10.1016/j.neuron.2014.11.010 

The first study advances the electrode end of the interface. A team of scientists led by Arto Nurmikko of Brown University developed an implant that requires no wires. The implant can pick up signals from 100 different electrodes. It contains microelectronics that can turn these signals into a Wi-Fi transmission broadcast at a rate of 200 Mb per second. The researchers implanted the device in monkeys and found that they could pick up signals from five yards away with a quality on par with signals delivered by cables. The monkeys went about their business freely, and the scientists could pick out signals they used to walk on a treadmill. When the monkeys fell asleep, the scientists could detect shifts in their brain waves. The whole apparatus runs for over two day straight on a double AA battery.

Yin et al IEEE Trans Biomed Circuits Syst. Apr 2013; 7(2): 115–128. doi:  10.1109/TBCAS.2013.2255874
Yin et al IEEE Trans Biomed Circuits Syst. Apr 2013; 7(2): 115–128. doi: 10.1109/TBCAS.2013.2255874

For now, this device will probably be most useful to researchers who study the behavior of animals. But Nurmikko and his colleagues are also learning lessons for the next generation of brain-machine interfaces for people. In another promising line of research, they have designed a prototype of a fully implantable device. The electrodes go in the brain, while the power source and transmitter sit atop the skull, below the scalp. In the future, scientists may be able to make new devices that take advantage of both studies–implants that can be sealed in the head, transmit a lot of data wirelessly, run efficiently on a long-lasting battery, and not heat up the way electronics sometimes do.

Meanwhile, at the other end of the interface, Scheuermann has been testing out a new and improved robot arm. The Pittsburgh team programmed four different positions that the hand could take, such as pinching the index and thumb together. The researchers had no idea if all those extra degrees of freedom would be too much for their interface to handle. Could it pick out signals in Scheuermann’s brain that were meaningful enough to make full use of the arm’s range of motion?

To train Scheuermann, the scientists had her start her practice on a virtual robot arm, which she used to grab virtual objects on a computer screen. The computer system learned how to recognize certain patterns of neuron signals as commands to change the shape of the robot hand. At the same time, Scheuermann’s own brain became more adept at controlling the robot arm, producing stronger signals. Finally, the scientists had Scheuermann try to pick up a number of different objects. Here’s a sampling of her successes:

Scheuermann, as the scientists had hoped, learned how to manage her new arm. It wasn’t a perfect education. Scheuermann sometimes failed to grab objects, and the scientists never managed to record a success on certain tasks, such as pouring water from one glass into another.

Still, the results were encouraging–and sometimes intriguing. The scientists found some groups of neurons that would fire together in distinctive patterns as Scheuermann moved the arm through all ten dimensions. In other words, these neurons weren’t limited to just bending the elbow or pinching a thumb. In the future, it may be possible to harness these flexible signals to make the arms even more proficient, and to fill the communication gap even more.

A Blog by Carl Zimmer

Flying Through Inner Space

It’s hard to truly see the brain. I don’t mean to simply see a three-pound hunk of tissue. I mean to see it in a way that offers a deep feel for how it works. That’s not surprising, given that the human brain is made up of over 80 billion neurons, each branching out to form thousands of connections to other neurons. A drawing of those connections may just look like a tangle of yarn.

As I wrote in the February issue of National Geographic, a number of neuroscientists are charting the brain now in ways that were impossible just a few years ago. And out of these surveys, an interesting new way to look at the brain is emerging. Call it the brain fly-through. The brain fly-through only became feasible once scientists started making large-scale maps of actual neurons in actual brains. Once they had those co-ordinates in three-dimensional space, they could program a computer to glide through it. The results are strangely hypnotic.

Here are three examples, from the small to the big. (Click on the cog-wheel icon if you can to make sure you’re watching them at high resolution.)

First is a video from a project called Eyewire. Volunteers play a game to map the structure of individual neurons. Here are a handful of neurons from the retina of a mouse. (More details about the video can be found here.)

The second video is a flight through the entire brain of a mouse, made possible by a new method called CLARITY. This method involves first adding chemicals to the brain to wash out the lipids and other chemicals that give it color. The brain is rendered transparent, even though its neurons remain intact.

Next, scientists douse the brain with compounds that only latch onto certain types of neurons, lighting them up. The researchers can then take pictures of the brain from different angles and combine them into a three-dimensional representation of the brain in which you can distinguish individual neurons. In this video, from the lab of Karl Deisseroth at Stanford University, a very common type of neuron is colored. Flying through the brain, we can start to get a feel for the large-scale connections that stretch across it.

And finally, we come to the newest method–one that didn’t even exist when I was working on my article. Adam Gazzaley of the University of California at San Francisco and his colleagues have made it possible to fly through a representation of a thinking human brain–as it thinks.

Here’s how they built this fly-through, which they call the Glass Brain. First, they gave volunteers a high-resolution MRI scan to get a very detailed picture of the overall shape of their brain. MRI doesn’t let you see individual neurons, but it does mark out the major structures of the brain in fine detail.

Next, they added in more anatomy with a method called diffusion tensor imaging. To use this method (known as DTI for short), scientists reprogram MRI scanners to measure the jostling of water molecules inside of neurons. Many of the neurons in the brain are located in the outer layers of the brain, and they extend long fibers across the inner regions and link up to the outer layers at a distant spot. Many of these fibers are organized together in pathways. The water molecules in the fibers jostle back and forth along that pathway, and so scientists can use their movement to reconstruct their shape.

The combination of MRI and DTI gave Gazzaley and his colleagues both the structures of the brain and the pathways connecting them, all lined up in the same three-dimensional space.

Now came the third ingredient: recordings of the brain’s activity. Gazzaley used EEG, a method that involves putting a cap of electrodes on someone’s head and measuring the electrical activity that reaches from the brain up through the skull to the scalp.

EEG is very fast, measuring changes in brain activity at a resolution of a tenth of a second or less. The drawback to EEG is that it’s like trying to eavesdrop on people in the next room over. A lot of detail gets blurred away as the signals travel from their source. To reconstruct the brain’s inner conversations, Gazzaley and his colleagues programmed a computer to solve mathematical equations that allow it to use the scalp recordings to infer where in the brain signals are coming from. Their program also measured how synchronized signals in different regions were with each other. Combining this information with their map of the brain’s pathways, the scientists could reconstruct how signals moved across the brain.

And here’s a video of what they ended up with. In this case, the volunteer was simply asked to open and shut her eyes and open and close her hand.

As gorgeous as this is simply as a video, there’s more to it. It didn’t take Gazzaley’s computer weeks to crunch all the data from the experiment, calculate the sources of the EEG signals and map them onto the brain. The system can create this movie in real time.

Imagine, if you will, putting on an EEG cap and looking at a screen showing you what’s happening in your brain at the moment you’re looking at it. That’s what this system promises.

I called Gazzaley to get the details of this new view of the brain. It took him and his team a year to build and to validate it–that is, to make sure that the patterns in the video have the same features that well-studied imaging technologies have found in the brain. Now Gazzaley hopes to start using it to record data during experiments and to test some prominent ideas about how the brain processes information.

And this imaging may be useful outside the lab. Gazzaley and his colleagues recently designed a video game that improved the cognition of older people. It may be possible to incorporate their new brain display into a game, allowing people to try to alter their brain activity through a kind of neuro-feedback.

Just recently, Gazzaley got another idea. He put an EEG cap on a colleague and then pushed the output to a set of Oculus Rift virtual reality goggles. Gazzaley put the goggles on and then used an Xbox joystick to fly through his colleague’s brain, which he could look at all around him in three dimensions.

“I had never seen a brain inside out before,” Gazzaley told me. “After that I couldn’t get back to work. I had to lay on the grass for a while.”

Tomorrow I will be speaking about brain mapping in Rochester, New York, in their Arts & Lectures series. You can get information about tickets here.

A Blog by Carl Zimmer

The Phantom Piano

When the brain goes awry, it can reveal to us some clues to how it works in all of us. In my latest “Matter” column for the New York Times, I look at a rare but fascinating disorder that causes people to hallucinate music. How someone could imagine that a piano was playing nearby–or a marching band or church choir–may tell us something about how our brains make sense of the world by making predictions about what comes next. Check it out.

A Blog by Carl Zimmer

Let Us Take A Walk In the Brain: My Cover Story For National Geographic

Some of the white-matter connections in my brain. (Thanks to Van Wedeen and colleagues at the Martinos Center for Biomedical Imaging)
White-matter connections in my brain ( Van Wedeen and colleagues at Martinos Center for Biomedical Imaging)

Over the past year, I’ve spent a lot of time around brains. I’ve held slices of human brains preserved on glass slides. I’ve gazed through transparent mouse brains that look like marbles. I’ve spent a very uncomfortable hour having my own brain scanned (see the picture above). I’ve interviewed a woman about what it was like for her to be able to control a robot arm with an electrode implanted in her brain. I’ve talked to neuroscientists about the ideas they’ve used their own brains to generate to explain how the brain works.

This has all been part of my research for the cover story in the current issue of National Geographic. You can find it on the newsstands, and you can also read it online.

On Monday, I was interviewed on KQED about the story, and you can find the recording here.

National Geographic has been doing a lot of interesting work to adapt their magazine stories for the web and tablets. For my story, the great photographs from Robert Clark are accompanied by some fine video.

Here’s one of my favorites–an interview with Jeff Lichtman, a neuroscientist Harvard. He’s one of the people I interviewed for the story, and it was an inescapable torture to have to boil down our  conversation to fit there. In this video, an unboiled Laitman talks about his project to see everything in the brain, with some of the mind-blowing visualizations he and his colleagues have created. I think these images are the clearest proof of just how big a task neuroscientists have taken on in trying to map the brain and understand how it works.

A Blog by Ed Yong

On Dolphins, Big Brains, Shared Genes and Logical Leaps

In 2012, a team of Chinese scientists showed that a gene called ASPM has gone through bouts of accelerated evolution in two very different groups of animals—whales and dolphins, and ourselves.

The discovery made a lot of sense. Many earlier studies had already shown that ASPM is one of several genes that affect brain size in primates. Since our ancestors split apart from chimps, our version of ASPM has changed with incredible speed and shows signs of intense adaptive evolution. And people with faults in the gene develop microcephaly—a developmental disorder characterised by having a very small brain. Perhaps this gene played an important role in the evolution of our big brains.

It seems plausible that it did something similar in whales and dolphins (cetaceans). They’re also very intelligent, and their brains are very big. Compared to a typical animal of the same size, dolphin brains are 4-5 times bigger than expected, and ours are 7 times bigger than expected. The Chinese team, led by Shixia Xu, concluded that “convergent evolution might underlie the observation of similar selective pressures acting on the ASPM gene in the cetaceans and primates”.

It made for a seductive story. I was certainly seduced. In my uncritical coverage of the study, I wrote: “It seems that both primates and cetaceans—the intellectual heavyweights of the animal world—could owe our bulging brains to changes in the same gene.”

Many other scientists were sceptical—check out the comments in my original post—and it seems they were right to be. Three British researchers—Stephen Montgomery, Nicholas Mundy and Robert Barton—have now published a response to Xu’s analysis, and found it wanting. “It’s a completely plausible hypothesis but they didn’t test it very well,” says Montgomery.

In the original paper, Xu’s team looked at how ASPM has changed in 14 species of cetaceans and 18 other mammals, including primates and hippos. ASPM encodes a protein, and some changes in the gene don’t affect the structure of the protein. These “synonymous mutations” are effectively silent. Other “non-synonymous mutations” do change the protein and can lead to dramatic effects (like microcephaly). The Chinese team claimed that a few cetacean families had a high ratio of non-synonymous to synonymous mutations in ASPM—a telltale sign of adaptive evolution.

But Montgomery’s team had two problems with this conclusion. First, it’s statistically weak. Second, it’s not unique to cetaceans. Xu’s team largely looked at brainy groups like cetaceans and primates, but the British trio found exactly the same signature of selection in other mammals, including those with average-sized brains. “It looks like ASPM evolved adaptively in all mammals,” says Montgomery. “It could be that ASPM is a general target of selection in episodes of brain evolution and isn’t specific to large brains.”

Xu’s team also failed to check if the changes they found in ASPM were actually related to differences in cetacean brains. If the gene is changing quickly under the auspices of natural selection, does that translate to equally fast changes in brain size? The Chinese team never explicitly addressed that question. Montgomery’s team did, and their answer was a resounding no.

“We felt a little bad picking on them because it’s quite a common problem,” says Montgomery. “People pick a gene to analyse because it’s linked to something interesting. They find that it’s got this pattern of evolution, and they infer that it’s doing what they thought it was doing. It’s a circular argument. “

“These analyses need to be followed up with experimental work (if that is possible) or treated with caution if not,” says Graham Coop from University of California, Davis. “At best, such studies can only act to generate hypotheses about the role of a particular gene in phenotypic evolution”. That’s because most genes do many jobs, “and we are profoundly ignorant of many of these roles and how they differ across organisms.”

ASPM, for example, isn’t a “brain gene”. It creates molecular structures that help cells to divide evenly. It’s activated in the embryonic cells that make neurons, so if it’s not working properly, fewer neurons are made and individuals end up with small brains. But ASPM is also activated in other parts of the body.

As Vincent Lynch pointed out in a comment to my earlier post, ASPM affects the development of the testes:

“This brain-testis connection was described by Svante Pääbo’s lab. They swapped the mouse and human ASPM genes, I assume hoping to breed a super-intelligent strain of mice, and surprisingly found that nothing happened. Bummer… But rather than uncovering a role for ASPM as a casual agent of increased brain size in the human lineage, these authors found ASPM was required for male fertility (yes, the jokes are obvious) and suggested that the signal of selection observed in humans and other primates is likely related to role in testis. It is on old observation that many testis expressed genes evolve rapidly, many under some form of positive selection.”

So, maybe ASPM’s fast evolution in primates is more a story about nuts than noggins. Then again, Montgomery’s team have indeed found that changes in primate ASPM are related to differences in the size of their brains but not their testes.

These conflicting results illustrate just how important it is to test hypotheses carefully, rather than finding bits of evidence that look nice together, and uniting them through conjecture. It’s a valuable cautionary note to both scientists and journalists alike.

Reference: Montgomery, Mundy & Barton. 2013. ASPM and mammalian brain evolution: a case study in the difficulty in making macroevolutionary inferences about gene–phenotype associations. Proceedings of the Royal Society B http://dx.doi.org/10.1098/rspb.2013.1743