Known Unknowns

Why no one can say exactly how much is safe to drink while pregnant

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I was waiting in the dining car of an Amtrak train recently when I looked up and saw that old familiar sign:

“According to the Surgeon General, women should not drink alcoholic beverages during pregnancy because of the risk of birth defects.”

One finds this warning everywhere: printed on bottles and menus or posted on placards at restaurants and even train cars barreling through Midwestern farmland in the middle of the night. The warnings are, of course, intended to reduce the number of cases of fetal alcohol syndrome in the United States. To that end, the Centers for Disease Control and Prevention (CDC) and the American Congress of Obstetricians and Gynecologists (ACOG) recommend that women avoid drinking any alcohol throughout their pregnancies.

Here’s how the CDC puts it:

“There is no known safe amount of alcohol to drink while pregnant.”

And here’s ACOG’s statement in 2008:

“. . . ACOG reiterates its long-standing position that no amount of alcohol consumption can be considered safe during pregnancy.”

Did you notice what they did there? These statements don’t actually say that no amount of alcohol is safe during pregnancy. They say that no safe amount is known and that no amount can be considered safe, respectively. Ultimately, these are statements of uncertainty. We don’t know how much is safe to drink, so it’s best if you don’t drink any at all.

Lest you think this is a merely a reflection of America’s puritanical roots, check out the recommendations of the U.K.’s National Health Service. While they make allowances for the fact that some women choose to drink, they still advise pregnant women to avoid alcohol altogether. As they say:

“If women want to avoid all possible alcohol-related risks, they should not drink alcohol during pregnancy because the evidence on this is limited.”

Yet it seems odd that the evidence is so limited. The damaging effects of binge drinking on fetal development were known in the 18th century and the first modern description of fetal alcohol syndrome was published in a French medical journal nearly 50 years ago. Six years later, in 1973, a group of researchers at the University of Washington documented the syndrome in The Lancet. Even then, people knew the cause of fetal alcohol syndrome: alcohol. And in the forty years since, fetal alcohol syndrome has become a well-known and well-studied illness. NIH alone devotes more than $30 million dollars annually to research in the field. So how come no one has answered the most pressing question (at least for pregnant women): How much is safe to drink?

One reason is that fetal alcohol syndrome isn’t like HIV. You can’t diagnose it with a blood test. Doctors rely on a characteristic pattern of facial abnormalities, growth delays and neural or mental problems – often in addition to evidence of prenatal alcohol exposure – to diagnose a child. Yet children exposed to and affected by alcohol during fetal development don’t always show all of these symptoms. Doctors and agencies now define fetal alcohol syndrome as the extreme end of a spectrum of disorders caused by prenatal alcohol exposure. The full spectrum, called fetal alcohol spectrum disorders (FASD), includes milder forms of the illness that involve subtler cognitive or behavioral problems and lack the classic facial features of the full-blown syndrome.

As you might imagine, milder cases of FASD are hard to identify. Pediatricians can miss the signs altogether. And there’s a fundamental difficulty in diagnosing the mildest cases of FASD. To put it crudely, if your child is slow, who’s to say whether the culprit is a little wine during pregnancy, genetics, too much television, too few vegetables, or god-knows-what-else? Unfortunately, identifying and understanding the mildest cases is crucial. These are the cases that worry pregnant women who drink lightly. They lie at the heart of the uncertainty voiced by the CDC, ACOG, and others. Most pregnant women would like to enjoy the occasional merlot or Sam Adams, but not if they thought it would rob their children of IQ points or otherwise limit their abilities – even just a little – down the line.

While it’s hard to pin down the subtlest cases in the clinic, scientists can still detect them by looking for differences between groups of children with different exposures. The most obvious way of testing this would be to randomly assign pregnant women to drink alcohol at different doses, but of course that experiment would be unethical and should never be done. Instead, researchers capitalize on the variability in how much women choose to drink during pregnancy (or at least how much they report that they drank, which may not always be the same thing.) In addition to interviewing moms about their drinking habits, the scientists test their children at different ages and look for correlations between prenatal alcohol exposure and test performance.

While essential, these studies can be messy and hard to interpret. When researchers do find correlations between moderate prenatal alcohol exposure and poor test performance, they can’t definitively claim that the former caused the latter (although it’s suggestive). A mysterious third variable (say, maternal cocaine use) might be responsible for them both. On the flip side, interpreting studies that don’t find correlations is even trickier.  It’s hard to show that one thing doesn’t affect another, particularly when you are interested in very small effects. To establish this with any confidence, scientists must show that it holds with large numbers of people and that they are using the right outcome measure (e.g., IQ score). FASD impairments can span language, movement, math skills, goal-directed behaviors, and social interactions. Any number of measures from wildly different tests might be relevant. If a given study doesn’t find a correlation between prenatal alcohol exposure and outcome measure, it might be because the study didn’t test enough children or didn’t choose the right test to pick up the subtle differences between groups.

When studies in humans get tricky, scientists often turn to animal models. FASD research has been no exception. These animal studies have helped us understand the physiological and biochemical mechanisms behind fetal alcohol syndrome, but they can’t tell us how much alcohol a pregnant woman can safely drink. Alcohol metabolism rates vary quite a bit between species. The sensitivity of developing neurons to alcohol may differ too. One study used computational modeling to predict that the blood alcohol level of a pregnant rat must be 10 times that of a pregnant human to wreak the same neural havoc on the fetus. Yet computational models are far from foolproof. Scientists simply don’t know precisely how a dose in a rat, monkey, or other animal would translate to a human mother and fetus.

And here’s the clincher: alcohol’s prenatal effects also differ between humans. Thanks to genetic differences, people metabolize alcohol at very different rates. The faster a pregnant woman clears alcohol from her system, the lower the exposure to her fetus. Other factors make a difference, too. Prenatal alcohol exposure seems to take a heavier toll on the fetuses of older mothers. The same goes for poor mothers, probably because of confounding factors like nutrition and stress. Taken together, these differences mean that if two pregnant women drink the same amount of alcohol at the same time, their fetuses might experience very different alcohol exposures and have very different outcomes. In short, there is no single limit to how much a pregnant woman can safely drink because every woman and every pregnancy is different.

As organizations like the CDC point out, the surest way to prevent FASD is to avoid alcohol entirely while pregnant. Ultimately, every expecting mother has to make her own decision about drinking based on her own understanding of the risk. She may hear strong opinions from friends, family, the blogosphere and conventional media. Lots of people will seem sure of many things and those are precisely the people that she should ignore.

When making any important decision, it’s best to know as much as you can – even when that means knowing how much remains unknown.

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Photo Credit: Uncalno Tekno on Flickr, used via Creative Commons license

Hurley TD, & Edenberg HJ (2012). Genes encoding enzymes involved in ethanol metabolism. Alcohol research : current reviews, 34 (3), 339-44 PMID: 23134050

Stoler JM, & Holmes LB (1999). Under-recognition of prenatal alcohol effects in infants of known alcohol abusing women. The Journal of Pediatrics, 135 (4), 430-6 PMID: 10518076

fMR-Why? Bad Science Meets Chocolate and Body Envy

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Imagine this: You have bulimia nervosa, a psychiatric condition that traps you in an unhealthy cycle of binge eating and purging. You’ve been recruited to participate in a functional MRI experiment on this devastating illness. As you lie in the scanner, you are shown pictures of pizza, chocolate and other high-calorie foods and you’re told to imagine eating them. You do this for 72 pictures of delicious, fatty foods. At other points in the experiment, you see pictures of bodies (sans heads) of models clipped from a women’s magazine. You are told to compare your body to each of the bodies in the pictures. You do this 72 times, once for each skinny (and probably retouched) model’s body. The experience would have been unsettling enough for normal women trying to eat healthier or feel happier with their not-so-super-model bodies. But for women with bulimia, it must have truly been a hoot and a half.

Luckily, the misery was worth it. When the researchers publish their findings, they claim to have shown that patients with bulimia process body images differently. In their conclusions, they say that their results can inform how psychotherapists should treat patients with the illness. They even suggest that it might someday lead to direct interventions, such as a targeted zap to the head using transcranial magnetic stimulation.

My recommendation? Cover your therapist’s ears and stay away from the head zapper. This study shows nothing of the sort.

Functional MRI is a widely used and quite powerful method of probing the brain, but it is only useful for experiments that are thoughtfully conceived and carefully interpreted. Unfortunately, many fMRI papers that make it to publication are neither.

One of the most common problems in fMRI is making bad comparisons. All fMRI studies rely on comparisons because brains are all different and scanners are all different. If you are going to say that Region X becomes active when you see a picture of chocolate, you first have to answer that crucial question: compared to what? If you’re interested in how the brain reacts to unhealthy food in particular, you might compare looking at pictures of chocolate with looking at pictures of raisins or eggplant. And if you’re comparing these comparisons across subject groups (such as patients versus non-patients), both groups had better have the same the control condition. Otherwise, you’re not even comparing apples to oranges. You’re comparing apples to gym socks.

Sadly, that is just what these experimenters did. They compared brain blood flow when the subjects looked either at junk food or skinny women with blood flow during 36-second stretches of time when subjects just stared at a small, white ‘+’ on the screen. The authors say that using a more similar control condition (say, imagining using non-food objects like a lamp or a door) would be bad because patients with bulimia might respond to these objects differently than healthy subjects. This argument is nonsensical. There’s no reason to believe that people with bulimia feel any differently about doors or lamps than anyone else, but there’s plenty of reason to believe that they would spend 36-second moments of downtime before or after comparing their bodies to those of models either obsessing or trying not to obsess about how their bodies ‘measure up.’

In fact, I suspect that could not help but wonder if the authors didn’t originally intend to use this ‘+’ as the control condition. They actually had less crappy control conditions built into the experiment. As a control for imagining eating pizza and chocolate, the participants were also shown non-food objects like tools and told to imagine using them. They also saw interior décor photos and had to compare the furniture to those in their own homes – a control for comparing each model’s body to one’s own.

When the authors did their analyses using these (better) control conditions, they found very few differences between patients and non-patients. None, in fact, for the imagine-eating-junk-food portion of the study. For the comparing-oneself-to-models portion, they only found that patients showed less activation than controls in two regions of visual cortex. These regions may correspond to areas that specifically process body images. But would less activation in these regions mean that patients with bulimia process body images differently than other people? Not at all. If the patients were not looking at the pictures as much as non-patients or were more distracted/less attentive to them, you would see the same pattern of results. In short, the authors had no story to tell when they used the better controls. They had a ‘null result’ that would not get published.

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Based on the design of their experiment, I suspect that find myself wondering if this was how they originally intended to analyze their data.* And it’s really the only sensible way to analyze these data. Experiments like these include the ‘+’ condition to establish a baseline (essentially, what you’re going to call ‘zero’). These ‘+’ blocks also correct for an unfortunate phenomenon called scanner drift that adds noise to the data.

It’s possible that I have to wonder if the authors decided to use the ‘+’ for their comparisons because they didn’t get any exciting results with the actual control conditions. If so, it unfortunately worked. Using the baseline condition, they found two differences between patient and non-patient activations in the food task and even more differences between the groups in the body task. Ultimately, the authors got their significant results and they got them published.  But those differences have nothing to do with the causes of bulimia and everything to do with what flits through people’s minds while they stare at a plus sign.

Unfortunately, this is just one example from a growing sea of bad fMRI studies out there. And while many people do wonderful work with the technique and advance the field, others do it a disservice and set us all back. From researchers to reviewers, publishers, science writers and reporters, we all need to proceed with caution and evaluate papers with a critical eye. The participants in our experiments deserve it. The public deserves it. Most of all, patients deserve the best information we can give them. Science done well and served to them straight.
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Update: I’ve made a few small changes to this post to clarify my intent. I don’t personally know the study’s authors and have no insight into their actions, intentions, or motivations. In writing the piece, I hoped to bring attention to a widespread problem in fMRI research. Of the study’s authors I can only say that they did some seriously flawed research. Why, when, or how is as much your guess as mine.

Since posting this piece, I’ve contacted the editor of BMC Psychiatry regarding my concerns with the paper. Not only have I received no reply from her, but this paper is still listed as one of the ‘Editor’s Picks’ on their website as of 1/5/14.

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*For curious fMRI folk: each run contained 6 food/body blocks, 6 non-food/décor blocks, and only 3 baseline ‘+’ blocks. That means they collected twice the data for the control conditions that they supposedly didn’t intend to use than for the ones that they did.

Photo #1 credit: MRI scanner, photo by Matthias Weinberger (cszar on Flickr), used via Creative Commons license

Photo #2 credit: Structural MRI of kiwi fruit by Dom McIntyre (McBadger on Flickr), used via Creative Commons license

Van den Eynde F, Giampietro V, Simmons A, Uher R, Andrew CM, Harvey PO, Campbell IC, & Schmidt U (2013). Brain responses to body image stimuli but not food are altered in women with bulimia nervosa. BMC Psychiatry, 13 (1) PMID: 24238299

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Another Time, Another Place

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Whenever I visit my childhood home outside of Chicago I try to make it to the local pancake house. The buttery pancakes would be reason enough, but they’re not the only reason I stop by. A stroll through that pancake house is truly a stroll down memory lane. Each table I pass triggers a memory of a meal shared with different people in different decades of my life. One moment I’m eating German pancakes with my college boyfriend. The next, I am passing menus to my new husband’s family.  The next, I am celebrating my eighth grade graduation with my parents and older brother.

Memories return you to a specific time and place. Consider so-called flashbulb memories, or vivid memories of  dramatic moments that caught you off-guard. I remember exactly where I was when I heard that a plane had struck one of the Twin Towers and, later, when I learned that my father had died. I remember that I was sitting on the living room rug in my Somerville apartment when I watched Columbia transform from a space shuttle into a streak of fire across the sky. Is it helpful to remember where I was sitting? Not in the slightest. But in the murky, mysterious realm of memory, when and what are inextricably linked with where.

Mention the word “memory” to neuroscientists and you’re sure to get them thinking of the hippocampus, a sliver of tissue nestled deep inside each hemisphere of the brain. The hippocampus has been synonymous with memory since the late 1950s, when William Scoville and Brenda Milner described a patient who was incapable of forming new memories after both of his hippocampi were removed. Since then, throngs of neuroscientists have devoted their careers to studying the hippocampus. Among other revelations, they’ve discovered a class of neurons called place cells that represent (you guessed it) information about place.

How do cells represent place? To illustrate, let’s say you’re in your favorite coffee shop. Some of the place cells in your hippocampus will fire like crazy when you walk through the entrance. Others save their enthusiasm until you are waiting in line to order your latte, stopping at the counter for milk and sugar, or settling in at your favorite table. When you physically occupy their place-of-interest, they go nuts – like a neural alarm signaling your location. At this moment, you are here!

The same principle applies to my experience at the pancake house. Different place cells fire at different tables. In essence, these sets of cells provide a unique neural code for each space I can occupy in the restaurant. And this code has been with me for a while. When I sat in the corner booth after my graduation from middle school, I formed a memory of that celebration that included the code for that particular spot. Decades later, sitting in that booth or even walking past it can trigger a similar code in my brain, one that elicits the rest of that dusty old memory.

While eternally cool, place cells have become old news in hippocampal research. The new hippocampal hotness is studying “time cells”. These recently discovered neurons prefer to fire at different intervals after an event (say, ten seconds versus one minute after you step into the coffee shop). This research fad is a bit amusing, as it turns out that place cells and “time cells” are one and the same. This fact hasn’t stopped scientists from referring to “time cells,” but it has forced them to typically use the term in quotation marks.

As scientists studied the time code in the hippocampal cells of rats, a flaw in their experiments became clear. Their studies recorded the neural activity of moving rats, which means that the firing patterns observed by the scientists could reflect changes in time, changes in the rat’s location, or in its motion.

Two recent papers addressed this issue and clarified the nature of “time cells” in the hippocampus. The first of these appeared in the journal Neuron in June of this year. The paper, by Benjamin Kraus, Michael Hasselmo, and collaborators at Boston University, describes an experiment that has as much to do with your time spent sweating it out at the gym as it does with your memory of past events. The scientists recorded the activity of hippocampal cells in rats as they ran on a treadmill or moved around in a simple maze. Since the rat remained in the same location as it ran on the treadmill, the researchers could decouple the rat’s location from the passage of time and the distance the rat ran. Since the authors could vary the speed of the treadmill, they could also piece apart the related variables of time and distance.

The scientists found that “time cells” still produced a time code when location was kept constant (on the treadmill). Using some fancy modeling, they also showed that the activity of most “time cells” reflected a combination of elapsed time and distance run, but a smaller number of “time cells” seemed to care only about time or distance. They also found that these same cells behaved like normal place cells when the rat walked around a simple maze. In short, place cells (a.k.a. “time cells”) can convey information about place, time, and distance travelled to varying degrees that also change under different conditions.

A second paper on the subject came out in a September issue of The Journal of Neuroscience. The authors, Christopher MacDonald, Howard Eichenbaum*, and colleagues (also from Boston University) eliminated the variable of location by physically restraining the rats from moving with a special headpiece that attached to the rats’ heads. This headpiece locked into the testing apparatus so that the rats couldn’t move their heads during testing. Unlike the fitness buff rats in the prior study, these rats were given a memory task. They got a whiff of an odor and then another whiff of an odor a few seconds later. If the second odor was the same as the first, the rat licked its waterspout and got a reward (a drop of water). If the two odors were different, the rat was not supposed to lick.

Even though the rats were completely immobile, the rats’ “time cells” showed a strong time code. Different cells fired at different times during the delay. These cells also seemed to represent what information (in this case, the odors presented for the task). The scientists found that the overall pattern of “time cell” firing was more similar when the rats remembered the same odor than when they remembered different odors across trials.

In short, place/time cells can represent what, when, and where in a variety of ways, depending on a variety of factors. This representation is flexible – just as memory must be in order for you to remember the date of your anniversary, the feel of your first kiss, and the items on your next shopping list. The remarkable thing about memory is that it is both flexible and robust, meaning that it is resistant to degradation or being swamped out by noise. It can return us to times, places, and experiences that are far away and decades past. For that, we can thank the hippocampus, neural codes, and a set of remarkable cells with an identity crisis.

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Photo credit: Stu Rapley on Flickr, used via Creative Commons License

*Howard Eichenbaum was also a middle author on the Neuron paper. Much of the recent work on “time cells” has come from his lab and affiliated labs at Boston University.

Kraus BJ, Robinson RJ 2nd, White JA, Eichenbaum H, & Hasselmo ME (2013). Hippocampal “time cells”: time versus path integration. Neuron, 78 (6), 1090-1101 PMID: 23707613

MacDonald CJ, Carrow S, Place R, & Eichenbaum H (2013). Distinct hippocampal time cell sequences represent odor memories in immobilized rats. The Journal of Neuroscience : the official journal of the Society for Neuroscience, 33 (36), 14607-14616 PMID: 24005311

Stuff and Brains Part 2: How Tools Come In Handy

298571748_c18ca5d78b_bHumans learn about objects by exploring them. I once described how my infant daughter explored objects, discovering their uses and properties through trial and error, observation, and plenty of dead ends. Her modest experiments illustrated a more universal truth: that from our earliest moments, our experience with objects in the world is fundamentally tied to our senses, to the ways we physically interact with them, and to the purposes they serve.

Last week I wrote about object-selective cortex, the part of visual cortex that lets us recognize people and stuff. I mentioned that this swath of the brain is speckled with several areas that specialize in processing certain object classes (e.g., faces, bodies, and scenes). If you consider object-selective cortex as a whole, you find that these specialized areas fit within a broader organization based on whether the to-be-recognized object is animate (a living, moving thing) or, if not, whether it’s large or small. While this may sound like a wacky way to divvy up object recognition, I mentioned some plausible reasons why your brain might map objects this way.

That’s the big-picture view. But what happens if we zoom in and explore one little bit of object-selective cortex in detail? Would we see a meaningful organization at this scale too? The answer, dear reader, is yes. In fact, this type of micro-organization can tell us volumes about how we recognize, understand, and use the objects around us.

For a beautiful example, let’s travel to the extrastriate body area (EBA).* The EBA is involved in visually recognizing bodies. Your EBA is active when you see a human body, regardless of whether the body is clothed or unclothed. It’s also active when you see parts of a body or even (to a lesser degree) when you see abstract body representations like stick figures. In 2010, scientists from Northumbria University used fMRI to ‘zoom in’ on the EBA in the left hemisphere. The team found that a chunk of the left EBA is specifically interested in pictures of hands, as opposed to other parts of the body. In essence, they found a micro-organization within the EBA, segregating hands from other body parts.

Before we talk more about hands, let’s visit another object-selective area in the same vicinity: the tool-selective area on the middle temporal gyrus. No kidding, your visual cortex has areas devoted to tools! The tool area on the middle temporal gyrus is engaged when you see a picture of a tool, be it a hammer, a stapler, or a fork. Patients with brain damage in this region tend to have trouble recalling information about the actions paired with common tools. But what counts as a tool for this region? One research group tried to answer this question by training adult subjects to use unfamiliar objects as tools. Using fMRI, the group showed that pictures of these objects activated the tool area after but not before training. In short, the brain dynamically reorganizes object recognition, or at least tool recognition, based on new experiences with objects.

But the story doesn’t end there. In 2012, the same group that discovered the hand area reported another find: that the hand area and the tool area overlap – a lot. What does this overlap mean? In essence, the same spot of cortex is active both when you see a hand and when you see a screwdriver or a pair of scissors. Notice that this goes against the broad divisions mentioned in my last post, since hands are animate and screwdrivers are not. Here, scale makes all the difference. When you zoom out, you see that object-selective cortex is broadly divvied up based on object animacy and size, but these divisions aren’t absolute and ubiquitous. Up close, you can find tiny bits of cortex that buck the trend, each with its own idiosyncratic combination of preferences.

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Figure from Bracci et al, 2012, showing the overlap of hand and tool areas in the left hemispheres of all but one of their subjects. Each slice represents the overlap (shown in cyan) in a different subject.

While each local mix of preferences may be idiosyncratic, it is probably not accidental. To save space and speed up reactions, brain organization is very well optimized. Chances are good that hands and tools overlap in the brain for a reason. But what might that reason be? It might stem from the fact that hands are intimately linked with tools in your visual experience. Since hands grip tools, you tend to see them together. You also tend to see faces and bodies together (that is, unless you’re watching a horror film.) And as it turns out, the face area and the body area on the bottom temporal surface of the right hemisphere appear to partially overlap as well. Could this be because faces and bodies, like hands and tools, tend to co-occur in our visual experience? It’s possible. Humans are quite sensitive to the statistical properties of our experience with objects.

But there’s another, quite different explanation for why faces overlap with bodies and tools overlap with hands in object-selective cortex. Brain organization tends to be dictated by where information needs to go next. (In essence, how the information will be used). The 2012 paper presents evidence that the overlapping hand/tool area is communicating with other areas of the brain that guide object-directed actions. The paper also cites another fMRI study that suggests the overlapping face and body areas in the right hemisphere communicate with parts of the brain involved in social interactions. In short, recognizing either a face or a body provides information that the social regions in your brain may need, while visual information about hands or tools may be invaluable when it comes time for reaching, grabbing, lifting, or stapling stuff.

Hands and tools. Faces and bodies. These are just a small sample of the many kinds of objects and creatures we see every day of our lives. Just imagine if we knew the micro-organization of every millimeter of object-selective cortex. Now that would be a map, one you started shaping from your earliest days on this earth. It would be a record of your lifetime of adventures with people and with stuff.

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*Is it just me or does this post seem like an episode of The Magic School Bus?

Photo credits

Hands photo: Carmen Maria on Flickr

Brain images: Bracci et al, 2012 in The Journal of Neurophysiology

Bracci S, Cavina-Pratesi C, Ietswaart M, Caramazza A, & Peelen MV (2012). Closely overlapping responses to tools and hands in left lateral occipitotemporal cortex. Journal of neurophysiology, 107 (5), 1443-56 PMID: 22131379

Mapping a World of Stuff onto the Brain

4872199920_660cd8fb05_bWhile we have five wonderful senses, humans rely most on our sense of sight. The allocation of real estate in the brain reflects this hegemony; a far greater chunk of your cerebral cortex is dedicated to vision than to any other sense. So when you encounter people, objects, and animals in the world, you typically use visual information to tell your lover from a toothbrush from your cat. And while it would be reasonable to expect your brain to process all of these items in the same way, it does nothing of the sort. Instead, the visual cortex segregates and plays favorites.

The most dramatic examples of this segregation occur whenever you look at other people.  Within the large chunk of  visual cortex dedicated to object recognition, two areas in each hemisphere specifically process faces (the FFA and OFA) and two areas in each hemisphere specifically process bodies (the FBA and EBA). In each case, one of these areas is located on the side of the brain (near the back) while the other is tucked away on the bottom surface of the temporal lobe. It’s clear that these areas are important for recognizing faces and bodies. Damage to the face area FFA can profoundly impair one’s ability to recognize faces, while direct electrical stimulation of the same area can temporarily distort perception of a face. And when scientists used a magnetic pulse to momentarily disrupt activity in either the face area OFA or the body area EBA of healthy adults, their participants had difficulty discriminating between similar faces or similar bodies, respectively.

Yet the segregation of objects in your visual cortex doesn’t end there. Scientists have long known that visual information about scenes – including the landmarks and buildings that often define them – is processed separately as well. In fact we have at least two scene areas per hemisphere in classic visual cortex: one on the side of the brain (TOS) and one on the lower surface (PPA).*

But what about other types of objects? If you looked at pictures of a trampoline, a screwdriver, a lamppost, and a toad, would they follow the same path through your visual cortex? The answer is no. In a recent study, Talia Konkle and Alfonso Caramazza at Harvard showed people pictures of a wide range of animals and objects while scanning them with fMRI. They studied the activations in visual cortex for each image and used them to compute something they called preference maps. The preference maps indicated whether each bit of cortex preferred animals or objects and, separately, small or large things. When they combined these maps they found zones of visual cortex that preferred large objects, small objects, or animals of any size.** For large objects and animals (with two zones each), one zone was located on the side of the brain and the other on the lower surface. The only zone that preferred small objects over both large ones and animals lay right at the edge of the brain, smack dab between the side of the brain and its lower surface. The face and body areas fit almost entirely within the animal zones, while the scene areas lay within the large-object zones.

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Figure from Konkle & Caramazza, 2013 showing where the face areas, body areas, scene areas, and ‘preference zones’ were in one participant. Each gray blob represents the right hemisphere of the brain, with the left side of each blob representing the back of the brain. The top two brains show a side view while the bottom two show the bottom surface of the cortex.

It may seem odd that object representation in visual cortex is organized based on such arbitrary dimensions. Why should it matter whether the thing you see is big or small, made of cotton or has a cottontail? The study’s authors argue that these divisions make sense if one considers the various ways we use different types of objects. For instance, small objects are generally useful because you can manipulate and interact with them. Recognizing an apple, axe, or comb allows you to eat, chop, or fix your ‘do, respectively – so long as the visual information about these objects gets passed along to brain areas involved in reaching and grasping movements.

Objects like buildings, trees, or couches are obviously too large to be lifted or manipulated. Since they stay put, you’re more likely to use them as landmarks to help you navigate through a neighborhood, park, or room. But you can only use these objects this way if you send the visual information about them to brain regions involved in navigation.

Finally, we have living things, which can move, bite, and behave unpredictably. While a large animal like an elephant might trample you, a small one like a venomous spider or snake could be more lethal still. And don’t even get me started on people! In short, an animal’s size doesn’t determine how you will or won’t interact with it; you need to be ready to predict any animal’s behavior and react accordingly. Should you pet that dog or run from it? Communication between the animal-preferring zones of visual cortex and the social prediction centers in your brain might help you reach the right answer before it’s too late.

What’s the upshot of all this using, manipulating, predicting and fleeing? A wonderful and miraculous map of all the stuff in your world. It’s a modest little map – no larger than a napkin and half the thickness of an iPhone 5 – that represents a vast array of creatures, things, and people based on what they mean to you. How frickin’ amazing is that?

* I’ll get back to this mysterious pattern in a future post.

** I find it interesting that people generally approach the game Twenty Questions with the same category distinctions. The first two questions are almost invariably: Is it alive? And is it bigger than a breadbox?

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Photo credits

Elephant and bird: Ludovic Hirlimann on Flickr, used via Creative Commons license

Figure with brains: Talia Konkle & Alfonso Caramazza in The Journal of Neuroscience

Konkle T, & Caramazza A (2013). Tripartite organization of the ventral stream by animacy and object size. The Journal of neuroscience : the official journal of the Society for Neuroscience, 33 (25), 10235-42 PMID: 23785139

Konkle T, & Caramazza A (2013). Tripartite organization of the ventral stream by animacy and object size The Journal of Neuroscience DOI: 10.1523/JNEUROSCI.0983-13.2013

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