worth a thousand words

December 10, 2009  |  Posted by John Medina | No Comments
When it comes to memory, researchers have known for more than 100 years that pictures and text follow very different rules. Put simply,the more visual the input becomes, the more likely it is to be recognized—and recalled. The phenomenon is so pervasive, it has been given its own name: the pictorial superiority effect, or PSE.

Human PSE is truly Olympian. Tests performed years ago showed that people could remember more than 2,500 pictures with at least 90 percent accuracy several days post-exposure, even though subjects saw each picture for about 10 seconds. Accuracy rates a year later still hovered around 63 percent. In one paper—adorably titled “Remember Dick and Jane?”—picture recognition information was reliably retrieved several decades later.

Sprinkled throughout these experiments were comparisons with other forms of communication. The favorite target was usually text or oral presentations, and the usual result was “picture demolishes them both.” It still does. Text and oral presentations are not just less efficient than pictures for retaining certain types of information; they are way less efficient. If information is presented orally, people remember about 10 percent, tested 72 hours after exposure. That figure goes up to 65 percent if you add a picture.

The inefficiency of text has received particular attention. One of the reasons that text is less capable than pictures is that the brain sees words as lots of tiny pictures. Data clearly show that a word is unreadable unless the brain can separately identify simple features in the letters. Instead of words, we see complex little art-museum masterpieces, with hundreds of features embedded in hundreds of letters. Like an art junkie, we linger at each feature, rigorously and independently verifying it before moving to the next. The finding has broad implications for reading efficiency. Reading creates a bottleneck. My text chokes you, not because my text is not enough like pictures but because my text is too much like pictures. To our cortex, unnervingly, there is no such thing as words.

That’s not necessarily obvious. After all, the brain is as adaptive as Silly Putty. With years of reading books, writing email, and sendingtext messages, you might think the visual system could be trained torecognize common words without slogging through tedious additional steps of letter-feature recognition. But that is not what happens. No matter how experienced a reader you become, you will still stop and ponder individual textual features as you plow through a book, and you will do so until you can’t read anymore. Perhaps, with hindsight, we could have predicted such inefficiency. Our evolutionary history was never dominated by text-filled billboards or Microsoft Word. It was dominated by leaf-filled trees and saber-toothed tigers. The reason vision means so much to us may be as simple as the fact that most of the major threats to our lives in the savannah were apprehended visually. Ditto with most of our food supplies. Ditto with our perceptions of reproductive opportunity.

The tendency is so pervasive that, even when we read, most of us try to visualize what the text is telling us. “Words are only postage stamps delivering the object for you to unwrap,” George Bernard Shaw was fond of saying. These days, there is a lot of brain science technology to back him up.

Learn more:
Getty Images video (from Brain Rules DVD)
Death by PowerPoint (from Brain Rules DVD)

Correction: I should have mentioned in my article “The cellular and molecular substrates of anorexia nervosa”, posted in the Psychiatric Times on (part 1) November 1 and (part 2) December 7 2009, that much of it was a digestion of an excellent review of the topic in Nature Reviews Neuroscience, 10, 573-584 (2009), by Walter H. Kaye, Julie L. Fudge and Martin Paulus.

I think I am going to talk about the neurobiology of happiness in my next column. The reason has to do with the nature of our 2-month journey into the biology of eating disorders—a subject that, considering the dearth of explanatory data, is tough to write about. It’s also a bit depressing, considering how difficult it can be to treat. This is the second installment in a 2-part series that focuses on the neurobiology of restricting-type anorexia nervosa (AN).

Last month, I discussed behavioral and cellular aspects of AN.1 A testable hypothesis was outlined: AN was described as a conflict between an un-acquired biological need to have food and an acquired negative reaction to it. Patients with AN recruit cortical executive reactions in response to appetite cues, reactions that insert a top-down “food-negative” bias into the normal drives for fuel. These executive reactions are consistently overstimulated in AN patients, leading to high anticipatory behavior and obsessive concern with future events. Derived mostly from noninvasive imaging studies, this notion of conflicting priorities (complete with a dysfunctional reward/punishment system) has surprising empirical support.

But it is hardly the complete story of AN. Besides behavioral and cellular concerns, there are also molecular interactions to consider. It is to these efforts that we turn, focusing on the “usual regulatory suspects” of dopamine and serotonin neurotransmitter biology.

A reason for genes

Many twin studies have been initiated in the attempt to characterize potential underlying genetic components to AN. There has been some success, and in 2 directions. Large-scale studies have demonstrated that between 50% and 85% of the variance observed in AN (and bulimia) can be attributed to genetic factors. The numbers actually suggest a continuum of diffuse but related behaviors—including weight dissatisfaction, weight preoccupation, and dietary restraint.

The second direction takes into account child temperament issues. It has been known for years that specific personality traits observed in adolescence can predispose an individual to AN. These include perfectionism, harm avoidance, and certain obsessive-compulsive behaviors. Genetic studies show these traits to be heritable as well. These are independent of body weight and can be present in unaffected family members.

One is continually confronted with complexity, confounders, and nuance. Not a welcome comment regarding a disease such as anorexia nervosa, which has the highest mortality rate of any psychiatric disorder.

The aggregation of these 2 lines of work gave researchers ample reasons to seek underlying genetic causes for the disorder. They are still looking. Investigation of the obvious choices—dopaminergic and serotonergic systems—has yielded some fruit. But the picture that emerges is far from complete, and at this point gives only tantalizing hints about potential molecular mechanisms.

Dopaminergic interactions

Behavioral work suggested ample reasons to suspect dopamine-pleasure responses might be dysfunctional in AN patients. Afflicted individuals often seem addicted to exercise. They are ascetic, anhedonic, and find precious little in their lives that is consistently rewarding (aside from the pursuit of weight loss). This “trait” versus “state” issue is strengthened because such behavioral patterns persist, albeit in reduced form, after successful treatment. Dysfunction in dopamine regulation, especially in the striatal circuits mentioned last month, might provide an important component to alterations in these behaviors. They may also play a role in the motor functions and decreased food consumption behaviors typically associated with AN.

Four lines of evidence support an involvement of dopaminergic processes in at least some types of AN:

• Concentrations of dopamine metabolites in the cerebrospinal fluid (CSF) of both affected individuals and recovered individuals are lower than in those without AN.

• Patients who have AN often present with difficulties in certain visual discrimination learning tasks. This is not a trivial finding. Studies show that such impairment often reflects a malfunction in dopamine-signaling.

• There are dopamine receptor (DR) D2 gene polymorphisms associated with those suffering from AN. (DRD2 is one of a family of dopamine receptors in the human genome.) A polymorphism is an aberration in its normal gene structure. The more tightly the polymorphism is associated with a given behavior, the more likely it is to exert an influence on it.

• Noninvasive imaging studies—positron emission tomography (PET) scans, mostly—found a surprising restoration in dopamine receptor binding activity in recovered patients. (Previous work had shown deficits.) Successfully treated patients presented with a dramatic increase in DRD3 binding—another dopamine receptor—in the ventral striatum. As you recall, the ventral striatum helps regulate reward stimuli. Its function played a prominent role in the cellular explanation put forward in the previous column. It must be noted that these PET scans are interpreted as changes in activity, but that could mean many things. The signals could indicate increased DRD3 densities, decreased extracellular dopamine, or both, in recovered patients.

Serotonin interactions

Compelling as some of these data are, dopamine is not the only neurotransmitter under active investigation. Other evidence suggests that compared with healthy individuals, people who are susceptible to AN have alterations in normal serotonin biology.

Click to EnlargeMost of the best work has focused on 2 ideas (Figure). The first posits that people vulnerable to an eating disorder have increased extracellular concentrations of serotonin—something that can actually be measured. The second is the presence of an imbalance in postsynaptic serotonin receptor activity. As you will recall, serotonin receptors consist of a family of related proteins. Two of these—the 5-HT1a and 5-HT2a receptors—have received a great deal of research attention.

There is reason to investigate alterations in ligand/receptor binding with these receptor systems in patients who have AN, especially given the mean age at onset. Gonadal steroid changes associated the menstruation (and stress) have been known to alter the activity of the 5-HT system during adolescence. Starvation-induced reductions in levels of extracellular 5-HT, for example, might result in reduced stimulation of postsynaptic 5-HT1a and 5-HT2a receptors, leading to behavioral alteration. The resulting dysphoria, normal in unaffected individuals, might be exaggerated in patients with AN.

There are testable questions surrounding these ideas. Forcing AN patients to eat, for example, might stimulate postsynaptic 5-HT1a and 5-HT2a receptor activity. This stimulation would lead to an elevation in dysphoric mood, transforming eating and weight gain activities into traumatic stress-inducing experiences. This might explain the no-win behaviors so common in AN patients. If the patient were allowed to continue to starve herself, anorexigenic information related to neuropeptide alterations (reduced b-endorphins, elevation in stress-related metabolism such as elevated corticotropic-releasing hormone), might exacerbate AN symptoms by driving food-restricting behaviors. Whether eating or starving, the same dysfunctional circuitry would be stimulated, all leading to the symptoms.

Do any of these speculations have empirical support? Two lines of evidence suggest an important involvement with serotonin in certain types of AN. However, the specific answers await further research.

• There is specific evidence that patients with AN present with an imbalance in postsynaptic 5-HT1a and 5-HT2a receptor activities in specific areas of the brain. These alterations might contribute to the feelings of abnormal satiety and excessive harm-avoiding, anxiety-riddled behavior.

• Persons with AN show unique anxiety-related 5-HIAA metabolic perturbations. The weight loss in these patients results in a reduction in 5-HIAA CSF levels. But they concomitantly show dramatically elevated 5-HIAA receptor binding in specific cortical and limbic structures—something not seen in healthy controls. Food might very well be anxiogenic in these individuals.

These findings, real as they are, do not provide many solid hints about molecular explanations for AN. To date, no single biochemical alteration has been shown to be both necessary and sufficient to produce the disease. Combined with the dopamine work, one might be tempted to say diseases.

And, I suppose, that is the frustrating point. Whether one is looking at behavioral components, neural circuitry, or molecular interaction, one is continually confronted with complexity, confounders, and nuance. Not a welcome comment regarding a disease such as AN, which has the highest mortality rate of any psychiatric disorder. It is one of the most expensive to treat, too, with no guarantee of success when therapy reaches its end point. Progress has been made, but we have a long way to go before we know everything. Given its urgency, that’s a very depressing thing to write.

Like I said, I think in my next column I am going to talk about the neurobiology of happiness.

Correction: I should have mentioned in my article “The cellular and molecular substrates of anorexia nervosa”, posted in the Psychiatric Times on (part 1) November 1 and (part 2) December 7 2009, that much of it was a digestion of an excellent review of the topic in Nature Reviews Neuroscience, 10, 573-584 (2009), by Walter H. Kaye, Julie L. Fudge and Martin Paulus.

Appetite regulation is made up of complex interlocking, incentive-driven motivational hormonal and neuronal circuitries . . . that can be pulled in many directions, especially where food is cheap and readily available.

The event that created the most indelible memories of my graduate experience recurred each morning as I trundled down to the lab. I always crossed paths with a well-disciplined jogger, heading in the opposite direction, running feverishly up a hill I was descending. It was very difficult not to stare at her, for she looked like someone freshly liberated from a concentration camp. Gaunt, pale, withered, bones protruding behind thin sheaths of skin, the jogger possessed a desperate, oddly determined look in her eyes. The look was unforgettable.

She grew paler and more emaciated as the months went by, and there came a time when we no longer crossed paths. I always wondered if she had moved away, got into a treatment program, or had simply died.

This month’s column—and the next—is all about the neural and molecular biology of anorexia nervosa, a disorder from which this jogger probably suffered. Starting with definitions and diagnostic criteria, then moving to the various neural circuits thought to be involved in its etiology, I will describe some recent findings from the 40,000-foot view of this most baffling disease. Here I discuss certain cellular interactions inside these circuits that may underlie the disease.

The field shows great promise, and some surprising recent research twists, in what turns out to be a very complex research story. Frustratingly, the field faces some real research challenges before consistently effective treatment strategies emerge and joggers like my morning friend become a thing of the past.


DSM-IV recognizes 2 types of restricting eating disorders whose most common feature is a deliberate alteration in caloric intake. As you know, bingeing/purging behavior is one type—classic bulimia nervosa—characterized mostly by the familiar intense restriction of food intake punctuated with temporary episodes of disinhibitory behavior. The other type, sometimes called restricting type anorexia nervosa, has few or no periods of disinhibition. Although many patients may freely transit between these behaviors, we focus our discussion on restricting anorexia, hereafter referred to as AN.

At first blush, researching the underlying neurobiological mechanisms behind AN might seem a fairly straightforward task. It has a fully known—even archetypal—set of symptoms. Its clinical course is well characterized. The disease has a surprisingly narrow age of onset (early puberty) and is mostly experienced by females, which easily makes AN one the most homogeneous of all psychiatric disorders. Would that investigating schizophrenia had such predictive luxury!

Scratching below the surface of the disorder reveals why research into AN has been such a challenge, however. Appetite regulation is made up of complex interlocking, incentive-driven motivational hormonal and neuronal circuitries. These circuits can be pulled in many directions, especially where the food supply is cheap and readily available to so many. From classic metabolic aberrations to more purely psycho-biological issues, there are many places where dysfunction could arise.

Given such variability, it is perhaps not surprising that AN has a bewildering, multifactorial etiology. There are sociocultural factors to consider; there are developmental factors to consider; and there are underlying genetic factors that may influence the psychosocial issues. (As we’ll see next month, AN shows surprising heritability.) Even in healthy populations, the factors that determine appetite are many and include an individual’s homeostatic needs, his or her perceptions of those needs, the tendency to favor certain consumptive strategies over others, and food’s natural rewarding (and also punishing) properties.

The bottom line? No single biochemical alteration has ever been shown to be both necessary and sufficient to produce the disease. One might be tempted to say diseases.

As if this isn’t complex enough, there is a powerful chicken-and-egg issue to consider. Severe caloric restriction can cause equally severe changes in the functioning of the brain. Patients with AN usually experience profound alterations in the metabolism of specific regions in the parietal, temporal, frontal, and cingulate cortices. They tend to have reduced brain volumes. Many regress to preadolescent gonadal function. Did the changes in the brain lead to the symptoms? Did the symptoms lead to changes in the brain? Did they exaggerate a premorbid trait? Or cause the predilection to come into existence?

Navigating the distance between trait and state is a difficult feat to perform under the best of circumstances. With disorders that involve appetite regulation, researchers face many challenges on the road to identify-ing their underlying neurobiological substrates.

Despite these hazards, real progress has been made, and one quite attractive hypothesis has been published that has many falsifiable features. It is to this work that we turn, beginning with an embarrassingly brief summary on the neurocircuitry of appetite control.


Although research on the specifics fills volumes, the functional circuit-ry needed to understand AN can be boiled down into 3 specific steps (Figure). We will take as an example the most studied topic (and perhaps the most delightful) . . . what happens when we bite into something sweet.

1. Initial stimulation

Chemoreceptors on the tongue detect a sweet stimulus and immediately broadcast the good news to the brain stem (via the spinal cord, medulla and, eventually, the nucleus tractus solitarii [NTS]). The NTS tosses the signal to the thalamic taste center in the middle of the brain.

2. Routing to the insula

The thalamus sends a stimulatory signal to the primary gustatory cortex, which is connected through a series of dense neural circuits to the anterior insula. That’s an important relationship. As you may know, the in-sula is involved in the process of interoception, which includes perceptions of temperature, muscle tension, itch, tickle, sensual touch, pain, perceptions of stomach pH, intestinal tension, and hunger. The insula creates an integrated perception of these disparate internal feelings, delivering to us a fairly unified appraisal of the physiological condition of our bodies. It is perhaps not surprising that when researchers looked for neurological substrates behind AN, alterations in the function of the insula were among their first targets.

3. Routing to the rest of the brain

Once the insula is stimulated, the signals become routed through an intricate series of reciprocating pathways. These pathways involve the amygdala, anterior cingulate cor-tex (ACC), and orbitofrontal cortex (OFC). Although complex, the route of stimulation can be divided into 2 overall cortical-striatal pathways: afferents from the cortical structures that are involved in the anterior insula and interconnected limbic structures (forming the so-called ventral neurocircuit) are directed to the ventral striatum. Cortical structures that help mediate more cognitive strategies send inputs to the dorsolateral striatum. These form a secondary dorsal neurocircuit.

These now fully aroused circuits chatter over interconnecting feedback loops that result not only in the perception of taste but also how you feel about it. The amygdala, for example, provides information about affective relevance, potentially stimulating reward systems in the brain. The ACC is involved in conflict monitoring, potentially mediating if not generating “eat” or “do not eat” commands. The OFC is involved in executive functions, and, thus, in planning future consequences and impulse control.

All these processes are stimulated by the simple act of eating a candy bar and eventually experiencing the sweet taste. As is evident here, however, such perception is not simple at all.


With these background pieces of information in mind, we are ready to discuss recent behavioral and imaging data that all converge on a single idea about the neurobiology of an-orexia. Some of the most interesting work has come from the finding that patients with AN have fundamentally different reactions to rewards and punishments and to the relationship between actions and outcomes than do unaffected controls.

The first set of experiments used classic “guessing game” behavioral protocols (usually involving positive and negative monetary reward exercises) while the participant’s brains were being imaged. Healthy participants generally show markedly differential activation profiles in the subgenual ACC and ventral striatum that are specific to both the positive and negative aspects of the game. These differences allow unaffect-ed subjects to discriminate between positive and negative feedback experiences in their psychological interiors. Women who had recovered from AN did not show differential activation profiles of the subgenual ACC and ventral striatal targets in these games. They showed equivalent profiles—and in both the positive and negative aspects of the protocol. That’s not a trivial finding. It is quite possible that individuals with AN have an impaired ability to perceive the difference between positive and negative feedback information. Subsequent behavioral work using different protocols confirmed this finding. Interestingly, and for whatever reason, this impairment led to a negative bias.

The second set of experiments also used imaging in conjunction with behavioral tasks. These tasks involved measuring connections between actions and outcomes. Healthy controls showed a relatively mild activation of the caudate-dorsal striatum and the regions that project to them (the dorsolateral prefrontal cortex and parietal cortex) in such tasks. As you recall, these areas are involved in planning and foresight, impulse control, and executive functions, as well as working memory. Participants who had recovered from AN showed a greatly elevated response in the same experiments. Behaviorally, they appeared to be looking for “rules” in the tasks where there were none and were overly concerned—even obsessively concerned—with making errors. They appeared to be overdriving a broad spread of their executive functions, an insight consistent with the imaging data, as well as other behavioral experiments.

Combining these 2 sets of experiments has suggested to some researchers that a behavioral “perfect storm” is brewing in the brains of affected subjects. Anorexic patients display an absence of appropriate reward processing responses; at the same time, they possess an increased activity in the neural substrates that are concerned with the consequences of their behavior. Perhaps the latter exists in an attempt to compensate for a lack of appropriate perceptive rewards and punishment feedback loops in the former.

This has led directly to a testable hypothesis, which explains AN as a conflict between an acquired negative reaction to food and the biological need to have it. Patients with AN recruit cortical executive functions in an attempt to settle the bias, all the while carrying dysfunctional rewards and punishment systems. These modulatory circuits become consistently overstimulated, leading to high anticipatory behavior and obsessive concern with future events.

How does that work? These modulatory circuits, sometimes referred to as top-down interoceptive circuits, meet information from the ascending interoceptive circuits that provide information about the body’s physiological state. (Remember our discussion about the insula?) These 2 neuroperceptive freight trains collide at the striatum. In this model, the top-down processes win. They alter the brain’s striatal reactions in response to food, resulting in the behavioral shifts and disease course.

These ideas represent just one way to interpret an increasingly large amount of imaging data, of course. Moreover, taken by themselves, these observations remain unsatisfying; they don’t have much explanatory power about the origins of the disease.

To answer questions of origins, we have to turn to a different set of experiments—ones involving genes and molecules and the neural substrates that carry them. As the Psychiatric Times resident geneticist, that is just what we will consider next month. A fairly consistent story (complete with maddeningly important confounders) is beginning to emerge, and there may be strong genetic underpinnings behind this otherwise baffling disorder.

It doesn’t make my memory of my morning running friend during graduate school any less vivid. But it may make it easier for me to understand why she looked the way she did.

John Medina writes the Molecules of the Mind column for the Psychiatric Times. Learn more about Brain Rules here.

Functional MRI, Round 3: Six Items to Keep in Mind

October 2, 2009  |  Posted by John Medina | No Comments

This is the third and final installment in a series on biophysical mechanisms of functional magnetic resonance imaging (fMRI) technologies. My overarching goal has been to explain why great care must be exercised when interpreting data derived from these magnets. The inspiration for the series came as I was reading a magazine article while waiting for a plane to take off—my reaction to what I read may have resulted in a bit of trauma to the seat pocket in front of me.

In part 1 , I talked about how innocent little protons spinning inside biologically important molecules end up giving us insights into brain function. The protons get caught in powerful magnetic fields; are blasted with radio waves; then allowed, gasping, to return to their former, lower-energy state. This gasping (energy release, actually) is what an fMRI machine actually detects.

In part 2, we discussed 2 facts:

• fMRI can only measure changes in blood flow (something called BOLD signals).

• There exists a somewhat ambiguous relationship between hemodynamic changes in the brain and neural activity. Although neural activity is supposed to be associated with an increase in blood flow, that’s not always true. Sometimes increases in neural activity result in a decrease in blood flow.

We ended by observing that there is an array of switching mechanisms from which the brain has to choose when assessing energy/oxygen needs; I then gave a detailed example of 1 such mechanism. A full understanding of all these mechanisms would have to occur before a completely accurate interpretation of imaging data emerges.

Here I formalize this need for caution by describing 6 items to keep in mind when examining fMRI images. The reason to end our series this way is altruistic (I hope): the view these scans provide about brain function is quite spectacular—and rendered all the more remarkable by conservative, thoughtful interpretations about what that revelation is.

Here then, are 6 things to keep in mind.

Item to Keep in Mind #1

You are always looking at machine-selected populations.

Not everyone who signs up for fMRI brain experiments can actually carry them out. Researchers estimate that up to 20% of the subject pool becomes claustrophobic as they climb into the machine. This makes it impossible for them to start (or in some cases, continue) the imaging process. Even those who stay put often report feelings of anxiety—especially as the machine groans into action. (This is not all that surprising, given the almost coffin-like tube into which the subject must crawl). Subjects also have to keep their heads perfectly still, sometimes from a few minutes to a few hours, while locked inside the tube (to keep the image as clear as possible). This stationary requirement is facilitated by packing the subject’s head into tight foam wedges before starting the scans.

Such conditions necessarily keep the subject pools from being completely randomized; their selection is biased by the needs of the machine. This may sound like a trivial matter, except when one beholds the sheer volume of fMRI papers that have been (and are currently being) published. Taken as a whole, we are not examining a randomized representation of a human family, but rather of a human family member who can stand to be in small spaces with his or her head in traction for a long time. There is no question that much information can be derived by imaging stress-tolerant individuals—but that is hardly the only population important out there.

Item to Keep in Mind #2

Resolution issues can be a problem.

Another issue has to do with the resolving power of the “typical” scanner, which affects the researcher’s ability to thoroughly characterize all relevant neural tissue recruited during an activity. The smallest block of tissue your garden-variety fMRI machine can image is a little cube a few millimeters on a side (these cubes are called voxels, a collision of the word volume and pixel). A few millimeters of brain tissue is a ridiculously large amount of cellular real estate, representing thousands upon thousands of neurons. But the machine can only examine a relatively large macroscopic block of tissue. That means there is a resolution problem—especially if you’d really like to see individual cells. It’s the equivalent of taking a picture of a 21st century battlefield using spy satellites from the early 1960s. As you know, many important neural activities do not happen in conspicuous, large blotches in the brain, but rather occur in subtle, more refined, electrically weaker networks distributed throughout the organ. fMRI technologies cannot currently capture these more subtle patterns. We are at constant risk for seeing only an incomplete picture of the brain’s response to the stimulus being examined.

The very idea of blotches themselves can be misleading, which leads to Item #3.

Item to Keep in Mind #3

Watch out for the edges.

Standard brain imaging scans often look like Doppler satellite images in a weather report: there are conspicuous regions of inactivity and sharply defined regions of activity. That sharpness can be misleading, however, because the activity levels between blotch and non-blotch regions are often quite small (in some cases, so small that the boundaries may be arbitrarily determined). One can easily be misled into believing that these sharply defined boundaries indicate just as sharply defined regions of activity, which may or may not be true.

This difficulty in determining the “actual” signal is confounded with the fact that the brain is hardly silent, even when no measured stimulus is occurring. The auditory cortex lights up after all in response to sound, which can be quite abundant in these machines, even when no experiment is in place. (There is also something called “dark energy” in the brain—electrical activity occurring throughout the organ in the complete absence of any stimulation.) Its a familiar signal-to-noise issue common to most engineering problems. Determining an acceptable threshold level, one capable of detecting the stimulus the researcher is after and not anything else, can surprisingly be difficult to achieve.

Item to Keep in Mind #4

Machine time and brain time may not be the same thing.

Temporal limitations of the technology must be taken into account when one is interpreting fMRI scans. The image most machines create develops quite slowly, usually over several seconds (that’s why it is important to keep the subject’s head so still). The big problem is that the brain’s neurons live in a world where firing rates often exceed hundreds of times per second. Combined with the fact that voxels are millimeters in size, exactly what is being imaged when a big blotch appears can be difficult to interpret precisely.

Getting accurate temporal data is further complicated by the fact that the machine is not actually measuring neural activity. It is measuring blood flow. There is usually about a 5-second delay between neuronal firing and observable blood-flow changes. The bottom line? Many fMRI images only display large groups of neurons whose cumulative firing efforts resulted in blood flow changes that are only observable long after triggering stimulus has exited. Assessing temporal activities can be very tricky business indeed!

Item to Keep in Mind #5

Every brain is wired differently.

Brain volumes can vary quite a bit from one person to the next, as can the absolute locations of specific brain structures. The wiring patterns of neural networks, which can include both structural and functional issues, also vary from one individual to the next. Because learning always involves changes in such patterns, and no 2 people learn the same things the same way, one can expect a wide variation in reactions in the brain to identical stimuli. All these issues must be taken into account when designing imaging experiments.

To overcome these individual variations, researchers usually recruit more than 1 person in the subject pool for imaging. And they take lots of images of each one of them. When the examinations are finished, the researchers line up all the images they receive, combine the data, and average what they see. At publication, the image obtained is usually representative not of one subject’s brain activity, but of the averaged brain activity of the entire experimental cohort.

Item to Keep in Mind #6

Be mindful of the dangers of reverse inference.

There is a great temptation to use activation profiles obtained with fMRI to infer a specific mental state. “This region of the brain is active, therefore this mental state must be occurring” is a mistake commonly made in the popular press and even occasionally by neuroscientists. It is usually called reverse inference, a convention that can be habit forming simply because in many cases, it actually reveals something useful. Broca and Wernicke speech centers really do light up when auditory information is being processed. Getting a stroke in those areas debilitates the function. Ergo, when these areas are active, speech is being processed.

Such reverse inferences may be fine for hyperspecialized regions like those of Broca and Wernicke, but there are many regions of the brain whose activation profiles are far more complex. The right prefrontal cortex lights up like a Christmas tree whenever the brain is trying to solve a hard problem. But it is also involved in impulse control, planning, foresight, and even the apprehension of mathematics. If the right prefrontal cortex lights up, is the brain working on solving a second order differential equation or reigning in the impulse to punch former math teachers in the nose? The amygdala, which is powerfully involved in mediating anxious emotions, is also involved in smelling popcorn—and in feeling sexually aroused. If it lights up, is its “owner” feeling afraid, hungry, or horny? When you have a series of choices but only one brain image, which mental state do you wish to infer?

There are many other objections that space here does not permit to describe. Yet, even considering just these 6, the cautious lessons about remembering contexts are very obvious here. One must be willing to constantly train a critical eye on the experimental conditions under which the images were obtained. One must also be willing to be skeptical about any claims concerning mental states. Any activation may narrow down the choices, but it does not a priori reveal our psychological interiors.


There are ample reasons not to end this series on a negative note. Many careful neuroscientists are leapfrogging over the inherent limitations of the technology to obtain meaningful results. One of the most promising approaches uses fMRI in combination with other technologies. Some researchers, for example, are using transcranial magnetic stimulation experiments to temporarily ablate activity in previously stimulated regions of the brain and then looking for the presence or absence of observable behavior. Others are combining their imaging work in people with electrophysiological recordings in animals. A typical experiment might involve following up human data with monkey data, for example, using single neuron recordings in the animals to verify a given observation. Still others are using more sophisticated statistical models and co-opting analytical tools originally derived from research into machine-based learning. This allows researchers to shift the focus on trying to apprehend brain region–specific activation profiles to a specific task to answering more global questions about brain processing in the presence of the given stimulus.

I would like to end our entire series by briefly describing one such hopeful approach, illustrated in the Click to Enlargeaccompanying Figure.

In a standard imaging experiment, researchers create an average of the fMRI activation profiles for adjacent voxels. This averaging makes things a bit easier to detect variations between experimental conditions alluded to previously (say you are having the subject first view the face of a famous movie star, then a boat). To do this averaging, you have to assume that neurons activated within specific voxels (all 10 gazillion of them) behave in an identical fashion. They don’t, most certainly. But there are statistical tools that, when used properly, can ferret out relevant information from these activation profiles and obtain meaningful results. Some of the most powerful of these tools are the so-called multivariate pattern classifiers. These classifiers produce finer-grained images by detecting activation patterns across many individual voxels without averaging any of them. As a result, they can detect signal differences that would normally escape conventional fMRI analysis.

The example in the Figure is an experiment that looked at how speakers process non-native language sounds.


The 3 articles in this series have attempted to summarize some of the basic science behind fMRI. Our journey took us from quark to voxel, and for a particular reason—to outline the strengths and limitations of noninvasive imaging technologies. One hopes that such wisdom will permit the clinician to train a critical eye on any image derived from these powerful magnets.

Despite these 6 caveats, I do not wish to leave you with the impression that fMRI images are not worth examining. I think fMRI, which can properly marry structure to function, represents one of the most powerful weapons in a cognitive neuroscientist’s arsenal. When used properly, fMRI takes on the powerful mantle of cartographer-in-chief—a valuable position in any expeditionary enterprise.

But just as there are limits to what a map can tell you about a country’s interior function, there are many limits to what these scans can tell us about a brain’s interior function. As long as we remember this, we will obtain a more nuanced opinion about fMRI images. This may help us get a clearer, ultimately more realistic view about how the brain actually processes information.

And it may also save a few airplane seats from being ripped apart at the seams when occupied by frustrated bioengineers.

Arik Korman interviews John Medina about the challenges facing the next generation. Watch the interview on YouTube or below.

1. The database is getting poorer.
Expert notion is shifting from knowing the knowledge outright to simply being reassured that it could be gotten "from somewhere." The students simply know where to get it, but the information is not immediately resident in their own brains.

2. The students' notion of intellectual toughness is shifting.
The amount of material they think is "hard" is growing and they don't like it.

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