The Cellular and Molecular Substrates of Anorexia Nervosa, Part 1

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.

DEFINITIONS AND CONFOUNDERS

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.

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.

AN IDEA ABOUT ANOREXIA

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

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.

ENDING ON THE POSITIVE

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 accompanying 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.

CONCLUSIONS

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.

7 Observations About the Next Generation -- And What to Do About Them (Part 1)

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.

Brain Rules in the News
- John Medina on Bob Rivers (video part 1)
- John Medina on Bob Rivers (video part 2)
- Oprah.com
- New Rules for Saving Your Memory (More magazine)
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- #8 on New York Times Business bestseller list

The Teenaged Brain: Part 2

In our last installment (The Teenaged Brain: Part 1), we discussed a familiar finding from the National Comorbidity Survey Replication (NCS-R): the peak age of onset for any mental health disorder is about 14 years. In an attempt to explain these data, we are exploring some of the known developmental changes in the teenaged brain at the level of gene, cell, and behavior.

We discussed in the last column a number of issues, ranging from alterations in gray matter volume to changes in adolescent executive function. Does a more complete understanding of these maturational processes provide enough hints to explain the NCS-R data?

Here we will attempt to answer this question. Three psychopathologies will be examined: affective disorders, anxiety disorders, and risk-taking activities—behaviors that may underlie substance abuse issues. As we go through some fairly recent findings, there may be an intellectual temptation to explain the NCS-R findings in terms of mutational (genetic) alterations inside otherwise normally functioning adolescent-related developmental processes.

As we will discover, demonstrating that this is indeed the biological basis for their numbers has proved to be surprisingly elusive. The relationship between the onset of psychiatric disorders and the typical changes seen in the developing brain of adolescents is complex, indirect, and not well understood. There is some evidence that certain disorders exist because of an exaggeration of otherwise typically functioning processes. But these are only hints. We are only in the beginning stages of our understanding of the relationship between adolescence and mental health. To date, no genetic anomalies have been shown to be responsible for the peak onset findings revealed by NCS-R in any mental health–related disorder.

ANXIETY AND DEPRESSION
Major depression is a serious and commonly experienced fact of life for many adolescents. Puzzling to some researchers, the adolescent-onset form often carries the most severe, most disabling symptoms of any form of human depression. Anxiety-related symptoms often precede the disorder, acting like a harbinger, and often first presenting in childhood.

Are there any cellular or even molecular explanations for these data? Unruly and wildly fluctuating hormonal changes are usually invoked to account for some of the risk associated with the adolescent-related affective disorders. That invocation may be well founded, with some of the strongest data coming from epidemiological investigations.

It is well established, for example, that changes in the prevalence for female depressive and anxiety disorders first show up midway through adolescence. The prepubertal ratio (female to male) is 1:1. By the time the adolescents have transited through Tanner stage III, that ratio has changed to 2:1. (As you may recall, the Tanner system [stages I through V] is devised to describe the typical transit through puberty for both males and females [See Table]). Corresponding to specific anatomical and hormonal events, the 5 stages are named prepubertal, beginning pubertal, midpubertal, advanced pubertal, and postpubertal. The sex-based change in the prevalence of these psychopathologies, first observed in the midpubertal stage, is stable into adulthood.

Are there any genetic explanations for this consistent observation? The answer is clearly “no,” or at least “not yet.” There is certainly reason to believe that hope may be on the horizon, however—mostly related to mouse studies examining the biochemistry of tetrahydroprogesterone (THP).

As you may recall, THP is released during stress. Normally, it provides an anxiolytic effect on mouse behavior. This behavioral change is mediated by the binding and subsequent activation of γ-aminobutyric acid, type A (GABAA) receptors—the same receptors activated by benzodiazepines and alcohol. Oddly enough, a specific subtype of the GABAA receptor, called an a4b2d receptor, is associated with an extraordinarily different response. If THP binds to this subtype, it increases anxiety in the animal, mediating a response directly opposed to the typical GABAA receptor behaviors. Researchers have recently shown that the expression of this subtype is greatly increased as the mouse transits through puberty. The increase is region-specific, most prevalent in the CA1 region of the hippocampus (known to be involved in memory formation). Convincingly, the increase in normally observable anxiety is blocked if the animal is prevented from making THP!

Though these hints are tantalizing, they say little about human behavior. For that we have to turn to examining the living brain, and these days that means functional MRI (fMRI). Do noninvasive brain-imaging studies reveal linkages to data from these images? Do they say anything about the anxiety-ridden or depressed teenaged brain? The answer to the first question is “no,” but the answer to the second question is a tentative “maybe.”

There is clear evidence that adolescents with both anxiety and depression have odd amygdala-associated reactions to typical social stimuli when compared with controls. The data are unsatisfying, mostly because they are associative. But combined with other research, an interesting picture may be emerging of the manner in which emotional stimuli are perceived in these youthful populations. Researchers have found evidence for anomalous adolescent cortical responses to emotional stimuli, for example.

Typical experiments (which usually involve both adults and adolescents in fMRI studies) consist of 2 phases:

• While their brains are being imaged, the subjects are asked to make emotional evaluations of faces bearing various kinds of emotion-competent expressions.

• While still in the machine, subjects are asked to make nonemotional statements about those same faces. When subjects encounter a joyous face, they might be asked, “How happy do you feel after seeing this?” Then they might be asked, “What color is the hair?”

The results are consistent: the adults in these studies actively engage the orbitofrontal cortex whenever this switch occurs. The adolescents do not. When these studies are combined with other data, the picture emerges that adolescents are abnormally engaging specific brain regions to externally presented emotionally competent stimuli. There are hints that these youngsters consistently provide unreal-istic evaluations of these emotions wherever they perceive them. These appraisals may act as triggering responses to depressive and anxiety-related disorders.

Read the rest of the article

Brain Rule #12: Exploration

The greatest Brain Rule of all is something I cannot prove or characterize, but I believe in it with all my heart. It is the importance of curiosity. Watch the Exploration video below or on YouTube.


Summary

Rule #12
We are powerful and natural explorers.

Babies are the model of how we learn—not by passive reaction to the environment but by active testing through observation, hypothesis, experiment, and conclusion.

Specific parts of the brain allow this scientific approach. The right prefrontal cortex looks for errors in our hypothesis (“The saber-toothed tiger is not harmless”), and an adjoining region tells us to change behavior (“Run!”)

We recognize and imitate behavior because of “mirror neurons” scattered across the brain.

Some parts of our adult brains stay as malleable as a baby’s, so we can create neurons and learn new things throughout our lives.

View the Exploration Tutorial

Teenaged Brain: Part 1

This statistic is as familiar as it is startling. According to the National Comorbidity Survey-Replication (NCS-R), the peak age of onset for any disease involving mental health is 14 years. True for bipolar disorder. True for anxiety. True for schizophrenia and substance abuse and eating disorders. The data suggest that most mental health challenges emerge during adolescence. If true, this brings to mind an important developmental question:

What is up with adolescence?

The teenaged years are chock-full of byzantine, intricately timed, molecular processes that have to be closely choreographed and deployed in a specific sequence to accomplish their sexual mission. Do these extraordinarily complex developmental processes go awry in some children as they evolve through adolescence? Do these changes create, or at least contribute, to future mental disorders? Is this one way to get at what are sometimes called genetic “trapdoors”—DNA-based psychopathologies that do not show up until a certain developmental milestone is reached?

These are important issues. Most of the mental health challenges that emerge during puberty have real staying power. The symptoms tend to be more severe. Many go undetected in the early formative stages of the illness and comorbid disorders often develop. These complications can create problems in determining the correct diagnosis, and make it difficult for the clinician to select the treatment strategies with the greatest probability of success.

Researchers face similar daunting challenges in attempting to understand the cellular and molecular basis for such disorders. Fortunately, fairly recent findings have provided a ray of hope—potential illumination for both clinician and scientist. From gene to cell, we are beginning to learn more and more about the neurobiological maturation of the brain transiting through adolescence. The question is, Does any of this knowledge help us understand the NCS-R data?

In this column and the next, we will explore the developmental biology of the so-called teenaged brain, focusing first on cellular studies, then on behavioral ones. In this first installment, we will address specific aspects of the brain’s developmental trajectory. We’ll look initially at structural changes and then focus on the notion of the canonical “teenage brain” behaviors.

In the second installment, we will discuss how changes in these developmental processes may contribute to the emergence of mental disorders.

Read the column

The Biological Threat of Stress: From the Jungle to Wall Street

If news about the economy isn’t stressful enough to make you drive your fist through the TV, wait until you hear what stress can do to your brain. Unrelenting stress can hurt the brain’s leading talent — which is learning — and, in its most potent forms, it can even lead to brain damage.

But before you read this admittedly depressing story, would you do me a favor? The article that accompanies this piece provides some practical advice about what you can do to ameliorate the effects of stress. Please promise to read it as well — because you can tame the impact of stress on your life. To underscore how important stress-relieving behaviors are, I am presenting the bad news first. But the bad news is neither the end, nor the most important part, of the story.

Definitions
You might be surprised to know that the negative linkages between stress and learning were not easy to measure in the laboratory. First, most of the time stress does not cause brain damage and, oddly enough, certain stressors can actually be quite good for learning.

Second, no one could find a single grouping of physiological states unique to stress. Indeed, it was discovered that a person’s overall responses to aversive stimuli were the same responses they had to their favorite chocolate bar. Or to sex.

Third, no two people react to stress in exactly the same way, which is another way of saying perceptions of stress were (and are) highly subjective.

So how are we going to define "unrelenting stress"? We actually do have definitions that make sense to a test tube these days, using insights uncovered many years ago and centering around a small but very powerful word: control. The principle is this: The more out of control you feel over some bad thing coming at you, the more likely you are to experience the type of stress that can hurt you.

“Out of control” is measured in two directions: an inability to control the frequency of the bad stuff coming at you, and an inability to control its severity once the bad stuff has arrived. This loss of control has been shown to greatly increase the probability of the brain slipping into an anxiety or clinical depression. That can profoundly affect learning, and even cause neurological harm.

The very kinds of experiences in which recessions are marinated, ranging from layoffs to the current slowdown’s favorite flavor — retirement erosion — can provide a perfect elixir for brain debilitation.

The Stress Response System and the Saber-toothed Tiger
Why should a system embedded so deeply in your psyche be so potentially dangerous to you? Stress responses play an extraordinarily important part in our evolutionary survival, after all.

The answer has less to do with biological systems than it has to do with social ones — and also with timing. The brain is well-adapted for solving stress-related problems that are short-term in duration. The saber-toothed tiger either ate you or you ran away from it, but the whole thing was over in less than five minutes.

Great for a jungle. Lousy for Wall Street. A recession doesn’t last for five minutes. Neither does a bad marriage, or a bad job. When you try to push a system that was adapted only for solving short-term problems into solving long-term ones, the system first becomes over-extended, then it becomes overwhelmed.

There are many lines of evidence supporting this insight. The metabolic machinery that would actually allow you to handle a stressful experience is almost completely exhausted in 30 minutes (a great deal of it consumed in the first five). If the system moves beyond this performance benchmark, it starts to deregulate, like a server with too many demands on its time.

Another line of evidence is a reaction to the first: Stress systems possess negative feedback loops that almost immediately ask the brain if it is OK to shut the systems down, even if it just started revving things up. Why? Because overlong activation hurts things, and your body simply does not have the resources to cope with sustained assaults to its metabolic first responders,

A final line of evidence has to do with the speed of our reactions and our conscious awareness of them. Our stress responses react so quickly that we often do not become aware we are reacting until after we have already started the process. We literally start running away from an aversive stimulus before we are even aware we are moving.

The reason? It simply takes too much time to tell the parts of the brain responsible for consciousness that a big feline is chasing you, time in which you could turn into lunch. So you start running and, in mid-stride, become aware of what you are doing. The delay is about 200 milliseconds.

Milliseconds? That’s less than the time it takes to blink your eyes. The performance envelope of our stress system is designed to solve problems of very short duration.

So what happens when you push a system designed to solve problems lasting less than an hour into an experience where the problems last for months? The answer is depressing. Severe stress experienced over long periods of time can result in physical brain damage.

Danger Zone: Long Term Stress
When you are stressed, your body gives you two options to respond. One option involves deploying a hormone called epinephrine (or, if you are from Great Britain, adrenalin), supervising the so-called fight-or-flight response. The second choice involves the hormone cortisol.

Which system you deploy first may be in part genetically determined, but the goals of both are the same: to shift enough blood flow to your thighs to get you to move out of the arena of danger — and to give your brain a reason for doing so quickly. Hormones rage through your body like a storm surge, energy resources are pumped wildly into far-flung tissues, you dump any excess waste your body is currently carrying, and your brain kicks you into a high state of surveillance.

We are going to follow one of the alert signals, the cortisol we just mentioned, to discuss why an over-exposure causes physical damage to specific regions in the brain.

When stress is moderate in severity, acutely experienced, or both, your stress systems work very well. Cortisol is secreted by your adrenal glands, organs that lie atop of your kidneys. This hormone is part of the Delta Force of your stress response, supervising not only the mission to get you out of danger, but helping to calm you down once the mission is accomplished.

Cortisol even goes into your brain, aiding and abetting regions that are involved in learning (specifically an area of the brain called the hippocampus). That makes sense; you want to learn quickly from the things that could threaten your biological future.

It is this brain access that provides a conduit for the killing, however. Left to its own devices, dumping cortisol onto unprotected hippocampal cells will kill them just as surely as acid burns skin. Fortunately, your brain “knows” this and has left the hippocampus with some pretty good protection. Hippocampal cells have within them an heroic protein called Brain Derived Neurotrophic Factor (mercifully shortened to BDNF). BDNF can protect a nerve cell from the toxic effects of cortisol. As long as the system is not overwhelmed, BDNF does a pretty good job of buffering against the negative effects of stress.

Watch this video explaining BDNF

When you begin to feel out of control, however, the system short-circuits. If too much cortisol floods into the brain, which is what happens with severe, sustained stress, BDNF cannot keep up the fight. Cells die. Cortisol has a fair number of dirty tricks up its sleeve when produced in large quantities, including the ability to turn off the gene that makes BDNF. Not only can cortisol take the field, it can render its victims incapable of mounting a counter-attack as well.

The Good News About Stress
There are many other issues involved in a complete description of this complex story, including the fact that some people are genetically wired to be more stress-tolerant than others. But the good news is powerful and does not require a genetic explanation.

The brain damage turns out in most cases not to be permanent. You can actually reverse this evil over-regulation in real time, and cure the negative effects listed here. This article discusses some of the ways this seeming miracle can occur.

Learn more about stress and the brain