Along with naming it, Mitchell proposed a theory to explain the phenomenon. He suggested that irritated nerve endings in the stump were sending signals to the brain, which interpreted them as sensations from the missing limb. Inspired by the theory, some surgeons tried amputating the stump, but this didn’t help. Today many neuroscientists believe a different theory: Phantom limbs are caused by a remapping of the cortex.
The reorganization is not of the entire cortex; it’s thought to be confined to a particular area. We previously learned about area 4, the strip in front of the central sulcus that controls movement. Just behind the central sulcus is area 3, which is involved in the bodily sensations of touch, temperature, and pain. In the 1930s the Canadian neurosurgeon Wilder Penfield mapped both areas in his patients by using electrical stimulation. After opening the skull to expose the brain for epilepsy surgery, Penfield applied his electrode to different locations in area 4. Each stimulation caused some part of the patient’s body to move. Penfield drew the correspondence between area 4 locations and body parts (Figure 12, right), calling the map a “motor homunculus.” (Homunculus is from the Latin for “little human.”) Likewise, after each stimulation of area 3, the patient reported feeling a sensation in some part of the body. Penfield mapped the “sensory homunculus” in area 3 (left), and it looked similar to the motor one. Both ran in parallel along opposite banks of the central sulcus. (Roughly speaking, these maps represent vertical planes passing through the brain from ear to ear. The plane of the sensory map is just behind the central sulcus, and that of the motor map just in front. Only the outer border is cortex; the rest is the interior of the cerebrum.)
Figure 12. Functional maps of cortical areas 3 and 4: the “sensory homunculus” (left) and the “motor homunculus” (right)
The face and hands dominate the maps, even though they are small parts of the body. Their cortical magnification reflects their disproportionate importance in sensation and movement. Could the sizes of their territories be changed by amputation, which suddenly reduces the importance of a body part to zero? Using such reasoning, the neurologist V. S. Ramachandran and his collaborators have proposed that phantom limbs are caused by remapping of area 3. If the lower arm is amputated, its territory in the sensory homunculus loses its function. The surrounding territories, dedicated to the face and upper arm, encroach upon the nonfunctional one by advancing their borders. (You can see the adjacencies in Penfield’s drawing.) These two intruders start to represent the lower arm as well as their original body parts, giving the amputee the sensation of a phantom limb.
According to the theory, the remapped face territory should represent the lower arm as well as the face. Therefore Ramachandran predicted that stimulation of the face would cause sensations in the phantom limb. Indeed, when he stroked the face of an amputee with a Q-tip, the patient reported feeling sensations not only in his face but also in his phantom hand. The theory likewise predicts that the remapped upper-arm territory should represent the lower arm as well as the upper arm. When Ramachandran touched the stump, the patient felt sensations in both the stump and his phantom hand. These ingenious experiments strikingly confirmed the theory that amputation caused remapping of area 3.
Ramachandran and his collaborators used technology no more advanced than a Q-tip. In the 1990s an exciting new method of brain imaging was introduced. Functional MRI revealed every region’s “activity,” or how much that part of the brain was being used. By now the images of functional MRI (fMRI) are familiar from their frequent appearance in the news media. They are usually shown superimposed on regular MRI images. The black-and-white MRI image shows the brain, and laid on top are the colored blotches of the fMRI image, which indicate the active regions. You can always recognize fMRI+MRI as “spots on brains,” while MRI is just brains.
Researchers imaged volunteers while they performed mental tasks in the laboratory. If a task activated a region, causing it to “light up” in the image, that was a clue to the region’s function. Neurology had always been hampered by the accidental nature of brain lesions, but fMRI enabled precise and repeatable experiments on localization of function. Brodmann’s map became indispensable as researchers worked hard to assign functions to each of its areas. The boom in scientific papers spurred many universities to invest large sums of money in fMRI machines, or “brain scanners.”
Researchers also repeated Penfield’s mapping of the sensory and motor homunculi. They observed which locations in area 3 were activated by touching parts of the body, and which locations in area 4 were activated when the subject moved parts of the body. It was thrilling to reproduce Penfield’s maps with fMRI rather than his crude method of opening up the skull. Researchers also studied remapping, verifying Ramachandran’s claim of a downward shift of the face representation in area 3 of amputees. As the theory predicted, the shift occurred only in those amputees who experienced phantom limb pain, not in pain-free amputees.
Amputation may not be injury to the brain, but it’s still a highly abnormal kind of experience. Do brains remap in more normal forms of learning? Violinists and other string musicians use the left hand to finger the strings of their instruments. Studies show enlargement of the left-hand representation within area 3, which is likely due to extensive musical practice. It’s impressive that fMRI can not only assign functions to Brodmann areas but also resolve fine changes within a single area. This research is far more sophisticated than studies of total brain size like Galton’s. It is bound to tell us more interesting things about cortical remapping, and it may even be useful for understanding crippling disorders of movement that seem to be caused by too much practice. Such disorders, known as focal dystonias, have tragically ended the careers of brilliant musicians.
Explaining learning in terms of the expansion of cortical areas or subareas, however, is still in the spirit of phrenology. It’s not so different in concept from the studies of cortical thickening, and the correlations are still statistically weak. The approach may be powerful, but it has limitations. For example, studies of Braille readers also show an enlarged hand representation. The remapping approach cannot easily distinguish between learning violin and Braille, which are two very different skills. And even if this particular problem can be solved, the general difficulty will remain.
Researchers have one other way of studying changes in the brain, which does not depend on the concept of remapping. Using fMRI, they have attempted to find differences in the level of activation of brain regions. For example, they have reported lower activation of the frontal lobe in schizophrenics performing certain mental tasks. At the moment such correlations are statistically weak, but this intriguing line of research may well tell us much about brain disorders and possibly lead to superior methods of diagnosing them.
At the same time, fMRI studies may have a fundamental limitation. Brain activation changes from moment to moment, roughly as quickly as thoughts and actions change. To find the cause of schizophrenia, we must identify some brain anomaly that is constant. Suppose that your car starts to shake whenever you drive faster than 30 miles per hour and turn the steering wheel to the right. This behavior is intermittent, so it’s only a symptom. It’s caused by something wrong with your car at a more basic level. Noticing symptoms is crucial, but it’s only the first step toward identifying the underlying cause.
Why are we still trying to use phrenology to explain mental differences? It’s not because the strategy is good. It’s because we have failed to come up with a better one. Do you know the joke about the policeman who comes upon a drunk crawling on the ground near a lamppost? The drunk explains, “I lost my keys around the corner.” The policeman asks, “Well, why don’t you search over there?” The drunk replies, “I would, but there’s more light under the lamppost.” Like the drunk who works with what he’s got, we know that size reveals little about function, but we look at it anyway because that’s what we can see with existing technologies.
To understand the failings of phrenology, can we
compare with a more successful example of relating function to size? Instead of investigating whether brainy people are smarter, let’s ask whether brawny people are stronger. The size of a muscle can be measured via MRI, and its strength with a machine that looks like one in the weight room at your health club. Researchers have found correlation coefficients ranging from 0.7 to 0.9, which is much stronger than the correlation between brain size and IQ. Muscle size accurately predicts strength, just as we’d expect.
Why are size and function so closely related for muscles but not for brains? Think of a muscle as operating like a factory in which all workers do the same thing. If every worker singlehandedly performs all the steps required for making an entire widget, doubling the size of the workforce will double the factory’s output of widgets. Likewise, every fiber of a muscle performs the same task. All the fibers are lined up in parallel, and all pull in the same direction. Their contributions to the force are additive (you can simply add them together to get the total), so a muscle with more fibers should be stronger.
Now consider a factory with a more complex organization. Each worker performs a different task, like fastening a screw or welding a joint. To make even a single widget, all the workers must cooperate. Economists say that such division of labor is efficient because specialization allows each worker to become highly skilled at each task. However, doubling the number of workers will likely fail to double the output of widgets. It’s not easy to integrate the new workers into the existing organization in a way that increases output. In fact, adding more workers could even reduce output by disrupting the workflow. As Brooks’ Law—a maxim of software engineers—puts it, “Adding more programmers to a late software project makes it later.”
The brain works like the more complex factory. Each of its neurons performs a tiny task, and they cooperate in intricate ways to carry out mental functions. That’s why performance depends less on the number of neurons and more on how they are organized.
The factory analogy explains the limitations of phrenology. Can it also explain remapping? The American neuropsychologist Karl Lashley believed that mental functions were widely distributed across the cortex, and charged that most of the boundaries of Brodmann’s map were figments of the imagination. Nevertheless, this archenemy of localizationism could not completely deny the experimental evidence in its favor. In 1929 he countered with his doctrine of cortical equipotentiality. Lashley granted that every cortical area is dedicated to a specific function, but every area also has the potential to assume some other function, he claimed.
Returning to our imaginary factory—the more complex one—let’s suppose that a worker is reassigned to a new task. The initial clumsiness will eventually give way to proficiency. Workers may be specialized, but they are also equipotential. When provided with new inputs, they can change their functions.
Lashley’s doctrine has some element of truth but is too sweeping. The cortex is not infinitely adaptable. If it were, every stroke patient would recover completely. To understand the limits of adaptation and develop ways to enhance it, we need a deeper understanding. We know that the cortex can remap, but how exactly does the function of an area change?
We can’t answer this without addressing a more basic issue: What defines the function of a cortical area in the first place? Broca’s and Wernicke’s regions are dedicated to language, and Brodmann areas 3 and 4 are dedicated to bodily sensation and movement. But why these functions? And how are they executed?
It’s hopeless to answer these questions by studying only brain regions, their sizes, and their activity levels. We must look at the organization of the brain on a much finer scale. A cortical area can contain over 100 million neurons. How are they organized to perform mental functions? In the next few chapters we’ll explore this question, along with the idea that brain function depends heavily on the connections between neurons.
Part II: Connectionism
3. No Neuron Is an Island
The neuron is my second-favorite cell. It’s a close runner-up to my favorite: sperm. If you have never looked into a microscope to see sperm swimming furiously, grab your favorite biologist by the lapels of his or her lab coat and demand a viewing session. Gasp at the urgency of their mission. Mourn their imminent death. Marvel at life stripped down to its bare essentials. Like a traveler with a single small suitcase, a sperm carries little. There are mitochondria, the microscopic power plants that drive the whipping motion of its tail. And there is DNA, the molecule that carries the blueprint of life. No hair, no eyes, no heart, no brain—nothing extraneous comes along for the ride. Just the information, please, written in DNA with the four-letter alphabet A, C, G, and T.
If your biologist friend is still game, ask to see a neuron. Sperm impress by their unceasing motion, but a neuron takes your breath away with its beautiful shape. Like a typical cell, a neuron has a boring round part, which contains its nucleus and DNA. But this cell body is only a small part of the picture. From it extend long, narrow branches that fork over and over, much like a tree. Sperm are sleek and minimalist, but neurons are baroque and ornate (see Figure 13).
Figure 13. My favorite cells: sperm fertilizing an egg (left) and a neuron (right)
Even in a crowd of 100 million, a sperm swims alone. At most one will achieve its mission of fertilizing the egg. The competition is winner take all. When one sperm succeeds, the egg changes its surface, creating a barrier that prevents other sperm from entering. Whether brought together by a happy marriage or a sordid affair, sperm and egg form a monogamous couple.
No neuron is an island. Neurons are polyamorous. Each embraces thousands of others as their branches entangle like spaghetti. Neurons form a tightly interconnected network.
The sperm and the neuron symbolize two great mysteries: life and intelligence. Biologists would like to know how the sperm’s precious cargo of DNA encodes half the information required for a human being. Neuroscientists would like to know how a vast network of neurons can think, feel, remember, and perceive—in short, how the brain generates the remarkable phenomena of the mind.
The body may be extraordinary, yet the brain reigns supreme in its mystery. The heart’s pumping of blood and the lung’s intake of air remind us of the plumbing in our houses. They may be complex, but they do not seem mysterious. Thoughts and emotions are different. Can we really understand them as the workings of the brain?
A journey of a thousand miles begins with a single step. To understand the brain, why not start with its cells? While a neuron may be a kind of cell, it is far more complex than any other. This is most obvious from its profuse branches. Even after many years of studying neurons, I am still thrilled by their majestic forms. I’m reminded of the mightiest tree on earth, the California redwood. Hiking in Muir Woods, or other redwood forests on the Pacific coast of North America, is a good way to feel small. You see trees that live for centuries or even millennia, enough time to grow to vertiginous heights.
Am I overreaching to compare a neuron to the towering redwood? In absolute size, yes, but consider further how these wonders of nature stack up against each other. The redwood’s twigs are as thin as one millimeter, a width 100,000 times smaller than the tree’s football-field height. A branch of a neuron, called a neurite, can extend from one side of the brain to the other, yet can also narrow to 0.1 micrometer in diameter. These dimensions differ by a factor of one million. In its relative proportions, a neuron puts a redwood to shame.
But why do neurons have neurites? And why do they branch to look like trees? In the case of a redwood, the reason for branches is obvious: The redwood’s crown captures light, which is a source of energy. A passing sunbeam will almost surely collide with a leaf rather than travel all the way to the ground. Likewise, a neuron is shaped to capture contacts. If a neurite passes through the branches of another neuron, it will likely collide with one of them. Just as a redwood “wants” to be struck by light, a neuron “wants” to be touched by other neurons.
Every time
we shake hands, caress a baby, or make love, we may be reminded that human life depends on physical contact. But why do neurons touch? Suppose that the sight of a snake causes you to turn and run. You respond because your eyes are able to communicate a message to your legs: Move! That message is conveyed by neurons, but how?
Neurites are much more densely packed than the branches of a forest or even a tropical jungle. Think instead of a plate of spaghetti—or microscopically fine capellini. Neurites entangle much like the jumbled strands on your plate, allowing one neuron to touch many others. Where two neurons touch, there can be a structure called a synapse, a junction through which the neurons communicate.
But contact alone does not make a synapse, which most commonly transmits chemical messages. A molecule known as a neurotransmitter is secreted by the sending neuron and sensed by the receiving neuron. Secretion and sensing are performed by still other types of molecules. The presence of such molecular “machinery” signifies that a contact point is actually a synapse, as opposed to a place where one neurite just goes past another.
These telltale signs are blurred in an ordinary microscope, which uses light to make images, but show up nicely with a more advanced microscope based on electrons rather than light. The image shown in Figure 14 is a highly magnified (100,000×) view of a cut through brain tissue. There are two large, round cross-sections of neurites (marked “ax” and “sp”). These are like the cut ends of strands that would be exposed if you sliced through spaghetti. The arrow points to a synapse between the neurites, which are separated by a narrow cleft. Now we see that the term contact point is not entirely accurate, as the neurites come extremely close to each other but do not really touch.
Connectome Page 5