By visualizing the brain at work, functional imaging has transformed cognitive neuroscience. We will illustrate some insights derived from functional imaging by considering one of the field's ultimate questions—the nature of consciousness.
The idea is quite simple: By comparing brain activity between conscious and unconscious states we should be able to identify brain regions in which the activity is correlated with consciousness. Because systematically manipulating consciousness is not trivial, translating this logic into a scientific experiment is difficult. To accomplish this task, scientists have relied on the fact that exposure to the identical external stimulus can alternately evoke a conscious or unconscious experience depending on other controllable factors. For example, as you read this chapter you have blinked numerous times; nevertheless, although your brain has recorded the flickered light caused by blinking, your consciousness has not. Now, once brought to your attention, you become aware of the perceptual effects of blinking (in fact, it is now difficult for you to suppress this awareness).
Just as sensory stimuli can be processed with and without conscious perception by the brain, the recall of objects from memory can also be conscious or unconscious. Consider running into someone you met once before. Viewing the person's face may activate conscious recall of the initial meeting—the place, the time, the person's name. Or, as is often the case, you might sense that the face is familiar, but you cannot quite connect it to a time or place—the face simply does not evoke conscious recall of the initial meeting. Even worse, you might not consciously recognize the face, even though (as can be demonstrated) some regions of your brain are responding unconsciously to the face (as if your brain remembers, but you do not).
By comparing the hemodynamic response associated with perception and recall, both with and without consciousness, functional imaging studies have begun to identify regions of the brain correlated with consciousness.
Imaging Perception with and Without Consciousness
Mapping of brain function began in the middle of the 19th century, fully 100 years before the advent of functional imaging of the brain. By correlating cognitive performance with the anatomical location of brain lesions, neurologists identified brain regions involved in specific cognitive functions (see Chapter 1). However, this approach has a number of important limitations that prevented many questions about function from being answered.
For example, just because an area of primary sensory cortex may be necessary for conscious perception, because it is involved in the initial processing of sensory information, does not mean it is responsible for the conscious experience. It may simply relay sensory information to higher-order cortex that is responsible for consciousness. In principle, functional imaging can help make these distinctions.
Indeed, neural correlates of conscious perception can be measured experimentally using visual illusions in which the percept is dissociated from the physical stimulus. One such illusion results from binocular rivalry, which occurs when different visual stimuli are presented simultaneously to each eye. Typically, awareness of one or the other stimulus is suppressed so that we are consciously aware of one stimulus at a time, never both. Thus one eye's view dominates consciousness for several seconds, only to be replaced by the other eye's view. What makes binocular rivalry so remarkable is that the perceptual experience fluctuates while the physical stimulus remains constant. Because of this dissociation, binocular rivalry presents a unique opportunity for studying the neural correlates of consciousness.
What systems are recruited when one eye's view becomes dominant? According to one idea, neurons in the early stages in visual processing respond to the physical stimulus of each eye, but in later stages the signals from these neurons are switched on and off, causing the perceptual alternations. That is, a later stage serves as a "gate" to visual consciousness.
Does such a gate exist? If so, what neurons in the brain have this gating function? Are the neurons localized in particular brain areas? Are they a particular cell type? Does the gating occur through modulation of the cells' firing rates or some other component of their responses (eg, spike timing, synchronous firing)? What are the neural circuits and neural computations that support the competition between the two stimuli?
Although we do not yet have firm answers to these questions, the evoked metabolic activity of the brain under conditions of binocular rivalry has been measured with fMRI. One fMRI experiment capitalized on an interesting aspect of this perceptual phenomenon; during an alternation one typically perceives a traveling wave in which one pattern emerges initially at one location and expands progressively as it renders the other pattern invisible. The physical stimulus does not change while this conscious perceptual change is taking place—it is all "in your brain." This experiment established that waves of activity in primary visual cortex (V1) accompanied the perceptual changes during binocular rivalry.
Because the primary visual cortex is topographically organized—adjacent neurons respond to adjacent locations in the visual field (see Chapter 27)—it was possible to show that neural activity propagated over subregions of primary visual cortex. The sequential activation of these subregions correlated with the dynamic perceptual changes experienced during binocular rivalry (Figure 20–6). Similar waves of activity propagated over the immediately adjacent secondary visual areas (V2 and V3).
Neural correlates of conscious visual perception.
(Reproduced, with permission, from Lee, Blake, and Heeger 2005.)
A. A subject is presented with a high-contrast spiral grating in the left eye and a low-contrast radial grating in the right, and each image is restricted to an annular region of the visual field. The subject perceives a traveling wave in which the low-contrast pattern is seen to spread around the annulus, starting at the top and progressively erasing the other image from awareness. (See part B for the explanation of the red and blue circles.)
B. An anatomical image cut through the posterior occipital lobe, perpendicular to the calcarine sulcus. The red circle identifies a subregion of primary visual cortex where cells represent the upper-right quadrant of the annular region of the visual field (depicted in part A). The blue circle identifies a sub region where cells represent the lower-right quadrant. The plot compares the fMRI measurements from these two subregions. Red and blue curves correspond to the red and blue outlined subregions; arrows indicate when these curves peak. The blue curve is delayed in time and larger in amplitude, as the high-contrast pattern remained visible for a longer period of time.
C. Propagation speed of the underlying neural activity, computed from the fMRI measurements of three subjects. Temporal latency of the neural activity is plotted as a function of cortical distance measured along the folded surface of the cerebral cortex. The dashed slope corresponds to a propagation speed of approximately 2 cm/s across the cortical surface.
Box 20–4 Diffusion Tensor Imaging
Diffusion tensor imaging (DTI) is another application of MRI, complementary to fMRI, for visualizing anatomical properties of the brain. DTI begins with measurements of how far water diffuses within the brain. The random displacements of molecules resulting from thermal agitation (Brownian motion) obey a statistical law that was described by Einstein in 1905.
In a homogeneous medium the average distance moved by the molecules increases linearly with the square root of time. For water at body temperature, 68% of the molecules will have moved less than 17 m during 50 ms. Water diffusion in the presence of large molecules or cell membranes is impeded.
It has been known for decades that MRI can be used to measure differences in the extent of water diffusion, called diffusion MRI, that depend on brain anatomy. One of the most successful clinical applications of diffusion MRI has been in the management of stroke. Michael Moseley discovered in 1990 that water diffusion decreases considerably in ischemic brain tissue within minutes of a restriction in blood flow. Diffusion MRI has since become a standard diagnostic procedure for the evaluation and management of stroke patients.
Peter Basser realized in 1994 that MRI could be used to characterize the anisotropy of water diffusion (differences in diffusion in different directions), which led to the development of DTI. DTI can be used to characterize the local orientation of the fiber bundles at each location in the white matter of the brain. This is because white matter is made up of bundles of axons (fascicles), and diffusion of water is approximately three to six times faster in the direction of the white matter fiber bundles than in the perpendicular direction.
A DTI measurement begins like all MRI measurements, by placing the subject in a strong magnetic field. A radio frequency pulse is applied so that the water protons wobble in phase with one another (see Box 20–3). Next a gradient in the magnetic field is introduced along one axis for a brief period of time. Let's assume for the moment that this gradient is initially applied in the rostral-caudal direction so that the magnetic field is stronger at the front of the brain than at the back of the brain; but we will see that each of several gradient directions will be used in sequence.
Because of the gradient, the rate of precession is faster for the water protons in the front of the brain than for those in the back of the brain. Indeed, the rate is slightly faster for the water protons at the front of each voxel than for those at the back of each voxel. When the gradient is turned off, therefore, the water protons dephase, each by a fixed amount depending on its front-back location.
A reversed gradient is then introduced with the same amplitude and duration but with the opposite direction (caudal-rostral in this example). If nothing has moved in the front-back direction, then this reversed gradient will perfectly rephase all of the water protons so that they are once again precessing in perfect synchrony.
Because of diffusion, however, each of the water molecules will have moved by some amount during the time period between the first gradient application and the reversed gradient. If diffusion (in the front-back direction) is less in one voxel than in another, the result will be better resynchronization and a brighter MRI image intensity in the voxel with less diffusion. For this example with rostral and caudal gradients, diffusion only in the front-back direction matters. If a water molecule diffuses rightward or leftward, then the dephasing and rephasing caused by the two gradients will perfectly cancel.
The measurement is repeated for each of several directions to characterize the diffusion anisotropy. A separate image of the brain is reconstructed for each direction. A voxel in white matter will typically exhibit greater diffusion in the direction of the fiber tract (dimmer image intensity in the corresponding image) and less diffusion in the other directions (brighter image intensities). These separate images can then be combined to show the degree of anisotropy and the dominant direction of anisotropy (Figure 20–5A).
The most advanced application of DTI is fiber tracking, the only noninvasive method currently available to characterize anatomical connectivity in the living human brain. Fiber tracking is a computational analysis of the DTI measurements, the basic idea of which is to follow the path of anisotropy (and hence the fiber track) from one location in the brain to another (Figure 20–5B).
There are, however, important limitations to the accuracy and precision with which fiber tracking can be done with DTI. Unlike the use of retro- and anterograde tracers that label connections established by individual axons, DTI reflects the statistical average of axon trajectories through each voxel of white matter tissue. Specifically, the intensity in each voxel of each diffusion MRI image depends on the average diffusion of all of the water molecules within that voxel.
Hence, only white matter bundles composed of large numbers of axons are visible (current methods fail to detect tracts smaller than 5 mm in cross-section diameter). The many thin tendrils of white matter connecting nearby cortical areas are not reliably detectable, nor are intracortical connections that remain entirely within gray matter.
In some white matter regions fiber tracking is difficult because different fiber bundles cross, so there is no single dominant diffusion direction. In other regions different fiber bundles travel together over some distance and then separate, which can cause fiber tracking software to make errors.
Even so, DTI is being used in conjunction with fMRI to characterize the normal development of human brain connectivity, and to identify subtle anomalies in brain function and connectivity in a variety of neurological diseases (eg, multiple sclerosis, Alzheimer disease), developmental disabilities (eg, dyslexia), and mental illnesses (eg, schizophrenia).
MRI measurement of diffusion anisotropy.
Water diffusion in white matter is anisotropic, and the anisotropy can be measured with MRI. The color and brightness at each location in the image represents the diffusion of a small volume (or voxel) of tissue. Brightness corresponds to the degree of diffusion anisotropy. White matter mostly appears bright (diffusion is highly anisotropic), whereas gray matter and ventricles are dark (isotropic diffusion). Colors represent the dominant orientation of white matter fibers: red indicates that diffusion is greatest in the right–left direction, green indicates diffusion is greatest in the front–back direction, and blue indicates diffusion is greatest in the up–down direction. (Reproduced, with permission, from Ben-Shachar, Dougherty, and Wandall 2007.)
White matter fiber tracts reconstructed from DTI.
Each "virtual fiber bundle" was computed from the DTI measurements by starting at one location in the brain and following the path of greatest anisotropy. Depicted are four fiber tracts that are believed to be important for reading. Yellow fibers are the superior longitudinal fasciculus that connects temporoparietal cortex (including Wernicke's area, which is critical for language comprehension) with lateral frontal cortex (including Broca's area, implicated in language production). The purple fibers are passing through the corpus callosum connecting regions of the two occipital lobes and regions of the two temporal lobes. Blue fibers are corona radiata fibers that pass through the posterior limb of the internal capsule. Finally, the orange fibers connect the posterior occipitotemporal cortex (including a region believed to be critical for letter recognition) with the lateral cortical surface at the border between the occipital and temporal lobes.
Another experiment showed, however, that the activity waves in primary visual cortex are not themselves sufficient for conscious perception. In this experiment subjects were temporarily distracted (their attention was diverted) so that they did not perceive the rival stimulus patterns presented to the two eyes. Waves of activity were still clearly evident in primary visual cortex, even though subjects did not experience a corresponding traveling wave. However, waves of activity were not evident in V2 and V3.
Indeed, activity in a number of brain areas other than the primary visual cortex correlates with the perceptual alternations of binocular rivalry, including higher-order visual areas in the inferior temporal lobe and areas in parietal and prefrontal cortex. It is likely that these different higher-order cortical areas play distinctive roles in visual perception during binocular rivalry. Attention, mediated by feedback from areas in parietal and prefrontal cortex, is thought to play a crucial role in coordinating the activity across these brain areas to yield conscious perceptual states, as discussed in a later section.
Perceptions are typically made up of multiple sensations, not just a single sensation isolated in experimental conditions. An introduction to a person, for example, involves visual, auditory, and often somatosensory and olfactory information, so that the conscious experience likely reflects activity in several higher-order sensory cortices. Although a multimodal percept arises from activity in numerous brain regions, we sense the conscious experience as a unified, seamless whole. The linkage between discrete functional systems in the brain that gives rise to a unified experience of consciousness is sometimes called the "binding problem." According to one view, a conscious experience occurs when neural activity in disparate regions of the brain is time locked: The activity in these areas becomes temporarily synchronous.
Imaging Memory with and Without Consciousness
Conscious perception and conscious memories have long been linked. According to one view, conscious recall occurs when a stimulus reactivates the brain regions that first encoded the conscious percept being remembered. An fMRI experiment by Randy Buckner and colleagues provided the first empirical evidence in support of this idea.
Buckner trained subjects to associate pictures or sounds with a written word. For example, the word "dog" was associated either with a picture of a dog or the sound of a dog barking. Subjects were scanned with fMRI after they were shown the word and asked to recall the associated picture or sound, thereby mapping memory-storage regions. In addition, they were scanned during exposure to the picture or sound, thereby mapping regions involved in perception.
Remarkably, first hearing and later recalling a sound stimulated some of the same higher-order regions in the auditory cortex, and viewing and later recalling a picture stimulated some of the same higher-order regions in the visual cortex. However, conscious recall of sounds or pictures did not activate areas of primary sensory cortex. These results provide evidence that conscious memory recall used some of the same regions of the brain that were used for conscious perception.
The notion that higher-order sensory cortex is recruited for memory is reinforced by studies of different stages of sleep. Although dreams are not faithful recollections of the external world, they are remarkably vivid, comparable to conscious memory. Functional imaging studies of rapid-eye-movement sleep, during which dreaming occurs, and slow-wave sleep, characterized by the absence of dreams, have found that dreaming is associated with activity throughout higher-order sensory cortical areas. Just as in Buckner's experiment, primary sensory cortex is not activated during dreaming.
Other imaging studies have found that when a stimulus induces only a sense of familiarity, not a full-blown recollection, brain activity tends to be confined to specific sensory regions representing one, or at most, a very few modalities. Taken together these studies demonstrate that, just as with conscious perception, simultaneous activity in several higher-order sensory regions underlies recollection (conscious recall of the stimulus along with the associated details of the context, when and where the stimulus was initially perceived, what else happened at the same time, etc.).
Imaging Attentional Modulation of Conscious Perception
Our brain is constantly bombarded by external and internal stimulation, yet at any given moment we are only aware of a small fraction of this input. Attention is one factor that influences the focus and scope of our awareness. As we mentioned earlier in Chapter 17, the American psychologist William James defined attention as "… the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneous possible objects or trains of thought." James captured the key element of attention—when confronted with more than one input the brain does not process all inputs equally.
Performance on a wide variety of perceptual discrimination and identification tasks is faster and more accurate when subjects attend to the right place at the right time. Several investigators have developed experimental protocols to characterize the behavioral consequences of attention. For example, in a visual attention experiment the subject is asked to fixate a spot at the center of a computer screen while visual stimuli are shown on either side. The subject is instructed to shift attention to one side of the screen, without moving the eyes, when a visual cue, such as an arrow, indicates an upcoming stimulus at the side of the screen. Behavioral performance (speed or accuracy) in a visual discrimination task is enhanced when subjects shift their attention to the side of the screen containing the stimulus.
On the basis of studies of patients with attention deficits as a consequence of a neglect syndrome following a stroke, the parietal and frontal lobes have long been implicated in the control of visual attention (Chapter 17). Using PET imaging, Michael Posner and his colleagues have confirmed that frontal and parietal lobe regions contribute to the control of attention. Similarly, as we saw in Chapter 17, electrophysiological studies of attention by Michael Goldberg have identified neurons in areas of the parietal lobe that respond more strongly to attended stimuli than to unattended stimuli.
William James described two different kinds of attention. One is passive, automatic, stimulus-driven, and transient, whereas the other is active, voluntary, conceptually driven, and sustained. In "passive immediate sensorial attention the stimulus is a sense-impression, either very intense, voluminous, or sudden … big things, bright things, moving things … blood." We now refer to passive, nonvoluntary attention as exogenous attention, whereas active and voluntary attention is called endogenous attention.
Functional imaging has revealed that the two types of attention recruit different subregions of the brain. During voluntary attention certain parietal areas (within the intraparietal sulcus) and frontal areas (frontal eye fields) are active when subjects are instructed to shift or maintain attention. Shifts in attention are mediated by transient responses in the frontal and parietal regions immediately following the presentation of a cue. The maintenance of attention, critical for our ability to focus on a particular location in the visual field over an extended period of time, is mediated by sustained activity in the visual cortex as well as some of the same parietal and frontal brain regions. Additional brain areas become active when attention is diverted by a particularly significant or unexpected stimulus. For example, the amygdala is involved in diverting attention to emotionally salient (particularly fearful) stimuli such as a fearful face or a snake (see Chapter 48).
Attention is believed to be controlled by a particular network of cortical and subcortical areas. But how does this give rise to the improved behavioral performance discussed above? One idea is that signals from higher-order cortical areas flow back down to sensory cortical areas to facilitate the sensory representation of an attended stimulus. Functional imaging experiments have found that in this way the neural representation of an attended stimulus is amplified. This amplification is correlated with, and is believed to cause, the improvements in behavioral performance that accompany attention (Figure 20–7).
Neural correlate of attention.
Subjects had to fixate on the center of a display (above) and were instructed to attend to one side or the other without moving their eyes. Here an axial (horizontal) slice through the occipital lobe of the brain (below) shows functional activity (red and orange) superimposed on the brain anatomy. The dashed outlines mark the boundaries of primary visual cortex. Activity in the left hemisphere increased when the subject attended to the right and vice versa (stimuli on the right are processed by neurons in the left hemisphere and vice versa). (Adapted, with permission, from Gandhi, Heeger, and Boynton 1999.)