Functional magnetic resonance imaging (fMRI):
A “window” into the brain
A.Parry and P.M.Matthews
Copyright  2002 A. Parry and P.M. Matthews
Centre for Functional Magnetic Resonance Imaging of the Brain
Department of Clinical Neurology
University of Oxford
The John Radcliffe Hospital
Headley Way
Headington, Oxford OX3 9DU UK
Correspondence to Prof. P. M. Matthews
Tel: 0044 1865 222729
Fax: 0044 1865 222717
Email: paul@fmrib.ox.ac.uk
Abstract
Functional MRI (fMRI) has enabled scientists to look for the first time into the human
brain in vivo, to literally “watch it while it works”. This has revealed exciting insights
into the spatial and temporal changes underlying a broad range of brain functions, such as
how we see, feel, move, understand each other and lay down memories. The technique is
safe, allowing repeated examinations to probe time-dependent changes such as those
involved in learning. fMRI also is improving our understanding of a variety of brain
pathologies. Some, such as the addictive behaviours of gambling or drug abuse, are
without structural brain changes. In other cases in which there are clear structural
changes, fMRI has shown that functional changes occur in widely distributed regions of
the normal appearing brain. Understanding these and the adaptive potential that they
confer promises to guide rational approaches to improved rehabilitation strategies. Here
we briefly review these and related applications of the technique for better understanding
of the healthy and diseased brain.
2
Magnetic resonance imaging: seeing brain structure
What connection could an ancient Greek shepherd have with modern brain science? The
answer is magnetism. Two thousand years ago a shepherd named Magnes came across a
rock with very peculiar qualities. It attracted bits of iron and seemed to orient itself as if
magically when held from a string. The rock was magnetite and the observations were
the first in a long history of physical studies of magnetism that extend to this day.
The connection with brain science comes because magnetism allows us to see the living
brain. You may not realise this, but your brain is composed of billions upon billions of
tiny magnets. Each of the hydrogen atoms in each of the molecules of water in your
brain is a tiny magnetic dipole. There are over 100,000,000,000,000,000,000,000 in even
a teaspoon-size scoop of your brain! When these tiny magnets in the brain tissue are
placed inside of a very strong magnetic field, they align with the field just as a compass
aligns with the earth’s magnetic field. Of course, a very strong magnetic field is needed
to make this happen. Typically, a supercooled (to a temperature of about 270o below
zero) magnet over 50,000 times more powerful than of the earth’s magnetic field is used.
A short pulse of radiofrequency energy perturbs these tiny magnets from their preferred
alignment. As they subsequently return to their original position they give off small
amounts of energy that can be detected and amplified with an antenna or “receiver coil”
placed directly around the head. The detected signal allows us to distinguish water
molecules and to distinguish the relative amounts in each part of the head.
3
Like other tissues in the body, over 70% of brain is composed of water. Thus, finding out
how the water is distributed in the head is just like localising brain tissue. Different parts
of the brain have slightly different amounts of water. Nerve cells, for example, are
relatively rich in water, whereas the fatty coating around the long nerve fibres and cells,
called myelin, has less. This generates contrast between the surface cortex and the
underlying white matter of the brain that can be used to provide exquisite details of
structure by the technique of magnetic resonance imaging or MRI (Fig. 1).
Figure 1. A. A sagittal MRI image from a healthy brain. Different structures in the brain
can be defined with exquisite detail. B. MRI imaging contrast is based primarily on the
distribution of water and differences in its physical environment in different tissues. Here
the differences between signals from the cortical grey and the underlying white matter are
illustrated (Images courtesy of Mr. P. Hobden and Dr. S. Clare).
4
The brain is a very unprepossessing organ at first glance. It has a grey and rather uniform
appearance. Through the “window” of MRI, however, a rich internal structure can be
defined. MRI can provide a detailed picture of the brain with resolution of less than 0.5
mm, about the size of the tip of a typical ballpoint pen. Even structures buried deep
within the centre of the brain immediately can be made apparent. The rich network of
arteries, including those penetrating deep into the substance of the brain that are as small
in diameter as a hair, also can be seen with MRI
However whilst MRI has provided us with a detailed knowledge of the structure of the
brain, it does not tell us how the brain functions. We would like to understand the neural
basis for our behaviour, such as how we are able to learn new skills, remember important
facts and integrate and make sense of the large amount of ever-changing stimuli in our
enviroment. With the development of functional imaging, we are now able to look inside
the brain of humans in “real time” and appreciate the neural mechanisms behind our
behaviour, rather than just observing the consequences of these effects.
A short history of “mapping” brain functions
The unassuming character of the surface of the brain led scientists to underestimate its importance until
relatively late in the history of medicine. Even after its significance was appreciated in the 17 th century, the
way in which it was organised was debated hotly. In keeping with the relatively uniform appearance of the
brain, many scientists believed in the theory of mass action which stated that areas of the brain worked
together for every task without much regional functional specialization 1.
5
In contrast, the phrenology school of the Viennese physician Thomas Gall argued that
specific types of brain function were associated with particular regions of the brain that
could be localised by palpating the overlying bumps on the skull surface. The notion that
particular regions of the brain acted as specific “functional modules” formed the
foundation for the concept of discreet cortical localization. Gall and his collaborators
however further assumed that the structure of the brain was linked directly to “character”,
with individual brain regions determining individual traits 2. Sadly for Gall, most of his
specific theories proved to be incorrect. However, the notion of cortical functional
localisation remains supported by current science, although we now think more in terms
of specific types of computational processes being performed by individual brain regions.
Phrenology gave way to a different localised approach to mapping the mind based on
lesion studies. Many scientists were impressed by the observation that damage to
different areas of the brain produced relatively specific deficits in patients. One
particularly well-known 19th century example was a patient made famous by the eminent
French neurologist Charcot and known to us as “Tan” because this was the only sound
that he could utter. Charcot was impressed that Tan’s inability to speak was not
associated with either mental deficiency, problems of movement, or impairment of
language comprehension. When his patient finally died, Charcot found a single large
tumour pushing on and damaging the lower left side of the front of the brain. He
therefore hypothesised this area of the brain had a specific function for spoken language
3
.
6
Although human lesion studies such as these provide valuable information concerning the
brain regions necessary to perform a task, they are limited in several respects. From a
practical perspective there are few instances in which a lesion (e.g., a brain tumour) is
confined only to one discreet brain region. Most lesions spill out into adjacent regions,
making it difficult to interpret the results unequivocally. In addition, the presence of
focal abnormal brain tissue may lead to changes in the functioning of adjacent “normal
looking” tissue by indirect effects. For example, an expanding tumour could cause
“bruising” of the adjacent brain tissue necessary for a specific task with subsequent
deterioration in brain function, the region occupied by the lesion itself not actually being
necessary for task performance.
A whole new appreciation of the ways in which the brain becomes structurally
specialised for sensation, cognition and direction of action is developing with use of the
extraordinary ability of magnetic resonance imaging to resolve small changes in the
healthy living brain that can be related to behaviour. Surprisingly reminiscent of aspects
of the phrenologists’ theories, there are now intriguing examples of situations in which
small, local variations in brain shape (rather than skull shape!) are correlated directly with
changes in brain function. For example, it has been reported that there is a relationship
between the size of the brain region which controls hand function and the number of
years of practice of a musical skill, suggesting that the structural variability in this brain
region occurs as a consequence of experience or use rather than reflecting individual
differences within the population 4. Similarly, a recent study reported that London taxi
drivers have an increased size of the part of the brain involved in the type of memory
7
used for map reading 5. The limitations of these cross-sectional studies however are that
they do not allow us yet to conclude confidently that structural variation arises as a
consequence of practiced behaviour.
Functional magnetic resonance imaging: seeing brain function
Functional magnetic resonance imaging (fMRI) is a relatively recent imaging technique
that is able to avoid some of the problems in the interpretation of lesion or purely
structural imaging studies. It aims to determine the neurobiological correlate of
behaviour by identifying the brain regions (or “functioning modules”) that become
“active” during the performance of specific tasks in vivo. In addition, the non-invasive
and relative safety of the technique allow repeated studies to be carried out within a given
subject so that important questions, such as the relationship between experiencedependent use and changes in brain structure or function can be addressed 6. To
appreciate how fMRI exploits the demands of neural activity to provide spatial and
temporal information on brain function it is important to first understand the basic
functional unit of the brain.
The basic functional unit of the brain is a cell called a neuron. A typical neuron has a cell
body (the central “command centre”), from which project short processes called dendrites
that receive “message” inputs from other neurons and then relay the information back to
the cell body. A neuron also has a long process called an axon that conducts electrical
impulses away from the cell body towards the dendrites of other neurons. The region
where the axon from one neuron and the dendrites from a second make contact is called
8
the synapse. When electrical impulses travelling down the first axon reach a certain
threshold they cause the release of local chemical neurotransmitters from the end of the
axon into the synapse and onto receptors on the nearby dendrites of the neighbouring
neuron. The contact of neurotransmitter with these receptors may then trigger the
transmission of a nerve impulse down the second neuron and then onto further neurons
until that a large number are activated coherently in a functional “network”. Although
the most interactions are “excitatory”, some synapses are “inhibitory” and limit
subsequent neuronal excitation. Importantly, after the neurotransmitters have been
released into the synapse, they are recycled and re-enter the neuron in a process that
requires energy. FMRI makes indirect use of this local need for energy to image the
regions of the brain recruited into the activated neural network 6, 7.
“Blush at the thought!” Like so many old phrases, there are many levels on which this
admonition can be appreciated. In fact, we do “blush” with thought. In the last decade of
the nineteenth century, the Oxford physiologist Charles Sherrington made the observation
that when a small area of exposed cat brain was stimulated electrically (an exaggerated
form of the changes that occur when the region becomes active in thought) there is a
flush of red blood 8. This increases the blood supply to the brain locally, ensuring an
adequate supply of oxygen to regions working harder in thinking.
This effect can be used to precisely map areas of the brain involved in brain function.
Fortuitously, the properties of the small bar magnets of water molecules in the brain
change slightly between areas that are near blood with its oxygen exhausted relative to
9
those near freshly oxygenated blood 9. The local increase in energy requirements arising
as a consequence of neuronal firing is largely met through an increase in oxygen-based
metabolism with the increased demand for oxygen being delivered seconds later by an
increase in the local blood flow (the hemodynamic response) 10. Changes in the
oxygenation level of the blood therefore occur as a consequence of neuronal activity and
so the magnitude of change in signal intensity can be used as an indirect measure of
excitatory input to neurons which is generally related closely to the cell firing rate 11, 12, 13.
Thus, when there is a “blush” from increased activity, there is a small increase in signal
intensity on the MRI scan.
If the MRI experiment is done while a mental task is given to a subject, a so-called
functional magnetic resonance image (or fMRI) image is generated. On such an image
we can see how different tasks activate different parts of the brain relatively specifically.
When we listen to music, for example, a specialised area in the so-called auditory cortex
along the sides of the brain shows increased signal 14. Vision activates a region in the
back of the brain (the occipital cortex), localised precisely to regions of the visual field 15
(Fig. 2). Touch brings increased signal along the side of the brain, particularly in the side
of the brain opposite to the part of the body that is touched 16 and movements activate
regions in the front and the top of the brain in cortex specialised for motor control 17.
Figure 2. A healthy subject was asked to listen to sentences being spoken while
watching a screen with a flashing checkerboard presented. The sentences started and
stopped at slightly different times than the flashing picture was turned on and off. On the
10
basis of the differences in timing activation in the brain, the areas responsible for hearing
(in the middle of the brain in grey) and vision (in the back of the brain in white) could be
localised by functional magnetic resonance imaging (fMRI) (Image courtesy of Dr. S.
Smith from www.fmrib.ox.ac.uk).
Visual
cortex
Auditory
cortex
FMRI therefore is able to identify the brain regions that become more active during
specific task performance. However, there are several caveats to interpretation of the
increase in fMRI signal. It is likely that the magnitude and extent of the hemodynamic
response is a measure of more than a single energy-requiring process. It may reflect not
only the frequency of local excitatory synaptic input, but also the extent of the postsynaptic depolarization (neuronal firing) 12. Whilst it may be less energy-consuming, the
firing of inhibitory neurons also could contribute to an increase in local energy
requirement and subsequent hemodynamic response 18, 19. Thus, regions that appear to be
“activated” on fMRI as a consequence of task could sometimes be selectively inhibited.
11
What may be more important is the varying inhibitory input could change the excitatory
input necessary for post-synaptic depolarization and thus the magnitude of the fMRI
response (reflecting the local metabolic “cost”).
A more fundamental issue in interpretation is that it is not the neuronal response that is
monitored directly, but a “surrogate”, the hemodynamic response. The hemodynamic
response (occurring over seconds) is much slower than the neuronal response (occurring
over tens to hundreds of milliseconds). Although there is typically a delay of between 46 seconds between the onset of the task and the peak of the hemodynamic response (the
latency of the response), the temporal resolution of fMRI as a technique would not be
compromised if the shape of the hemodynamic response function was fixed and was
simply convolved with the time course of the task-induced neural activity. However,
there is some evidence that characteristics of the hemodynamic response may vary
between different individuals 20, different brain regions, with the nature of the task 21 or
in the presence of disease 22. The unknown potential variability in the latency of the
hemodynamic response limits precise interpretations of the magnitude and the temporal
resolution of the signal 23.
The latter problem however does not generally limit uses of fMRI as a “mapping”
technique. For such experiments, the time-course of the response is itself not the most
critical issue. A standard method of task presentation uses a “box-car” or “block” design
where blocks of the stimulus or task are presented typically for 20-30 seconds, alternating
with periods of rest or a “control” condition. The “control’ task is chosen carefully such
12
that it activates all of the neural processes common to the stimulus-task with the
exception of the cognitive process of interest. By subtracting the brain regions recruited
during the performance of the control task from the brain regions recruited during the test
condition, the areas of the brain whose activity is associated specifically with the
cognitive process of interest can be identified.
An alternative experimental approach is to present stimuli as isolated brief events
separated in time so that the individual response to single events can be identified. The
principle advantage of this “event-related” approach is that it avoids the potential
confounding factors of habituation or fatigue which may arise in a block design as a
consequence of the presentation of repeated identical stimuli. While this approach can
preserve much of the temporal information in the hemodynamic response, it often is not
used.
Using fMRI to investigate the normal and diseased brain
By using a combination of these and other experimental approaches fMRI has provided
important insights into the neural basis of a multitude of normal human cognitive
processes, such as our ability to memorise information 24 or recognise faces 25, how we
experience pain 26 and how subjective experiences can be modulated by the context of the
stimulus presentation 27. FMRI also has provided a greater understanding of the
neurobiological basis for problems of drug addiction or gambling! 28, 29.
13
One of the main advantages of fMRI is that the same subject can be studied a number of
times in a longitudinal design. This approach has been used to investigate both how
healthy subjects learn new complex tasks 30 and also to understand how people with
initially disabling neurological disease, such as a stroke, improve functionally over time
31
. The clinical relevance of fMRI also has been strengthened by recent experiments
which have explored the neurobiological basis of specific rehabilitation strategies
associated with the recovery of function 32. Other clinical applications of fMRI such as
the pre-surgical mapping of eloquent cortex 33 and new approaches to the early diagnosis
of disease 34 also suggest that fMRI will soon play an important role in the routine
management of patients with neurological disease.
How do we remember?
Most people have sat in an exam room and been unable to remember a specific fact or
figure, despite knowing that they had previously revised the topic area well. How is it
that we are able to remember some facts and not others? Is it a random process related to
the failure to retrieve the information from our memory “bank”, or is it directly related to
how well we encoded the information when we were revising for the exam?
Unfortunately, simple behavioural tests of memory are not be able to distinguish these
possibilities as the assessment of memory encoding is limited by the necessity to retrieve
the previously encoded facts 35. However, the ability of event-related (a series of single
trials) fMRI to break down the complex cognitive processes involved in memory into the
separate components of memory encoding and retrieval, avoids this confound.
14
Brewer and colleagues 36 scanned subjects whilst they were shown a series of pictures
and had to decide whether each picture represented either an inside or an outside scene.
Thirty minutes after completion of the test, subjects were given an unexpected memory
test and had to indicate whether a picture presented had been included in the original
picture list. By comparing the brain activation during the presentation of a picture in the
original test which the subjects later correctly identified as being included in the original
test, to the brain activation occurring when subjects were shown a picture in the original
test which they subsequently did not recall as being in the original picture list, the
experimenters were able to demonstrate that how well a picture was remembered was
related to the amount of activity in a particular bit of the brain. This suggests that the
likelihood of us correctly remembering a piece of information is related directly to how
well we absorb the information initially, or the strength of the encoding process.
In addition to remembering facts we learnt weeks or months previously, we sometimes
have to remember a number of different facts in a short space of time and apply them to
solving immediate problems. How do we juggle and retain these facts in our mind so that
we can remember them all at the appropriate time? Braver and colleagues 37 investigated
the neural basis for this type of memory using fMRI with a parametric design. In
essence, parametric experimental designs aim to identify brain regions where the pattern
of activation changes in accordance with the different demands of the task across several
levels of difficulty, thus increasing the likelihood that the brain regions identified are
specific to the cognitive processes involved in the task. The scientists used a sequential
letter task in which the difficulty of the task varied in an incremental manner, thereby in
15
effect generating a “dose-response” curve for the neural activity relative to task and
demonstrated that two specific regions in the front of the brain appeared important for
correct task performance.
Recognising faces and objects
Even for a given sense, such as vision, the patterns of brain activation can be highly
specific, because different parts of the brain work together in order to generate the
complexity of thought. For example, while the primary visual cortex in the back of the
brain is the most active region with simple visual stimulus such as a flashing
checkerboard, if we see a familiar face, a very specific area along the base of the back of
the brain in the so called fusiform gyrus becomes active 25. This is an area that appears
specialised for complex types of visual experience about which we have some form of
“expert” knowledge.
Andrews and colleagues provided a lovely illustration of this in a study of brain activity
during observation of the Rubin vase ambiguous character. This black and white figure
is perceived alternatively as either an old woman’s face or a vase. Using a special trick,
Andrews was able to study subjects whose attention had been arrested in one state or the
other perception. He found that when subjects viewed the same stimulus as a face, the
fusiform gyrus at the base of the brain in the back became active, but when they viewed it
as a vase, this activity disappeared- although the image had not itself changed! 38
16
A similar, exquisite functional specialisation holds in other areas of the brain. For
example, in areas of the lateral temporal lobe, on the lower, middle part of the brain, there
are regions specialised for processing of sound and different regions become active
depending on the precise nature of the sound being heard. Narain and colleagues have
found that meaningless or noise sound is perceived more towards the front of the socalled superior temporal sulcus, whereas meaningful sound interpreted as speech is heard
behind this area 39
Everything depends on context
Depending on the context, even the same stimulus can be perceived very differently.
Most of us have experienced the downside of gluttony, the glorious first taste of our
favorite pudding changing to the horrible sense of over-satiation when we have overindulged!
Edmund Rolls and his colleagues have studied just this phenomenon. They presented the
taste of bananas to subjects while they were being studied with fMRI. All of the subjects
found the taste pleasant and activated in an area along the middle of the lower part of the
front of the brain in the orbital frontal cortex. The experiment then was repeated after the
subjects had been asked to eat half a dozen bananas. But now when they had the banana
taste in their mouth, with full stomachs and the memory of bananas they had just eaten,
the taste was no longer pleasant and activated the orbital- frontal cortex in a different
way 40
17
This experiment also illustrates that fMRI can be used as a window on entirely
subjective, individual experience. One particularly exciting application for this is using
the techniques to tell us about the experience of pain. Tracey and colleagues have been
studying the way in which the brain processes pain. They have used a novel way of
testing in which they train subjects that when they see one particular colour of light, they
will experience a painful burning sensation. By studying the brain response that follows
the coloured lights separately from the brain responses following the painful stimuli,
Tracey’s group have been able to differentiate the distinct aspects of anticipation and
response to pain.
They found that the anticipation involves areas of the brain known to be important in
“alerting” responses. The pain response involves areas known to respond to touch.
However, what may be the most important is that activation of emotional centres of the
brain in the limbic system was found only with anticipation of pain or pain itself 26.
Also activated by pain is a small region deep in the lower part of the brain known as the
periaqueductal grey. This is a very primitive part of the brain, through which sensory
information from the body must pass to go to the brain. Although it is well known that
the perception of pain can be modulated by psychological factors such as stress, the
neural basis for this was poorly understood. Tracey and her colleagues aimed to define
the neuroanatomical location where attention could modulate the perception of pain.
During painful stimuli, subjects were asked either not to pay attention to the pain or to
focus on the pain. When the subjects were distracted, the response to pain in the
18
periaqueductal grey was changed 39. It appeared as though this area in the lower part of
the brain (the brainstem), between the spinal cord input and the cerebral cortex, was
gating the responses. With such experiments we can begin to build up a model circuit in
which functionally distant areas in the brain interact in order to produce full subjective
responses.
The modulation of our perception of pain therefore may explain common phenomena,
such as why when we are emotionally stressed that we feel pain much more. Similarly,
these experiments may help us to understand the mechanism behind reports of humans
claiming not to perceive pain from a real injury when their attention is focused on a more
immediate danger e.g., rescuing their child from a burning house. The definition of such
networks is important, because it is a first step towards designing ways of controlling
their responses. With a direct “readout” of the response of the brain to pain using fMRI,
it is possible to measure the effects of pain-relieving drugs and to develop new strategies
for pain relief.
Why do gamblers gamble?
FMRI also may cast a window into the neural mechanisms underlying certain
maladaptive behaviours. Most people at some point have placed a monetary bet on a
horse or on a game. If we won we might be tempted to place another bet, especially if
the odds looked favorable. However if we lost a lot of money we would be unlikely to
keep gambling for fear of losing even more. However some people are unable to stop,
and seem almost oblivious to consequences of the loss they are incurring.
19
O’Doherty and colleagues used fMRI to probe the neural basis of monetary reward and
punishment using a “gambling “experiment. Subjects were presented with two stimuli,
one that was associated with a high monetary reward and comparatively low monetary
punishment, and an alternative stimuli where the converse was true. Subjects had to use
trial and error to determine which stimulus was the more profitable to choose, to keep
track of the profitability and to reverse their choice of profitable stimuli when the
experimenters reversed the rewards and punishments associated with each type of stimuli.
The scientists found that particular areas of the brain were associated with reward and
punishment respectively and that there was a correlation between the magnitude of the
brain activation and the magnitude of the rewards and punishments received, thus
suggesting that these regions were involved in the representation of the size of rewards or
punishments. They concluded that damage to this region of the brain may lead to the
inability to represent the size losses or gains incurred and consequently result in difficulty
in judging the degree to which a particular stimulus choice is advantageous on the basis
of cumulative monetary gain 28.
Learning how we learn
One of the main advantages of fMRI is that as a non-invasive and safe technique, studies
can be performed again and again in the same individuals. This has allowed scientists to
monitor directly the ability of the brain to adapt with time 41. The reason that most of you
are currently reading this is article is that you are trying to learn. Learning involves
20
strengthening new connections in the brain to change the relationship between brain input
and output.
In studies, of how a motor skill is learned, for example, fMRI allows the way the brain
changes functionally over time to be followed, contributing to an understanding of which
brain circuits are involved in developing the skills 42. As one example, Heal and
colleagues (R. Heal, unpublished data) have been looking at what happens when people
begin to learn a complex button pressing sequence which is rather like learning to play a
simple piano tune. Initially, the sequence is unknown and subjects have to attend very
closely, remembering carefully each finger movement before going on to the next. This
creates large demands in the prefrontal cortex, part of which is involved in processing for
short-term memories, as well as in areas more traditionally recognised as important for
motor functions. A surprising observation has been that when subjects are trying to learn
something they use motor areas on both sides of the brain, while when they perform the
same button pressing task under conditions in which they are not required to be learning
the task, they activate a much smaller region, mostly in the hemisphere on the opposite
side of the brain to that in which the hand is moved. Thus, the regions of the brain that
becomes active for the same movement task depend upon the context in which the
movement has been performed (unpublished observations). It also may be relevant that
under the conditions of learning in which the movement is taxing, a greater amount of
brain becomes active. It is as if the effortfulness of the task is related to the amount of
brain involved.
21
Similar studies have demonstrated that increases in activation in the main motor areas
persist for several weeks after training is stopped 30. The authors suggest that the
repeated practise of a new task may lead to modifications in how the relevant brain
regions recruited in the task behave. One possible mechanism for this may include the
strengthening of links between different motor neurons as a consequence of repeated
correlated firing. Structural changes also may occur as a consequence of repeated
activity 30 and be mediated via a change in the strength of the synaptic connections 43
which may be associated with changes in the morphology of dendrites, the extensively
branching “arms” of neurons on which the synapses form 44.
Similarly, numerous fMRI experiments have demonstrated that repeated perception of a
stimulus may lead to functional reorganization in the area of the brain (the somatosensory
cortex) which detects sensations, e.g., touch to the hand. The representation of the digits
of the skilled hand is expanded in string musicians 45 and also in blind Braille readers in
comparison to the unskilled hand 46 in this region of the brain, suggesting use-dependent
reorganization.
Watching the brain heal itself
The experiments outlined above have suggested that cortical activity may be modulated
by learning and experience. Analogous to the changes in brain function that arise during
the learning of a complex skill in a healthy subject, could patients with damage (e.g.,
caused by a stroke) to a part of a neural network used to perform a specific task regain the
lost function by recruiting a “new” area of the brain to take over? Patients who have had
22
an acute stroke have been shown to make spontaneous recovery of function beyond the
period where healing of the damaged tissue appears to have reached a plateau 47. This
suggests that mechanisms other than resolution of the initial injury may contribute to
recovery. If such changes could be defined with fMRI, then results could be used to
harness the mechanisms to maximise recovery after injury, e.g., with rehabilitative
physiotherapy.
A number of fMRI experiments have now provided exciting evidence that the brain may
be capable of functional re-organization after injury. Similar to the way that healthy
subjects learn a complex task, a number of studies have shown that subjects who have
recovered successfully after a stroke show increased activity in motor areas on the same
side as the affected hand, rather than just in the opposite side of the brain normally used
to move the hand (Fig. 3) 48, 49, 50. This suggests that parallel motor pathways from both
sides of the brain, normally kept relatively inhibited in the healthy individual, are
activated to compensate for injury to one of them.
Figure 3. The pattern of brain activation associated with a simple hand movement
changes after a stroke causing arm weakness. FMRI activity with movement of the
affected hand shows recruitment of both sides of the brain as an adaptive response to the
injury, rather than primarily just the side of the brain opposite the hand moved, as with
the unaffected hand (Image courtesy of Ms. H. Johansen-Berg).
23
Subtle changes in the location of brain activity observed in the motor areas controlling
the affected hand also have been reported. As one example, Pineiro and colleagues
studied the movement of the right hand in right-handed healthy controls and also in
patients who had suffered a stroke affecting the function of that hand. In addition to
showing a greater relative increase in the extent of the activation in the motor area on the
same side as the affected hand, they also noted that the centre of activation in the main
motor area opposite to the side of the affected hand in patients was shifted posteriorly
relative to that of control subjects, thus confirming altered patterns of activation in both
sides of the brain 31. This suggests that modulation of widely distributed parts of the
cortical network for motor control are associated with changes in the pattern of brain
activation that may contribute to functional recovery after acute injury.
24
We have found similar results in patients with multiple sclerosis, a chronic neurological
disease with superimposed acute exacerbations, radiologically characterised by the
presence of multiple widely distributed and predominantly white matter lesions on MRI.
FMRI can be combined with more conventional MRI approaches to provide simultaneous
information on both structural and functional pathology. Studies have shown that
patterns of brain function are altered by the lesions and that the degree to which cortical
activation is altered is correlated with the severity of the underlying brain injury 51.
“Recruitment” of new brain areas may be adaptive and able to limit the progression of
manifestations of disease 52.
Providing compelling evidence for this remains challenging. A critical question is
whether the altered patterns of activation in patients truly reflect an adaptive response
that contributes to the recovery process, or are only a bystander reaction of a damaged
brain? To address this problem, the ability to use fMRI to perform serial examinations
can be exploited to relate dynamic changes in altered patterns of fMRI activation both to
the resolution of brain injury and to the extent of functional recovery. For example,
Reddy and colleagues serially examined a patient with multiple sclerosis during an acute
clinical exacerbation causing impaired hand function, which occurred secondary to a new
large lesion in the brain. In addition to serial clinical assessment and fMRI measures of
changes in cortical activation, the severity of the damage in the brain was quantified (by
measuring the amount of the chemical, N-acetyl-aspartate, a marker of axonal injury). It
was demonstrated that functional recovery from disability preceded the recovery of
axonal function (the amount of N-acetyl-aspartate) around the lesion. However,
25
functional recovery was associated with early increased recruitment of motor cortex (on
the same side as the affected hand), consistent with the hypothesis that parallel neural
pathways may become unmasked during injury to take over task performance and thus
enable functional recovery 53.
The clinical potential of fMRI as a tool to evaluate the effects of clinical interventions
also has been explored. Rehabilitation initiated relatively long after the occurrence of a
stroke has still been shown to produce functional gains, further supporting the concept
that compensatory reorganization can occur in response to injury and that this may be
enhanced by a focused physiotherapy approach. Johansen-Berg and colleagues have
been trying to explore the neural basis underlying functional improvement during a
focused physiotherapy programme by relating changes in fMRI cortical activity to
changes in behaviour. Specifically, Johansen-Berg et al tried to test whether
enhancement of activity in the “premotor” area (a region situated directly in front of the
primary motor cortex) is responsible for the improvements that come with successful
rehabilitation therapy. She and her colleagues took a small group of patients who had
suffered from stroke and gave them an intensive 2-week period of hand training. This
involved restraint of the unaffected arm to force the subjects to use the affected hand, as
well as a graded series of exercises for the affected hand. After the therapy period ended,
some subjects showed significant improvements in their ability to use the previously
impaired hand. Correlated with the degree of improvement, was increased activity in the
so-called premotor cortex on both sides of the brain (Fig. 4).
26
Figure 4. Rehabilitation after a stroke can improve function by enhancing activation of
the brain in intact regions. For this study, patients with weakness on one side were
studied before and after a two week period of intensive rehabilitation. After the
rehabilitation period, in patients whose arm function improved there was an associated
increase in brain activation with movement- a “learned” recruitment of additional areas.
This was not found with patients who did not show functional improvements, confirming
that the brain changes are related directly with the behavioural improvements (Image
courtesy of Ms. H. Johansen-Berg).
To confirm the functional significance of this special area, Johansen-Berg then
investigated what would happen if the newly recruited pre-motor regions were transiently
inactivated. Of course, it is not possible to take out an area of the brain just to test its
27
importance. However, magnetic fields can be used in another exciting way. If a small
electrical coil is placed over the brain and a brief pulse of electricity pushed through it, a
local magnetic field is generated for what is known as transcranial magnetic stimulation
(TMS). This magnetic field can transiently (over periods of fractions of a second only)
interfere with the function of nerves in the underlying cortex. By using special
techniques, the position of the TMS coil could be correlated with the underlying brain
anatomy as determined from the MRI scanning and the areas of activation known from
the fMRI experiment. The scientists showed that the pre-motor areas in the unaffected
side of the brain become particularly important to functions of the impaired hand in
patients after injury. The degree to which they become important seems related directly
to the extent of the activation seen in the fMRI experiment 50
Helping to guide the surgeon’s knife
Currently surgery is often used in the management of patients with brain tumours or
pharmacological-resistant epileptic foci. However, there is a trade-off between the
margin of excision used to ensure complete removal and the potential loss of function
that may arise as a consequence of removing normal surrounding brain tissue. There are
several invasive approaches used in neurosurgery to define eloquent areas of the cortex
prior to surgical excision. One approach is to perform electrophysiological mapping of
the cortex in the awake patient at the time of the operation. A second approach routinely
used prior to surgery for epilepsy is the Wada test, where the predominant side of the
brain used for language and memory is identified by the invasive sequential injection of
sodium amytal (which transiently stops the brain from working) into each of the two
28
main blood vessels supplying different sides of the brain, during standard
neuropsychological testing.
An alternative non-invasive approach is to use fMRI to localise activations associated
with important tasks such as limb movement or speech production. A number of groups
have now reported the effectiveness of fMRI in correctly identifying the localisation of
the main motor strip or language area pre-operatively in patients with lesions near these
eloquent regions 33, 54, 55. The cost of performing such an fMRI study is at least three
times cheaper than admitting a patient into hospital for preoperative assessment using the
Wada test. Further refinement of these techniques therefore may lead to the
discontinuation of the former expensive and invasive approaches.
Seeing disease before symptoms
The role of fMRI as a diagnostic tool for neurological conditions also has been explored.
One such example is Alzheimer’s disease, a progressive neurodegenerative condition that
is characterised clinically by progressive memory loss thought to arise secondary to
pathological change in the brain regions important for memory, such as the temporal and
frontal lobe 56. A number of fMRI studies have shown differing patterns of activation in
the brains of patients with Alzheimer’s disease relative to healthy age-matched controls,
consistent with localised pathological dysfunction 57.
However, the insidious onset of the memory disturbance often leads to a delay in the
diagnosis of the disease. Similarly, the characteristic pathological changes found at post-
29
mortem have been detected before the onset of symptoms. This delay in the diagnosis of
the disease may be important if there are therapeutic agents that can slow the progression
of the disease. Bookheimer and colleagues used fMRI to examine whether this technique
could be used as an early diagnostic tool and predict subsequent memory decline in
patients genetically at risk for the disease 34. Specifically, the experimenters wanted to
see firstly, if the pattern of brain activation in patients at risk for Alzheimer’s disease was
different to those not at risk when doing a memory task and secondly to determine
whether the baseline brain activation pattern was correlated with subsequent memory
decline.
The scientists compared the patterns of brain activation in genetically at-risk individuals
to those of healthy controls when performing a memory test. Although none of the
subjects at-risk for Alzheimer’s disease scored abnormally on standard tests of memory
performed outside of the scanner, Bookheimer et al found that the magnitude and extent
of brain activation in the regions of the brain commonly affected by Alzheimer’s disease
were greater amongst the genetically at-risk group. This suggested that increased brain
activation in these brain areas may be an early marker for late-onset disease. To test this
hypothesis, the experimenters reexamined the subjects 2 years later and found that the
magnitude of the baseline brain activation correlated with the degree of decline in
memory over the following two years. The authors interpreted the baseline differences in
activation as reflecting the recruitment of extra neural and cognitive processes to
compensate for the decline in function of the necessary neural network normally used for
the memory task.
30
If the differences in observed brain activation are true markers of pre-symptomatic
disease, then fMRI may provide a sensitive index of sub-clinical neural dysfunction and
allow the identification and monitoring of asymptomatic “disease progression”. FMRI
also may prove to be a sensitive tool for assessing the impact of preventative and
therapeutic interventions.
Defining disease based on patterns of brain function
FMRI could be used to define the range of functional characteristics or “phenotypes” of
patients with a clinical syndrome. Defining such phenotypes is often a first step in
moving from descriptions of syndromes to specific diseases (i.e., entities with a
specifically defined cause and progression). This may be especially relevant in diseases
such as dyslexia, which are difficult to classify on the basis of clinical assessment and for
which the conventional tests may not have identified abnormalities. In addition to
providing a sensitive marker of the functional trait of the disease, fMRI studies may
provide further insights into the pathophysiology of the disease process.
A number of observations using fMRI have supported the notion that patients with
dyslexia may have abnormalities in a specific part of their visual system which leads to
deficits in the processing of moving stimuli. Eden et al 58 presented a moving stimulus to
control subjects and patients with dyslexia and demonstrated that in the patients, this
stimulus did not activate a particular region of the “motion-sensitive” visual cortex which
receives specific inputs from the magnocellular pathway, whereas the presentation of
31
stationary stimuli did not result in any abnormal pattern of brain activation. Subsequent
studies have extended this observation and suggest that deficits in the integrity of the
magnocellular pathway could be used as an objective clinical marker of the disorder 59.
The functional definition of sub-groups of patients should facilitate the understanding of
the disorder at the molecular and genetic level.
Conclusions
Together these studies illustrate how magnetism has been used in exciting ways to learn
about both the structure and the function of the brain. With the exquisite detail provided
by modern MRI techniques, we are beginning to learn how brain structure varies between
individuals and how it is related to different behaviours. With fMRI it is possible to link
information about brain function directly to information about brain structure to begin to
understand the brain basis of mind. Not only should this lead to new insights into
fundamental problems such as the nature of consciousness, but it also points the way
towards developing new strategies for understanding and treating disease.
Acknowledgements
The authors wish to thank their many colleagues and collaborators in the Oxford Centre
for Functional Magnetic Resonance Imaging of the Brain for continuing to freely share
information and excitement in this new area for cognitive neuroscience. They gratefully
acknowledge support for work in the Oxford Centre particularly from the Medical
Research Council and the MS Society of Great Britain and Northern Ireland.
32
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