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APPLIED NEUROPSYCHOLOGY, 16: 295–306, 2009
Copyright # Taylor & Francis Group, LLC
ISSN: 0908-4282 print=1532-4826 online
DOI: 10.1080/09084280903297891
Downloaded By: [Canadian Research Knowledge Network] At: 16:32 10 October 2010
Utilizing Virtual Reality to Improve the Ecological Validity
of Clinical Neuropsychology: An fMRI Case Study
Elucidating the Neural Basis of Planning by Comparing
the Tower of London with a Three-Dimensional
Navigation Task
Zachariah Campbell, Konstantine K. Zakzanis, Diana Jovanovski, and Steve Joordens
Department of Psychology, University of Toronto Scarborough, Toronto, Canada
Richard Mraz
Information Technology, Sunnybrook Health Sciences Center, Toronto, Canada
Simon J. Graham
Imaging Research, Sunnybrook Health Sciences Center, Rotman Research Institute,
Baycrest Center for Geriatric Care, Department of Medical Biophysics, University of
Toronto, and Heart and Stroke Foundation Centre for Stroke Recovery, Toronto, Canada
Virtual reality (VR) was used to create an ecologically valid spatial-navigation task in
hand with functional magnetic resonance imaging (fMRI) to articulate the neural basis
of planning behavior. A virtual version of a traditional planning measure, the Tower of
London, was also developed to ascertain convergent and divergent validity in terms of
planning behavior and functional neuroanatomy. This VR-fMRI case study experiment
was performed at 3.0 Tesla on a young healthy male subject. The obtained image data
suggest both convergent and divergent specificity between the two conditions in terms of
location and overall intensity of activation. Overall, the present case study provides
supportive evidence that the activity of various brain regions associated with planning
tasks is largely modulated by the ecological validity of the measure being used. This
finding may extend to all domains of inquiry in neuropsychological research and assessment when deductive conclusions are formulated on the results of neuropsychological
test measures that could be considered contrived in nature.
Key words:
executive function, functional MRI, neuropsychology, planning, virtual reality
During the past century, our understanding of the
relationship between brain and behavior has been
elucidated in a monumental way. Early researchers
profited from the lesion deficit model and attempted to
Address correspondence to Konstantine K. Zakzanis, Department
of Psychology, University of Toronto Scarborough, 1265 Military
Trail, Toronto, ON, M1C 1A4, Canada. E-mail: zakzanis@
utsc.utronto.ca
define the parameters of associations observed between
a lesion and its behavioral sequelae. This method
was crude at best, however, as it was very difficult to
ascribe a function to a particular region that had been
damaged. The most obvious reason is that pathological,
as opposed to experimental, lesions seldom conform
to functionally homogenous neuroanatomical systems.
Fortunately, with the advent of structural and functional
Downloaded By: [Canadian Research Knowledge Network] At: 16:32 10 October 2010
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CAMPBELL ET AL.
neuroimaging techniques, research has accumulated
where neuropsychological processes have been correlated to specific neural regions with reliable certainty.
Indeed, the field of neuropsychology has evolved in such
a manner where a growing number of researchers are
combining several forms of technology (e.g., electroencephalography [EEG], magnetoencephalography
[MEG], single photon emission tomography [SPECT],
positron emission tomography [PET], functional
magnetic resonance imaging [fMRI], etc.) to better define
the neuroanatomical architecture that underlies higher
cognition in humans (e.g., Benson et al., 1999; Courtney,
Ungerleider, Keil, & Haxby, 1997; Wagner et al., 1998).
One aspect of cognition that has received a considerable amount of attention is that of ‘‘executive functioning.’’ This neuropsychological domain encompasses a
number of specific processes, one of which is planning
ability (Lezak, Howieson, & Loring, 2004; Miller &
Cummings, 1998). The ability to plan is of critical concern given that it encompasses motivation, purposive
action, and self-regulation, and hence, it is no surprise
it has lent itself to scientific experimentation as it acts
as a bottom-up process (see Stuss & Benson, 1986).
In general, planning entails the act of comparing and
properly organizing a number of alternative steps of
action for the benefit of achieving a primary goal.
In clinical and research terms, the majority of
neuropsychological test measures employed to articulate
planning behavior are primarily based on either one of
two designs. The first, are maze tests that are essentially
pencil-and-paper tasks that require subjects to navigate
through two-dimensional allocentric mazes. In order
to reduce errors on this task, one must look ahead in
an abstract fashion and plan a proper route. A common
example of such a task is the Porteus Maze Test
(Porteus, 1959). The second type of measure often
employed to index planning behavior includes
tower-type tests such as the Tower of Hanoi (Simon,
1975), Tower of London (Shallice, 1982), and Tower
of Toronto (Saint-Cyr, Taylor, & Lang, 1988) which
all index planning ability using a simple tabletop
three-dimensional apparatus. These measures all consist
of several pegs that hold a number of items (e.g., beads
or rings) in a particular array. An examiner presents the
subject with a stimulus card of a desired pattern, and it
is the responsibility of the subject to achieve that particular design as efficiently as possible (i.e., in the least
number of moves). These measures require subjects to
formulate a series of actions in their minds that will lead
them to the goal state. In the case of the Tower of
London, each successive trial requires a higher number
of minimum moves to achieve the goal state. Moreover,
an early response that immediately fulfills one portion of
the design may actually inhibit progression to the final
pattern. That is, subjects are initially required to make
non-obvious responses (e.g., moving a bead to a peg that
is not depicted as being affixed to the stimulus card) in
order to complete some of the more difficult trials
(i.e., four- and five-move problems) in a minimum
number of moves. The act of mentally testing and selecting all possible alternative action sequences, prior to
physically engaging in the task, is considered to be the
essence of planning and thus necessary for exemplary
performance on these tests.
To date, the research literature has shown that the
sensitivity and specificity of the Tower of London is limited. For instance, when the Tower of London was first
introduced, Shallice (1982) reported it was effective in
differentiating frontal lobe patients from controls, but
the results were inconclusive when patients with anterior
and posterior lesions were compared. Many studies have
since been conducted and have shown conflicting
findings (e.g., Andres & Van der Linden, 2001; Colvin,
Dunbar, & Grafman, 2001), although a general consensus suggests that performance on the Tower of London
Test is largely dependent upon frontal lobe functioning
(e.g., Lazeron et al., 2000; van den Heuvel et al.,
2003). This consensus is largely supported by those studies that employed functional neuroimaging techniques
and less so by those studies employing patients with circumscribed brain lesions. That is, some researchers have
shown that frontal lobe damage is a good predictor of
Tower of London performance (e.g., Colvin et al.,
2001; Owen, Downes, Sahakian, Polkey, & Robbins,
1990; Owen, Sahakian, Semple, Polkey, & Robbins,
1995). In addition, the performance of patients with
fronto-temporal dementia and patients with circumscribed frontal lesions have also been shown to be
similar as both types of patients perform significantly
worse than matched controls (Carlin et al., 2000). On
the other hand, several recent studies have actually
failed to discriminate between patients with strict frontal
lesions and controls using this test (Andres & Van der
Linden, 2001; Cockburn, 1995).
With regard to studies that employ functional neuroimaging techniques, the data reported are generally
more consistent in terms of implicating frontal regions
as well as various other important regions (e.g., Lazeron
et al., 2000; van den Heuvel et al., 2003). That is, early
research of this kind combined computerized versions
of the Tower of London with SPECT or PET to
illuminate the areas of neural activation purportedly
associated with planning (Baker et al., 1996; Morris,
Ahmed, Syed & Toone, 1993; Owen, Doyon, Petrides,
& Evans, 1996; Rezai et al., 1993). For example, Rezai
et al. combined SPECT with the Tower of London
and discovered that it activated the frontal cortex bilaterally. In another study, Morris et al. also used SPECT
and found Tower of London performance to activate
two particular areas in the left hemisphere, namely, the
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THE NEURAL BASIS OF PLANNING
dorsolateral prefrontal cortex (DLPFC) and superior
prefrontal cortex. Owen et al. (1996) used PET and
found left mid-dorsolateral frontal activation. More
recently, Dagher, Owen, Boecker, and Brooks (1999)
used PET and confirmed the common finding of
DLPFC activation and noted other areas of activation
such as lateral premotor, anterior cingulate, and caudate
regions. With regard to fMRI, a recent study found that
the DLPFC, the anterior part of the cingulate cortex,
the cuneus and precuneus, the supramarginal and
angular gyrus in the parietal lobe, and the frontal opercular area of the insula as regions that were activated
during Tower of London planning performance
(Lazeron et al.). In another fMRI study, similar areas
of activation were also noted (van den Heuvel et al.,
2003). Cazalis et al. (2003) went so far as to compare
superior and standard performers using fMRI. They
found the same network of activation, but higher activation in the DLPFC for superior performers and higher
activation in the anterior cingulate for standard performers. Hence, studies employing functional neuroimaging
appear to implicate various regions within the prefrontal
cortex, most notably the DLPFC, as well as other key
brain regions. Despite some evidence of reliability, however, it remains unclear as to exactly what neuroanatomical regions are consistently implicated within and beyond
the frontal lobes during planning. This may be largely due
to issues surrounding the limited spatial resolution of
SPECT- and PET-based imaging, the insufficient use of
more precise instrumentation (e.g., fMRI or MEG), and
finally any methodological differences that may exist
between studies.
In addition, and more poignantly, standardized
neuropsychological measures such as the Tower of
London are considered far removed, albeit by necessity
of experimental control, from the everyday tasks for
which they attempt to predict performance. That is,
how does a patient’s ability to place ringlets over towers
to achieve a desired pattern relate to successful planning
that one engages in within the real world? Indeed, it is
evident that performance on a contrived paper-andpencil test does not express law-like behavior that
transcends into real-world experience (Zakzanis,
Graham, Campbell, & Mraz, 2004). For this reason,
the utility of traditional neuropsychological measures
has been questioned in terms of their ecological validity.
Ecological Validity and the Use of Virtual Reality
A common problem for a great majority of neuropsychological measures is that they lack the attribute of
ecological validity. This term refers to how well a given
test reflects the tasks we perform in the ‘‘real’’ world.
More formally, the concept of ecological validity, as
currently defined, implies that there is a functional and
297
predictive relationship between the patient’s performance
on a set of neuropsychological tests and the patient’s
behavior in a variety of real-world settings (Burgess,
Alderman, Evans, Emslie, & Wilson, 1998). Indeed,
there have been recent attempts to improve the ecological validity of neuropsychological measures, specifically
within the domain of executive functioning (e.g., Behavioral Assessment of the Dysexecutive Syndrome
[BADS]; Wilson, Alderman, Burgess, Emslie, & Evans,
1996). The impetus for the development of the BADS
was underscored by reports that, although patients with
frontal lobe impairment can present as unimpaired on
traditional measures of executive function, they often
tend to fare quite poorly on real-world executive tasks
at the same time (e.g., Alderman, Burgess, Knight, &
Henman, 2003; Shallice & Burgess, 1991). Although,
there has been some reported success with test measures
of executive functioning and their predictive validity in
the real world (Burgess et al., 1998).
There are also problems with the degree of association
across measures of executive functioning. For example,
the Tower of London has not been found to reasonably
correlate with other executive measures that purportedly
measure planning function as well (e.g., Kafer & Hunter,
1997; Krikorian, Bartok, & Gay, 1994). Thus, it would seem
appropriate to continue to design new measures of planning
that better reflect this particular ability in more ecologically valid terms. Moreover, it would be important to
develop an ecologically valid measure of planning whilst
elucidating its neuroanatomical underpinning so to
attempt to understand its clinical specificity.
Accordingly, given the established and successful use
of basic computerized environments, it seems logical to
take a virtual step forward and consider immersive
virtual reality (VR) as a tool to improve the ecological
validity of neuropsychological measures. Indeed,
Maguire et al. (1998) created a virtual environment in
which subjects were required to navigate between different destinations in a city corridor with the initial aid of
an iconic map. That is, subjects would be initially shown
their goal destination on a two-dimensional map, after
which they would attempt to successfully navigate this
route while maintaining the map image in memory. The
authors of the experiment were explicitly interested in
the neural substrate of accurate navigation ability using
fMRI. The task that conceivably had the largest planning
component consisted of placing random roadblocks in
the subject’s path to increase the difficulty level of reaching the destination. In their results, the authors reported
in detail which areas were associated with accurate navigation. However, since planning was not the focus of their
investigation, they merely mentioned that the trials containing roadblocks were associated with a variety of left
frontal activations. They attributed this pattern of activation to the ‘‘non-spatial components of navigation.’’
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CAMPBELL ET AL.
Accordingly, in keeping with the research described
above, a logical extension would be to create a
novel experiment that would combine two different
approaches to the assessment of planning while attempting to elucidate the underlying neural substrate. Specifically, it was of interest to compare performance on the
Tower of London with performance on an ecologically
valid planning task to elucidate whether there is a
common neuroanatomic substrate for planning, regardless of the task employed, and the degree of convergent
and divergent specificity. It was also of interest to
determine whether the commonalities in neuroactivation
between these tasks support the notion that the Tower
of London task, as a clinical measure, is at least
somewhat predictive of everyday planning ability.
Accordingly, the purpose of the present study was to
address the following questions:
1. In terms of neuroactivation patterns, how does a
traditional neuropsychological test measure of
planning (e.g., Tower of London) compare in
terms of convergent and divergent specificity to
a novel and more ecologically valid task of
planning?
2. What implications do the obtained findings have
for both the experimental and clinical neuropsychologist?
With these considerations in mind, a VR city was
developed that incorporated a novel roadblock task.
In addition, a VR analog of the Tower of London task
was developed for comparative purposes. Using a case
study design, these environments and their associated
procedures were ultimately combined with fMRI to
compare and contrast the patterns of neuroactivation
that each task was to elicit.
METHOD
Participant
This study used data from one healthy, 21-year-old,
right-handed male. Handedness was determined by
The Handedness Inventory (Briggs & Nebes, 1975). The
subject was an undergraduate student with 15 years of
education. He denied having any personal history of
medical problems such as serious illnesses, surgeries,
or injuries. He specifically denied having any history
of psychiatric disorder, neurological disorder, previous
head injuries, or losses of consciousness. He also
reported that he was a nonsmoker with no history of
substance abuse or dependence and that he was not
taking any medication at the time of this study. In terms
of MRI compatibility, this subject passed a screening
protocol that ruled out any unsafe hazards. The ethical
review board at Sunnybrook and Women’s College
Health Sciences Center approved the study, and the
participant provided informed consent prior to beginning the experiment. Finally, the participant was
provided with monetary compensation (i.e., $50) for
his involvement in the present study.
Materials
Before experimentation, a VR city was designed within
Sense8’s three-dimensional simulation platform called
WorldUp (Release 5). All of the city buildings and
worldly fixtures were built from Discreet’s threedimensional modeling software called 3DMax. In addition, Adobe Photoshop (version 7) was used to create
the graphics and textures added to the geometric models
created in the 3DMax application. In its final form, the
VR city was composed of numerous city blocks and
corridors. The main features of this city were a variety
of buildings, which included office, commercial, and
residential structures. An aerial view of the city, which
illustrates its complexity and overall size, is depicted in
Figure 1. In addition, an attempt was made to increase
the realistic nature of the virtual environment. This
was accomplished by including a number of ‘‘presence’’
adding features such as sky, sidewalks, roads, streetlights, parking lots, cars, park space, trees, grass,
garbage bins, building signs, bus stops, and newspaper
stands. The term ‘‘presence’’ refers to the subjective
experience of actually being immersed in a virtual
environment by way of the similarities it shares with real
environments. After the city was completely constructed, computer programming for the learning and
assessment trials was designed, tested, and implemented.
It is also important to note that models of roadblocks
were created and appropriately programmed for their
future placement during the fMRI testing trial. To this
end, eight roadblocks were designed and placed strategically throughout the city. They were placed in locations
thought to elicit the highest amount of effort when
planning an alternative route (i.e., correct decisions on
which alternative path to choose were not obvious to
the subject). Four roadblocks were allocated to each
of the two routes that were randomly designated as
either permanent or temporary. Thus, there were two
permanent and two temporary roadblocks per route.
Images of the roadblock’s overall form and the alternative signs are depicted in Figures 2a and 2b, respectively.
In addition to the design of a VR city, a virtual,
three-dimensional Tower of London task was developed
to allow brain activity to be measured during performance on a traditional test of planning. This was accomplished using the same software and workstation as
mentioned above. This virtual Tower of London was
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THE NEURAL BASIS OF PLANNING
299
FIGURE 1 An aerial view of the entire virtual city with landmark labels.
ultimately designed to represent closely the actual
administration of the original test (i.e., as it would be
given in a clinical setting; see Figure 3).
The learning protocol for the roadblock-navigation
task took place by means of a VR interface. This was
set up on a state-of-the-art ZX-10 Intergraph Computer
Workstation with a 21-inch flat screen Trinitron
monitor. A joystick was used as the input device for this
platform. The participant initially navigated within the
VR city (to be described in detail within the procedures
section) via a joystick interface while watching his
first-person movements on a monitor.
Finally, the testing component of this study (i.e.,
within the fMRI magnet) was divided into the
roadblock-navigation task and the virtual Tower of
London task. For the roadblock portion (i.e., within
the fMRI magnet), the same computer setup was used
to run the software; however, a number of magnetic resonance (MR)-compatible devices were also employed.
This included an Avotec SV4021 head-mounted display
(MR Compatible, 832 ! 624 SVGA, 60 Hz) which was
used to present the visual scene to the subject while in
the magnet. In addition, an MR-compatible joystick
was also employed to allow the user to interact within
the VR city while in the magnet. Finally, for the virtual
Tower of London task, the same set up as mentioned
above was employed; however, a LUMItouch device
was used instead of a joystick to act as the response
interface for this task. This particular piece of equipment
consists of two paddles (one for each hand) with two
buttons on each paddle and was employed to make the
response interface more intuitive during the virtual
Tower of London task.
Behavioral Procedures
The subject underwent a series of three training sessions,
ultimately followed by one testing session that took
place within an fMRI scanner. All of the three learning
trials and the testing session were separated from one
another by one day (i.e., Monday, Wednesday, Friday,
and Sunday). Prior to engaging in the first learning
session, the subject was presented with a hypothetical
scenario on which the task was based (please note that
complete copies of the instructional sets used in this
study are available from the authors upon request).
In brief, the subject was told that he had obtained a
new job in a new location and had to become familiar
with his new city of residence. He was instructed that
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300
CAMPBELL ET AL.
FIGURE 2 The components of the roadblock models. (a) A perspective view of the overall roadblock formation, and (b) images of the permanent
and temporary roadblock signs.
his task during the training sessions was to learn two
particular routes that he would soon have to travel when
he started his new job. The first route consisted of the
subject navigating from his fictitious home to a day care
center (e.g., to drop off his child or sibling, etc.) and
then on to his final destination which was his workplace.
The second route consisted of the subject navigating
from his workplace to a supermarket (e.g., for milk or
bread) and then on to his final destination which was
his home for this path. Thus, the subject learned an
extensive route through the city that brought him full
circle.
The first learning session began with the computer
program automatically showing the first path to the
subject. That is, the program demonstrated the exact
route that was to be navigated. The subject was then
instructed to re-navigate the route that was just shown
to him using the joystick interface. If the subject was
observed to make a substantial error (i.e., turning down
the wrong street or missing a turn and traveling too far
(i.e., 10 virtual meters), that particular trial was terminated and the path was re-demonstrated by the computer once more. As long as the subject did not deviate
significantly from the designated path during a trial
run, the computer did not re-demonstrate the route.
Once the subject was able to navigate the first path
accurately (without significant error) three times, he
moved on to learning the second path in the exact same
fashion. Finally, once the subject had fulfilled the learning criteria for path two, he moved on to the final phase
of the training session, which was a familiarity task.
The subject was told that he was to search out and
explore the remainder of the city. He was also told that
once he had navigated through an unspecified but
sufficient amount of the city, the program would
self-terminate. This was achieved by employing invisible
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THE NEURAL BASIS OF PLANNING
FIGURE 3 The virtual Tower of London task’s environment.
checkpoints throughout the city within a 4 ! 4 grid
structure. Thus, the subject had to travel through the
16 subdivided portions of the city before the trial ended.
The purpose of the familiarity trial was to serve as a
natural means of accustoming the subject to the environment and streets surrounding his learning path much
like one would normally do in everyday life. During this
trial, the subject had access to an overhead map (by
pressing the ‘‘m’’ key on a keyboard) to accelerate his
level of familiarization. This is an ecologically valid
feature of the learning task as it is analogous to pulling
out a paper map while navigating in the real world. This
map was identical to the one depicted in Figure 1.
Overall, the familiarity trial is necessary so that the
individual has some implicit knowledge of the entire city
from which to draw on during the roadblock-navigation
component of the testing phase.
In the two subsequent training sessions, all of the
learning sequences were the same as described above
except the subject was not shown the route initially.
Here it was assumed that the subject had retained
knowledge of the path since his previous training
session. Moreover, we were attempting to reduce the
frequency with which the path was automatically
demonstrated, as it was not considered the most ecologically valid feature. Although, one could equate this
activity to a friend guiding someone along a new route,
albeit in an extremely controlled fashion. For the testing
component (i.e., within the magnetic bore), the subject
was initially told that he would be engaging in the same
spatial-navigation task that he had been exposed to all
week. As such, he was naı̈ve to the roadblock task that
was going to be implemented in keeping with the goal of
achieving the highest level of ecological validity. That is,
roadblocks tend to occur without any explicit forewarning. He was also naı̈ve to the fact that he would be
301
completing the virtual Tower of London task as well.
Thus, at the beginning of the testing trial, the subject
was instructed to navigate accurately each of the routes
that he had previously navigated during the learning
trials. Following the successful navigation of each route,
the subject was administered a novel set of instructions.
He was essentially told that while navigating along his
regular routes in the coming trials, he might encounter
a construction roadblock, which will obstruct his path.
The subject was also informed that the roadblock would
appear as an arrangement of construction pylons with a
pocket in the center (see Figure 2a). He was instructed to
approach the roadblock and enter the pocket at which
time he would become immobile, and one of two
construction signs would appear. The sign would either
indicate that it was a permanent roadblock or a temporary roadblock (see Figure 2b). If a permanent
roadblock, he was instructed to spend the next 15–20
seconds planning the most efficient alternative route,
which could only involve traveling on roads or sidewalks. He was also told that after approximately
15–20 seconds, the roadblock sign would disappear
(but not the roadblock itself), at which time he would
become free to move from his immobilized state. He
was then permitted to re-navigate according to the route
he had supposedly planned during the delay. If the subject was presented with a temporary roadblock, he was
instructed to rest and not think about anything in particular. He was also told that after 15–20 seconds, the
roadblock sign and complete roadblock model would
disappear, and he would be permitted to continue along
his original route as usual. In sum, it is within these
windows of planning (i.e., stimulus condition) and
non-planning (i.e., baseline condition) that are of utmost
interest to the present study. That is, the difference in
neuroactivation patterns between these two states were
interpreted as integral to planning performance, at least
within this context.
The final component of the testing phase consisted of
the VR version of the Tower of London task. To reiterate the nature of this task, subjects are required to
arrange the balls on a grid of three pegs (of varying
height) according to some goal state that is indicated
on a stimulus card (see Figure 3). The subject must do
so by moving only one ball at a time to a valid location
(i.e., where space is permitted) and achieving the goal
design in the least number of moves possible.
Immediately prior to the administration of the virtual
Tower of London task, the subject was instructed on
how to perform this task and how to respond via the
LUMItouch paddles. The four buttons across the two
paddles were used as selecting devices. The first three
buttons (from the left side) were used to select the
top-most ball in the three respective positions of
the Tower of London pegs. When a ball was selected,
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302
CAMPBELL ET AL.
it became illuminated to indicate it had been chosen.
The next tap of the first three buttons indicated the
destination of that ball. If the chosen destination was
invalid (e.g., you could not fit another ball on a particular peg), the item was deselected. Otherwise, if the
chosen location was valid, the movement of the ball to
that location was demonstrated to the subject. To allow
for maximum comparability to the roadblocknavigation paradigm, the nature of the virtual Tower
of London task was slightly modified to tease out the
planning component of the subject’s performance. That
is, prior to engaging in a response to the problem (i.e.,
making a physical move), the subject was instructed to
spend at least 15–20 seconds planning his entire
sequence of moves. The subject was also told on half
the trials administered that he should spend 15–20
seconds not planning anything despite observing the
same visual display. As in the roadblock-navigation
paradigm previously described, these two conditions
of planning and non-planning were contrasted with
one another to isolate planning performance. In sum,
the subtraction procedures for both the roadblocknavigation task and the virtual Tower of London task
were methodologically analogous.
Functional MRI Procedures
This fMRI experiment was conducted on a whole-body
3.0 T MRI system (Signa Eclipse, GE Medical Systems)
using a standard quadrature, bird-cage head coil. Functional images were obtained using a single-shot spiral
acquisition sequence with offline gridding and reconstruction, as well as magnetic field homogeneity correction. Twenty-six slices (5 mm thick) were acquired
during each acquisition period (FOV ¼ 20, TE=TR=h ¼
30=2,000=70).1 A series of high-resolution anatomical
images was also acquired with a fast spoiled gradient
echo sequence (FOV ¼ 22, TE=TR=h ¼ 4.2=10.1=15)
prior to the functional scans, to serve as the anatomical
underlay for the maps of brain activity.
Data Analysis
Activation maps were calculated using Analysis of
Functional NeuroImages freeware (AFNI; Cox, 1996).
For each task, the first five time points in all the time
series data were discarded to eliminate the fMRI signal
decay associated with magnetization while reaching a
state of equilibrium. All remaining fMRI data were
co-registered to the first remaining time sample to correct for the confounding effects of small head motions
during task performance. For this subject, head motion
1
FOV ¼ Field of View; TE ¼ Echo Time; TR ¼ Repetition Time;
h ¼ Flip Time.
was less than 1 mm throughout. The final brain activation maps were produced using a deconvolution routine
provided in AFNI that contrasted the stimulus periods
with the baseline periods. The corresponding periods
for the roadblock-navigation task consisted of the stationary planning period and the stationary
non-planning period (i.e., standing in front of the permanent and temporary roadblocks, respectively). The
corresponding periods for the virtual Tower of London
task also consisted of stationary planning periods and
stationary non-planning periods (i.e., viewing the stimulus ‘‘goal’’ card while planning a sequence of moves and
while not planning anything at all, respectively). The
final maps for each condition contained voxels based
on blood-oxygen level dependent contrast. These voxels
were coded according to z-score values, which indicated
the magnitude of difference between the stimulus and
baseline periods. Within each of the 26 slices or planes,
there were 64 voxels along both the x and y dimensions.
Therefore, there were 106,496 voxels being compared
between the stimulus and baseline periods for each
condition. To compensate for the increased risk of
making a Type I error, a Bonferroni corrected p value
was calculated. Using an initial p value of 0.05, the
Bonferroni corrected p value for 106,496 comparisons
is 4.695 ! 10#7. This latter p value corresponds to a ZScore of 4.904. Thus, only voxels of brain tissue that
showed a Z-score increase in activation of 4.904 or
greater would be considered significant for interpretation purposes. These final brain maps were analyzed
plane by plane to document the areas of neuroanatomical activation that were common and exclusive to each
of the two stimulus conditions. Finally, the Talairach
and Tournoux (1988) coordinate system was used for
the procedures of mapping and defining the location
of different brain regions.
RESULTS
With regard to behavioral performance, the subject did
not make any errors in the learning phase or during the
testing trial. It is important to note that ceiling effects
are not of concern in this study as the focus is not on
learning and memory but with planning. Upon debriefing, however, the subject reported that he found all of
the learning and testing components of the various tasks
to be challenging and claimed that he needed to put
forth a sizeable amount of effort to be successful. The
subject also reported an excellent sense of presence while
interacting within both of the virtual environments
employed (i.e., the virtual city and virtual Tower of
London).
In terms of the acquired neuroimaging data, the notable areas of neuroactivation for the roadblock-navigation
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THE NEURAL BASIS OF PLANNING
303
TABLE 1
Regions of Neuroactivation for the Spatial Navigation (‘‘Roadblock’’)
Planning Task
TABLE 2
Regions of Neuroactivation for the Virtual Tower of London Planning
Task
Neuroanatomical Region
Neuroanatomical Region
Left Insula
Right Insula
Right Anterior Cingulate
Left Anterior Cingulate
Left Caudate
Right Caudate
Right Hippocampus
Left Hippocampus
Right Posterior Cingulate
Left Posterior Cingulate
Left Middle Frontal Gyrus
Right Middle Frontal Gyrus
Between LMFG and LAC
Between RMFG and RAC
Left Angular Gyrus
Right Angular Gyrus
Left Supramarginal Gyrus
Right Supramarginal Gyrus
Left Superior Frontal Gyrus
Right Superior Frontal Gyrus
Left Medial Frontal Gyrus
Right Medial Frontal Gyrus
Left Post-Central Gyrus
Right Post-Central Gyrus
Left Precuneus
Right Precuneus
Left Middle Temp Gyrus
Right Middle Temp Gyrus
Left Inferior Parietal Lobule
Right Inferior Parietal Lobule
Left Thalamus
Right Thalamus
Left Nodule=Dentate
Right Nodule=Dentate
x
y
z
Z-Score
!32
32
11
!8
!19
10
26
!26
6
!8
!31
31
24
!24
!32
36
!37
37
!17
17
!9
9
!22
22
!18
16
!35
35
!52
52
!20
20
!15
8
10
10
40
40
19
15
!38
!38
!50
!50
29
29
24
22
!61
!57
!45
!45
19
19
26
26
!29
!29
!60
!60
!60
!60
!33
!33
!26
!26
!57
!64
!5
!5
10
10
4
13
7
7
10
10
28
28
25
25
34
34
37
37
49
49
40
40
55
55
34
34
18
18
24
24
6
6
!26
!26
6.3
ns
5.35
5.02
7.66
10.82
5.5
ns
5.52
4.84
6.82
ns
7.66
8.65
5.59
7.7
ns
5
9.99
ns
7.43
ns
4.17
ns
ns
ns
ns
ns
ns
ns
ns
ns
7.99
5.97
Left Insula
Right Insula
Right Anterior Cingulate
Left Anterior Cingulate
Left Caudate
Right Caudate
Right Hippocampus
Left Hippocampus
Right Posterior Cingulate
Left Posterior Cingulate
Left Middle Frontal Gyrus
Right Middle Frontal Gyrus
Between LMFG and LAC
Between RMFG and RAC
Left Angular Gyrus
Right Angular Gyrus
Left Supramarginal Gyrus
Right Supramarginal Gyrus
Left Superior Frontal Gyrus
Right Superior Frontal Gyrus
Left Medial Frontal Gyrus
Right Medial Frontal Gyrus
Left Post-Central Gyrus
Right Post-Central Gyrus
Left Precuneus
Right Precuneus
Left Middle Temp Gyrus
Right Middle Temp Gyrus
Left Inferior Parietal Lobule
Right Inferior Parietal Lobule
Left Thalamus
Right Thalamus
Left Nodule=Dentate
Right Nodule=Dentate
x
y
Z
Z-Score
!32
32
11
!6
!11
14
26
!26
5
!9
!23
34
24
!24
!36
36
!37
37
!28
28
!9
9
!55
55
!18
16
!35
35
!52
52
!20
20
!15
12
10
10
40
26
15
5
!38
!38
!57
!57
38
30
24
22
!60
!57
!45
!45
35
35
26
26
!27
!27
!60
!60
!60
!60
!33
!33
!26
!26
!57
!55
!5
!5
10
25
10
16
7
7
16
16
39
36
25
25
37
34
37
37
55
55
40
40
42
42
34
34
18
18
24
24
6
6
!26
!26
ns
ns
ns
8.03
6.33
5.46
ns
ns
10.9
6.52
10.95
6.97
ns
ns
12.29
ns
ns
6.13
10.15
ns
ns
ns
12.11
ns
10.97
8.98
13
ns
ns
5.96
10.27
ns
10.12
7.32
"
Note. LMFG ¼ Left Middle Frontal Gyrus, LAC ¼ Left Anterior
Cingulate; RMFG ¼ Right Middle Frontal Gyrus; RAC ¼ Right
Anterior Cortex. These coordinates are based on the Talairach and
Tournoux (1988) stereotaxic system.
"
Note. LMFG ¼ Left Middle Frontal Gyrus, LAC ¼ Left Anterior
Cingulate; RMFG ¼ Right Middle Frontal Gyrus; RAC ¼ Right
Anterior Cortex. These coordinates are based on the Talairach and
Tournoux (1988) stereotaxic system.
and virtual Tower of London tasks are provided in
Tables 1 and 2, respectively. In addition, Figure 4 illustrates dorsal plane images for the roadblock-navigation
task (top row) and virtual Tower of London task
(bottom row), while Figure 5 illustrates ventral plane
images with the roadblock-navigation task again on
top and the virtual Tower of London task on the bottom.
It is important to note that conventional image orientation is depicted in the figures (i.e., the right side of the
image corresponds to the left side of the body).
When comparing the tables and images of the two
conditions, one will note a number of similarities as
well as distinct differences. First, it is important to
state that, upon brief glance, the virtual Tower of
London task elicited a more widespread and generally
higher level of brain activity when compared to the
roadblock-navigation task. Moreover, it is necessary
FIGURE 4 Dorsal images for both the roadblock-navigation task
(top row) and virtual Tower of London task (bottom row).
304
CAMPBELL ET AL.
Discussion
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FIGURE 5 Ventral images for both the roadblock-navigation task
(top row) and virtual Tower of London task (bottom row).
to keep in mind that both neuroactivation maps were
obtained using the same threshold z-score value and that
the images are displayed at identical planes of reference.
Thus, the obtained differences are real and not reflective
of any differences in analysis or methodology.
Overall, there does appear to be a moderate
amount of overlap between the two conditions. In
fact, many of the neuroanatomical areas with significant activations listed for the roadblock-navigation
task are also listed for the virtual Tower of London
task. These regions include the following structures:
left anterior cingulate, bilateral caudate nucleus, bilateral posterior cingulate, left middle frontal gyrus, left
angular gyrus, right supramarginal gyrus, left superior
frontal gyrus, left post-central gyrus, and bilateral
dentate nucleus.
In terms of differences, several exclusive brain regions
were only activated during one of the two planning
tasks. For the roadblock-navigation task, bilateral
portions of tissue between the cingulate cortex and
prefrontal cortex, left medial frontal gyrus, right hippocampus, right anterior cingulate, left insula, and right
angular gyrus were intrinsic components of its neuroanatomical substrate. For the virtual Tower of London
task, the right middle frontal gyrus, left supramarginal
gyrus, bilateral precuneus, right inferior parietal lobule,
and left thalamus mainly distinguished this task from
the roadblock-navigation task. To articulate the overall
difference in magnitude of activation between these two
tasks, mean z-scores were computed. That is, the zscores for all of the areas of congruent activation were
obtained, and an average was determined for each task.
For the roadblock-navigation task, a mean z-score of
6.6 was calculated. For the virtual Tower of London
task, a mean z-score of 8.9 was obtained. Overall, there
were both qualitative (i.e., location) and quantitative
(i.e., degree of activation) differences between these
two conditions despite a moderate amount of overlap.
The purpose of the present case study was to address
how a traditional neuropsychological test measure of
planning (e.g., Tower of London) would compare in
terms of convergent and divergent specificity to a novel
and more ecologically valid task of planning and to this
end, what implications the obtained findings would have
for both the experimental and clinical neuropsychologist. With these considerations in mind, a VR city that
employed a novel roadblock task was developed. In
addition, a VR analog of the Tower of London task
was also designed for comparative purposes. These
environments and procedures were ultimately combined
with fMRI to elucidate the patterns of neuroactivation
elicited by each task.
The present research found evidence of both convergent and divergent specificity between these two tasks of
planning. First, convergent regions of neuroactivation
were found within and outside of the frontal lobes (see
Figures 4 and 5). Indeed, overlap occurred within key
areas that have been previously reported by researchers
who examined neuroactivation patterns and Tower of
London performance (e.g., van den Heuvel et al.,
2003). Figure 4 illustrates some of the most noticeable
overlap. Specifically, this figure depicts an overlap in
activation within the (left) DLPFC that occurs across
several planes of view. This overlap in activation is particularly interesting given that the left frontal lobe is
what Maguire et al. (1998) ascribed to being the
‘‘non-spatial components of navigation’’ in their VR
study. Collectively, there is support for the notion that
these separate tasks are at the very least measuring similar aspects of cognition by way of their correlative functional neuroanatomy. In contrast, however, there are
clear differences between these two tasks. In fact, several
functional imaging planes appear to stand in direct contrast to one another. This is well illustrated in Figure 5
where successive planes of fMRI data appear to oppose
one another such that more anterior activation is associated with the roadblock-navigation task and more
posterior activation is comparably associated with the
virtual Tower of London task. Moreover, the overall
difference in magnitude of activation between these
two tasks, at least in reference to areas of congruent
activity, was found to be quite large and in favor of
the Tower of London task (i.e., difference of 2.3
z-score units).
Overall, the obtained results lend preliminary support
to the notion that the virtual Tower of London task is
much more multifactorial in nature than the
roadblock-navigation task in terms of its neuroanatomical specificity. That is, the larger and more widespread
areas of activation that the Tower of London appears
to elicit may indicate that it is sensitive to many other
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THE NEURAL BASIS OF PLANNING
aspects of cognition beyond planning per se. As such, it
seems that performance on this task requires an amount
of cortical tissue much larger and more widespread than
what is required for the more ecologically valid task
employed in this study. Indeed, several investigators
have concluded that the Tower of London does not
reliably nor specifically measure planning performance
(e.g., Berg & Byrd, 2002; Kafer & Hunter, 1997). Thus,
the present study may question further the notion that
the Tower of London is a reliable and specific measure
of planning and therefore, an index of frontal lobe
function or dysfunction. The implication of this finding
relates to both the experimental setting where one
might attempt to infer brain-behavior relationships in
terms of successful (or unsuccessful) planning, as well
as in a clinical context in which the clinician is asked
to predict everyday planning ability in patients with a
presumed lesion.
An obvious limitation to the present findings,
however, is that of its use of a single subject design.
Hence, it is difficult to say with much certainty that
the obtained findings are reliable, and therefore generalizable. Whilst no case study should purport its findings
as being reliable, we can draw strength on the fact that
there is consistency between our findings and similar
experiments in the present literature. That is, similar
patterns of activation were observed for the Tower of
London task in previous studies that have employed
the same neuroimaging technique as used in the present
research (i.e., fMRI; Lazeron et al., 2000; van den
Heuvel et al., 2003). Accordingly, if we tentatively
accept that the present results are replicable, it appears
as though the context and content of planning measures,
whether contrived or realistic, play a large role in the
neuroactivity observed during performance on such
tasks. Indeed, one can speculate that the development
of new and hopefully more ecologically valid planning
tasks will show similar results (i.e., moderate overlap
with distinctive differences across tasks). That is, the
obtained patterns of neuroactivation will vary as a result
of different frames of reference, use of different strategies, and incorporation of different stimuli. However,
they will all conceivably share the same theoretical
construct of planning and, hence, an overlap in
neuroactivation.
Although the present research contributes to the
long-standing notion that planning is at least partially
attributable to the frontal lobes and various other areas
of importance, it also illustrates the complex dynamic
between brain and behavior as well (Miller &
Cummings, 1998; Stuss & Benson, 1986). More
importantly, the obtained findings underscore that the
way in which we operationally define planning ability,
or other cognitive domains for that matter, is integral
to the progress of scientific inquiry in clinical neuropsy-
305
chology. In sum, the motivating factor for pursuing the
present research stemmed from the idea that traditional
or standard measures of neuropsychological function
(in this case planning) are not entirely reflective and
therefore may not be predictive of normal everyday
ability. The present case study provides supportive
evidence that the pattern and intensity of brain activity
associated with planning performance is, in part, modulated by the ecological validity of the instrument being
used. This finding may extend to all domains of inquiry
in neuropsychological research and assessment when
such deductive conclusions are formulated on the results
of what might be largely contrived neuropsychological
test measures. To this end, future research should focus
on the development of more ecologically valid tasks,
perhaps using VR technology, in an attempt to bring
standard approaches to measuring neuropsychological
constructs up to date. Indeed, experimental and clinical
neuropsychologists alike stand to benefit from additional test measures that better reflect, and accurately
predict, real-world performance.
ACKNOWLEDGEMENTS
This work was funded in part by grants from the
Canadian Foundation for Innovation the Natural
Sciences and Engineering Research Council, and the
Premier’s Research Excellence Award. The authors also
wish to acknowledge Diano Marrone for his assistance
with the creation of the virtual city environment developed in this study.
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