Frontostriatal System in Planning Complexity: A Parametric Functional Magnetic Resonance Version of...

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Frontostriatal system in planning complexity: a parametric functionalmagnetic resonance version of Tower of London task

Odile A. van den Heuvel,a,b,* Henk J. Groenewegen,c Frederik Barkhof,d

Richard H.C. Lazeron,e Richard van Dyck,a and Dick J. Veltmana,b

a Department of Psychiatry, VU Medical Centre, Amsterdam, The Netherlandsb Clinical PET Centre, VU Medical Centre, Amsterdam, The Netherlands

c Department of Anatomy, VU Medical Centre, Amsterdam, The Netherlandsd Department of Radiology, VU Medical Centre, Amsterdam, The Netherlandse Department of Neurology, VU Medical Centre, Amsterdam, The Netherlands

Received 1 April 2002; revised 27 June 2002; accepted 6 August 2002

Abstract

In the present study, we sought to investigate which brain structures are recruited in planning tasks of increasing complexity. For this

purpose, a parametric self-paced pseudo-randomized event-related functional MRI version of the Tower of London task was designed. We

tested 22 healthy subjects, enabling assessment of imaging results at a second (random effects) level of analysis. Compared with baseline,

planning activity was correlated with increased blood oxygenation level-dependent (BOLD) signal in the dorsolateral prefrontal cortex,

striatum, premotor cortex, supplementary motor area, and visuospatial system (precuneus and inferior parietal cortex). Task load was

associated with increased activity in these same regions. In addition, increasing task complexity was correlated with activity in the left

anterior prefrontal cortex, a region supposed to be specifically involved in third-order higher cognitive functioning.

© 2003 Elsevier Science (USA). All rights reserved.

Keywords: Planning; Tower of London; Functional magnetic resonance imaging; Parametric; Prefrontal cortex; Basal ganglia

Introduction

Planning, i.e., the ability to achieve a goal through a

series of intermediate steps, is an essential component of

higher-order cognitive processes, such as problem solving.

Central to the concept of the neural substrate for cognitive

planning is the position of the dorsolateral prefrontal cortex,

but other brain regions, like the premotor, cingulate, and

insular cortices and the striatum, have also been implicated

in cognitive planning (Owen, 1997; Robbins, 1998).

Whereas until recently insight into the role of brain struc-

tures in planning processes was based primarily on lesion

studies in patients, contemporary studies are able to employ

human brain imaging techniques. The results of such func-

tional neuroimaging studies have suggested that a distrib-

uted corticocortical and corticosubcortical network is being

activated during complex planning tasks, although there is

yet no complete agreement about the contribution of the

various components of such neuronal networks.

A direct and very frequently used test to probe planning

processes is the Tower of London task, originally developed

by Shallice (1982). The Tower of London task has been

modified in various ways to make it suitable for testingdifferent components of cognitive planning in clinical and

experimental psychological settings. The complexity of the

task can range from trivial, requiring only one obvious

move, to highly complex. The more complex versions of the

task require the planning of intermediate, in many instances

counterintuitive, steps to successfully solve a problem. In-

dividuals with decreased prefrontal functioning, such as

those with neurosurgical lesions (Shallice, 1982), neurode-

generative diseases like Huntington’s and Parkinson’s dis-

* Corresponding author: Clinical PET Centre, VU Medical Centre, PO

Box 7057, 1007 MB Amsterdam, The Netherlands. Fax: 31-20-4449636.

E-mail address: [email protected] (O.A. van den Heuvel).

R

Available online at www.sciencedirect.com

NeuroImage 18 (2003) 367–374 www.elsevier.com/locate/ynimg

1053-8119/03/$ – see front matter © 2003 Elsevier Science (USA). All rights reserved.

PII: S 1 0 5 3 -8 1 1 9 (0 2 )0 0 0 1 0 -1



eases (Dagher et al., 1999; Watkins et al., 2000; Hodgson et

al., 2002), or psychiatric disorders like obsessive– compul-

sive disorder (Veale et al., 1996) and schizophrenia (Pan-

telis et al., 1997), are impaired on the task, in particular

when complexity increases.

The Tower of London task is also very suitable for

studying in healthy subjects the patterns of brain activationin the context of planning tasks using positron emission

tomography (PET) (Owen et al., 1996; Baker et al., 1996;

Dagher et al., 1999; Rowe et al., 2001) or single-photon

emission computed tomography (SPECT) (Rezai et al.,

1993; Morris et al., 1993). The results of these imaging

studies agree on the involvement of the dorsolateral pre-

frontal cortex and parieto-occipital regions (visuospatial

system) during planning. However, activation of other brain

regions, such as the cingulate and insular cortices and the

striatum, has not been found across all mentioned studies.

Furthermore, the results of a functional MRI study using the

Tower of London task of Lazeron et al. (2000) confirmed

activation of the dorsolateral prefrontal, parietal, cingulate,

and insular cortices but failed to show striatal activation.

Several methodological differences between these studies

may explain the inconsistencies in the results. First, differ-

ent baseline conditions have been used, ranging from low-

level conditions (watching a blank screen) to conditions

involving motor activity (moving beads without a goal) or

cognitive functioning (counting beads). Second, differences

in the execution of the task exist: whereas in some studies

subjects used a touch screen to perform the task (Owen et

al., 1996; Dagher et al., 1999; Rowe et al., 2001), in other

designs subjects were requested to execute their moves

mentally (Baker et al., 1996; Lazeron et al., 2000). A thirdlikely source of inconsistency concerns the differences in

task load. Because in subtraction designs the specificity of

active versus baseline differences for the (cognitive) func-

tion under study is often questionable (Friston et al., 1996;

Sidtis et al., 1999), several researchers have used designs in

which task load during performance of the Tower of Lon-

don task was manipulated. For example, Baker et al.,

(1996), in comparing “dif ficult” (four or five moves) and

“easy” trials (two or three moves), found task load to be

correlated with increased regional cerebral blood flow

(rCBF) in rostrolateral and dorsolateral prefrontal cortex,

and the parietal and cingulate cortices. Owen et al. (1996)reported increased activation in striatum and thalamus, but

not in cortical areas. Lazeron et al. (2000) did not find any

difference between their “easy” and “dif ficult” versions, but

they used a more dif ficult design comparing two to four

moves with five to seven moves. The only study employing

a parametric approach in studying task load (Dagher et al.,

1999) used five levels of planning and showed a correlation

between planning complexity and increased rCBF in the

right caudate nucleus and prefrontal and cingulate areas.

The involvement of visuospatial areas in the parietal lobe,

however, seemed not to be related to the task load. A

general methodological drawback in the studies performed

so far is the lack of control over performance differences,

due to the use of blocked designs. Since performance is

likely to deteriorate at higher complexity, this is especially

relevant for parametric tasks.

It must be assumed that the various cortical and subcor-

tical brain structures that have been demonstrated to be

involved in planning processes execute these functions as“nodes” in distributed neuronal networks (Owen, 1997).

Employing a neuronal network model for planning pro-

cesses, Dehaene and Changeux (1997) proposed multiple

hierarchic levels with ascending and descending streams for

execution of planning and evaluation of this behavior, re-

spectively. This model also stresses that planning behavior

cannot be related to a single region, but rather relies on

multiple neuronal circuits coding for specialized subpro-

cesses such as working memory, plan generation, and in-

ternal reward. These subprocesses may differentially con-

tribute to different levels of planning behavior; some

subprocesses may be relatively independent of task load,while other subprocesses are involved mainly at higher

levels of planning behavior. The main aspect in increasing

planning complexity is the increasing amount of subgoals

and counterintuitive moves, while holding in mind the over-

all main goal. Koechlin et al. (2000) introduced the term

branching for this process of integrating working memory

with attentional resource allocation, a third-order higher

cognitive function. They found, using a multitask experi-

ment, branching to rely on activation of the rostral part of

the prefrontal cortex. To be able to investigate these specific

subprocesses and to differentiate between different brain

regions in their contribution to different levels of planning

behavior, parameterization of the task is essential. There-

fore, the main aim of the present study was to determine the

effects of task load in planning execution.

The present study employs event-related functional MRI

as a method to study activation patterns on a trial-by-trial

basis. This enabled us (i) to randomize items with respect to

task complexity, and (ii) to include correct responses only.

A large number of subjects (n 22) were included, with the

aim of performing second-level or random effects analyses,

allowing for generalization of our results. On the basis of

the considerations above, we hypothesized that increased

planning complexity would be associated with increased

activity in dorsolateral prefrontal, visuospatial, and striatalregions. In addition, we expected rostral areas of the pre-

frontal cortex to be specially involved in the higher levels of

planning execution.

Materials and methods

Subjects

Twenty-two healthy right-handed subjects (11 men and

11 women; mean age 29.9 years, range 21– 49 years) per-

368 O.A. van den Heuvel et al. / NeuroImage 18 (2003) 367–374



formed the Tower of London task, while functional MRI

data were collected. Subjects were recruited among univer-

sity students and staff. The ethical review board of the VU

Medical Centre approved of the study and all participants

provided written informed consent.

Task paradigm

To ensure that participants were familiar with the proce-

dure, the test was explained and practiced outside the scan-

ner before MR imaging was performed. The present version

of the Tower of London task consisted of six conditions: a

baseline condition and five planning conditions ranging

from one to five moves. In the planning condition, subjects

were presented, on a single screen, a starting configuration

and a target configuration with the instruction “count the

amount of steps” displayed at the top and two answers at the

bottom (Fig. 1B). In both configurations, three colored

beads are placed on three vertical rods, which can accom-modate one, two, and three beads, respectively. One bead

can be moved at a time and only when there is no other bead

on top. Subjects were requested to determine the minimum

number of moves necessary to reach the target configuration

and to press the button corresponding to the side of the

screen (left or right) where the correct answer was pre-

sented. In the baseline condition, subjects simply had to

count the total of yellow and blue beads, a task that does not

require any planning activity, the display being similar to

the planning condition, but the instruction now being “count

the total amount of yellow and blue beads” (Fig. 1A).

Moreover, the numbers of beads of each color in the twoconfigurations, used for the baseline condition, were un-

equal, with the aim of preventing planning activity. We used

a pseudo-randomized, self-paced design with a maximum

response duration of 30 s for each trial. We adopted a

pseudo-randomized design to control for any overflow ef-

fects (i.e., persevering of task-related cognitive processes

after a dif ficult trial). Therefore, each trial of three or more

moves was followed by a baseline trial. No feedback re-

garding the answers was provided during the task.

Data acquisition

Imaging was performed on a 1.5-T Sonata MR system

(Siemens, Erlangen, Germany) with a standard circularly

polarized head coil. Stimuli were generated by a Pentium

PC and projected on a screen at the end of the scanner table,

which was seen through a mirror mounted above the sub-

ject’s head. Two magnet-compatible four-key response

boxes were used to record subject’s performance and reac-

tion times (RTs). To reduce motion artifacts, the subject’s

head was immobilized using foam pads; in addition, we

used automatic online motion correction.

Anatomic imaging included a coronal 3D gradient-echo

T1-weighted sequence (matrix 256 160, voxel size 1 1

1.5 mm, 160 sections). For functional MRI, an echo

planar imaging sequence (TR 3.045 s, TE 45 ms, matrix 64

64, field of view 192 192 mm, flip angle 90°) was used,

creating transversal whole-brain acquisitions (35 slices, 3

3-mm in-plane resolution, 2.5-mm slice thickness, 0.5-mm

interslice gap). In total, 433 EPI volumes per subject were

scanned. The distribution frequency of event types wasbased on RT data of a pilot study, so that a similar amount

of scans (around 70 EPI volumes) was acquired per subject

for each of the six conditions.

Data analysis

Psychometric data were analyzed using a standard sta-

tistical package. Imaging data were analyzed using SPM99

(Wellcome Departmentof Cognitive Neurology, http://www.

fil.ion.ucl.ac.uk/spm). After the first four volumes were dis-

carded, time series were corrected for differences in sliceacquisition times and realigned. Spatial normalization into

approximate Talairach and Tournoux space was performed

using a standard SPM EPI template. Data were resliced to 2

2 2-mm voxels and spatially smoothed using a Gauss-

ian kernel of 6 mm.

Next, data were analyzed in the context of the general

linear model, using delta functions convolved with a canon-

ical hemodynamic response function to model responses of

varying length to each type of stimulus. In addition, error

trials were modeled separately as a condition of no interest.

For each subject, weighted contrasts were computed for

simple main effects (all active conditions vs baseline) andfor task load. Contrast images containing parameter esti-

mates for main effects and task load were entered into a

second level (random effects) analysis. All results are re-

ported at P 0.05 ( z score 5.00) corrected for multiple

comparisons.

Results

Task performance

Mean performance scores were 94.2% (SD 1.68) for

baseline (mean RT 3.7 s, SD 0.88), 97.6% (SD 2.02)

for one move (mean RT 4.4 s, SD 1.11), 95.4% (SD

3.99) for two moves (mean RT 5.8 s, SD 1.27), 96.7%

(SD 3.58) for three moves (mean RT 7.4 s, SD

1.90), 89.9% (SD 7.17) for four moves (mean RT

10.1 s, SD 2.51), and 82.2% (SD 12.59) for five moves

(mean RT 15.0 s, SD 4.02). Both the decrease in

performance (F (4,18) 12.4, P 0.0001) and the increase

in R T (F (4,18) 64.3, P 0.0001) were statistically

significant.

369O.A. van den Heuvel et al. / NeuroImage 18 (2003) 367 – 374

http://www.fil.ion.ucl.ac.uk/spm






Imaging data

Planning versus baseline

Regions showing increased BOLD signal during plan-

ning compared with baseline (Table 1 and Fig. 2) were

bilateral precuneus (Brodmann’s area (BA) 7), bilateral

inferior parietal cortex (BA 40), bilateral premotor cortex

(BA 6), right dorsolateral prefrontal cortex (BA 9 and BA

46), left supplementary motor area, left caudate nucleus,

right globus pallidum, and right insular cortex.

Task load

Increased task load (Table 2) was correlated with in-

creased BOLD signal in those areas that already showed an

increased signal compared with the baseline condition, i.e.,bilateral precuneus (BA 7), bilateral inferior parietal cortex

(BA 40), bilateral premotor cortex (BA 6 and BA 8), right

dorsolateral prefrontal cortex (BA 9 and BA 46), and left

supplementary motor area. In addition, a number of areas

showed increased BOLD signal specifically related to in-

creased task load (Fig. 3 and 4), i.e., left dorsolateral pre-

frontal cortex, left insular cortex, right pallidum, and right

caudate nucleus. The left rostral prefrontal cortex (BA 10)

was also involved in the highest levels of planning com-

plexity only. The reverse contrast (decreased BOLD signal

associated with increased task load) did not show significant

activations except for bilateral primary visual cortex.

Discussion

In this article we present a parametric event-related func-

tional MRI version of the Tower of London task, suitable

for investigating the effects of increasing complexity in

Fig. 1. Display screen of the Tower of London task as used in the present

study: (A) baseline condition, (B) planning condition.

Fig. 2. BOLD signal increase in right dorsolateral prefrontal and bilateral

motor and visuospatial cortex during planning compared with baseline.

370 O.A. van den Heuvel et al. / NeuroImage 18 (2003) 367 – 374



planning processes. In agreement with the results of earlier

imaging studies, the present fMRI data show that the pro-

cess of planning, compared with baseline, was strongly

correlated with activation of the right dorsolateral prefrontalcortex (BA 9 and BA 46), bilateral premotor cortex (BA 6

and BA 8), bilateral precuneus (BA 7) and inferior parietal

cortex (BA 40), left supplementary motor area (BA 32),

right insular cortex, and bilateral striatum (Morris et al.,

1993; Rezai et al., 1993; Baker et al., 1996; Owen et al.,

1996; Dagher et al., 1999; Lazeron et al., 2000; Rowe et al.,

2001; Fincham et al., 2002). Our parametric approach also

shows that with increasing planning complexity a number of

additional areas come into play, i.e., the left dorsolateral

(BA 9 and BA 46) and rostral prefrontal cortices (BA 10) as

well as the right caudate nucleus. These are, at least in part,

novel observations.Involvement of the dorsolateral and rostral prefrontal

areas is in agreement with the findings of Baker et al.

(1996). It has been postulated that manipulation processes,

operating on information already stored in memory, engage

dorsolateral prefrontal cortex (Baddeley, 1996). Rostral pre-

frontal cortex activation, on the other hand, may reflect a

more complex third level of executive control, holding

temporarily in mind an ongoing goal while first completing

intermediate or subgoals, although we cannot rule out the

alternative explanation, i.e., that rostral prefrontal activation

merely reflects increasing task dif ficulty. Burgess et al.

(2001) investigated the phenomenological correlates of pro-

spective memory and found activation of the rostral pre-

frontal areas correlated with maintenance of intention. This

higher-order supervisory cognition, specific for humans,

functions beyond the manipulation in dorsolateral prefrontal

cortex and maintenance in ventrolateral prefrontal regions

(Burgess et al., 2000; Baddeley, 1996).

We found dorsolateral prefrontal activation to be rightlateralized in the comparison planning versus baseline, but

was bilaterally occurring as a function of task load. Later-

alization probably exists, with spatial predominance at the

right hemisphere and nonspatial predominance at the left

side, but there is no consensus about the exact nature of this

lateralization. While Baker et al. (1996) found right rostral

prefrontal cortex activity to be strongly correlated with

planning complexity, in the present study left lateralization

was found for this rostral prefrontal area (BA 10).

In the present study, right striatal activation (head of the

caudate nucleus) was correlated with task load. Striatal

involvement in planning complexity has been describedbefore (Owen et al., 1996; Dagher et al., 1999). In conjunc-

tion with the increased activity of dorsolateral and rostral

prefrontal areas, this suggests a role for the frontostriatal

system in complex planning tasks. In terms of forebrain

circuits, this indicates an involvement of particular basal

ganglia–thalamocortical circuits (Alexander et al., 1986;

Groenewegen et al., 1990) which are directed at various

parts of the prefrontal cortex. Interactions between the pre-

frontal cortex and the basal ganglia are thought to play a

Fig. 3. BOLD signal increase in bilateral dorsolateral prefrontal cortex and

right caudate nucleus, correlating with increased task load.

Fig. 4. BOLD signal increase in left Brodmann’s area 10 and right palli-

dum, correlating with increased task load.




role in the selection and switching of behavioral compo-

nents (Wise et al., 1996), which are crucially important for

the execution of a (complex) planning process. In the con-

text of the neuronal network model of Dehaene and Chan-

geux (1997), our finding of involvement of various cortical

and subcortical regions related to increased planning com-

plexity strengthens the idea of distributed corticocortical

and corticosubcortical networks as a neural basis for such

higher-order cognitive processes. The involvement of the

striatum might represent, at least in part, the predicted role

Table 1

Maxima of regions showing significant (P 0.05 ( z 5.00), corrected) BOLD signal increase during planning compared with baseline

Region of activation Cluster size

(voxels)

Left/

right

Brodmann’s

area

Talairach coordinate (mm) Z value

x y z

Dorsolateral PFC 5 R 46 46 36 28 5.57

7 R 9 40 30 30 5.17

Premotor cortex 12 L 6 24 20 50 5.17

31 L 6 26 6 52 5.17

7 L 6 26 4 54 5.08

90 R 6 26 4 54 6.05

SMA 3 L 32 6 20 46 5.02

Precuneus 277 L 7 4 56 48 6.42

R 7 6 58 50 5.47

Inferior parietal cortex 12 L 40 38 78 34 5.34

2 L 40 60 36 44 5.14

60 R 40 34 84 28 5.61

7 R 40 28 72 40 5.20

7 R 40 48 42 44 5.18

1 R 40 40 66 26 5.03

Caudate nucleus 8 L 8 8 0 5.01

Globus pallidus 41 R 12 6 6 5.53

Insular cortex 23 R 32 24 4 5.59

Table 2

Maxima of regions showing significant (P 0.05 ( z 5.00), corrected) BOLD signal increase correlating with increased task load

Region of activation Cluster size

(voxels)

Left/

right

Brodmann’s

area

Talairach coordinate (mm) Z value

x y z

Anterior PFC 2 L 10 36 60 8 5.02

Dorsolateral PFC 15 L 46 40 50 16 5.52

23 L 46 44 32 24 5.27

33 L 9 44 36 38 5.37

134 R 46 40 36 24 5.72

R 9 44 32 32 5.13

Premotor cortex 197 L 6 24 16 56 5.98

3 L 6 18 12 58 5.19

23 L 6 32 4 58 5.29

212 R 6 26 10 58 5.69

33 R 8 28 34 42 5.60

SMA 94 L 32 6 16 50 5.97

Precuneus 323 L 7 12 56 52 6.86

R 7 6 60 48 5.50

Inferior parietal cortex 51 L 40 42 40 36 5.89

87 L 40 48 42 46 5.73

7 L 40 40 78 34 5.47

15 L 40 40 50 16 5.52

5 L 40 24 76 50 5.37

52 R 40 38 72 34 5.83

20 R 40 50 42 42 5.64

3 R 40 48 42 50 5.00

3 R 40 34 64 44 5.06

Caudate nucleus, caput 23 R 18 6 18 5.43

Globus pallidus 23 R 12 4 4 5.58

Insular cortex 53 L 32 20 0 5.88




of a reward system in the cognitive processes (Dehaene and

Changeux, 2000).

As mentioned before, most previous studies used a block

design with an easy and a dif ficult planning condition,

thereby restricting the investigation of task load effects to a

subtraction analysis of both conditions. That no task load

effects were found in the functional MRI study of Lazeronet al. (2000) may be explained by this operationalization of

“easy” and “dif ficult” task conditions. The main aspect of

planning complexity is presumably the amount of subgoals

to be attained before reaching the main goal. The “easy”

condition of Lazeron et al. consisted of two- to four-move

items. Part of the three-move items and all four-move items

need the counterintuitive intermediate steps characteristic of

planning complexity. In the “dif ficult” condition subjects

had to solve five- to seven-move items, which are quite hard

to perform. Therefore, the difference between “easy” and

“dif ficult” may have been obscured by the presence of

complex items in the “easy” condition and the occurrence of ceiling effects in the “dif ficult” condition.

Our results are also at variance with Dagher et al. (1999),

who conducted a parametric analysis and divided areas

involved in planning behavior into those that did and those

that did not correlate with task complexity. Brain regions

where rCBF correlated with planning complexity included

bilateral dorsolateral prefrontal, premotor, and anterior cin-

gulate cortex and right caudate nucleus. In contrast with our

results, Dagher et al. (1999) found parietal activation not to

be correlated with task complexity. Since this PET study

consisted of only 6 subjects with 12 measurements per

subject, all data were analyzed using a fixed effects analysis,and results were presented uncorrected for multiple com-

parisons. Another important confounding factor may have

been that subjects had to perform the planning task by

touching the screen to move the beads. Motor execution is

not essential for planning and may produce motion artifacts,

confounding experimental effects. For this reason, we asked

our subjects to execute their plan by making the moves

mentally. Even without motor execution, massive activation

of motor and visuospatial areas was found. Parietal activa-

tion caused by motor execution, independent of specific

planning behavior, is not correlated with task complexity,

while visuospatial activity necessary for planning is indeed

correlated with task complexity.

Compared with previous studies, the present version of

the Tower of London task has two advantages. First, a

self-paced, parametric design allows flexible responding as

well as comparisons between subjects/groups at each task

level, providing us with the opportunity to investigate sub-

jects and groups with varying levels of performance. The

increased comparability across groups, using the same task,

is important for clinical studies, for instance, in patients

with decreased functioning of the frontostriatal system. Sec-

ond, in addition to variation in reaction time, performance

may also vary in hit percentage. Event-related analysis en-

ables a selective analysis of correct responses, by rejecting

the false responses. Moreover, adequate randomization of

trials is possible in event-related designs, in contrast to

block designs.

In summary, frontostriatal and visuospatial systems are

strongly involved in planning behavior and their level of

activity is related to task complexity as demonstrated by thedescribed self-paced, parametric event-related version of

the Tower of London task. By correcting for task perfor-

mance differences, it will be possible to investigate planning

behavior in subjects with decreased frontostriatal function.

Acknowledgments

This work was supported by the Dutch Organization for

Scientific Research (NWO), MW 940-37-018.

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