Differential Brain Activation Patterns in Adult Attention-Deficit
Hyperactivity Disorder (ADHD) Associated With Task Switching
Pauline Dibbets, Elisabeth A. T. Evers,
Petra P. M. Hurks, and Katja Bakker
VU University Amsterdam
Objective: The main aim of the study was to examine blood oxygen level–dependent response during
task switching in adults with attention-deficit/hyperactivity disorder (ADHD). Method: Fifteen male
adults with ADHD and 14 controls participated and performed a task-switching paradigm. Results:
Behaviorally, no specific executive control problems were observed in the ADHD participants, although
they did display more errors in general. The neuroimaging data did show remarkable differences between
the ADHD and control adults: Adults with ADHD engaged more strongly the dorsal anterior cingulate
cortex, middle temporal gyrus, precuneus, lingual gyrus, precentral gyrus, and insula than did the healthy
controls during task switching. Controls displayed more task-related activity in the putamen, posterior
cingulate gyrus, medial frontal gyrus, thalamus, orbitofrontal cortex, and postcentral gyrus. Conclusions:
ADHD adults did not display specific executive control problems at a behavioral level, but did engage
different brain areas during task switching compared with healthy controls. The results are discussed in
the framework of the executive frontostriatal circuitry, conflict detection, and attentional networks.
Keywords: attention-deficit/hyperactivity disorder, executive control, task switching, neuroimaging,
Attention-deficit/hyperactivity disorder (ADHD) is a develop-
mental disorder that is characterized by a pattern of chronic symp-
toms, including attention problems, hyperactivity, and impulsivity
(American Psychiatric Association, 1994). Over the past decades,
evidence has accumulated that ADHD is not restricted to child-
hood, but that symptoms of ADHD persist into adulthood at least
for 60% of children diagnosed with ADHD (Barkley, 1996; Spen-
cer, Biederman, & Mick, 2007). Like children, adults with ADHD
encounter highly comparable clinical features, comorbidities, neu-
ropsychological deficits, and failures in major life domains, such
as work, academic, and social problems (Biederman, Mick, &
Faraone, 2000; Faraone et al., 2000).
Several studies on ADHD have indicated that not only children
but also adults can experience executive control deficits (for an
overview, see Seidman, 2006). Executive control is an umbrella
term that comprises a collection of interrelated cognitive processes
responsible for goal-directed behavior. There are numerous exec-
utive control processes for example, planning, attentional control,
mental flexibility, utilization of feedback, working memory, and
self-regulation, such as response inhibition or resistance to inter-
ference. The deficits in executive control in adult ADHD have
been observed in several domains, such as response inhibition
(Epstein, Johnson, Varia, & Conners, 2001; Murphy, 2002b; Oss-
mann & Mulligan, 2003), problem solving (Murphy, 2002a), and
switching between rules or tasks (Tamm, Menon, Ringel, & Reiss,
2004; White & Shah, 2006). Neuroimaging studies suggest that
deficits in executive control in children and adolescents with
ADHD are associated with aberrant activation in the (pre)frontos-
triatal circuitry (for reviews, see Bush, Valera, & Seidman, 2005;
Durston, 2003; Tripp & Wickens, 2009). This circuitry comprises,
among other structures, the lateral prefrontal cortex (PFC), puta-
men, caudate nucleus, and anterior cingulate cortex.
With the increased recognition that ADHD symptoms persist
into adulthood, the amount of neuroimaging studies on adult
ADHD also steadily increases. Several studies have investigated
brain activation related to executive control in adults with this
disorder. Executive control functions that have been examined are,
for example, delay of reward aversion (Plichta et al., 2009),
sustained and transient attentional control (Banich et al., 2009),
cognitive interference (Bush et al., 1999), response inhibition and
feedback processing (Dibbets, Evers, Hurks, Marchetta, & Jolles,
2009), and working memory load (Hale, Bookheimer, McGough,
Phillips, & McCracken, 2007; Valera, Faraone, Biederman,
Poldrack, & Seidman, 2005). These studies reveal that, compared
with normal controls, adults with ADHD display aberrant brain
activity patterns in several brain areas during the performance of a
cognitive task. The most common observations are activity
changes in the dorsolateral prefrontal cortex (DLPFC), with both
increased (Hale et al., 2007) and decreased (Banich et al., 2009;
Bush et al., 1999) activity; decreased activity of medial cingulate
Pauline Dibbets, Department of Clinical Psychological Science, Faculty
of Psychology and Neuroscience, Maastricht University, the Netherlands;
Elisabeth A. T. Evers, Petra P. M. Hurks, and Katja Bakker, Department of
Neuropsychology and Psychopharmacology, Faculty of Psychology and
Neuroscience, Maastricht University, the Netherlands; Jelle Jolles, Depart-
ment of Special Education, Faculty of Psychology and Education, VU
University Amsterdam, Amsterdam, the Netherlands.
We thank Etienne Lemaire and Walter Backes for providing comments
and help on the MRI protocol. Further, we thank Dymphie Scholtissen-in
de Braek and Natalie Marchetta for the active recruitment and testing of the
Correspondence concerning this article should be addressed to Pauline
Dibbets, Maastricht University, Universiteitssingel 40, Clinical Psycholog-
ical Science, PO Box 616, 6200 MD Maastricht, the Netherlands. E-mail:
2010, Vol. 24, No. 4, 413–423
© 2010 American Psychological Association
areas (Banich et al., 2009; Bush et al., 1999); hyporesponsiveness
in the ventral–striatal reward system, including the nucleus accum-
bens (Dibbets et al., 2009; Plichta et al., 2009); hyperactivity in
dorsal parts of the striatum, including the caudate nucleus (Bush et
al., 1999; Plichta et al., 2009) and putamen (Bush et al., 1999); and
increased thalamic (Banich et al., 2009; Bush et al., 1999) and
insular activity (Bush et al., 1999).
The present study was set up to further investigate brain acti-
vation in adults with ADHD during the performance of a task
measuring another aspect of executive control, notably switching
between response rules and tasks.
The task used for this study is based on a task-switching para-
digm (Dibbets, Bakker, & Jolles, 2006; Dibbets & Jolles, 2006).
This paradigm involves the induction of a response–conflict situ-
ation and is, therefore, appropriate for exploration of executive
(dys)control. In this task, two relatively easy tasks are presented
(Monsell, 1996; Rogers & Monsell, 1995). In the “nonswitch”
condition, the participant is repeatedly presented one of the two
tasks (e.g., AAAA or BBBB). In the other condition, the “switch”
condition, the participant switches from one task to the other (e.g.,
ABBABA). Performance is usually slower and less accurate in the
switch than in the nonswitch condition. These so-called switch
costs, expressed as the difference in performance between the
switch and nonswitch conditions, are thought to reflect a stronger
engagement of executive processes during the switch condition,
such as the inhibition of the irrelevant task set and the accompa-
nying response, switching to the relevant task set, and the main-
tenance and manipulation of two different mental task sets in
working memory (Rogers & Monsell, 1995). A deficit in executive
control will, therefore, mostly be pronounced in the switch condi-
tion and not, or to a lesser extent, in the nonswitch condition. As
a result, the switch costs will be enlarged in individuals with
ADHD. This increase in switch costs is exactly what has been
observed in children (Cepeda, Cepeda, & Kramer, 2000) and
adults with ADHD (White & Shah, 2006).
Several functional MRI (fMRI) studies have examined task
switching in normal adults (e.g., DiGirolamo et al., 2001; Dove,
Pollmann, Schubert, Wiggins, & von Cramon, 2000; Ruge et al.,
2005; Rushworth, Hadland, Paus, & Sipila, 2002). Additional
activity during the switch condition was mainly observed in the
dorsolateral and medial–frontal areas, the presupplementary motor
area, cingulate areas, the caudate nucleus, and parietal areas.
Except for the parietal areas, all these structures are involved in the
frontostriatal circuitry, which is known to be affected in ADHD
(Bradshaw, 2001). Indeed, an fMRI study on task switching in
adolescents and children with ADHD revealed, among other
changes in activation, underactivation of the PFC compared with
healthy controls (Smith, Taylor, Brammer, Toone, & Rubia, 2006).
Separate group analyses of switch minus nonswitch trials indicated
that healthy controls additionally recruited striatal areas, such as
the caudate nucleus and putamen; no such additional activity in
these areas was observed in the ADHD group. These results
indicate that a task-switching paradigm can detect anomalies in the
frontostriatal circuitry associated with ADHD. The main aim of the
present study was to further investigate neural activity in adult
ADHD using a task-switching paradigm. Based on previous re-
search, it was hypothesized that—compared with normal con-
trols—adults with ADHD should display a different neural acti-
vation pattern in the frontostriatal circuitry and parietal areas.
More specifically, it was expected that, compared with healthy
controls, adults with ADHD should display aberrant activity in
lateral and medial prefrontal areas, the caudate nucleus, putamen,
and parietal cortex.
Method and Materials
This study was part of a larger project on ADHD and executive
control in children (e.g., Hurks et al., 2004) and adults (e.g.,
Marchetta, Hurks, De Sonneville, Krabbendam, & Jolles, 2008;
Marchetta, Hurks, Krabbendam, & Jolles, 2008). A part of our data
concerning feedback- and inhibition-related brain activity in adult
ADHD has been published elsewhere (Dibbets et al., 2009).
The study sample (N ? 34) existed of two groups: 17 adults
with ADHD and 17 education level- and age-matched normal
controls. The ADHD group consisted of 16 persons with combined
attention and hyperactivity problems (ADHD) and one adult with
predominantly inattentive symptoms (attention deficit disorder
[ADD]). All participants were male and right-handed. The reason
for selecting only male participants was to create a more homo-
geneous sample as the menstrual cycle can influence both neuro-
psychological and neurophysiological parameters, including corti-
cal activation patterns (e.g., Dietrich et al., 2001). The mean age of
the ADHD group was 28.8 years (SD ? 6.25, range ? 21.9–41.9),
and 28.6 years (SD ? 6.45, range ? 21.3–41.3) for the control
group. The education level was 5.00 (SD ? 1.62, range ? 2–7) for
the ADHD group and 5.29 (SD ? 1.40, range ? 3–8) for the
control group, representing an average level of preuniversity edu-
cation (De Bie, 1987).
Control participants were recruited via advertisements in local
newspapers. Participants of the ADHD group were recruited by
spreading information brochures after group interventions for
adults with ADHD and by advertisements on Web sites of ADHD
associations. Only participants in the group intervention that had
no other Axis I or II diagnosis were approached for participation.
Prior to the group intervention, diagnosis was set by an experi-
enced ADHD team including a psychiatrist, a clinical psycholo-
gist, a neuropsychologist, and a registered psychiatric nurse.
Briefly, the clinical assessment included a standard diagnostic
protocol, including a clinical, semistructured interview with both
the patient and a cross-informant (usually a parent or a spouse). At
the very least, some elements of this interview were conducted by
all disciplines separately. In these interviews, the following topics
were collected: demographic, academic, and work history; devel-
opmental course; the Diagnostic and Statistical Manual of Mental
Disorders (4th ed. [DSM–IV]) ADHD symptoms and criteria, as
well as ADHD-related cognitive complaints, for example, execu-
tive dysfunctions; and the comorbid or co-occurring psychopathol-
ogy. In addition, self-report rating scales, including a Dutch ver-
sion of the Wender Utah Rating Scale (Ward, Wender, & Reim-
herr, 1993) and a Dutch version of the ADHD Rating Scale based
on the DSM–IV criteria (comparable to the one described in the
Questionnaires and Neuropsychological Tests section), were com-
pleted. An extended neuropsychological examination (e.g., intelli-
gence, attention, inhibitory control, and learning) and a review of
school reports, if available, were also part of the assessment. Final
DIBBETS, EVERS, HURKS, BAKKER, AND JOLLES
diagnosis was based on clinical consensus in which all the data
mentioned above were judged. This multidisciplinary procedure is in
line with earlier published reports on classifying ADHD in adults
(e.g., Barkley, 2005; Kooij et al., 2008). Subsequently, each ADHD
participant received, next to the information package, a letter empha-
addition, we asked each ADHD participant to provide a medical or
diagnostic report of a (former) specialist or general practitioner. Un-
fortunately, only nine reports were received. However, this was not
considered a problem as these reports served only as an additional
control measure. Finally, all participants were asked via a short
interview whether they had any other disorders in addition to the
diagnosed ADHD. This whole procedure resulted in the following
distribution of co-occurring characteristics, that is, no full DSM–IV
classification: ADHD and depressive symptoms (n ? 2), ADHD
and obsessive–compulsive symptoms (n ? 1), ADHD and sub-
stance abuse (cannabis, n ? 1), and ADHD and learning problems
(dyslexia, n ? 1). Furthermore, one participant received epilepsy
medication (Depakine) in addition to Ritalin to reduce ADHD-
related complaints, one received antihistamine, and one cholesterol
medication. Fourteen ADHD participants used methylphenidate-
based medication (Ritalin n ? 11; Concerta n ? 3). None of the
control participants reported Axis I or II problems. One control
participant reported the use of antiasthma medication (Seritide
Exclusion criteria for all participants were presence of any
current Axis I psychiatric diagnosis other than ADHD, IQ ? 80
(based on education level and Wechsler Adult Intelligence Scale—
Revised [WAIS–R] Block Design; see below), neurological
trauma or disorder, and contraindication for MRI. On suspicion of
ADHD problems in the control group during testing, the Current
Symptoms Scale and Childhood Symptoms Scale was adminis-
tered to all participants (ADHD and controls) after completion of
the experiment (see description in Neuropsychological Tests be-
low). This checklist enabled verification of the presence of ADHD
problems in the ADHD group and the absence of these problems
in the control group. Inclusion criteria for the ADHD group were
a minimum score of six symptoms on at least one of the current
symptoms subscales and retrospective childhood subscales (Bark-
ley & Murphy, 1998). Inclusion criteria for the control group were
a maximum of two symptoms on each of the current symptoms
scales and a maximum of three symptoms on each of the retro-
spective childhood subscales (mean norm scores; Barkley & Mur-
The study was carried out in accordance with the Declaration of
Helsinki and was started after approval of the local Medical
Ethical Committee (Academic Hospital Maastricht, azM, the Neth-
erlands, nr: 05.045.4). Before onset of the study, all participants
provided written informed consent. Each participant was paid 50
Euros and received a compact disk with their anatomical brain
Questionnaires and Neuropsychological Tests
The Current Symptoms Scale and Childhood Symptoms
Scale (Barkley & Murphy, 1998).
Scale and Childhood Symptoms Scale is a short, self-report screen-
ing questionnaire to measure ADHD in adults. The questions are
based on the ADHD symptom list of the DSM–IV (normative data
The Current Symptoms
are published in Barkley & Murphy, 1998). The questionnaire
contains a list concerning current symptoms of ADHD and a
retrospective list concerning symptoms during childhood. For the
present study, participants with ADHD were provided with two
copies of the current symptoms list, one copy to fill in judging their
behavior while using ADHD medication, one copy for judging
their behavior during an off-medication period. All control partic-
ipants received only one current symptoms list. In addition, all
participants received one retrospective list containing items related
to childhood behavior. Each list contains nine items measuring
inattention and nine items measuring hyperactivity (six items) and
impulsivity (three items) problems. Each item can be scored on a
4-point-scale ranging from 1 (never) to 4 (very often) A score is
considered a symptom if an item is answered with often or very
often, leading to a maximum of nine symptoms for inattention and
nine symptoms for hyperactivity/impulsivity (Barkley & Murphy,
The WAIS–R Block Design (Uterwijk, 2000).
Design subtest of the WAIS–R measures visuospatial abilities and
is frequently used as an estimator of general intelligence (Kaufman
& Lichtenberger, 1999). Furthermore, if matched for level of
education, no difference in performance on the WAIS–R Block
Design between adults with ADHD and normal control is expected
(Bridgett & Walker, 2006; Seidman, Biederman, Weber, Hatch, &
Faraone, 1998). Therefore, in the present study, where groups were
matched for level of education, WAIS–R Block Design was used
to check for similar levels of general intelligence in each group, or
in case a difference was observed between the groups, it func-
tioned as a covariate in the data analyses.
Functional MRI Task: The Switch Task (ST)
The stimuli were presented on a screen and viewed via a mirror
mounted on the head coil. The software used was programmed in
E-prime (Psychology Software Tools, Version 220.127.116.11, http://www
.pstnet.com/). The task used was the Switch Task (ST), which was
developed at our department (for a detailed description, see Dib-
bets et al., 2006; Dibbets & Jolles, 2006). This relatively easy task
is based on a standard switch paradigm and can be used in studies
with children and adults. The ST is presented as a simple computer
game and consists of two similar, yet conflicting, tasks: the “day
task” and the “night task” (see Figure 1).
During the day task, an orange house and a blue house with a
white cat in between them were presented against a daytime sky.
The left and right position of the houses varied across trials. One
of the two houses could be selected by pressing the accompanying
button (i.e., left or right). The role of the orange and blue house
and the order of the day and night tasks were counterbalanced. For
sake of brevity, only one version is described. During the day task,
selecting the orange house (i.e., the correct response) was re-
warded by replacing this house with a treasure chest. Selecting the
blue house (i.e., the incorrect response) was punished by marking
this house with a pink cross. The absence of a response was neither
rewarded nor punished. During the night task, the houses and cat
were presented against a nighttime sky and selection of the blue
house was rewarded and selection of the orange house was pun-
The duration of each trial was 6,500 ms (2 TRs, repetition time
between successive pulses applied to the same brain slice) and
NEUROIMAGING IN ADULTS WITH ADHD
both response time (RT) and response accuracy were recorded.
During the scan session, two experimental conditions were pre-
sented: the nonswitch condition and switch condition (block de-
sign). In the nonswitch condition, one task was repeatedly pre-
sented for eight trials (e.g., day–day–day or night–night–night). In
the switch condition, eight trials of day and night were randomly
mixed (e.g., day–night–night–day–night). Within each switch
block, at least five switches between day and night took place
(M ? 5.25 switches per block). A signal screen before each block
indicated whether only the day task, only the night task, or a mix of
both tasks would be presented for the next eight trials. A total of eight
8-trial blocks was presented with alternating nonswitch (total ? 4
blocks) and switch conditions (total ? 4 blocks). The onset of each
block was synchronized with the scanner and the time between
two blocks was at least 6 s. During the scan session, the white cat on
the screen was gradually filled with colors to give an indication of the
remaining test time. The task started with a baseline condition (15
TRs) in which no response was required. The participants viewed
a picture of a crown covered with gems placed in the center of a
green screen. During this baseline, some of the gems occasionally
sparkled (on average, seven sparkles) to preserve attention to the
picture. The participant was instructed to passively watch the
scene. The task lasted for 10 min. The ST was practiced in a
simulation scanner about 2 weeks prior to the actual scan session.
This practice version was identical to the scan version with the
exception that no baseline was presented.
The participants were scanned using a 1.5T MRI whole-body
scanner (Philips Gyroscan ACS-NT, Eindhoven, the Netherlands)
with a synergy head coil. Head fixation was accomplished by using
foam padding. Each scan session started with a sagittal T1-
weighted anatomical image (150 slices, imaging matrix ? 256 ?
256, slice thickness 1 mm, no gap) directly followed by the ST.
Functional images were based on a T2?weighted gradient echo
sequence with the following parameters: flip angle ? 90°, TE ? 48
ms, TR ? 3,250 ms, field of view ? 224 ? 224, imaging matrix ?
128 ? 128, slice thickness ? 3 mm, slice gap ? 0 mm, 32 slices.
Scanning orientation was axial and slice order was interleaved. A
total of 185 volumes was collected.
The experiment was conducted in two 2-hr sessions, about 2
weeks apart. In Session 1, the ST was practiced in a simulation
scanner and the Block Design subtest of the WAIS–R was admin-
istered. Participants with ADHD were requested to refrain from
any ADHD medication at least 24 hr prior to the second session,
during which anatomical and functional scans were made. After
completion of the entire experiment, all participants were asked to
complete the Current Symptoms Scale and Childhood Symptoms
Behavioral Data Analysis
The WAIS–R Block Design data were transformed to standard
T scores (standardized mean ? 50, SD ? 10). Dependent measures
of the ST were the total number of errors per condition, including
omissions, and the mean RT of correct responses in each condition
(nonswitch and switch). Switch costs were calculated by subtract-
ing the mean RT in the nonswitch condition from the mean RT in
the switch condition (RT costs), and error costs were calculated by
subtracting the total number of errors in the nonswitch condition
from the total number of errors in the switch condition.
The data of the WAIS–R Block Design, the symptom checklists,
and ST were analyzed for group differences using parametric tests
(analysis of variance [ANOVA] and repeated measures general
linear model [GLM]). In each analysis, group (ADHD vs. control)
functioned as a between-subjects factor. In the repeated measures
GLM, condition (switch vs. nonswitch) served as a within-subject
factor. Partial eta squared (?p
size. Bonferroni tests were used to adjust the significance level
when multiple comparisons were made. The rejection criterion was
set at p ? .05.
2) was used as a measure for effect
an example of an incorrect trial of the night task. Note that the maximum response time is 4,000 ms; if the
participant responds within this time limit, the intertrial duration is proportionally enlarged to fill up to the total
trial time to 6,500 ms.
The Switch Task: On the left side is an example of a correct trial of the day task, on the right side
DIBBETS, EVERS, HURKS, BAKKER, AND JOLLES
The fMRI data were statistically analyzed using Brainvoyager
QX Version 18.104.22.1680 software (Brain Innovation, Maastricht,
the Netherlands). Preprocessing procedures consisted of 3D mo-
tion correction, slice time correction, spatial Gaussian smoothing
(FWHM 8 mm), and temporal filtering (highpass filter: 3 cycles/
block and linear trend removal). For each participant, functional
and anatomical images were spatially coregistered and trans-
formed to standard Talairach space (Talairach & Tournoux, 1988).
After individual GLM analyses, we ran a random effect analysis
(separate subject predictors, z transformation) to assess task-re-
lated activity and to detect differences in brain activity between the
ADHD and control groups. In this model, two predictors were
incorporated, the switch and nonswitch conditions. These predic-
tors were obtained using a model of the hemodynamic response
locked to the onset of each block. For each participant, a general
response level was estimated and activation on the predictors was
expressed relative to this baseline value. Subsequently, a random
effects analysis of covariance (ANCOVA) was run using the
random effect multisubject GLM as input. In this analysis, the
nonswitch and switch condition served as within-subjects factor
and group (ADHD vs. control) functioned as a between-subjects
factor. Finally, overlay random effects ANCOVAs were used to
map the several contrasts (see below). Cluster-level thresholding
(1,000 iterations) was applied as a correction method for multiple
comparisons and to reduce the likelihood of Type I errors to less
than .05 (Forman et al., 1995; Goebel, Esposito, & Formisano,
2006). Effect sizes were calculated using ?p
2. All analyses were
Brain activity related to task switching was assessed by com-
paring the switch blocks with the nonswitch blocks. This task-
related contrast was calculated for all data (two groups together).
Next, differences in switch-related activity (switch–nonswitch)
between groups were assessed. Anatomical labeling of the clusters
was carried out using the Talairach and Tournoux atlas (1988).
Demographic and Neuropsychological Data
Five participants were excluded. The participant with predom-
inantly attention problems displayed too few symptoms of
AD(H)D on the current and childhood subscales of the Current
Symptoms Scale and Childhood Symptoms Scale (maximum of
three symptoms per scale). Three participants of the control group
displayed too many ADHD symptoms (five or more symptoms on
the current or childhood subscale). Finally, one ADHD participant
was excluded because of very poor task performance, indicating
either a lack of motivation or poor task comprehension, which
could also have effects on the brain activity observed. The average
RT of this participant (1,804 ms) on the ST deviated more than 5
standard deviations from the remainder of included ADHD partic-
ipants (M ? 826 ms, SD ? 181), the total number of errors (only
one error) deviated less than 1 standard deviation from the group
(M ? 3.07, SD ? 2.25). Both of the two excluded AD(H)D partic-
ipants used Ritalin and both reported only ADHD-related symptoms.
Table 1 summarizes the demographic and neuropsychological data of
the remaining participants (14 controls; 15 ADHD). The two groups
did not significantly differ with regard to age, education level, and
Demographic and Behavioral Data for the ADHD and Control Groups
VariableADHD (n ? 15) Control (n ? 14)
Mean (SD) age (years)
Mean (SD) education level
Mean (SD) Block Design T score
Mean (SD) current symptoms
No medication inattention
No medication hyperactivity/impulsivity
Mean (SD) childhood symptoms
Mean (SD) ST RTs
Mean (SD) ST errors (total per condition)
symptoms ? Current Symptoms Scale; childhood symptoms ? Childhood Symptoms Scale.
?Significant difference between the ADHD and control group, p ? .05.
ADHD ? attention-deficit/hyperactivity disorder; ST ? Switch Task; RT ? response time; Current
NEUROIMAGING IN ADULTS WITH ADHD
WAIS–R Block Design score, Fs(1, 28) ? 1.54. As expected, the
ADHD group displayed more inattentive and hyperactivity impulsiv-
ity symptoms on the Current Symptoms Scale and Childhood Symp-
toms Scale, Fs(1, 28) ? 40.63, ps ? .001, ?p
ipants scored themselves as less inattentive and hyperactive during an
on-medication period, Fs(1, 14) ? 38.25, ps ? .001, ?p
compared with an off-medication period.
2? .58. ADHD partic-
The Switch Task (ST)
Table 1 summarizes the mean RTs and errors for each group on the
ST. As expected, the analysis of the RTs revealed that participants
responded faster in the nonswitch than in the switch condition, F(1,
27) ? 98.46, p ? .001, ?p
Condition interaction was observed, Fs(1, 27) ? 1.27, ps ? .27. A
separate analysis of the RT costs revealed no difference in switch
costs between the two groups, F(1, 28) ? 1.27, p ? .27, indicating no
specific executive control deficit in the ADHD group.
The analysis of the total number of errors in each condition
revealed that the ADHD group made, overall, significantly more
errors (range ? 0–7) than did the control group (range ? 0–3),
F(1, 27) ? 7.87, p ? .01, ?p
or Group ? Condition interaction was observed, Fs(1, 27) ? 1.28,
ps ? .21. Because of the low error rates, no error costs were
calculated. These results indicate that the increase of errors in the
ADHD group was not specific for the switch condition. A closer
look at the type of errors made revealed that in both groups more
than 80% of the errors were caused by selecting the incorrect
house and less than 20% through response omission. This distri-
bution was similar in both groups, Fs ? 1.
2? .78. No group effect or Group ?
2? .23. No main effect of condition
For all participants, the amount of movement was within accept-
able ranges (?voxel size). Analyses of the six parameters generated
during motion correction revealed no significant group differences in
absolute mean translation movement (ADHD, 0.16 ? 0.26 mm;
control, 0.19 ? 0.20 mm) and the absolute mean rotation movement
(ADHD, 0.18 ? 0.21 degrees; control, 0.20 ? 0.27 degrees), Fs ? 1.
Task-Related Activity: Switch Versus Nonswitch
The comparison of the switch condition versus the nonswitch
condition represents the neural correlates of additional cognitive
processes related to task switching. For results, see Table 2. Three
large task-related activation clusters were observed in the right
superior frontal gyrus (BA 6), the left superior parietal lobule (BA
7), and the left orbitofrontal cortex (BA 47).
Group Differences in Task Switching
Table 3 and Figure 2 display the interaction between group and
condition. This interaction reflects group differences in additional
brain activation associated with task switching (switch vs. non-
switch). The ADHD group revealed more activation than did the
control group in the right middle temporal gyrus (BA 19), the right
dorsal anterior cingulate cortex (BA 32), the right precuneus (BA
31), the right lingual gyrus (BA 17), the left precentral gyrus
(BA 6, supplementary motor area), and the left insula (BA 13). The
control group displayed more activation in the right putamen, the
right dorsal posterior cingulate cortex (BA 31), the left medial
frontal cortex (BA 6), the right thalamus, the left orbitofrontal
cortex (BA 47) extending to the claustrum, and the postcentral
gyrus (BA 2).
The present fMRI study evaluated differences in brain activity
between normal adults and adults with ADHD during the perfor-
mance of a task-switching paradigm, the ST. In this task, partici-
pants had to switch between two similar yet conflicting tasks
(switch condition) or perform only one of these two tasks (non-
switch condition). The behavioral data of the ST are in line with
other task-switching studies (Allport, Styles, & Hsieh, 1994; Dib-
bets & Jolles, 2006). That is, both groups responded slower in the
switch than in the nonswitch condition, reflecting a stronger en-
gagement of executive control processes during the switch condi-
tion. However, contrary to previous studies, no specific executive
control deficits were observed in the ADHD group (e.g., White &
Shah, 2006). The switch costs, defined as a difference in RT or
error performance between the switch and nonswitch conditions,
did not differ between the control and ADHD groups. The only
group difference was a higher overall error rate for the ADHD
group (M ? 3.1 errors) compared with the control group (M ? 1.2
errors). Although the absence of a difference in switch costs
between the two groups was somewhat unexpected, this lack of an
executive deficit has been observed more often in adolescents
(Smith et al., 2006) and adults with ADHD (for reviews, see
Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005; Woods, Love-
joy, & Ball, 2002). Altogether, the behavioral data of the ST do not
provide evidence for a specific executive control deficit in adults
with ADHD, but suggest a more general information-processing
deficit such as attention or inhibition problems.
Task-related brain activation (switch vs. nonswitch condition)
was observed in large clusters in the right superior frontal gyrus,
the left superior parietal lobule, and the inferior frontal (ventrolat-
eral PFC) gyrus extending to the claustrum. These results concord
Brain Regions Showing Greater Activation During the Switch Condition Compared With the
Nonswitch Condition (Whole-Brain Analyses)
TAL Cluster size
Brain region BA
[13, 15, 65]
[?22, ?57, 32]
[?26, 25, 0]
Superior frontal gyrus
Superior parietal lobule
Inferior frontal gyrus/claustrum
expressed in anatomical voxels (1 ? 1 ? 1 mm). TAL ? Talairach coordinates (x, y, z); BA ? Brodmann Areas.
Switch–nonswitch: attention-deficit/hyperactivity disorder (ADHD) and controls (n ? 29). The cluster size is
DIBBETS, EVERS, HURKS, BAKKER, AND JOLLES
with results of other imaging studies examining task switching
(Braver, Reynolds, & Donaldson, 2003; Crone, Wendelken, Do-
nohue, & Bunge, 2006; DiGirolamo et al., 2001).
The most important comparison of the present study was that of
the task-related activation differences, that is, the switch–non-
switch activation, between the ADHD and healthy controls. This
contrast revealed that adults with ADHD more strongly engaged
the right middle temporal gyrus, dorsal anterior cingulate cortex
(dACC), precuneus, lingual gyrus, and the left precentral gyrus
(supplementary motor area) and insula during task switching.
Remarkably, the controls also engaged these regions, but during
the nonswitch rather than the switch condition, Fs ? 6.71, ps ?
dorsal posterior cingulate cortex, thalamus, and the left medial
frontal gyrus, orbitofrontal cortex (ventrolateral PFC), and post-
central gyrus. Likewise, these areas were also engaged in the
ADHD group, but rather during the nonswitch than switch condi-
tion, Fs ? 7.46, ps ? .05, ?p
14) ? 4.32, p ? .057. An ad hoc analysis of the ? values of each
cluster with the switch condition of one group and the nonswitch
condition of the other group as between-subjects factors revealed
a similar pattern. Only one cluster revealed a group difference, that
is, the activity in the medial frontal gyrus was higher in the
nonswitch condition of the ADHD compared with the switch
condition of the control group, t ? 3.31, p ? .005. This might
indicate that similar brain areas are involved during the switch
task, but that the conditions that evoke the recruitment of these
areas differ between the control and ADHD groups.
The results confirm our hypothesis that adults with ADHD
should display different frontostriatal and parietal activation than
healthy controls during the performance of an executive control
task. Furthermore, the data concord with other adult ADHD stud-
ies in that the ADHD group showed a relative decrease in activa-
tion in the thalamus (Banich et al., 2009), posterior cingulate gyrus
(Hale et al., 2007), and inferior frontal gyrus (Banich et al., 2009;
Hale et al., 2007). A relative increase was observed in the precen-
tral gyrus (Sunshine et al., 1997), temporal areas (Banich et al.,
2009; Hale et al., 2007), and the insula (Banich et al., 2009; Bush
et al., 1999). Compared with our go/no-go task, in which almost
2? .31. Controls more strongly engaged the right putamen,
2? .34, but, inferior frontal gyrus, F(1,
the same research sample was used, only one corresponding region
was observed. During positive feedback, the ADHD group dis-
played less activation in the left inferior frontal cortex (orbitofron-
tal part) compared with the healthy controls. No other similarities
A more important comparison is that with the neuroimaging
study of Smith and colleagues (2006). In that study, adolescents
with ADHD and healthy controls performed a switch task. Al-
though their group results deviate from the present data, the
separate group analyses of Smith et al. do show similarities. That
is, compared with the healthy controls, the ADHD group addition-
ally recruited the dACC during task switching but failed to activate
The most remarkable result of the present study is the increase
rather than decrease in activation of the dACC in the ADHD
group. Even though the study of Smith et al. (2006) also observed
this increase, most functional neuroimaging studies report a hypo-
functioning of the dACC in ADHD (for reviews, see Bush et al.,
2005; Schneider, Retz, Coogan, Thome, & Rosler, 2006). Neural
activity of the dACC is associated with error responses and neg-
ative feedback (Holroyd et al., 2004). Although no specific exec-
utive control deficit was observed in the ADHD group, separate
ANOVAs indicate that the ADHD group made significantly more
errors during the switch condition, F(1, 28) ? 4.29, p ? .05, but
not during the nonswitch condition (p ? .16), than did the control
group. It is, therefore, possible that the observed increase in the
dACC in the ADHD group is caused by this difference in errors
and the accompanying negative feedback. Although this might
seem a sound explanation, one has to bear in mind that the number
of errors and negative feedback was small (ADHD M ? 3.07;
controls M ? 1.21). In addition, a repeated measures GLM anal-
ysis with the extracted ? values (z transformation, baseline cor-
rected, switch and nonswitch condition) from the ACC activity
revealed that incorporating the total number of errors as a covariate
did not abolish the observed Group ? Condition interaction, F(1,
27) ? 12.98, p ? .005.
Next to error and feedback processing, dACC activation is also
strongly associated with the detection of conflicting information
(for an overview, see Carter & van Veen, 2007). It is suggested
Differences in Brain Activation During the Switch Task Between the ADHD and the Control Groups (Whole-Brain Analyses)
TAL Cluster size
Brain region BA
ADHD ? controls
[38, ?57, 18]
[22, 31, 24]
[14, ?54, 30]
[19, ?82, 0]
[?35, ?12, 33]
[?29, ?39, 21]
Controls ? ADHD
[23, 6, 2]
[20, ?30, 44]
[?10, ?3, 56]
[3, ?12, 11]
[?28, 11, ?4]
[?35, ?30, 33]
Middle temporal gyrus
Anterior cingulate gyrus
Medial frontal gyrus
Inferior frontal gyrus/claustrum
(x, y, z); BA ? Brodmann Areas.
ADHD ? attention-deficit/hyperactivity disorder. The cluster size is expressed in anatomical voxels (1 ? 1 ? 1 mm). TAL ? Talairach coordinates
NEUROIMAGING IN ADULTS WITH ADHD
that following the detection of a conflict, the DLPFC is engaged in
order to reduce this conflict. Task switching is known to induce
response competition and, therefore, can result in the engagement of
the dACC. As adults with ADHD experience more inhibition prob-
lems (Epstein et al., 2001; Murphy, 2002b; Ossmann & Mulligan,
2003), one can argue that they more strongly engaged the dACC to
reduce this conflict in order to perform at the same level as the
control group. Unfortunately, no subsequent increase in the
DLPFC in the ADHD group was observed to support this notion.
An alternative explanation is that the dACC was more strongly
engaged to maintain the focus of attention. The study of Weiss-
man, Gopalakrishnan, Hazlett, and Woldorff (2005) indicated a
role for dACC in healthy participants in focusing attention on
relevant stimuli, especially when distracting events are present. In
the case of our study, one can argue that the ADHD group more
strongly engaged the dACC to boost attention during the distract-
ing switch condition in order to obtain a normal level of task
performance. This attention-related explanation is in line with the
changing nature of ADHD symptoms across time. As children with
ADHD mature, hyperactivity/impulsivity symptoms decline, but
inattentive symptoms remit in fewer cases (Biederman et al.,
In line with the previous attentional explanation, more activity
deviations were observed in attention-related brain structures. At-
tention can be subdivided in three relatively independent aspects,
that is, alerting, orienting, and resolving conflict among responses
(executive attention), each linked to separable brain areas (Posner
& Petersen, 1990). Structures that are associated with the alerting
network are the right frontal lobe, right parietal lobe, thalamic
areas, and locus coeruleus; the orienting network is associated with
the parietal lobe and the fusiform gyrus; the executive attention
network seems to include midline frontal areas including the ACC,
supplementary motor area, and portions of the basal ganglia
(Berger & Posner, 2000; Fan, McCandliss, Fossella, Flombaum, &
Posner, 2005). Although the task used for the present study was
not designed to measure separate attention aspects, we observed
disorder (ADHD) and control groups (whole-brain analysis) and accompanying ? values. Red indicates more
activation in the ADHD group, blue indicates more activation in the control group. A ? anterior cingulate cortex;
B ? precentral gyrus; C ? insula; D ? medial frontal gyrus; E ? putamen; and F ? thalamus.
Differences in task-related activation (switch–nonswitch) between the attention-deficit/hyperactivity
DIBBETS, EVERS, HURKS, BAKKER, AND JOLLES
relatively increased activity in the ACC and supplementary motor
area, and decreased activation in the putamen, inferior frontal
gyrus, and medial frontal gyrus in the ADHD group. These struc-
tures are related to the executive attention network. In addition, we
observed decreased activity in the thalamus, a structure that is
involved in activation of the alerting network (e.g., Fan et al.,
2005; for an overview, see Raz, 2004).
A final network that can be linked to ADHD symptoms is the
default attention network proposed by Raichle and colleagues
(2001). This network comprises a set of brain regions located at the
midline areas of the brain (e.g., posterior cingulate gyrus, precu-
neus, medial PFC, and ACC; see also Fassbender et al., 2009) and
is thought to suppress brain activity to allow goal-directed behav-
ior. In accordance with this default network explanation, Fassbender
and colleagues (2009) observed that increased distractibility in chil-
dren with ADHD coincided with the inability to sufficiently suppress
activity in the medial PFC, which is part of the default-mode attention
network. In the present research, the amount of inattention (based on
the Current Symptoms Scale) significantly correlated with an in-
crease in activity during the complex switch condition (extracted
z-transformed and normalized ? values) in the ACC, ? ? 0.543,
p ? .01. That is, more attention problems coincided with more
activity, which might indicate less suppression. This speculation is
in line with the notion that default-mode network areas might be
insufficiently suppressed in ADHD. However, contrary to the
findings of Fassbender et al., medial frontal activity during the
switch condition significantly correlated with fewer attention prob-
lems, ? ? ?0.423, p ? .05. No other significant correlations
between attention problems and posterior cingulate and precuneus
activity were observed, ps ? .10. A possible explanation for this
discrepancy is that, depending on the task, different suppression
patterns are observed. In addition, it is thinkable that lack of
suppression in the ACC evokes more suppression in other areas of
the default-mode network, such as the medial PFC, in order to
successfully accomplish the task.
Summarizing the results, it seems that the ADHD group showed
a hypoactivation of the alerting system and more strongly engaged
parts of the executive attention system. In addition, hypo- and
hyperactivation in the default-mode attention network, linked to
executive control, were observed. These results are in line with the
notion that ADHD is characterized by deviations in the alerting,
executive attention (Berger & Posner, 2000), and default-mode
networks (Fassbender et al., 2009). The increase in executive areas
is probably to compensate for lapses of attention and to perform
the task at the same level as normal adults.
Although the results are promising, the current study does have
a number of limitations. First, the control participants were not
screened as thoroughly as the ADHD participants. This might have
resulted in a deviating control sample. Although we tried to correct
this by excluding all participants with a relatively high inattention
or hyperactivity/impulsivity score, this does not guarantee that the
included participants did not have other (neuro)psychological
problems. Therefore, in the future, we would strongly recommend
using a standard neuropsychological assessment for all partici-
pants. Second, the baseline condition of the ST was not a tradi-
tional resting state, but involved watching a fluctuating visual
presentation. This condition might have tapped cognitive pro-
cesses, such as sustained attention, that are known to be disturbed
in adults with ADHD (e.g., Marchetta, Hurks, De Sonneville, et
al., 2008). The subtraction of this nonneutral baseline condition
might have obscured possible group differences regarding these
cognitive processes. Therefore, in a future experiment, a less
cognitive demanding resting state should be incorporated. A third
topic that needs to be mentioned is that a block rather than an
event-related design was used. An advantage of an event-related
design is that it can disentangle actual task-switching effects from
working memory load effects. This can be accomplished by com-
paring the switch trials and nonswitch trials within a switch block
(e.g., Kray & Lindenberger, 2000). In the present study, this
resulted in too few nonswitch events in the switch blocks for data
analyses (seven nonswitch trials). The reason that we did not use
an event-related design is that we plan to offer the current task
to children and adolescents with ADHD in the near future. This
will enable us to track developmental changes in brain activa-
tion patterns associated with task switching in ADHD. A pilot
study in normal control children indicated that presenting the
ST in an event-related fashion resulted in many errors, with
some children not mastering the task at all.
In conclusion, the data from the present study add to the view
that ADHD is associated with altered brain activation patterns in
the frontostriatal circuitry during the performance of an executive
control task. The expected deficit in executive control was not
behaviorally expressed in enlarged switch costs, but visible as
hypo- and hyperactivity of several brain regions. This discrepancy
between behavioral and fMRI findings stresses the importance of
the use of neuroimaging techniques in cognitive research. Not only
do these techniques provide additional, otherwise unnoticed, in-
formation, they also create (future) clinical application possibili-
ties, such as treatment evaluations.
Allport, D. A., Styles, E. A., & Hsieh, S. (1994). Shifting intentional set:
Exploring the dynamic control of tasks. In C. Umilta & M. Moscovitch
(Eds.), Attention and performance 15: Conscious and nonconscious
information processing. Attention and performance series (pp. 421–
452). Cambridge, MA: MIT Press.
American Psychiatric Association. (1994). Diagnostic and statistical man-
ual of mental disorders (4th ed.). Washington DC: Author.
Banich, M. T., Burgess, G. C., Depue, B. E., Ruzic, L., Bidwell, L. C.,
Hitt-Laustsen, S., . . . Willcutt, E. G. (2009). The neural basis of sus-
tained and transient attentional control in young adults with ADHD.
Neuropsychologia, 14, 3095–3104.
Barkley, R. A. (1996). Attention deficit-hyperactivity disorder. In E. J.
Mash & R. A. Barkley (Eds.), Child psychopathology (pp. 63–112). New
York: Guilford Press.
Barkley, R. A. (2005). Attention-deficit hyperactivity disorder: A handbook
for diagnosis and treatment (3rd ed.). New York: Guilford Press.
Barkley, R. A., & Murphy, K. R. (1998). Attention-deficit hyperactivity
disorder: A clinical workbook (2nd ed.). New York: Guilford Press.
Berger, A., & Posner, M. I. (2000). Pathologies of brain attentional
networks. Neuroscience & Biobehavioral Reviews, 24, 3–5.
Biederman, J., Mick, E., & Faraone, S. V. (2000). Age-dependent decline
of symptoms of attention deficit hyperactivity disorder: Impact of re-
mission definition and symptom type. The American Journal of Psychi-
atry, 157, 816–818.
Boonstra, A. M., Oosterlaan, J., Sergeant, J. A., & Buitelaar, J. K. (2005).
Executive functioning in adult ADHD: A meta-analytic review. Psycho-
logical Medicine, 35, 1097–1108.
NEUROIMAGING IN ADULTS WITH ADHD
Bradshaw, J. L. (2001). Developmental disorders of the frontostriatal
system: Neuropsychological, neuropsychiatric and evolutionary per-
spectives. Hove, England: Psychology Press.
Braver, T. S., Reynolds, J. R., & Donaldson, D. I. (2003). Neural mecha-
nisms of transient and sustained cognitive control during task switching.
Neuron, 39, 713–726.
Bridgett, D. J., & Walker, M. E. (2006). Intellectual functioning in adults
with ADHD: A meta-analytic examination of full-scale IQ differences
between adults with and without ADHD. Psychological Assessment, 18,
Bush, G., Frazier, J. A., Rauch, S. L., Seidman, L. J., Whalen, P. J., Jenike,
M. A., . . . Biederman, J. (1999). Anterior cingulate cortex dysfunction
in attention-deficit/hyperactivity disorder revealed by fMRI and the
Counting Stroop. Biological Psychiatry, 45, 1542–1552.
Bush, G., Valera, E. M., & Seidman, L. J. (2005). Functional neuroimaging
of attention-deficit/hyperactivity disorder: A review and suggested fu-
ture directions. Biological Psychiatry, 57, 1273–1284.
Carter, C. S., & van Veen, V. (2007). Anterior cingulate cortex and conflict
detection: An update of theory and data. Cognitive, Affective, & Behav-
ioral Neuroscience, 7, 367–379.
Cepeda, N. J., Cepeda, M. L., & Kramer, A. F. (2000). Task switching and
attention deficit hyperactivity disorder. Journal of Abnormal Child Psy-
chology, 28, 213–226.
Crone, E. A., Wendelken, C., Donohue, S. E., & Bunge, S. A. (2006).
Neural evidence for dissociable components of task-switching. Cerebral
Cortex, 16, 475–486.
De Bie, S. E. (1987). Standaardvragen 1987: Voorstellen voor uniform-
ering van vraagstellingen naar achtergrondkenmerken en interviews
[Standard questions 1987: Proposal for uniformization of questions
regarding background variables and interviews] (2nd ed.). Leiden, the
Netherlands: Leiden University Press.
Dibbets, P., Bakker, K., & Jolles, J. (2006). Functional MRI of task
switching in children with specific language impairment (SLI). Neuro-
case, 12, 71–79.
Dibbets, P., Evers, L., Hurks, P., Marchetta, N., & Jolles, J. (2009).
Differences in feedback- and inhibition-related neural activity in adult
ADHD. Brain and Cognition, 70, 73–83.
Dibbets, P., & Jolles, J. (2006). The Switch Task for Children: Measuring
mental flexibility in young children. Cognitive Development, 21, 60–71.
Dietrich, T., Krings, T., Neulen, J., Willmes, K., Erberich, S., Thron, A., &
Sturm, W. (2001). Effects of blood estrogen level on cortical activation
patterns during cognitive activation as measured by functional MRI.
NeuroImage, 13, 425–432.
DiGirolamo, G. J., Kramer, A. F., Barad, V., Cepeda, N. J., Weissman,
D. H., Milham, M. P., . . . McAuley, E. (2001). General and task-specific
frontal lobe recruitment in older adults during executive processes: A
fMRI investigation of task-switching. NeuroReport, 12, 2065–2071.
Dove, A., Pollmann, S., Schubert, T., Wiggins, C. J., & von Cramon, D. Y.
(2000). Prefrontal cortex activation in task switching: An event-related
fMRI study. Cognitive Brain Research, 9, 103–109.
Durston, S. (2003). A review of the biological bases of ADHD: What have
we learned from imaging studies? Mental Retardation and Developmen-
tal Disabilities Research Reviews, 9, 184–195.
Epstein, J. N., Johnson, D. E., Varia, I. M., & Conners, C. K. (2001).
Neuropsychological assessment of response inhibition in adults with
ADHD. Journal of Clinical and Experimental Neuropsychology, 23,
Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I., & Posner, M. I.
(2005). The activation of attentional networks. NeuroImage, 26, 471–
Faraone, S. V., Biederman, J., Spencer, T., Wilens, T., Seidman, L. J.,
Mick, E., & Doyle, A. E. (2000). Attention-deficit/hyperactivity disorder
in adults: An overview. Biological Psychiatry, 48, 9–20.
Fassbender, C., Zhang, H., Buzy, W. M., Cortes, C. R., Mizuiri, D.,
Beckett, L., & Schweitzer, J. B. (2009). A lack of default network
suppression is linked to increased distractibility in ADHD. Brain Re-
search, 1273, 114–128.
Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A.,
& Noll, D. C. (1995). Improved assessment of significant activation in
functional magnetic resonance imaging (fMRI): Use of a cluster-size
threshold. Magnetic Resonance in Medicine, 33, 636–647.
Goebel, R., Esposito, F., & Formisano, E. (2006). Analysis of functional
image analysis contest (FIAC) data with Brainvoyager QX: From single-
subject to cortically aligned group general linear model analysis and
self-organizing group independent component analysis. Human Brain
Mapping, 27, 392–401.
Hale, T. S., Bookheimer, S., McGough, J. J., Phillips, J. M., & McCracken,
J. T. (2007). Atypical brain activation during simple and complex levels
of processing in adult ADHD. Journal of Attention Disorders, 11,
Holroyd, C. B., Nieuwenhuis, S., Yeung, N., Nystrom, L., Mars, R. B.,
Coles, M. G., & Cohen, J. D. (2004). Dorsal anterior cingulate cortex
shows fMRI response to internal and external error signals. Nature
Neuroscience, 7, 497–498.
Hurks, P. P., Hendriksen, J. G., Vles, J. S., Kalff, A. C., Feron, F. J., Kroes,
M., . . . Jolle, J. (2004). Verbal fluency over time as a measure of
automatic and controlled processing in children with ADHD. Brain and
Cognition, 55, 535–544.
Kaufman, A. S., & Lichtenberger, E. O. (1999). Essentials of WAIS–III
assessment. New York: Wiley.
Kooij, J. J. S., Boonstra, A. M., Swinkels, S. H., Bekker, E. M., De Noord,
I., & Buitelaar, J. K. (2008). Reliability, validity, and utility of instru-
ments for self-report and informant report concerning symptoms of
ADHD in adult patients. Journal of Attention Disorders, 11, 445–458.
Kray, J., & Lindenberger, U. (2000). Adult age differences in task switch-
ing. Psychology and Aging, 15, 126–147.
Marchetta, N. D., Hurks, P. P., De Sonneville, L. M., Krabbendam, L., &
Jolles, J. (2008). Sustained and focused attention deficits in adult
ADHD. Journal of Attention Disorders, 11, 664–676.
Marchetta, N. D., Hurks, P. P., Krabbendam, L., & Jolles, J. (2008).
Interference control, working memory, concept shifting, and verbal
fluency in adults with attention-deficit/hyperactivity disorder (ADHD).
Neuropsychology, 22, 74–84.
Monsell, S. (1996). Control of mental processes. In V. Bruce (Ed.),
Unsolved mysteries of the mind: Tutorial essays in cognition (pp. 93–
148). Oxford, England: Erlbaum (UK) Taylor & Francis.
Murphy, P. (2002a). Cognitive functioning in adults with attention-deficit/
hyperactivity disorder. Journal of Attention Disorders, 5, 203–209.
Murphy, P. (2002b). Inhibitory control in adults with attention-deficit/
hyperactivity disorder. Journal of Attention Disorders, 6, 1–4.
Ossmann, J. M., & Mulligan, N. W. (2003). Inhibition and attention deficit
hyperactivity disorder in adults. The American Journal of Psychology,
Plichta, M. M., Vasic, N., Wolf, R. C., Lesch, K. P., Brummer, D., Jacob,
C., . . . Grön, G. (2009). Neural hyporesponsiveness and hyperrespon-
siveness during immediate and delayed reward processing in adult
attention-deficit/hyperactivity disorder. Biological Psychiatry, 65, 7–14.
Posner, M. I., & Petersen, S. E. (1990). The attention system of the human
brain. Annual Review of Neuroscience, 13, 25–42.
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard,
D. A., & Shulman, G. L. (2001). A default mode of brain function.
Proceedings of the National Academy of Sciences, USA, 98, 676–682.
Raz, A. (2004). Anatomy of attentional networks. The Anatomical Record
Part B: The New Anatomist, 281, 21–36.
Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between
simple cognitive tasks. Journal of Experimental Psychology: General,
DIBBETS, EVERS, HURKS, BAKKER, AND JOLLES
Ruge, H., Brass, M., Koch, I., Rubin, O., Meiran, N., & von Cramon, D. Y. Download full-text
(2005). Advance preparation and stimulus-induced interference in cued
task switching: Further insights from BOLD fMRI. Neuropsycholo-
gia, 43, 340–355.
Rushworth, M. F., Hadland, K. A., Paus, T., & Sipila, P. K. (2002). Role
of the human medial frontal cortex in task switching: A combined fMRI
and TMS study. Journal of Neurophysiology, 87, 2577–2592.
Schneider, M., Retz, W., Coogan, A., Thome, J., & Rosler, M. (2006).
Anatomical and functional brain imaging in adult attention-deficit/hy-
peractivity disorder (ADHD)—A neurological view. European Archives
of Psychiatry and Clinical Neuroscience, 256(Suppl. 1), i32–i41.
Seidman, L. J. (2006). Neuropsychological functioning in people with
ADHD across the lifespan. Clinical Psychology Review, 26, 466–485.
Seidman, L. J., Biederman, J., Weber, W., Hatch, M., & Faraone, S. V.
(1998). Neuropsychological function in adults with attention-deficit hy-
peractivity disorder. Biological Psychiatry, 44, 260–268.
Smith, A. B., Taylor, E., Brammer, M., Toone, B., & Rubia, K. (2006).
Task-specific hypoactivation in prefrontal and temporoparietal brain
regions during motor inhibition and task switching in medication-naive
children and adolescents with attention deficit hyperactivity disorder.
The American Journal of Psychiatry, 163, 1044–1051.
Spencer, T. J., Biederman, J., & Mick, E. (2007). Attention-deficit/hyper-
activity disorder: Diagnosis, lifespan, comorbidities, and neurobiology.
Journal of Pediatric Psychology, 32, 631–642.
Sunshine, J. L., Lewin, J. S., Wu, D. H., Miller, D. A., Findling, R. L.,
Manos, M. J., & Schwartz, M. A. (1997). Functional MR to localize
sustained visual attention activation in patients with attention deficit
hyperactivity disorder: A pilot study. American Journal of Neuroradi-
ology, 18, 633–637.
Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the
human brain. New York: Thieme.
Tamm, L., Menon, V., Ringel, J., & Reiss, A. L. (2004). Event-related
fMRI evidence of frontotemporal involvement in aberrant response
inhibition and task switching in attention-deficit/hyperactivity disorder.
Journal of the American Academy of Child & Adolescent Psychiatry, 43,
Tripp, G., & Wickens, J. R. (2009). Neurobiology of ADHD. Neurophar-
macology, 57, 579–589.
Uterwijk, J. (2000). WAIS–III Nederlandstalige bewerking. Technische
handleiding [WAIS-III Dutch version. Technical manual]. Lisse, the
Netherlands: Swets & Zeitlinger.
Valera, E. M., Faraone, S. V., Biederman, J., Poldrack, R. A., & Seidman,
L. J. (2005). Functional neuroanatomy of working memory in adults
with attention-deficit/hyperactivity disorder. Biological Psychiatry, 57,
Ward, M. F., Wender, P. H., & Reimherr, F. W. (1993). The Wender Utah
Rating Scale: An aid in the retrospective diagnosis of childhood atten-
tion deficit hyperactivity disorder. The American Journal of Psychiatry,
Weissman, D. H., Gopalakrishnan, A., Hazlett, C. J., & Woldorff, M. G.
(2005). Dorsal anterior cingulate cortex resolves conflict from distract-
ing stimuli by boosting attention toward relevant events. Cerebral Cor-
tex, 15, 229–237.
White, H. A., & Shah, P. (2006). Training attention-switching ability in
adults with ADHD. Journal of Attention Disorders, 10, 44–53.
Woods, S. P., Lovejoy, D. W., & Ball, J. D. (2002). Neuropsychological
characteristics of adults with ADHD: A comprehensive review of initial
studies. Clinical Neuropsychology, 16, 12–34.
Received March 30, 2009
Revision received November 23, 2009
Accepted November 23, 2009 ?
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NEUROIMAGING IN ADULTS WITH ADHD