Cerebral Cortex May 2013;23:1179–1189
doi:10.1093/cercor/bhs107
Advance Access publication May 10, 2012
Methylphenidate Effects on Neural Activity During Response Inhibition in Healthy Humans
Anna Costa1, Michael Riedel1,4, Oliver Pogarell1, Frank Menzel-Zelnitschek1, Markus Schwarz1, Maximilian Reiser2,
Hans-Jürgen Möller1, Katya Rubia5, Thomas Meindl2 and Ulrich Ettinger1,3,6
1
Department of Psychiatry, 2Institute for Clinical Radiology and 3Department of Psychology, Ludwig-Maximilians-University,
Munich, Germany, 4Clinic for Psychiatry, Psychotherapy, Gerontopsychiatry and Neurology, Rottweil, Germany, 5Institute of
Psychiatry, King’s College London, London, UK and 6Department of Psychology, University of Bonn, Bonn, Germany
Address correspondence to Ulrich Ettinger, Department of Psychology, University of Bonn, Kaiser-Karl-Ring 9, 53111 Bonn, Germany.
Email: [email protected].
Methylphenidate (MPH) is a catecholamine transporter blocker,
with dopamine agonistic effects in the basal ganglia. Response inhibition, error detection, and its mediating frontostriatal brain activation are improved by MPH in patients with attention-deficit/
hyperactivity disorder. However, little is known about the effects of
MPH on response inhibition and error processing or its underlying
brain function in healthy individuals. Therefore, this study employed
functional magnetic resonance imaging (fMRI) and 2 response inhibition tasks in 52 healthy males. Subjects underwent fMRI during a
go/no-go task and a tracking stop-signal task after administration of
40 mg MPH and placebo in a double-blind, placebo-controlled, repeated-measures design. Results revealed task- and conditionspecific neural effects of MPH: it increased activation in the
putamen only during inhibition errors but not during successful inhibition and only in the go/no-go task. We speculate that task specificity of the effect might be due to differences in the degree of error
saliency in the 2 task designs, whereas errors were few in the go/
no-go task and thus had high saliency and the stop-signal task was
designed to elicit 50% of errors in all subjects, diminishing the
error saliency effect. The findings suggest that neural MPH effects
interact with the saliency of the behavior under investigation.
Keywords: dopamine, fMRI, human, methylphenidate, response inhibition
Introduction
Methylphenidate is a catecholamine reuptake inhibitor that
predominantly blocks dopamine transporters in the basal
ganglia, leading to enhanced striatal dopamine availability. In
frontal regions, methylphenidate blocks both dopamine and
noradrenaline transporters, leading to enhanced availability of
both catecholamines (Arnsten 2006a; Volkow et al. 2009).
Methylphenidate is the treatment of choice for attentiondeficit/hyperactivity disorder (ADHD) (Greenhill et al. 1999;
Müller 2008). The therapeutic effects of methylphenidate are
thought to be due to its increasing extracellular levels of noradrenaline and dopamine in the brain, particularly of striatal
dopamine (Volkow et al. 2001), a neurotransmitter involved
in cognition (Nieoullon 2002), reward, and motivation
(Schultz 2002), as well as motor response inhibition (Hershey
et al. 2004).
Motor response inhibition is the ability to inhibit reflexive
or inappropriate motor actions (Mostofsky and Simmonds
2008). Poor motor response inhibition is observed in ADHD
on the go/no-go and stop-signal tasks (Willcutt et al. 2005;
Alderson et al. 2007; Rubia, Smith, Taylor, Brammer 2007).
Evidence for impairment in other inhibitory tasks such as
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interference inhibition is less consistent (van Mourik et al.
2005; Lansbergen et al. 2007).
The go/no-go and stop-signal tasks share the requirement
to suppress a contextually inappropriate prepotent motor
response and activate frontostriatal neural circuitry (Rubia
et al. 2001; Rubia, Smith et al. 2003; Rubia et al. 2006; Rubia,
Smith, Taylor, Brammer 2007; Garavan et al. 2002). However,
there are also factors on which the tasks differ. The go/no-go
task requires responding on frequent go trials and inhibiting
this response on infrequent no-go trials. The go or no-go
signal is always presented at the beginning of the trial, indicating immediately whether a response or nonresponse is required. The task thus measures selective inhibition in the
context of response selection. In contrast, the stop-signal task
requires inhibiting responses to a stop signal presented unexpectedly shortly after some of the go stimuli. Therefore, the
subject does not know at the beginning of the trial whether
the go signal is a “true” go signal and needs to be responded
to or is converted into a “no response” signal by the subsequent stop signal. The stop-signal task, therefore, measures
a later, more challenging inhibitory process, that is, the retraction of a motor response that is triggered by the go signal and
already on its way to execution (Rubia et al. 2001).
There is also likely a difference in error-processing mechanisms between the tasks due to differences in error frequency. In the tracking version of the stop-signal task
(Verbruggen and Logan 2009), the delay at which the stop
signal appears after the go signal is individually adjusted so
as to achieve a 50% rate of successful inhibition. Therefore,
response errors cannot be avoided in about half the stop trials
and consequently occur relatively frequently. In comparison,
response suppression in the go/no-go task is entirely under
the control of the subject; in healthy samples, the task typically yields a relatively low error rate, meaning that errors are
highly salient. Errors represent violation of reward prediction.
When a reward is smaller than predicted or fails to occur,
activity in dopamine neurons is inhibited (Schultz et al. 1997),
and the inhibitory dopamine signaling suppresses future
actions that result in punishment (Bromberg-Martin et al.
2010). Therefore, although a similar and domain-independent
error detection mechanism may be active in both tasks, there
are likely to be differences in the dopaminergic errorprocessing mechanisms between the 2 tasks due to differences in the saliency of errors and the level of control the
subject has over them.
The present study investigated the effects of methylphenidate on motor response inhibition and the neural networks
underlying inhibition as well as error processing. Methylphenidate improves motor response inhibition (Trommer et al.
1991; Vaidya et al. 1998; Aron et al. 2003a; Bedard et al. 2003;
DeVito et al. 2009) as well as error processing and performance monitoring (Krusch et al. 1996; Groen et al. 2009) in
the go/no-go and stop-signal tasks in ADHD. Functional
magnetic resonance imaging (fMRI) studies have shown that
these improvements are associated with upregulation of inferior frontostriatal inhibitory (Vaidya et al. 1998; Epstein et al.
2007; Rubia, Halari, Cubillo et al. 2011) as well as with
striatal error-processing networks (Rubia, Halari, Mohammad
et al. 2011).
However, despite this evidence from ADHD and the widespread use of response inhibition paradigms in cognitive and
clinical neuroscience (Aron and Poldrack 2005; Aron 2007;
Eagle et al. 2008), only few studies have investigated methylphenidate effects on motor response inhibition in healthy
subjects (Turner et al. 2003; Nandam et al. 2011) and only
one study has investigated methylphenidate effects on the
neural correlates of motor response inhibition in healthy children (Vaidya et al. 1998), which, however, does not easily
extrapolate to adults, given developmental influences on
both inhibitory control (Durston et al. 2002) and dopamine
signaling (Seeman et al. 1987). No study has investigated the
neural mechanisms of methylphenidate effects on motor
response inhibition and response error processing in healthy
adults. Such a study would be important not only to better
understand this clinically relevant compound but also to
further our understanding of the neurotransmitter mechanisms underlying cognitive control. Although patient studies
are useful in this regard, factors such as clinical heterogeneity,
comorbidity, interfering symptoms, and long-term medication may confound experimental effects; these can be circumvented in studies of healthy subjects (Berto et al. 2000;
Koren 2003).
Therefore, we used fMRI and the stop-signal and go/no-go
tasks to investigate methylphenidate effects on the neural networks of successful and erroneous motor response inhibition
in healthy males. We hypothesized that the neural effect of
methylphenidate would be primarily in the striatum, where
the largest amount of dopamine transporters is located
(Arnsten 2006b), with additional effects in frontal regions.
Additionally, we hypothesized that methylphenidate would
improve performance as previously shown in individuals with
ADHD (Epstein et al. 2007; Lee et al. 2009) and healthy subjects (Nandam et al. 2011).
Materials and Methods
Subjects
Subjects were recruited through advertisements placed around the
community and universities. The recruitment criteria were male
gender, between 18 and 45 years old, nonsmokers, right-handed, of
European origin, good command of the German language, and physically, neurologically, and psychiatrically healthy. All potential subjects were first prescreened by telephone. If they fulfilled the general
study criteria, they were invited to participate in the baseline screening session that included an electroencephalogram, an electrocardiogram, a blood test, and a detailed interview to exclude any
psychiatric, neurological, and medical illness, including alcohol and
drug abuse. Other exclusion criteria were current consumption of prescription or over-the-counter medication, current or recent (within
the last 12 months) use of drugs, metallic implants, and claustrophobia. They were asked to refrain from alcohol 24 h prior to each study
appointment. Additionally, subjects had to also refrain from
1180 Methylphenidate Effects on Neural Activity
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Costa et al.
consuming caffeine before the appointment on the days when they
were scanned. The study was approved by the local Ethics Committee,
and all subjects provided written informed consent and received monetary compensation for their participation.
Study Design and Procedure
A randomized, double-blind, placebo-controlled design was used.
Each subject was scanned twice, approximately 1 week between the 2
sessions. During each test session, 2 capsules containing either an
oral dose of 40 mg of methylphenidate or placebo (lactose) was administered to the subject. The chosen dose is comparable to doses reported in previous studies investigating methylphenidate effects in
healthy subjects (Mehta et al. 2000; Volkow et al. 2001; Dodds et al.
2008; Clatworthy et al. 2009; Schlosser et al. 2009; Finke et al. 2010).
Furthermore, according to NICE guidelines (www.nice.org.uk),
methylphenidate medication for adults is typically titrated for each
subject’s responsiveness and side effects from a minimum of 15 mg to
a maximum of 100 mg. Therefore, 40 mg is within the typical clinical
range of drug administration. After capsule administration, the subjects relaxed in a waiting room, where they were allowed to do activities such as read, listen to music, or work on their laptop. Subjects
were not allowed to eat or drink, with the exception of water, during
the waiting period. The fMRI scan started 60 min after capsule administration. Before scanning, on each session, subjects had to carry out a
practice test of both tasks outside the scanner. Subjects also had to fill
out visual analog rating scales (VARSs) at 3 time points (before
capsule administration, immediately before scan, and immediately
after scan) during each session. A blood sample (one 7.5 mL tube) for
methylphenidate plasma level analysis was taken by venepuncture at
the end of each session.
Go/No-Go Task
The go/no-go task was implemented in an event-related design
similar to that used by Chikazoe et al. (2009). Three types of trials
were used: 200 go trials (77%), 30 oddball trials (11.5%), and 30
no-go trials (11.5%). Each trial consisted of a colored circle presented
for 500 ms on a black background in the middle of the screen, followed by an average interstimulus interval of 1300 ms ( jittered
between 1100 and 1500 ms) where only the black background was
shown. Go trials were indicated by a gray circle, and no-go and
oddball trials were indicated by yellow or blue circles. The circle color
for the no/go and oddball trials was counterbalanced across subjects.
The purpose of the oddball trials was to allow the investigation of the
inhibition process independent of the confounds of the visuospatial
attentional “oddball effect” of the processing of low frequency no-go
relative to the high frequency go trials. The order of the trials was
quasi-randomized with at least 3 go trials between no-go and oddball
trials and between no-go trials. Subjects had to press a button with
their right index finger on go trials and oddball trials, but had to withhold the button press on no-go trials. The whole task lasted 7 min 59 s.
The dependent variables were the percentage of incorrect no-go, go,
and oddball trials and the mean reaction times (MRTs) and the
intra-individual coefficient of variation (ICV) of RTs during incorrect
no-go, correct go, and correct oddball trials. The ICV was calculated
using the following formula: ICV = standard deviation (SD) go RT/
MRT (Nandam et al. 2011).
Stop-Signal Task
The stop-signal task was implemented in an event-related design as
described previously (Rubia, Smith et al. 2003b). There were 234 go
trials and 60 no-go trials in total. Each trial consisted of a white arrow
pointing right or left presented for 500 ms on a black background in
the middle of the screen, followed by an average interstimulus interval of 1300 ms ( jittered between 1100 and 1500 ms). The basic task
was a choice RT task in which subjects had to press a left or right
button with the index or middle finger, respectively, of the right hand
corresponding to the direction of the arrow (go trials). In 20% of the
trials, pseudorandomly interspersed, the go signals are followed unpredictably (about 250 ms later) by arrows pointing upwards (stop
signals). Subjects had to inhibit their motor responses on these trials.
The initial interval between go and stop stimulus was 250 ms. A tracking algorithm then adapted this time interval according to each subject’s performance by recalculating the percentage of correct stop
trials after each stop trial. The time interval between go and stop
signal (stop-signal delay) increased by 50 ms when the subjects’
overall inhibition was higher than 50%, making the task more difficult, or decreased by 50 ms when the percentage of inhibition was
lower than 50%, making the task easier for the subject. The algorithm
elicits about 50% successful and 50% failed stop trials for each
subject. The whole task lasted 9 min. The main dependent inhibitory
variable is the stop-signal RT, which measures the speed of the inhibitory process, and is calculated by subtracting the stop-signal delay
from the mean response time to go trials (Logan et al. 1997). Other
dependent variables are the MRT and ICV on correct go trials.
Visual Analog Rating Scale
VARSs were used based on Norris (1971). The VARS consisted of 10
items and was carried out on paper. The items were “Attentive,” “My
thoughts are racing,” “Irritable,” “Restless,” “Tired,” “Energetic,” “I
have self control,” “Anxious,” “Untroubled,” and “Happy.” Each item
had a corresponding 10 cm line, with the beginning and end of each
line representing “not at all” and “very,” respectively. Subjects indicated their answers by marking the line according to how they felt at
the moment.
Behavioral Data Analysis
All performance variables from the go/no-go and stop-signal tasks
were analyzed with paired 2-tailed t-tests to compare performance
between drug and placebo. Effect sizes for the t-tests were calculated
using Cohen’s d. Each item on the VARS was analyzed with a withinsubject 2 × 3 (Drug × Time) repeated-measures analysis of variance
(ANOVA), with Drug (methylphenidate and placebo) and Time
(before capsule administration, before scan, and after scan) as withinsubject factors. Effect sizes for the repeated-measures ANOVA were
calculated using the effect size estimator partial η 2.
fMRI Data Acquisition and Analysis
T2*-weighted whole-brain MR echo planar images of the blood oxygenation level-dependent (BOLD) response were collected on a
Siemens Verio scanner at 3 T field strength. About 264 and 298 functional images were acquired for the go/no-go and stop-signal tasks,
respectively, with a repetition time (TR) of 1.8 s on each task. The
first 4 volumes were discarded to allow for establishment of
steady-state longitudinal magnetization. Each image volume compromised 28 axial slices, each 4 mm thick with an interslice gap of 0.8
mm and an in-plane resolution of 3 × 3 mm. For each sequence, the
flip angle was 80° and echo time (TE) was 30 ms. Slices were acquired
in the ascending sequence (inferior to superior) parallel to the AC-PC
line.
Imaging data were preprocessed and analyzed using SPM5
(http://www.fil.ion.ucl.ac.uk/spm/) running in MATLAB R2008a (The
MathWorks Inc.). All images were aligned to the first image in the
time series, normalized to the Montreal Neurological Institute (MNI)
template, and spatially smoothed using an 8 mm full-width halfmaximum Gaussian filter. The data were high-pass filtered (128 s),
and the onsets of the stimuli were modeled as events. For the go/
no-go task, the conditions (1) successful no-go trials, (2) unsuccessful
no-go trials, (3) correct oddball trials, (4) incorrect oddball trials, and
(5) incorrect go trials were modeled using a synthetic canonical hemodynamic response function. The data for the stop-signal task were
similarly modeled for the conditions (1) successful stop trials, (2) unsuccessful stop trials, and (3) incorrect go trials. For all conditions, we
modeled the onsets of the trials and not the responses, if any occurred. Go trials on both tasks were not included in the model and
served as a baseline (see e.g. Chamberlain et al. 2009; Chikazoe et al.
2009). Individual realignment parameters were included in the model
as multiple regressors.
For each task, we focussed on the BOLD data underlying successful and unsuccessful inhibition. The analysis of these data used a
within-subject 2 × 2 full factorial model with Drug (methylphenidate
and placebo) and Condition (successful inhibition and unsuccessful
inhibition) as factors, separately for each task. Incorrect oddball or go
trials on the go/no-go task were not analyzed at the second level due
to the small number of responses made. Results involving correct
oddball trials on the go/no-go task are shown in Supplementary
Material.
For all analyses, the threshold for statistical significance was set at
P < 0.05 and family-wise error (FWE) corrected at the voxel level
across the whole brain. An additional minimum cluster size criterion
of 20 voxels was applied. Conversion from MNI to Talairach coordinates of peak voxels within a cluster was performed using a nonlinear
transformation (Brett et al. 2002), and identification of anatomic areas
was determined using the stereotaxic atlas by Talairach and Tournoux
(1988).
Results
Fifty-four subjects (mean ± SD: 23.65 ± 2.97 years; range: 18–
30 years; all right-handed, male nonsmokers) completed the
study. However, 1 subject failed to comply with the exclusion
criteria. One subject showed excessive motion artifacts during
fMRI (>5 mm), and 2 subjects performed no errors on the go/
no-go task. Due to technical problems, data of 11 subjects
could not be obtained for the stop-signal task and methylphenidate plasma levels could not be obtained for 1 subject.
Therefore, the final sample consisted of 50 subjects for the
VARS and the go/no-go task, 42 subjects for the stop-signal
task, and 49 subjects for the plasma analysis.
Behavioral Data, VARS, and Plasma Levels
There was an effect of methylphenidate on the ICV of
correct go trials on the go/no-go task [t(49) = −1.99, P = 0.05,
d = −0.40]. A trend-level effect was also observed on the
intra-individual SD of RT of go trials on the go/no-go task
Table 1
Descriptive statistics of behavioral variables on the go/no-go and stop-signal tasks during
methylphenidate and placebo
Go/no-go variables
Methylphenidate (N = 50)
Placebo (N = 50)
Mean
SD
Mean
SD
Percentage of incorrect no-go
Percentage of incorrect Go
Percentage of incorrect oddball
Mean RT correct go
Mean RT incorrect no-go
Mean RT correct oddball
SD RT correct go
SD RT incorrect no-goa
SD RT correct oddball
ICV correct go
ICV incorrect no-goa
ICV correct oddball
17.13
0.60
0.67
321.50
389.75
378.21
62.36
152.84
86.81
0.19
0.30
0.23
10.21
0.99
1.90
38.63
160.61
50.92
20.89
242.46
25.20
0.05
0.31
0.06
18.40
0.60
0.53
322.98
409.22
374.13
67.69
176.88
83.48
0.21
0.37
0.22
10.57
1.10
1.41
35.85
249.29
50.98
19.25
233.24
24.02
0.05
0.36
0.05
Stop-signal variables
Methylphenidate (N = 42)
Placebo (N = 42)
Mean RT correct go
Stop-signal reaction time
SD RT correct go
ICV correct go
Mean
503.76
64.76
131.03
0.25
Mean
497.82
66.85
128.90
0.25
SD
124.03
103.38
52.32
0.07
SD
124.80
125.19
54.25
0.07
Note: N, number of subjects.
a
Due to the low number of errors of some subjects, their SD values could not be calculated.
Therefore, N = 48 and 47 for the ICV incorrect no-go during methylphenidate and placebo,
respectively.
Cerebral cortex May 2013, V 23 N 5 1181
(t(49) = −1.82, P = 0.075, d = −0.27). There were no other significant behavioral effects of methylphenidate on the go/
no-go or stop-signal tasks (all P > 0.46). Descriptive statistics
of the variables of both tasks can be seen in Table 1.
For VARSs, significant interactions between Drug and
Time were observed for the items my thoughts are racing
(F2,98 = 11.53, P < 0.001, partial η 2 = 0.19), energetic (F2,98 =
7.40, P = 0.001, partial η 2 = 0.13), attentive (F2,98 = 3.42,
P = 0.04, partial η 2 = 0.07), restless (F2,98 = 18.47, P < 0.001,
partial η 2 = 0.28), and tired (F2,98 = 5.43, P = 0.006, partial
η 2 = 0.10). Methylphenidate increased ratings in these variables more strongly than placebo, with the exception of tired,
where ratings increased with placebo but decreased with
methylphenidate. Further statistical analysis of VARS items
can be found in Supplementary Material.
The mean and SD of plasma levels were 14.39 ± 5.63 ng/mL
following drug administration and 0 following placebo
administration.
fMRI Data
Task Main Effects
As described earlier, the threshold for statistical significance
of fMRI results was set at P < 0.05 and FWE-corrected at the
voxel level across the whole brain.
The within-subject ANOVA revealed significant task-related
activation on both tasks. During successful inhibition in the
go/no-go task (no-go trials) compared with baseline, extensive activation in widespread cortical and subcortical networks included the bilateral inferior frontal cortex, middle
and superior frontal cortex, superior temporal cortex, posterior cingulate, occipital regions, thalamus, putamen, and
cerebellum (Fig. 1A). The activation during unsuccessful inhibition compared with baseline on the go/no-go task included
the bilateral inferior and superior frontal cortex, inferior parietal cortex, insula, middle temporal cortex, middle frontal
cortex, superior temporal cortex, anterior cingulate, thalamus,
precentral gyrus, and precuneus (Fig. 1B).
Similarly on the stop-signal task during successful inhibition compared with baseline, significant and extensive activation was seen in widespread networks, including the
bilateral middle frontal cortex, right middle and superior temporal cortex and left inferior and middle temporal cortex, left
postcentral, precentral, and occipital regions (Fig. 2A). During
unsuccessful inhibition compared with baseline, activation
was seen in the bilateral superior temporal cortex, right
middle temporal cortex, left inferior and middle frontal
cortex, right superior frontal cortex, anterior cingulate, left
insula, and precentral gyrus (Fig. 2B).
We then directly compared successful with unsuccessful
inhibition trials. In the go/no-go task, there was significantly
stronger activation in the putamen bilaterally and in the right
middle occipital gyrus during successful inhibition when
compared with unsuccessful inhibition (Fig. 3A and Table 2).
The inverse contrast (unsuccessful inhibition > successful
inhibition) on the go/no-go task yielded significant clusters in
the anterior cingulate cortex, bilateral inferior frontal gyrus,
left putamen, left inferior parietal lobe, and head of the right
caudate (Fig. 3B and Table 2). In the stop-signal task, greater
activation was seen during successful inhibition when compared with unsuccessful inhibition in the bilateral putamen,
as well as in the bilateral middle frontal gyrus, bilateral
1182 Methylphenidate Effects on Neural Activity
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Costa et al.
Figure 1. Neural effects of successful and unsuccessful inhibition in the go/no-go
task. (A and B) Activation of the contrast successful > baseline (P < 0.05, FWE) and
unsuccessful > baseline, respectively, on the go/no-go task (P < 0.05, FWE).
middle occipital gyrus, right inferior frontal gyrus, and cerebellar regions (Fig. 4A and Table 2). The inverse contrast
showed greater activation during unsuccessful inhibition than
during successful inhibition in the left insula (Fig. 4B and
Table 2).
Drug Effects
For the go/no-go task, there were no main effects of Drug for
any contrast. However, there was a significant interaction
between Drug and Condition in a cluster in the right putamen
(x = 26, y = −2, z = −3, Z = 5.79, cluster size = 72 voxels),
which is located closely within the putamen cluster of the
contrast successful > unsuccessful (Fig. 3A). In order to
explore whether the effect was in fact lateralized to the right
putamen, we changed the statistical correction to P < 0.05
false discovery rate; this analysis also revealed an interaction
effect in a cluster in the left putamen (x = −26, y = −2, z = 6,
Z = 4.77, cluster size = 182 voxels), which showed the same
pattern as in the right hemisphere.
To clarify the origin of this interaction, we carried out post
hoc t-tests in SPM5 using criteria for significance as described
earlier. It was found that with placebo, there was significantly
greater BOLD signal in the putamen bilaterally (x = 28, y = 0,
z = −3, Z = 7.54, cluster size = 492 voxels; x = −26, y = −4,
z = 8, Z = 7.00, cluster size = 364 voxels) and the right middle
occipital gyrus (x = 32, y = −84, z = 21, Z = 4.92, cluster size = 24
voxels) during successful inhibition when compared with unsuccessful inhibition; however, no such difference was seen
with methylphenidate. Post hoc t-tests also showed that
methylphenidate significantly increased activation during the
Figure 2. Neural effects of successful and unsuccessful inhibition in the stop-signal
task. (A and B) Activation of the contrast successful > baseline (P < 0.05, FWE) and
unsuccessful > baseline on the stop-signal task (P < 0.05, FWE).
unsuccessful inhibition condition in the right putamen compared with placebo (x = 30, y = −4, z = −1, cluster size = 22
voxels), but not during the successful inhibition condition
(not significant). Together, these findings suggest that methylphenidate increased the BOLD signal in the putamen during
incorrect no-go trials to the levels observed during correct
no-go trials. Figure 5 illustrates this pattern for the cluster,
which showed the significant Drug by Condition interaction
in the ANOVA model.
For the stop-signal task, there were no significant main
effects of Drug and there was no significant interaction
between Drug and Condition. In an attempt to explore
whether a similar effect as on the go/no-go task might exist in
the data, we changed the correction to P < 0.05 false discovery
rate; again, no main effect of drug or interaction effect was
found. t-tests, analogous to those carried out on the go/no-go
task, showed significantly stronger BOLD signal in right (x =
26, y = 8, z = 7, cluster size = 874) and left (x = −24, y = 10,
z = −3, cluster size = 738) putamen during correct than incorrect trials during placebo. This effect remained significant
with methylphenidate in the left (x = −24, y = 8, z = −2, cluster
size = 363 voxels) and right (x = 24, y = 9, z = −7, cluster
size = 363 voxels) putamen, suggesting that unlike in the go/
no-go task, methylphenidate did not alter striatal BOLD
during error trials on the stop-signal task.
In order to further confirm the specificity of the methylphenidate effect on the go/no-go but not the stop-signal task, the
parameter estimates were extracted from the cluster in the
right putamen that showed a significant interaction effect in
the go/no-go task ( peak coordinate: x = 26, y = −2, z = −3).
Data for this cluster were extracted for both the go/no-go and
stop-signal tasks using the MarsBaR toolbox (Brett et al. 2002;
see http://marsbar.sourceforge.net/). A 2 (methylphenidate
and placebo) × 2 (go/no-go and stop signal) × 2 (successful
inhibition and unsuccessful inhibition) repeated-measures
ANOVA was carried out on these data in PASW Statistics,
release version 19.0 (SPSS Inc. 2010). A significant 3-way
interaction was found (F1,40 = 11.88, P < 0.001, partial η 2 =
0.23), confirming that the effect of methylphenidate on BOLD
signal in the right putamen is task-dependent, that is, specific
to the go/no-go task.
Figure 3. Neural effects of methylphenidate in the go/no-go task. (A) Bilateral activation of the putamen in the contrast successful > unsuccessful inhibition (red) and the interaction
between Condition and Drug (blue) on the go/no-go task (P < 0.05, FWE). (B) Activation of the contrast unsuccessful > successful on the go/no-go task (P < 0.05, FWE).
Cerebral cortex May 2013, V 23 N 5 1183
Table 2
Activated areas during successful and unsuccessful inhibition on the go/no-go and stop-signal
tasks
Activated region
Go/no-go task
Succ > Unsucc
Right putamen
Left putamen
Right middle occipital
gyrus
Unsucc > Succ
Anterior cingulate cortex,
extending into superior
frontal gyrus
Left inferior frontal gyrus,
insula
Right inferior frontal gyrus
Right cingulate gyrus
Left putamen, extending
into thalamus
Left inferior parietal lobule
Pons
Left midbrain
Right caudate (head)
Stop-signal task
Succ > Unsucc
Left putamen
Right putamen
Left middle frontal gyrus
Right precuneus extending
into superior parietal lobule
Right middle frontal gyrus
Right middle frontal gyrus
Right middle frontal gyrus
Right precuneus
Right medial frontal gyrus
Right middle occipital
gyrus
Left middle frontal gyrus
Left middle occipital gyrus
Right middle occipital
gyrus
Right lingual gyrus
Right precentral gyrus
Cerebellum
Right inferior frontal gyrus
Unsucc > Succ
Left insula, extending into
inferior frontal gyrus
Talairach
coordinates
Z-value
T-value
Cluster
size
BA
6.23
5.66
5.36
6.56
5.90
5.57
153
124
44
–
–
19
10.44
3429
32
X
y
z
26
−28
40
2
−9
−70
5
6
2
2
28
24
−40
17
−11
7.81
8.50
1413
47
50
2
−10
21
−14
2
−3
38
−2
6.84
6.21
5.59
7.28
6.54
5.83
818
135
134
47
24
-
−63
0
−4
10
−39
−21
−29
6
31
−23
0
3
5.44
5.36
5.19
5.05
5.65
5.58
5.38
5.23
27
36
37
32
40
–
–
–
−20
26
−42
24
5
12
49
−50
−10
−1
3
49
Inf
Inf
7.34
5.81
10.01
9.54
8.00
6.13
1226
1392
419
455
–
–
10
7
53
30
20
30
10
34
34
−3
34
−70
−10
−85
24
52
−15
35
63
19
5.71
5.66
5.58
5.49
5.44
5.41
6.02
5.96
5.87
5.76
5.70
5.67
66
361
45
46
31
113
46
6
11
6
6
19
−53
−36
44
27
−73
−66
28
9
−3
5.32
5.27
5.21
5.56
5.51
5.44
68
21
50
46
39
37
16
24
−10
53
−80
−21
−71
13
−1
54
−18
25
5.18
5.18
5.15
4.98
5.41
5.41
5.37
5.19
32
22
32
30
18
4
–
9
−44
12
6
5.21
5.44
20
44
Inf
Note: Statistical significance is set at P < 0.05 (FWE) with a minimum cluster size of 20 voxels.
Succ, successful inhibition (correct no-go); Unsucc, unsuccessful inhibition (incorrect no-go); BA,
Brodmann area; Inf, infinity. Cluster size is given as the number of voxels.
We also reran the analysis of the go/no-go data excluding
subjects who committed less than 2 (N = 5) or less than 3
errors (N = 15) (not including the subjects already excluded
from the main analysis). There were no major changes in the
results when these subjects were excluded.
Finally, in order to explore whether drug effects at behavioral and neural levels were correlated, we carried out Pearson’s
correlations between magnitude of change (methylphenidate–
placebo/methylphenidate) in the BOLD signal in the putamen and magnitude of change in no-go error trials as well
as stop-signal RT. None of these correlations were significant
(all P > 0.47).
Discussion
This study used fMRI and the go/no-go and tracking stopsignal tasks to investigate neural and behavioral effects of
1184 Methylphenidate Effects on Neural Activity
•
Costa et al.
methylphenidate on motor response inhibition and error
monitoring in healthy adults. At the behavioral level, methylphenidate decreased intra-individual response time variability
on the go/no-go task without affecting response inhibition
performance on either task. Methylphenidate also increased
self-ratings of perceived activity levels. At the neural level,
methylphenidate administration led to an increase in the
BOLD signal in the putamen during response inhibition
errors on the go/no-go but not the stop-signal task.
fMRI Task Main Effects
On both tasks, extensive activation was seen during successful inhibition compared with baseline in cortical and subcortical areas, in line with previous findings in healthy adults
(Garavan et al. 2002; Rubia, Smith et al. 2003; Rubia, Smith,
Taylor, Brammer 2007; Aron et al. 2007). Furthermore,
greater activation was found in the putamen during successful
inhibition compared with unsuccessful inhibition on both
tasks, supporting previous findings on the involvement of the
putamen in both healthy subjects and patients with ADHD
during motor inhibition in the go/no-go task (Garavan et al.
2002; Durston et al. 2003; Bedard et al. 2010) as well as in the
stop-signal task (Vink et al. 2005; Rubia, Smith, Taylor,
Brammer 2007; Zandbelt and Vink 2010). In the stop-signal
task, there was additional right inferior frontal as well as
middle frontal activation in this contrast, consistent with previous evidence (Aron et al. 2003b; Rubia, Smith et al. 2003;,
Rubia, Smith, Taylor, Brammer 2007; Verbruggen and Logan
2008; Chambers et al. 2009).
The results of the comparison of unsuccessful inhibition
with baseline were similar in both tasks, showing greater activation during inhibition errors in the anterior cingulate, temporal and parietal areas, and inferior and superior frontal
cortices with stronger bilateral frontal activation in the go/
no-go task (Liddle et al. 2001; Menon et al. 2001; Rubia,
Smith et al. 2003; Rubia, Smith, Taylor, Brammer 2007b).
However, activation during unsuccessful compared with successful inhibition was stronger for the go/no-go task than the
stop-signal task: in the stop-signal task, only a cluster in the
left insula was activated, whereas in the go/no-go task, there
was activation in the anterior cingulate, frontal, and parietal
cortex. The stronger activation in the go/no-go task for this
contrast could have been due to the greater saliency of errors
in this task: as the error rate in the go/no-go task was smaller
than that of the stop-signal task, it can be inferred that go/
no-go errors had a higher saliency than stop-signal errors.
Neural Effects of Methylphenidate
Methylphenidate increased the BOLD signal in the putamen
during unsuccessful go/no-go inhibition trials, but did not
affect the BOLD signal during successful inhibition trials.
These findings may be reconciled with methylphenidate
effects in ADHD, in which methylphenidate has been found
to increase activation in the caudate and putamen during
tasks of interference inhibition (Rubia, Halari, Cubillo et al.
2011), motor inhibition (Rubia, Halari, Mohammad et al.
2011b), attention (Shafritz et al. 2004), and timing functions
(Rubia, Halari, Christakou et al. 2009).
Evidence from our study together with prior studies thus
suggests that methylphenidate may have a stronger effect on
striatal-mediated performance monitoring than on inhibitory
Figure 4. (A) Activation of the contrast successful > unsuccessful inhibition on the stop-signal task (P < 0.05, FWE). (B) Activation during the contrast unsuccessful >
successful inhibition on the stop-signal task (P < 0.05, FWE).
Figure 5. BOLD signal in the right putamen during successful and unsuccessful
inhibition on the go/no-go task. The graph shows the significant increase in the BOLD
signal with methylphenidate during the unsuccessful inhibition condition in the right
putamen compared with placebo. Also, the BOLD signal significantly differed
between successful and unsuccessful inhibition during placebo. The error bars
indicate the standard error of the mean.
networks (Cubillo et al. 2011) and that the neural effects of
methylphenidate of upregulating basal ganglia activation are
not only present in patients with ADHD, but also in healthy
adults. Together, these findings also provide support for
hypotheses concerning a modulating role of dopamine in
error processing related to executive function (Braver and
Cohen 2000; Hazy et al. 2007).
However, our findings also suggest that the neural effects
of methylphenidate may differ between healthy individuals
and ADHD patients in frontal areas. In ADHD, methylphenidate has been shown to upregulate frontal activation during
motor response inhibition (Vaidya et al. 1998; Epstein et al.
2007; Prehn-Kristensen et al. 2011; Rubia, Halari, Cubillo
et al. 2011; Rubia, Halari, Mohammad et al. 2011) and other
cognitive functions (Bush et al. 2008; Rubia, Halari, Christakou et al. 2009; Rubia, Halari, Cubillo 2009; Lee et al. 2010),
although there have also been negative findings (Shafritz
et al. 2004; Kobel et al. 2009; Peterson et al. 2009). In contrast, in healthy subjects, methylphenidate effects on frontal
regions appear to be more inconsistent and more strongly taskdependent. For example, methylphenidate reduced frontal
activation during a working memory task (Mehta et al. 2000)
but enhanced frontal activation during a rewarding working memory task (Marquand et al. 2011). Methylphenidate
has also been shown to modulate striatal activation during
response reversal, but to modulate prefrontal activation
during simple repetitive executive processes (Dodds et al.
2008).
A noteworthy finding of the present study is that the neural
effects of methylphenidate were anatomically highly specific
to the putamen. This localization is compatible with methylphenidate’s mechanism of action of blocking about 60–70%
of striatal dopamine transporters (Volkow et al. 1998) and increasing extracellular levels of dopamine in the human striatum (Volkow et al. 2001).
Another important finding of the present study is that the
result in the putamen was task-specific, which is observed on
the go/no-go but not the stop-signal task. A number of differences between the 2 tasks may be invoked to explain this
pattern.
A first explanation concerns the type of response inhibition
required in the 2 tasks. Schachar et al. (2007) distinguished
between action restraint and action cancellation. Action restraint is the inhibition of the motor response before it has
started, which describes the form of inhibition on the go/
no-go task. Action cancellation refers to inhibition during the
execution of the motor response, which describes the form of
inhibition on the stop-signal task. The 2 forms of inhibition
differ in the way they assess the amount of time required to
inhibit a motor response. The processing of the no-go stimulus during action restraint takes place before motor execution,
and therefore, the time taken to process the no-go stimulus
includes both response inhibition and decision making or
response selection (Rubia et al. 2001). However, during
action cancellation, each trial begins as a go trial and the
subject has to inhibit the motor response as soon as the stop
stimulus appears, thereby demanding the withdrawal of a
response that is already on its way to execution, without implicating selective attention or decision making at the stimulus
onset (Rubia et al. 2001). Studies on the neurotransmitter substrates of these processes suggest that action restraint may be
Cerebral cortex May 2013, V 23 N 5 1185
related to serotonin (Anderson et al. 2002; Evers et al. 2006;
Eagle et al. 2008), whereas action cancellation may be associated with noradrenaline (Eagle et al. 2008).
The observed task specificity of methylphenidate effects
may also be explained by differences in error processing
between the 2 tasks. As outlined earlier, errors on the go/
no-go task were fully avoidable, less frequent, and therefore
likely more salient than errors on the stop-signal task. A relevant study by Volkow et al. (2004) found that methylphenidate increased dopamine levels during a rewarded
mathematical task but not during a neutral task. They proposed that methylphenidate-induced dopamine enhances the
saliency only of the task that is already salient to the subjects.
Subjects in our study were aware that they had no control
over the overall rate of errors on the stop-signal task; this
might have led to a state of loss of control similar to what has
been described in the literature on learned helplessness
(Maier and Seligman 1976; Bauer et al. 2003). As a result,
stop-signal errors may not have been as salient as go/no-go
errors. Furthermore, error trials in the go/no-go task were less
frequent than those in the stop-signal task, further adding to a
saliency effect. Considering that dopamine release is enhanced by salient stimuli (Braver and Cohen 2000) and the
striatal effects of methylphenidate are associated with blockade of dopamine transporters and dopamine signaling, the
upregulation effects of methylphenidate on performance
monitoring in the stop-signal task may not have been observed in healthy adults due to relatively lower saliency of
stop errors of this task, which may only have reached the
necessary saliency threshold in the go/no-go task.
While methylphenidate has been shown to enhance striatal
saliency processing in the stop-signal task in patients with
ADHD (Rubia, Halari, Cubillo et al. 2011), it is possible that
higher saliency is needed to interact with methylphenidate to
elicit a neural effect in healthy subjects. In medication-naive
ADHD patients, lower levels of dopamine and striatal dopamine transporters have been reported in comparison to
healthy subjects. The neurofunctional striatal upregulation
effects observed in ADHD patients with methylphenidate are,
therefore, likely to take place at a lower threshold of saliency
(Volkow et al. 2007) in line with the conclusions of Volkow
et al. (2004).
Taylor 2007) and found to be associated with overall performance levels (e.g. Ettinger and Corr 2001; Flehmig et al. 2007).
Limitations
A limitation of the study was that the sample consisted of
healthy young volunteers drawn from a university population.
While such a screened sample is of course highly suitable for
pharmacological challenge studies, their high level of performance on the tasks might have contributed to a ceiling
effect.
Secondly, the sample consisted of only males. Some (SonugaBarke et al. 2007) but not all studies (Gray and Kagan 2000;
Owens et al. 2003) have reported gender differences in
methylphenidate response in ADHD. Therefore, generalizability of the present findings to the general population may be
limited.
A final limitation is that there were relatively few error trials
on the go/no-go task, which may have resulted in low statistical power. However, exclusion of subjects with very few inhibition errors on the go/no-go task did not affect the
methylphenidate effects reported here.
Conclusions
In summary, neural effects of methylphenidate during motor
response inhibition tasks in healthy adults are observed in the
putamen, where the drug modulates neural activity only
during errors and specifically in the go/no-go task, but not in
the stop-signal task. This task specificity may be due to differences in error saliency between the 2 tasks.
Supplementary Material
Supplementary material can be found at: http://www.cercor.
oxfordjournals.org/.
Funding
This work was supported by the Emmy Noether Programme
of the Deutsche Forschungsgemeinschaft (ET 31/2-1).
Notes
Conflict of Interest: None declared.
Behavioral Effects of Methylphenidate
Methylphenidate had a stimulating effect at the level of subjective self-ratings, leading to increases (compared with
placebo) in perceived levels of activation and attentiveness
but also in restlessness.
Methylphenidate furthermore reduced intra-individual RT
variability, but did not affect inhibitory performance. RT
variability in a number of tasks is increased in ADHD (Klein
et al. 2006; Rubia, Smith, Taylor 2007a) and reduced by
methylphenidate (Rubia, Noorloos, Smith et al. 2003; Epstein
et al. 2006; Lee et al. 2009; Spencer et al. 2009). Similarly, in a
previous study of healthy subjects, methylphenidate improved
response time variability but not the MRT of go trials on the
stop-signal task (Nandam et al. 2011).
Intra-individual RT variability is an important, although
sometimes neglected, feature of task performance, which has
been considered an indicator of sustained attention (LethSteensen et al. 2000; Bellgrove et al. 2004; Rubia, Smith,
1186 Methylphenidate Effects on Neural Activity
•
Costa et al.
References
Alderson RM, Rapport MD, Kofler MJ. 2007. Attention-deficit/hyperactivity disorder and behavioral inhibition: a meta-analytic review of
the stop-signal paradigm. J Abnorm Child Psychol. 35:745–758.
Anderson IM, Clark L, Elliott R, Kulkarni B, Williams SR, Deakin JF.
2002. 5-HT(2C) receptor activation by m-chlorophenylpiperazine
detected in humans with fMRI. Neuroreport. 13:1547–1551.
Arnsten AF. 2006a. Fundamentals of attention-deficit/hyperactivity
disorder: circuits and pathways. J Clin Psychiatr. 67(Suppl 8):7–12.
Arnsten AF. 2006b. Stimulants: therapeutic actions in ADHD. Neuropsychopharmacology. 31:2376–2383.
Aron AR. 2007. The neural basis of inhibition in cognitive control.
Neuroscientist. 13:214–228.
Aron AR, Dowson JH, Sahakian BJ, Robbins TW. 2003a. Methylphenidate improves response inhibition in adults with attention-deficit/
hyperactivity disorder. Biol Psychiatr. 54:1465–1468.
Aron AR, Durston S, Eagle DM, Logan GD, Stinear CM, Stuphorn V.
2007. Converging evidence for a fronto-basal-ganglia network
for inhibitory control of action and cognition. J Neurosci. 27:
11860–11864.
Aron AR, Fletcher PC, Bullmore ET, Sahakian BJ, Robbins TW. 2003b.
Stop-signal inhibition disrupted by damage to right inferior frontal
gyrus in humans. Nat Neurosci. 6:115–116.
Aron AR, Poldrack RA. 2005. The cognitive neuroscience of response
inhibition: relevance for genetic research in attention-deficit/hyperactivity disorder. Biol Psychiatr. 57:1285–1292.
Bauer H, Pripfl J, Lamm C, Prainsack C, Taylor N. 2003. Functional neuroanatomy of learned helplessness. Neuroimage. 20:
927–939.
Bedard AC, Ickowicz A, Logan GD, Hogg-Johnson S, Schachar R,
Tannock R. 2003. Selective inhibition in children with attentiondeficit hyperactivity disorder off and on stimulant medication. J
Abnorm Child Psychol. 31:315–327.
Bedard AC, Schulz KP, Cook EH, Jr, Fan J, Clerkin SM, Ivanov I, Halperin JM, Newcorn JH. 2010. Dopamine transporter gene variation
modulates activation of striatum in youth with ADHD. Neuroimage. 53:935–942.
Bellgrove MA, Hester R, Garavan H. 2004. The functional neuroanatomical correlates of response variability: evidence from a response
inhibition task. Neuropsychologia. 42:1910–1916.
Berto D, Peroni M, Milleri S, Spagnolo AG. 2000. Evaluation of the
readability of information sheets for healthy volunteers in phase-I
trials. Eur J Clin Pharmacol. 56:371–374.
Braver TS, Cohen JD. 2000. On the control of control: the role of
dopamine in regulating prefrontal function and working memory.
Control Cogn Process Attent Perform XVIII. 18:713–737.
Brett M, Anton JL, Valabregue R, Poline JB. 2002. Region of interest
analysis using an SPM toolbox [abstract]. Paper Presented at the
8th International Conference on Functional Mapping of the
Human Brain, Sendai, Japan.
Bromberg-Martin ES, Matsumoto M, Hikosaka O. 2010. Dopamine in
motivational control: rewarding, aversive, and alerting. Neuron.
68:815–834.
Bush G, Spencer TJ, Holmes J, Shin LM, Valera EM, Seidman LJ,
Makris N, Surman C, Aleardi M, Mick E et al. 2008. Functional
magnetic resonance imaging of methylphenidate and placebo in
attention-deficit/hyperactivity disorder during the multi-source interference task. Arch Gen Psychiatr. 65:102–114.
Chamberlain SR, Hampshire A, Muller U, Rubia K, Del Campo N,
Craig K, Regenthal R, Suckling J, Roiser JP, Grant JE et al. 2009.
Atomoxetine modulates right inferior frontal activation during
inhibitory control: a pharmacological functional magnetic resonance imaging study. Biol Psychiatr. 65:550–555.
Chambers CD, Garavan H, Bellgrove MA. 2009. Insights into the
neural basis of response inhibition from cognitive and clinical
neuroscience. Neurosci Biobehav Rev. 33:631–646.
Chikazoe J, Jimura K, Asari T, Yamashita K, Morimoto H, Hirose S,
Miyashita Y, Konishi S. 2009. Functional dissociation in right
inferior frontal cortex during performance of go/no-go task. Cereb
Cortex. 19:146–152.
Clatworthy PL, Lewis SJ, Brichard L, Hong YT, Izquierdo D, Clark L,
Cools R, Aigbirhio FI, Baron JC, Fryer TD et al. 2009. Dopamine
release in dissociable striatal subregions predicts the different
effects of oral methylphenidate on reversal learning and spatial
working memory. J Neurosci. 29:4690–4696.
Cubillo A, Smith A, Barrat N, Giampietro V, Rubia K. 2011. Taskdependent drug-specific upregulation effects of methylphenidate
and atomoxetine on brain function in medication-naïve children
with ADHD. Eur J Psychopharmacol. 19:S303.
DeVito EE, Blackwell AD, Clark L, Kent L, Dezsery AM, Turner DC,
Aitken MR, Sahakian BJ. 2009. Methylphenidate improves
response inhibition but not reflection-impulsivity in children with
attention deficit hyperactivity disorder (ADHD). Psychopharmacology (Berl). 202:531–539.
Dodds CM, Muller U, Clark L, van Loon A, Cools R, Robbins TW.
2008. Methylphenidate has differential effects on blood oxygenation level-dependent signal related to cognitive subprocesses of
reversal learning. J Neurosci. 28:5976–5982.
Durston S, Thomas KM, Yang Y, Ulug AM, Zimmerman RD, Casey B.
2002. A neural basis for the development of inhibitory control.
Dev Sci. 5:F9–F16.
Durston S, Tottenham NT, Thomas KM, Davidson MC, Eigsti IM, Yang
Y, Ulug AM, Casey BJ. 2003. Differential patterns of striatal activation in young children with and without ADHD. Biol Psychiatr.
53:871–878.
Eagle DM, Bari A, Robbins TW. 2008. The neuropsychopharmacology
of action inhibition: cross-species translation of the stop-signal
and go/no-go tasks. Psychopharmacology (Berl). 199:439–456.
Epstein JN, Casey BJ, Tonev ST, Davidson MC, Reiss AL, Garrett A,
Hinshaw SP, Greenhill LL, Glover G, Shafritz KM et al. 2007.
ADHD- and medication-related brain activation effects in concordantly affected parent–child dyads with ADHD. J Child Psychol
Psychiatr. 48:899–913.
Epstein JN, Conners CK, Hervey AS, Tonev ST, Arnold LE, Abikoff
HB, Elliott G, Greenhill LL, Hechtman L, Hoagwood K et al. 2006.
Assessing medication effects in the MTA study using neuropsychological outcomes. J Child Psychol Psychiatr. 47:446–456.
Ettinger U, Corr PJ. 2001. The Frequency Accrual Speed Test (FAST):
psychometric intelligence and personality correlates. Eur J Person.
15:143–152.
Evers EA, van der Veen FM, van Deursen JA, Schmitt JA, Deutz NE,
Jolles J. 2006. The effect of acute tryptophan depletion on the
BOLD response during performance monitoring and response
inhibition in healthy male volunteers. Psychopharmacology
(Berl). 187:200–208.
Finke K, Dodds CM, Bublak P, Regenthal R, Baumann F, Manly T,
Muller U. 2010. Effects of modafinil and methylphenidate on
visual attention capacity: a TVA-based study. Psychopharmacology
(Berl). 210:317–329.
Flehmig HC, Steinborn M, Langner R, Scholz A, Westhoff K. 2007.
Assessing intraindividual variability in sustained attention:
reliability, relation to speed and accuracy, and practice effects.
Psychol Sci. 49:132.
Garavan H, Ross TJ, Murphy K, Roche RA, Stein EA. 2002. Dissociable
executive functions in the dynamic control of behavior: inhibition,
error detection, and correction. Neuroimage. 17:1820–1829.
Gray JR, Kagan J. 2000. The challenge of predicting which children
with attention deficit-hyperactivity disorder will respond positively
to methylphenidate. J Appl Dev Psychol. 21:471–489.
Greenhill LL, Halperin JM, Abikoff H. 1999. Stimulant medications. J
Am Acad Child Adolesc Psychiatr. 38:503–512.
Groen Y, Mulder LJ, Wijers AA, Minderaa RB, Althaus M. 2009.
Methylphenidate improves diminished error and feedback sensitivity in ADHD: an evoked heart rate analysis. Biol Psychol.
82:45–53.
Hazy TE, Frank MJ, O’Reilly RC. 2007. Towards an executive without
a homunculus: computational models of the prefrontal cortex/
basal ganglia system. Phil Trans R Soc Lond B Biol Sci.
362:1601–1613.
Hershey T, Black KJ, Hartlein J, Braver TS, Barch DM, Carl JL, Perlmutter JS. 2004. Dopaminergic modulation of response inhibition:
an fMRI study. Brain Res Cogn Brain Res. 20:438–448.
Klein C, Wendling K, Huettner P, Ruder H, Peper M. 2006. Intrasubject variability in attention-deficit hyperactivity disorder. Biol
Psychiatr. 60:1088–1097.
Kobel M, Bechtel N, Weber P, Specht K, Klarhofer M, Scheffler K,
Opwis K, Penner IK. 2009. Effects of methylphenidate on working
memory functioning in children with attention deficit/hyperactivity disorder. Eur J Paediatr Neurol. 13:516–523.
Koren G. 2003. Healthy children as subjects in pharmaceutical research. Theor Med Bioeth. 24:149–159.
Krusch DA, Klorman R, Brumaghim JT, Fitzpatrick PA, Borgstedt AD,
Strauss J. 1996. Methylphenidate slows reactions of children with
attention deficit disorder during and after an error. J Abnorm
Child Psychol. 24:633–650.
Lansbergen MM, Kenemans JL, van Engeland H. 2007. Stroop interference and attention-deficit/hyperactivity disorder: a review and
meta-analysis. Neuropsychology. 21:251–262.
Cerebral cortex May 2013, V 23 N 5 1187
Lee SH, Song DH, Kim BN, Joung YS, Ha EH, Cheon KA, Shin YJ,
Yoo HJ, Shin DW. 2009. Variability of response time as a predictor
of methylphenidate treatment response in korean children with
attention deficit hyperactivity disorder. Yonsei Med J. 50:650–655.
Lee YS, Han DH, Lee JH, Choi TY. 2010. The effects of methylphenidate on neural substrates associated with interference suppression
in children with ADHD: a preliminary study using event related
fMRI. Psychiatr Investig. 7:49–54.
Leth-Steensen C, King Elbaz Z, Douglas VI. 2000. Mean response
times, variability, and skew in the responding of ADHD children:
a response time distributional approach. Acta Psychol.
104:167–190.
Liddle PF, Kiehl KA, Smith AM. 2001. Event-related fMRI study of
response inhibition. Hum Brain Mapp. 12:100–109.
Logan GD, Schachar RJ, Tannock R. 1997. Impulsivity and inhibitory
control. Psychol Sci. 8:60.
Maier SF, Seligman ME. 1976. Learned helplessness: theory and evidence. J Exp Psychol Gen. 105:3.
Marquand AF, De Simoni S, O’Daly OG, Williams SC, Mourao-Miranda
J, Mehta MA. 2011. Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine,
and placebo in healthy volunteers. Neuropsychopharmacology.
36:1237–1247.
Mehta MA, Owen AM, Sahakian BJ, Mavaddat N, Pickard JD, Robbins
TW. 2000. Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human
brain. J Neurosci. 20:RC65.
Menon V, Adleman NE, White CD, Glover GH, Reiss AL. 2001. Errorrelated brain activation during a Go/NoGo response inhibition
task. Hum Brain Mapp. 12:131–143.
Mostofsky SH, Simmonds DJ. 2008. Response inhibition and response
selection: two sides of the same coin. J Cogn Neurosci.
20:751–761.
Müller U. 2008. Pharmacological treatment. In: Cappa SF, Abutalebi J,
Démonet J-F, Fletcher P, Garrard P. editors. Cognitive neurology: a
clinical textbook. Oxford: Oxford University Press. p. 475–498.
Nandam LS, Hester R, Wagner J, Cummins TD, Garner K, Dean AJ,
Kim BN, Nathan PJ, Mattingley JB, Bellgrove MA. 2011. Methylphenidate but not atomoxetine or citalopram modulates inhibitory
control and response time variability. Biol Psychiatr. 69:902–904.
Nieoullon A. 2002. Dopamine and the regulation of cognition and
attention. Prog Neurobiol. 67:53–83.
Norris H. 1971. The action of sedatives on brain stem oculomotor
systems in man. Neuropharmacology. 10:181–191.
Owens EB, Hinshaw SP, Kraemer HC, Arnold LE, Abikoff HB, Cantwell DP, Conners CK, Elliott G, Greenhill LL, Hechtman L. 2003.
Which treatment for whom for ADHD? Moderators of treatment
response in the MTA. J Consult Clin Psychol. 71:540.
Peterson BS, Potenza MN, Wang Z, Zhu H, Martin A, Marsh R, Plessen
KJ, Yu S. 2009. An FMRI study of the effects of psychostimulants
on default-mode processing during Stroop task performance in
youths with ADHD. Am J Psychiatr. 166:1286–1294.
Prehn-Kristensen A, Krauel K, Hinrichs H, Fischer J, Malecki U,
Schuetze H, Wolff S, Jansen O, Duezel E, Baving L. 2011. Methylphenidate does not improve interference control during a working
memory task in young patients with attention-deficit hyperactivity
disorder. Brain Res. 1388:56–68.
Rubia K, Halari R, Christakou A, Taylor E. 2009. Impulsiveness as a
timing disturbance: neurocognitive abnormalities in attentiondeficit hyperactivity disorder during temporal processes and normalization with methylphenidate. Phil Trans R Soc Lond B Biol
Sci. 364:1919–1931.
Rubia K, Halari R, Cubillo A, Mohammad AM, Brammer M, Taylor E.
2009. Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in
medication-naive children with ADHD during a rewarded continuous performance task. Neuropharmacology. 57:640–652.
Rubia K, Halari R, Cubillo A, Smith AB, Mohammad AM, Brammer M,
Taylor E. 2011. Methylphenidate normalizes fronto-striatal underactivation during interference inhibition in medication-naive boys
1188 Methylphenidate Effects on Neural Activity
•
Costa et al.
with attention-deficit hyperactivity disorder. Neuropsychopharmacology. 36:1575–1586.
Rubia K, Halari R, Mohammad AM, Taylor E, Brammer M. 2011.
Methylphenidate normalizes frontocingulate underactivation
during error processing in attention-deficit/hyperactivity disorder.
Biol Psychiatr. 70:255–262.
Rubia K, Noorloos J, Smith A, Gunning B, Sergeant J. 2003. Motor
timing deficits in community and clinical boys with hyperactive
behavior: the effect of methylphenidate on motor timing. J
Abnorm Child Psychol. 31:301–313.
Rubia K, Russell T, Overmeyer S, Brammer MJ, Bullmore ET, Sharma
T, Simmons A, Williams SC, Giampietro V, Andrew CM et al. 2001.
Mapping motor inhibition: conjunctive brain activations across
different versions of go/no-go and stop tasks. Neuroimage.
13:250–261.
Rubia K, Smith AB, Brammer MJ, Taylor E. 2003. Right inferior
prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection. Neuroimage.
20:351–358.
Rubia K, Smith AB, Taylor E. 2007. Performance of children with attention deficit hyperactivity disorder (ADHD) on a test battery of
impulsiveness. Child Neuropsychol. 13:276–304.
Rubia K, Smith AB, Taylor E, Brammer M. 2007. Linear age-correlated
functional development of right inferior fronto-striato-cerebellar
networks during response inhibition and anterior cingulate during
error-related processes. Hum Brain Mapp. 28:1163–1177.
Rubia K, Smith AB, Woolley J, Nosarti C, Heyman I, Taylor E,
Brammer M. 2006. Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of
cognitive control. Hum Brain Mapp. 27:973–993.
Schachar R, Logan GD, Robaey P, Chen S, Ickowicz A, Barr C. 2007.
Restraint and cancellation: multiple inhibition deficits in attention deficit hyperactivity disorder. J Abnorm Child Psychol. 35:
229–238.
Schlosser RG, Nenadic I, Wagner G, Zysset S, Koch K, Sauer H. 2009.
Dopaminergic modulation of brain systems subserving decision
making under uncertainty: a study with fMRI and methylphenidate challenge. Synapse. 63:429–442.
Schultz W. 2002. Getting formal with dopamine and reward. Neuron.
36:241–263.
Schultz W, Dayan P, Montague PR. 1997. A neural substrate of prediction and reward. Science. 275:1593–1599.
Seeman P, Bzowej NH, Guan HC, Bergeron C, Becker LE, Reynolds
GP, Bird ED, Riederer P, Jellinger K, Watanabe S et al. 1987.
Human brain dopamine receptors in children and aging adults.
Synapse. 1:399–404.
Shafritz KM, Marchione KE, Gore JC, Shaywitz SE, Shaywitz BA. 2004.
The effects of methylphenidate on neural systems of attention
in attention deficit hyperactivity disorder. Am J Psychiatr. 161:
1990–1997.
Sonuga-Barke EJS, Coghill D, Markowitz JS, Swanson JM, Vandenberghe M, Hatch SJ. 2007. Sex differences in the response of children with ADHD to once-daily formulations of methylphenidate. J
Am Acad Child Adolesc Psychiatr. 46:701–710.
Spencer SV, Hawk LW, Jr, Richards JB, Shiels K, Pelham WE, Jr, Waxmonsky JG. 2009. Stimulant treatment reduces lapses in attention
among children with ADHD: the effects of methylphenidate on
intra-individual response time distributions. J Abnorm Child
Psychol. 37:805–816.
SPSS. 2010. PASW STATISTICS 19.0 Command Syntax Reference.
Chicago: SPSS Inc.
Talairach J, Tournoux P. 1988. Co-planar stereotaxic atlas of the
human brain. New York (NY): Thieme.
Trommer BL, Hoeppner JA, Zecker SG. 1991. The go-no go test in
attention deficit disorder is sensitive to methylphenidate. J Child
Neurol. 6(Suppl):S128–S131.
Turner DC, Robbins TW, Clark L, Aron AR, Dowson J, Sahakian
BJ. 2003. Relative lack of cognitive effects of methylphenidate
in elderly male volunteers. Psychopharmacology (Berl).
168:455–464.
Vaidya CJ, Austin G, Kirkorian G, Ridlehuber HW, Desmond JE,
Glover GH, Gabrieli JD. 1998. Selective effects of methylphenidate
in attention deficit hyperactivity disorder: a functional magnetic
resonance study. Proc Natl Acad Sci USA. 95:14494–14499.
van Mourik R, Oosterlaan J, Sergeant JA. 2005. The Stroop revisited: a
meta-analysis of interference control in AD/HD. J Child Psychol
Psychiatr. 46:150–165.
Verbruggen F, Logan GD. 2009. Models of response inhibition in the
stop-signal and stop-change paradigms. Neurosci Biobehav Rev.
33:647–661.
Verbruggen F, Logan GD. 2008. Response inhibition in the stop-signal
paradigm. Trends Cogn Sci. 12:418–424.
Vink M, Kahn RS, Raemaekers M, van den Heuvel M, Boersma M,
Ramsey NF. 2005. Function of striatum beyond inhibition
and execution of motor responses. Hum Brain Mapp. 25:
336–344.
Volkow ND, Wang GJ, Fowler JS, Gatley SJ, Logan J, Ding YS, Hitzemann R, Pappas N. 1998. Dopamine transporter occupancies in
the human brain induced by therapeutic doses of oral methylphenidate. Am J Psychiatr. 155:1325–1331.
Volkow ND, Wang G, Fowler JS, Logan J, Gerasimov M, Maynard L,
Ding Y, Gatley SJ, Gifford A, Franceschi D. 2001. Therapeutic
doses of oral methylphenidate significantly increase extracellular
dopamine in the human brain. J Neurosci. 21:RC121.
Volkow ND, Wang GJ, Fowler JS, Telang F, Maynard L, Logan J, Gatley
SJ, Pappas N, Wong C, Vaska P et al. 2004. Evidence that methylphenidate enhances the saliency of a mathematical task by increasing dopamine in the human brain. Am J Psychiatr. 161:1173–1180.
Volkow ND, Wang GJ, Kollins SH, Wigal TL, Newcorn JH, Telang F, Fowler
JS, Zhu W, Logan J, Ma Y et al. 2009. Evaluating dopamine reward
pathway in ADHD: clinical implications. JAMA. 302:1084–1091.
Volkow ND, Wang GJ, Newcorn J, Fowler JS, Telang F, Solanto MV,
Logan J, Wong C, Ma Y, Swanson JM et al. 2007. Brain dopamine
transporter levels in treatment and drug naive adults with ADHD.
Neuroimage. 34:1182–1190.
Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. 2005. Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatr. 57:1336–1346.
Zandbelt BB, Vink M. 2010. On the role of the striatum in response
inhibition. PLoS One. 5:e13848.
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