Response inhibition is associated with white matter microstructure in children.
ABSTRACT Cognitive control of thoughts, actions and emotions is important for normal behaviour and the development of such control continues throughout childhood and adolescence. Several lines of evidence suggest that response inhibition is primarily mediated by a right-lateralized network involving inferior frontal gyrus (IFG), presupplementary motor cortex (preSMA), and subthalamic nucleus. Though the brain's fibre tracts are known to develop during childhood, little is known about how fibre tract development within this network relates to developing behavioural control. Here we examined the relationship between response inhibition, as measured with the stop-signal task, and indices of regional white matter microstructure in typically-developing children. We hypothesized that better response inhibition performance would be associated with higher fractional anisotropy (FA) in fibre tracts within right IFG and preSMA after controlling for age. Mean FA and diffusivity values were extracted from right and left IFG and preSMA. As hypothesized, faster response inhibition was significantly associated with higher FA and lower perpendicular diffusivity in both the right IFG and the right preSMA, possibly reflecting faster speed of neural conduction within more densely packed or better myelinated fibre tracts. Moreover, both of these effects remained significant after controlling for age and whole brain estimates of these DTI parameters. Interestingly, right IFG and preSMA FA contributed additively to the prediction of performance variability. Observed associations may be related to variation in phase of maturation, to activity-dependent alterations in the network subserving response inhibition, or to stable individual differences in underlying neural system connectivity.
Article: The influence of emotion on cognitive control: relevance for development and adolescent psychopathology.[show abstract] [hide abstract]
ABSTRACT: The last decade has witnessed an explosion of research into the neural mechanisms underlying emotion processing on the one hand, and cognitive control and executive function on the other hand. More recently, studies have begun to directly examine how concurrent emotion processing influences cognitive control performance but many questions remain currently unresolved. Interestingly, parallel to investigations in healthy adults, research in developmental cognitive neuroscience and developmental affective disorders has provided some intriguing findings that complement the adult literature. This review provides an overview of current research on cognitive control and emotion interactions. It integrates parallel lines of research in adulthood and development and will draw on several lines of evidence ranging from behavioral, neurophysiological, and neuroimaging work in healthy adults and extend these to work in pediatric development and patients with affective disorders. Particular emphasis is given to studies that provide information on the neurobiological underpinnings of emotional and cognitive control processes using functional magnetic resonance imaging. The findings are then summarized and discussed in relation to neurochemical processes and the dopamine hypothesis of prefrontal cortical function. Finally, open areas of research for future study are identified and discussed within the context of cognitive control emotion interactions.Frontiers in psychology. 01/2011; 2:327.
[show abstract] [hide abstract]
ABSTRACT: Callosal volume reduction has been observed in patients with bipolar disorder, but whether these deficits reflect genetic vulnerability to the illness remains unresolved. Here, we used computational methods to map corpus callosum abnormalities in a population-based sample of twin pairs discordant for bipolar disorder. Twenty-one probands with bipolar I disorder (mean age 44.4 ± 7.5 years; 48% female), 19 of their non-bipolar co-twins, and 34 demographically matched control twin individuals underwent magnetic resonance imaging. Three-dimensional callosal surface models were created to visualize its morphologic variability and to localize group differences. Neurocognitive correlates of callosal area differences were additionally investigated in a subsample of study participants. Bipolar (BPI) probands, but not their co-twins, showed significant callosal thinning and area reduction, most pronounced in the genu and splenium, relative to healthy twins. Altered callosal curvature was additionally observed in BPI probands. In bipolar probands and co-twins, genu and splenium midsagittal areas were significantly correlated with verbal processing speed and response inhibition. These findings suggest that aberrant connections between cortical regions--possibly reflecting decreased myelination of white matter tracts--may be involved in bipolar pathophysiology. However, findings of callosal thinning appear to be disease related, rather than reflecting genetic vulnerability to bipolar illness.Cerebral Cortex 03/2011; 21(10):2415-24. · 6.54 Impact Factor
Article: Developmental trajectories during adolescence in males and females: a cross-species understanding of underlying brain changes.[show abstract] [hide abstract]
ABSTRACT: Adolescence is a transitional period between childhood and adulthood that encompasses vast changes within brain systems that parallel some, but not all, behavioral changes. Elevations in emotional reactivity and reward processing follow an inverted U shape in terms of onset and remission, with the peak occurring during adolescence. However, cognitive processing follows a more linear course of development. This review will focus on changes within key structures and will highlight the relationships between brain changes and behavior, with evidence spanning from functional magnetic resonance imaging (fMRI) in humans to molecular studies of receptor and signaling factors in animals. Adolescent changes in neuronal substrates will be used to understand how typical and atypical behaviors arise during adolescence. We draw upon clinical and preclinical studies to provide a neural framework for defining adolescence and its role in the transition to adulthood.Neuroscience & Biobehavioral Reviews 05/2011; 35(8):1687-703. · 8.65 Impact Factor
Neuropsychologia 48 (2010) 854–862
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/neuropsychologia
Response inhibition is associated with white matter microstructure in children
Kathrine Skak Madsena,b,c,∗, William F.C. Baaréa,b, Martin Vestergaarda,
Arnold Skimmingea, Lisser Rye Ejersboe, Thomas Z. Ramsøya, Christian Gerlache,
Per Åkesona, Olaf B. Paulsona,b,c, Terry L. Jernigana,b,c,d
aDanish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark
bCenter for Integrated Molecular Brain Imaging, Copenhagen, Denmark
cFaculty of Health Sciences, University of Copenhagen, Denmark
dCenter for Human Development, University of California, San Diego, CA, United States
eLearning Lab Denmark, Danish School of Education, University of Aarhus, Copenhagen, Denmark
a r t i c l e i n f o
Received 24 March 2009
Received in revised form 27 August 2009
Accepted 4 November 2009
Available online 10 November 2009
Diffusion tensor imaging
a b s t r a c t
Cognitive control of thoughts, actions and emotions is important for normal behaviour and the devel-
opment of such control continues throughout childhood and adolescence. Several lines of evidence
suggest that response inhibition is primarily mediated by a right-lateralized network involving infe-
rior frontal gyrus (IFG), presupplementary motor cortex (preSMA), and subthalamic nucleus. Though the
brain’s fibre tracts are known to develop during childhood, little is known about how fibre tract develop-
ment within this network relates to developing behavioural control. Here we examined the relationship
between response inhibition, as measured with the stop-signal task, and indices of regional white mat-
ter microstructure in typically-developing children. We hypothesized that better response inhibition
performance would be associated with higher fractional anisotropy (FA) in fibre tracts within right IFG
and preSMA after controlling for age. Mean FA and diffusivity values were extracted from right and left
IFG and preSMA. As hypothesized, faster response inhibition was significantly associated with higher
FA and lower perpendicular diffusivity in both the right IFG and the right preSMA, possibly reflecting
faster speed of neural conduction within more densely packed or better myelinated fibre tracts. More-
over, both of these effects remained significant after controlling for age and whole brain estimates of
these DTI parameters. Interestingly, right IFG and preSMA FA contributed additively to the prediction
of performance variability. Observed associations may be related to variation in phase of maturation,
to activity-dependent alterations in the network subserving response inhibition, or to stable individual
differences in underlying neural system connectivity.
© 2009 Elsevier Ltd. All rights reserved.
ues throughout childhood and adolescence (Williams, Ponesse,
Schachar, Logan, & Tannock, 1999). Cognitive control of thoughts,
actions, and emotions is important for normal behaviour, and
deficits in behavioural control are prominent in a variety of
psychiatric disorders, e.g., attention deficit/hyperactive disorder
(Pliszka et al., 2006) and obsessive–compulsive disorder (Enright &
Beech, 1993). In recent years, investigators have developed exper-
imental paradigms designed to measure motor control, or more
specifically the capacity to inhibit primed, or prepotent, motor
ofcognitive controlofbehaviour contin-
∗Corresponding author at: Danish Research Centre for Magnetic Resonance, MR-
Department, Section 340, Copenhagen University Hospital, Hvidovre, Kettegaard
Allé 30, 2650 Hvidovre, Denmark. Tel.: +45 3632 3323; fax: +45 3647 0302.
E-mail address: firstname.lastname@example.org (K.S. Madsen).
responses (Chambers, Garavan, & Bellgrove, 2009). Among these
is the Go/NoGo task. It is relatively easy to measure variability in
the speed of a motor response using response latency; however,
it is more difficult to assess the time a subject needs to inhibit
a response, since no response occurs on successful “NoGo” trials.
is the number of errors of commission during inhibit conditions.
The stop-signal task (SST) provides a more continuous measure of
a subject’s ability to inhibit a prepotent manual response (Logan
& Cowan, 1984). The subject’s task in the SST is to make a motor
quent trials an acoustic “stop” signal occurs at some delay after
the visual target. When the stop signal is detected, the subject
when the stop signal occurs very late in the trial (long stop-signal
delay), the subject will not be able to inhibit the response, whereas
when it occurs very soon after the visual target (short stop-signal
delay), success in inhibiting the response is much more likely. The
0028-3932/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
stop-signal delay is varied dynamically during the task so that the
subject succeeds in inhibiting the response on stop trials approx-
imately 50% of the time. A “stop-signal reaction time”, or SSRT, is
then computed for each subject by subtracting this average stop-
signal delay from the median response latency for Go trials (on
which no stop-signal occurs). In this way, a continuous measure is
constructed of the estimated time needed to inhibit a response.
Studies using the SST have implicated several brain structures
in a neural network subserving response inhibition. Responding
is thought to be mediated by a premotor-striatal-pallidal-motor
cortical network, whereas a primarily right-lateralized network,
involving prominently the inferior frontal gyrus (IFG), as well as
the presupplementary motor area (preSMA), and the subthalamic
nucleus (STN), has been implicated in response inhibition (Aron,
Behrens, Smith, Frank, & Poldrack, 2007; Aron & Poldrack, 2006).
shown that inhibiting a prepotent response consistently activates
prefrontal regions, particularly the right IFG, in adults (Aron et al.,
2007; Aron & Poldrack, 2006; Chevrier, Noseworthy, & Schachar,
2007; Rubia, Smith, Brammer, & Taylor, 2003) as well as in children
(Cohen et al., 2007). Moreover, human lesion studies suggest that
the right IFG is critical for response inhibition (Rieger, Gauggel, &
impair response inhibition (Aron, Fletcher, Bullmore, Sahakian, &
Robbins, 2003; Aron, Robbins, & Poldrack, 2004). The latter has
been confirmed in a study using transcranial magnetic stimulation,
where temporary deactivation of the right pars opercularis selec-
tively impaired the ability to stop an initiated response (Chambers
et al., 2006). In addition, a recent study found that FA within the
right IFG correlated with response inhibition measured with the
Simon task in a small group of adults (Forstmann et al., 2008).
Further, fMRI studies have found the right preSMA to be acti-
vated during stop trials (Aron et al., 2007; Aron & Poldrack, 2006).
The involvement of the right preSMA in response inhibition has
also been corroborated by human lesion studies (Floden & Stuss,
2006; Nachev, Wydell, O’neill, Husain, & Kennard, 2007). More-
over, weak microstimulation of neurons in the supplementary eye
field improves stop task performance by delaying saccadic initia-
tion in monkeys (Stuphorn & Schall, 2006). Another monkey study,
using a switching task designed to examine control over automatic
responses, found that successful switching from an automatic to
a volitionally controlled response selectively increased the activ-
ity of preSMA neurons. In addition, electrical stimulation in the
evidenced by fMRI studies showing this region to be activated dur-
ing stop trials (Aron et al., 2007; Aron & Poldrack, 2006), and by the
finding that deep-brain stimulation of the STN improves response
et al., 2006). Consistently, STN lesions impair inhibition in rodents
(Eagle et al., 2008). Finally and importantly, evidence from a trac-
tography study (Aron et al., 2007) suggests that the three regions
implicated in response inhibition are interconnected. A possible
preSMA-IFG connection is further supported by another tractog-
raphy study in humans (Johansen-Berg et al., 2004), whereas the
existence of direct fibre connections between the preSMA and STN
is supported by tract tracing in monkeys (Inase, Tokuno, Nambu,
Akazawa, & Takada, 1999).
Brain maturation is a complex ongoing process during child-
hood and early adulthood. Conventional structural MRI studies
have demonstrated morphological changes in grey and white mat-
ter structures during childhood and adolescence consistent with
cellular maturational processes, i.e., synaptic pruning and myeli-
nation (Giedd et al., 1999; Gogtay et al., 2004; Jernigan, Trauner,
Hesselink, & Tallal, 1991; Paus et al., 1999; Sowell et al., 2004); and
different grey matter structures exhibit distinct maturational tra-
jectories (Gogtay et al., 2004; Jernigan et al., 1991; Sowell et al.,
2004). Furthermore, gradual increases in global white matter vol-
ume as well as regional white matter density, have been reported
in axonal diameter and ongoing myelination across this age range
(Giedd et al., 1999; Paus et al., 1999, 2001). However, conventional
structural MRI provides limited information about the underlying
white matter microstructural properties.
Diffusion tensor imaging (DTI) measures the diffusion of water
molecules in tissue. In white matter, diffusion perpendicular to
highly organised fibre bundles is hindered relative to diffusion par-
allel to the fibres, causing diffusion anisotropy (Beaulieu, 2002).
An estimate of the degree of diffusion directionality, fractional
anisotropy (FA), as well as diffusivity parallel (??) and perpen-
dicular (?⊥) to the principal diffusion direction can be derived
by fitting the diffusion measurements of each voxel to the diffu-
sion tensor model (Basser, Mattiello, & LeBihan, 1994; Beaulieu,
2002). In recent years, DTI has been applied in studies of typically-
developing children and adolescents (Eluvathingal, Hasan, Kramer,
Fletcher, & Ewing-Cobbs, 2007; Lebel, Walker, Leemans, Phillips,
& Beaulieu, 2008; Snook, Paulson, Roy, Phillips, & Beaulieu, 2005).
Age-related increases in FA have been reported in multiple loca-
tions within white matter, reflecting a disproportionate decrease
in ?⊥relative to ??, possibly due to ongoing myelination and/or
an increase in fibre density (Eluvathingal et al., 2007; Lebel et al.,
fully understood, a previous study of infants correlated increased
FA (and decreased ?⊥) with apparently increased neural conduc-
tion speed, as reflected in decreased latency of the first positive
wave of the visual evoked potential (Dubois et al., 2008). Different
fusion parameters in some tracts approaching adult levels earlier
than others (Lebel et al., 2008).
The relationship between age-related changes in white mat-
ter microstructural properties and the concurrent development
of cognitive control in children is poorly understood. White mat-
ter microstructural changes in the fronto-striatal network have
been correlated with faster reaction times in the Go/NoGo task,
particularly in conditions more demanding of inhibition, in a
small group of children (Liston et al., 2006). However, processing
factors other than inhibitory function may also influence reac-
tion times on Go trials of the Go/NoGo task. There are currently
no reports of associations between white matter microstructure
and the specific measure of response inhibition from the SST,
the stop-signal reaction time, in children. Here we report asso-
ciations between response inhibition performance and regional
white matter microstructure in typically-developing children. The
major hypothesis of the study was that better response inhibition,
adjusted for age, would be associated with higher FA in fiber tracts
ships might also be observed for tracts in the right preSMA region.
The parallel and perpendicular diffusivities were investigated to
further explore the observed effects, since these diffusion param-
eters may provide additional information about the underlying
white matter microstructure. Post-hoc analyses, including whole
brain or left hemisphere region-of-interest estimates of FA and ?⊥,
were also performed to further explore the anatomical specificity
of the effects.
2. Materials and methods
Ninety-two typically-developing children aged 7–13 from three schools
(1st–6th graders) in the Copenhagen area were inducted into the study. Prior to
participation, all children assented to the procedures and informed written con-
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
Demographic data for the included subjects.
Parents’ average years of education (mean±SD)
Abbreviation: SD=standard deviation.
aChildren enrolled in the study were scanned either in the months just before (when in 1st, 3rd or 5th grade) or just after (when in 2nd, 4th or 6th grade) the summer
sent was obtained from the parents/guardians after oral and written explanation
of the study aims and study procedures. The study was approved by the local Dan-
ish Committee for Biomedical Research Ethics (H-KF-01-131/03), and conducted in
accordance with the Declaration of Helsinki.
excluded for the following reasons: incidental findings on MRI (1 subject), not com-
pleting the scanning session (3 subjects), not assessed on the SST due to failure
of the response box (6 subjects), and reduced image quality as described below
(17 subjects). Thus, 65 subjects were included in the present study (mean age±std:
no significant differences between the included and the excluded subjects on age,
gender, parental education, or handedness (as assessed with the Edinburgh hand-
edness inventory). Demographic data from the included subjects are presented in
2.2. Stop-signal task
The stop-signal task (SST) was administered using the Cambridge Neuropsy-
chological Test Automated Battery (Cambridge Cognition Ltd., Cambridge, UK). The
SST consists of go and stop trials (Fig. 1). Subjects sit in front of a computer monitor,
with each index finger resting on a response button. A circle is presented for 500ms,
followed by an arrow pointing either left or right. Subjects are instructed to press
the left or right button (i.e., with the left or right index finger), depending on the
The test consists of two parts. In the first part, there are 16 ‘Go’ training trials with-
out an auditory stop-signal to introduce the subjects to the press pad. In the second
and longer part, an auditory stop-signal occurs in 25% of the trials. When the tone
occurs, subjects must try to withhold their responses. The time between the onset
of the arrow and the auditory stop signal, i.e., the stop-signal delay (SSD), changes
adaptively throughout the test, depending on the subject’s past performance, so
that responses are inhibited successfully approximately 50% of the time for each
subject. The shorter the SSD, the more likely it is that the subject will be able to
Fig. 1. The stop-signal task consists of go and stop-signal trials. A circle is presented
for 500ms, followed by a presentation of an arrow pointing either left or right.
Subjects are instructed to respond as fast as possible by pressing a left or right
button, depending on the direction of the arrow. In the stop trials, an auditory stop-
to withhold their responses. The latency to the sound (the stop-signal delay [SSD])
varies dynamically throughout the study to produce the SSD50, where subjects are
able to inhibit approximately 50% of their responses. The stop-signal reaction time
(SSRT) is calculated as the median go RT minus the SSD50, according to the race
model (Logan & Cowan, 1984).
inhibit his or her response. The SST is administered in 5 blocks of 64 trials. Each
block is divided into four sub-blocks of 16 trials (12 go trials and 4 stop trials in ran-
dom order). There is no gap between the sub-blocks and they are not evident to the
subject. After each block, a feedback screen is displayed showing a histogram repre-
sentation of the subject’s reaction time on ‘Go’ trials. The histogram shown after the
first block is identical for all subjects. The test administrator explains to the subject
that if he/she can go faster, it will show in the next histogram, before encouraging
him/her to go faster. The feedback after each of the last four blocks is the subjects’
go reaction time in relation to the first block, and consists of a histogram contain-
ing the first block and the relative performance of all previous blocks. The primary
behavioural outcome measure is the stop-signal reaction time (SSRT), which mea-
the race model (Logan & Cowan, 1984) by subtracting the SSD50, where subjects
are able to inhibit 50% of their responses, from the median go RT. The race model
independent (Boucher, Palmeri, Logan, & Schall, 2007; Logan & Cowan, 1984). To
get a stable estimate of the SSRT, the trials in the first half of the dataset were
treated as training trials to familiarize subjects with the auditory stop-signal, and
the SSRT used for statistical analysis was estimated from trials in the last half of the
2.3. Image acquisition
All subjects were scanned using a 3T Siemens Magnetom Trio MR scanner
(Siemens, Erlangen, Germany) with an eight-channel head coil (Invivo, FL, USA).
were aligned parallel to the anterior commisure–posterior commisure (AC–PC) line.
Diffusion-weighted (DW) images of the whole brain were acquired using a twice-
refocused balanced spin echo sequence that minimised eddy current distortion
(Reese, Heid, Weisskoff, & Wedeen, 2003). Ten non-DW images (b=0) and 61 DW
images, encoded along independent collinear diffusion gradient orientations (Cook,
Symms, Boulby, & Alexander, 2007; Jansons & Alexander, 2003), were acquired
with a b value of 1200s/mm2(TR=8200ms; TE=100ms, FOV=220mm×220mm,
matrix=96×96, GRAPPA: acceleration factor=2; number of reference lines=48, 61
transverse slices with no gap, 2.3mm×2.3mm×2.3mm voxels, NEX=1, acqui-
sition time=9.50min). A gradient echo based field map sequence (TR=530ms,
transverse slices with no gap, voxel size=2mm×2mm×3mm, NEX=1, acqui-
sition time=2.18min) was acquired to correct geometric distortions caused
by B0 magnetic field inhomogeneities. T2-weighted images of the whole head
were acquired using a 3D turbo spin echo sequence (TR=3000ms, TE=354ms,
FOV=282mm×282mm, matrix=256×256, 192 sagittal slices with no gap,
1.1mm×1.1mm×1.1mm voxels, NEX=1, acquisition time=8.29min) for gener-
ating brain masks. T1-weighted and proton density weighted images were also
acquired in the imaging session, but these images were not used in the analysis
2.4. Image evaluation
inspection, 17 subjects had significantly reduced image quality due to movement or
susceptibility artefacts and were excluded from further analysis.
2.5. Image analysis
SPM2 routines. DW images were oriented to the MNI coordinate system and cor-
rected for geometric distortions due to B0inhomogeneities. The first B0image was
coregistered to MNI space using a rigid transformation (6 parameter mutual infor-
mation), after which all DW images were coregistered (no reslicing) to the first B0
image. Next, all coregistered images were corrected for geometric distortions using
the acquired B0field map (Andersson, Hutton, Ashburner, Turner, & Friston, 2001)
and resliced into the MNI coordinate system using trilinear interpolation. Note that
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
Fig. 2. (a) Coronal and sagittal slices of the mean skeleton (yellow) overlaid on the
target FA image. The blue segments are the IFG (inferior frontal gyrus, pars oper-
cularis) ROIs and the red segments are the preSMA (presupplementary motor area)
ROIs. (b) Midsagittal view showing the planes (in green) of the three coronal sec-
tions in the top row. The planes indicated in white and green represent the coronal
views of the effect-size maps shown in Figs. 3 and 4.
this procedure involves only one reslicing step. The diffusion tensor was fitted using
the RESTORE algorithm (Chang, Jones, & Pierpaoli, 2005) implemented in Camino
(Cook et al., 2006), and FA, ??(?1) and ?⊥((?2+?3)/2) were calculated. A brain mask
based on the T2-weighted image was automatically created using SPM2 segmen-
tation routines and morphological operations and applied to the FA and diffusivity
we used a region-of-interest (ROI) approach to extract FA and diffusivity measures
tics (TBSS) (Smith et al., 2006) to accomplish spatial normalisation and to align the
fibre tracts across children. Specifically, FSL 4.0.1 (Smith et al., 2004) was used to
align all subjects’ FA images into a common space using the nonlinear registration
IRTK (Rueckert et al., 1999). A study-specific target, the group’s most representa-
tive FA image, was identified by aligning each subject’s FA image to every other
subject’s FA image. The target FA image was aligned to 1mm3MNI space using
affine registration, and subsequently all subjects’ FA images were then aligned to
this study-specific target FA image. A cross-subject mean FA image was created and
thinned to create a mean FA skeleton, representing the centres of all tracts common
to the group. The mean FA skeleton was thresholded at FA>0.25, and contained
103,503 1mm3interpolated isotropic voxels, corresponding to approximately one
quarter of the voxels with FA above 0.25. Each subject’s aligned FA image was then
projected onto the study-specific skeleton by locating the voxels with the highest
local FA value in the direction perpendicular to the skeleton tracts and assigning
the value of these voxels to the skeleton at this standardised location. Note that this
results in a mapping of each voxel location in the skeleton to a specific voxel in the
individual FA maps. In addition, the nonlinear warps and the skeleton projections
were applied to the ??and ?⊥data.
Regions-of-interest (ROIs) were drawn onto the study-specific skeleton over-
laid on the target FA map. Since the TBSS procedure projects individual FA values
and other DTI derived parameters onto a common framework, i.e., the mean study-
specific skeleton, one can extract each subject’s individual measures directly by
delineating a ROI once. The ROIs were placed in the right and left IFG (pars opercu-
located using a brain atlas (Duvernoy, 1999) and published information about the
morphology of the pars opercularis (Tomaiuolo et al., 1999). The boundaries of the
IFG ROIs were found by using anatomical information visible in the target FA map,
where the vertical ramus of the lateral fissure and the inferior part of the precentral
sulcus, indicated by low FA values, were used as landmarks to define the anterior
and posterior boundaries of the pars opercularis. The skeleton defined the superior
boundary, in that the lateral part of the pars opercularis segment was perpendicular
els and the left contained 322 voxels. The location of the preSMA ROIs was based on
MNI coordinates derived from published functional and structural studies (Behrens,
Jenkinson, Robson, Smith, & Johansen-Berg, 2006; Johansen-Berg et al., 2004), and
defined between MNI x=0 and x=20. However, based on the anatomical informa-
tion provided by the target FA map, the posterior and anterior boundaries were set
to MNI x=5 and x=14 for the right and x=6 and x=15 for the left preSMA ROI,
respectively. The right preSMA ROI included 295 voxels and the left ROI included
311 voxels. Mean FA, ??and ?⊥values from all four ROIs and the whole skeleton
were extracted for each subject for statistical analyses. We initially attempted to
include a ROI in the internal capsule, given that the subthalamic nucleus (STN) has
been implicated in response inhibition (Aron & Poldrack, 2006; van den Wildenberg
et al., 2006) and fibre tracts connecting the IFG and preSMA with the STN have been
found (Aron et al., 2007). However, due to the lack of clear landmarks delimiting the
relevant segments of the internal capsule, it was deemed not feasible to define an
appropriate ROI on the mean skeleton.
2.7. Statistical analysis
The statistical analyses were performed with SPSS 15.0 using multiple linear
regression models predicting SSRT. Multicollinearity between the predictors was
assessed for all models. The statistical tests were performed hierarchically. The
nificantly and negatively associated with SSRT were tested with ˛=0.025. Planned
follow-up analyses were contingent on observing significant associations with FA in
the primary analyses. Follow-up analyses were done to assess the anatomical speci-
ficity of the effects, either by adjusting for the whole skeleton FA or by adjusting
for the corresponding left hemisphere ROI FA. The parallel and perpendicular dif-
fusivities were investigated to further explore the nature of these observed effects
by following the same statistical analysis protocol as for the FA data. An additional
preSMA FA measures simultaneously. Exploratory models including either gender
or age by gender interaction effects revealed no significant gender effects and, thus,
models presented do not include gender.
Two effect-size maps are presented to provide further anatomical information
about the association between FA and SSRT (Jernigan, Gamst, Fennema-Notestine,
& Ostergaard, 2003). The effect-size maps are t-maps of the correlation between
FA and SSRT, however the sign of the correlations are reversed so that correlations
between high FA and better response inhibition performance (lower reaction time)
adjusting for age, respectively. The t-maps were generated using the Monte Carlo
within FSL (Nichols & Holmes, 2002).
Behavioural measures for the SST and the FA measures for the
four ROIs are presented in Table 2. The subjects inhibited approx-
imately 50% of their responses in the stop trials, indicating that
the adaptive tracking algorithm performed as expected. Separate
statistics are provided for boys and girls, but no gender differences
approached significance (Ps>0.37).
Behavioural measures of the SST and fractional anisotropy measures of the region-of-interest.*.
Behavioural measureAll subjects Girls Boys
Median correct Go RT (ms)
Mean SSD 50% (ms)
504.4 ± 103.5
287.1 ± 104.9
50.5 ± 0.1
217.3 ± 60.4
510.3 ± 114.1
297.5 ± 109.0
50.9 ± 0.1
212.9 ± 59.1
497.0 ± 90.2
274.1 ± 99.9
49.9 ± 0.1
222.8 ± 62.5
Region-of-interest All subjects FAGirls FABoys FA
0.405 ± 0.025
0.435 ± 0.034
0.411 ± 0.031
0.444 ± 0.024
0.404 ± 0.026
0.435 ± 0.034
0.414 ± 0.030
0.442 ± 0.027
0.407 ± 0.024
0.436 ± 0.034
0.407 ± 0.331
0.446 ± 0.021
Abbreviations: SST=stop-signal task, SD=standard deviation, RT=reaction time, SSRT=stop-signal reaction time, SSD=stop-signal delay.
*All values are Mean±SD.
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
Linear regression models predicting SSRT from FA: a priori hypotheses.a.
Right IFGRight preSMA Age
Each model is referred to with a number in the leftmost column, representing the models:  right IFG FA, adjusted for age;  right preSMA FA, adjusted for age;  right
IFG FA;  right preSMA FA;  right IFG and right preSMA FA;  right IFG and right preSMA, adjusted for age. Models  and  test the a priori hypotheses (see text for
details). Abbreviations: SSRT=stop-signal reaction time, IFG=inferior frontal gyrus, PreSMA=presupplementary motor area, and FA=fractional anisotropy.
aLinear regression models are presented in rows. Predictors are presented in columns.
3.1. Associations between SSRT and DTI parameters within IFG
in Table 3, where each row represents a separate, planned model
predicting SSRT. As hypothesized, right IFG FA, adjusted for age,
was significantly and negatively associated with SSRT (Fig. 3a and
ciated with higher mean FA values within the right IFG. A similar
significant relationship was observed for right preSMA FA (Fig. 3b
and Table 3, model 2). When not controlling for age, which itself
was significantly and negatively correlated with SSRT (R2=0.212,
ˇ=−0.460, P=0.0001) the effects were slightly larger (Table 3,
FA remained significant predictors of SSRT (Table 3, models 5 and
6), suggesting that these effects are additive.
Results from follow-up analyses of the FA data, testing for the
anatomical specificity of the above-mentioned effects, are pre-
sented in Table 4. When adjusting for age and whole skeleton
FA, right IFG FA as well as right preSMA FA remained significant
predictors of SSRT (Table 4, models 1 and 3), suggesting that the
association between SSRT and right IFG and preSMA FA were not
mediated by global increase in FA. In both models, the additional
associated with SSRT (R2=0.288, ˇ=−0.304, P=0.013). Further-
more, the right hemisphere FA values remained significant when
controlling for age and the corresponding left hemisphere ROI FA
values (Table 4, models 2 and 4). The additional contributions of
the latter did not approach significance in either case. Although left
IFG FA by itself was correlated with SSRT (R2=0.075, P=0.028), this
association was not significant after controlling for age (P=0.09),
and left preSMA FA did not exhibit a significant association with
The parallel and perpendicular diffusivities were investigated
to further explore the nature of these effects. Both right IFG and
preSMA ?⊥were significantly and positively associated with SSRT
models 2 and 6) or for left hemisphere ROI ?⊥(Table 5, models
4 and 8). When controlling for both age and whole skeleton ?⊥,
the effect of right IFG ?⊥remained significant, though the effect of
right preSMA ?⊥did not (Table 5, models 3 and 7, respectively).
Whole skeleton ?⊥, adjusted for age, was significantly and pos-
itively related with SSRT (R2=0.307, ˇ=0.333, P=0.005). These
effects contrasted with those for parallel diffusivity. Neither right
IFG ??nor right preSMA ??was significantly associated with SSRT,
with or without adjusting for age (P>0.5). However, whole skele-
ton ??adjusted for age was significantly and positively correlated
with SSRT (R2=0.267, ˇ=0.242, P=0.035).
Collinearity diagnostics were performed for all of the regres-
in the models was low (tolerance=0.46–0.99, variance inflation
3.2. Effect-size map
The present study was designed to test specific anatomical
hypotheses about the relationship between right IFG and preSMA
fibre tract microstructure and variability in SST performance in
children. Therefore neither a whole brain, voxel-wise analysis of
the effects (appropriately adjusted for test-multiplicity), nor a
restricted voxel-wise analysis with small volume correction was
deemed appropriate for testing these a priori hypotheses. How-
ever, since the analysis we used produces estimates of the effect
size at each voxel, we have provided visualisation of the effect-size
Fig. 3. Partial regression plots of the stop-signal reaction time (SSRT) as a function of (a) right inferior frontal gyrus (IFG) FA, adjusted for age and (b) right presupplementary
motor area (preSMA) FA, adjusted for age.
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
Linear regression models predicting SSRT from FA: follow-up analyses.a.
Right IFGAgeWhole skeletonLeft IFG
Right PreSMA AgeWhole skeletonLeft PreSMA
Each model is referred to with a number in the leftmost column, representing the models: [1,3] right ROI, adjusted for age and whole skeleton FA; [2,4] right ROI, adjusted
for age and corresponding left ROI FA. Abbreviations: SSRT=stop-signal reaction time, IFG=inferior frontal gyrus, PreSMA=presupplementary motor area, and FA=fractional
aMultiple linear regression models are presented in rows. Predictors are presented in columns.
els, and are presented to provide additional information about the
anatomical distribution of associations between response inhibi-
tion performance and FA in white matter. Two maps are provided,
the first (Fig. 4) displaying the association between FA and SSRT
controlling for age, and the second (Fig. 5) displaying the direct
association of FA and SSRT. The contrast has been coded so that
association of lower SSRT (better response inhibition) with higher
FA yields positive t-values. Increasing positive t-values are shown
in warm colours ranging from red to yellow, whereas decreasing
negative t-values are shown in cool colours ranging from dark blue
to light blue. The position of the depicted coronal slices is indicated
in Fig. 2b. Slices 5, 10, and 13 are through the ROI locations and
are the same as those shown in Fig. 2a, top row. Slice 18 shows the
effects in the hand area in the motor cortex. In general, the associ-
ations between higher FA and better response inhibition appear to
be modestly stronger when not correcting for age. In Fig. 6, sagittal
views of the effect-size map show clusters of voxels with relatively
high t-values within the internal capsule. Some clusters appear to
be located in the posterior limb of the internal capsule.
The present study examined associations between response
inhibition performance and white matter microstructure within
IFG (pars opercularis) and preSMA in children aged 7–13 years. As
hypothesized, higher FA in fibre tracts within right IFG and preSMA
was significantly associated with better response inhibition. Since
both FA (Lebel et al., 2008) and response inhibition (Williams et
al., 1999) increase throughout childhood and adolescence, a cor-
relation between these measures could represent a nonspecific
association attributable to unmeasured factors indexed by chrono-
logical age. For example, since height also increases in children
over this age range, one might expect to find a simple correlation
between height and response inhibition within a group of children
with varying ages. However, since height has little direct relation-
ship to cognitive functioning in children, one would expect the
correlation between these variables to be substantially reduced
after controlling for age statistically. Thus, we hypothesized that
the associations between SSRT and FA within the right IFG and
preSMA would remain significant after controlling for age; i.e., that
even among children of similar age, those with higher FA in right
IFG and right preSMA would exhibit stronger response inhibition
performance. These hypotheses were confirmed.
Several planned follow-up analyses were conducted to explore
the nature of these associations. We were interested to assess the
anatomical specificity of the associations between response inhi-
bition performance and fibre structure within the right IFG and
preSMA, i.e., the extent to which response inhibition exhibited a
relatively specific relationship to FA in this fibre tract relative to
others. This is an important question, since it is known that FA
et al., 2008). Mean FA for the whole skeleton, adjusted for age,
was modestly correlated with SSRT, suggesting that global individ-
ual differences in white matter microstructure between children
of similar age are mediating this association. However, the effects
on SSRT of FA in the right IFG and in the right preSMA remained
significant when this global measure of FA was included as a
Linear regression models predicting SSRT from ?⊥: follow-up analyses.a.
Right IFGAge Wholeskeleton Left IFG
Right PreSMA AgeWholeskeletonLeft PreSMA
Each model is referred to with a number in the leftmost column, representing the models: [1,5] right ROI ?⊥; [2,6] right ROI ?⊥, adjusted for age; [3,7] right ROI ?⊥, adjusted
for age and whole skeleton ?⊥; [4,8] right ROI ?⊥, adjusted for age and corresponding left ROI ?⊥. Abbreviations: SSRT=stop-signal reaction time, IFG=inferior frontal gyrus,
PreSMA=presupplementary motor area, and ?⊥=perpendicular diffusivity.
aLinear regression models are presented in rows. Predictors are presented in columns.
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
Fig. 4. Effect-size map of the negative association between SSRT and FA adjusted
for age. The effect-size map is overlaid on the target FA image. The colour bar shows
the mapping of colours to the values of the t-statistics. All voxels on the skeleton
are coloured. Images are shown with the subject’s right side on the right side (neu-
rological convention). The MNI coordinates for the coronal images are given under
each image. The IFG and preSMA ROIs were located in slice 5, 10 and 13 (see Fig. 2a,
Fig. 5. Effect-size map of the negative association between SSRT and FA. The effect-
size map is overlaid on the target FA image. The colour bar shows the mapping
of colours to the values of the t-statistics. All voxels on the skeleton are coloured.
Images are shown with the subject’s right side on the right side (neurological con-
vention). The MNI coordinates for the coronal images are given under each image.
The IFG and preSMA ROIs were located in slice 5, 10 and 13 (see Fig. 2a, top row).
Fig. 6. Effect-size map of the negative association between SSRT and FA with (a)
and without (b) adjusting for age. The effect-size map is overlaid on the target FA
image. The colour bar shows the mapping of colours to the values of the t-statistics.
All voxels on the skeleton are coloured. The sagittal slices were selected to show
clusters of voxels with relatively high t-values within the internal capsule. The MNI
coordinates for the images are given above the images.
covariate, and in these models the effect of the global measure
no longer approached significance. This suggests that the relation-
ships between response inhibition and FA within the right IFG and
preSMA are not likely to be mediated by global white matter FA
differences and may be more strongly related to the structure in
specific tracts. Moreover, results from similar analysis controlling
for FA in the left hemisphere ROIs, suggested that the effects of
structure in the right hemisphere tracts were more robust than
effects of structure in the left hemisphere tracts. This pattern is
consistent with a significant degree of anatomical specificity of the
effects relating FA and SSRT performance; and it is consistent with
previous studies suggesting that a primarily right-lateralized net-
et al., 2007, 2003; Chambers et al., 2006; Floden & Stuss, 2006;
Nachev et al., 2007). Interestingly, when predicting SSRT perfor-
mance with right IFG and right preSMA FA simultaneously (with
or without adjusting for age), both regions remained significant
predictors. This suggests that increased FA in fibre tracts within
the right IFG and right preSMA may contribute additively to better
response inhibition performance.
ative to prior methods for performing spatial normalisation of FA
images (Smith et al., 2006) and the method obviates the need for
extensive spatial smoothing. Thus, the use of TBSS in the present
study aimed to increase the sensitivity of our ROI approach relative
to ROI methods used in previous DTI studies in children. We aver-
aged FA over the segments of the tract skeleton that corresponded
to the expected location of the targeted tracts after applying TBSS
to align the tracts across subjects. This was considered to be a more
objective way of estimating FA within a comparable portion of the
tract within each individual than subjective (manual) delineation
of regions within the white matter, and a more powerful test of
our hypothesis than a voxel-wise approach. However, the method
produces only an approximation of FA in the targeted tracts, and in
raphy methods that define the tracts based on connectivity, or new
methods using probabilistic atlases (Hagler et al., 2009) to more
definitively assess the structure in particular fibre tracts.
We performed additional analyses of parallel and perpendic-
ular diffusivities to further investigate the effects found in the
right IFG and preSMA, since these diffusion parameters may pro-
vide additional information about the underlying white matter
microstructure. We found a similar relationship between SSRT and
?⊥ to that observed with FA in the fibre tracts within the right
IFG and preSMA. The effects of ?? appeared weaker and were
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
not significant. This suggests that the increase in FA associated
with better response inhibition performance is mainly driven by
decreased ?⊥. Although interpretation of changes in DTI parame-
ters is not straightforward, previous studies suggest that ?⊥may
be more sensitive to changes in myelination (Song et al., 2003,
2005). Using a mouse model of retinal ischemia, a DTI and his-
tology study revealed a gradual decrease in relative anisotropy,
caused by, at first, decrease in ??, corresponding with the tim-
ing of axonal degeneration, followed by increase in ?⊥, associated
with optic nerve demyelination (Song et al., 2003). Another rodent
study, using a model of experimental de- and remyelination of
treatment caused demyelination of the callosal fibres, reflected by
increased ?⊥. When the treatment was discontinued, the effects
were reversed, and the progression of fibre remyelination was
consistent with decrease in ?⊥ (Song et al., 2005). Although ?⊥
does not measure myelin directly, these findings suggest that the
degree of myelination may be contributing to the observed effects
in the present study. Age-related FA increases in fibre tracts have
been linked to decreases in ?⊥ in previous studies (Lebel et al.,
2008). However, other tissue parameters, such as axonal diameter,
packing density, spacing, or number; extracellular volume frac-
tion; or tract geometry may also contribute to changes in FA and
?⊥(Beaulieu, 2002; Madler, Drabycz, Kolind, Whittall, & MacKay,
2008; Schwartz et al., 2005).
Previous studies have linked the STN to response inhibition
(Aron & Poldrack, 2006; van den Wildenberg et al., 2006), and fibre
tracts connecting the IFG and preSMA with STN pass through the
internal capsule (Aron et al., 2007). Due to the lack of clear land-
marks delimiting these fibres within the internal capsule, we were
the associations between higher FA and better response inhibition
performance, clusters with relatively high t-values were observed
in the internal capsule (Fig. 6). Though it is not possible to conclude
whether or not these connections are contained in the observed
clusters, the pattern observed in the effect-size maps suggests that
the internal capsule may be an important part of the neural circuit
mediating variability in response inhibition in this study. Future
studies employing tractography may provide additional evidence.
As mentioned above, these findings suggest that even among
children of similar age, higher FA and lower ?⊥in right IFG and
preSMA are associated with better response inhibition perfor-
mance. Many questions remain about what such age-adjusted
variability in FA and ?⊥might represent. It may reflect individual
tem subserving response inhibition, i.e., differences in the phase
of IFG and preSMA development among children of similar age.
It is plausible that this variability could mediate, at least to some
extent, the association between response inhibition performance
and white matter microstructure found in the present study, since
both white matter structure (Lebel et al., 2008) and inhibitory con-
trol (Williams et al., 1999) continue to develop across this age
structure of the fibre tracts (perhaps reflecting differences in the
underlying neural system connectivity) that emerge earlier during
individual differences in behavioural performance have been asso-
ciated with FA variability in adults (Forstmann et al., 2008; Gold,
Powell, Xuan, Jiang, & Hardy, 2007; Wolbers, Schoell, & Buchel,
2006) as well as children.
It is also plausible that the difference in microstructure of the
fibre tracts are influenced by dynamic processes, possibly associ-
ated with activity levels in the neural circuits. In a recent DTI study,
accelerated white matter development (higher FA) in the sagittal
stratum was found in preterm relative to full-term infants at term
equivalent age, possibly as a result of increased intensity of sen-
sorimotor stimulation associated with extrauterine life (Gimenez
et al., 2008). Thus, the differences observed in the present study
could reflect variability in the experiences and learning of the chil-
dren. Finally, genetic variability may play a role in mediating these
FA and ?⊥within the right IFG and preSMA exhibit associations
with SSRT that are not attributable to age and the associations do
not appear to reflect behavioural effects of global white matter
development. Further, the contributions of right IFG and preSMA
FA to the prediction of response inhibition appear to be additive.
Children may vary in the phase of maturation in the network sub-
serving response inhibition, and this variability may mediate the
associations. Alternatively, the associations could be mediated by
individual differences among the children in underlying neural
system connectivity, or to more transient differences associated
with dynamic (perhaps activity-dependent) processes. Longitudi-
cil, University of Copenhagen’s Research Priority Area Body and
Mind, and the Lundbeck Foundation.
Andersson, J. L., Hutton, C., Ashburner, J., Turner, R., & Friston, K. (2001). Modeling
geometric deformations in EPI time series. Neuroimage, 13, 903–919.
Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J., & Poldrack, R. A. (2007). Triangulat-
ing a cognitive control network using diffusion-weighted magnetic resonance
imaging (MRI) and functional MRI. Journal of Neuroscience, 27, 3743–3752.
signal inhibition disrupted by damage to right inferior frontal gyrus in humans.
Nature Neuroscience, 6, 115–116.
Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2004). Inhibition and the right inferior
frontal cortex. Trends in Cognitive Sciences, 8, 170–177.
Basser, P. J., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and
imaging. Biophysical Journal, 66, 259–267.
technical review. NMR in Biomedicine, 15, 435–455.
consistent relationship between local white matter architecture and functional
specialisation in medial frontal cortex. Neuroimage, 30, 220–227.
and brain: An interactive race model of countermanding saccades. Psychological
Review, 114, 376–397.
Chambers, C. D., Bellgrove, M. A., Stokes, M. G., Henderson, T. R., Garavan, H., Robert-
son, I., et al. (2006). Executive “brake failure” following deactivation of human
frontal lobe. Journal of Cognitive Neuroscience, 18, 444–455.
Chambers, C. D., Garavan, H., & Bellgrove, M. A. (2009). Insights into the neural basis
of response inhibition from cognitive and clinical neuroscience. Neuroscience
and Biobehavioral Review, 33, 631–646.
by outlier rejection. Magnetic Resonance in Medicine, 53, 1088–1095.
Chevrier, A. D., Noseworthy, M. D., & Schachar, R. (2007). Dissociation of response
inhibition and performance monitoring in the stop signal task using event-
related fMRI. Human Brain Mapping, 28, 1347–1358.
Cohen, J. R., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J., Asarnow, R.
F., et al. (2007). Neural substrates of response inhibition in children examined
with fMRI. 13th annual meeting of the organization of human brain mapping.
Neuroimage, 36(Suppl. 1).
Cook, P. A., Bai, Y., Nedjati-Gilani, S., Seunarine, K. K., Hall, M. G., Parker, G. J., et
al. (2006). Camino: Open-source diffusion-MRI reconstruction and processing.
In 14th scientific meeting of the international society for magnetic resonance in
medicine Seattle, WA, USA, (p. 2759).
Cook, P. A., Symms, M., Boulby, P. A., & Alexander, D. C. (2007). Optimal acquisition
orders of diffusion-weighted MRI measurements. Journal of Magnetic Resonance
Imaging, 25, 1051–1058.
K.S. Madsen et al. / Neuropsychologia 48 (2010) 854–862
Dubois, J., Dehaene-Lambertz, G., Soares, C., Cointepas, Y., Le Bihan, D., & Hertz-
Pannier, L. (2008). Microstructural correlates of infant functional development:
Example of the visual pathways. Journal of Neuroscience, 28, 1943–1948.
section anatomy (2nd ed.). Wien: Springer.
Eagle, D. M., Baunez, C., Hutcheson, D. M., Lehmann, O., Shah, A. P., & Robbins, T. W.
(2008). Stop-signal reaction-time task performance: Role of prefrontal cortex
and subthalamic nucleus. Cerebral Cortex, 18, 178–188.
Eluvathingal, T. J., Hasan, K. M., Kramer, L., Fletcher, J. M., & Ewing-Cobbs, L. (2007).
normally developing children and adolescents. Cerebral Cortex, 17, 2760–2768.
Enright, S. J.,& Beech,A.R. (1993).
obsessive–compulsive disorder. British Journal of Clinical Psychology, 32(Pt
Floden, D., & Stuss, D. T. (2006). Inhibitory control is slowed in patients with
right superior medial frontal damage. Journal of Cognitive Neuroscience, 18,
Forstmann, B. U., Jahfari, S., Scholte, H. S., Wolfensteller, U., van den Wildenberg,
W. P., & Ridderinkhof, K. R. (2008). Function and structure of the right inferior
frontal cortex predict individual differences in response inhibition: A model-
based approach. Journal of Neuroscience, 28, 9790–9796.
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., et
MRI study. Nature Neuroscience, 2, 861–863.
Gimenez, M., Miranda, M. J., Born, A. P., Nagy, Z., Rostrup, E., & Jernigan, T. L. (2008).
Accelerated cerebral white matter development in preterm infants: A voxel-
based morphometry study with diffusion tensor MR imaging. Neuroimage, 41,
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., et
al. (2004). Dynamic mapping of human cortical development during childhood
through early adulthood. Proceedings of the National Academy of Sciences United
States of America, 101, 8174–8179.
Gold, B. T., Powell, D. K., Xuan, L., Jiang, Y., & Hardy, P. A. (2007). Speed of lex-
ical decision correlates with diffusion anisotropy in left parietal and frontal
white matter: Evidence from diffusion tensor imaging. Neuropsychologia, 45,
Hagler, D. J., Jr., Ahmadi, M. E., Kuperman, J., Holland, D., McDonald, C. R., Halgren, E.,
et al. (2009). Automated white-matter tractography using a probabilistic diffu-
sion tensor atlas: Application to temporal lobe epilepsy. Human Brain Mapping,
corticosubthalamic input zones from the presupplementary motor area in the
macaque monkey: Comparison with the input zones from the supplementary
motor area. Brain Research, 833, 191–201.
Isoda, M., & Hikosaka, O. (2007). Switching from automatic to controlled action by
monkey medial frontal cortex. Nature Neuroscience, 10, 240–248.
Jansons, K. M., & Alexander, D. C. (2003). Persistent angular structure: New insights
from diffusion MRI data. Information Processing in Medical Imaging, 18, 672–683.
Jernigan, T. L., Gamst, A. C., Fennema-Notestine, C., & Ostergaard, A. L. (2003). More
“mapping” in brain mapping: Statistical comparison of effects. Human Brain
Mapping, 19, 90–95.
Jernigan, T. L., Trauner, D. A., Hesselink, J. R., & Tallal, P. A. (1991). Maturation
of human cerebrum observed in vivo during adolescence. Brain, 114(Pt 5),
J. M., et al. (2004). Changes in connectivity profiles define functionally distinct
regions in human medial frontal cortex. Proceedings of the National Academy of
Sciences United States of America, 101, 13335–13340.
Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008). Microstructural
maturation of the human brain from childhood to adulthood. Neuroimage, 40,
Frontostriatal microstructure modulates efficient recruitment of cognitive con-
trol. Cerebral Cortex, 16, 553–560.
Logan, G. D., & Cowan, W. B. (1984). On the ability to inhibit thought and action: A
theory of an act of control. Psychological Review, 91, 295–327.
Madler, B., Drabycz, S. A., Kolind, S. H., Whittall, K. P., & MacKay, A. L. (2008). Is
diffusion anisotropy an accurate monitor of myelination? Correlation of mul-
Reducedcognitive inhibition in
ticomponent T2 relaxation and diffusion tensor anisotropy in human brain.
Magnetic Resonance Imaging, 26, 874–888.
Nachev, P., Wydell, H., O’neill, K., Husain, M., & Kennard, C. (2007). The role of the
pre-supplementary motor area in the control of action. Neuroimage, 36(Suppl.
neuroimaging: A primer with examples. Human Brain Mapping, 15, 1–25.
Paus, T., Collins, D. L., Evans, A. C., Leonard, G., Pike, B., & Zijdenbos, A. (2001). Mat-
uration of white matter in the human brain: A review of magnetic resonance
studies. Brain Research Bulletin, 54, 255–266.
Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., et al.
(1999). Structural maturation of neural pathways in children and adolescents:
In vivo study. Science, 283, 1908–1911.
Pliszka, S. R., Glahn, D. C., Semrud-Clikeman, M., Franklin, C., Perez, R., III, Xiong, J.,
et al. (2006). Neuroimaging of inhibitory control areas in children with atten-
tion deficit hyperactivity disorder who were treatment naive or in long-term
treatment. American Journal of Psychiatry, 163, 1052–1060.
Reese, T. G., Heid, O., Weisskoff, R. M., & Wedeen, V. J. (2003). Reduction of eddy-
current-induced distortion in diffusion MRI using a twice-refocused spin echo.
Magnetic Resonance in Medicine, 49, 177–182.
Rieger, M., Gauggel, S., & Burmeister, K. (2003). Inhibition of ongoing responses
following frontal, nonfrontal, and basal ganglia lesions. Neuropsychology, 17,
tex mediates response inhibition while mesial prefrontal cortex is responsible
for error detection. Neuroimage, 20, 351–358.
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L., Leach, M. O., & Hawkes, D. J. (1999).
Nonrigid registration using free-form deformations: Application to breast MR
images. IEEE Transactions on Medical Imaging, 18, 712–721.
Schwartz, E. D., Cooper, E. T., Fan, Y., Jawad, A. F., Chin, C. L., Nissanov, J., et al. (2005).
roreport, 16, 73–76.
Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C.
diffusion data. Neuroimage, 31, 1487–1505.
Berg, H., et al. (2004). Advances in functional and structural MR image analysis
and implementation as FSL. Neuroimage, 23(Suppl. 1), S208–S219.
Snook, L., Paulson, L. A., Roy, D., Phillips, L., & Beaulieu, C. (2005). Diffusion tensor
imaging of neurodevelopment in children and young adults. Neuroimage, 26,
Song, S. K., Sun, S. W., Ju, W. K., Lin, S. J., Cross, A. H., & Neufeld, A. H. (2003). Diffu-
sion tensor imaging detects and differentiates axon and myelin degeneration in
mouse optic nerve after retinal ischemia. Neuroimage, 20, 1714–1722.
Song, S. K., Yoshino, J., Le, T. Q., Lin, S. J., Sun, S. W., Cross, A. H., et al. (2005).
Demyelination increases radial diffusivity in corpus callosum of mouse brain.
Neuroimage, 26, 132–140.
Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W.
(2004). Longitudinal mapping of cortical thickness and brain growth in normal
children. Journal of Neuroscience, 24, 8223–8231.
Stuphorn, V., & Schall, J. D. (2006). Executive control of countermanding saccades
by the supplementary eye field. Nature Neuroscience, 9, 925–931.
Tomaiuolo, F., MacDonald, J. D., Caramanos, Z., Posner, G., Chiavaras, M., Evans, A.
C., et al. (1999). Morphology, morphometry and probability mapping of the pars
of Neuroscience, 11, 3033–3046.
van den Wildenberg, W. P., van Boxtel, G. J., van der Molen, M. W., Bosch, D. A.,
Speelman, J. D., & Brunia, C. H. (2006). Stimulation of the subthalamic region
Journal of Cognitive Neuroscience, 18, 626–636.
Williams, B. R., Ponesse, J. S., Schachar, R. J., Logan, G. D., & Tannock, R. (1999). Devel-
opment of inhibitory control across the life span. Developmental Psychology, 35,
Wolbers, T., Schoell, E. D., & Buchel, C. (2006). The predictive value of white matter
organization in posterior parietal cortex for spatial visualization ability. Neu-
roimage, 32, 1450–1455.