Neuropsychologia 48 (2010) 3037–3044
Contents lists available at ScienceDirect
Neuropsychologia
journal homepage: www.elsevier.com/locate/neuropsychologia
Early-life stress is associated with impairment in cognitive control in
adolescence: An fMRI study夽
Sven C. Mueller a,∗ , Francoise S. Maheu a,b , Mary Dozier c , Elizabeth Peloso c ,
Darcy Mandell a , Ellen Leibenluft d , Daniel S. Pine a , Monique Ernst a
a
Section of Developmental and Affective Neuroscience, Mood and Anxiety Disorders Program, National Institute of Mental Health,
National Institutes of Health, 15K North Drive, Bethesda, MD 20892, USA
b
Research Centre of the CHU Ste-Justine, and Psychiatry Department, University of Montreal, Canada
c
Department of Psychology, University of Delaware, Newark, DE, USA
d
Section of Bipolar Spectrum Disorders, Mood and Anxiety Disorders Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
a r t i c l e
i n f o
Article history:
Received 30 November 2009
Received in revised form 18 May 2010
Accepted 10 June 2010
Available online 16 June 2010
Keywords:
Stress
Development
Inhibition
Task switching
Maltreatment
Change task
a b s t r a c t
Early-life stress (ES) has been associated with diverse forms of psychopathology. Some investigators
suggest that these associations reflect the effects of stress on the neural circuits that support cognitive
control. However, very few prior studies have examined the associations between ES, cognitive control,
and underlying neural architecture. The present study compares adolescents with a documented history
of ES to typical adolescents on a cognitive control task using functional magnetic resonance imaging
(fMRI). Twelve ES adolescents who were adopted because of early caregiver deprivation (9 females,
age = 13 years ± 2.58) and 21 healthy control adolescents without a history of ES (10 females, age = 13
years ± 1.96) who resided with their biological parents performed the change task (Nelson, Vinton et al.,
2007) – a variant of the stop task – during fMRI. Behaviourally, ES adolescents took longer to switch from a
prepotent response (“go”) to an alternative response (“change”) than control adolescents. During correct
“change” responses vs. correct “go” responses, this behavioural group difference was accompanied by
higher activation in ES subjects than controls. These differences were noted in regions involved in primary
sensorimotor processes (pre- and postcentral gyri), conflict monitoring (dorsal anterior cingulate gyrus),
inhibitory and response control (inferior prefrontal cortex and striatum), and somatic representations
(posterior insula). Furthermore, correct “change” responses vs. incorrect “change” responses recruited
the inferior prefrontal cortex (BA 44/46) more strongly in ES subjects than controls. These data suggest
impaired cognitive control in youth who experienced ES.
Published by Elsevier Ltd.
1. Introduction
Early-life stress (ES) influences brain development and confers
risk for later psychopathology, including anxiety, depression, posttraumatic stress disorder (PTSD), substance abuse, and psychosis
(Bremner, Southwick, Johnson, Yehuda, & Charney, 1993; Carrion,
Weems, et al., 2009; Fisher et al., 2009; Kilpatrick et al., 2003;
Ritchie et al., 2009; Schenkel, Spaulding, DiLillo, & Silverstein, 2005;
Stein et al., 1996). These psychopathological consequences might
be mediated by the disruption of cognitive processes and their
associated neural underpinnings (Bremner & Vermetten, 2001). In
夽 The views expressed in this article do not necessarily represent the views of
the National Institute of Mental Health, National Institutes of Health, or the United
States Government.
∗ Corresponding author. Tel.: +1 301 402 6955; fax: +1 301 402 2010.
E-mail address: [email protected] (S.C. Mueller).
0028-3932/$ – see front matter. Published by Elsevier Ltd.
doi:10.1016/j.neuropsychologia.2010.06.013
humans, the impact of ES on cognitive functions such as memory, cognitive control, visuospatial processing, language, attention
processing, and manual dexterity has been documented (Chugani
et al., 2001; McEwen, 1998; Nelson, Zeanah et al., 2007; Pears &
Fisher, 2005). Concomitantly, some initial studies found an association between ES and neural perturbations in affective (Maheu
et al., 2010), reward (Dillon et al., 2009), memory (Carrion, Haas,
Garrett, Song, & Reiss, 2010), and executive (Carrion, Garrett,
Menon, Weems, & Reiss, 2008) processing.
Notably, substantial improvement in cognitive control occurs
across development (Davidson, Amso, Anderson, & Diamond, 2006;
Rubia et al., 2006), with mature skills finally emerging during
early adulthood (Bunge & Wright, 2007). These final cognitive
refinements are accompanied by changes in neural activation,
particularly in structures implicated in executive control (Bunge,
Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Luna et al., 2001)
and goal-directed behaviour (Ernst & Mueller, 2008; Ernst, Pine,
& Hardin, 2006). Specifically, these structures include the infe-
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S.C. Mueller et al. / Neuropsychologia 48 (2010) 3037–3044
rior prefrontal cortex for inhibitory processes (Aron, Robbins, &
Poldrack, 2004), the dorsal anterior cingulate cortex (dACC) for
conflict detection/resolution (Botvinick, Nystrom, Fissell, Carter,
& Cohen, 1999), and the striatal network involved in response
coding and switching (Atallah, Lopez-Paniagua, Rudy, & O’Reilly,
2007; Casey et al., 2004; Hikosaka & Isoda, 2010; Loose, Kaufmann,
Tucha, Auer, & Lange, 2006). Developmental neuroimaging studies
have shown that these same regions are recruited in adolescents during tasks that involve inhibition of a prepotent response
(Durston, Mulder, Casey, Ziermans, & van Engeland, 2006), conflict between two responses (e.g., stop-signal task) (Leibenluft et
al., 2007; Pliszka et al., 2006), task switching (Casey et al., 2004), or
planning/execution of action (Bunge & Wright, 2007). Unlike traditional studies of task switching that require a switch between
abstract categories such as letters and numbers (Mueller, Swainson,
& Jackson, 2007; Rogers & Monsell, 1995), the current change task
(Nelson, Vinton et al., 2007) is similar to the antisaccade task, in
which a prepotent response must be inhibited and replaced with
an alternative one (Hallett & Adams, 1980). Electrophysiological
studies identified distinct inhibitory components when switching
from prepotent to non-prepotent responses (Mueller, Swainson,
& Jackson, 2009). Consequently, these types of tasks inherently
require a strong motoric response. Not surprisingly, activations
have also been reported in primary motor and sensorimotor cortices in adults (Menon, Adleman, White, Glover, & Reiss, 2001) and
adolescents (Schulz et al., 2004) completing go/no-go tasks.
Separation from the primary caregiver, as well as experiencing
maltreatment or neglect, are all associated with severe physiological stress responses. Such experiences have been shown to alter
neuroendocrine function (McEwen, 1998; Sanchez et al., 2010). For
example, one study found that children in foster care exhibit disturbed daytime patterns of cortisol production (Dozier, Manni, et
al., 2006). Moreover, work with monkeys has found that excessive
exposure to cortisol can impair inhibitory control and prefrontal
cortical function (Lyons, Lopez, Yang, & Schatzberg, 2000). Therefore, ES may have important influences on prefrontal cortical
function via the neuroendocrine system.
Systems in flux, like the adolescent brain, are typically unstable and vulnerable to disruption. The current study addressed the
hypothesis that adolescents with a history of ES would display perturbed performance on a cognitive control task. We predicted that,
relative to a group of healthy peers, ES adolescents would show
deficits in cognitive control on the change task. Specifically, we
hypothesized that reaction time [RT] on trials requiring a switch
from a prepotent to a non-prepotent response would be slower in
the ES group than the control group. With respect to neural correlates, we expected altered regional activation within the inferior
prefrontal cortex, dorsal anterior cingulate cortex and striatum in
ES adolescents compared to controls (Bunge et al., 2002; Carrion
et al., 2008; Casey et al., 1997; Durston et al., 2006). Consistent
with the motoric requirements of this task (Menon et al., 2001)
and the differences in sensorimotor processing reported between
controls and youth displaying psychopathology (Schulz et al., 2004;
Suskauer et al., 2008), we also predicted functional alterations in
primary motor and sensorimotor cortices.
2. Materials and methods
2.1. Subjects
Twelve ES adolescents (13.16 years SD 2.58, 9 female) who resided with their
adoptive parents and 21 non-ES adolescents (13.86 years SD 1.96, 10 female)
who resided with their biological parents participated in the study. We chose
to recruit ∼50% more controls than ES subjects in order to increase statistical power, reduce inter-subject variance, and obtain a truer representation of
the mean for typical adolescents, given that individual developmental trajectories can differ greatly in adolescent samples. Groups did not differ on IQ,
socioeconomic status (SES) (Hollingshead, 1975), sex distribution, parental edu-
cation, or State/Trait anxiety scores (Spielberger, Gorsuch, & Lushene, 1970) (all
p > .05) (Table 1). All subjects were carefully assessed by physical examination
and structured psychiatric interviews (Kiddie-Schedule-for-Affective-Disorders –
Present-and-Lifetime-version (KSADS); Kaufman et al., 1997). Parents (adoptive
parents for the ES group, biological parents for the control group) were also interviewed about the adolescent participant’s behaviours. The psychiatric interviews
were conducted by experienced clinicians and achieved excellent inter-rater reliability (k > .75). IQ was assessed using the Wechsler Abbreviated Scale of Intelligence
for Adolescents (Wechsler, 1999).
Nine of the twelve ES participants had a history of neglect and maltreatment
prior to their adoption. The other three ES participants had experienced unstable
early environments as reflected by two to three foster placements before adoption. ES adolescents had resided in U.S. foster care or international orphanages for
an average of 28.36 months (SD = 30.22) prior to their adoptive placement. All ES
youths experienced their first placement between the ages of 1 and 74 months
(mean = 16.25 months, SD 25.87 months); they had been placed in foster care
between one and five times (mean 2.42, SD 1.16). ES adolescents were recruited
as part of a larger, ongoing study and have been well characterized in terms of their
early-life experiences (Dozier, Peloso, et al., 2006; Dozier, Peloso, Lewis, Laurenceau,
& Levine, 2008). In the ES group, one adolescent suffered from enuresis, one from
Oppositional Defiant Disorder, one from Generalised Anxiety Disorder, and two from
Specific Phobia. None of these adolescents met criteria for a mood or anxiety disorder, including PTSD. Only one subject from the ES group was receiving medication
(sertraline and methylphenidate). This subject discontinued methylphenidate for
30 h prior to the fMRI study. With regards to prenatal risk in the ES group, one participant was born with a low birth weight and the mothers of four participants had
suspected use of alcohol and/or drugs/cigarettes during pregnancy.
Adolescents from the control group were screened for an absence of medical
or psychiatric problems, IQ greater than 70, and no history of maltreatment (as
evaluated by the trauma section of the KSADS). The parents of participants provided written informed consent, and the adolescents provided written assent to
participate in protocols approved by the Institutional Review Boards of the National
Institute of Mental Health (NIMH) and the University of Delaware.
2.2. Experimental paradigm
Cognitive control was probed using a variant of the stop-signal task, the change
task (McClure et al., 2005; Nelson, Vinton et al., 2007). The change task measures the
ability to inhibit a prepotent response (go response) and to switch to an alternative
non-prepotent response (change response). To establish prepotency, go trials constituted 66% of the total experimental trials (excluding blank trials), and change trials
33%. Each trial began with a 500 ms fixation cross at the center of the screen. Fixation
was then replaced with an “X” or an “O”, which was displayed for 1000 ms. Subjects
were instructed to press button 1 when they saw an “X”, and button 2 when they
saw an “O”. However, for 33% of the trials, a blue square appeared in the background
after the appearance of the “X” or the “O”. On these trials, subjects were required
to press button 3. Trials without the appearance of the blue square constituted the
go trials, and those with the appearance of the blue square constituted the change
trials.
On the first change trial, the change signal was displayed 250 ms after the go signal. Consistent with the Stop/Signal paradigm (Logan, Schachar, & Tannock, 1997),
the delay between the onset of the go signal (“X” or “O”) and the onset of the change
signal (blue square) varied from trial to trial and was adjusted to the subject’s performance in order to ensure an approximately 50% correct response rate on change
trials for all participants. If the previous change trial was performed correctly, the
onset of the subsequent change signal was delayed by 50 ms, thereby making execution of a correct change response more difficult. If, on the other hand, the previous
change trial was performed incorrectly, the onset of the change signal occurred 50 ms
closer to the onset of the X/O signal, making execution of a correct change response
easier. In addition, 88 blank fixation trials were randomly distributed across the
task and served as the implicit fMRI baseline. The task was conducted over four
3.5-min runs inside the scanner. Subjects were trained to proficiency prior to entering the scanner, i.e., mean reaction time of less than 1000 ms on go trials. Subjects
also received feedback after each block, prompting them to speed up if the mean go
reaction time exceeded 1000 ms.
2.3. fMRI data acquisition
All fMRI data were collected at the NIMH. Scanning took place in a General Electric 3 Tesla magnet scanner (Waukesha, WI, USA). Images were acquired with echoplanar single shot gradient echo T2* weighting. A total of 23 5-mm axial slices were
acquired with the following parameters: repetition time (TR) 2000 ms; echo time
(TE) 40; field of view (FOV) 240 mm; matrix 64 × 64. A single high-resolution structural image was also acquired for each subject to assist with spatial normalization.
This image consisted of a spoiled gradient (SPGR) sequence: 180 1-mm axial slices;
TR 11.4; TE 4.4; FOV 240 mm; number of acquisitions (NEX) 1; matrix 256 × 256;
inversion time (TI) 300; bandwidth 130 Hz, pixel, 33 kHz/256 pixels. The paradigm
was presented to the subjects inside the magnet via Avotec Silent Vision goggles
(Stuart, FL, USA) mounted on the head coil directly above subjects’ eyes. Each of the
four runs consisted of 86 trials: 44 go trials, 20 change trials, and 22 blank fixation tri-
S.C. Mueller et al. / Neuropsychologia 48 (2010) 3037–3044
3039
Table 1
Demographic information: demographic information for ES and control groups including mean age, IQ, social economic status, use of medication, and any co-morbid disorders.
Demographic information
Adopted youth (n = 12)
Unaffected controls (n = 21)
P-Value
Age
IQ
SES income
SES education
Female
BMI (Mean, SD)
State anxiety (STAI)
Trait anxiety (STAI)
Medication
Anxiety disorder
ODD
Enuresis
13.16 (2.58)
105.67 (10.47)
7.25 (1.96)
5.83 (1.19)
9
21.16 (4.21)
28.08 (1.24)
29.58 (1.47)
1
3
1
1
13.85 (1.96)
109.62 (12.05)
6.82 (1.67)
5.72 (.89)
10
20.91 (2.35)a
26.41 (1.05)a
28.67 (1.20)a
–
–
–
.39
.35
.53
.77
.13
.85
.32
.62
Behavioural performance data
Mean Go RT (ms)
Accuracy go correct (%)
CSRT (ms)
Mean inhibit delay (ms)
920.78 (39.58)
91.28 (2.53)
289.03 (17.84)
434.31 (40.18)
914.37 (29.62)
89.20 (1.89)
231.09 (13.35)
490.70 (30.07)
.90
.52
.02
.28
The bold value indicates that significance (at p < .05) was met.
a
Unfortunately, due to some missing scores, the comparison of BMI in the healthy volunteers is based on a subset of 14 controls and the STAI state/trait comparison on a
subset of 17 controls for whom these data were available. The remaining data for these controls were missing. Behavioural performance data show the performance of subjects
on the change task with regards to reaction time to go stimuli (ms), errors committed on these trials, mean reaction time to execute the change command (CSRT), and mean
inhibit delay time (MID) of the change command.
als. Go trials, change trials, and fixation trials were distributed randomly across each
run. In this event-related design, randomly interspersed fixation trials (2250 ms in
duration each) allowed the experimental and statistical techniques to successfully
deconvolve unique events occurring relatively close in time (Friston et al., 1998;
Zarahn & Slifstein, 2001). In this statistical approach (Zarahn, Aguirre, & D’Esposito,
1997), fixation trials were not modelled and provided an implicit baseline.
2.4. fMRI analysis
Pre-processing steps of imaging data included slice timing correction, realignment, spatial normalization, and reslicing. To facilitate comparison with studies
from our laboratory that used the same task in different populations (Leibenluft et
al., 2007; Nelson, Vinton et al., 2007), we analysed the data with SPM99 (Wellcome
Department of Imaging Neuroscience, UCL, London, UK) using the same parameters
as in earlier studies. A statistical model was generated for each subject for correct
change trials, incorrect change trials, correct go trials, and incorrect go trials. With
regards to the number of trials used for modelling data, and in line with the group
accuracy rates provided in Table 1, the statistical analyses included 160 (±3) go trials
and 88 (±3) fixation trials on average for the ES group, and 156 (±3) go trials and 88
(±3) fixation trials on average for the control group. With regards to successful vs.
unsuccessful change trials, 40 (±5) correct and 40 (±5) incorrect change trials were
used on average for the ES group, and 38 (±4) correct and 42 (±4) incorrect change
trials on average for controls. The wave form used to model each event consisted of a
rectangular pulse convolved with the hemodynamic response function provided by
Fig. 1. (A) Axial slices through the brain showing increased activation in the ES group (relative to controls) during the correct change vs. correct go trials contrast. The threshold
was set at p < .001, uncorrected. IFG = inferior frontal gyrus, PreCG = precingulate gyrus, PostCG = post-cingulate gyrus, CingG = cingulate gyrus, Cl/ins = Claustrum/insula. (B)
Extracted peak voxel BOLD signal changes for correct inhibition vs. fixation and correct go trials vs. fixation as a comparison contrast are shown for the most representative
ROI, given that all clusters showed the same pattern of activation. Data for the ES group are displayed with dashed bars and the control group in grey. Error bars denote S.E.M.
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S.C. Mueller et al. / Neuropsychologia 48 (2010) 3037–3044
Fig. 2. (A) Axial and coronal slices (left) showing increased activation in the ES group (relative to controls) during the correct vs. incorrect changes contrast. (B) Bar graphs show
BOLD signal changes from baseline for the peak activations for correct changes and incorrect changes relative to fixation for comparison are shown for the most representative
ROI, given that all clusters showed the same pattern of activation. The threshold was set at p < .001, uncorrected. IFG = inferior frontal gyrus, PreCG = precingulate gyrus,
Cl/insula = Claustrum/insula.
SPM99. A high-pass filter of .024 Hz was applied to each subject’s data prior to model
estimation to improve the signal-to-noise ratio. Contrast images were proportionally scaled to session baseline and smoothed with an isotropic Gaussian kernel of
11.4-mm full width-half maximum (FWHM) (Leibenluft et al., 2007). Individual subjects’ contrasts were then entered into a second order (i.e., random effects) group
level analysis.
All group level analyses employed t-tests of the contrast values that each individual subject’s data generated in the pre-determined event contrasts. The statistical
threshold was set at p < .001 uncorrected for multiple comparisons with a spatial
extent of at least 10 contiguous voxels (Nelson, Vinton et al., 2007). Sex was used as
a covariate of nuisance in all analyses because of a trend towards group differences
in the distribution of this variable (x2 (1) = 2.34, p = .13).
To examine the neural correlates involved in inhibiting a planned response and
switching to an alternative response, we compared correct change responses to correct go responses. Moreover, to examine the neural correlates of brain responses
during incorrect changes, we examined correct vs. incorrect change responses. To
confirm that significant activations on the correct change vs. correct go contrast
resulted from the change responses and not from mere responding to go trials,
contrasts were decomposed into correct change trials vs. baseline and correct go
trials vs. baseline (Fig. 1B). Similarly, to tease out whether the activity in the correct vs. incorrect change trials stemmed from either correct or incorrect responding,
each component was compared against baseline (Fig. 2B). All activations are in MNI
coordinates (mm).
Finally, to control for a potential confounding effect of group differences in
response time in the [correct change vs. correct go] contrast, a multivariate ANCOVA
was conducted on extracted individual peak voxel values with Group as the
between-subjects factor and individual change signal reaction time (CSRT) as the
covariate of nuisance. Statistical threshold was set at p < .05, corrected using the
step-down Bonferroni–Holm procedure.
2.5. Behavioural analysis
Behavioural data were analysed using Multivariate Analysis of Variance
(MANOVA). In addition to go and change accuracy, we examined the mean inhibit
delay (MID) and the CSRT. The MID is an index of task difficulty and is calculated by
subtracting the mean onset time of the change signal from the mean onset time of
Table 2
Significantly (p < .001, uncorrected) activated regions on correct change trials (correct change vs. correct go trials) and erroneous change trials (correct changes vs. incorrect
changes) along with the direction of the effect (ES > control or control > ES), coordinates, Broadmann area, T-value, and cluster size.
Region
Hemisphere
Coordinates
BA
t-Score
Correct change vs. correct go
ES > Control
Precentral gyrus
Postcentral gyrus
Cingulate gyrus
Cingulate gyrus
Inferior frontal gyrus/precentral gyrus
Caudate
Putamen/lentiform nucleus
Claustrum/insula
Right
Right
Right
Left
Left
Left
Left
Left
46 −8 42
22 −28 46
22 6 40
−20 18 32
−50 18 8
−8 10 16
−20 18 6
−30 −22 10
BA4
BA3
BA24
BA32
BA44/45
4.08
4.06
3.59
3.66
3.53
3.60
3.75
3.63
154
149
26
65
63
23
144
46
Correct change vs. incorrect change
ES > Control
Precentral gyrus
Inferior frontal gyrus
Claustrum/insula
Left
Left
Left
−58 4 8
−40 34 6
−26 26 −2
BA44
BA46
4.38
3.44
3.68
596
10
64
BA13
Cluster size
S.C. Mueller et al. / Neuropsychologia 48 (2010) 3037–3044
the go signal. The CSRT is an estimation of the speed of the subject’s change response.
When a subject changed successfully on 50% of change trials, the CSRT was the mean
go reaction time minus the MID (Logan et al., 1997). Because a subject’s accuracy
could deviate from 50%, an interpolation algorithm was used to calculate CSRT, such
that CSRT = go reaction time at the Xth percentile minus mean MID, where X is the
subject’s percent accuracy on change trials (Nelson, Vinton et al., 2007). As with the
fMRI data, sex was used as a covariate in all behavioural analyses.
3041
claustrum/insula (xyz = −26 26 −2) (Table 2/Fig. 2A). Further analysis of this contrast revealed that the significant trial by group
interaction was primarily related to group differences in correct
change trials (all F(1,29) > 5.50, all p < .03), and not incorrect change
trials (all F(1,29) < .82, ns). However, the deactivation of the inferior
frontal gyrus seen in the ES group during these incorrect trials may
also have contributed to the interaction (Fig. 2B).
3. Results
3.1. Behavioural performance (Table 1)
ES youths switched to an alternative response on change
trials (CSRT: 289.03 ms ± 17.84) significantly more slowly than
controls (CSRT: 231.09 ms ± 13.35) (F(1,30) = 6.54, p = .02). Accuracy for go trials did not differ significantly between the ES
(91.282 ± 2.53%) and control groups (89.20 ± 1.89%) (F(1,30) = .42,
p = .52). Similarly, the mean RT of go trials (ES group: 920 ± 39 ms;
controls: 914 ± 29 ms) and the MID time (ES: 434 ± 40 ms; control: 490 ± 30 ms) did not differ between groups (F(1,30) = .16,
p = .89 and F(1,30) = 1.22, p = .28, respectively). Finally, because the
paradigm was designed to keep error rates around 50%, ES and
control groups did not differ on errors on change trials (49.95%
vs. 46.88%) (F(1,30) = .42, p = .52) (Table 1). With respect to sex,
the error rate for change trials was lower for males (41%) than
females (53%) (F(1,30) = 8.26, p < .05). CSRT did not correlate with
state (r2 (29) = .16, p = .38) or trait (r2 (29) = −.05, p = .83) measures
of anxiety.
3.2. fMRI
3.2.1. Correct change trials vs. correct go trials
The SPM analysis of correct change (vs. correct go) trials revealed
significantly higher activation (all at p ≤ .001, uncorrected) for the
ES group relative to controls in a set of regions involved in motor
and cognitive control (see Table 2). Specifically, these regions
included: the left inferior prefrontal cortex (BA44, xyz = −50 18 8),
typically involved in response inhibition and task switching; the
right dorsal anterior cingulate cortex (dACC, BA24, xyz = 22 6 40)
and left anterior cingulate cortex (BA32, xyz = −20 18 32), both
involved in conflict monitoring; striatal structures (left putamen, xyz = −20 18 6 and left caudate, xyz = −8 10 16), involved in
response planning and execution; and the primary motor (BA4,
xyz = 46 −8 42) and sensorimotor cortices (BA3, xyz = 22 −28 46)
underlying the motor and sensorimotor aspects of the task. In
addition to expected changes seen in these regions, a group difference emerged in the posterior left claustrum/insula (BA13,
xyz = −30 −22 10) (Table 2), also reflecting enhanced activation in
the ES group relative to controls (Fig. 1A). No regional activations
were found to be greater in the controls than in the ES group.
To examine whether the significantly increased activations for
the ES group reflected the switch process (change trials) or were a
mere result of motor response (go trials), the contrasts were further
separated into change trials vs. fixation, and go trials vs. fixation.
The results revealed that the contributions to the significant activation stemmed from the change trials (all F(1,29) > 6.00, p < .02,
except for the cingulate activation BA32 which approached significance (F(1,29) = 3.93, p = .057)), and not from the go trials (all
F(1,29) < 1.26, all p > .21) (Fig. 1B).
3.3. Correct change vs. incorrect change trials
In contrast to correct change trials vs. incorrect change trials, the ES group showed significantly greater activation in three
regions (all at p < .001, uncorrected) compared to controls: the
left inferior prefrontal gyrus (BA46, xyz = -40 34 6), the left precentral/inferior frontal gyrus (BA44, xyz = -58 4 8), and the left
3.4. Additional analyses
A multivariate ANCOVA controlling for individual CSRT (covariate of nuisance) in the go vs. change contrast revealed that group
differences in activations in the ACC (BA24), postcentral gyrus and
IFG remained significant (at p < .05, corrected), while the other areas
were trending (p < .10, corrected). These results suggest that the
observed group differences were not accounted for by group differences in motoric speed.
4. Discussion
The present study addressed the extent to which ES is associated with perturbations in cognitive control, which, in turn, could
confer risk for psychopathology in this population. Our findings
support this hypothesis at both the behavioural and neural level. In
this study, and consistent with previous reports of perturbations on
cognitive control tasks in adopted youth (Lewis, Dozier, Ackerman,
& Sepulveda-Kozakowski, 2007), the ES group showed prolonged
reaction times compared to controls when inhibiting a prepotent
response and executing an alternative one. Furthermore, group differences in neural activity showed greater activation in the ES group
than in controls in several regions involved in cognitive control.
These included the inferior frontal cortex (BA junction 44/45 and
46), a region previously identified as playing a critical role in cognitive inhibitory control (Aron et al., 2004; Bunge et al., 2002; Durston
et al., 2006; Leung & Cai, 2007), and the striatum, a region involved
in response control (Atallah et al., 2007; Casey et al., 2004; Loose et
al., 2006).
The current study found these regions to be more active in
the ES group than in controls during correct change trials relative to go trials (BA44/45, striatum) as well as during correct vs.
incorrect change responses (BA44/46). Comparisons against fixation confirmed that the greater activation seen in the ES group vs.
controls occurred during the change trials and not during the go trials, suggesting an impaired behavioural control aspect of the task
rather than impaired simple motor response. In addition, IFG, dACC
and postcentral gyrus findings remained robust, while the remaining regions were trending, when controlling for CSRT, suggesting
that differences in neural activation were not due to the slower
execution of the alternative response in the ES group.
Studies in psychiatric samples indicate that psychopathology
may disrupt the same prefrontal-striatal circuitry whose function
differed between our ES sample and controls (Carrion et al., 2008;
Durston et al., 2006; Leibenluft et al., 2007; Nelson, Vinton et al.,
2007; Schulz et al., 2004). Indeed, the neural pattern of our data
is consistent with neuroimaging findings of disorders for which ES
populations show enhanced risk. Findings in adults, for instance,
have associated depression with distinct patterns of activation in
the inferior frontal and anterior cingulate regions during inhibitory
control tasks (Langenecker et al., 2007). Likewise, fronto-striatal
and somatosensory network alterations during cognitive inhibition
have been reported in individuals with PTSD (Falconer et al., 2008).
In youths, prior studies of ES have focused primarily on morphological differences (De Bellis & Kuchibhatla, 2006; Eluvathingal et
al., 2006; Mehta et al., 2009; Teicher et al., 2004); nevertheless,
preliminary investigations using functional imaging have yielded
3042
S.C. Mueller et al. / Neuropsychologia 48 (2010) 3037–3044
interesting patterns. Maheu et al. (2010) reported medial temporal
lobe dysfunction during emotional face processing in ES subjects,
some of whom were included in the current sample. In another
sample of maltreated youth, Dillon et al. (2009) found decreased
responding of the basal ganglia to reward stimuli. The current findings extend prior reports to a clinical non-PTSD sample.
Notably, current IFG activations were left-lateralized. Studies in
adults commonly associate the right IFG with inhibitory processes
(e.g., Aron et al., 2004). Three possible explanations can account for
this discrepancy. First, a recent meta-analysis of inhibitory control
studies that included a set of patients with selective left inferior
frontal gyrus lesions reported a critical involvement of this region
in inhibitory control (Swick, Ashley, & Turken, 2008), suggesting
a bilateral contribution of the IFG to inhibitory control. Second,
activation in the left IFG could also account for the shift process
(pressing button 3 during the change signal instead of buttons 1
or 2), in addition to the inhibitory process. Indeed, Loose et al.
(2006) reported left-sided IFG activations during a response switching task. Finally, significantly activated clusters in left IFG have also
been reported in children during successful resolution of response
ambiguity (Bunge et al., 2002). More research in pediatric populations is needed to disentangle the contribution of each of these
processes to the current findings.
Similar to the group differences in inferior frontal gyrus and
striatum activation, the ES group showed heightened activations
in motor and sensorimotor cortices than controls. Prior studies
in youths with ADHD (Schulz et al., 2004) and healthy volunteers
(Menon et al., 2001) have also documented activations in BAs 3/4
in go/no-go studies. Given that fine motor skills of the fingers are
required to flexibly select and press the correct out of 3 possible
buttons on the current task, our findings are further consistent
with a report of impaired manual dexterity in neglected youth
(Chugani et al., 2001). However, the absence of group differences in
reaction times to go stimuli in the present study complicate interpretation. Regardless, heightened activation of these regions could
reflect developmental disruptions of primary sensorimotor areas
in ES.
Likewise, the anterior cingulate cortex has also been implicated
in response conflict but not in stimulus conflict during task switching (Liston, Matalon, Hare, Davidson, & Casey, 2006; Rushworth,
Hadland, Paus, & Sipila, 2002). In this study, increased activation
was present in two regions of the dACC (BA24 and BA32). Prior functional studies of youths with cognitive control deficits (Pliszka et al.,
2006; Rubia et al., 1999; Tamm, Menon, Ringel, & Reiss, 2004) and
PTSD after maltreatment (Carrion et al., 2008) documented changes
in dACC function during inhibitory control tasks. In particular, in a
similar study in youths with post-traumatic stress symptoms that
used a block design to compare go and no-go trials, Carrion et al.
(2008) reported hyperactivations in both BAs 24 and 32. Likewise,
previous morphological studies documented smaller grey matter
volumes in the dACC of adults who experienced adverse life events
in childhood (Cohen et al., 2006) or adulthood (Ganzel, Kim, Glover,
& Temple, 2008). Consequently, our findings are in-line with prior
data linking this region with cognitive control and adverse experiences; they also extend previous research and suggest that ES might
perturb a network involved in motor control aspects of executive
tasks leading to a difficulty in resolving response conflict.
Finally, an interesting but unexpected finding was the hyperactivation of the posterior insula/claustrum observed in the ES
group. Although the posterior insula is commonly associated with
pain and visceral sensory functions (Augustine, 1996), other studies have documented a role in working memory (Paulesu, Frith,
& Frackowiak, 1993) and divided attention (Corbetta, Miezin,
Dobmeyer, Shulman, & Petersen, 1991). The current task required
divided attention given that participants had to distinguish
between letters as well as pay attention to a sudden change in
background colour when a switch in responses was required.
Moreover, previous studies have reported perturbed attentional
emotional processing in maltreated youth (Pine et al., 2005), supporting the idea of impaired attention processing following ES.
Taken together, the present study demonstrates the impact of ES on
several brain regions including fronto-striatal circuitry, dACC, and
insula/claustrum during cognitive control. However, the small sample precludes us from examining the contribution of the specific
types of stress experienced. Future studies using animal models
could further explore the vulnerability of particular brain regions
to specific stressors.
One strength of this sample is that participants were recruited
from a very well-characterized and longitudinally followed cohort
(Dozier et al., 2008; Dozier, Peloso, et al., 2006). However, a few limitations of the study also need to be addressed. One concern is the
heterogeneity of the relatively small sample. A common problem
in conducting studies in ES youth is recruitment; indeed, previous studies have suffered from similar limitations in their ability to
report differences in the type of maltreatment experienced in their
sample (Carrion et al., 2008; Dillon et al., 2009) or in samples coming from different orphanages (Chugani et al., 2001; Eluvathingal
et al., 2006). Another problem is the extent to which prenatal factors, such as malnutrition or prenatal exposure to drugs, may have
contributed to the present findings. This issue is very difficult to
resolve because of poor historical information regarding drug use
in the biological parents. The use of comparison groups of low birth
weight or those with known prenatal exposure to drugs might
help to clarify these factors. It is important to note, however, that
although youths may have experienced different types of stressors, one commonality is that all stressors are likely to trigger the
same determinants of the stress response such as stress-related
hormones (e.g., cortisol) and neurotransmitters (e.g., serotonin). A
final issue concerns the fact that four adopted participants suffered
from a psychiatric disorder. However, when these four subjects
were removed from the analysis (data not shown), the pattern of
results was unchanged, suggesting that psychiatric morbidity had
little impact on the present data.
In summary, the present study extends previous behavioural
evidence and demonstrates perturbations in the neural circuits
critical for cognitive and motor control in youths with ES.
ES, here, included early separation from the biological parent, several separations from caregivers before adoption, and early
neglect and abuse. Future studies should replicate and refine these
findings. In particular, critical periods of development, the type
of maltreatment experienced, and other prenatal factors such as
exposure to drugs will need to be investigated.
Conflict of interest
Dr. Pine has received compensation for activities related to
teaching, editing, and clinical care that pose no conflict. All other
authors declare that, except for income received from their primary
employer, no financial support or compensation has been received
from any individual or corporate entity over the past 3 years for
research or professional service and there are no personal financial
holdings that could be perceived as constituting a potential conflict
of interest.
Acknowledgements
This study was supported by a National Alliance for Research
on Schizophrenia and Depression (NARSAD) Young Investigator Award and a postdoctoral fellowship from the Fonds de la
Recherche en Santé du Québec (FRSQ) to FSM, and by the NIMH
Intramural Research Program. Ioline Henter provided invaluable
S.C. Mueller et al. / Neuropsychologia 48 (2010) 3037–3044
editorial assistance. We would also like to thank the three anonymous reviewers for their very helpful and constructive comments
in improving earlier versions of this manuscript.
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