Altered neural circuits related to sustained attention and executive control in children with ADHD: An event-related fMRI study

Clinical Neurophysiology 124 (2013) 2181–2190

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Altered neural circuits related to sustained attention and executive control in children with ADHD: An event-related fMRI study Suhong Wang a,b, Yilin Yang b, Wei Xing c, Jie Chen c, Chunhong Liu d, Xuerong Luo a,⇑ a

Mental Health Institute, The Second Xiangya Hospital, Central South University, China Department of Neuroscience, The Third Affiliated Hospital of Soochow University, China c Department of Radiology, The Third Affiliated Hospital of Soochow University, China d Department of Radiology, Beijing Anding Hospital, Capital Medical University, China b

a r t i c l e

i n f o

Article history: Accepted 18 May 2013 Available online 22 June 2013 Keywords: Attention deficit hyperactivity disorder Sustained attention Executive control Functional magnetic resonance imaging Continuous performance task

h i g h l i g h t s  Sustained attention was first evaluated in children with ADHD using cued continuous performance

tasks (AX-CPTs).  Fronto-temporo-limbic circuits and the cerebellum are involved in ADHD.  Subjects with ADHD exhibited altered patterns of brain activation under the Lure condition.

a b s t r a c t Objective: The aim of this study was to investigate the neural basis of sustained attention, executive processing, and cognitive control in children with attention deficit hyperactivity disorder (ADHD). Methods: Event-related functional magnetic resonance imaging (fMRI) was used to compare brain activation of 28 medication-naïve children with ADHD aged 7–12 years and 31 healthy controls during a cued continuous performance task (AX-CPT) in three stimulus context conditions (Go, NoGo, Lure). Results: The children with ADHD showed increased activation in the left middle frontal gyrus, bilateral middle temporal gyrus, left precuneus and right cerebellum posterior lobe under the Lure condition compared to the controls. In the Lure condition, in contrast to the NoGo condition, an increased activation in the left inferior frontal gyrus, right medial frontal gyrus and right inferior parietal gyrus was observed in ADHD children. Conclusions: The results demonstrate that medication-naïve ADHD children show spatial and temporal abnormalities in neural activities involved in sustained attention and executive control. Significance: These findings show that there are distinct alternations in neural circuits related to sustained attention and executive control in children with ADHD, and further improve our understanding of the neural substrates of cognitive impairment in children with ADHD. Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland All rights reserved.

1. Introduction Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood behavioral disorders affecting approximately 5% of school-age children (Polanczyk et al., 2007). Children and adults with ADHD have consistently exhibited deficits in sustained attention or vigilance (Tucha et al., 2009; Wang et al., 2011; Perera et al., 2012). They often exhibit higher rates of errors of ⇑ Corresponding author. Address: Mental Health Institute, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, Hunan 410011, China. Tel./fax: +86 731 8529 2124. E-mail address: [email protected] (X. Luo).

commission and omission, slower response speed and greater response variability than healthy controls in sustained attention tasks such as the continuous performance task (CPT). However, few studies have investigated the neural basis of sustained attention performance in children with ADHD (Cubillo et al., 2012). Sustained attention/vigilance is the ability to voluntarily maintain the focus of attention to infrequently occurring critical events and is most consistently affected in ADHD (Johnstone et al., 2013; Christakou et al., 2013). CPTs, initially developed by Rosvold et al. (1956), are widely used to examine vigilance and sustained attention (Meier et al., 2012). Since CPTs are sensitive to input-related perceptual processes, central cognitive attention-controlled processes, and output-related processes of response selection and

1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.05.008

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execution (Riccio et al., 2002), the difficulties children with ADHD have with these tasks may reflect altered function of the dorsolateral prefrontal cortex, parietal cortex and basal ganglia (Konrad et al., 2006). This is underpinned by electrophysiological evidence for diminished central NoGo P3 component (Fallgatter et al., 2004; Doehnert et al., 2010), as well as reduced amplitude of contingent negative variation (CNV) which has proved to be a reliable index for preparatory expectation in children with ADHD (Banaschewski et al., 2008; Doehnert et al., 2013). Although relatively few functional magnetic resonance imaging (fMRI) studies have tested the abnormalities associated with ADHD in the underlying processes and substrates of sustained attention, recent results indicate that subjects with ADHD have atypical activation in the ventrolateral/inferior prefrontal cortex, striato-thalamic regions, anterior cingulate cortex, and cerebellum compared to healthy controls (Rubia et al., 2009a, 2011; Schneider et al., 2010; Christakou et al., 2013). In this study, a numeral version of cued continuous performance task (AX-CPT) is conducted containing three stimulus context conditions (Go, NoGo, Lure). They are used to distinguish visual attention processing related to the target and preparatory processes related to response execution (Dhar et al., 2010; Bickel et al., 2012). For Go condition, participants were asked to press the button when a numeral ‘‘1’’ (cue stimulus) was immediately followed by the numeral ‘‘9’’ (target probe); under NoGo condition, participants were asked to withhold responding to cued non-target pairs (i.e. when numerals other than ‘‘9’’ follow the ‘‘1’’); under Lure condition, participants were asked to inhibit responses to when the numeral ‘‘9’’ follows a numeral other than ‘‘1’’. Although the NoGo and Lure conditions both require withholding a response, the underlying mechanisms are different. For the NoGo condition, as the ‘‘1’’ is not followed by a ‘‘9’’, subjects should inhibit their motor preparation triggered by the preceding ‘‘1’’. Because subjects had a history of responding to the numeral ‘‘9’’ when it followed ‘‘1’’ (Go condition), the presentation of ‘‘9’’ at other times (the Lure condition) was expected to activate a practiced response tendency that had now to be suppressed by a larger or more rapid inhibitory effect than NoGo condition (Roberts et al., 1994). For the Lure condition, subjects had to inhibit their response induced by target ‘‘9’’ which is vulnerable to retrieval-based interference. Chatham et al. (2009) found that 3.5-year-old children showed a larger reactive peak during target probes under Lure condition relative to 8-year-old children. Despite the current evidence of working memory impairments and cognitive control deficits in ADHD children (Martinussen et al., 2005; Martel et al., 2011), no fMRI studies on the Lure condition have been investigated. The aim of the present study was to compare brain function of children with ADHD and age-matched healthy controls while they performed a modified AX-CPT task with three types of conditions (Go, NoGo and Lure). We hypothesized that (1) ADHD children would show abnormal activation in the fronto-cerebellar and parietal attentional network during the Go and NoGo conditions; (2) ADHD children would show atypical activation related to the Lure condition; and (3) ADHD children would show some differences in the temporal characteristics of their cognitive control processes compared to healthy children.

2. Methods 2.1. Participants Sixty-eight children aged 7–12 years participated in the study. All the subjects were right-handed, native Chinese speakers and had normal or corrected to normal vision. The data from four

ADHD children and five healthy controls were discarded due to low correct response rates (<80%, 2 ADHD, 2 Controls) or excessive movement (2 ADHD, 3 Controls). Following these exclusions, the final groups consisted of 28 children with ADHD (three females) and 31 healthy children (15 females; see Table 1). Children with ADHD were recruited from the outpatient clinic of Child Psychiatry of the Third Affiliated Hospital of Soochow University, diagnosed according to DSM-IV criteria after careful clinical examination. None of the participants had previously received any kind of stimulant medication or behavior therapy. The Kiddie-schedule for affective disorders and schizophrenia-present and lifetime version (K-SADS-PL interview, Kaufman et al., 1997) was administered to obtain severity ratings of symptomatology and assess current and lifetime history of psychiatric disorders. All children underwent the full scale of the Wechsler Intelligence Scale for Chinese Children-Revised (WISCC-R, Gong and Cai, 1993). In addition, the parents of participants completed Conners’ Parent Symptom Questionnaire (PSQ, Goyette et al., 1978; Su et al., 2001). The inclusion criteria for children with ADHD were: (1) ADHD combined type (ADHD-C), (2) no other psychiatric disorders, and no history of any kind of central nervous system (CNS) disorder in the last 6 months, (3) intelligence quotient (IQ) above 90. Participants could not meet the criteria for oppositional defiant disorder, conduct disorders, tics, epilepsy, or affective disorders. Healthy, IQ, handedness, and age-matched controls were recruited from two local elementary schools. The PSQ and K-SADS-PL interviews were also administered. None of the comparison group met current or lifetime criteria for any DSM-IV ADHD subtype or any other psychiatric disorders. All participants were asked to abstain from foods and liquids containing caffeine or other substances that can influence activity levels for at least 24 h prior to fMRI scanning. After a complete description of procedures, written informed consent was obtained from the parents or caregivers of all participants. After task completion, all subjects were financially reimbursed and given small gifts. All procedures were approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University.

2.2. Experimental paradigm The modified AX-CPT included rare Go, NoGo and Lure conditions embedded in a vigilance task with a pseudorandom sequence of 500 white Arabic numeral symbols (1, 2, 3, 4, 5, 6, 7, 8 and 9) presented in the center of a black screen (subtending 1.63° of visual angle horizontally and 2.86° vertically; see Fig. 1). Each numeral was presented for 300 ms, separated by a 1200 ms blank screen. The numeral ‘‘1’’ (24% probability) served as a cue initiating a Go–NoGo task and inducing response preparation. Participants were instructed to press a button on an MRI-compatible optical fiber pad (Sinorad Medical Electronics Inc., Shenzhen, China) with the index finger of their dominant hand as fast as possible when the numeral ‘‘1’’ was followed directly by the numeral ‘‘9’’ (Go condition, 12% probability), but had to withhold response to the numeral ‘‘1’’ when it was not followed by ‘‘9’’ (NoGo condition, also 12% probability). Moreover, the single ‘‘9’’ preceded by a number other than ‘‘1’’ (Lure condition, 12% probability) also required no response. A total of 160 numeral sequences involving neither the numeral ‘‘1’’ nor the numeral ‘‘9’’ (Background condition, 64% probability) were presented, the duration of which varied from 6 to 12 s (6, 9 or 12 s, mean = 7.5 s). The procedure was designed and controlled using E-Prime 2.0 software (Psychology Software Tools Inc., Pittsburgh, Pennsylvania, USA). The projection of figures was performed with a computerguided projector onto a screen. Subjects watched the screen through a mirror on the head coil positioned above their eyes.

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S. Wang et al. / Clinical Neurophysiology 124 (2013) 2181–2190 Table 1 Demographic and clinical characteristics of ADHD children and healthy controls, and their behavioral performance on the CPT. ADHD (n = 28)

Control (n = 31)

t value

Mean

SD

Mean

SD

Age

9.60

1.61

10.23

1.83

1.40

WISCC-R Verbal IQ Performance IQ Estimated full-scale IQ

111.2 97.2 105.4

14.1 10.6 11.4

111.6 103.0 108.2

11.7 8.8 9.3

0.11 2.29* 1.11

Conners’ Parent Symptom Questionnaire Conduct problem Learning problem Psychosomatic Impulsivity-hyperactive Anxiety Hyperactivity index

0.865 1.733 0.276 1.348 0.495 1.353

0.434 0.592 0.304 0.606 0.393 0.936

0.420 0.674 0.121 0.545 0.371 0.506

0.260 0.528 0.229 0.421 0.349 0.342

4.72* 7.26** 2.26* 5.96** 1.28 4.52*

Performance on the CPT task Reaction time (ms) Hit rate (%) Go omissions rate (%) NoGo commission rate (%) Number of Lure commission

501.66 92.00 8.01 4.47 1.21

111.69 8.02 8.02 4.16 0.96

468.31 95.84 4.16 2.61 0.94

87.31 4.13 4.13 2.86 0.85

1.28 2.28* 2.28* 1.98* 1.15

Abbreviations: ADHD, attention deficit hyperactivity disorder; CPT, continuous performance task; WISCC-R, Wechsler Intelligence Scale for Chinese Children-Revised; IQ, intelligence quotient. * p < 0.05. ** p < 0.01.

Fig. 1. The modified continuous performance task (CPT-AX). Participants were instructed to press one key using their right index finger only when the numeral ‘‘1’’ was directly followed by the numeral ‘‘9’’. All other conditions, including the NoGo and the Lure conditions, were to be ignored.

2.3. Data acquisition Event-related fMRI was performed with a 1.5-T MAGNETOM Avanto scanner (SiemensÒ Medical Systems, Erlangen, Germany) equipped with a standard head coil using a gradient-echo echoplanar imaging (EPI) sequence with the following parameters: 20 axial slices, repetition time (TR) = 3000 ms, echo time (TE) = 50 ms, flip angle (FA) = 90°, field of view (FoV) = 240  240 mm2, voxel size = 3.75  3.75  6.00 mm3, and 250 volumes each run. The beginning of a scanning series triggered the stimulus presentation via Sinorad hardware and E-Prime software. In addition, a T1weighted anatomical three-dimensional VIBE (volumetric interpolated breath-hold sequence) transaxial image covering the whole brain was acquired (slices = 160, TR = 9.0 ms, TE = 2.38 ms, FA = 10°, FoV = 240  240 mm2, voxel size = 1.3  0.9  1.0 mm3). Participants were asked to lay supine with their heads snugly fixed by VAC-FIX foam pads (S&S Par Scientific, Houston, USA) to minimize movement. 2.4. Imaging and statistical analysis Data were pre-processed and analyzed using SPM8 software (Wellcome Department of Imaging Neuroscience, London, UK; http://www.fil.ion.ucl.ac.uk/spm/). The first five volumes were discarded to remove saturation effects. Slice time correction was conducted to adjust for time differences due to multi-slice imaging acquisition. For each participant, functional images were then spatially realigned to the mean of all the functional images for that participant, minimizing the effects of head movement. Individual runs exhibiting more than 2 mm maximum translation on the x, y or z axes or 1° of angular rotation throughout the course of the

scan were excluded from further analysis. The anatomical T1 image was co-registered with the mean functional image and then spatially normalized to the standard Montreal Neurological Institute (MNI) template brain implemented in the SPM8 International Consortium for Brain Mapping space template – East Asian brains. This normalization resulted in a voxel size of 3 mm3 for the functional images. Functional images were smoothed with a 4 mm full width at half maximum (FWHM) isotropic Gaussian kernel. Three event types of interest were defined at the first level of analysis: Go, NoGo and Lure. The event types were time-locked to the onset of stimuli by a canonical synthetic hemodynamic response function (HRF) and its first-order temporal derivative. For the second-level analysis, three planned one-sample t-tests were conducted to identify neural correlates of the Go, NoGo, and Lure conditions. Two-sample t-tests were then performed to investigate group differences in activation between the ADHD group and the controls. Activations for one-sample and two-sample t-tests were all reported at a level of significance of p < 0.01 (cluster size > 136 voxels) and p < 0.05 (cluster size > 54 voxels) respectively, corrected with the custom written REST AlphaSim program (www.restfmri.net) determined by Monte Carlo simulations (Ledberg et al., 1998). Additionally, the activity found during the NoGo condition was subtracted from activity found during the Lure condition to identify the specific neural response for the Lure condition. We used a combined threshold of voxelwise p < 0.05 and cluster size > 54 voxels to determine significant differences. MNI stereotactic coordinates were transformed to Talairach space (Talairach and Tournoux, 1998). Furthermore, all significantly activated clusters of voxels in one-sample tests were defined as the regions of interest (ROIs). The signal in every ROI was analyzed by first averaging the time

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course data of all voxels constituting the ROI, then computing statistical parameters for the time course using the general linear model (GLM, Worsley et al., 2002). A rapid event-related design was used to determine the level of brain activation, such that the blood oxygen level dependent (BOLD) signal recorded at any given point was the sum of the BOLD impulse responses from several preceding task events. The GLM method was adopted to perform both the statistical analysis and the linear de-convolution. The ROI-GLMs were calculated after a z-transformation of the signal level from each subject and corrected for the number of serial correlations within the ROI time course (Bledowski et al., 2004). The time course of activity in each ROI was computed by event-related averaging of the activity over every TR for each of the three conditions. In addition, a Pearson correlation was performed to evaluate the temporal correlation coefficient of changes in the BOLD signal within 10 TRs (i.e. 30 s) of the stimulus onset between the ADHD group and the comparison group. 3. Results 3.1. Clinical and demographic features Independent sample t-tests indicated that there were no significant differences for age (p = 0.146), Verbal IQ (p = 0.906) or Full Scale IQ (p = 0.271) except within the Performance IQ (ADHD < Control, p = 0.025). As expected, post-hoc analyses showed the ADHD children scored significantly higher than controls on five PSQ factors such as psychosomatic, impulsivity-hyperactive and hyperactivity indices, as well as conduct and learning problems. However, the two groups did not differ from each other in the PSQ anxiety factor. 3.2. Behavioral data Although children with ADHD appeared to respond (501.66 ± 111.69 ms) slightly slower than the control group (468.31 ± 87.31 ms, see Table 1), these differences were not significant in two sample t-test analysis (p = 0.201). However, there was a significant difference in hit rate (i.e. accuracy, p < 0.05), such that the ADHD group made more errors, including a higher omission rate (incorrect response to Go condition, p = 0.022) and a higher commission rate (incorrect response to NoGo condition, p = 0.047). All participants made few mistakes on Lure condition, and there is no group difference of commission between the two groups (F(1, 57) = 1.399, p = 0.242).

3.3. fMRI results The neural responses to each condition (i.e. Go, NoGo, Lure) were compared separately to the neural responses to the Background condition to examine the brain mechanisms engaged when sustained attention or vigilance must be involved in response selection. Within-group brain activations for the three contrast conditions are shown in Fig. 2 and Table 2. 3.3.1. Go condition Controls showed increased activation in the right medical frontal gyrus and left postcentral gyrus under Go condition compared to background condition. Children with ADHD markedly differed from this pattern, exhibiting increased activation in a more widespread network including the left medial frontal gyrus, left postcentral gyrus, bilateral insula, and right cerebellar anterior lobe. However, hits on Go condition were associated with deactivation in the left posterior cingulate in ADHD children (Fig. 2A). Two

sample t-test showed that the controls exhibited increased activation in the right parahippocampal gyrus under Go condition compared to background condition relative to the ADHD children (p < 0.001). However, the ADHD children had increased activation in the right cerebellar anterior lobe, right middle frontal gyrus, and bilateral insula relative to the controls (p < 0.002) (Table 3).

3.3.2. NoGo condition All subjects showed decreased activation in the medial frontal gyrus and left posterior cingulate cortex under NoGo condition compared to background condition. Additionally, the controls showed increased activation in the left anterior cingulate cortex and decreased activation in the left middle temporal cortex (Fig. 2B). Two sample t-test showed that controls had increased activation in the left anterior cingulate cortex, right precentral gyrus, left middle temporal gyrus, and bilateral parahippocampus gyrus relative to ADHD children (p < 0.005). Relative to the controls, ADHD children had increased activation relative to controls in the left middle frontal gyrus, left middle temporal gyrus, left middle occipital gyrus, left putamen, left posterior cingulate cortex, left precuneus, and right cerebellar anterior lobe (p < 0.005) (see Table 3).

3.3.3. Lure condition Within-group activation maps for controls and children with ADHD were shown in Fig. 2C. Controls showed only decreased activation in the medial frontal gyrus. However, the ADHD group showed increased activation in the right medial frontal gyrus, right inferior frontal gyrus, left middle frontal gyrus, bilateral inferior parietal gyrus, and right cerebellar posterior lobe. Two sample ttest analyses showed that ADHD children had increased activation in left middle frontal gyrus, bilateral middle temporal gyrus, left precuneus, and right cerebellar posterior lobe relative to controls (p < 0.001) (see Table 3). Because the subjects were required to inhibit their response under both the NoGo and Lure conditions, a contrast between the two conditions was used to identify the specific neural circuits underlying the Lure condition. Both control and ADHD subjects exhibited significant Lure-specific activation in the left precuneus and posterior cingulate cortex (Table 4). However, ADHD children exhibited activation in a more widespread cortical network including the left precentral gyrus, left middle temporal gyrus, bilateral angular gyrus and the left precuneus (p < 0.05, corrected) (Fig. 3). 3.3.4. ROI-GLM analyses A correlation analysis of the time course extracted from the ROIs with significant activation was conducted for each group in different conditions to examine the temporal features of patterns of brain activation (see Table 2). Although functionally distinct networks were involved in Go and NoGo conditions, the time courses generated by scaling the curves with the canonical BOLD response model of the GLM were similar for ADHD children and controls. In the Go condition, high correlations of all the ROIs were observed between the two groups. In the NoGo condition, the correlation coefficient indicated that the activity patterns of the bilateral medial frontal gyrus and the left posterior cingulate cortex were similar between the two groups. However, the anterior cingulate cortex responded to inhibition in different ways in each group (Pearson’s r = 0.513, p = 0.129). For the Lure condition, all ROIs except the left inferior frontal gyrus exhibited different temporal characteristics between the ADHD group and controls (see Fig. 4). Children with ADHD exhibited greater activation in the bilateral inferior frontal

S. Wang et al. / Clinical Neurophysiology 124 (2013) 2181–2190

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Fig. 2. Activation patterns associated with Go, NoGo and Lure conditions in the continuous performance task in healthy children and in children with attention deficit hyperactivity disorder (ADHD).

gyrus, inferior parietal gyrus, and right cerebellar posterior lobe than the healthy controls. The peak latencies of the signal time course curve of the bilateral inferior frontal gyrus and bilateral inferior parietal gyrus in ADHD children were longer than those in the controls. The longer peak latency of activation in the right declive indicated that the cerebellum was activated more rapidly in ADHD children than in the healthy subjects.

4. Discussion To our knowledge, this is the first event-related fMRI study elucidating the differences in the neural circuits underlying AX-CPT performance in children with and without ADHD. The major finding of the present study is an abnormal activation pattern within fronto-temporo-limbic networks and enhanced activation within posterior and cerebellar regions in children with ADHD. In agreement with the literature concerning functional abnormalities in children, adolescents and adults with ADHD (Epstein et al., 2009; Schneider et al., 2010; Cubillo et al., 2012), the regions suggested to be involved in ADHD pathophysiology were also found to be affected in the anterior cingulate cortex (ACC), the primary motor cortex, the supplementary motor area, the striatum, the insula, the parietal cortex, and the cerebellum.

4.1. Behavioral performance There were differential group effects of behavioral performance during the Go condition. ADHD children exhibited fewer correct Go responses and a higher omission rate than controls, in accord with the significant problems of inattention reported by their parents on the PSQ and the reports of symptom severity in our preceding KSADS-PL interviewing. Moreover, children with ADHD made significantly more commission errors than healthy children. Commission errors are traditionally assumed to reflect symptoms of impulsivity (Riccio et al., 2002; Wang et al., 2011). Recent studies indicate that more commission errors in medication naïve children with ADHD could reflect weaker sustained attention and less topdown attentional control over the task (Johnson et al., 2008). In line with some studies of children and adults with ADHD (Schneider et al., 2010; Spronk et al., 2008; Nazari et al., 2010), our results did not show slower reaction time in the AX-CPT task. This could be due to higher response variability or behavioral heterogeneity in ADHD children. 4.2. Go and NoGo conditions The brain activation data provides clear evidence of the differential fronto-temporo-limbic activation between the children with

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Table 2 Regional activation and the correlation coefficients of mean event-related BOLD time course during sustained attention (Go, NoGo and Lure conditions vs. Background conditions) in ADHD and healthy controls. Brain regions of activation

(a) Go condition (Go vs. Background) ADHD; increased activation Medial frontal gyrus Postcentral gyrus Insula Insula Cerebellar anterior lobe ADHD; decreased activation Posterior cingulate cortex Control; increased activation Medial frontal gyrus Postcentral gyrus

Side

BA

Talairach coordinates

Voxels

z Score

Pearson’s r

46 51 3 1 2

580 410 192 252 1744

4.80 4.32 4.22 4.03 5.90

0.930*** 0.883*** 0.913*** 0.885** 0.891**

45

33

193

4.39

0.891**

33

20 18

49 48

358 176

4.00 4.36

0.784** 0.906***

3

55 42

5 33

138 247

3.63 3.99

0.854** 0.666*

12

19

32

136

3.74

0.513

x

y

z

11 18 0 12 53

L L L R R

32 3 48 48

3 50 39 42 9

L

23

3

R L

6 4

9

10 23

0

(b) NoGo condition (NoGo vs. Background) ADHD; decreased activation Medial frontal gyrus Inter Posterior cingulate cortex L Control; increased activation Anterior cingulate cortex L Control; decreased activation Medial frontal gyrus Inter Middle temporal gyrus L Posterior cingulate cortex L (c) Lure condition (Lure vs. Background) ADHD; increased activation Medial frontal gyrus R Inferior frontal gyrus R Middle frontal gyrus L Inferior parietal gyrus L Inferior parietal gyrus R Cerebellar posterior lobe R Control; decreased activation Medial frontal gyrus Inter

32 10 19 30

0 39 6

57 71 46

6 34 5

434 169 572

4.47 4.08 4.77

8 47 44 7 7

6 39 45 27 24 6

25 26 10 53 47 77

43 4 27 39 41 16

922 157 179 521 633 137

4.42 3.98 4.37 5.11 5.12 4.01

0.105 0.336 0.649* 0.599 0.616 0.195

10

0

55

0

386

3.67

0.165

Abbreviations: BOLD, blood oxygen level dependent; ADHD, attention deficit hyperactivity disorder; BA, Brodmann’s area; L, left; R, right; Inter, inter hemisphere; vs., versus. Activation is reported at a level of significance of p < 0.01 and cluster size > 136 voxels (corrected by AlphaSim, surface connected). p < 0.05. ** p < 0.01. *** p < 0.001. *

ADHD and controls. The contrast between Go and Background conditions, labeled ‘‘sustained attention contrast,’’ measures the brain response to infrequent stimuli over time (Cubillo et al., 2012). The present study adopted an AX-CPT task, which required participants to respond to the numeral ‘‘9’’ but only when it followed a cue numeral, ‘‘1’’, which acted as a warning stimulus. The distinct brain activation in children with and without ADHD reveals that during response execution ADHD children exhibit abnormal target and preparatory visual attention processing. Likewise, the contrast between NoGo and Background conditions, labeled ‘‘motor control contrast,’’ measures the brain response to inhibitory control during which the participants have to withhold their response to NoGo condition (Dhar et al., 2010). Ventrolateral prefrontal and ACC abnormalities have consistently been reported in ADHD literature (Rubia et al., 2009b; Hart et al., 2012). As a part of the limbic system, the ACC plays a crucial role in a form of attention that serves to regulate both cognitive and emotional processing (Bush et al., 2005). Another finding in our study was a consistent dysfunction of the parahippocampus gyrus in children with ADHD compared to the controls. The parahippocampus gyrus is a region of the brain surrounding the hippocampus. It plays an important role in memory encoding and retrieval. Because both the Go and NoGo conditions required the subjects to retain the Cue (‘‘1’’), our results could explain the disturbances of executive functions, including working memory, in ADHD children, which refers to the ability to transiently store and flexibly manipulate task-relevant information (Fassbender et al., 2011; Massat et al., 2012).

The enhanced activation in posterior and cerebellar regions in children with ADHD could be compensatory for the dysfunction of fronto-temporo-limbic networks (Cubillo et al., 2012; Rubia et al., 2009b). Hyperactivation of the insular cortex has also been found in a previous AX-CPT fMRI study in ADHD adults (Schneider et al., 2010). The insula cortex is an important limbic-related region integrating perceptual information into higher cognitive and emotional processes. The hyperactivity in this region in ADHD children may be associated with increased processing of task-irrelevant information (Fassbender et al., 2011). Also, in some functional connectivity studies, subjects with ADHD-combined also exhibit prominent atypical connectivity in midline default network components, as well as in the insular cortex (Fair et al., 2012). In addition to the increased activation in posterior and subcortical regions, such as the cerebellar posterior lobe, middle temporal gyrus, and precuneus, in ADHD patients may reflect a compensatory recruitment of accessory brain regions to accomplish a cognitive task (Dickstein et al., 2006). In other words, given the atypical activation within sustained attention regions, the children with ADHD might activate an alternative network. 4.3. Lure condition The areas of activation in the frontal gyri, including the right medial frontal gyrus, right inferior frontal gyrus and left middle frontal gyrus, under the Lure condition have been previously associated with both anatomical and functional pathology of ADHD

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S. Wang et al. / Clinical Neurophysiology 124 (2013) 2181–2190 Table 3 Differences in brain activation between children with ADHD and healthy controls. Brain regions of activation

Side

BA

Talairach coordinates x

(a) Go condition (Go vs. Background) Control > ADHD Parahippocampus gyrus ADHD > Control Middle frontal gyrus Insula Insula Insula Cerebellar anterior lobe

4.15

<0.001**

15 30 30 42 5

34 8 26 12 50

9 11 1 1 5

101 59 72 104 2084

3.48 3.76 3.40 3.17 4.15

<0.001** <0.001** <0.001** 0.001* <0.001**

3 33 53 30 30 24

7

25 36 4 15 14 15

121 181 64 61 93 67

2.89 2.67 2.92 3.48 2.99 2.89

0.002* 0.004* 0.002* <0.001** 0.001* 0.002*

50 42 45 21 9 12 12

22 61 79 9 46 67 53

29 3 6 0 11 50 5

86 93 141 116 127 61 1088

2.96 3.21 2.96 3.08 2.86 2.58 4.79

0.002* <0.001** 0.002* 0.001* 0.002* 0.005* <0.001**

18 42 21 27 18

21 58 44 33 77

21 6 44 29 16

915 176 484 493 240

4.48 3.79 4.29 3.96 3.96

<0.001** <0.001** <0.001** <0.001** <0.001**

R L L R R

11 48 47 48

(c) Lure condition (Lure vs. Background)⁄ ADHD > Control Middle frontal gyrus L Middle temporal gyrus L Middle temporal gyrus R Precuneus L Cerebellar posterior lobe R

29 7

37

z

77

33

44 37

p values

14

20

21 20 20 34

z Score

27

R

(b) NoGo condition (NoGo vs. Background) Control > ADHD Anterior cingulate cortex L Precentral gyrus R Middle temporal gyrus L Parahippocampus gyrus L Parahippocampus gyrus R Parahippocampus gyrus R ADHD > Control Middle frontal gyrus L Middle temporal gyrus L Middle occipital gyrus L Putamen L Posterior cingulate cortex L Precuneus L Cerebellar anterior lobe R

y

Voxels

10 26 9 27 7

Abbreviations: ADHD, attention deficit hyperactivity disorder; BA, Brodmann’s area; L, left; R, right; vs., versus. Activation are reported at a level of significance of p < 0.05 and cluster size > 54 voxels (corrected by AlphaSim, surface connected). * p < 0.01. ** p < 0.001.

Table 4 Brain regions with increased activation under Lure conditions compared with NoGo conditions in children with and without ADHD. Brain regions of activation

Side

ADHD group; increased activation Precentral gyrus Middle temporal gyrus Angular gyrus Angular gyrus Precuneus

L L L R L

Control group; increased activation Precuneus

L

BA

44 21 39 39

Talairach coordinates

Voxels

z Score

30 2 42 42 41

161 122 300 238 841

3.27 3.40 3.20 3.73 3.92

36

57

3.09

x

y

z

45 56 42 48 6

10 32 62 56 50 56

0

Abbreviations: ADHD, attention deficit hyperactivity disorder; L, left; R, right. Activation is reported at a level of significance of p < 0.05 and cluster size > 54 voxels (corrected by AlphaSim, surface connected).

(Shaw et al., 2011; Cubillo et al., 2011). Studies have shown that in subjects with ADHD the fronto-striatal network contributes to dysfunctions of executive control (Mansouri et al., 2009; Sebastian et al., 2012) and attention allocation (Rubia et al., 2011; Hart et al., 2013). Although the exact biological or neurological responses to the Lure condition remain unclear, it has been related to the modulation of the effect of irrelevant stimuli. As a piece of supporting evidence, Banaschewski et al. (2008) demonstrated that ADHD children showed smaller contingent negative variation (CNV) potentials and a functionally irrelevant over-activation of the ipsilateral motor area. The increased activation in the frontal lobe under the Lure condition might contribute to a lapse of excitation related to sensorimotor preparation in children with ADHD.

Successful performance under Lure conditions was also related to left precuneus activation in both healthy controls and ADHD children. A previous report suggested that, together with the posterior cingulate cortex, the precuneus plays a central role in a wide range of highly integrated tasks, including visuospatial imagery, episodic memory retrieval, and self-processing operations (Cavanna and Trimble, 2006). However, the parietal and cerebellar hyperactivation in the ADHD group which occurred during the Lure condition, in contrast to during the NoGo condition, is a novel finding. Based on the findings of neurophysiological and functional imaging studies in healthy adults, it has been argued that the posteromedial parietal cortex acts in concert with the lateral parietal areas to elaborate information about the egocentric and allocentric

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Fig. 3. Axial slices show the specific neural circuits underlying the responses of ADHD children (blue) and healthy controls (red) to Lure condition by subtracting brain responses to NoGo condition from those of the Lure condition. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

control of body movement as well as voluntary attention shifts (Simon et al., 2002). Additionally the posterior parietal cortex plays a critical role in action planning when there is response competition (Coulthard et al., 2008). While participants were instructed to refrain from responding during the Lure condition in this study, children with ADHD are prone to fail to maintain the previous numeral in a series but will conceptually process the numeral ‘‘9’’, even when they are not sure whether the processing of perceptual features is useful. The increased activation in the parietal regions under the Lure condition might be associated with more executive attention related resources allocated to irrelevant stimuli, increased mental load of retrieval, and/or working memory (Dorfel et al., 2009); or it might alternately be related to inefficient regulation of supervisory higher control systems (Banaschewski et al., 2008). Moreover, ADHD children exhibited additional increased activation of the bilateral angular gyrus during the Lure condition in

contrast to the NoGo condition. A recent study reported that the dorsomedial angular gyrus is involved in semantic processing of visual stimuli, and the ventrolateral angular gyrus is involved in the conceptual identification of visual inputs (Seghier et al., 2010). Taken together with evidence of a neural circuit involved in downregulating salience in healthy controls (Mevorach et al., 2010), our findings indicate that children with ADHD may develop compensatory strategies to cope with the reduced engagement of their frontal–striatal–temporal–parietal networks (Vaidya et al., 2005). 4.4. Time course of ROI activation A robust finding in this study was the different temporal features of the neural response in ROIs that were significantly activated under the Lure condition in two groups. During the performance of attention-demanding cognitive tasks, a specific set of frontal and parietal cortical regions have been found to routinely

S. Wang et al. / Clinical Neurophysiology 124 (2013) 2181–2190

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Fig. 4. The mean event-related BOLD time course of healthy children (solid line) and ADHD children (dashed line) under the Lure condition from six selected ROIs. Healthy children exhibit significantly decreased activity in (A) the right medial frontal gyrus (GFd), exhibiting significant activity decrease in healthy children; children with ADHD exhibit activity in (B and C) the bilateral inferior frontal gyrus (IFG), (D and E) the bilateral inferior parietal gyrus (IPG), and (F) the right cerebellar declive.

exhibit activity increases; however, a different set of regions, including the posterior cingulate, medial and lateral parietal, and medial prefrontal cortex, have been found to routinely exhibit activity decreases (Fox et al., 2005). The spontaneous fluctuations of the BOLD signal found during the resting state in the prefrontal cortex have been reported to correlate with both the bilateral dorsal attention system and the right-lateralized ventral attention system (Fox et al., 2006). The present study demonstrated that the neural substrates of CPT performance in ADHD children were also altered temporally. Also observed was a different time course of activation in the ACC in children with and without ADHD. These findings are consistent with previous studies on executive dysfunction in ADHD, reporting that fronto-striatal abnormalities, especially in the ACC, modulate the ability to hold task-relevant information online, allocate attention, inhibit distraction, and process reward contingencies (Schulz et al., 2004; Pliszka et al., 2006; Wittfoth et al., 2009). Abnormalities in a diverse range of brain structures are linked to ADHD, and changes in activation within these regions (the prefrontal cortex, dorsal and ventral striatum, posterior cingulate cortex, and cerebellum) may contribute to impairments of attention control (Depue et al., 2010). Although there is variability across the different studies, it is widely accepted that abnormalities exist in the mesocortical and nigrostriatal dopamine system in those with ADHD.

5. Conclusion In contrast to most previous fMRI studies, participants in this study were medication-naïve, without extensive comorbidity, and were matched with controls in terms of age and IQ. In accordance with previously reported clinical findings, the current results implicated widespread increases in activation, including the bilateral medial frontal gyrus, inferior frontal gyrus, precuneus, and cerebellum during CPT performance in children with ADHD. The current study provides evidence that CPT performance in the presence of Lure stimuli depends on different neural circuits in children with and without ADHD. In addition, our results support the notion

that the left fronto-temporal area and angular gyrus exhibit functional abnormalities related to ADHD. Acknowledgments This article was supported by the National Nature Science of Foundation of China (NSFC Grant Nos. 81101018, 61103172), Hunan Provincial Natural Science Foundation of China (Grant No. 12JJ5066), Open Project of Key Laboratory Incubation Base (Grant No. 2012NZDJ06), Capital Medical University Fundamental and Clinical Foundations of China (Grant No. 12JL73), and Changzhou Science and Technology Application Project (Grant No. CJ20112009). The authors thank Yufeng Zang, Xiaolin Zhou, Yuan Zhong for technical assistance, the anonymous reviewers for their insightful comments and suggestions, and the volunteers who took part in the experiment. The authors declare no conflict of interest. References Banaschewski T, Yordanova J, Kolev V, Heinrich H, Albrecht B, Rothenberger A. Stimulus context and motor preparation in attention-deficit/hyperactivity disorder. Biol Psychol 2008;77:53–62. Bickel S, Dias EC, Epstein ML, Javitt DC. Expectancy-related modulations of neural oscillations in continuous performance tasks. Neuroimage 2012;62:1867–76. Bledowski C, Prvulovic D, Goebel R, Zanella FE, Linden DE. Attentional systems in target and distractor processing: a combined ERP and fMRI study. Neuroimage 2004;22:530–40. Bush G, Valera EM, Seidman LJ. Functional neuroimaging of attention-deficit/ hyperactivity disorder: a review and suggested future directions. Biol Psychiatry 2005;57:1273–84. Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 2006;129:564–83. Chatham CH, Frank MJ, Munakata Y. Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control. Proc Natl Acad Sci U S A 2009;106:5529–33. Christakou A, Murphy CM, Chantiluke K, Cubillo AI, Smith AB, Giampietro V, et al. Disorder-specific functional abnormalities during sustained attention in youth with Attention Deficit Hyperactivity Disorder (ADHD) and with autism. Mol Psychiatry 2013;18:236–44. Coulthard EJ, Nachev P, Husain M. Control over conflict during movement preparation: role of posterior parietal cortex. Neuron 2008;58:144–57. Cubillo A, Halari R, Giampietro V, Taylor E, Rubia K. Fronto-striatal underactivation during interference inhibition and attention allocation in grown up children with attention deficit/hyperactivity disorder and persistent symptoms. Psychiatry Res 2011;193:17–27.

2190

S. Wang et al. / Clinical Neurophysiology 124 (2013) 2181–2190

Cubillo A, Halari R, Smith A, Taylor E, Rubia K. A review of fronto-striatal and frontocortical brain abnormalities in children and adults with Attention Deficit Hyperactivity Disorder (ADHD) and new evidence for dysfunction in adults with ADHD during motivation and attention. Cortex 2012;48:194–215. Depue BE, Burgess GC, Willcutt EG, Bidwell LC, Ruzic L, Banich MT. Symptomcorrelated brain regions in young adults with combined-type ADHD: their organization, variability, and relation to behavioral performance. Psychiatry Res 2010;182:96–102. Dhar M, Been PH, Minderaa RB, Althaus M. Information processing differences and similarities in adults with dyslexia and adults with Attention Deficit Hyperactivity Disorder during a Continuous Performance Test: a study of cortical potentials. Neuropsychologia 2010;48:3045–56. Dickstein SG, Bannon K, Castellanos FX, Milham MP. The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis. J Child Psychol Psychiatry 2006;47:1051–62. Doehnert M, Brandeis D, Imhof K, Drechsler R, Steinhausen HC. Mapping attentiondeficit/hyperactivity disorder from childhood to adolescence – no neurophysiologic evidence for a developmental lag of attention but some for inhibition. Biol Psychiatry 2010;67:608–16. Doehnert M, Brandeis D, Schneider G, Drechsler R, Steinhausen HC. A neurophysiological marker of impaired preparation in an 11-year follow-up study of attention-deficit/hyperactivity disorder (ADHD). J Child Psychol Psychiatry 2013;54:260–70. Dorfel D, Werner A, Schaefer M, von Kummer R, Karl A. Distinct brain networks in recognition memory share a defined region in the precuneus. Eur J Neurosci 2009;30:1947–59. Epstein JN, Delbello MP, Adler CM, Altaye M, Kramer M, Mills NP, et al. Differential patterns of brain activation over time in adolescents with and without attention deficit hyperactivity disorder (ADHD) during performance of a sustained attention task. Neuropediatrics 2009;40:1–5. Fair DA, Bathula D, Nikolas MA, Nigg JT. Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proc Natl Acad Sci U S A 2012;109:6769–74. Fallgatter AJ, Ehlis AC, Seifert J, Strik WK, Scheuerpflug P, Zillessen KE, et al. Altered response control and anterior cingulate function in attention-deficit/ hyperactivity disorder boys. Clin Neurophysiol 2004;115:973–81. Fassbender C, Schweitzer JB, Cortes CR, Tagamets MA, Windsor TA, Reeves GM, et al. Working memory in attention deficit/hyperactivity disorder is characterized by a lack of specialization of brain function. PLoS One 2011;6:e27240. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 2006;103:10046–51. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005;102:9673–8. Gong YX, Cai TS. Manual of Chinese Revised Wechsler Intelligence Scale for Children. Changsha: Hunan Atlas; 1993. Goyette CH, Conners CK, Ulrich RF. Normative data on revised Conners Parent and Teacher Rating Scales. J Abnorm Child Psychol 1978;6:221–36. Hart H, Radua J, Mataix-Cols D, Rubia K. Meta-analysis of fMRI studies of timing in attention-deficit hyperactivity disorder (ADHD). Neurosci Biobehav Rev 2012;36:2248–56. Hart H, Radua J, Nakao T, Mataix-Cols D, Rubia K. Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attentiondeficit/hyperactivity disorder: exploring task-specific, stimulant. JAMA Psychiatry 2013;70:185–98. Johnson KA, Barry E, Bellgrove MA, Cox M, Kelly SP, Daibhis A, et al. Dissociation in response to methylphenidate on response variability in a group of medication naive children with ADHD. Neuropsychologia 2008;46:1532–41. Johnstone SJ, Barry RJ, Clarke AR. Ten years on: a follow-up review of ERP research in attention-deficit/hyperactivity disorder. Clin Neurophysiol 2013;124: 644–57. Kaufman J, Birmaher B, Brent DA, Rao U, Flynn C, Moreci P, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 1997;36:980–8. Konrad K, Neufang S, Hanisch C, Fink GR, Herpertz-Dahlmann B. Dysfunctional attentional networks in children with attention deficit/hyperactivity disorder: evidence from an event-related functional magnetic resonance imaging study. Biol Psychiatry 2006;59:643–51. Ledberg A, Akerman S, Roland PE. Estimation of the probabilities of 3D clusters in functional brain images. Neuroimage 1998;8:113–28. Mansouri FA, Tanaka K, Buckley MJ. Conflict-induced behavioural adjustment: a clue to the executive functions of the prefrontal cortex. Nat Rev Neurosci 2009;10:141–52. Martel MM, Roberts B, Gremillion M, von Eye A, Nigg JT. External validation of bifactor model of ADHD: explaining heterogeneity in psychiatric comorbidity, cognitive control, and personality trait profiles within DSM-IV ADHD. J Abnorm Child Psychol 2011;39:1111–23. Martinussen R, Hayden J, Hogg-Johnson S, Tannock R. A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2005;44:377–84. Massat I, Slama H, Kavec M, Linotte S, Mary A, Baleriaux D, et al. Working memoryrelated functional brain patterns in never medicated children with ADHD. PLoS One 2012;7:e49392.

Meier NM, Perrig W, Koenig T. Neurophysiological correlates of delinquent behaviour in adult subjects with ADHD. Int J Psychophysiol 2012;84:1–16. Mevorach C, Hodsoll J, Allen H, Shalev L, Humphreys G. Ignoring the elephant in the room: a neural circuit to downregulate salience. J Neurosci 2010;30:6072–9. Nazari MA, Berquin P, Missonnier P, Aarabi A, Debatisse D, De Broca A, et al. Visual sensory processing deficit in the occipital region in children with attentiondeficit/hyperactivity disorder as revealed by event-related potentials during cued continuous performance test. Neurophysiol Clin 2010;40:137–49. Perera S, Crewther D, Croft R, Keage H, Hermens D, Clark CR. Comorbid externalising behaviour in AD/HD: evidence for a distinct pathological entity in adolescence. PLoS One 2012;7:e41407. Pliszka SR, Glahn DC, Semrud-Clikeman M, Franklin C, Perez 3rd R, Xiong J, et al. Neuroimaging of inhibitory control areas in children with attention deficit hyperactivity disorder who were treatment naive or in long-term treatment. Am J Psychiatry 2006;163:1052–60. Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 2007;164:942–8. Riccio CA, Reynolds CR, Lowe P, Moore JJ. The continuous performance test: a window on the neural substrates for attention? Arch Clin Neuropsychol 2002;17:235–72. Roberts LE, Rau H, Lutzenberger W, Birbaumer N. Mapping P300 waves onto inhibition: Go/No-Go discrimination. Electroencephalogr Clin Neurophysiol 1994;92:44–55. Rosvold HE, Mirsky AF, Sarason I, Bransome Jr ED, Beck LH. A continuous performance test of brain damage. J Consult Psychol 1956;20:343–50. Rubia K, Halari R, Cubillo A, Mohammad AM, Brammer M, Taylor E. Methylphenidate normalises activation and functional connectivity deficits in attention and motivation networks in medication-naive children with ADHD during a rewarded continuous performance task. Neuropharmacology 2009a;57:640–52. Rubia K, Smith AB, Halari R, Matsukura F, Mohammad M, Taylor E, et al. Disorderspecific dissociation of orbitofrontal dysfunction in boys with pure conduct disorder during reward and ventrolateral prefrontal dysfunction in boys with pure ADHD during sustained attention. Am J Psychiatry 2009b;166:83–94. Rubia K, Cubillo A, Woolley J, Brammer MJ, Smith A. Disorder-specific dysfunctions in patients with attention-deficit/hyperactivity disorder compared to patients with obsessive-compulsive disorder during interference inhibition and attention allocation. Hum Brain Mapp 2011;32:601–11. Schneider MF, Krick CM, Retz W, Hengesch G, Retz-Junginger P, Reith W, et al. Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults – a functional magnetic resonance imaging (fMRI) study. Psychiatry Res 2010;183: 75–84. Schulz KP, Fan J, Tang CY, Newcorn JH, Buchsbaum MS, Cheung AM, et al. Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: an event-related FMRI study. Am J Psychiatry 2004;161:1650–7. Sebastian A, Gerdes B, Feige B, Kloppel S, Lange T, Philipsen A, et al. Neural correlates of interference inhibition, action withholding and action cancelation in adult ADHD. Psychiatry Res 2012;202:132–41. Seghier ML, Fagan E, Price CJ. Functional subdivisions in the left angular gyrus where the semantic system meets and diverges from the default network. J Neurosci 2010;30:16809–17. Shaw P, Gilliam M, Liverpool M, Weddle C, Malek M, Sharp W, et al. Cortical development in typically developing children with symptoms of hyperactivity and impulsivity: support for a dimensional view of attention deficit hyperactivity disorder. Am J Psychiatry 2011;168:143–51. Simon O, Mangin JF, Cohen L, Le Bihan D, Dehaene S. Topographical layout of hand, eye, calculation, and language-related areas in the human parietal lobe. Neuron 2002;33:475–87. Spronk M, Jonkman LM, Kemner C. Response inhibition and attention processing in 5- to 7-year-old children with and without symptoms of ADHD: an ERP study. Clin Neurophysiol 2008;119:2738–52. Su L, Li X, Huang C, Luo X, Zhang J. Norms of the Conners Parent Symptom Questionnaire in Chinese Urban Children. Chin J Clin Psychol 2001;9:241–52. Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. Stuttgart: Thieme; 1998. Tucha L, Tucha O, Walitza S, Sontag TA, Laufkotter R, Linder M, et al. Vigilance and sustained attention in children and adults with ADHD. J Atten Disord 2009;12:410–21. Vaidya CJ, Bunge SA, Dudukovic NM, Zalecki CA, Elliott GR, Gabrieli JD. Altered neural substrates of cognitive control in childhood ADHD: evidence from functional magnetic resonance imaging. Am J Psychiatry 2005;162:1605–13. Wang LJ, Huang YS, Chiang YL, Hsiao CC, Shang ZY, Chen CK. Clinical symptoms and performance on the continuous performance test in children with attention deficit hyperactivity disorder between subtypes: a natural follow-up study for 6 months. BMC Psychiatry 2011;11:65. Wittfoth M, Schardt DM, Fahle M, Herrmann M. How the brain resolves high conflict situations: double conflict involvement of dorsolateral prefrontal cortex. Neuroimage 2009;44:1201–9. Worsley KJ, Liao CH, Aston J, Petre V, Duncan GH, Morales F, et al. A general statistical analysis for fMRI data. Neuroimage 2002;15:1–15.