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  • Forty-two children aged 8–12 years and one of their parents gave their written informed consent to participate in this study approved by the Ethics Committee of the Erasme University Hospital, ULB, Bruxelles, Belgium. Out of 42 participants, 3 children with ADHD were excluded from the analyses following the discovery of anatomical brain abnormalities, 2 children with ADHD were excluded due to excessive head motion during MRI scanning, and 4 children (2 with ADHD, 2 healthy) were excluded based on insufficient performance during the N-back task (>1.96 SD below mean group performance). Behavioural and neuroimaging analyses were thus conducted on 19 right-handed children fulfilling DSM-IV criteria for the ADHD combined type (9 boys) and 14 healthy volunteers (8 boys). Mean age was 10.75±1.31 years in ADHD and 10.05±1.28 years in control children (t = 1.53,df = 31, p = 0.13). Population consistency was also ensured with respect to handedness, age range, diagnosis of combined-type and absence of co-morbidity. Children with ADHD were recruited from the Department of Neuropediatrics, outpatient clinic in Erasme Hospital, ULB University. Healthy children agreed to participate upon announcement or personal query. All participants were identically assessed by the same child psychiatrist (IM). Diagnosis for ADHD was based on clinical features including typical history and behavioural reports. The Kiddie Schedule for Affective Disorders and Schizophrenia for School Aged Children-Present and Lifetime Version (K-SADS-PL [47]), was completed at screening for each subject to establish the diagnosis according to the DSM-IV criteria. Exclusion criteria in ADHD and controls were the presence of psychiatric co-morbidity, history of prematurity, current and past medical and neurological disorders and contraindications to MRI. All children were living in a family home and were attending normal primary schools, without educational problem, and had a scale IQ above 85 as measured by the age-appropriate Weschler Abbreviated Scale of Intelligence, WASI [48]. Finally, all children were naïve for any medication and had never been treated with any psychotropic drug during lifetime. WM performance and underlying cerebral activity were measured using a verbal N-back task under two different conditions [5], [9]. In both cases, stimuli were black numbers (Arial font, size 74) displayed on a white background on the centre of the screen, successively presented in pseudo-random order. In the vigilant/control 0-back (N0) condition, subjects had merely to press a button with the right hand whenever the number “2” was displayed. In the WM 2-back (N2) condition, subjects had to press the button when the displayed number was identical to the number displayed two trials before. During the fMRI session, subjects were administered 5 blocks in the N0 condition alternated with 5 blocks in the N2 condition. Each block consisted of a sequence of 30 trials (including 10 targets) each displayed for 1750 ms with an interstimulus interval of 250 ms. Each block was followed by a resting period of random duration ranging 11–16 seconds, during which the instruction relative to the forthcoming condition was displayed (i.e. either “number 2” [N0] or “same than two numbers before” [N2]). A fixation cross replaced the instruction 2.5 seconds before the start of a novel series of 30 numbers. All participants performed the whole task outside of the fMRI environment once before scanning. During the fMRI session, stimuli were projected on a translucent screen that was seen via a mirror fixed to the head coil and located in front of the subject, and responses were made with the right hand on a commercially available MRI compatible keypad system (fORP; Current Design, Vancouver) connected with a PC. The timing of MR image acquisitions and stimuli presentations was synchronised using the clock signal of the MRI scanner. Head stabilization was achieved using a head-restraining foam and MR scanner noise was attenuated using foam earplugs and headphones. fMRI Data Acquisition and Image Analysis: Data were acquired on a Philips Achieva 3-T (Philips Medical Systems, Best, the Netherlands) scanner using a T2* sensitive gradient echo (EPI) sequence (TR = 2130 ms, TE = , 40 ms, FA 90°, SENSE acceleration factor 2.5, matrix size 64×64×32; voxel size: 3.06×3.06×3 mm3). Thirty-two contiguous transverse slices were acquired, covering the whole brain. Anatomical images were obtained using a T1-weigthed sagittal 3D TFE sequence (TR 1960 ms, TE 4.60 ms, TI 1040 ms, flip angle 8°, FOV 250×250 mm2, matrix size 320×320×160, interpolated voxel size: 0.78×0.78×1.0 mm). The MR scanner was equipped with the Quasar imaging gradients (maximum amplitude and slew rate: 30 mT/m and 200 mT/m/ms) and a 8-channel SENSE head coil. Functional MRI data were pre-processed and analyzed using Statistical Parametric Mapping SPM8 (Wellcome Department of Cognitive Neurology, London) software implemented in MATLAB 7.8 (Mathworks Inc., Sherbom, MA). The first five functional volumes in the acquisition were discarded to avoid transient spin saturation effects. Preprocessing included realignment and adjustment for movement related effects, co-registration of functional and anatomical data, spatial normalization into standard stereotactic MNI space and spatial smoothing using a Gaussian kernel of 8 mm full width at half maximum (FWHM). Subjects (n = 2) showing excessive scan-to-scan head motion (>4 mm) were excluded from the analyses. Additionally, the magnitude of head motion at each time point for translation and rotation parameters was obtained for each subject, and averaged within each group. No between-groups difference was evidenced using either the maximum head motion or the mean head motion measurements (ps >0.8), indicating similar movement patterns during scanning. Data were analysed using a mixed-effects model aimed at showing a stereotypical effect in the population from which the subjects were drawn [49]. For each subject, a first-level intra-individual analysis aimed at modelling data to partition observed neurophysiological responses into components of interest, confounds and error, using a general linear model [50]. The regressors of interest were built using box cars positioned at each block (N2 and N0) presentation. These regressors were secondarily convolved with the canonical hemodynamic response function. Movement parameters derived from realignment of the functional volumes (translations in x, y and z directions and rotations around x, y and z axes) were included as covariates of no interest in the design matrix. High-pass filtering was implemented in the matrix design using a cut-off period of 256 seconds to remove low drift frequencies from the time series. Serial correlations were estimated with a restricted maximum likelihood (ReML) algorithm using an intrinsic first order autoregressive model during parameter estimation. Effects of interest were then tested by linear contrasts, generating statistical parametric maps [SPM(T)]. Here, the contrast of interest was the difference of activation between N2 and N0 conditions (N2 vs. N0) as the best approximation of neural activity associated with WM. Summary statistic images were then further spatially smoothed (6 mm FWHM Gaussian kernel) and entered in a second-level analysis in which subjects were treated as a random effect (RFX). One-sample t tests were used to assess the N2 vs. N0 contrast in the ADHD and control groups separately. Two-sample t tests were used for a direct comparison of the N2 vs. N0 contrast between ADHD and control subjects. Conjunction null analyses were used to identify the brain areas commonly activated in ADHD and controls in contrasts of interest [26]. Restricted maximum likelihood estimates of variance components were used to allow possible departure from the sphericity assumptions in RFX conjunction analyses [49]. Additionally, psychophysiological interaction (PPI) analyses [28], [29] were computed to test the hypothesis that areas showing group- and/or condition-specific neural activity might establish differential functional connections in ADHD than Control groups with other brain regions involved in WM. Coordinates of interest (COI) were determined based on results from RFX analyses described above. For each subject and each COI, the N2 vs. N0 contrast effect (corresponding to the summary statistic images entered in the RFX analysis) was computed at the individual level and the local maximum of activation determined in a small spherical volume in a 6 mm radius around the COI. This peak value was selected, unless identified outside of the brain structure of interest upon visual inspection of the individual normalized anatomical T1 image and verification of localization in SPM toolbox Anatomy atlas [51], in which case the maximum value that fitted the anatomical location was selected. A new linear model was then generated at the individual level, using three regressors. One regressor represented the task condition (N2 or N0). The second regressor was the average activity in a sphere (radius 4 mm) centred on the coordinate of the subject-specific peak value. The third regressor represented the interaction of interest between the first (psychological) and the second (physiological) regressors. To build this regressor, the underlying neuronal activity was first estimated by a parametric empirical Bayes formulation, combined with the psychological factor and subsequently convolved with the hemodynamic response function [29]. The design matrix also included the movement parameters. A significant psychophysiological interaction indicated a change in the regression coefficients between any reported brain area and the reference region related to the task condition. Individual summary statistic images obtained at the first level (fixed effects) analysis were then spatially smoothed (6 mm FWHM Gaussian kernel) and entered into a second-level (random effects) analysis using One-sample t-tests to test for condition-specific effects within each group separately, or two-sample t-tests for between–group comparisons. In all the analyses presented above, the resulting set of voxel values for each contrast constituted a map of the t statistic [SPM(T)], thresholded at p<0.001 (uncorrected for multiple comparisons). Statistical inferences were then obtained after corrections at the voxel level using Gaussian random field theory [52], either pcorr <.05 corrected for multiple comparisons in the whole brain volume and a minimal cluster size of 20 voxels (except for small structures), or psvc <.05 corrected in a small spherical volume (radius 6–16 mm) around a priori locations of activation in structures of interest, taken from previous fMRI studies examining the N-back task in adults [12], [41], adolescents [15], and children [16], [17] with ADHD.
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