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The attention network test: a characteristic pattern of deficits in children with ADHD

The attention network test: a characteristic pattern of deficits in children with ADHD Background: The Attention Network test (ANT) gives measures of different aspects of the complex process of attention. We ask if children with Attention Deficit Hyperactivity Disorder (ADHD) will show a characteristic pattern of deficits on this test. Methods: The sample included 157 children (M = 10 years) who performed the child version of ANT as participants of the Bergen Child Study. Children with an ADHD diagnosis (N = 45) were compared to a group of children with other diagnoses (N = 55) and a group of children without any diagnosis (N = 57). Results: The group of children with ADHD showed low accuracy scores and a variable response set, indicating an inattentive response style. No differences were found between the groups on RT and accuracy measures of the alerting, orienting, and conflict networks. A high correlation between full scale IQ (FSIQ) and ANT measures was only found in the ADHD group. When FSIQ score was included as a covariate, the group differences were not statistically significant on any ANT measure. Conclusion: The present study showed that accuracy and variability measures rather than measures of the three attention networks conveyed the characteristic pattern of deficits in children with ADHD. The results emphasized the importance of including these measures to extend the sensitivity of the ANT, and the importance of reporting results both with and without FSIQ as a covariate. attention network model is of special interest in studies of Background Attention is a complex cognitive function, dependent on attentional disorders, e.g. the Attention Deficit Hyperac- interacting neural systems of the brain. According to the tivity Disorder (ADHD). Attention Network theory the systems can be subdivided into an alerting or vigilance network, a network of orien- The three networks have been widely explored by using tation or selection, and an executive or conflict network cue-target reaction time (RT) tasks [7] and tasks evoking a [1]. A range of experimental, neuroimaging, and clinical conflict (e.g. [8]). Recently, Fan, Posner and collaborators studies have supported the theory [2-4] and Berger and [6] developed an experimental task called the Attention Posner [5] as well as Fan et al. [6] have argued that the Network Test (ANT), combining a cue-target and a flanker Page 1 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 test to obtain measures of the efficiency and accuracy of far as we know, no study has generated error and variabil- the three networks. Recent studies have used different ver- ity measures from ANT in a study of children with ADHD. sions of ANT to study cognitive characteristics associated with ADHD. Booth [9] used the original child version and The aim of the present study was to find characteristic pat- found no differences between children with ADHD and terns of ANT results in children with ADHD by including control children on any of the three networks. An Event measures of error types and variability in addition to the Related Potential (ERP) study by Rodriguez [10] demon- conventional measures of the three attention networks strated a deviant ERP activation pattern on the alerting (Table 1). From earlier studies we assumed that the con- and conflict networks in young adults with the DSM-IV flict network was affected in children with ADHD. Fur- defined inattentive subtype of ADHD. A deviant activa- thermore, we expected the extended measures to add tion pattern was also found in a Functional magnetic res- information about behavior characteristics of the ADHD onance imaging (fMRI) study by Konrad and colleagues group, i.e. we expected to find lower accuracy scores and [11]. This affected all three networks, but the behavioral higher response variability in children with ADHD than data showed that only the conflict network was less effi- in their non-ADHD peers. cient in ADHD children than in control children. These results suggest that the neural basis of the attentional net- Methods works may be affected in children with ADHD, even when Participants this is not reflected in behavior measures. The present study is part of the Bergen Child Study (BCS). The protocol and population of the stages in the first wave Most studies using ANT have focused on the RT measures of BCS are described in detail in separate publications of the three attention networks, even though studies using [19,20], and only a short presentation will be given here. measures from continuous performance tasks have shown Briefly, the original BCS included three stages: screening that accuracy measures are more affected than RT meas- for behavior problems and psychiatric disorder of the ures in children with ADHD [12,13]. Furthermore, chil- whole Bergen 7-9-year-old population using the Strengths dren with ADHD are shown to be impaired on measures and Difficulties Questionnaire (SDQ) [21], the Autism of sustained attention and vigilance [12,14,15], they show Spectrum Screening Questionnaire (ASSQ) [22], and a more variable RT and report more errors of omissions items pertaining DSM-IV symptoms of ADHD and ODD and commissions than their non-ADHD peers [16,17]. from Swanson, Nolan, and Pelham, version IV (SNAP-IV) Most studies reporting such findings have used the Con- [23], supplemented with a number of items designed spe- tinuous Performance Test (CPT). A recent study by Ober- cifically for use in the BCS (stage 1); Development and lin et al. [18] included variability and error measures in Well-Being Assessment (DAWBA) [24] interviews with their analysis of ANT results. They found that these meas- parents of children defined as screen positive in stage 1 ures discriminated adults with ADHD from controls. As and a sample of screen negative children (stage 2), and in- depth neuropsychiatric/neuropsychological assessment of Table 1: Definition of variables Variable Definition Reaction time Mean reaction time (RT) for each cue and flanker condition Hits Number of correct responses Overall errors Number of overall errors Wrong responses All other error responses than omissions, perservations and outliers Omissions RT = 0 ms. Perservations RT > 0 ms. < 100 ms. Outliers Flanker × Cue RT - Stdev < 3*Stdev. RT = 0 can not be both omission and outlier and priority was given to omissions Hits RT Median RT for correct responses Hits RT SE Standard error of RT for correct responses. Measure of consistency of responses Variability of SE Standard deviation of the 3 standard error values calculated for each block. Measures within respondent variability Hits RT block change The slope of change in RT between blocks. Measure of vigilance Hits SE block change The slope of change in standard error of RT between blocks. Measure of consistency and vigilance Attention Networks Calculated both for RT and errors: Alerting = RT/error for no cue - RT/error for double cue Orienting = RT/error for central cue - RT/error for orienting cue Conflict: = RT/error for incongruent flanker - RT/error for congruent flanker Page 2 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 a subsample of "DAWBA positive" (i.e. children who Oppositional Defiant Disorder (ODD) without ADHD (N obtained a diagnosis according to DAWBA) and "DAWBA = 11) were excluded. The last group was excluded due to negative" children from stage 2. Children with a "chronic the overlap in symptomatology. The final sample (N = somatic disorder" reported by parents in stage 1 of the 157) included all children with a definite ADHD diagno- study [25] were included, regardless of participation in sis (N = 45), children with other definite diagnoses (N = stage 2. 55), and children without any diagnosis or ADHD symp- toms (N = 57) (Figure 1). The ADHD group was further The aim of stage 3 was to investigate the neuropsycholog- divided into a group of children taking central stimulants ical function (motor, emotional and cognitive) of chil- at the inclusion of the study (N = 9) and children not tak- dren with known mental health problems and normal ing any central stimulants (N = 36). The last group controls. A total of 329 children met together with their included mainly newly diagnosed children. All children parents for a 6 hours examination procedure at the Neu- taking central stimulants had an ADHD diagnosis at the ropsychology outpatient clinic at the University of Bergen. entry of the study, and their parents were asked to with- The examination included a diagnostic semi-structured hold the medication on the day of clinical examination. interview of parent and child (Kiddie-Sads-Present and Because of the low number of girls in the ADHD group (N Lifetime Version) [26]; the Wechsler's Intelligence Scale = 13), gender differences were not investigated in the for Children, third version (WISC-III) [27], and the AANT present study. [28]. Of the 286 children who completed the ANT with an accuracy scores above 50%, all children with a diagnosis The study was approved by the Regional Committee of in remission (N = 8) and the group of children with Ethics on Medical Research in Western Norway and by the F Figure 1 low chart visualizing the selection procedure Flow chart visualizing the selection procedure. Page 3 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 Ombudsman for Privacy in Research, Norwegian Social Statistical analysis Science Data Services Ltd. A Pearson correlation analysis with Bonferroni correction was computed, including the FSIQ score, age, and ANT Measures measures. For FSIQ score, age, network measures, error Kiddie-Sads-Present and Lifetime Version (Kiddie-Sads-PL) measures, RT, standard error (SE) of RT, variability of SE, [26] was used as a diagnostic instrument. Kiddie-Sads-PL and vigilance measures, separate one-way between-groups is a reliable semi-structured interview designed to evaluate ANOVAs were calculated. Main effects were further current and past episodes of psychopathology in children explored with post-hoc tests. The Tukey HSD was used to according to the DSM-IV criteria [26,29]. The diagnosis explore differences between groups where equivalence of was ascertained through an interview in two separate ses- variances were assumed, and the Games-Howell test was sions on the same day, first with one or both of the par- used if variances were heterogeneous. Bonferroni cor- ents and then with the child. Diagnoses were scored by the rected independent-samples t-test was used to compare interviewer immediately after the assessment of both the results of children in the ADHD group who regularly informants as either definite, probable (≥75% of symp- took central stimulants and the results of the non-medi- tom criteria met), in remission, or not present [26]. cated children in the same diagnostic group. The ANT used in the present study is the original "child Results version" [30] downloaded from Jin Fan's webpage [28]. Age and FSIQ scores The test has four cue conditions (no cue, center, double, The age range of the participants was 7.9 to 11.9 years, orienting) and three flanker conditions (congruent, with a mean age of 10. A one-way ANOVA showed a sta- incongruent, neutral), and has been described in detail tistically significant difference between the three diagnos- elsewhere [30,31]. All combinations of conditions are tic groups in age, F(2, 154) = 3.311, p = .039, but Tukey randomly presented in three blocks of 48 trials each. HSD between group comparison revealed that the differ- Overview of the calculations used in the present study is ence between the ADHD group and the other groups only given in Table 1. The calculated measures are based on an bordered on significance (other diagnoses: p = .07; with- Excel macro downloaded from Jin Fans webpage [28], out any diagnosis: p = .06). The mean FSIQ score for all supplemented by measures calculated according to the participants was 90.9 (Table 2). A one-way ANOVA formulas given by Conners and collaborators [32]. This revealed a statistically significant difference between the file was imported into SPSS 13.0, which was used for all groups on the FSIQ measure, and a Games-Howell post- further analyses. hoc test showed that the ADHD group obtained signifi- cantly lower FSIQ scores than the non-ADHD groups WISC-III [27] was used to assess intellectual function. The (Table 2 and 3). Full Scale IQ (FSIQ) was included in the present study, with scaled scores derived from Swedish norms [33]. Attention Networks A one-way between groups ANOVA revealed no statisti- Procedure cally significant differences between the three diagnostic The clinical examination was performed in an outpatient groups on the RT and error measures of the three attention clinic at the University of Bergen. Trained psychologists networks (Table 2 and 3). administered the Kiddie-Sads-PL interview. The WISC-III and the ANT were administered by trained test-assistants, Error measures in a quiet room designed for testing. It was run on E-Prime The overall accuracy on the ANT was 90.7%, and an anal- software, on a stationary computer with a 17" computer ysis of the overall number of errors showed that 97.0% of screen. The children sat at a comfortable distance from the all errors were wrong responses and omissions (Table 2). screen and used left and right thumb to press the left or Outliers and perservations represented only 3.0% of all the right mouse button, corresponding to a left and right errors and less than 1% of all responses, and were not pointing fish. The children were instructed to help feed included in further analyses. A one-way between groups the hungry fish as fast as they could by pushing the left or ANOVA of the remaining error measures showed a statis- right button, according to which direction the fish was tically significant main effect of group (Table 3). A Games- pointing. They were told that sometimes the fish would Howell post-hoc test revealed statistically significant appear alone, and other times it would swim together higher overall error scores in the ADHD group than the with other fishes. In all cases, they were told to concen- non-ADHD groups, with significantly more wrong trate on the fish in the middle. They were also asked to responses than the non-ADHD groups (Table 2 and 3). keep their eyes on the fixation point during the presenta- The number of omission errors was also higher in the tions. The completion time was approximately 25 min- ADHD group than in the non-ADHD groups, but the dif- utes. ferences were not statistically significant (compared to the Page 4 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 Table 2: Means and SDs of the ADHD and non-ADHD groups on demographic variables and selected ANT variables. ADHD definite diagnosis N = 45 Other definite Diagnoses N = 55 Without diagnosis N = 57 Overall N = 157 RT/Error M (SD) RT/Error M (SD) RT/Error M (SD) RT/Error M (SD) FSIQ and age FSIQ 78,7 (17,6) 93,6 (15,2) 97,8 (11,5) 90,9 (16,7) Age 10,3 (0,8) 9,9 (1,0) 9,9 (0,9) 10,0 (0,9) Accuracy and error variables Hits 125,1 (16,7) 132,4 (8,6) 133,1 (9,7) 130,6 (12,2) Overall errors 18,9 (16,7) 11,6 (8,6) 10,9 (9,7) 13,4 (12,2) Wrong response 5,4 (4,5) 3,3 (3,3) 3,2 (3,3) 3,9 (3,8) Omissions 12,9 (14,6) 7,9 (7,1) 7,3 (7,9) 9,1 (10,3) Perservations 0,2 (0,5) 0,1 (0,3) 0,1 (0,2) 0,1 (0,4) Outliers 0,3 (0,6) 0,3 (0,5) 0,3 (0,8) 0,3 (0,7) Consistency and variability variables Hits RT 828,3 (148,4) 791,9 (123,7) 791,8 (124,7) 802,3 (131,8) Hit RT SE 22,5 (5,6) 20,2 (4,1) 19,7 (5,0) 20,7 (5,0) Variability of SE 5,2 (2,9) 5,4 (2,4) 5,3 (3,8) 5,3 (3,1) Hit RT Block Change -3,1 (48,9) -27,8 (52,7) -13,9 (66,7) -15,7 (57,7) Hit SE Block Change 0,3 (4,9) -0,1 (4,9) 0,5 (5,4) 0,2 (5,1) Networks – RT Alerting 109,2 (81,6) 101,0 (63,6) 80,8 (74,4) 96,0 (73,5) Orienting 27,7 (60,6) 37,5 (59,3) 32,0 (58,7) 32,7 (59,2) Conflict 92,2 (85,6) 81,0 (60,2) 87,3 (56,1) 86,5 (66,9) Networks – Errors % Alerting 2,6 (9,9) 2,4 (5,1) 3,3 (6,6) 2,8 (7,3) Orienting 0,3 (7,0) 0,3 (5,9) 0,2 (5,7) 0,3 (6,1) Conflict 4,6 (11,2) 4,4 (7,4) 3,4 (6,1) 4,1 (8,2) group with other diagnoses, p = .09 and the group without variability measures. Table 4 shows that this only applied any diagnosis, p = .06). to the ADHD group, showing that the lower the FSIQ scores, the less accurate the children responded and with Variability measures less consistency. A one-way between groups ANOVA was performed sepa- FSIQ score as covariate rately for the overall Hit RT and the variability measures (Hit RT SE, variability of SE, Hit RT block change, Hit SE When FSIQ was included as a covariate in the ANOVA block change). A statistically significant main effect of analyses, the main effect for group was no longer statisti- group was found on the Hit RT SE measure (Table 2 and cally significant for the overall error measure (F(2, 153) = 3). Between group Tukey HSD comparisons revealed that 0,689, p = .504) and the Hit RT SE measure (F(2, 153) = the ADHD group showed significantly larger Hits RT SE 0,316, p = .729). than the group without diagnosis and border on signifi- cance when compared to the group with other diagnoses Influence of medication (p = .054). No main effect of group was found on the The group of ADHD children taking central stimulants other measures. regularly (N = 9) was compared to the other ADHD chil- dren (N = 36) on selected ANT measures (overall errors, Age and FSIQ scores correlation Hits RT, Hits RT SE) and FSIQ scores. Bonferroni corrected A Bonferroni corrected Pearson's correlation analysis was independent-samples t-test showed no statistically signif- performed between FSIQ scores, age and ANT measures icant difference between the two groups. (Table 4). The analysis revealed a high correlation between age and the overall Hit RT and the variability Discussion measures, showing that the older the children, the faster The aim of the present study was to characterize patterns they responded, with a higher level of consistency. Table of ANT performance in children with an ADHD diagnosis. 4 shows that this only applied to the two non-ADHD On the measures of the three attention networks, the groups. Correlations between FSIQ scores and the ANT results revealed no statistically significant differences measures were more widespread, including both error and between the group of children with ADHD, the group of Page 5 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 Table 3: A one-way ANOVA showing the difference between the wrong responses and showed a trend towards more omis- groups on selected ANT variables. sions errors compared to the non-ADHD groups. The high number of omission errors in the ADHD group indicated Main effect of between groups ANOVA a higher level of inattention than in children belonging to the two other groups, a finding that confirms earlier find- Df F Between ings in studies using the Conners' CPT [16,17]. The results Covariate did also support the prediction of high response variabil- FSIQ 2, 154 22.66*** 1 < 2,3 ity in the ADHD group. However, the variability of SE was Accuracy and error not different between the three groups. According to Con- variables ners [32], a pattern of higher Hits SE RT than Variability Overall errors 2, 154 6.77** 1 > 2,3 SE suggests a poor consistency of responses that did not Wrong respons 2, 154 5.58** 1 > 2,3 change as the test progressed. This supports the idea that Omissions 2, 154 4.54* ns problems related to inattention rather than vigilance are Consistency and variability variables characteristic of children with ADHD. Hits RT 2, 154 1.23 ns Hit RT SE 2, 154 4.41** 1 > 3 Our findings with respect to vigilance are in conflict with Variability of SE 2, 154 .05 ns results from studies using CPT, suggesting that loss of vig- Hit RT Block Change 2, 154 2.34 ns ilance as the test progresses is characteristic of children Hit SE Block Change 2, 154 .22 ns with an ADHD diagnosis [12,14,15]. It is well known that Networks Alerting – RT 2, 154 2.09 ns children perform better on tasks with a vivid feedback and Orienting – RT 2, 154 .34 ns on tasks that have an underlying story [30]. The child ver- Conflict – RT 2, 154 .35 ns sion of the ANT is more similar to a computer-game with Alerting – Error 2, 154 .21 ns immediate and clear feedback on performance than the Orienting – Error 2, 154 .01 ns Conners' CPT. The ANT includes a character (the fish), a Conflict – Error 2, 154 .34 ns narrative (is hungry, help feed him), and auditory and vis- ual feedback (fish blowing bubbles and wagging its tail as * = p < .05 ** = p < .01 *** = p < .001 Group: 1 = ADHD; 2 = Other diagnoses; 3 = No diagnosis well as exciting sound), and these features have been found to improve the performance on more game-like children with other psychiatric diagnoses and the group of versions of the CPT [34]. This and the fact that the ANT children without any diagnosis. However, the children has just three time blocks compared to six in the CPT, may diagnosed with ADHD showed a lower accuracy score as have made the vigilance measure less sensitive to a core well as a more variable response pattern (i.e. a higher SE problem of children with ADHD [12,14-16]. of RT) than the other groups. The FSIQ score was strongly correlated with both error The results confirmed the expectation of lower accuracy and variability measures, but only in the ADHD group. scores in the ADHD group compared to what was shown When included as a covariate, all differences between the in the other two groups. The ADHD group reported more ADHD group and the two other groups became non-sig- Table 4: Correlations between selected ANT variables within the three groups and for all participants. ADHD definite diagnosis N = 45 Other definite Diagnoses N = 55 Without diagnosis N = 57 Overall N = 157 Age FSIQ score Age FSIQ score Age FSIQ score Age FSIQ score Accuracy and error variables Overall errors -.167 -.509** -.298 -.255 -.314 -.294 -.164 -.455** Wrong response -.031 -.497** -.111 -.149 -.264 -.152 -.073 -.373** Omissions -.191 -.408* -.315 -.236 -.274 -.277 -.174 -.388** Consistency and variability variables Hits RT -.164 -.309 -.469** -.101 -.411** -.183 -.320** -.235* Hit RT SE -.094 -.472** -.321 -.239 -.235 -.207 -.159 -.381** Variability of SE .051 -.084 .181 -.054 .066 -.036 .089 -.041 Hit RT Block Change .083 .069 .287 -.176 .415** .098 .312** -.058 Hit SE Block Change .034 -.108 .119 -.240 .299 .009 .164 -.100 * = Correlation is significant at the 0.05 level 2 tailed ** = Correlation is significant at the 0.01 level 2 tailed Page 6 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 nificant. There is an ongoing debate whether or not one detect this difference [11]. However, the high number of should control for IQ in studies of cognitive function in errors reported by children with ADHD in the present children with ADHD [15,35-37]. Several studies have study may be used to support the idea of a less effective shown that children with ADHD tend to obtain lower IQ use of strategies in children with ADHD than in their non- scores than other children [35,37], and that the neurocog- ADHD peers. nitive disorders of ADHD in itself can cause poor perform- Strengths and weaknesses ance on intelligence tests [38]. Actually, a meta-analysis found a strong association between ADHD and FSIQ (d = As in all research including children with an ADHD diag- .61) [35]. This is supported by the results in the present nosis, the present results are colored by the high degree of study, showing that the highest and most widespread cor- heterogeneity within the diagnostic group. In the present relations were found in the ADHD group. If reduced IQ is study, information about subgroups of ADHD and symp- a developmental consequence of the ADHD disorder, tom load was not included. Both Booth [9] and Rodriguez then, by controlling for IQ, one may very well control for [10] found a difference between the DSM-IV defined diag- a part of the disorder [35,36]. This has led Barkley [38,39] nostic subgroups of ADHD on the network measures. On to argue that it is probably unwise to control for IQ score the other hand, Seidman [36] argues that there are more in studies comparing ADHD groups and controls, and similarities between the subgroups of ADHD than dissim- that studies of ADHD should rather report results with ilarities when it comes to measures of cognitive functions. and without controlling for IQ scores [40], as done in the We have calculated the within response variability accord- present study. ing to Conners [32]. According to Russell et al. [41], more extended calculations may give more adequate measures While the IQ scores showed the strongest associations of variability and should be considered in further studies. with errors, age was more strongly associated with the Hit RT and variability measures. Age was correlated with faster The main strength of the study was the case-control overall RT in the non-ADHD groups, confirming earlier selected sample of children with ADHD, and that the findings that RT improves with age [30,31]. However, age results probably are less biased by co-morbid problems was not significantly correlated to any of the dependent than clinical studies. However, no child was excluded due ANT measures in the ADHD group, suggesting that chil- to a low FSIQ score, although some of the BCS partici- dren with ADHD do not show the expected improvement pants with very low FSIQ score were excluded because of RTs as they age. The results revealed no significant they were unable to perform the ANT. The high correla- group differences on the efficiency and error measures of tions between the FSIQ score and error and variability the three attention networks. These results are in accord- measures in the ADHD group indicate that by excluding ance with Booth's [9] findings in a study using the same children with low total IQ scores, one may have excluded child version of the ANT as in the present study. However, a specific group of ADHD children [39]. the results did not support the findings of Konrad et al. Clinical implications [11], who showed a significant deficit in the efficiency of the conflict network. One explanation may be that they Although there have been several studies of the neuropsy- used a modified ANT procedure. Rueda et al. [30] found chological characteristics and the neural basis of ADHD, that the fish target used in the present study as well as a the deficits of attention in children with this behavioral paradigm including only valid cues generates a smaller diagnosis are still poorly understood. To conduct studies interference effect of incongruent flankers than the arrow of this complex issue, appropriate neurocognitive models target. This implies that the paradigm used in the present that operationalize different aspects of the attention sys- study may have made it easier for the children to solve the tem are necessary. The attention network theory provides conflict between the congruent and incongruent flankers one such model and can be used both in group studies than in the study of Konrad and collaborators [11], and and in the clinical evaluation of individual children. In a may indicate a need for revision of the child version of the neuropsychological examination, the range of variables ANT in future studies of children with an ADHD diagno- from the ANT may help to characterize the strengths and sis. difficulties of a child. Studies of the attention networks in children with ADHD may contribute to a better under- Based on the behavioral measures of the attention net- standing of the disorder and to the development of appro- works, one should not exclude the possibility of a charac- priatetraining and treatment methods [42]. teristic neural activity in children with ADHD, as suggested by Rodriguez [10] and Konrad et al. [11]. From Conclusion these studies one may argue that children with ADHD use The results in the present study support the notion that different strategies for completing tasks than their peers, accuracy measures rather than RT measures are sensitive and that behavioral measures are not sensitive enough to to characteristic deficits in children with ADHD [12,13]. Page 7 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 tion deficit/hyperactivity disorder: evidence from an event- The results also demonstrate the importance of including related functional magnetic resonance imaging study. Biol accuracy measures and variability measures to extend the Psychiatry 2006, 59:643-651. sensitivity of the ANT to deficits that characterize children 12. Stins JF, Tollenaar MS, Slaats-Willemse DI, Buitelaar JK, Swaab-Barn- eveld H, Verhulst FC, Polderman TC, Boomsma DI: Sustained with an ADHD diagnosis. Nevertheless, there is a need of attention and executive functioning performance in atten- developing the test measures, and to perform studies tion-deficit/hyperactivity disorder. Child Neuropsychol 2005, 11:285-294. investigating the clinical significance of the errors and var- 13. Wilding J: Is attention impaired in ADHD? Brit J Dev Psychol 2005, iability shown by children with ADHD. 23:487. 14. Swaab-Barneveld H, de Sonneville L, Cohen-Kettenis P, Gielen A, Buitelaar J, Van Engeland H: Visual sustained attention in a child Competing interests psychiatric population. J Am Acad Child Adolesc Psychiatry 2000, The author(s) declare that they have no competing inter- 39:651-659. ests. 15. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF: Validity of the executive function theory of attention-deficit/hyperac- tivity disorder: a meta-analytic review. Biol Psychiatry 2005, Authors' contributions 57:1336-1346. 16. Epstein JN, Erkanli A, Conners CK, Klaric J, Costello JE, Angold A: SA has been responsible for the data analysis and the writ- Relations Between Continuous Performance Test Perform- ing of the manuscript. AJL designed and coordinated the ance Measures and ADHD Behaviors. J Abnorm Child Psychol study, supervised the data analysis and the writing proc- 2003, 31:543. 17. Losier BJ, McGrath PJ, Klein RM: Error patterns on the continu- ess. LS participated in the acquisition of data, discussions ous performance test in non-medicated and medicated sam- about the data analyses and commented on the written ples of children with and without ADHD: a meta-analytic review. J Child Psychol Psychiatry 1996, 37:971-987. drafts of the manuscript. All authors have read and 18. Oberlin BG, Alford JL, Marrocco RT: Normal attention orienting approved the final manuscript. but abnormal stimulus alerting and conflict effect in com- bined subtype of ADHD. Behav Brain Res 2005, 165:1-11. 19. Posserud MB, Lundervold AJ, Gillberg C: Autistic features in a List of abbreviations total population of 7-9-year-old children assessed by the ADHD: Attention Deficit Hyperactivity Disorder; ANT: ASSQ (Autism Spectrum Screening Questionnaire). J Child Attention Network Test; BCS: Bergen Child Study; CPT: Psychol Psychiatry 2006, 47:167-175. 20. Heiervang E, Stormark KM, Lundervold AJ, Heimann M, Goodman R, Continuous Performance Test; FSIQ: Full Scale IQ; ODD: Posserud MB, Ullebo AK, Plessen KJ, Bjelland I, Lie SA, Gillberg C: Oppositional Defiant Disorder; RT: Reaction time; Kid- Psychiatric disorders in Norwegian 8- to 10-year-olds: an epi- demiological survey of prevalence, risk factors, and service die-Sads-PL: Kiddie-Sads-Present and Lifetime Version; use. J Am Acad Child Adolesc Psychiatry 2007, 46:438-447. WISC-III: Wechsler Intelligence Scale for Children-III. 21. Goodman R: The extended version of the Strengths and Diffi- culties Questionnaire as a guide to child psychiatric caseness and consequent burden. J Child Psychol Psychiatry 1999, Acknowledgements 40:791-799. The present study was supported by the University of Bergen, the Norwe- 22. Ehlers S, Gillberg C, Wing L: A screening questionnaire for gian Directorate for Health and Social Affairs, and the Western Norway Asperger syndrome and other high-functioning autism spec- Regional Health Authority. We are grateful to the children, parents and trum disorders in school age children. J Autism Dev Disord 1999, 29:129-141. teachers who participated in the BCS, and to the BCS project group for 23. Swanson JM, Kraemer HC, Hinshaw SP, Arnold LE, Conners CK, making the study possible. Abikoff HB, Clevenger W, Davies M, Elliott GR, Greenhill LL, et al.: Clinical relevance of the primary findings of the MTA: suc- cess rates based on severity of ADHD and ODD symptoms References at the end of treatment. J Am Acad Child Adolesc Psychiatry 2001, 1. Posner MI, Petersen SE: The attention system of the human 40:168-179. brain. Annu Rev Neurosci 1990, 13:25-42. 24. Goodman R, Ford T, Richards H, Gatward R, Meltzer H: The Devel- 2. Callejas A, Lupianez J, Tudela P: The three attentional networks: opment and Well-Being Assessment: description and initial on their independence and interactions. Brain Cogn 2004, validation of an integrated assessment of child and adoles- 54:225-227. cent psychopathology. J Child Psychol Psychiatry 2000, 41:645-655. 3. Posner MI, Raichle ME: Images of mind New York: Scientific American 25. Hysing M, Elgen I, Gillberg C, Lie SA, Lundervold AJ: Chronic phys- Library/Scientific American Books; 1994. ical illness and mental health in children. Results from a 4. Fan J, McCandliss BD, Fossella J, Flombaum JI, Posner MI: The acti- large-scale population study. J Child Psychol Psychiatry 2007, vation of attentional networks. Neuroimage 2005, 26:471-479. 48:785-792. 5. Berger A, Posner MI: Pathologies of brain attentional networks. 26. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, William- Neurosci Biobehav Rev 2000, 24:3-5. son D, Ryan N: Schedule for Affective Disorders and Schizo- 6. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI: Testing the phrenia for School-Age Children-Present and Lifetime efficiency and independence of attentional networks. J Cogn Version (K-SADS-PL): initial reliability and validity data. J Am Neurosci 2002, 14:340-347. Acad Child Adolesc Psychiatry 1997, 36:980-988. 7. Posner MI: Orienting of attention. Q J Exp Psychol 1980, 32:3-25. 27. Wechsler D: Wechsler Intelligence Scale for Children – III (Norwegian ver- 8. Eriksen BA, Eriksen CW: Effects of noise letters upon the iden- sion) Stockholm: Harcourt Assessment; 2003. tification of a target letter in a nonsearch task. Percept Psycho- 28. Fan J: The Attention Network Test. Child edition 2001. phys 1974, 16:143. 29. Ambrosini PJ: Historical development and present status of 9. Booth JE, Carlson CL: Cognitive processes of inattention in the schedule for affective disorders and schizophrenia for attention deficit hyperactivity disorder subtypes. The Univer- school-age children (K-SADS). J Am Acad Child Adolesc Psychiatry sity of Texas at Austin 2003. 2000, 39:49-58. 10. Rodriguez PD: Attention networks in attention-deficit/hyper- 30. Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, activity disorder subtypes: An event-related potentials Posner MI: Development of attentional networks in child- study. University of South Carolina, Department of Psychology 2005. hood. Neuropsychologia 2004, 42:1029-1040. 11. Konrad K, Neufang S, Hanisch C, Fink GR, Herpertz-Dahlmann B: Dysfunctional attentional networks in children with atten- Page 8 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 31. Mezzacappa E: Alerting, orienting, and executive attention: developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Dev 2004, 75:1373-1386. 32. Conners CK: Conners' Continuous Performance Test (CPT II). Version 5 for Windows. Technical Guide and Software Manual Toronto: Multi- Health Systems Inc; 2000. 33. Wechsler D: Manual for Wechsler Intelligence Scale for Children (Swedish version) 3rd edition. Stockholm: Psykologiforlaget; 1999. 34. Shaw RM: The effect of context on the performance of chil- dren with ADHD on a series of computerised tasks and games [abstract]. The Open University 2004. 35. Frazier TW, Demaree HA, Youngstrom EA: Meta-analysis of intel- lectual and neuropsychological test performance in atten- tion-deficit/hyperactivity disorder. Neuropsychology 2004, 18:543-555. 36. Seidman LJ: Neuropsychological functioning in people with ADHD across the lifespan. Clin Psychol Rev 2006, 26:466-485. 37. Schwean VL, Saklofske DH: WISC-III assessment of children with attention deficit/hyperactivity disorder. In WISC-III clinical use and interpretation: Scientist-practitioner perspectives Edited by: Prifit- era A, Saklofske DH. San Diego: Academic Press; 1998:91-118. 38. Barkley RA: Associated Problems. In Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment 2nd edition. Edited by: Barkley RA. New York: Guilford Press; 1998:97-138. 39. Barkley RA: Symptoms, Diagnosis, Prevalence, and Gender differences. In Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment 2nd edition. Edited by: Barkley RA. New York: Guilford Press; 1998:56-96. 40. Barkley RA: ADHD and the nature of self-control New York: Guilford Press; 1997. 41. Russell VA, Oades RD, Tannock R, Killeen PR, Auerbach JG, Johansen EB, Sagvolden T: Response variability in Attention-Deficit/ Hyperactivity Disorder: a neuronal and glial energetics hypothesis. Behav Brain Funct 2006, 2:30. 42. Posner MI: Progress in Attention Research. In Cognitive neuro- science of attention Edited by: Posner MI. New York: Guilford Press; 2004:3-9. Publish with Bio Med Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." 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The attention network test: a characteristic pattern of deficits in children with ADHD

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Springer Journals
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Copyright © 2008 by Adólfsdóttir et al; licensee BioMed Central Ltd.
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Biomedicine; Neurosciences; Neurology; Behavioral Therapy; Psychiatry
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1744-9081
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10.1186/1744-9081-4-9
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18269768
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Abstract

Background: The Attention Network test (ANT) gives measures of different aspects of the complex process of attention. We ask if children with Attention Deficit Hyperactivity Disorder (ADHD) will show a characteristic pattern of deficits on this test. Methods: The sample included 157 children (M = 10 years) who performed the child version of ANT as participants of the Bergen Child Study. Children with an ADHD diagnosis (N = 45) were compared to a group of children with other diagnoses (N = 55) and a group of children without any diagnosis (N = 57). Results: The group of children with ADHD showed low accuracy scores and a variable response set, indicating an inattentive response style. No differences were found between the groups on RT and accuracy measures of the alerting, orienting, and conflict networks. A high correlation between full scale IQ (FSIQ) and ANT measures was only found in the ADHD group. When FSIQ score was included as a covariate, the group differences were not statistically significant on any ANT measure. Conclusion: The present study showed that accuracy and variability measures rather than measures of the three attention networks conveyed the characteristic pattern of deficits in children with ADHD. The results emphasized the importance of including these measures to extend the sensitivity of the ANT, and the importance of reporting results both with and without FSIQ as a covariate. attention network model is of special interest in studies of Background Attention is a complex cognitive function, dependent on attentional disorders, e.g. the Attention Deficit Hyperac- interacting neural systems of the brain. According to the tivity Disorder (ADHD). Attention Network theory the systems can be subdivided into an alerting or vigilance network, a network of orien- The three networks have been widely explored by using tation or selection, and an executive or conflict network cue-target reaction time (RT) tasks [7] and tasks evoking a [1]. A range of experimental, neuroimaging, and clinical conflict (e.g. [8]). Recently, Fan, Posner and collaborators studies have supported the theory [2-4] and Berger and [6] developed an experimental task called the Attention Posner [5] as well as Fan et al. [6] have argued that the Network Test (ANT), combining a cue-target and a flanker Page 1 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 test to obtain measures of the efficiency and accuracy of far as we know, no study has generated error and variabil- the three networks. Recent studies have used different ver- ity measures from ANT in a study of children with ADHD. sions of ANT to study cognitive characteristics associated with ADHD. Booth [9] used the original child version and The aim of the present study was to find characteristic pat- found no differences between children with ADHD and terns of ANT results in children with ADHD by including control children on any of the three networks. An Event measures of error types and variability in addition to the Related Potential (ERP) study by Rodriguez [10] demon- conventional measures of the three attention networks strated a deviant ERP activation pattern on the alerting (Table 1). From earlier studies we assumed that the con- and conflict networks in young adults with the DSM-IV flict network was affected in children with ADHD. Fur- defined inattentive subtype of ADHD. A deviant activa- thermore, we expected the extended measures to add tion pattern was also found in a Functional magnetic res- information about behavior characteristics of the ADHD onance imaging (fMRI) study by Konrad and colleagues group, i.e. we expected to find lower accuracy scores and [11]. This affected all three networks, but the behavioral higher response variability in children with ADHD than data showed that only the conflict network was less effi- in their non-ADHD peers. cient in ADHD children than in control children. These results suggest that the neural basis of the attentional net- Methods works may be affected in children with ADHD, even when Participants this is not reflected in behavior measures. The present study is part of the Bergen Child Study (BCS). The protocol and population of the stages in the first wave Most studies using ANT have focused on the RT measures of BCS are described in detail in separate publications of the three attention networks, even though studies using [19,20], and only a short presentation will be given here. measures from continuous performance tasks have shown Briefly, the original BCS included three stages: screening that accuracy measures are more affected than RT meas- for behavior problems and psychiatric disorder of the ures in children with ADHD [12,13]. Furthermore, chil- whole Bergen 7-9-year-old population using the Strengths dren with ADHD are shown to be impaired on measures and Difficulties Questionnaire (SDQ) [21], the Autism of sustained attention and vigilance [12,14,15], they show Spectrum Screening Questionnaire (ASSQ) [22], and a more variable RT and report more errors of omissions items pertaining DSM-IV symptoms of ADHD and ODD and commissions than their non-ADHD peers [16,17]. from Swanson, Nolan, and Pelham, version IV (SNAP-IV) Most studies reporting such findings have used the Con- [23], supplemented with a number of items designed spe- tinuous Performance Test (CPT). A recent study by Ober- cifically for use in the BCS (stage 1); Development and lin et al. [18] included variability and error measures in Well-Being Assessment (DAWBA) [24] interviews with their analysis of ANT results. They found that these meas- parents of children defined as screen positive in stage 1 ures discriminated adults with ADHD from controls. As and a sample of screen negative children (stage 2), and in- depth neuropsychiatric/neuropsychological assessment of Table 1: Definition of variables Variable Definition Reaction time Mean reaction time (RT) for each cue and flanker condition Hits Number of correct responses Overall errors Number of overall errors Wrong responses All other error responses than omissions, perservations and outliers Omissions RT = 0 ms. Perservations RT > 0 ms. < 100 ms. Outliers Flanker × Cue RT - Stdev < 3*Stdev. RT = 0 can not be both omission and outlier and priority was given to omissions Hits RT Median RT for correct responses Hits RT SE Standard error of RT for correct responses. Measure of consistency of responses Variability of SE Standard deviation of the 3 standard error values calculated for each block. Measures within respondent variability Hits RT block change The slope of change in RT between blocks. Measure of vigilance Hits SE block change The slope of change in standard error of RT between blocks. Measure of consistency and vigilance Attention Networks Calculated both for RT and errors: Alerting = RT/error for no cue - RT/error for double cue Orienting = RT/error for central cue - RT/error for orienting cue Conflict: = RT/error for incongruent flanker - RT/error for congruent flanker Page 2 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 a subsample of "DAWBA positive" (i.e. children who Oppositional Defiant Disorder (ODD) without ADHD (N obtained a diagnosis according to DAWBA) and "DAWBA = 11) were excluded. The last group was excluded due to negative" children from stage 2. Children with a "chronic the overlap in symptomatology. The final sample (N = somatic disorder" reported by parents in stage 1 of the 157) included all children with a definite ADHD diagno- study [25] were included, regardless of participation in sis (N = 45), children with other definite diagnoses (N = stage 2. 55), and children without any diagnosis or ADHD symp- toms (N = 57) (Figure 1). The ADHD group was further The aim of stage 3 was to investigate the neuropsycholog- divided into a group of children taking central stimulants ical function (motor, emotional and cognitive) of chil- at the inclusion of the study (N = 9) and children not tak- dren with known mental health problems and normal ing any central stimulants (N = 36). The last group controls. A total of 329 children met together with their included mainly newly diagnosed children. All children parents for a 6 hours examination procedure at the Neu- taking central stimulants had an ADHD diagnosis at the ropsychology outpatient clinic at the University of Bergen. entry of the study, and their parents were asked to with- The examination included a diagnostic semi-structured hold the medication on the day of clinical examination. interview of parent and child (Kiddie-Sads-Present and Because of the low number of girls in the ADHD group (N Lifetime Version) [26]; the Wechsler's Intelligence Scale = 13), gender differences were not investigated in the for Children, third version (WISC-III) [27], and the AANT present study. [28]. Of the 286 children who completed the ANT with an accuracy scores above 50%, all children with a diagnosis The study was approved by the Regional Committee of in remission (N = 8) and the group of children with Ethics on Medical Research in Western Norway and by the F Figure 1 low chart visualizing the selection procedure Flow chart visualizing the selection procedure. Page 3 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 Ombudsman for Privacy in Research, Norwegian Social Statistical analysis Science Data Services Ltd. A Pearson correlation analysis with Bonferroni correction was computed, including the FSIQ score, age, and ANT Measures measures. For FSIQ score, age, network measures, error Kiddie-Sads-Present and Lifetime Version (Kiddie-Sads-PL) measures, RT, standard error (SE) of RT, variability of SE, [26] was used as a diagnostic instrument. Kiddie-Sads-PL and vigilance measures, separate one-way between-groups is a reliable semi-structured interview designed to evaluate ANOVAs were calculated. Main effects were further current and past episodes of psychopathology in children explored with post-hoc tests. The Tukey HSD was used to according to the DSM-IV criteria [26,29]. The diagnosis explore differences between groups where equivalence of was ascertained through an interview in two separate ses- variances were assumed, and the Games-Howell test was sions on the same day, first with one or both of the par- used if variances were heterogeneous. Bonferroni cor- ents and then with the child. Diagnoses were scored by the rected independent-samples t-test was used to compare interviewer immediately after the assessment of both the results of children in the ADHD group who regularly informants as either definite, probable (≥75% of symp- took central stimulants and the results of the non-medi- tom criteria met), in remission, or not present [26]. cated children in the same diagnostic group. The ANT used in the present study is the original "child Results version" [30] downloaded from Jin Fan's webpage [28]. Age and FSIQ scores The test has four cue conditions (no cue, center, double, The age range of the participants was 7.9 to 11.9 years, orienting) and three flanker conditions (congruent, with a mean age of 10. A one-way ANOVA showed a sta- incongruent, neutral), and has been described in detail tistically significant difference between the three diagnos- elsewhere [30,31]. All combinations of conditions are tic groups in age, F(2, 154) = 3.311, p = .039, but Tukey randomly presented in three blocks of 48 trials each. HSD between group comparison revealed that the differ- Overview of the calculations used in the present study is ence between the ADHD group and the other groups only given in Table 1. The calculated measures are based on an bordered on significance (other diagnoses: p = .07; with- Excel macro downloaded from Jin Fans webpage [28], out any diagnosis: p = .06). The mean FSIQ score for all supplemented by measures calculated according to the participants was 90.9 (Table 2). A one-way ANOVA formulas given by Conners and collaborators [32]. This revealed a statistically significant difference between the file was imported into SPSS 13.0, which was used for all groups on the FSIQ measure, and a Games-Howell post- further analyses. hoc test showed that the ADHD group obtained signifi- cantly lower FSIQ scores than the non-ADHD groups WISC-III [27] was used to assess intellectual function. The (Table 2 and 3). Full Scale IQ (FSIQ) was included in the present study, with scaled scores derived from Swedish norms [33]. Attention Networks A one-way between groups ANOVA revealed no statisti- Procedure cally significant differences between the three diagnostic The clinical examination was performed in an outpatient groups on the RT and error measures of the three attention clinic at the University of Bergen. Trained psychologists networks (Table 2 and 3). administered the Kiddie-Sads-PL interview. The WISC-III and the ANT were administered by trained test-assistants, Error measures in a quiet room designed for testing. It was run on E-Prime The overall accuracy on the ANT was 90.7%, and an anal- software, on a stationary computer with a 17" computer ysis of the overall number of errors showed that 97.0% of screen. The children sat at a comfortable distance from the all errors were wrong responses and omissions (Table 2). screen and used left and right thumb to press the left or Outliers and perservations represented only 3.0% of all the right mouse button, corresponding to a left and right errors and less than 1% of all responses, and were not pointing fish. The children were instructed to help feed included in further analyses. A one-way between groups the hungry fish as fast as they could by pushing the left or ANOVA of the remaining error measures showed a statis- right button, according to which direction the fish was tically significant main effect of group (Table 3). A Games- pointing. They were told that sometimes the fish would Howell post-hoc test revealed statistically significant appear alone, and other times it would swim together higher overall error scores in the ADHD group than the with other fishes. In all cases, they were told to concen- non-ADHD groups, with significantly more wrong trate on the fish in the middle. They were also asked to responses than the non-ADHD groups (Table 2 and 3). keep their eyes on the fixation point during the presenta- The number of omission errors was also higher in the tions. The completion time was approximately 25 min- ADHD group than in the non-ADHD groups, but the dif- utes. ferences were not statistically significant (compared to the Page 4 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 Table 2: Means and SDs of the ADHD and non-ADHD groups on demographic variables and selected ANT variables. ADHD definite diagnosis N = 45 Other definite Diagnoses N = 55 Without diagnosis N = 57 Overall N = 157 RT/Error M (SD) RT/Error M (SD) RT/Error M (SD) RT/Error M (SD) FSIQ and age FSIQ 78,7 (17,6) 93,6 (15,2) 97,8 (11,5) 90,9 (16,7) Age 10,3 (0,8) 9,9 (1,0) 9,9 (0,9) 10,0 (0,9) Accuracy and error variables Hits 125,1 (16,7) 132,4 (8,6) 133,1 (9,7) 130,6 (12,2) Overall errors 18,9 (16,7) 11,6 (8,6) 10,9 (9,7) 13,4 (12,2) Wrong response 5,4 (4,5) 3,3 (3,3) 3,2 (3,3) 3,9 (3,8) Omissions 12,9 (14,6) 7,9 (7,1) 7,3 (7,9) 9,1 (10,3) Perservations 0,2 (0,5) 0,1 (0,3) 0,1 (0,2) 0,1 (0,4) Outliers 0,3 (0,6) 0,3 (0,5) 0,3 (0,8) 0,3 (0,7) Consistency and variability variables Hits RT 828,3 (148,4) 791,9 (123,7) 791,8 (124,7) 802,3 (131,8) Hit RT SE 22,5 (5,6) 20,2 (4,1) 19,7 (5,0) 20,7 (5,0) Variability of SE 5,2 (2,9) 5,4 (2,4) 5,3 (3,8) 5,3 (3,1) Hit RT Block Change -3,1 (48,9) -27,8 (52,7) -13,9 (66,7) -15,7 (57,7) Hit SE Block Change 0,3 (4,9) -0,1 (4,9) 0,5 (5,4) 0,2 (5,1) Networks – RT Alerting 109,2 (81,6) 101,0 (63,6) 80,8 (74,4) 96,0 (73,5) Orienting 27,7 (60,6) 37,5 (59,3) 32,0 (58,7) 32,7 (59,2) Conflict 92,2 (85,6) 81,0 (60,2) 87,3 (56,1) 86,5 (66,9) Networks – Errors % Alerting 2,6 (9,9) 2,4 (5,1) 3,3 (6,6) 2,8 (7,3) Orienting 0,3 (7,0) 0,3 (5,9) 0,2 (5,7) 0,3 (6,1) Conflict 4,6 (11,2) 4,4 (7,4) 3,4 (6,1) 4,1 (8,2) group with other diagnoses, p = .09 and the group without variability measures. Table 4 shows that this only applied any diagnosis, p = .06). to the ADHD group, showing that the lower the FSIQ scores, the less accurate the children responded and with Variability measures less consistency. A one-way between groups ANOVA was performed sepa- FSIQ score as covariate rately for the overall Hit RT and the variability measures (Hit RT SE, variability of SE, Hit RT block change, Hit SE When FSIQ was included as a covariate in the ANOVA block change). A statistically significant main effect of analyses, the main effect for group was no longer statisti- group was found on the Hit RT SE measure (Table 2 and cally significant for the overall error measure (F(2, 153) = 3). Between group Tukey HSD comparisons revealed that 0,689, p = .504) and the Hit RT SE measure (F(2, 153) = the ADHD group showed significantly larger Hits RT SE 0,316, p = .729). than the group without diagnosis and border on signifi- cance when compared to the group with other diagnoses Influence of medication (p = .054). No main effect of group was found on the The group of ADHD children taking central stimulants other measures. regularly (N = 9) was compared to the other ADHD chil- dren (N = 36) on selected ANT measures (overall errors, Age and FSIQ scores correlation Hits RT, Hits RT SE) and FSIQ scores. Bonferroni corrected A Bonferroni corrected Pearson's correlation analysis was independent-samples t-test showed no statistically signif- performed between FSIQ scores, age and ANT measures icant difference between the two groups. (Table 4). The analysis revealed a high correlation between age and the overall Hit RT and the variability Discussion measures, showing that the older the children, the faster The aim of the present study was to characterize patterns they responded, with a higher level of consistency. Table of ANT performance in children with an ADHD diagnosis. 4 shows that this only applied to the two non-ADHD On the measures of the three attention networks, the groups. Correlations between FSIQ scores and the ANT results revealed no statistically significant differences measures were more widespread, including both error and between the group of children with ADHD, the group of Page 5 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 Table 3: A one-way ANOVA showing the difference between the wrong responses and showed a trend towards more omis- groups on selected ANT variables. sions errors compared to the non-ADHD groups. The high number of omission errors in the ADHD group indicated Main effect of between groups ANOVA a higher level of inattention than in children belonging to the two other groups, a finding that confirms earlier find- Df F Between ings in studies using the Conners' CPT [16,17]. The results Covariate did also support the prediction of high response variabil- FSIQ 2, 154 22.66*** 1 < 2,3 ity in the ADHD group. However, the variability of SE was Accuracy and error not different between the three groups. According to Con- variables ners [32], a pattern of higher Hits SE RT than Variability Overall errors 2, 154 6.77** 1 > 2,3 SE suggests a poor consistency of responses that did not Wrong respons 2, 154 5.58** 1 > 2,3 change as the test progressed. This supports the idea that Omissions 2, 154 4.54* ns problems related to inattention rather than vigilance are Consistency and variability variables characteristic of children with ADHD. Hits RT 2, 154 1.23 ns Hit RT SE 2, 154 4.41** 1 > 3 Our findings with respect to vigilance are in conflict with Variability of SE 2, 154 .05 ns results from studies using CPT, suggesting that loss of vig- Hit RT Block Change 2, 154 2.34 ns ilance as the test progresses is characteristic of children Hit SE Block Change 2, 154 .22 ns with an ADHD diagnosis [12,14,15]. It is well known that Networks Alerting – RT 2, 154 2.09 ns children perform better on tasks with a vivid feedback and Orienting – RT 2, 154 .34 ns on tasks that have an underlying story [30]. The child ver- Conflict – RT 2, 154 .35 ns sion of the ANT is more similar to a computer-game with Alerting – Error 2, 154 .21 ns immediate and clear feedback on performance than the Orienting – Error 2, 154 .01 ns Conners' CPT. The ANT includes a character (the fish), a Conflict – Error 2, 154 .34 ns narrative (is hungry, help feed him), and auditory and vis- ual feedback (fish blowing bubbles and wagging its tail as * = p < .05 ** = p < .01 *** = p < .001 Group: 1 = ADHD; 2 = Other diagnoses; 3 = No diagnosis well as exciting sound), and these features have been found to improve the performance on more game-like children with other psychiatric diagnoses and the group of versions of the CPT [34]. This and the fact that the ANT children without any diagnosis. However, the children has just three time blocks compared to six in the CPT, may diagnosed with ADHD showed a lower accuracy score as have made the vigilance measure less sensitive to a core well as a more variable response pattern (i.e. a higher SE problem of children with ADHD [12,14-16]. of RT) than the other groups. The FSIQ score was strongly correlated with both error The results confirmed the expectation of lower accuracy and variability measures, but only in the ADHD group. scores in the ADHD group compared to what was shown When included as a covariate, all differences between the in the other two groups. The ADHD group reported more ADHD group and the two other groups became non-sig- Table 4: Correlations between selected ANT variables within the three groups and for all participants. ADHD definite diagnosis N = 45 Other definite Diagnoses N = 55 Without diagnosis N = 57 Overall N = 157 Age FSIQ score Age FSIQ score Age FSIQ score Age FSIQ score Accuracy and error variables Overall errors -.167 -.509** -.298 -.255 -.314 -.294 -.164 -.455** Wrong response -.031 -.497** -.111 -.149 -.264 -.152 -.073 -.373** Omissions -.191 -.408* -.315 -.236 -.274 -.277 -.174 -.388** Consistency and variability variables Hits RT -.164 -.309 -.469** -.101 -.411** -.183 -.320** -.235* Hit RT SE -.094 -.472** -.321 -.239 -.235 -.207 -.159 -.381** Variability of SE .051 -.084 .181 -.054 .066 -.036 .089 -.041 Hit RT Block Change .083 .069 .287 -.176 .415** .098 .312** -.058 Hit SE Block Change .034 -.108 .119 -.240 .299 .009 .164 -.100 * = Correlation is significant at the 0.05 level 2 tailed ** = Correlation is significant at the 0.01 level 2 tailed Page 6 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 nificant. There is an ongoing debate whether or not one detect this difference [11]. However, the high number of should control for IQ in studies of cognitive function in errors reported by children with ADHD in the present children with ADHD [15,35-37]. Several studies have study may be used to support the idea of a less effective shown that children with ADHD tend to obtain lower IQ use of strategies in children with ADHD than in their non- scores than other children [35,37], and that the neurocog- ADHD peers. nitive disorders of ADHD in itself can cause poor perform- Strengths and weaknesses ance on intelligence tests [38]. Actually, a meta-analysis found a strong association between ADHD and FSIQ (d = As in all research including children with an ADHD diag- .61) [35]. This is supported by the results in the present nosis, the present results are colored by the high degree of study, showing that the highest and most widespread cor- heterogeneity within the diagnostic group. In the present relations were found in the ADHD group. If reduced IQ is study, information about subgroups of ADHD and symp- a developmental consequence of the ADHD disorder, tom load was not included. Both Booth [9] and Rodriguez then, by controlling for IQ, one may very well control for [10] found a difference between the DSM-IV defined diag- a part of the disorder [35,36]. This has led Barkley [38,39] nostic subgroups of ADHD on the network measures. On to argue that it is probably unwise to control for IQ score the other hand, Seidman [36] argues that there are more in studies comparing ADHD groups and controls, and similarities between the subgroups of ADHD than dissim- that studies of ADHD should rather report results with ilarities when it comes to measures of cognitive functions. and without controlling for IQ scores [40], as done in the We have calculated the within response variability accord- present study. ing to Conners [32]. According to Russell et al. [41], more extended calculations may give more adequate measures While the IQ scores showed the strongest associations of variability and should be considered in further studies. with errors, age was more strongly associated with the Hit RT and variability measures. Age was correlated with faster The main strength of the study was the case-control overall RT in the non-ADHD groups, confirming earlier selected sample of children with ADHD, and that the findings that RT improves with age [30,31]. However, age results probably are less biased by co-morbid problems was not significantly correlated to any of the dependent than clinical studies. However, no child was excluded due ANT measures in the ADHD group, suggesting that chil- to a low FSIQ score, although some of the BCS partici- dren with ADHD do not show the expected improvement pants with very low FSIQ score were excluded because of RTs as they age. The results revealed no significant they were unable to perform the ANT. The high correla- group differences on the efficiency and error measures of tions between the FSIQ score and error and variability the three attention networks. These results are in accord- measures in the ADHD group indicate that by excluding ance with Booth's [9] findings in a study using the same children with low total IQ scores, one may have excluded child version of the ANT as in the present study. However, a specific group of ADHD children [39]. the results did not support the findings of Konrad et al. Clinical implications [11], who showed a significant deficit in the efficiency of the conflict network. One explanation may be that they Although there have been several studies of the neuropsy- used a modified ANT procedure. Rueda et al. [30] found chological characteristics and the neural basis of ADHD, that the fish target used in the present study as well as a the deficits of attention in children with this behavioral paradigm including only valid cues generates a smaller diagnosis are still poorly understood. To conduct studies interference effect of incongruent flankers than the arrow of this complex issue, appropriate neurocognitive models target. This implies that the paradigm used in the present that operationalize different aspects of the attention sys- study may have made it easier for the children to solve the tem are necessary. The attention network theory provides conflict between the congruent and incongruent flankers one such model and can be used both in group studies than in the study of Konrad and collaborators [11], and and in the clinical evaluation of individual children. In a may indicate a need for revision of the child version of the neuropsychological examination, the range of variables ANT in future studies of children with an ADHD diagno- from the ANT may help to characterize the strengths and sis. difficulties of a child. Studies of the attention networks in children with ADHD may contribute to a better under- Based on the behavioral measures of the attention net- standing of the disorder and to the development of appro- works, one should not exclude the possibility of a charac- priatetraining and treatment methods [42]. teristic neural activity in children with ADHD, as suggested by Rodriguez [10] and Konrad et al. [11]. From Conclusion these studies one may argue that children with ADHD use The results in the present study support the notion that different strategies for completing tasks than their peers, accuracy measures rather than RT measures are sensitive and that behavioral measures are not sensitive enough to to characteristic deficits in children with ADHD [12,13]. Page 7 of 9 (page number not for citation purposes) Behavioral and Brain Functions 2008, 4:9 http://www.behavioralandbrainfunctions.com/content/4/1/9 tion deficit/hyperactivity disorder: evidence from an event- The results also demonstrate the importance of including related functional magnetic resonance imaging study. Biol accuracy measures and variability measures to extend the Psychiatry 2006, 59:643-651. sensitivity of the ANT to deficits that characterize children 12. Stins JF, Tollenaar MS, Slaats-Willemse DI, Buitelaar JK, Swaab-Barn- eveld H, Verhulst FC, Polderman TC, Boomsma DI: Sustained with an ADHD diagnosis. Nevertheless, there is a need of attention and executive functioning performance in atten- developing the test measures, and to perform studies tion-deficit/hyperactivity disorder. Child Neuropsychol 2005, 11:285-294. investigating the clinical significance of the errors and var- 13. Wilding J: Is attention impaired in ADHD? Brit J Dev Psychol 2005, iability shown by children with ADHD. 23:487. 14. Swaab-Barneveld H, de Sonneville L, Cohen-Kettenis P, Gielen A, Buitelaar J, Van Engeland H: Visual sustained attention in a child Competing interests psychiatric population. J Am Acad Child Adolesc Psychiatry 2000, The author(s) declare that they have no competing inter- 39:651-659. ests. 15. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF: Validity of the executive function theory of attention-deficit/hyperac- tivity disorder: a meta-analytic review. Biol Psychiatry 2005, Authors' contributions 57:1336-1346. 16. Epstein JN, Erkanli A, Conners CK, Klaric J, Costello JE, Angold A: SA has been responsible for the data analysis and the writ- Relations Between Continuous Performance Test Perform- ing of the manuscript. AJL designed and coordinated the ance Measures and ADHD Behaviors. J Abnorm Child Psychol study, supervised the data analysis and the writing proc- 2003, 31:543. 17. Losier BJ, McGrath PJ, Klein RM: Error patterns on the continu- ess. LS participated in the acquisition of data, discussions ous performance test in non-medicated and medicated sam- about the data analyses and commented on the written ples of children with and without ADHD: a meta-analytic review. J Child Psychol Psychiatry 1996, 37:971-987. drafts of the manuscript. All authors have read and 18. Oberlin BG, Alford JL, Marrocco RT: Normal attention orienting approved the final manuscript. but abnormal stimulus alerting and conflict effect in com- bined subtype of ADHD. Behav Brain Res 2005, 165:1-11. 19. Posserud MB, Lundervold AJ, Gillberg C: Autistic features in a List of abbreviations total population of 7-9-year-old children assessed by the ADHD: Attention Deficit Hyperactivity Disorder; ANT: ASSQ (Autism Spectrum Screening Questionnaire). J Child Attention Network Test; BCS: Bergen Child Study; CPT: Psychol Psychiatry 2006, 47:167-175. 20. Heiervang E, Stormark KM, Lundervold AJ, Heimann M, Goodman R, Continuous Performance Test; FSIQ: Full Scale IQ; ODD: Posserud MB, Ullebo AK, Plessen KJ, Bjelland I, Lie SA, Gillberg C: Oppositional Defiant Disorder; RT: Reaction time; Kid- Psychiatric disorders in Norwegian 8- to 10-year-olds: an epi- demiological survey of prevalence, risk factors, and service die-Sads-PL: Kiddie-Sads-Present and Lifetime Version; use. J Am Acad Child Adolesc Psychiatry 2007, 46:438-447. WISC-III: Wechsler Intelligence Scale for Children-III. 21. Goodman R: The extended version of the Strengths and Diffi- culties Questionnaire as a guide to child psychiatric caseness and consequent burden. J Child Psychol Psychiatry 1999, Acknowledgements 40:791-799. The present study was supported by the University of Bergen, the Norwe- 22. Ehlers S, Gillberg C, Wing L: A screening questionnaire for gian Directorate for Health and Social Affairs, and the Western Norway Asperger syndrome and other high-functioning autism spec- Regional Health Authority. We are grateful to the children, parents and trum disorders in school age children. J Autism Dev Disord 1999, 29:129-141. teachers who participated in the BCS, and to the BCS project group for 23. Swanson JM, Kraemer HC, Hinshaw SP, Arnold LE, Conners CK, making the study possible. 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