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Attention Network Test in adults with ADHD - the impact of affective fluctuations

Attention Network Test in adults with ADHD - the impact of affective fluctuations Background: The Attention Network Test (ANT) generates measures of different aspects of attention/executive function. In the present study we investigated whether adults with ADHD performed different from controls on measures of accuracy, variability and vigilance as well as the control network. Secondly, we studied subgroups of adults with ADHD, expecting impairment on measures of the alerting and control networks in a subgroup with additional symptoms of affective fluctuations. Methods: A group of 114 adults (ADHD n = 58; controls n = 56) performed the ANT and completed the Adult ADHD Rating Scale (ASRS) and the Mood Disorder Questionnaire (MDQ). The latter was used to define affective fluctuations. Results: The sex distribution was similar in the two groups, but the ADHD group was significantly older (p = .005) and their score on a test of intellectual function (WASI) significantly lower than in the control group (p = .007). The two groups were not significantly different on measures of the three attention networks, but the ADHD group was generally less accurate (p = .001) and showed a higher variability through the task (p = .033). The significance was only retained for the accuracy measure when age and IQ scores were controlled for. Within the ADHD group, individuals reporting affective fluctuations (n = 22) were slower (p = .015) and obtained a lower score on the alerting network (p = .018) and a higher score on the conflict network (p = .023) than those without these symptoms. The significance was retained for the alerting network (p = .011), but not the conflict network (p = .061) when we controlled for the total ASRS and IQ scores. Discussion: Adults with ADHD were characterized by impairment on accuracy and variability measures calculated from the ANT. Within the ADHD group, adults reporting affective fluctuations seemed to be more alert (i.e., less impacted by alerting cues), but slower and more distracted by conflicting stimuli than the subgroup without such fluctuations. The results suggest that the two ADHD subgroups are characterized by distinct patterns of attentional problems, and that the symptoms assessed by MDQ contribute to the cognitive heterogeneity characterizing groups of individuals with ADHD. Background than impulsivity/hyperactivity as the main persistent Attention-deficit/hyperactivity disorder (ADHD) is a symptoms [5,6]. neuropsychiatric disorder characterized by motor rest- Neuropsychological studies have related changes in lessness and symptoms of impulsivity and inattention. neural networks involving the frontal lobe to impairment The prevalence in the child population is estimated to on tests of attention, primarily those defined within the be about 5%, and the disorder frequently persists into concept of executive function (EF) [7,8]. The importance adulthood [1-4], with symptoms of inattention rather of EF is emphasized by the fact that impairment in child- hood tends to increase into adulthood [9], is associated with severity of ADHD symptoms [10] and overall cogni- * Correspondence: astri.lundervold@psych.uib.no tive and everyday functioning [11]. However, it is well Department of Biological and Medical Psychology, University of Bergen, documented that not all individuals with ADHD show Bergen, Norway impairment on all core tests of EF [12,13], and multiple Full list of author information is available at the end of the article © 2011 Lundervold et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 2 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 pathway models have been developed to link different to impairment of EF and motivation [34-36], and that aspects of EF to neurobiology [14,15]. Furthermore, not children with ADHD and affective disorder (i.e., anxiety) all impairments associated with ADHD can be explained are cognitively distinct from and more impaired than within the concept of EF [16]. Alterations in more basic individuals in either condition on measures defined perceptual processing [17,18], activation [19] and tempo within the concept of EF [32,37]. This may be explained of information processing [20,21] are reported to influ- by the heightened arousal characterizing individuals with affective disorders [38], and that this arousal contributes ence everyday functioning of individuals with ADHD. to cognitive impairment through its effect on EF [39]. A cognitive model of ADHD should therefore describe Due to the high frequency of affective symptoms in and operationalize different levels of information proces- sing, their interactions and neurobiological substrates adults with ADHD [40], these results motivate further [15]. studies using the ANT to investigate characteristics of Posner and colleagues have presented one such model alerting and control networks. [22]. Their Attention Network Model defines an alerting Theaim of thepresent studywas twofold. First, we or vigilance network, an orientation network and an investigated ANT results in a group of adults with executive or conflict network. The alerting network ADHD and a control group from the general popula- maintains a high sensitivity to incoming stimuli, the tion. From earlier studies we expected the ADHD group orienting network is involved in selection of information to show impairment on the ANT measure of the control from sensory input, whereas the control network is network, as well as on measures of accuracy, vigilance involved in resolving conflicts between thoughts, feelings and variability. Secondly, we investigated ANT results in and responses [22]. These networks have been associated an ADHD subgroup reporting affective fluctuations. with different anatomical locations, neurotransmitter sys- From earlier studies we expected that high arousal and tems and genetic markers [23,24], and the model has EF impairment in this subgroup would influence their inspired studies of attention deficits in ADHD as well as results on the alerting and control networks. Finally, we in other neuropsychiatric disorders (see [25]). asked if the results would change when we controlled The Attention Network Model has been used to for ADHD symptoms and intellectual function. develop test paradigms that have become popular during the last decades. Fan and collaborators developed the Methods Attention Network Test (ANT) [23,26], and presented The present study included a subset of adults participat- updated versions on the Internet. The ANT has recently ing in a Norwegian national study of adults with ADHD. been used to study cognitive characteristics of individuals with ADHD. A deviant activation pattern in all three net- Participants in the national study works has been found in fMRI studies of children with The adults with ADHD were recruited from a national ADHD, but impairment was only found on the control registry of adults diagnosed with ADHD in Norway from network when the test was administrated according to 1997 to May 2005. Three national expert committees for standard procedure outside the scanner [27,28]. A recent ADHD/Hyperkinetic disorders were responsible for the Norwegian population-basedstudy could notconfirm diagnostic assessment, based on information from clinical impairment on any measure of the attention networks in records provided by the referring clinicians. The records a group of primary school children with ADHD, but a included information collected and evaluated according more detailed analysis showed that the children in gen- to the official diagnostic system in Norway, the ICD-10. eral performed less accurate and more variable through- However, allowance was made for the inattentive subtype out the task than controls [29]. These characteristics in DSM-IV to be sufficient for the diagnosis, so that the were confirmed in an ANT study of college students (age assessment also could be comparable with the DSM-IV. 18-30 yrs) with a combined type of ADHD [30]. A total of 1700 invitation letters were sent from 2005 to The impact of ADHD symptoms on cognitive function 2007, mainly targeting individuals referred after year is well documented (e.g., [20,31]). Recent studies have 2000. Adults with ADHD were also referred directly also shown that symptoms associated with affective dis- from Norwegian psychiatrists or psychologists to include orders are crucial to understand characteristics of cogni- individuals diagnosed later than May 2005. These adults tion in children [32] and adults [8] with ADHD. Murphy were assessed by specialists in clinical psychiatry or psy- and Barkley illustrated a close association between what chology according to the national guidelines based on the they referred to as emotional regulation and metacogni- criteria used by the national expert committees, but with- tion [8], and a longitudinal study emphasized the predic- out their mandatory evaluation. tive value of such symptoms on future cognitive and The control group was recruited through a random everyday functioning [33]. Other studies have shown that selection from the Norwegian population, using the data- symptoms associated with affective disorders are related base of The Medical Birth Registry of Norway (MBRN), Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 3 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 which includes all Norwegians born after January 1st [44,45]. More recent studies have been critical to this 1967. Invitation letters were sent to a randomly selected diagnostic specificity [46], and have even shown an sample of 2963 individuals who were between 18-40 overlap between MDQ and ADHD reported symptoms years old. In addition, a subsample was recruited by dif- [40,47]. We therefore decided to use the more global ferent advertisements. The control group was not term of affective fluctuations to describe the functionally screened for ADHD before entering the study. However, impairing symptoms assessed by the MDQ, and strict the prevalence in the population was expected to be low, criteria to define affective fluctuation in order to distin- as the estimate among Norwegian primary school chil- guish MDQ symptoms from ADHD symptoms. In the present study affective fluctuations equals a screen posi- dren has been reported to be only 1.7 percent [41]. All participants completed a set of questionnaires (see below tive MDQ score (MDQ+), defined as: seven or more for more details). The project was approved by the Regio- answers of “yes” on the first 13 symptom items, “yes” on nal Committee for Medical Research Ethics of Western the next question (co-occurrence of symptoms), and Norway and the Norwegian Social Science Data Services “level 3 or more” on the last question (i.e., moderate to (NSD). severe impairment caused by the reported symptoms). MDQ screen negative (MDQ-) was defined as not fulfill- Participants in the present study ing these criteria. We invited randomly selected participants from the main study, living geographically close to the city of Bergen, to Experimental procedure take part in a neuropsychological examination including The Attention Network Test (ANT) used in the present the Attention Network Test and two subtests from the study is the original standard version [26], downloaded Wechsler Abbreviated Scale of Intelligence (WASI) [42]. from the webpage of Jin Fan in 2005. In this version, the The ADHD group comprised 32 females and 26 males, participants have to decide whether an arrow points to and the control group 34 females and 22 males. Fifty- the left or right. The arrows are presented either above nine percent of the adults with ADHD used medication or below a fixation point, and may be accompanied by related to ADHD, of whom 80 percent used methylphe- flankers. The test has four cue- (no cue, center, double, nidate (Ritalin or Concerta). They were asked not to take orienting) and three flanker conditions (congruent, medication at the day of testing. incongruent, neutral). All combinations of these are randomly presented in three blocks. The calculations Intellectual function were based on an Excel macro downloaded from Jin Fans Two subtests from the WASI, the Vocabulary and Matrix webpage, supplemented by measures of reaction-time, Reasoning, were used to estimate intellectual function accuracy, vigilance and variability based on the calcula- according to the norms presented in the test-manual tions presented in the manual of the Conners’ Continu- [42]. The examination was performed at an outpatient ous Performance Test, second edition [48] (Table 1). The clinic at the University of Bergen, where an experienced error rates for the cue and flanker conditions included in technician administrated the tests. the calculations of the attention networks were very low. Symptom scales Table 1 Definitions of variables The Adult ADHD Self-Report Scale (ASRS) is a rating Variable Definition scale designed to measure current ADHD symptoms, Alerting RT for no cue - RT for double cue representing the 18 DSM-IV symptoms of ADHD. The network symptoms were rated on a 5-point scale (0 = never/seldom Orienting RT for central cue - RT for orienting cue network and 4 = very often), yielding a total score between 0 and Conflict RT for incongruent flanker - RT for congruent flanker 72. In this study we included the total ASRS score [43]. network The Mood Disorder Questionnaire (MDQ) was included Hit reaction Median RT for correct responses across the test to define a subgroup with affective fluctuations [44,45]. time The first 13 items are related to lifetime presence of hypo- Accuracy Number of correct responses manic/manic symptoms, answered yes or no, followed by Omissions Number of omissions a single yes/no question about whether the symptoms SE all blocks Standard error of RT for correct responses have been experienced at the same time. A final question Variability SE Standard deviation of the 3 standard error values evaluates the level of impairment caused by the symptoms, calculated for each block rated on a four-point scale (no problem, minor problem, RT block The slope of change in RT between blocks change moderate problem, and severe problems). SE block The slope of change in standard error of RT between TheMDQ wasoriginallydesignedand validatedasa change blocks screening instrument for bipolar spectrum disorder Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 4 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 The attention networks were therefore calculated from Table 2 Demographic variables in the control and ADHD groups the RT measures of correct responses in the present study. Controls ADHD ADHD MDQ+ ADHD MDQ- The ANT was administered in a quiet test room. It was n = 56 n = 58 n = 22 n = 36 run on E-Prime software, on a stationary computer with Sex 34F/22M 32F/26M 11F/11M 21F/15M a17” computer screen. The participants sat at a comfor- Age 29.2(7.1) 33.6(9.3) 34.2(9.3) 33.2(9.5) table distance from the screen and responses were col- IQ total score 115.3(9.6) 108.9(14.7) 103.2(14.2) 112.1(14.1) lected via two input keys on the keyboard, corresponding ASRS total 22.4(9.1) 48.7(9.3) 52.3(6.2) 46.4(10.4) to a left or right pointing arrow. Each administration was done individually with a research technician present in the room. The participants were asked to decide as quick two participants in the control group were defined as as possible the direction of the middle arrow by pushing MDQ+ (ANT results not shown). the left or right mouse button. The completion time was approximately 25 minutes. ANT results in the ADHD and the control group Table 3 shows the means and standard deviations (SD) Statistical analysis for the selected ANT variables in the control group and SPSS, version 18, was used to analyze group differences the ADHD group. between individuals with ADHD and controls. The three Attention networks network scores were included in a multivariate analysis A multivariate analysis of variance (MANOVA), includ- within the GLM package, with group as a fixed factor. ing the reaction-time measures of the three attention Univariate post-hoc tests with Tukey corrections for networks, showed a non-significant effect of group, multiple comparisons were run to investigate group differ- Wilks’ l = .965, F =1.3, p = .264. This was confirmed ences on each of the three networks. The analyses were by theunivariateanalysisfor the alerting p = .404, the repeated by including covariates, i.e., demographic vari- orienting p = .244, and the conflict network p = .165. ables that were significantly different between the groups. Reaction time and accuracy measures Group comparisons on the ANT measures of accuracy, Hit reaction time (RT) for correct responses was not sig- variability and vigilance were investigated by using sepa- nificantly different between the ADHD and the control rate univariate analyses of variance within the GLM pack- group, but the total number of hits was significantly age, including covariates in case of statistically significant lowerinthe ADHD groupthaninthe controlgroup, results. The statistical procedure was repeated within the F = 12.8, p = .001, d = .71. The difference remained sig- two ADHD subgroups, by including MDQ score as a fixed nificant after including age and intellectual function as binary factor. Effect sizes (d values) were calculated and covariates, F = 12.4, p = .001. The ADHD group com- interpreted according to general guidelines (d =0.20is mitted significantly higher number of omission errors small, d =0.50is moderate; d = 0.80 is large) [49]. than the controls, F = 13.6, p< 001, d = .68, a difference that was retained when age and intellectual function Results were included as covariates, F = 13.4, p < .001. Description of the sample Variability Participants in the ADHD group were significantly older The SE of all blocks, measuring the consistency of (mean age = 33.6/29.2 yrs, t = 2.86, p = .005, d = .53) responses across the task, was not significantly different and scored significantly lower on the test of intellectual between the two groups. The variability SE score, mea- function than the control group (mean IQ = 108.9/ suring the within respondent variability throughout the 115.3, t = 2.73, p = .007, d = .52). The two groups did task, was significantly higher in the ADHD than in the not differ in gender distribution. As expected, the total control group, F =4.2, p = .033, d =.71.Atrend ASRS score was considerably higher in the ADHD than towards statistical significance was retained when intel- in the control group (mean ASRS = 48.7/22.4, t = 15.0, lectual function was included as a covariate (p = .052), p < .001, d = 2.86) (Table 2). but not when age was added in the statistical model. Participants within the ADHD subgroups (i.e., ADHD Vigilance MDQ+ and ADHD MDQ-) did not differ with respect The RT block change, measuring the slope of change in to age (mean age = 34.2/33.2). However, the group of reaction time throughout the test, did not differ between individuals defined as MDQ+ obtained a significantly the ADHD and control group. The SE block change, mea- lower total IQ (mean IQ = 103.2/112.1, t =2.2, p =.30, suring the slope of change in standard error of reaction d = .63), and higher ASRS score (52.3/46.4, t =2.6, p = time between the three blocks, was significantly higher in .010, d = .69) than the MDQ- group (Table 2). Only the ADHD than in the control group, F = 5.0, p = .027, d Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 5 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 Table 3 ANT results in the control and ADHD groups Controls ADHD ADHD MDQ+ ADHD MDQ- n=56 n=58 n=22 n=36 Alerting network 36.0(26.2) 32.1(23.5) 22.8(28.9) 37.8(17.9) Orienting network 42.5(25.4) 36.5(29.0) 35.4(34.3) 36.7(26.3) Conflict network 128.2(50.3) 142.1(55.9) 162.9(54.1) 129.5(53.7) Hit Reaction time 556.1(70.2) 576.0(92.5) 610.4(91.1) 554(88.0)) Accuracy 267.2(13.3) 258.0(12.6) 259.3(10.7) 257.4(13.3) Ommissions 13.8(12.0) 21.0(8.8) 21.6(8.0) 20.7(9.3) SE All Blocks 117.7(31.5) 129.1(39.2) 135.4(35.5) 125.3(41.3) Variability SE 10.3(7.2) 13.5(8.7) 13.5(8.6) 13.5(9.2) RT Block Change -11.3(19.2) -7.2(23.0) -4.5(18.3) -8.8(25.6) SE Block Change 1.1(8.0) 5.1(10.5) 7.5(9.1) 3.5(11.2) = .43. The difference was still significant when intellectual change in standard error of reaction time between the function was included as covariate, F =4.4, p = .038, but three blocks, and the RT block change, measured as the not when adding age in the statistical model. slope of change in RT throughout the task, were not sig- nificantly different between the two ADHD subgroups. ADHD subgroups: MDQ+ and MDQ- The results for the selected ANT variables for the two Discussion ADHD subgroups are shown in the two right panels of The present study showed that adults with ADHD did Table 3. not differ from controls on ANT measures of the three Attention networks attention networks, but they showed a lower accuracy, a A MANOVA, including the reaction-time measures of higher intra-individual variability, and lower vigilance the three attention networks, showed a statistically signif- across the task. The effect sizes were mainly moderate, icant difference between the two MDQ-groups, Wilks’ but only the accuracy measures retained statistical sig- l = .836, F = 3.5, p = .021. Post-hoc tests showed that the nificance when we controlled for age and intellectual MDQ+ subgroup obtained an overall lower score on the function. An important and novel aspect of the present alerting network than the MDQ-subgroup, F =6.0, p = study was the inclusion of a binary MDQ score to define .018, d =.64.Onthe conflict network,the MDQ+ sub- an ADHD subgroup with affective fluctuations. In this group obtained a significantly higher score than the subgroup we found an impact on the alerting and con- MDQ- subgroup, F =5.3, p = .026, d = .62, indicating trol networks, and on the measure of hit reaction time. that the former was more distracted by incongruent flan- Thesubgroup was significantlymoredistractedbycon- kers than the latter. The group difference was non-signif- flicting stimuli and slower to respond. At the same time icant on the orienting network. their results on the measure of the alerting network sug- A MANCOVA including the total ASRS and IQ gest that they were more alert. Furthermore, the total scores as covariates was still statistically significant, ASRS and IQ scores had a major impact on the reaction Wilks’ l = .837, F =3.3, p = .027. A statistical signifi- time and the control network in this ADHD subgroup, cant difference was confirmed by the univariate analyses while the alerting network was left unaffected. for the alerting network, F =7.1, p = .011, but not for Given the original description of the test, it was some- the conflict network, p = .061. what surprising that adults with ADHD and controls did Reaction time and accuracy measures not differ on any measure of attention networks. The hit reaction time for correct responses was significantly According to findings in previous studies we expected slower in the MDQ+ than in the MDQ-subgroup, F = 5.3, to find impairment of the control network in the ADHD p =.025, d = .90. This difference was no longer statistically group [27,28,50]. Functions such as inhibition and cog- significant when the total IQ and ASRS scores were nitive flexibility are essential to solve cognitive conflicts included as covariates (p = .078). The overall number of as they are presented in the task. Dysfunction of these hits across the three blocks and the number of omissions EFs are regarded as core cognitive deficits in the daily were not significantly different between the two life of adults with ADHD [12,51,52], and are described subgroups. as essential to understand their core symptoms [8]. In Variability and vigilance the present study, EF deficits as assessed by the conflict The variability SE, measuring within respondent varia- network were only found in the ADHD subgroup bility, the SE block change, measuring the slope of defined with affective fluctuations. The clinical Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 6 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 importance of this finding is emphasized by the high The present study showed impairment in the ADHD frequency of affective symptoms among adults with group on measures of accuracy, variability and vigilance, ADHD [40], and that persistent affective symptoms tend supporting findings from earlier studies of children to contribute to impaired function in occupational and [29,55,56] and adults [30,35,57]. Impaired accuracy and academic areas of life [33]. Due to the lower intellectual vigilance remained when intellectual function (IQ) was function and higher ADHD symptom scores in the included as a covariate. Earlier studies have demon- strated a more general impact of IQ on ANT measures MDQ+ subgroup, our results may be explained by allo- in children with ADHD (e.g., [29]). The retained group cation of the individuals with the most severe ADHD to differences in the present study indicates that results on this subgroup. Although symptoms assessed by MDQ are frequently reported by individuals with ADHD [47], an IQ test are not as important for ANT results in we argue that we used a definition of the term affective adults as they are in children. However, intellectual fluctuations that indicates an add on to the core ADHD function did influence the results when ADHD symp- symptoms: the participants should answer “yes” to the toms were combined with affective symptoms, illustrat- symptoms, but also confirm that the symptoms co- ing the complex relation between core and comorbid occurred and caused moderate to severe impairment in symptoms of ADHD, EF and intellectual function. their daily life. The present study has limitations related to the diag- Arousal and alertness are known to be affected in nosis of ADHD and the definition of affective fluctua- ADHD [19], with a level that was expected to be dif- tions. The adults with ADHD included in the study ferent from the one shown by the control group. This were evaluated by several clinicians before inclusion in was only suggested by the results in the ADHD sub- the study, probably yielding a large heterogeneity within group with affective fluctuations. As anxiety symptoms the ADHD group. On the other hand, our ADHD sam- are frequently found in individuals with ADHD and ple probably represents adults with an ADHD diagnosis since anxiety symptoms are associated with high arou- as encountered in a clinical setting. Affective fluctua- sal, we speculate that individuals with such symptoms tions were assessed according to a self-report question- were mainly allocated to this ADHD subgroup. The naire, originally designed to measure symptoms of adjustment to the new setting of ANT may have made bipolar spectrum disorders. However, it has been shown them more prone to increased arousal. According to that most adults with ADHD with a positive MDQ Eysenck, an individual with anxiety and ADHD will score do not fulfill criteria for this disorder [40], and that apositiveMDQ scoremaybefoundina rangeof also be affected by a top-down influence of the EF dys- psychiatric diagnoses [47]. The conclusions of the pre- function associated with ADHD [38]. We suggest that this may explain why both the alerting and conflict sent study will therefore not be restricted to a specific networks are affected in the ADHD subgroup defined comorbidity group. It is also a limitation that the adults with affective fluctuations. Such a complex interaction with ADHD were only asked not to take medication at between the two networks has been illustrated in stu- the day of testing. Although we thus do not know if the dies by Fan and collaborators and is supported by the wash-out of the medication was complete, our results fact that the alerting and conflict networks share brain are supported by the fact that methylphenidate use was networks [53,54]. MacLeod and collaborators suggested more frequently reported in the MDQ- than the MDQ+ another difference between the two networks that may group. Finally, the study did not include biomarkers of contribute to explain the results: the executive network the attention networks. Previous studies suggest that is more trait-like and the alerting network more state- data derived from imaging techniques may be more sen- like [25]. It is tempting to assume that both trait and sitive than behavioral data collected in a standard test state have influenced the results in the ADHD sub- condition outside the scanner [27,28,50]. To extend the group with affective fluctuations. When we controlled exploration of competition between the neural sub- for the ASRS and IQ scores, probably reflecting traits strates of the alerting and conflict networks to neuroi- associated with ADHD, only the state-like alerting net- maging of the here described subgroups would therefore work was influenced by the affective fluctuations be an important asset. assessed by the binary MDQ score. However, it is important to emphasize that further studies are neces- Conclusions sary to obtain firm conclusions about alertness in The present study suggests that adults with ADHD are adults with ADHD and symptoms of affective disor- less accurate, have a higher level of variability and a ders. The interpretations of the results in the present lower vigilance than adults without ADHD, and that study should be considered with caution, not at least affective fluctuations make adults with ADHD more because the results on the alerting network are com- alert, but slower and more distracted by conflicting sti- bined with slow RT in the MDQ+ subgroup. muli. Our results indicate that the cognitive Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 7 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 7. Bush G: Attention-deficit/hyperactivity disorder and attention networks. heterogeneity among adults with ADHD at least partly is Neuropsychopharmacology 2010, 35:278-300. explained by affective symptoms. By these results the 8. Barkley RA: Differential diagnosis of adults with ADHD: the role of present study emphasizes the importance of characteriz- executive function and self-regulation. J Clin Psychiatry 2010, 71(7):e17.. 9. Lijffijt M, Kenemans JL, Verbaten MN, van Engeland H: A meta-analytic ing and taking these symptoms into account in research review of stopping performance in attention-deficit/hyperactivity and clinical work with adults with ADHD. disorder: deficient inhibitory motor control? J Abnorm Psychol 2005, 114(2):216-222. 10. Forssman L, Bohlin G, Lundervold AJ, Taanila A, Heiervang E, Loo S, Acknowledgements Jarvelin MR, Smalley S, Moilanen I, Rodriguez A: Independent contributions The data are generated from the project ADHD in adults - from molecular of cognitive functioning and social risk factors to symptoms of ADHD in mechanisms to clinical characterization, which is part of the K.G. Jebsen two nordic populations-based cohorts. Dev Neuropsychol 2009, Research Center on Neuropsychiatric Disorders. The project has received 34(6):721-735. financial support from the Norwegian Research Council of Norway, the 11. Loo SK, Humphrey LA, Tapio T, Moilanen IK, McGough JJ, McCracken JT, Regional Health Authority of Western Norway and K.G. Jebsen Foundation to Yang MH, Dang J, Taanila A, Ebeling H, Jarvelin MR, Smalley SL: Executive Haavik. We thank the participants, Liv Heldal (test technician) and the other functioning among Finnish adolescents with attention-deficit/ members of the research team. hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2007, 46(12):1594-1604. Author details 12. Nigg JT, Willcutt EG, Doyle AE, Sonuga-Barke EJS: Causal heterogeneity in Department of Biological and Medical Psychology, University of Bergen, attention-deficit/hyperactivity disorder: do we need Bergen, Norway. Uni Research, Regional Centre for Child and Adolescent neuropsychologically impaired subtypes? Biol Psychiatry 2005, 3 4 Mental Health, Bergen, Norway. Solli Hospital, Bergen, Norway. Department 57(11):1224-1230. of Biomedicine, University of Bergen, Bergen, Norway. Department of 13. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF: Validity of the Psychiatry, Haukeland University Hospital, Bergen, Norway. 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SA: Data analysis, comments on the manuscript. 17. Dockstader C, Gaetz W, Cheyne D, Wang F, Castellanos FX, Tannock R: MEG HH: Cognitive assessment, comments on the manuscript event-related desynchronization and synchronization deficits during KP: Comments on the manuscript. basic somatosensory processing in individuals with ADHD. Behav Brain AH: Comments on the manuscript. Funct 2008, 4:8. JH: Head of the national ADHD project, comments on the manuscript. 18. Dockstader C, Cheyne D, Tannock R: Cortical dynamics of selective All co-authors have read and accepted the final version of the manuscript. attention to somatosensory events. Neuroimage 2010, 49(2):1777-1785. 19. Sergeant J: The cognitive-energetic model: an empirical approach to Competing interests attention-deficit hyperactivity disorder. Neurosci Biobehav Rev 2000, The authors declare that they have no competing interests. 24:7-12. 20. 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Attention Network Test in adults with ADHD - the impact of affective fluctuations

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Springer Journals
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Copyright © 2011 by Lundervold et al; licensee BioMed Central Ltd.
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Biomedicine; Neurosciences; Neurology; Behavioral Therapy; Psychiatry
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Abstract

Background: The Attention Network Test (ANT) generates measures of different aspects of attention/executive function. In the present study we investigated whether adults with ADHD performed different from controls on measures of accuracy, variability and vigilance as well as the control network. Secondly, we studied subgroups of adults with ADHD, expecting impairment on measures of the alerting and control networks in a subgroup with additional symptoms of affective fluctuations. Methods: A group of 114 adults (ADHD n = 58; controls n = 56) performed the ANT and completed the Adult ADHD Rating Scale (ASRS) and the Mood Disorder Questionnaire (MDQ). The latter was used to define affective fluctuations. Results: The sex distribution was similar in the two groups, but the ADHD group was significantly older (p = .005) and their score on a test of intellectual function (WASI) significantly lower than in the control group (p = .007). The two groups were not significantly different on measures of the three attention networks, but the ADHD group was generally less accurate (p = .001) and showed a higher variability through the task (p = .033). The significance was only retained for the accuracy measure when age and IQ scores were controlled for. Within the ADHD group, individuals reporting affective fluctuations (n = 22) were slower (p = .015) and obtained a lower score on the alerting network (p = .018) and a higher score on the conflict network (p = .023) than those without these symptoms. The significance was retained for the alerting network (p = .011), but not the conflict network (p = .061) when we controlled for the total ASRS and IQ scores. Discussion: Adults with ADHD were characterized by impairment on accuracy and variability measures calculated from the ANT. Within the ADHD group, adults reporting affective fluctuations seemed to be more alert (i.e., less impacted by alerting cues), but slower and more distracted by conflicting stimuli than the subgroup without such fluctuations. The results suggest that the two ADHD subgroups are characterized by distinct patterns of attentional problems, and that the symptoms assessed by MDQ contribute to the cognitive heterogeneity characterizing groups of individuals with ADHD. Background than impulsivity/hyperactivity as the main persistent Attention-deficit/hyperactivity disorder (ADHD) is a symptoms [5,6]. neuropsychiatric disorder characterized by motor rest- Neuropsychological studies have related changes in lessness and symptoms of impulsivity and inattention. neural networks involving the frontal lobe to impairment The prevalence in the child population is estimated to on tests of attention, primarily those defined within the be about 5%, and the disorder frequently persists into concept of executive function (EF) [7,8]. The importance adulthood [1-4], with symptoms of inattention rather of EF is emphasized by the fact that impairment in child- hood tends to increase into adulthood [9], is associated with severity of ADHD symptoms [10] and overall cogni- * Correspondence: astri.lundervold@psych.uib.no tive and everyday functioning [11]. However, it is well Department of Biological and Medical Psychology, University of Bergen, documented that not all individuals with ADHD show Bergen, Norway impairment on all core tests of EF [12,13], and multiple Full list of author information is available at the end of the article © 2011 Lundervold et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 2 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 pathway models have been developed to link different to impairment of EF and motivation [34-36], and that aspects of EF to neurobiology [14,15]. Furthermore, not children with ADHD and affective disorder (i.e., anxiety) all impairments associated with ADHD can be explained are cognitively distinct from and more impaired than within the concept of EF [16]. Alterations in more basic individuals in either condition on measures defined perceptual processing [17,18], activation [19] and tempo within the concept of EF [32,37]. This may be explained of information processing [20,21] are reported to influ- by the heightened arousal characterizing individuals with affective disorders [38], and that this arousal contributes ence everyday functioning of individuals with ADHD. to cognitive impairment through its effect on EF [39]. A cognitive model of ADHD should therefore describe Due to the high frequency of affective symptoms in and operationalize different levels of information proces- sing, their interactions and neurobiological substrates adults with ADHD [40], these results motivate further [15]. studies using the ANT to investigate characteristics of Posner and colleagues have presented one such model alerting and control networks. [22]. Their Attention Network Model defines an alerting Theaim of thepresent studywas twofold. First, we or vigilance network, an orientation network and an investigated ANT results in a group of adults with executive or conflict network. The alerting network ADHD and a control group from the general popula- maintains a high sensitivity to incoming stimuli, the tion. From earlier studies we expected the ADHD group orienting network is involved in selection of information to show impairment on the ANT measure of the control from sensory input, whereas the control network is network, as well as on measures of accuracy, vigilance involved in resolving conflicts between thoughts, feelings and variability. Secondly, we investigated ANT results in and responses [22]. These networks have been associated an ADHD subgroup reporting affective fluctuations. with different anatomical locations, neurotransmitter sys- From earlier studies we expected that high arousal and tems and genetic markers [23,24], and the model has EF impairment in this subgroup would influence their inspired studies of attention deficits in ADHD as well as results on the alerting and control networks. Finally, we in other neuropsychiatric disorders (see [25]). asked if the results would change when we controlled The Attention Network Model has been used to for ADHD symptoms and intellectual function. develop test paradigms that have become popular during the last decades. Fan and collaborators developed the Methods Attention Network Test (ANT) [23,26], and presented The present study included a subset of adults participat- updated versions on the Internet. The ANT has recently ing in a Norwegian national study of adults with ADHD. been used to study cognitive characteristics of individuals with ADHD. A deviant activation pattern in all three net- Participants in the national study works has been found in fMRI studies of children with The adults with ADHD were recruited from a national ADHD, but impairment was only found on the control registry of adults diagnosed with ADHD in Norway from network when the test was administrated according to 1997 to May 2005. Three national expert committees for standard procedure outside the scanner [27,28]. A recent ADHD/Hyperkinetic disorders were responsible for the Norwegian population-basedstudy could notconfirm diagnostic assessment, based on information from clinical impairment on any measure of the attention networks in records provided by the referring clinicians. The records a group of primary school children with ADHD, but a included information collected and evaluated according more detailed analysis showed that the children in gen- to the official diagnostic system in Norway, the ICD-10. eral performed less accurate and more variable through- However, allowance was made for the inattentive subtype out the task than controls [29]. These characteristics in DSM-IV to be sufficient for the diagnosis, so that the were confirmed in an ANT study of college students (age assessment also could be comparable with the DSM-IV. 18-30 yrs) with a combined type of ADHD [30]. A total of 1700 invitation letters were sent from 2005 to The impact of ADHD symptoms on cognitive function 2007, mainly targeting individuals referred after year is well documented (e.g., [20,31]). Recent studies have 2000. Adults with ADHD were also referred directly also shown that symptoms associated with affective dis- from Norwegian psychiatrists or psychologists to include orders are crucial to understand characteristics of cogni- individuals diagnosed later than May 2005. These adults tion in children [32] and adults [8] with ADHD. Murphy were assessed by specialists in clinical psychiatry or psy- and Barkley illustrated a close association between what chology according to the national guidelines based on the they referred to as emotional regulation and metacogni- criteria used by the national expert committees, but with- tion [8], and a longitudinal study emphasized the predic- out their mandatory evaluation. tive value of such symptoms on future cognitive and The control group was recruited through a random everyday functioning [33]. Other studies have shown that selection from the Norwegian population, using the data- symptoms associated with affective disorders are related base of The Medical Birth Registry of Norway (MBRN), Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 3 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 which includes all Norwegians born after January 1st [44,45]. More recent studies have been critical to this 1967. Invitation letters were sent to a randomly selected diagnostic specificity [46], and have even shown an sample of 2963 individuals who were between 18-40 overlap between MDQ and ADHD reported symptoms years old. In addition, a subsample was recruited by dif- [40,47]. We therefore decided to use the more global ferent advertisements. The control group was not term of affective fluctuations to describe the functionally screened for ADHD before entering the study. However, impairing symptoms assessed by the MDQ, and strict the prevalence in the population was expected to be low, criteria to define affective fluctuation in order to distin- as the estimate among Norwegian primary school chil- guish MDQ symptoms from ADHD symptoms. In the present study affective fluctuations equals a screen posi- dren has been reported to be only 1.7 percent [41]. All participants completed a set of questionnaires (see below tive MDQ score (MDQ+), defined as: seven or more for more details). The project was approved by the Regio- answers of “yes” on the first 13 symptom items, “yes” on nal Committee for Medical Research Ethics of Western the next question (co-occurrence of symptoms), and Norway and the Norwegian Social Science Data Services “level 3 or more” on the last question (i.e., moderate to (NSD). severe impairment caused by the reported symptoms). MDQ screen negative (MDQ-) was defined as not fulfill- Participants in the present study ing these criteria. We invited randomly selected participants from the main study, living geographically close to the city of Bergen, to Experimental procedure take part in a neuropsychological examination including The Attention Network Test (ANT) used in the present the Attention Network Test and two subtests from the study is the original standard version [26], downloaded Wechsler Abbreviated Scale of Intelligence (WASI) [42]. from the webpage of Jin Fan in 2005. In this version, the The ADHD group comprised 32 females and 26 males, participants have to decide whether an arrow points to and the control group 34 females and 22 males. Fifty- the left or right. The arrows are presented either above nine percent of the adults with ADHD used medication or below a fixation point, and may be accompanied by related to ADHD, of whom 80 percent used methylphe- flankers. The test has four cue- (no cue, center, double, nidate (Ritalin or Concerta). They were asked not to take orienting) and three flanker conditions (congruent, medication at the day of testing. incongruent, neutral). All combinations of these are randomly presented in three blocks. The calculations Intellectual function were based on an Excel macro downloaded from Jin Fans Two subtests from the WASI, the Vocabulary and Matrix webpage, supplemented by measures of reaction-time, Reasoning, were used to estimate intellectual function accuracy, vigilance and variability based on the calcula- according to the norms presented in the test-manual tions presented in the manual of the Conners’ Continu- [42]. The examination was performed at an outpatient ous Performance Test, second edition [48] (Table 1). The clinic at the University of Bergen, where an experienced error rates for the cue and flanker conditions included in technician administrated the tests. the calculations of the attention networks were very low. Symptom scales Table 1 Definitions of variables The Adult ADHD Self-Report Scale (ASRS) is a rating Variable Definition scale designed to measure current ADHD symptoms, Alerting RT for no cue - RT for double cue representing the 18 DSM-IV symptoms of ADHD. The network symptoms were rated on a 5-point scale (0 = never/seldom Orienting RT for central cue - RT for orienting cue network and 4 = very often), yielding a total score between 0 and Conflict RT for incongruent flanker - RT for congruent flanker 72. In this study we included the total ASRS score [43]. network The Mood Disorder Questionnaire (MDQ) was included Hit reaction Median RT for correct responses across the test to define a subgroup with affective fluctuations [44,45]. time The first 13 items are related to lifetime presence of hypo- Accuracy Number of correct responses manic/manic symptoms, answered yes or no, followed by Omissions Number of omissions a single yes/no question about whether the symptoms SE all blocks Standard error of RT for correct responses have been experienced at the same time. A final question Variability SE Standard deviation of the 3 standard error values evaluates the level of impairment caused by the symptoms, calculated for each block rated on a four-point scale (no problem, minor problem, RT block The slope of change in RT between blocks change moderate problem, and severe problems). SE block The slope of change in standard error of RT between TheMDQ wasoriginallydesignedand validatedasa change blocks screening instrument for bipolar spectrum disorder Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 4 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 The attention networks were therefore calculated from Table 2 Demographic variables in the control and ADHD groups the RT measures of correct responses in the present study. Controls ADHD ADHD MDQ+ ADHD MDQ- The ANT was administered in a quiet test room. It was n = 56 n = 58 n = 22 n = 36 run on E-Prime software, on a stationary computer with Sex 34F/22M 32F/26M 11F/11M 21F/15M a17” computer screen. The participants sat at a comfor- Age 29.2(7.1) 33.6(9.3) 34.2(9.3) 33.2(9.5) table distance from the screen and responses were col- IQ total score 115.3(9.6) 108.9(14.7) 103.2(14.2) 112.1(14.1) lected via two input keys on the keyboard, corresponding ASRS total 22.4(9.1) 48.7(9.3) 52.3(6.2) 46.4(10.4) to a left or right pointing arrow. Each administration was done individually with a research technician present in the room. The participants were asked to decide as quick two participants in the control group were defined as as possible the direction of the middle arrow by pushing MDQ+ (ANT results not shown). the left or right mouse button. The completion time was approximately 25 minutes. ANT results in the ADHD and the control group Table 3 shows the means and standard deviations (SD) Statistical analysis for the selected ANT variables in the control group and SPSS, version 18, was used to analyze group differences the ADHD group. between individuals with ADHD and controls. The three Attention networks network scores were included in a multivariate analysis A multivariate analysis of variance (MANOVA), includ- within the GLM package, with group as a fixed factor. ing the reaction-time measures of the three attention Univariate post-hoc tests with Tukey corrections for networks, showed a non-significant effect of group, multiple comparisons were run to investigate group differ- Wilks’ l = .965, F =1.3, p = .264. This was confirmed ences on each of the three networks. The analyses were by theunivariateanalysisfor the alerting p = .404, the repeated by including covariates, i.e., demographic vari- orienting p = .244, and the conflict network p = .165. ables that were significantly different between the groups. Reaction time and accuracy measures Group comparisons on the ANT measures of accuracy, Hit reaction time (RT) for correct responses was not sig- variability and vigilance were investigated by using sepa- nificantly different between the ADHD and the control rate univariate analyses of variance within the GLM pack- group, but the total number of hits was significantly age, including covariates in case of statistically significant lowerinthe ADHD groupthaninthe controlgroup, results. The statistical procedure was repeated within the F = 12.8, p = .001, d = .71. The difference remained sig- two ADHD subgroups, by including MDQ score as a fixed nificant after including age and intellectual function as binary factor. Effect sizes (d values) were calculated and covariates, F = 12.4, p = .001. The ADHD group com- interpreted according to general guidelines (d =0.20is mitted significantly higher number of omission errors small, d =0.50is moderate; d = 0.80 is large) [49]. than the controls, F = 13.6, p< 001, d = .68, a difference that was retained when age and intellectual function Results were included as covariates, F = 13.4, p < .001. Description of the sample Variability Participants in the ADHD group were significantly older The SE of all blocks, measuring the consistency of (mean age = 33.6/29.2 yrs, t = 2.86, p = .005, d = .53) responses across the task, was not significantly different and scored significantly lower on the test of intellectual between the two groups. The variability SE score, mea- function than the control group (mean IQ = 108.9/ suring the within respondent variability throughout the 115.3, t = 2.73, p = .007, d = .52). The two groups did task, was significantly higher in the ADHD than in the not differ in gender distribution. As expected, the total control group, F =4.2, p = .033, d =.71.Atrend ASRS score was considerably higher in the ADHD than towards statistical significance was retained when intel- in the control group (mean ASRS = 48.7/22.4, t = 15.0, lectual function was included as a covariate (p = .052), p < .001, d = 2.86) (Table 2). but not when age was added in the statistical model. Participants within the ADHD subgroups (i.e., ADHD Vigilance MDQ+ and ADHD MDQ-) did not differ with respect The RT block change, measuring the slope of change in to age (mean age = 34.2/33.2). However, the group of reaction time throughout the test, did not differ between individuals defined as MDQ+ obtained a significantly the ADHD and control group. The SE block change, mea- lower total IQ (mean IQ = 103.2/112.1, t =2.2, p =.30, suring the slope of change in standard error of reaction d = .63), and higher ASRS score (52.3/46.4, t =2.6, p = time between the three blocks, was significantly higher in .010, d = .69) than the MDQ- group (Table 2). Only the ADHD than in the control group, F = 5.0, p = .027, d Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 5 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 Table 3 ANT results in the control and ADHD groups Controls ADHD ADHD MDQ+ ADHD MDQ- n=56 n=58 n=22 n=36 Alerting network 36.0(26.2) 32.1(23.5) 22.8(28.9) 37.8(17.9) Orienting network 42.5(25.4) 36.5(29.0) 35.4(34.3) 36.7(26.3) Conflict network 128.2(50.3) 142.1(55.9) 162.9(54.1) 129.5(53.7) Hit Reaction time 556.1(70.2) 576.0(92.5) 610.4(91.1) 554(88.0)) Accuracy 267.2(13.3) 258.0(12.6) 259.3(10.7) 257.4(13.3) Ommissions 13.8(12.0) 21.0(8.8) 21.6(8.0) 20.7(9.3) SE All Blocks 117.7(31.5) 129.1(39.2) 135.4(35.5) 125.3(41.3) Variability SE 10.3(7.2) 13.5(8.7) 13.5(8.6) 13.5(9.2) RT Block Change -11.3(19.2) -7.2(23.0) -4.5(18.3) -8.8(25.6) SE Block Change 1.1(8.0) 5.1(10.5) 7.5(9.1) 3.5(11.2) = .43. The difference was still significant when intellectual change in standard error of reaction time between the function was included as covariate, F =4.4, p = .038, but three blocks, and the RT block change, measured as the not when adding age in the statistical model. slope of change in RT throughout the task, were not sig- nificantly different between the two ADHD subgroups. ADHD subgroups: MDQ+ and MDQ- The results for the selected ANT variables for the two Discussion ADHD subgroups are shown in the two right panels of The present study showed that adults with ADHD did Table 3. not differ from controls on ANT measures of the three Attention networks attention networks, but they showed a lower accuracy, a A MANOVA, including the reaction-time measures of higher intra-individual variability, and lower vigilance the three attention networks, showed a statistically signif- across the task. The effect sizes were mainly moderate, icant difference between the two MDQ-groups, Wilks’ but only the accuracy measures retained statistical sig- l = .836, F = 3.5, p = .021. Post-hoc tests showed that the nificance when we controlled for age and intellectual MDQ+ subgroup obtained an overall lower score on the function. An important and novel aspect of the present alerting network than the MDQ-subgroup, F =6.0, p = study was the inclusion of a binary MDQ score to define .018, d =.64.Onthe conflict network,the MDQ+ sub- an ADHD subgroup with affective fluctuations. In this group obtained a significantly higher score than the subgroup we found an impact on the alerting and con- MDQ- subgroup, F =5.3, p = .026, d = .62, indicating trol networks, and on the measure of hit reaction time. that the former was more distracted by incongruent flan- Thesubgroup was significantlymoredistractedbycon- kers than the latter. The group difference was non-signif- flicting stimuli and slower to respond. At the same time icant on the orienting network. their results on the measure of the alerting network sug- A MANCOVA including the total ASRS and IQ gest that they were more alert. Furthermore, the total scores as covariates was still statistically significant, ASRS and IQ scores had a major impact on the reaction Wilks’ l = .837, F =3.3, p = .027. A statistical signifi- time and the control network in this ADHD subgroup, cant difference was confirmed by the univariate analyses while the alerting network was left unaffected. for the alerting network, F =7.1, p = .011, but not for Given the original description of the test, it was some- the conflict network, p = .061. what surprising that adults with ADHD and controls did Reaction time and accuracy measures not differ on any measure of attention networks. The hit reaction time for correct responses was significantly According to findings in previous studies we expected slower in the MDQ+ than in the MDQ-subgroup, F = 5.3, to find impairment of the control network in the ADHD p =.025, d = .90. This difference was no longer statistically group [27,28,50]. Functions such as inhibition and cog- significant when the total IQ and ASRS scores were nitive flexibility are essential to solve cognitive conflicts included as covariates (p = .078). The overall number of as they are presented in the task. Dysfunction of these hits across the three blocks and the number of omissions EFs are regarded as core cognitive deficits in the daily were not significantly different between the two life of adults with ADHD [12,51,52], and are described subgroups. as essential to understand their core symptoms [8]. In Variability and vigilance the present study, EF deficits as assessed by the conflict The variability SE, measuring within respondent varia- network were only found in the ADHD subgroup bility, the SE block change, measuring the slope of defined with affective fluctuations. The clinical Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 6 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 importance of this finding is emphasized by the high The present study showed impairment in the ADHD frequency of affective symptoms among adults with group on measures of accuracy, variability and vigilance, ADHD [40], and that persistent affective symptoms tend supporting findings from earlier studies of children to contribute to impaired function in occupational and [29,55,56] and adults [30,35,57]. Impaired accuracy and academic areas of life [33]. Due to the lower intellectual vigilance remained when intellectual function (IQ) was function and higher ADHD symptom scores in the included as a covariate. Earlier studies have demon- strated a more general impact of IQ on ANT measures MDQ+ subgroup, our results may be explained by allo- in children with ADHD (e.g., [29]). The retained group cation of the individuals with the most severe ADHD to differences in the present study indicates that results on this subgroup. Although symptoms assessed by MDQ are frequently reported by individuals with ADHD [47], an IQ test are not as important for ANT results in we argue that we used a definition of the term affective adults as they are in children. However, intellectual fluctuations that indicates an add on to the core ADHD function did influence the results when ADHD symp- symptoms: the participants should answer “yes” to the toms were combined with affective symptoms, illustrat- symptoms, but also confirm that the symptoms co- ing the complex relation between core and comorbid occurred and caused moderate to severe impairment in symptoms of ADHD, EF and intellectual function. their daily life. The present study has limitations related to the diag- Arousal and alertness are known to be affected in nosis of ADHD and the definition of affective fluctua- ADHD [19], with a level that was expected to be dif- tions. The adults with ADHD included in the study ferent from the one shown by the control group. This were evaluated by several clinicians before inclusion in was only suggested by the results in the ADHD sub- the study, probably yielding a large heterogeneity within group with affective fluctuations. As anxiety symptoms the ADHD group. On the other hand, our ADHD sam- are frequently found in individuals with ADHD and ple probably represents adults with an ADHD diagnosis since anxiety symptoms are associated with high arou- as encountered in a clinical setting. Affective fluctua- sal, we speculate that individuals with such symptoms tions were assessed according to a self-report question- were mainly allocated to this ADHD subgroup. The naire, originally designed to measure symptoms of adjustment to the new setting of ANT may have made bipolar spectrum disorders. However, it has been shown them more prone to increased arousal. According to that most adults with ADHD with a positive MDQ Eysenck, an individual with anxiety and ADHD will score do not fulfill criteria for this disorder [40], and that apositiveMDQ scoremaybefoundina rangeof also be affected by a top-down influence of the EF dys- psychiatric diagnoses [47]. The conclusions of the pre- function associated with ADHD [38]. We suggest that this may explain why both the alerting and conflict sent study will therefore not be restricted to a specific networks are affected in the ADHD subgroup defined comorbidity group. It is also a limitation that the adults with affective fluctuations. Such a complex interaction with ADHD were only asked not to take medication at between the two networks has been illustrated in stu- the day of testing. Although we thus do not know if the dies by Fan and collaborators and is supported by the wash-out of the medication was complete, our results fact that the alerting and conflict networks share brain are supported by the fact that methylphenidate use was networks [53,54]. MacLeod and collaborators suggested more frequently reported in the MDQ- than the MDQ+ another difference between the two networks that may group. Finally, the study did not include biomarkers of contribute to explain the results: the executive network the attention networks. Previous studies suggest that is more trait-like and the alerting network more state- data derived from imaging techniques may be more sen- like [25]. It is tempting to assume that both trait and sitive than behavioral data collected in a standard test state have influenced the results in the ADHD sub- condition outside the scanner [27,28,50]. To extend the group with affective fluctuations. When we controlled exploration of competition between the neural sub- for the ASRS and IQ scores, probably reflecting traits strates of the alerting and conflict networks to neuroi- associated with ADHD, only the state-like alerting net- maging of the here described subgroups would therefore work was influenced by the affective fluctuations be an important asset. assessed by the binary MDQ score. However, it is important to emphasize that further studies are neces- Conclusions sary to obtain firm conclusions about alertness in The present study suggests that adults with ADHD are adults with ADHD and symptoms of affective disor- less accurate, have a higher level of variability and a ders. The interpretations of the results in the present lower vigilance than adults without ADHD, and that study should be considered with caution, not at least affective fluctuations make adults with ADHD more because the results on the alerting network are com- alert, but slower and more distracted by conflicting sti- bined with slow RT in the MDQ+ subgroup. muli. Our results indicate that the cognitive Lundervold et al. Behavioral and Brain Functions 2011, 7:27 Page 7 of 8 http://www.behavioralandbrainfunctions.com/content/7/1/27 7. 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Forssman L, Bohlin G, Lundervold AJ, Taanila A, Heiervang E, Loo S, Acknowledgements Jarvelin MR, Smalley S, Moilanen I, Rodriguez A: Independent contributions The data are generated from the project ADHD in adults - from molecular of cognitive functioning and social risk factors to symptoms of ADHD in mechanisms to clinical characterization, which is part of the K.G. Jebsen two nordic populations-based cohorts. Dev Neuropsychol 2009, Research Center on Neuropsychiatric Disorders. The project has received 34(6):721-735. financial support from the Norwegian Research Council of Norway, the 11. Loo SK, Humphrey LA, Tapio T, Moilanen IK, McGough JJ, McCracken JT, Regional Health Authority of Western Norway and K.G. Jebsen Foundation to Yang MH, Dang J, Taanila A, Ebeling H, Jarvelin MR, Smalley SL: Executive Haavik. 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