We find a higher SNP heritability for ADHD + DBDs (h2SNP = 0.34) when compared to ADHD without DBDs (h2SNP = 0.20), high genetic correlations between ADHD + DBDs and aggressive (rg = 0.81) and anti-social behaviors (rg = 0.82), and an increased burden (polygenic score) of variants associated with ADHD and aggression in ADHD + DBDs compared to ADHD without DBDs. Our results suggest an increased load of common risk variants in ADHD + DBDs compared to ADHD without DBDs, which in part can be explained by variants associated with aggressive behavior.Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood psychiatric disorder often comorbid with disruptive behavior disorders (DBDs). Here, we report a GWAS meta-analysis of ADHD comorbid with DBDs (ADHD + DBDs) including 3802 cases and 31,305 controls. We identify three genome-wide significant loci on chromosomes 1, 7, and 11. A meta-analysis including a Chinese cohort supports that the locus on chromosome 11 is a strong risk locus for ADHD + DBDs across European and Chinese ancestries (rs7118422, P = 3.15×10 ADHD is often found to be comorbid with disruptive behavior disorders, but the genetic loci underlying this comorbidity are unknown. Here, the authors have performed a GWAS meta-analysis of ADHD with disruptive behavior disorders, finding three genome-wide significant loci in Europeans, and replicating one in a Chinese cohort. Comorbidity with other psychiatric disorders is common among children with ADHD, and disruptive behavior disorders (DBDs) are the most frequently co-occurring conditions2. DBDs comprise oppositional defiant disorder and conduct disorder. Both have a childhood onset and are characterized by persistent patterns of oppositional, defiant, disobedient and disruptive behavior and antisocial rule-breaking, and aggressive behaviors such as being destructive, physically cruel towards others, and rule violations3. DBDs have a prevalence of 3–10%5 among children and, are around twice as frequent in males compared to females6 and DBDs are associated with a 3-fold increased risk of premature death7, with higher mortality rates than in ADHD8. Different comorbidity rates of ADHD with DBDs have been reported, some studies have found that approximately 30–40% of children with ADHD have comorbid DBDs (ADHD + DBDs)11, while a study of 1.92 million individuals from Denmark found that 17% of those with ADHD were also diagnosed with DBDs8. Among those with DBDs in the same Danish cohort, more than half (57%) had a comorbid diagnosis of ADHD7. ADHD in combination with diagnosed DBDs or excessive aggressive and disruptive behaviors increases the risk for several detrimental outcomes in individuals with ADHD including increased risks for substance use disorders14, in-patient psychiatric admission15, transgression17, risky behavior18, and premature death compared to those diagnosed with ADHD only8.Attention-Deficit/Hyperactivity Disorder (ADHD) is a common childhood onset behavioral disorder affecting around 5% of children and 2.5% of adults19 and 0.40–0.7022, respectively. Twin studies have also suggested that ADHD + DBDs is a more severe and genetically loaded subtype of ADHD than ADHD without comorbid DBDs23. Siblings of individuals with ADHD + DBDs have a higher recurrence risk to develop ADHD + DBDs compared to siblings of individuals having ADHD without DBDs (ADHDwoDBDs)25. Individuals with ADHD + DBDs also have an increased polygenic burden of common ADHD risk variants compared to individuals with only ADHD, further supporting the hypothesis that ADHD + DBDs reflect a higher load of genetic risk26. However, it seems unlikely that ADHD risk variants alone can fully account for the underlying genetic risk that mediates aggressive and disruptive behaviors in individuals with ADHD + DBDs. Family studies have found that ADHD and DBDs have distinct genetic architectures with moderate to high genetic overlap in the range of 0.34–0.7429. The existence of genetic risk factors specific to the aggressive and disruptive component of ADHD + DBDs finds support from a twin study, where DBDs had an estimated heritability of 0.33–0.64 after controlling for ADHD30.Both genetic and environmental factors influence the risk for ADHD and DBDs with twin heritability estimates of 0.7432 or aggressive and anti-social behaviors34, with only limited success in identifying genome-wide significant loci and no conclusive, replicated findings34. Only two genome-wide studies have focused specifically on ADHD + DBDs. One small genome-wide linkage study examined DBDs in individuals with ADHD35 and another, while not assessing diagnosed DBDs, examined aggressive behaviors in individuals with ADHD36. Neither studies reported genome-wide significant loci.Several genome-wide association studies (GWASs) have focused on diagnosed DBDsIn the current study we perform a large GWAS meta-analysis of ADHD + DBDs using a Danish nation-wide cohort from iPSYCH and samples from the Psychiatric Genomics Consortium (PGC). We identify three genome-wide significant loci for ADHD + DBDs, located on chromosomes 1, 7, and 11, and show evidence of transancestral association for the locus on chromosome 11 in a Chinese cohort and, we find high polygenic overlap of ADHD + DBDs with childhood aggression and antisocial behavior in the general population, higher than found for ADHDwoDBDs.P = 5 × 10−8); these were located on chromosome 1 , 7 , and 11   and six European ancestry PGC cohorts . All cases were diagnosed with both ADHD and DBDs or had a diagnosis of hyperkinetic conduct disorder, which according to the ICD10 criteria implies that both disorders are present. Selection of controls was population-based and they were not diagnosed with either ADHD or DBDs. Results were in total based on 3802 cases and 31,305 controls and included 8,285,688 variants after filtering. Three loci passed the threshold for genome-wide significance (6) Table . The dir6) Table .Table 1R38. The genetic correlation between the iPSYCH cohort and the meta-analyzed PGC cohorts was high  supporting consistency of the ADHD + DBDs phenotypes analyzed in the cohorts. In addition, no variants demonstrated significant heterogeneity between studies  rather than confounding factors. The estimated remaining contribution of confounding factors was small and non-significant .LD score regression analysis indicated that the observed deviation of the genome-wide test statistics from the null distribution  .P < 1 × 10−4) from the GWAS meta-analysis of ADHD + DBDs. A consistent direction of association was observed for 221 loci (out of the 281 loci) in the ADHDwoDBDs GWAS (sign test P < 2.2 × 10−16), while all the 281 loci demonstrated consistent direction of association in the ADHD + DBDs vs. ADHDwoDBDs GWAS. The proportion of variants having a consistent direction of association in the ADHD + DBDs vs. ADHDwoDBDs GWAS was significantly larger than the proportion in the ADHDwoDBDs GWAS (P = 7.7 × 10−16), which suggests that the associations in the GWAS meta-analysis of ADHD + DBDs reflects association with the comorbid phenotype beyond association with risk for ADHD alone.The summary statistics from the two secondary GWASs were used to evaluate the direction of association of the top loci  remained strong in the ADHD + DBDs vs. ADHDwoDBDs GWAS  confirming a stronger effect of the risk allele for these two loci in ADHD + DBDs compared to ADHDwoDBDs (Supplementary Data P = 8.97 × 10−9), which showed no evidence for association with ADHDwoDBDs  versus a suggestive evidence of association with ADHD + DBDs vs. ADHDwoDBDs .All three genome-wide significant loci demonstrated higher effect sizes in ADHD + DBDs compared to ADHDwoDBDs  none of the three loci reached genome-wide significance for a direct effect on ADHD + DBDs, although the locus on chromosome 11 retained the most robust signal after correction for ADHDwoDBDs  were significantly associated with ADHD + DBDs  are located in the genome-wide significant locus on chromosome 1, and two genes  in the genome-wide significant locus on chromosome 11. One gene (MAML3) located on chromosome 4 had not been identified as a risk locus in the single variant analysis.A gene-based association analysis was performed using MAGMAP < 10−3, 79 genes) using the results from the two secondary GWASs. The gene set was significantly associated with ADHDwoDBDs (beta = 0.312 (SE = 0.02), P = 9 × 10−4), but had a stronger association in the ADHD + DBD vs. ADHD only GWAS (beta = 1.1 (SE = 0.07), P = 9.28 × 10−32).To evaluate if the gene-based association signals reflected the aggressive and disruptive component of the ADHD + DBDs phenotype rather than ADHD alone, we did a gene-set test of the most associated genes from the primary ADHD + DBDs GWAS meta-analysis (41 (version 6p) using MetaXcan42. Depending on the tissue, 2042–6094 genes were tested (Supplementary Data P < 1.16 × 10−6); RRM1 (chromosome 11) was less expressed in cases, while RAB3C (chromosome 5) and LEPRE1 (chromosome 1) showed a higher expression in cases when compared to controls  was estimated using a prevalence of ADHD + DBDs in the population of 1–2%, based on studies reporting that around 20–40% of individuals with ADHD have comorbid DBDs11. The estimated h2SNP for ADHD + DBDs was 0.25 (SE = 0.03) using LD score regression37 with a prevalence estimate of 2% in the population  compared to ADHDwoDBDs . This pattern remained similar when using GCTA and was stable when assuming a lower prevalence of ADHD + DBDs (1%) and higher prevalence of ADHDwoDBDs (3%)  and antisocial behavior in 16,400 individuals34 ) using LD score regression37. We found a high genetic correlation of ADHD + DBDs with aggression in children  and antisocial behavior  . Analyzing only the iPSYCH cohort, ADHD + DBDs demonstrated a nominally higher positive genetic correlation than ADHDwoDBDs with aggression in children  and antisocial behavior . The differences in the genetic correlations, however, were not statistically significant when assessed using the jackknife method43. Finally, we estimated the genetic correlation of ADHD + DBDs vs ADHDwoDBDs .We estimated the genetic correlations of ADHD + DBDs with aggression-related phenotypes using GWAS results from analyses of aggressive behaviors in 18,988 children33  and ADHD  were observed for ADHD + DBDs. Additionally, PGS for increased cognitive performance was negatively associated with ADHD + DBDs compared to ADHDwoDBDs . Finally, PGS for having children at an older age was negatively associated with ADHD + DBDs compared to ADHDwoDBDs . Only a small proportion of the variance in ADHD + DBDs among individuals with ADHD was explained by the PGSs, with the maximum Nagelkerke’s R2 = 0.36% for the ADHD PGS. The odds ratio for ADHD + DBDs was increasing across quintiles of the polygenic load of variants associated with aggression, and ADHD, and decreasing with higher load of variants associated with cognition and age at first birth  analyses were done to evaluate whether ADHD + DBDs cases are enriched for variants associated with 22 relevant phenotypes related to personality, cognition, and psychiatric disorders compared with cases having ADHDwoDBDs. Seven phenotypes were significantly associated with ADHD + DBDs compared with ADHDwoDBDs after multiple testing correction  in the endoplasmatic reticulum (ER) that acts as a sensor of calcium. Upon calcium depletion from the ER, STIM1 is responsible for an influx of calcium ions from the extracellular space through store-operated calcium channels to refill ER stores54. Store-operated calcium entry may also be involved in neuronal calcium signaling55, and recent evidence indicates that STIM1 plays a role in synaptic plasticity affecting learning and memory56. These results are interesting in the light of the observed learning deficits associated with aggressive behaviors and accumulating evidence that suggests calcium signaling is involved in several psychiatric disorders59. Alternatively, analysis of genetically regulated gene expression suggested that the variants in the genome-wide significant locus might affect expression of RRM1, with a decreased RRM1 expression being associated with ADHD + DBDs. RRM1 is oriented in a tail-to-head configuration with STIM1, which lies 1.6 kb apart, and encodes a subunit of a reductase involved in the biosynthesis of deoxyribonucleotides from the corresponding ribonucleotides necessary for DNA replication. To our knowledge, this gene has not previously been associated with psychiatric disorders.The locus most strongly associated with ADHD + DBDs on chromosome 11 is located in RRM1 and STIM1) and with three  out of the remaining four located in or near the genome-wide significant locus on chromosome 1. The top-associated genes (79 genes) seem to mainly reflect association with the aggressive and disruptive component of the ADHD + DBDs phenotype. The geneset was significantly associated with ADHDwoDBDs but even more strongly associated in the ADHD + DBDs vs. ADHDwoDBDs GWAS (where the effect of ADHD is corrected out). Likewise, the most strongly associated single markers (with P < 1 × 10−4) in the GWAS meta-analysis of ADHD + DBDs showed high consistency in the direction of association in the GWAS of ADHDwoDBDs, but even higher consistency in the ADHD + DBDs vs. ADHDwoDBDs GWAS, reinforcing the notion that the associations mainly reflect the aggressive and disruptive component of the phenotype.In the gene-based analysis six genes were exome-wide significantly associated with ADHD + DBDs, including two implicated by variants in or near the genome-wide significant locus on chromosome 11  compared to ADHDwoDBDs  47, which included individuals with and without comorbid DBDs. Conditional on an ADHD diagnosis, the aggressive and disruptive behavioral component of the ADHD + DBDs phenotype also has a genetic component involving common variants . PGS analyses suggested the higher SNP heritability of ADHD + DBDs compared to ADHDwoDBDs is partly explained by a higher burden of common ADHD risk variants , and probably biased downwards by heterogeneity in the cohorts analyzed33. The studies do therefore not capture the full impact of common variants in aggressive behavior, and the observed high genetic correlation of the two phenotypes with ADHD + DBDs involves variants that only explain a small proportion of variance in aggression.It should also be noted that the SNP heritabilities of childhood aggression47. However, it might also involve variants associated with antisocial behavior, which also has a negative genetic correlation with educational performance34. This latter idea is consistent with epidemiological studies linking aggression to decreased educational attainment68. Finally, we found a significantly higher load of variants associated with younger age at birth of first child in ADHD + DBDs compared to ADHDwoDBDs, in line with the observed positive genetic correlations of ADHD and antisocial behavior with having children earlier47 and evolutionary theories suggesting that aggression has played a role when competing for access to mates69.We have shown that individuals with ADHD + DBDs have an increased load of variants associated with worse cognition compared to individuals having ADHDwoDBDs  (see a detailed description in the ref. 70). A biological sample of the included individuals were obtained from the Newborn Screening Biobank at Statens Serum Institute, Denmark. DNA was extracted from dried blood spot samples and whole genome amplified in triplicates72. Genotyping and calling of genotypes were performed as described in our previous publications70.The iPSYCH cohort is a population-based nation-wide cohort which includes 79,492 genotyped individuals . The cohort was selected, based on register information from a baseline birth cohort of all singletons born in Denmark between May 1st, 1981 and December 31, 2005 (73. Cases with ADHD + DBDs had a diagnosis of hyperkinetic conduct disorder (F90.1) or an ADHD diagnosis (ICD-10 F90.0) occurring together with a diagnosis of ODD (ICD-10 F91.3) or conduct disorder . Distribution of cases with ADHD + DBDs over diagnosis codes is presented in Supplementary Data For this study cases and controls were identified based on diagnoses given in 2016 or earlier in the Danish Psychiatric Central Research Registerisorder F0.1 or anhttps://genome.au.dk).The study was approved by the Danish Data Protection Agency and the Scientific Ethics Committee in Denmark. All analyses of the iPSYCH cohort were performed at the secured national high performance-computing cluster, GenomeDK  provided by PGC with information about diagnoses of ADHD + DBDs were included. An overview of the cohorts including genotyping information and diagnosis criteria can be found in Supplementary Data 75. Pre-imputation quality control allowed an inclusion of individuals with a call rate > 0.98 (>0.95 for iPSYCH) and genotypes with a call rate >0.98, difference in SNP missingness between cases and controls < 0.02, no strong deviation from Hardy–Weinberg equilibrium (P > 1 × 10−6 in controls or P > 1 × 10−10 in cases) and low individual heterozygosity rates (|Fhet | < 0.2). Genotypes were phased and imputed using SHAPEIT76 and IMPUTE277 and the 1000 Genomes Project phase 3 (1KGP3)78 as imputation reference panel (the East Asian reference genome was used for imputation of the Chinese sampels). Trio imputation was done with a case-pseudocontrol setup.Quality control, imputation, and primary GWASs of the iPSYCH and PGC cohorts (including the Chinese cohort) were done separately for each using the bioinformatics pipeline RicopiliP > 1 × 10−4 and SNP call rate >0.98) pruned for linkage disequilibrium (LD) resulting in ~30,000 pruned variants . Genetic relatedness was estimated using PLINK v1.981 to identify first and second-degree relatives  > 0.05, HWE 80 using additive logistic regression and the imputed marker dosages, covariates from principal component analyses  and other relevant covariates ) from LD score regression was used to evaluate the relative contribution of polygenic effects and confounding factors to the observed deviation from the null in the genome-wide distribution of the χ2 statistics of the GWAS meta-analysis of ADHD + DBDs.The ratio (ratio = (intercept−1)/ and covariates indicating genotyping waves.r2 < 0.2, 281 variants) with association P-values less than 1 × 10−4 in the GWAS meta-analysis of ADHD + DBDs.The summary statistics from the two secondary GWASs were used to evaluate direction of association of the top loci associated with ADHD + DBDs using a sign test based on LD distinct variants  was performed using variants that were genome-wide significant in the GWAS of ADHDwoDBDs (P < 5 × 10−8), and not in linkage disequilibrium . No variants were removed due to evidence of pleiotropy (HEIDI-outlier threshold of P = 0.01).We also did an mtCOJO40 and summary statistics from the GWAS meta-analysis. Variants were annotated to genes using the NCBI37.3 gene definitions and no window around genes was used. MAGMA summarizes association signals observed for variants located in a gene into a single P-value while correcting for LD in a reference genome. For this the European samples from the 1000 Genomes phase 3 were used.Gene-based association analysis was done using MAGMA 1.05P < 10−3 Supplementary Data P-values were generated using summary statistics from the two secondary GWASs (ADHD + DBDs vs. ADHD-only GWAS and ADHDwoDBDs GWAS) and subsequently gene-set tests were done using MAGMA 1.0540. MAGMA performs a competitive test to analyze if the gene set is more strongly associated with the phenotype than other genes, while correcting for a series of confounding effects such as gene length and size of the gene set.The most associated genes in the GWAS meta-analysis of ADHD + DBDs  using MetaXcan42 imlemented in the R-package metaxcanr (https://github.com/drveera/metaxcanr). MetaXcan is an extension of PrediXcan87 that can be used to test for differences in gene expression using summary statistics. We used high-performance prediction models for MetaXcan based on variants located within 1 Mb +/− of transcription start site and trained using elastic net regression and 10-fold cross-validation4 downloaded from http://predictdb.org. MetaXcan also requires covariance matrices of the variants within each gene model for each tissue. Covariance matrices calculated from 503 individuals with European ancestry from the 1000 genomes project88 available with the prediction models at http://predictdb.org were used.Association of the genetically regulated gene expression with ADHD + DBDs was analyzed in 12 brain tissues from GTExh2SNP) was estimated using LD score regression37 and the summary statistics from the GWAS meta-analysis of ADHD + DBDs. The heritability was estimated on the liability scale assuming a population prevalence of ADHD + DBDs of 2 and 1%.The SNP heritability  and the other group was used to estimate h2SNP of ADHDwoDBDs . The analysis using independent controls was done using prevalances of 2 and 1% for ADHD + DBDs and 3 and 4% for ADHDwoDBDs. Test for difference in h2SNP between ADHD + DBDs and ADHDwoDBDs was done using Eq. Zdiff is the Z-score for the difference in h2SNP and SE is the standard errors for the heritabilities. We calculated two-tailed P-values in R.In order to be able to test for difference in χ2 = 1.06).Additionally, we evaluated how much of the variance in the ADHD + DBDs phenotype could be explained by common genetic variation in the context of ADHD. For this we did a case-only approach including 1959 cases with ADHD + DBDs and 13,539 individuals with ADHDwoDBDs. This was only done using GCTA due to a low polygenic signal in the ADHD + DBDs vs. ADHDwoDBDs GWAS  obtained by questionnaires filled by their parents. Another is a GWAS meta-analysis of antisocial behavior conducted by the Broad Antisocial Behavior Consortium (BroadABC) including 16,400 individuals34. Both children and adults were accessed for a broad range of antisocial measures, including aggressive and non-aggressive domains. The BroadABC study has a minor overlap with the EAGLE aggression GWAS with respect to the included cohorts. We also estimated the genetic correlations of ADHDwoDBDs and ADHD+DBDs  with the two aggression-related phenotypes.The genetic overlap of ADHD + DBDs with aggression-related phenotypes was evaluated by estimating genetic correlations using LD score regressionrg = 0.97; SE = 0.06), and not statistically different from one.The genetic correlation between ADHD + DBDs and ADHDwoDBDs was calculated using LD score regression and summary statistics from the GWAS meta-analysis of ADHD + DBDs and the GWAS of ADHDwoDBDs, the latter based on iPSYCH data only. To supplement the LD score regression analysis we used iPSYCH genotypes and GCTA to estimate the genetic correlation between ADHD + DBDs  and ADHDwoDBDs , showing results consistent with LD score regression  were estimated multiplying the natural log of the odds ratio of each variant by the allele-dosage of each variant and whole-genome PGS was obtained by summing values over variants for each individual. The ADHD PGS was generated using the approach described in Demontis et al.47: in short ADHD cases and controls in the iPSYCH cohort were split into five independent samples, then one sample was left out in an ADHD GWAS meta-analysis of iPSYCH data and data from the Psychiatric Genetics Consortium. The results were then used as training for generating PGS in the sample that was left out. This “leave-one sample out” procedure was repeated until ADHD PGSs were generated for all individuals in the iPSYCH cohort. For each P-value threshold the variance in the ADHD + DBDs phenotype explained by PGS was estimated using Nagelkerke’s R2 (R package “BaylorEdPsych”) and conversion to Nagelkerke’s R2 on the liability scale was done using the method suggested by Lee et al.90 and a prevalence of 0.2 for comorbid DBDs among ADHD cases. Association of PGS with ADHD + DBDs compared to ADHDwoDBDs was estimated using logistic regression including the same covariates used in the GWAS.Case-only polygenic score (PGS) analyses were done using GWAS summary statistics from 22 GWASs related to cognition and education (six phenotypes), personality (nine phenotypes), psychiatric disorders (five phenotypes), reproduction/fitness (two phenotypes) .Subsequently individuals were divided into quintiles based on their PGS. OR for ADHD + DBDs compared to ADHDwoDBDs was estimated within each quintile with reference to the lowest risk quintile (using the training data Further information on research design is available in the Supplementary InformationPeer ReviewReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1-10


Altered brain activity that has been observed in attention deficit hyperactivity disorder (ADHD) while performing cognitive control tasks like the stop signal task (SST) has generally been interpreted as reflecting either weak (under-active) or compensatory (over-active) versions of the same functions as in healthy controls. If so, then regional activities that correlate with the efficiency of inhibitory control  in healthy subjects should also correlate with SSRT in ADHD. Here we test the alternate hypothesis that BOLD (blood-oxygen-level-dependent) differences might instead reflect the redirection of neural processing resources normally used for task-directed inhibitory control, towards actively managing symptomatic behaviour. If so, then activities that correlate with SSRT in TD should instead correlate with inattentive and hyperactive symptoms in ADHD.We used fMRI  in 14 typically developing (TD) and 14 ADHD adolescents performing the SST, and in a replication sample of 14 healthy adults. First, we identified significant group BOLD differences during all phases of activity in the SST . Next, we correlated these phases of activity with SSRT in TD and with SSRT, inattentive and hyperactive symptom scores in ADHD. We then identified whole brain significant correlations in regions of significant group difference in activity.Only three regions of significant group difference were correlated with SSRT in TD and replication groups  during error detection and hypothalamus during post-error slowing). Consistent with regions of altered activity managing symptomatic behaviour instead of task-directed behaviour, left IFG correlated with greater inattentive score, right IFG correlated with lower hyperactive score and hypothalamus correlated with greater inattentive score and oppositely correlated with SSRT compared to TD.Stimuli that elicit task-directed integration of neural processing in healthy subjects instead appear to be directing integrated function towards managing symptomatic behaviour in ADHD. The ability of the current approach to determine whether altered neural activities reflect comparable functions in ADHD and control groups has broad implications for the development and monitoring of therapeutic interventions. Attention deficit hyperactivity disorder (ADHD) is associated with cognitive difficulties, particularly in the form of decreased inhibitory control , 2, and Neuroimaging studies of ADHD using the SST have consistently found deficient inhibitory control to be associated with altered activity and connectivity –9. For tOne indication that altered activities in ADHD do not simply reflect under- or over-activation is observations of opposite activity changes with respect to baseline compared to healthy control subjects. We found four such patterns of opposite activity in a recent study of ADHD and typically developing (TD) adolescents performing the SST. First, we observed opposite activity in task-related (deactivation instead of activation) and default mode networks (activation instead of deactivation) during response phases indicative of categorically altered preparation . Second,Rather than reflecting relatively weak or compensatory versions of normal function, we propose that altered activity in ADHD might instead reflect altered integration of distributed neural processing resources towards non task-related goals. Processing resources that are used for effective task performance in healthy subjects might instead be directed towards supporting or suppressing symptomatic behaviours like wandering attention and impulsive behaviour in ADHD. If so, then neural differences would be more analogous to optical rivalry  than to One form of optical rivalry is apparent when viewing bistable images, in which the contours of an image can be perceived as two distinct objects (e.g. duck or rabbit) but both cannot be perceived simultaneously, because the dynamics of component neural processing resources (e.g. edge detection) can only represent one unified object at a time. Similarly, when subjects perform the SST, their component neural processing resources can only support one integrated focus of attention at a time. For example, activity in left inferior frontal gyrus, which has been consistently found to be altered in ADHD , is invoWe propose that in the kind of distracted and impulsive states associated with ADHD, the task itself might be processed as noise rather than signal. If so, then activities in regions that normally predict improved performance (i.e. SSRT) should instead predict symptoms in ADHD. For instance, neural activities in left and right inferior frontal gyri that, in typically developing individuals, perform inhibitory control and interference suppression processing, might, in individuals with ADHD, be directed towards suppressing impulsive behaviour and supporting wandering attention. This distinction is crucial for the development of appropriate neurocognitive models that are increasingly being used to inform and monitor therapeutic interventions.Here, we attempt to determine whether altered activity in ADHD reflects either (1) weak or compensatory versions of normal function, or (2) the same neural processing resources managing wandering attention and hyperactive behaviour instead of supporting efficient task performance. We test this hypothesis using the straight forward approach of examining the actual correlates these activities which, to our knowledge, has not been done before.Firstly, we perform intersubject correlation analyses on all phases of SST activity Fig.   stopped taking medication 24 h prior to the scan to eliminate drug-induced BOLD changes [This study is the third stage of analyses performed on data from TD and ADHD adolescents presented in , 10. Fou changes .ADHD subjects and their parents were interviewed separately and together using the parent interview for child symptoms (PICS-IV) . IntelliNine ADHD subjects were diagnosed with ADHD combined subtype, five met criteria for inattentive subtype, and two also met DSM-5 criteria for ODD. Control subjects were assessed in a comparable manner and reported no psychiatric or medical disorders. All subjects were right-handed and had normal vision and hearing.The replication sample used for comparing results of behavioural correlations in TD consisted of 14 healthy young adults on a placebo dose of methylphenidate ) age = 24.0 ± 2.8 years). Subjects from the replication sample, recruited through advertisements in local hospitals, gave informed, written consent, and the study was approved by the Hospital For Sick Children institutional research ethics board. These data also served as the replication sample in our previous paper examining error processing activities .The stop signal task (SST)  involvest tests.Inter-trial interval (ITI) was jittered to maximize the number of independent equations in the deconvolution analysis, using trials of 2.5 or 3.5 s. Every 14th trial was followed by a 17.5-s rest (blank screen). Trial order was pseudo-randomized so that the current trial did not predict the subsequent type of trial. Mean go response time (RT) was observable from the 67% of trials in which no stop signal appeared. Stop signal reaction time (SSRT) was estimated by subtracting the mean delay on stop signal trials from the mean RT on trials with no stop signal. Behavioural scores (within-group means and between-group differences) were analyzed using two-tailed Imaging was done with a General Electric (GE) LX 1.5 T MRI scanner . Anatomical data were acquired with a standard high-quality anatomical sequence  24 cm, 256 × 256 matrix). Functional data were collected using an echo planar imaging (EPI) sequence with an eight-channel head coil (echo time (TE) = 40 ms; repetition time (TR) = 2000 ms; flip angle = 90°; 24 slices; 6-mm thick; FOV 24 cm; 100 kHz readout bandwidth; 64 × 64 in-plane resolution). Behavioural data were collected using a fibre-optic response system interfaced to a laptop running the SST.Functional data were analyzed using AFNI software package for the analysis of functional neuroimages version 16.0.09 . Images 3 resolution), and smoothed using a 6-mm full-width at half maximum Gaussian kernel.The following event types were used in the deconvolution analysis (as in ): fixate3) with a minimum Z score of 1.96 for an overall α < 0.05. The low threshold and large cluster size used for correction is based on the expected effect sizes from our previous results using the same approach [Single-subject activation maps were entered into a random-effects ANOVA analysis for ADHD and TD groups. Statistics for group-wise ANOVA output maps were distributed as a t* statistic with 13 degrees of freedom. Group difference maps were generated using a nested repeated measures 3-factor ANOVA  to identify significantly different activities between TD and ADHD adolescents. Statistics for group difference ANOVA output maps were distributed as a t* statistic with 26 degrees of freedom. Output from all ANOVA analyses  were corrected for multiple comparisons using AFNI’s 3dClustSim program   warning phases (Fixate); (2) response phases ((X + O)/2); (3) reactive inhibition (SI-(X + O)/2); (4) error detection (Detect); and (5) post-error slowing (PES). Statistics for slope (B1) term estimates from correlation analyses were distributed as a t* statistic with 12 degrees of freedom. All correlation maps were whole brain corrected as described above and inspected for significant clusters in regions that exhibited significant group differences. In TD, only SSRT correlations that replicated in healthy young adults were reported.p = 0.024). TD and ADHD groups showed no significant difference in post-error slowing , go reaction time , percent correct go-responses  or the percent of successful stop trials . The only behavioural difference was in stop signal reaction time, which was longer  in the ADHD (233 ± 51.0 ms) than in the TD group (198 ± 33.0 ms). SSRT was not correlated with inattentive , hyperactive  or total  symptom scores in the ADHD group. Behavioural performance in the replication sample was in normal range . Subject motion and the number of censored time points did not differ between TD and ADHD groups .The 14 TD (mean (± SD) age 15.4 ± 1.6) and 14 ADHD (age 13.7 ± 2.1) adolescents showed significant age difference  was present in the left inferior frontal gyrus  during error detection, the result of activation in ADHD  but not in TD   and replication groups , but positively correlated with inattentive  and total  symptom scores in ADHD. Therefore, greater error detection activity in the left inferior frontal gyrus predicted improved SSRT in TD and replication groups, whereas greater activity in ADHD instead predicted increased inattentive and total symptom scores.Firstly, significantly different activity  was present in the right inferior frontal gyrus  during error detection, the result of activation in ADHD  but not in TD   and replication groups  similar to right inferior frontal. However, right inferior frontal activity negatively correlated with hyperactive  symptom scores in ADHD, in contrast with correlation of right inferior frontal with inattentive scores. Therefore, greater error detection activity in the right inferior frontal gyrus predicted improved (i.e. shorter) SSRT in TD and replication groups, whereas greater activity in ADHD instead predicted lower hyperactive scores.Secondly, similar to the left inferior frontal gyrus, significantly different activity  was present in the hypothalamus  during post-error slowing, the result of deactivation in TD  but not in ADHD   and replication groups , but correlated with inattentive  symptom scores and negatively correlated with SSRT  in ADHD. Therefore, greater deactivation in the hypothalamus during post-error slowing predicted improved (i.e. shorter) SSRT in TD and replication groups, but instead predicted lower inattentive scores and worse (i.e. longer) SSRT in ADHD.Thirdly, significantly different activity  was also oppositely correlated with SSRT compared to TD and replication groups.It is noteworthy that activities that predict SSRT in TD but symptoms in ADHD were apparent during errors and not other phases of the task. Errors are important moments for integrating information that becomes available upon outcomes, and our results point to this level of integration being fundamentally altered in ADHD. Further, identifying these effects requires the separation of error detection from post-error slowing, which has not been done in any previous approach. Our results point to the need for separating error detection from post-error slowing in the SST, because the functional roles of these two stages of processing, although tightly related, are highly distinct; error detection must interrupt ongoing processing and initiate the operating conditions necessary for adjustment, whereas post-error slowing must actually carry out the adjustment. Further, we showed in previous work , 51 thatNegative correlation of left and right inferior frontal activity during error detection with SSRT in TD and replication groups suggests that healthy subjects who activate inferior frontal gyri more strongly at moments when errors are detected have shorter SSRT. By contrast, activation of left inferior frontal gyrus involved in interference suppression   correlatThese findings clearly demonstrate that activity differences in ADHD do not simply reflect the over- or under-functioning of component processes, but the integration of these processing resources towards the management of symptoms instead of towards the task. Correlation of left inferior frontal activity with inattention is also consistent with our initial speculation, noted in the introduction, that inattentive and hyperactive-impulsive states associated with ADHD might cause task-related information to be targeted and processed in the brain as noise rather than as signal. Most current models would hold that ADHD involves degraded neural processing of agreed upon signals and noises, rather than a different mapping of events into signals and noises. However, if task-related stimuli are not being processed by the brain as signals but rather as noise, then interventions that can strengthen signal to noise in the context of normal function might have little or potentially opposite effects on information that is being processed as noise. Although stimulant medications increase signal to noise of task-directed attention and performance in both healthy subjects and those with ADHD, our findings indicate that improvements in ADHD might be more due to a shift in the susceptibility of attentional networks to automatically process task-directed stimuli as signals rather than noises. The current approach could be used to test whether stimulant medications cause modulations in the intensity of altered activities in ADHD or a shift towards relatively normal processing.The importance of inferior frontal activities at moments that errors are detected might signify the central role of the efference copy of ongoing events in striatal learning. The term efference copy refers to the convergence of signals representing context, action and reward in the striatum that are necessary for reinforcement learning –56. In cOur results indicate that the efference copy of events at moments that errors are detected, which reflect the goals towards which neural processing is directed, exert a dominant influence on overall task performance in healthy subjects. However, in ADHD, altered inferior frontal activity does not likely reflect the degree of task-directed efference copy or reinforcement learning. Rather, inferior frontal activities that normally contribute to the efference copy of task-directed processing instead appear to be engaged in managing inattentive and hyperactive symptoms and thus cannot provide any task-related efference copy. If we cannot evoke an image  of what happened leading up to the error, then there is no leverage for effective adjustment in the brief time window allowed for reinforcement learning. Therefore, therapeutic interventions aimed at strengthening such non-occurring processes would be expected to have little impact.Post-error slowing activity in the hypothalamus correlated with inattentive symptoms in ADHD and was the only replicated correlation with SSRT in TD and healthy young adults that also oppositely correlated with SSRT in ADHD. The hypothalamus is a cognitive-motivational interface , 31, whiThe hypothalamus plays a central role in mobilizing and unifying distributed processing in the brain towards specific goals  based on internal states  –32. HoweHere, we found heightened amygdala activity on post-error slowing correlated with greater inattentive symptoms in ADHD, but was not correlated with SSRT in TD adolescents or our replication sample of healthy young adults. These results are consistent with the amygdala driving symptoms in ADHD but not affecting task performance in healthy subjects. We also found that amygdala activity correlated with inattentive symptom scores in ADHD during all phases of the SST except reactive inhibition phases on successful stop trials. In a previous paper , we showRather than simply reflecting a degraded form of normal reinforcement learning on errors in ADHD, altered striatal function in the form of limbic-motor interfacing is in fact harmonious with the absence of an efference copy required for reinforcement learning. In other words, our results are consistent with an entirely different integration of distributed processing in ADHD that is not amenable to the reinforcement learning models that appropriately describe neural function in TD. Despite the similarity of stimuli and outcomes  in both TD and ADHD groups when performing the SST, our results point to categorically different neural representations and therefore subjective experience of the same task-related stimuli and events in ADHD compared to TD.In addition to the kind of altered integration of distributed neural processing that would be caused by heightened amygdala activity, heightened activity and altered correlations among neurotransmitter nuclei that compete for control of dopamine  would alNeural processing resources that are directed towards the regulation of inattentive and hyperactive symptoms cannot simultaneously be used for task performance. Therefore, therapeutic interventions that attempt to directly enhance a component function that improves task performance in healthy subjects might only affect the expression of symptoms in ADHD without affecting task-directed behavioural control in general. It remains to be seen whether interventions that alleviate symptoms would cause a shift towards relatively normal functioning in ADHD. This is an important distinction for monitoring the effects and predicting outcomes of therapeutic interventions.The current approach can test whether therapeutic interventions cause a shift towards relatively normal function in several ways, including (1) testing whether activities that previously correlated with symptoms  become correlated with SSRT in ADHD as they do in TD adolescents, (2) testing whether therapy-induced reduction in amygdala activity causes a transition from limbic-motor interfacing to reinforcement learning-related thresholding influences in the striatum on errors, and (3) testing for a normalization of the competition for control of dopamine evident in normalized activities and correlations in neurotransmitter nuclei on errors. The use of such measures can improve therapeutic interventions and the neurocognitive models used to inform them.Several methodological limitations of this study restrict the interpretations of results and point to future work necessary for validating and extending the current work. Firstly, the hypothalamus and neurotransmitter nuclei identified here are small and in the brainstem, which is generally associated with increased susceptibility artefacts. However, we are confident that activities and correlations in these nuclei reflect true positives and not artefact-related noise, because they are all in specific locations that are relatively free of susceptibility artefact , 63, andOur results support an interpretation that differs from the usual one which holds that altered BOLD responses in ADHD compared to TD arise from relatively weak or compensatory versions of normal task-related processing. Instead, our results are consistent with alerting stimuli that normally cause the preparation and adjustment of task-directed processing instead engaging processes involved in mediating inattentive and hyperactive symptoms, and partially in direct opposition to task-directed processing. The correlation analyses performed here demonstrate that activities during errors and preparatory periods like warning and response phases that precede the appearance of stop signals, and not those during reactive inhibition, are most strongly predictive of ADHD symptoms and of overall inhibitory control estimated by the SSRT, highlighting the importance of separating these phases of activity. A better structural understanding of the altered neural representations of task-related stimuli and events, which underlie altered cognitive control, can help us move beyond deficit-based theories and ask how to gain some leverage on the integration of component processes that would be necessary for more effective therapeutic interventions.Additional file 1. Supplementary tables of group BOLD differences and correlations with SSRT and ADHD symptomsAdditional file 2. Supplementary figures


Reading disability (RD) and Attention Deficit Hyperactivity Disorder (ADHD)symptoms often co-occur in school-age children.rd and 4th grades on “cool” executive function(EF) abilities and phonological processing. The children were divided intothree groups: those with ADHD symptoms only, those with RD only, andcontrols.The present study evaluated the performance of 216 Brazilian children from3MANOVA analyses, controlling for age and nonverbal intelligence, showed worseperformance for the RD group, compared to the ADHD symptoms group, onmeasures of phonological processing  and “cool” EF components. The ADHD symptoms groupdid not differ from the control group on the majority of the “cool” EFtasks. Compared to the control group, the ADHD symptoms group and the RDgroup both showed significantly more errors in rapid automatized naming offigures, which evaluates the inhibition component of EF; performance on thistask was similar for these groups.We conclude that children with RD have greater impairment in phonologicalprocessing and “cool” EF compared to those with ADHD symptoms. Furthermore,deficits in inhibitory control may be shared among children with bothconditions. Thesestudies compared groups with RD-only, ADHD-only, comorbid profiles, and typicallydeveloping children.Purvis and TannockSince ADHD and RD are two of the most common neurodevelopmental disorders, andconsidering their high comorbidity and negative impact on children’s lives, it isimportant to analyze and specify the neuropsychological profiles associated with thoseconditions, as suggested by the multiple deficit model of neurodevelopmentaldisorders.,-,Thus, this study aimed to compare performance in “cool” EF and phonological processingbetween ADHD-only children, RD-only children, and typically developing children. Weexpected to find the following results: 1) higher impairment in general “cool” EF andphonological processing among RD children compared to children with ADHD symptoms and tocontrols and, specificallyM = 8.94, SD = .71), from the3rd (n = 62) and 4th grades(n = 154). All children attended public elementary schoolsin Porto Alegre  and Belo Horizonte ;The sample consisted of 216 children, 55.1% girls, aged between 8 and 11 years(n = 150) . The excThis study was approved by the Research Ethics Committee of the FederalUniversity of Minas Gerais (protocol number 939.562) and the Ethics Committee ofthe Institute of Psychology at the Federal University of Rio Grande do Sul(protocol number 1.023.371). Two evaluation sessions  were conducted with the children whose parents/guardianssigned the Informed Consent Form. The research aims and procedures wereexplained to the children and parents. Assessments were conducted only after thechild’s written and oral informed consent and the parent’s written informedconsent were obtained.The RCPMThis study has a quasi-experimental design of contrasting groups.n = 20): comprising children who scored at least 6items as either “pretty much” or “very much” in questions 1 to 9(symptoms of inattention) of the MTA-SNAP-IV questionnaireth percentile on the LPIa) Inattention/hyperactivity symptoms group :comprising children who scored below the cut-off point described aboveon the MTA-SNAP-IV questionnaire,b) RD without ADHD symptoms group :children who scored below the cut-off point described in section“a” of the MTA-SNAP-IV questionnaire,c) Control group  Raven’s Colored Progressive Matrices (RCPM):,b) MTA-SNAP-IV:,,c) Oral Single-Word and Pseudoword Reading Task (LPI):d) Phoneme Elision Task:e) Digit Span (forward and backward):f) Letter span: This instrument is analogous to the digit span task andmeasures short-term phonological memory and working memory. The taskcontains both forward and backward letter span conditions, each of whichis scored on a scale from 2 to 9. The instrument was developed by theauthors and showed internal consistency  of .66.g) Corsi Block-Tapping Task:h) Five Digit Test:,i) Phonemic and Semantic Verbal Fluency:j) Contingency Naming Task (CNT):We performed descriptive analyses and examined the dependent variables to detectgroup differences to control for in further analyses. We conducted MultivariateAnalysis of Variance (MANOVA), controlling for age and nonverbal reasoning, tocompare performances on the neuropsychological tasks between the groups,employing the Bonferroni correction for multiple comparisons. Analyses were runin the SPSS software v. 20.0 at a significance level of 5%.t-test for independent samples showed differences between cities(Porto Alegre vs. Belo Horizonte) in both reading and phonological processing. Thesedifferences may be related to the schooling process.t(204) = 4.37, p < .001, whereas childrenfrom Belo Horizonte were faster in RAN of letters and figures,t(213) = 3.58, p < .001 andt(213) = 3.39, p = .001, respectively. Also, inRAN of figures, children from Belo Horizonte made fewer errors than those from PortoAlegre, t(213) = 2.28, p = .05. There were nosignificant differences between cities in phonological short-term memory or workingmemory, nor in reading of regular words, irregular words, or pseudowords.A F = 3.608,p = .029, Wilk’s Λ = .865, η2 =.03]. They performed worse than the other groups (controls and ADHD symptoms) inphonemic elision  ; orthographic verbal fluency  ; figure RAN (more errors)  ; and response time – inhibitory component (processing speed and inhibitorycontrol)   [2 = .10] .p > .05). The only measure in which the ADHD symptoms groupexhibited worse performance (larger mean number of errors) than the control groupwas figure RAN – inhibitory component (inhibitory control). Nevertheless, theirperformance did not differ significantly from that of the RD group, which, asdescribed previously, also made significantly more errors than the controls , verbal andvisuospatial working memory, and phonological processing, the performance of theADHD symptoms group did not differ from that of the control group and phonological processing  compared to both the ADHDsymptoms and control groups.,There is currently a consensus in the literature showing consistent associationsbetween deficits in phonological processing and RD.,It is noteworthy that the tasks we employed to measure EF , in which children with RD performed worse than childrenwith ADHD symptoms only and control children, also require language skills. Thechildren with RD in this study differed from the other groups in verbal memory andoral and written language abilities, such as phonological awareness andlexical-semantic processing.,Additionally, as our results showed, EF deficits are not always present in childrenwith ADHD symptoms.-Despite the fact that children with RD displayed a more prominent profile ofexecutive deficits in the present study, we also found a shared inhibitory controldeficit between RD and ADHD children on one of the tasks, even after controlling forage and nonverbal reasoning. Inhibitory control deficits are described as animportant endophenotype for the study of ADHD. This important EF component may helpexplain difficulties in both attention regulation and hyperactive/impulsivebehaviors in children.There has been speculation regarding the distinct mechanisms underlying inhibitorycontrol deficits in ADHD and RD. It has been suggested that children with ADHD mayhave a more pervasive inhibitory deficit compared to typically developing children,which leads to behavioral impulsivity, whereas children with RD may havedifficulties in inhibition due to impairments in rapid information processing.,In conclusion, the current results corroborate the hypothesis that RD isfundamentally characterized by neuropsychological impairments, where these are lessprominent in children with ADHD symptoms.The results presented here must be interpreted with caution since the study hassome limitations. These limitations include small sample sizes, especially forthe ADHD symptoms group, and a difference in sample sizes between the twocities. It is important to observe that the proportion of girls in the RD groupis twice that of boys, which contradicts the literature showing equal or greaterproportion of boys with RD,We also emphasize the need to include fine measures for other EF components,especially “hot” components. Furthermore, measures of other modalities ofinhibitory control, such as motor inhibition, are needed, since the inhibitorycontrol task employed here specifically involves inhibition of verbal responsesto competing visual stimuli. Additionally, the groups in our sample were formedbased solely on observed reading difficulties and ADHD symptoms, not properdiagnosis of dyslexia and ADHD. This may limit comparisons with studies thatincluded samples selected following standardized diagnostic procedures for bothconditions. We also did not split the ADHD symptoms group by subtype due to the small sample size.However, grouping children with unspecified ADHD symptoms is consistent with thecurrent version of the Diagnostic and Statistical Manual of Mental Disorders(DSM-5),,In conclusion, the present study indicates that phonological measures may aid thediagnosis and intervention of RD.


In the last few years, several computerized tasks have been developed to increase the objectivity of the diagnosis of attention-deficit hyperactivity disorder (ADHD). This article proposes the “running raccoon” video game to assess the severity of inattention in patients diagnosed with ADHD. Unlike existing tests, the proposed tool is a genuine video game in which the patient must make a raccoon avatar jump to avoid falling into different gaps. The distance to the gap is recorded for each jump. To evaluate the proposed game, an experiment was conducted in which 32 children diagnosed with ADHD participated. For each participant, the median and interquartile range of these distances were calculated, along with the number of omissions. Experimental results showed a significant correlation between the participants’ inattention  inattention subscale) with each of these three measures. In addition to its accuracy, other benefits are its short duration and the possibility of being run on both standard computers and mobile devices. These characteristics facilitate its acceptance in clinical environments or even its telematic use. The obtained results, together with the characteristics of the video game, make it an excellent tool to support clinicians in the diagnosis of ADHD. Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with an estimated prevalence in children and adolescents of 7.2% according to a systematic review published recently . ADHD isUsually, ADHD diagnosis is based on the judgment of the health professional using a clinical history often supported by scales filled out by caregivers and/or teachers. Therefore, the ADHD diagnosis depends primarily on health professionals’ expertise and the caregiver/teacher’s observational skills . SeveralIn addition to the observational capabilities of the medical professionals and caregivers, the assessment’s accuracy may also be influenced by various weaknesses associated with the use of questionnaires. As an example, the veracity of the responses may not be guaranteed. In the case of ADHD, some of the reasons for which patients tend to exaggerate or attenuate their symptoms are: to justify academic failure, to access to stimulant drugs, to obtain certain social/academic benefits, or to refuse that they have the disorder ,12. FurtIn order to surpass these limitations, in recent years, some authors have proposed to analyze patients’ behavior while performing a computerized task . As willThe rest of the article is structured as follows. As mentioned above, a significant number of computerized tasks have been developed in recent years aimed at identifying patients with ADHD . A commoSome of these works extend the traditional CPT to make it more similar to a video game. For example, Berger, Slobodin, and Cassuto proposed the MOXO-CPT in which the letters were replaced by cartoons, and visual and auditory distracters were added . ConciseA different extension was proposed by Keller et al. . In theiLater, these authors replicated the study by replacing the previous predictive techniques with a logistic regression . In thisAnother work in which the interaction between the participant and the computer was carried out through movement was developed by Delgado-Gomez et al. ,22. In tIn order to incorporate ecological validity into the assessment, Rizzo et al., proposed the use of virtual reality . In 2006The following section describes the proposed raccoon runner game. Unlike the previous computerized tasks, the proposed video game does not follow a go-no-go paradigm. It is a standard video game that most children are familiar with, which increases the ecological validity of the test. It also has the advantage of not needing specific hardware. This considerably reduces its cost and allows it to run on personal computers, tablets, or mobile devices, allowing remote evaluations.A group composed of 32 children  referred to the Child and Adolescent Psychiatry Unit of the Department of Psychiatry at Fundación Jiménez Díaz Hospital  and diagnosed with ADHD according to DSM-5 criteria participated in the study . All parThe running raccoon game is a video game based on the genre of infinity runners in which a raccoon must jump several gaps before reaching the goal. The game was implemented using the widely used Unity 3D game engine . Figure In detail, the raccoon has to jump 180 gaps which are grouped into 18 blocks. Each block is identified by the raccoon’s speed, the trunk length, and gap length. The length of the trunk and the speed of the avatar define the inter stimuli (IS) time, which is approximately 1.5, 2.5, and 3.5 s while the gap’s width defines the difficulty of the jump. The settings of the different blocks are shown in The Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behavior rating scale (SWAN) is a parent/caregiver report inventory developed for screening ADHD . It exteDuring the experiment, a trained psychiatrist accompanied each of the patients while they conducted the task. While each child was performing the test, the corresponding caregiver or legal tutor filled the inattention subscale of the SWAN scale. The average score obtained was −7.1 and the standard deviation was 10.7.The predictors that are widely used in the literature were computed. These are the median and interquartile range (IQR) of the recorded distances along with the number of omissions for each participant. The recorded distance is the distance from the jump point to the beginning of the gap, while the number of omissions represents the number of times the participant did not jump. Pearson’s correlation was calculated for each of these measures and the score obtained in the inattention subscale of the SWAN scale. In addition, a multiple regression analysis was conducted to analyze if these predictors are independent.Caregivers were required to sign their informed consent after been explained the test in detail. The consent form and the study protocol were reviewed and approved by the Institutional Review Board of Fundación Jiménez Díaz Hospital of Madrid. During the experiment, a trained psychiatrist accompanied each of the patients while they conducted the task. While each child was performing the test, the corresponding caregiver filled the inattention subscale of the SWAN scale.p-value) of the median and the interquartile range of the jump distances and the number of omissions made by each participant with respect to the score obtained by them on the inattention subscale of the SWAN scale. Columns 2 to 4 in The first column in p-values associated with the coefficients of the variables included in the different possible linear regression models. The fact that the variables are significant in the simple linear regression models and no longer in the multiple models shows the collinearity of these variables.A relevant aspect to investigate is whether these widely used predictors are independent. This result makes sense since, for example, the shorter the jump distance, the more likely it is to commit an omission . For this reason, the following analyses are performed using only the median of the jump distances.Another important aspect to investigate is whether the value of these correlations depends on the length of the test. That is, whether the correlations obtained with these three measures are higher at the beginning or the end of the test. p-value = 0.005). To get a better understanding, In order to verify the last statement, an ANOVA test was conducted in which the dependent variable is the correlation and the factor is the time between stimuli. The analysis showed that at least one of the means was significantly different from the others (n = 24) were used to estimate the parameters of the linear regression model, while the remaining 25% were used to evaluate it. The linear regression model was built with three predictors. These were the medians calculated using the jump distances of each of the three blocks with similar interstimulus times. For each observation in the validation set, the inattention of the associated participant was estimated by the built linear regression model using the three participants’ predictors. The correlation of these estimates with the scores obtained by those participants in the SWAN inattention subscale was calculated. To obtain more significant results, 10,000 repetitions were performed and the average of the obtained correlations was calculated. The mean correlation obtained was 0.53.To assess the performance of the proposed game, a repeated validation experiment was performed ,36. To dIn the present study an adaptation of a traditional video game of the infinity runner genre has been proposed to assess the degree of inattention of children with ADHD. This work differs from previous studies in two aspects. Firstly, with the exception of the work of Shaw, Grayson and Lewis , it is aOur results suggest that the number of times the avatar does not jump, as well as the median and interquartile range of the jump distances, show a significant correlation with the severity of patients’ inattention. In addition, this correlation tends to be greater when the time between stimuli increases. This could be explained because when the time between stimuli is short, the patient is immersed in the game, whereas whenever this time is longer, ADHD patients have difficulty maintaining the attention. This finding suggests giving more importance to the jumps in which the interstimulus time is longer .The proposed methodology has several advantages. First of all, unlike the existing methods that last more than 15 min, the developed test takes approximately seven minutes. Furthermore, our results indicate that a shorter test could be sufficient to accurately evaluate ADHD. This feature makes it especially attractive in clinical environments where time is scarce. Second, the test does not require complicated or expensive hardware such as virtual reality equipment. A standard computer has been used in this work, but devices such as tablets or mobile devices could also be used. The limitations of our study are the sample size, the use of a single assessment scale and the absence of a healthy control group. Future studies with larger samples and administered with different assessment scales will help to confirm our pilot results. In addition, the availability of a control group will also allow us to analyze whether the proposed game is capable of discriminating children with ADHD from the participants in the control group.In conclusion, the results obtained open up new lines of research. Firstly, to find out what is the optimal length and configuration (time between stimuli) of the test. Secondly, since the game can be run on any device, to analyze the possibility of performing the test remotely. More importantly, our study indicates to use other video game genres  as diagnostic tools for ADHD or any other mental disorders.


Internet trolling, or the intentional disturbance or upsetting of others on social media for personal amusement, has become increasingly prevalent in recent years . Current research focuses on these destructive social media behaviors in younger populations, therefore this study set out to investigate the gender differences of trolling behaviors in Baby Boomers. Participants (N = 140), ages 54 and older, were recruited from the Amazon Mechanical Turk and were compensated for their participation. Participants completed a survey investigating their likelihood to engage in trolling behaviors, the extent to which they enjoy trolling, and their feelings while trolling. Results indicated that while there were no significant differences between men and women in their need, intensity of use, or addiction to social media, men were significantly more likely to engage in trolling behaviors than women. Men reported posting to upset others (p = .018), as well as commenting to upset others (p = .053), more often than women. Furthermore, when engaging in these behaviors, men reported feeling intelligent (p = .013), confident (p = .024), superior (p = .053), and happy (p = .012), more often than women. However, these results could be indicative of a more sinister issue. Men also reported more often that their reasons for engaging in trolling behaviors were feelings of loneliness (p = .005) and anxiety (p = .010). This indicates that these trolling behaviors may then be a way for men to seek out some form of “social support” in the online community.