ADRA2A rs553668, DRD2 rs1124491 and SLC6A4 rs6354 were identified to be associated with ADHD. A significant multiplicative and additive gene-environment interactions were observed between the PSE and the ADRA2A rs553668 in relation to ADHD and ADHD-ODD. The risk of the genetic variants in ADHD was increased significantly if the child had prenatal tobacco exposure. The genetic risk for ADHD could be influenced by the presence of environmental risks. The environmental and the genetic risks are not distinct to each other. More gene-environment interaction studies were needed to reveal the etiology of ADHD.The purpose of our study was to test the hypothesis that prenatal tobacco smoking exposure (PSE) could modulate the association of genetic variants with ADHD. A community based case-control study was conducted among Chinese children and 168 ADHD patients and 233 controls were recruited by using combination diagnosis of DSM-IV, SNAP-IV and semi-structured clinical interview. Logistic regression analysis was performed to estimate the effect of prenatal tobacco smoking exposure and genotype frequencies on ADHD susceptibility individually by adjustment for potential confounders. Multiplicative and additive interaction analysis were performed to evaluate the interactions between risk genes and PSE with regard to ADHD. Prenatal tobacco smoke exposure was a significant risk factor of ADHD even after adjusted for other potential confounders.  Previous studies reveal that individuals affected by ADHD frequently lead to various social functional impairment, such as social communication disorder, bad peer relationships, academic performance difficulties3, develop personality disorder, substance abuse and even criminality5.Attention-deficit/hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder characterized by developmentally inappropriate level of inattentiveness and hyperactivity. It is one of the most frequently diagnosed disorders among children and adolescents, with the pooled prevalence of 7.2%7. The genetic architecture of ADHD is complex and numerous single nucleotide polymorphisms (SNPs) of genes involved in the regulation of neurotransmitter systems are identified to be associated with ADHD10.Among them, genes involved in the dopaminergic, serotonergic and noradrenergic pathways are the most common candidate genes for ADHD studies13.As a complex multi-factorial disease, exact etiology of ADHD is still elusive. The biologic basis for ADHD is thought to be largely genetic14. Some prenatal and perinatal factors are reported to be associated with ADHD, such as younger maternal pregnant age15, caesarean, induced delivery, preterm birth, low birth weight16, and mother smoking during pregnancy. With regard to prenatal smoking, it is constantly identified to be a risk factor for offspring hyperactivity symptom and ADHD19. Evidence from epidemiological researches suggests that maternal cigarette smoking and environmental tobacco exposure during pregnancy are all in association with high risk of ADHD. Hyunjoo Joo20 and his colleagues have described that independent and combined exposure to pre- and postnatal secondhand smoke were associated with ADHD, particularly with the hyperactivity symptom domain. A large population-based birth cohort have provided that maternal smoking during pregnancy was associated with children hyperactivity symptom19. Although large numbers of studies demonstrated a relationship between maternal smoking exposure during pregnancy and ADHD, most of previous studies have investigated the effect of smoking exposure only and there were few studies on gene–environment interactions which were more likely to play the real role in the etiology of ADHD21.Adding to genetic influence, environmental factors are assumed to be co-involved in the etiology of ADHD21, but it may be required to uncover the genetic liability14. The studies about the G ene× Environment studies of gene and smoking exposure on ADHD are limited. A gene-environment interplay study reports that the co-exist group of prenatal smoking exposure and 10 R/10 R genotype of DAT gene showed a higher mean scores of hyperactivity-impulsivity and inattentiveness than other groups22. But the findings remain speculative since the participants are lack of a clinical diagnosis of ADHD, and the ADHD scores of the participants was derived from the Conners’ Parent Rating Scale which will lead to the overdiagnosis of the ADHD.Importantly, the environmental risk is not distinct to the genetic influence23. Therefore, gene and environment interaction research will be an inevitable way to explore the etiologic mechanisms of ADHD. However, studies about the joint effects of gene and environmental factors on ADHD is limited14. To fill the knowledge gap, we conducted the case-control study to elucidate the potential synergistic effect of the prenatal tobacco exposure during pregnancy and children genetic variants on children diagnosed ADHD and its symptom domains.Developing of novel technologies have made it more common in identifying genetic risk factors of ADHD, however, proved the genetic architecture of ADHD to be far more complex, since the genetic influences expressed only additional risks to disease susceptibilityp < 0.05). There were no significant difference between ADHD patients and controls in terms of family history of neural system diseases, parental marital status, preterm birth (pregnancy < 37weeks), Low birth weight(birth weight <2500 g) and delivery mode, levels of blood lead and postnatal tobacco exposure. Meanwhile, ADHD children scored higher of SNAP-IV scales  and of CBCL total scores than controls . Thus they were excluded from further analysis. Consequently, a total of 19 SNPs remained in the subsequent analysisA total of 22 SNPs were detected. The genotype distributions of ADRA2A rs553668, DRD2 rs1124491 and SLC6A4 rs6354 were nominally significant at the uncorrected 0.05 significance level with adjustment for age and gender. Among these SNPs, ADRA2A rs553668G allele was associated with an increased risk of ADHD in the additive model, and SLC6A4 rs6354 (GG + GT) was associated with an increased risk of ADHD, whereas DRD2 rs1124491 (AA) was associated with a decreased risk of ADHD. After FDR adjustment, SLC6A4 rs6354 remained statistically significant , but the SNP was not associated with ADHD-ODD. Additionally, individuals who carried DRD2 rs1124491 AG or GG genotype displayed a risk association with inattention symptom compared to AA genotype . Compared with those carrying TT genotype, the risk association with ADHD-ODD was increased significantly in individuals carrying the SLC6A4 rs6354 GT or GG genotype , but no association was observed in other ADHD symptom domains. The ORs and 95% CIs changed slightly in different adjustment model  showed a risk association with ADHD and all symptom domains, and slight change of the OR and 95% CI was observed in different adjustment model . The strong association was shown in ODD symptom domain  and in the hyperactivity symptom domain , and RERI was 1.88(95% CI = 0.06–4.12), Meanwhile, multiplicative interaction was displayed in this group (Pmul = 0.003). The multiplicative and additive interactions were found in all symptom domain of ADHD. However, no multiplicative and additive interactions were detected between DRD2 rs1124491 polymorphism and PSE, nor between SLC6A4 rs6354 polymorphism and PSE , compared with AA carrier without PSE. But the OR for the rs553668 GG or AG carriers with PSE, was significantly increased to 3.54 in children, especially a strong association with hyperactivity and ADHD-ODD, and the effect size did not diminished even after adjusted by potential environmental influencing covariates. In addition, the study found three SNPs of ADRA2A rs553668 GG or AG genotype were in concordance with the findings of several studies, and indicated that rs553668, being known as the DraI site in the 3′-untranslated region of ADRA2A gene, A-to-G variant increased the risk of ADHD25. Meanwhile, the findings showed the DRD2 rs1124491 in the significant association with inattention type of ADHD. DRD2 gene (Dopamine Receptor D2 gene), encodes the D2 subtype of the dopamine receptor. A population-based birth cohort have reported an association of DRD2 rs1124491 with ADHD in males and low persistence in female ADHD patients27. Moreover, a common variant SLC6A4 rs6354 was identified to be in association with ADHD-ODD. SLC6A4 rs6354 located in the untranslated(exon 2) 5′-UTR region of SLC6A4 gene—a potential module of exonic splicing enhancer or silencer by bioinformatics prediction— and may affect the expression level or exonic splicing of serotonin28. Evidences show that one repeat length polymorphism-STin2 located in the untranslated exon 2 of SLC6A4 gene may be significantly associated with major depressive disorder and suicide29.With regard to the findings in the genetic analysis, the results of risk association with ADHD in the children carrying 30 and his colleagues found maternal smoking during pregnancy was shown a significant association with offspring ADHD symptom scores in twins study after adjustment by genetic factors, although the main effect of cigarette exposure was small. One gene-environment interaction study evaluated maternal smoking and the DAT gene. In the cases with co-exist of maternal smoking during pregnancy and 10 R/10 R genotype of DAT gene, the mean score of hyperactivity-impulsivity and inattentiveness derived from the Conners’ Parent Rating Scale were higher than other groups22. The findings in the current study infer that the susceptibility of ADHD for the unfavorable ADHD genotypes carriers might increase when the children having the history of in-utero exposure to cigarette smoking.A limited number of G × E studies have shown that genetic factors interact with maternal smoking to heighten risk for ADHD. Anita Thapar31. The neurotoxic effects of nicotine exposure on human fetal brain development have not been studied directly. However, a large number of animal studies have found that nicotine has obvious neurotoxic effects on fetal brain development32. Animal models confident that the nicotine can readily cross the placental barrier into fetal circulation, and the blood-brain barrier, which protects the adult brain from toxic chemicals, is not fully formed until about six months after birth33. So, nicotine can cross the blood-brain barrier and specifically binds to nicotinic acetylcholine receptors (nAChRs) in the fetal brain34, and nAChRs have an important role in regulating synaptic plasticity and brain development. Concerning of animal models, chronic nicotine exposure to animal brain developmentally leads to long-term changes of decreasing presynaptic nAChRs function, which cause altering nicotine-stimulated release of those neurotransmitters and the altering continues for a long time even after nicotine exposure has been removed36. And neurotransmitters dysregulation of acetylcholine, dopamine and serotonin are assumed to be the reason of ADHD21. The decreased presynaptic nAChRs function may increase the risks of dysfunctions of dopaminergic, serotonergic and noradrenergic neurotransmission caused by genetic variants. That may explain the strong joint effects of prenatal tobacco exposure and genetic variants on all ADHD subtypes. But the exact reasons of the increasing risks require more extensive research.However, the molecular mechanisms responsible for the interaction remain unknown. Nicotine, the main psychoactive component of tobacco, can accumulated in human brain fast when they are smoking38. Moreover, evidences support that longer term low level exposure (<10 µg/dL) associated with intellectual impairment40. Unfortunately, the effects of lead exposure on ADHD are less clear. In the current study, no statistically significant association was found between low blood lead levels (<10 µg/dL) and diagnosed ADHD and all the symptoms. A growing body of evidence now support that low level exposure (<5 µg/dL) also associated with cognitive impairment, suggesting that there is no threshold for developmental neurotoxicity42. Stephani Kim5 and his colleagues examined the relationship between blood lead levels and ADHD in children, and found that the odds ratios for the risk of ADHD is 4.63 (95% CI: 1.36–15.72) in the “≥2 mg/dL” group and the odds ratios increased to 7.25 (1.66–31.67) in the “≥3 mg/dL” group. However, some studies reported that the association between blood lead levels and ADHD did not exhibit a dose-response relationship43.Lead is a heavy metal that exists in the environment in nature, and it is a most widely used neurotoxicants. Many studies have shown that high blood lead levels (≥10 µg/dL) are harmful to children neurodevelopment21, aiming to minimize the overdiagnosis or underdiagnosis. However, several limitations have to be considered during interpretation of the findings. The major limitation of this study is that the small sample size has decreased the power to detect the effects of the SNPs and the gene-environment interactions. One inherent methodologic problem in this case-control design is that the retrospective assessment of PSE might introduce systematic error in the calculation of the association between PSE and ADHD because of recall bias. In order to diminish the recall bias, we recruited the new diagnosed patients from the community survey. Although the regression model controlled for important confounding variables such as age, gender, mother and father education, and postnatal smoke exposure status, it did not take into account other possible environmental pollutants, such as manganese, mercury, arsenic and bisphenol A and phthalates45, which can be a focus of future research. Psychological and social environment are also the important for child psychological development, but those did not measured in our study. This is a weakness of the study. The participants in our study were enrolled from one city of China, which limited the results interpretation in general populations. Finally, its cross-sectional nature limits explication about the causal effects of G × E interactions. Future research may focus on a longitudinal study to confirm the causality.To the best of our knowledge, this paper is the first G × E interaction research about SNPs and prenatal tobacco smoke exposure in association to ADHD and all the subtypes. The major strength of this study is the standardized procedures of ADHD assessment in children and the precise and rigorous diagnosisThis study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology, and all methods were performed in accordance with the approved guidelines and regulations. Informed consent was obtained from all participants and their parents.Children who were diagnosed for ADHD by pediatric psychiatrists were recruited as the cases in the Liuzhou Women and Children Healthcare Hospital, and 6–12-year-old healthy children from 2 elementary schools in Liuzhou City were also recruited as the controls. The exclusion criteria for both groups were: 1) a history of mental retardation, autism spectrum disorders, bipolar disorder, language disorders or learning disabilities, pervasive developmental disorder; 2) a history of brain disease, seizure disorder, or other neurological disorders; 3) intelligence quotient (IQ) < 70; and 4) the presence of any chronic physical disease; 5) a blood lead concentration ≥10(µg/dL).Permissions were given for the eligible patients and controls to participate in the study, and those children whose parents could not be contacted or denied consent for participation were excluded. As a result, a total of 168 cases and 233 controls (6–12 years of age) were recruited.46. The 4-point response is scored 0–3  on SNAP-IV scale. Subscale scores are calculated by summing the scores on the items in the specific symptom domains , in which average score 0–1 means no features, 1.1–1.5 means subclinical, 1.6–2.0 means significant, and >2.1 means very severe. We defined oppositional defiant disorder as the ODD symptom domain score ≥1.6. The Child Behavior Checklist (CBCL) scale was conducted to evaluate child potential behavior problems and the Wechsler Intelligence Scale for Children (WISC) was used to evaluate full-scale IQ of all participants. All instruments used in the current study are validated Chinese versions.The status of both the case and control subjects were confirmed with a semi-structured clinical interview with the parents and children that was conducted according to the DSM-IV ADHD Rating Scale by a pediatric psychiatrist. Additionally, diagnoses were made that included both in categories and severity ratings using the 26-item Chinese version of SNAP-IV scaleTo measure lead concentrations in blood, 2–3 mL of whole blood was drawn from each child and kept it in heparin-containing tubes. Blood lead level in whole blood was measured using graphite furnace atomic absorption spectrometry . The limit of detection for blood lead was 0.1 µg/dL. None of the blood samples showed below the limit of detection.Genomic DNA was extracted from peripheral blood leukocytes using the TIANamp® Blood DNA Kit DP318 .47, and included 7 genes . The screening procedure for the candidate single nucleotide polymorphism(SNP) was as follow: First, we searched the F-SNP database (http://compbio.cs.queensu.ca/F-SNP) to retrieve information about the predictive functions of the SNPs within these genes and those of potential effects on splicing, transcription, translation, and post translation processes were selected. Second, we filtered the SNPs by the MAF of CHB  in the database of 1000 genomes (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/) and selected SNPs with MAFs > 5% for the next step. Third, we tested the linkage disequilibrium (LD) among selected SNPs using SNAP Pairwise LD (http://www.broadinstitute.org/mpg/snap/ldsearchpw.php), and only one was reserved if several SNPs were in strong LD with each other (r2 ≥ 0.80). Consequently, a total of 16 SNPs were retained. High-quality genotyping was performed on the Sequenom MassARRAY platform  by BIO MIAO BIOLOGICAL TECHNNOLOGY  following the standard experimental procedures from the manufacturer. In addition, samples with a call rate <80% or Hardy-Weinberg equilibrium (HWE) < 0.05 in the control group were removed.The candidate genes within the dopamine, norepinephrine and serotonin neurotransmitter pathways were selected based on recent findingsTobacco exposure status was obtained from the children’s parents by a questionnaire. The questions were constructed in two parts: mother tobacco exposure and child secondhand smoke exposure. Firstly, we asked whether the mother smoked before pregnancy or not (>100 cigarettes) and whether she smoked during pregnancy. If yes, we then asked how many cigarette expensed per day. Only three of the mothers reported being an ex-smoker or having smoked during pregnancy. Besides, we inquired whether or not she was exposed to secondhand smoke during pregnancy at home or in the workplace and how long she stayed in the smoking circumstance per day/week. Secondly, we investigated whether any family members smoked at home after the child birth and how many cigarette the members expensed per day/week.A face-to-face questionnaire survey was administered for their parents by a trained interviewer to collect socio-demographic variables, prenatal and perinatal characteristics. Socio-demographic variables included age, gender, and parental marital status . Prenatal and perinatal characteristics included birth weight, pregnancy age of mother, preterm birth and the mode of delivery . The family history of neural system diseases and ADHD were asked additionally, but no participant declared of ADHD family history in their families.t test, when appropriate. Hardy-Weinberg equilibrium (HWE) for genotypes was calculated in controls by a goodness-of-fit chi-squared test. Logistic regression analysis was used to estimate the effect of PSE and genotype frequencies on ADHD susceptibility by computing odds ratios (ORs) and 95% confidence intervals (95% CIs). Four inheritance models  were estimated for each SNP. Meanwhile, the statistical power to detect the effects of the SNPs was calculated by Power v3.0.049, and for SNPs with MAF of 0.11(rs6354), 0.43(rs1124491), and 0.44(rs553668), for example, we calculated that the power for our sample size to detect an OR of 1.50 was 43.6, 74.1 and 74.8%, respectively.Differentials in the distribution of socio-demographic variables, prenatal and perinatal characteristics between patients and controls were compared by chi-squared test or https://sourceforge.net/projects/mdr/) was used to assess the gene-environment interactions between risk genes and the environmental risks. The details analytic protocol was described in the previous study51. Furthermore, multiplicative and additive interactions were computed. Multiplicative interaction was evaluated by the interaction term of logistic regression model. Synergy index (SI), attributable proportion due to interaction (AP), and relative excess risk due to interaction (RERI) and their corresponding 95% CIs were calculated by additive interaction analysis, as suggested by Tomas Andersson52. If there is no additive interaction, RERI and AP are equal to 0, or SI are equal to 1. The multiplicative and additive interaction analyses were adjusted for age of the child, gender, pregnancy age of mother, and postnatal smoking exposure. Since a higher proportion of males and younger ADHD children(<9 years old) was noted in our study.The Multifactor Dimensionality Reduction (MDR) 2.0 beta 8.1 program . All tests were two-tailed, and statistical significance was defined at supplementary table


Performance deficits and diminished brain activity during cognitive control and error processing are frequently reported in attention deficit/hyperactivity disorder (ADHD), indicating a “top-down” deficit in executive attention. So far, these findings are almost exclusively based on the processing of static visual forms, neglecting the importance of visual motion processing in everyday life as well as important attentional and neuroanatomical differences between processing static forms and visual motion. For the current study, we contrasted performance and electrophysiological parameters associated with cognitive control from two Flanker-Tasks using static stimuli and moving random dot patterns. Behavioral data and event-related potentials were recorded from 16 boys with ADHD (combined type) and 26 controls (aged 8–15 years). The ADHD group showed less accuracy especially for moving stimuli, and prolonged response times for both stimulus types. Analyses of electrophysiological parameters of cognitive control revealed trends for diminished N2-enhancements and smaller error-negativities (indicating medium effect sizes), and we detected significantly lower error positivities (large effect sizes) compared to controls, similarly for both static and moving stimuli. Taken together, the study supports evidence that motion processing is not fully developed in childhood and that the cognitive control deficit in ADHD is of higher order and independent of stimulus type. Attention Deficit/Hyperactivity Disorder (ADHD) is a common early-onset neurodevelopmental disorder, characterized by severe and age-inappropriate levels of pervasive inattention, hyperactivity and impulsivity that occurs in about 5% of school-aged children with a strong overrepresentation of boys . ADHD isCognitive control comes into play when task demands or performance errors require rapid adaptation. This can be tapped by the Eriksen Flanker Task, which is a demanding paradigm that requires responding to a target stimulus in the presence of competing distractors . ChildreThe associated brain electrical activity underlying those task demands, performance difficulties and hence potential deficits in patients can be tracked with high temporal resolution using Electroencephalography (EEG) and thereof extracted Event-Related Potentials (ERP) . Task deCognitive control may be also implicated after performance errors that require adapting response strategies. In the response-locked ERP, errors are generally accompanied by a negative component peaking approximately 40–120 ms after the erroneous response at fronto-central sites  with sources in the anterior cingulate cortex. Several theories ascribe Ne a crucial role in response monitoring, error detection or reinforcement learning , and somNumerous studies and several meta-analyses suggest that these electrophysiological parameters of cognitive control (N2) and error processing  may be diminished in ADHD . MoreoveMost ADHD studies of cognition in general and cognitive control in particular have used static visual stimuli such as letters or forms . This fThis ventral “what” processing stream is paralleled by a dorsal “where” pathway of cortical areas specialized for the spatial layout of the visual environment and visual movement, thought to support accurate visually guided movements . Moving The central question of our study is whether children with ADHD display specific problems with motion processing during cognitive control versus processing of static forms. Although beyond the objectives of our experiment, this may well be a consequence of difficulties in the magnocellular pathway and amongst others in functioning of the superior colliculus, which play a role in visual motion processing as well as voluntary attention shifts, that may be implicated in several difficulties observed in ADHD . In the However, especially children with ADHD have been associated with hypoarousal at rest and activation difficulties during task performance that may partly explain impaired performance in terms of slower and more variable responses in a variety of demands . This haThis study was carried out in accordance with the recommendations of the ethics committee of the University Medical Center Göttingen with written informed consent from all subjects . All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the ethics committee of the University Medical Center Göttingen.A total of 20 boys with ADHD and 31 male controls, aged 8–15 years, participated in the study. Children in the ADHD group were patients of the Clinic for Child and Adolescent Psychiatry and Psychotherapy at the University of Göttingen or outpatients of a nearby board-certified private practice. The control group was recruited after information talks given in schools in the region of Göttingen. Detailed information sheets about the study were provided to parents and children, and written informed consent was obtained from both. Children taking stimulants were off medication for at least 48 h before testing. All children received small prizes and a financial compensation (15 €) for participation.All subjects had normal or corrected-to-normal vision, an full-scale IQ above 85  and no 2(1) = 0.13, p = 0.72]. The included samples of 16 children with ADHD and 26 controls were age-matched [F = 0.08, p = 0.8, F = 3.9, p = 0.05, F > 7.7, p < 0.01, see Table Due to too few correct responses, too many performance errors (more than 50% errors in the congruent condition) or artifacts in the EEG leading to less than 20 sweeps in the respective ERP, 9 datasets  had to be excluded, but the exclusion ratio did not differ between groups [χ2 = 0.13, Cognitive control during processing of static visual forms was tested with a classical arrowhead version of the Eriksen Flanker-Task  in noiseSubjects had to press response buttons with the index finger of the left or right hand corresponding to the target direction. On congruent trials, flanker and target pointed to the same, on incongruent trials to opposite, horizontal directions. Congruency (congruent vs. incongruent stimuli), and target direction (left vs. right), were balanced and randomized. Reaction times were measured with respect to the onset of the target.Altogether, 400 trials were presented in ten blocks á 40 trials intermitted by written feedback displayed on screen for 8 s at the end of each block: if more than 10% errors on congruent or more than 40% errors on incongruent trials were committed, the child was instructed to be more accurate. If less than 10% errors in the congruent and less than 10% errors in incongruent trials occurred, the child was instructed to respond faster; otherwise, it was told to continue in the same way.The duration of the Flanker-Tasks including two practice blocks of 24 trials was approximately 15 min each, and arrowhead and RDP Flanker-Tasks were presented in randomized order after 3 min of resting EEG with eyes open and –closed condition.The electroencephalogram was recorded with Ag/AgCl electrodes from 23 sites according to the extended 10–20 System altogether with the electro-oculogram recorded from additional electrodes placed above and below the right eye and at the canthi with FCz as recording reference and a ground electrode placed at the forehead using a BrainAmp amplifier. Sampling rate was 500 Hz with low and high cutoff filters set to 0.016 and 100 Hz, respectively, and a 50 Hz notch filter. Impedances were kept below 10 kΩ.Offline processing was performed using Brain Vision Analyzer software . The EEG was downsampled to 256 Hz, re-referenced to the average and filtered offline with 0.1 – 15 Hz, 24 dB/oct Butterworth filters. Ocular artifacts were corrected using the method of Gratton and Coles without raw average subtraction . If the p > 0.12, All averages contained at least 20 sweeps (more details can be found in the respective Tables of the Results). ERPs comprised more accepted sweeps in correct responses than errors and congruent vs. incongruent trials and also group-differences in numbers of sweeps . Signal to noise ratio (SNR) was for response-locked ERPs higher in the condition with static arrowheads than moving RDPs, and also higher for correct responses than errors, and for stimulus-locked ERPs higher in ERPs from incongruent than congruent trials and particularly for incongruent trials ERPs highest at electrode Cz. However, there were no significant group differences or interaction effects involving Group  with 10% Hanning window on 4 s non-overlapping artifact-free epochs to extract absolute Alpha (8–13 Hz) band power.Analyses of performance data  were conducted using a General Linear Model with between subjects factor ADHD (children with ADHD vs. Controls) and within subject factors Condition (static arrowhead vs. moving RDP Flanker-Task) and Congruency . The electrophysiological parameters of cognitive control were also tested regarding factor “Electrode” . Error processing was tested with factors “ADHD,” “Error”  and Electrode . Alpha power was logarithmus naturalis (LN) transformed to achieve normal distribution and tested in an ANOVA with factors Group, Condition and Site .d ≤ 0.9 two-tailed) with a power of 1-β = 0.80 at the conventional significance level α = 0.05. Significant effects are indicated by ∗∗p < 0.01, ∗p < 0.05, and trends by +p < 0.1.The available sample size allows the detection of large between-subjects (ADHD-) effects  = 9.4, p < 0.01, F = 125.4, p < 0.01, F = 2.7, p = 0.11, F = 1.6, p = 0.21, Post hoc comparisons of the Congruency effect revealed a significant group difference for static arrowheads , but not for RDPs .Reaction times  = 13.3, p < 0.01, F = 1.8, p = 0.18, F = 14.2, p < 0.01, The intra-individual reaction time variability  = 116.8, p < 0.01, F = 57.8, p < 0.01, All children made significantly more errors in incongruent compared to congruent trials [Congruency: F = 2.5, p = 0.12, F = 5.7, p = 0.02, F = 6.1, p = 0.02, While the groups’ error rates do not differ overall [Group: F = 11.9, p < 0.01, F = 3.4, p = 0.07, F = 2.8, p = 0.10, F= 3.3, 𝜀 = 0.61∗∗, p = 0.07, F = 0.1, p = 0.72, The stimulus-locked ERPs show fronto-central negativity, peaking around 200–400 ms after onset of the stimulus at FCz, where the N2 peaks and the N2 enhancement were most pronounced. Incongruent stimuli evoke enhanced N2 amplitudes compared to congruent ones for both groups and both tasks [Congruency: Typically, the responses to attention-demanding tasks are more error-prone in children with ADHD. We therefore analyzed error processing components of the ERP, generally embodied by a negative deflection peaking at fronto-central sites approximately 40–120 ms after the error response  that is followed by a more parietal positive deflection  within 200–500 ms after the response . Both Nepost hoc comparisons with confidence intervals (p = 0.05). The error negativity (measured as the peak within 150 ms following the response) is most pronounced at the fronto-central electrode FCz, whereas the adjacent error positivity (quantified as mean amplitude 200–500 ms following the response) is maximal at the centro-parietal electrode Pz.Figure F = 2.2, p = 0.15, F = 51.0, p < 0.01, F = 2.2, p = 0.15, ∗Error: F = 2.1, p = 0.15, ∗Group: F = 3.3, p = 0.08, F = 0.2, p = 0.66, F = 7.7, p < 0.01, The amplitude for the early response negativity is overall similar in static arrowheads and RDPs [Stimulus: rror: F1,0 = 51.0,rror: F1,0 = 51.0,F = 166.5, p < 0.01, F = 7.8, p < 0.01, F = 11.1, p < 0.01, ∗Error∗Group: F = 2.9, p = 0.09, F = 4.0, p = 0.05, F = 4.7, p = 0.04, F = 2.8, p = 0.10, F = 18.2, p < 0.01, The following adjacent response positivity was larger following errors than correct responses [Error: F1,0 = 166.5F1,0 = 166.5F = 53.2, p < 0.01, F = 4.0, p = 0.05, p = 0.05), while no group differences emerged when processing static arrowheads  smaller in the motion Flanker-Task, which was particularly driven by larger N2-amplitudes already in the congruent condition, a similar pattern of results as detected for reaction times, again suggesting some ambiguity when processing visual motion which potentially taps into cognitive control already in the congruent condition. Our findings regarding brain electrical activity associated with cognitive control complement and differentiate findings from performance data: we detected N2-enhancement and error-negativity and -positivity, indicating – even if the maturation of the visual motion processing system is still ongoing – that cognitive control may be diminished in children with ADHD not only when processing static visual forms, but also when processing visual motion in Flanker-Tasks.Additional evidence for motion processing deficits in ADHD comes from a recent study with biological motion. In a simple identification task where moving walkers had to be identified from scrambled moving random dots, children with ADHD did not differ in identification rate from typically developed children, but they showed a lowered vN1 (labeled as N200) with a more diffuse activation in occipital-temporal regions . Taken tIn the response-locked ERP, we observed  smaller error negativity- and (significantly) lower positivity- amplitudes (assessed as difference amplitudes with respect to correct response Nc and Pc) for children with ADHD compared to controls. These findings may indicate reduced conflict between error and correct response, and also diminished motivational error assessment particularly in ADHD. But things tend to be more complicated, as the RDP Flanker-Task evoked enhanced early negativity to correct responses (Nc) similarly in both groups, and diminished Pe difference amplitude particularly in ADHD, which may be explained by higher error-rates leading to less error salience in that condition. Both enhanced Nc and lower Pe (difference) amplitudes may thus indicate some more ambiguity in responding to RDPs compared to static arrowheads. This is at least regarding Performance in stark contrast to our previous study with healthy adults that showed very similar Flanker-Task performance for both static arrowheads and RDPs. We may conclude so far that the motion processing Flanker-Task is more difficult for children than adults, which may be a consequence of ongoing maturation of the dorsal processing stream with full development probably not reached until adolescence . This maAssessment of absolute Alpha activity during task performance yielded elevated power in Controls during RDP processing, suggesting that control children had lower activation during Flanker-Task motion processing - or payed less attention to RDPs. Elevated alpha activity during task performance in controls has also been reported by d ≥ 0.8) (d ≥ 0.5) with a power of only 60%. As a consequence, a number of important effects may remain undiscovered in the current study, whilst trends need to be considered with care. However, the current study aims extending previous perspectives on Cognitive control in ADHD, so we explicitly test hypotheses which warrant the current approach.The limited sample size of 26 Controls and 16 children with ADHD, and an alpha error set to a conventional 5% allows the detection of large phenotype effects (d ≥ 0.8)  with a pAnother difficulty, especially with visually evoked potentials, is the consideration of eye movements throughout the task, as eye movements induce artifacts into the recorded brain activity and may indicate that something interrupted the task performance. For the current study, a consequent rejection of contaminated trials would lead to an additional dropout of 30% of the sample. In a supplementary analysis we compared the impact of ocular correction versus blink rejection in the remaining sample. While the applied regression-based ocular correction procedure may effectively eliminate ocular artifacts, it also reduces activity at fronto-polar sites. However, effects on the fronto-central electrodes included in the current analyses were rather small and are unlikely to have compromised the reported results.Another difficulty with the current design is the fixed flanker-target asynchrony which led to an overlap of the ERP components evoked by target and flankers as well as responses. While the latter may be ameliorated by techniques like Adjar  or RIDE Assessments of medication effects were beyond the scope of this study. While discontinuing medication with stimulants for 48 h prior testing is a good standard in research with patients, the ongoing use of medication using norepinephrine reuptake inhibitors like Atomoxetine© may lead to underestimated ADHD effects.In sum, although some weaknesses of the current study cannot be ruled out, these obstacles will not compromise the validity of the reported results.The current study confirms medium effect-sized deficits in cognitive control in children with ADHD compared to typically developing controls when processing static visual forms in a Flanker-Task regarding performance (reaction time and -variability and error-rate) as well as brain electrical activity (N2-Enhancement due to stimulus incongruency and in elevated Ne and Pe amplitudes after errors). This not so surprising finding was extended with data from a parallel form of the Flanker-Task using moving RDPs that may capture automatic “bottom-up” attention and may thus lead to more severe deficits in ADHD.The current findings revealed that processing moving RDPs was more challenging for both groups of children, leading to slower RTs and higher error-rates already in the congruent condition and generally higher RT-SD, which was paralleled by elevated error processing deficits, probably as a consequence of immature visual motion processing capabilities at this age. In contrast to our hypothesis, deficits in cognitive control were similarly present when processing static and motion Flanker-Tasks, indicating a higher order cognitive control deficit in ADHD.BL-M conceived the study, organized data collection, and drafted the manuscript. ST conceived the study and drafted the manuscript. AR conceived the study and drafted the manuscript. BA conceived the study, organized data collection, performed analyses, and drafted the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Continuous 64-electrode electroencephalography (EEG) was collected to assess neurological responses to each condition. The AV multisensory condition resulted in the shortest response times for both populations. Analysis using the race model  process audiovisual (AV) multisensory stimuli using behavioral and neurological measures. Adults with a clinical diagnosis of ADHD ( Miller,  demonstr Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. The most common symptoms associated with a diagnosis of ADHD are hyperactivity, impulsivity, and inattention  and electroencephalography  MSI involves the specific integration of auditory and visual stimuli that occur close in temporal and spatial proximity. This form of sensory integration occurs throughout daily life. When in a classroom setting the nervous system is constantly processing all of the auditory stimuli from things that one is hearing as well as all of the visual stimuli from things that they are seeing. Since these stimuli most often occur in close temporal and spatial proximity, AV MSI contributes to the formation of coherent perceptions, such as when detecting and identifying a presented stimulus  in AV multisensory vs. unisensory conditions. We hypothesized that due to the altered brain structure in regions involved in multisensory processing that MSI would occur differently in those with ADHD compared to controls; particularly in regions known to have altered structure in those with ADHD, i.e., the parietal and occipital regions. Further, we hypothesized that these neurological differences would be reflected in behavioral differences in MSI.n = 11, three females) was 21.3 ± 3.0 years old and for the ADHD group  was 24.1 ± 3.5 years old. The mean age of ADHD diagnosis was 13.1 ± 7.4 years old.This study was carried out in accordance with the recommendations of the University of Ontario Institute of Technology (UOIT) Research Ethics Board (REB) and participants gave written informed consent prior to participating. This study was performed according to the principles set out by the Declaration of Helsinki for the use of humans in experimental research. Participants were recruited from the student body at the UOIT. Recruitment was done through the use of word of mouth, in-course announcements, and posters placed throughout the campus. Participants recruited were young adults between the ages of 18–35 years old that had and had not received a clinical diagnosis of ADHD at some point in their life. Adults that reported receiving a diagnosis of ADHD, self-reported the age at which they were diagnosed as well as any medication that was currently being taken to control their symptoms related to ADHD. The mean age for neurotypical controls  was used to assess each participant’s symptoms associated with ADHD. The ASRS has a total of 18 questions, which are in line with the ADHD diagnostic criteria set out in the DSM-IV  from speakers placed bilaterally to the computer screen. The volume control was adjusted to the halfway mark to ensure a comfortable and easily discernible sound level, which was the same volume for each participant.A red circle (diameter 30 cm) appeared on the screen for 60 ms, placed centrally in the vertical and horizontal plane.The auditory and visual stimuli occurred simultaneously from speakers and a computer screen adjacent to one another.®  response device was used to receive and collect responses. Response time was calculated as the time from stimulus onset to when a button press response on the device was made. This device was used for its 1 ms temporal accuracy.A simple response time task was utilized to measure MSI. This paradigm was designed using E-Prime 2.0 Professional . The task consisted of three different stimuli conditions  all presented in random order with an inter-trial interval of 1,000–3,000 ms. The trial events are depicted in E-Prime 2.0 Professional  was utilized to run, collect, and record responses. While performing the simple response time task on a desktop computer, continuous EEG was recorded.® .A Waveguard™ 64-electrode EEG cap  was used to collect surface brain electrical activity in response to each stimulus type. The use of a 64-electrode cap allows for a more robust analysis of brain activity, as acquisition is not limited to a few electrodes. The Waveguard™ cap was connected to a TMSi REFA-8 amplifier with 64 EEG channels, four bipolar channels, and four auxillary channels; which was run through asaLab™ (Netherlands) to collect and record each session at a 2,048 Hz sampling frequency. Event-related potential (ERP) analysis was completed on a separate laptop using Advanced Source Analysis , Matlab™ , and SPSSData was cleaned and removed of any artifacts prior to running any analyses. Artifacts which were a result of muscle activity and ocular activity were removed. EEG data was filtered using a band-pass filter with a low cut-off of 1.6 Hz and a high cut-off of 45 Hz and a slope of 24 db/octave was utilized. The low cut-off of 1.6 removes any slow-wave activity that would otherwise be represented twice in the “sum” waveform during analysis. The 45 Hz high cut-off removes any artifacts that are a result of surrounding electrical equipment. Artifact rejection was performed, with the exclusion criteria being ±100 μV. Finally, data for each trial were averaged into 600 ms epochs per participant per condition, which included 100 ms pre-stimulus and 500 ms post-stimulus-onset . Average waveforms for each unisensory condition were summed  for comparison to the multisensory waveform  was used to report effect size, where a small effect was noted as 0.01, medium as 0.06, and a large effect as 0.14  version 24 . Outliers were removed, and any responses that were within ±2 SDs from their individual condition average were included when calculating each participant’s average per condition, with the caveat being that the lower limit could not be any faster than 100 ms; for participants where –2 SDs was in fact lower than 100 ms, the lower limit was then set to 100 ms. A 2 group  by 3 sensory condition  mixed factors analysis of variance (ANOVA), with repeated measures on the last factor, was used to determine whether there were any significant differences in response time dependent on diagnostic status and/or sensory condition. Alpha was set as Miller’s “race model” was applied Miller,  to test x-axis and quantiles on the y-axis. This is representative of sensory integration at earlier response latencies as this is when the integration of unisensory inputs is expected to fulfill the multisensory response criterion, before either unisensory input is independently processed  within a given latency window, or quantile. This test is based upon statistical probability and cumulative density functions (CDFs) of responses acquired from the redundant multisensory condition as compared to the two unisensory conditions. The purpose is to determine whether improvements in response time that occur in the multisensory conditions results from MSI, as opposed to the faster of the two unisensory stimuli triggering the response  deviations for each response quantile were identified  script provided by Ulrich et al. . It shou®  software. As has been done in the previous literature  version 24 , Matlab™ , and SPSSF = 587.89, p < 0.001, partial η2 = 0.969, revealed that responses to the multisensory stimulus were significantly faster than responses to either of the unisensory conditions  = 2.709; p = 0.116; partial η2 = 0.125). There was no group by stimulus condition interaction (F = 0.021).The mean response time for both the ADHD and control group are presented in ions see . Althougt(9) = 2.81, p = 0.020, while controls did not  = 16.293; p < 0.001; partial η2 = 0.462, indicated a significant difference between the average multisensory vs. sum waveforms in this latency window (see F = 0.390) and there was no significant interaction (F = 0.9).There was localized negative activity for the time period of 100–140 ms in the central parietal region (electrodes CPz and Pz). A significant effect of stimulus type, ndow see . This inF = 7.295; p = 0.014; partial η2 = 0.277, and stimulus type, F = 5.420; p = 0.031; partial η2 = 0.222 , which was observed as localized negative activity. This can be observed in the heat map in .222 see . SpecifiF = 4.988; p = 0.038; partial η2 = 0.208. However, this interaction did not reveal MSI in either group. Instead, it showed that the sum waveform was more positive than the multisensory waveform for controls while the inverse was found for the ADHD group  as seen in F = 4.336; p = 0.051, η2 = 0.186, indicating a large effect size. This suggests a trend toward the pattern of electrical activity in response to the multisensory stimulus being different in both groups at this time point and brain region . This area is illustrated in gion see .To our knowledge, this study was the first to examine multisensory processing in a population of young adults who have received a diagnosis of ADHD. Literature pertaining to ADHD is often assessing childhood ADHD, resulting in very little being known regarding adult ADHD and associated neurophysiological characteristics, particularly during AV multisensory processing. This is an important area of inquiry, as multisensory processing is fundamental to optimal human sensory functioning, and alterations to this neurological process may be related to some of the well-known behavioral characteristics of ADHD. This process is relevant to numerous environments that adults may interact within on a daily basis, such as sensory-rich environments while at work or school. By using a simple response time task while recording continuous EEG, distinct behavioral  and neurophysiological  patterns of MSI were observed in both neurotypical controls and adults with ADHD.In the response time task, both groups responded fastest to the multisensory stimulus, which was predicted due to the typical speeding of responses that occurs when individuals are presented with more than one percept. Previous studies utilizing a similar paradigm had found that an AV multisensory condition resulted in the shortest response time when compared to an auditory or visual unisensory condition , where neurotypical controls did not have violation in the early quantiles. This race model violation suggests that MSI likely contributed to the faster response times in the multisensory condition, as opposed to the faster of the two independently processed unisensory stimuli Miller, . OverallThis study was the first of its kind to assess MSI in adults with ADHD and combined behavioral and neurophysiological measures. Based on methodology from previous literature assessing MSI, we found that specific patterns of MSI were apparent. MSI was found to have occurred in both study groups, however, there were some differences in how that activity occurred in each group . In particular, MSI occurred in both groups over central parietal regions from 100 to 140 ms. The parietal region is often discussed as being a sensory integration site (Brandwein et al., Differences in brain activity between those with and without ADHD were also found in the present study. For instance, from 110 to 120 ms those with ADHD had significantly smaller ERPs than neurotypical controls. At this latency, controls also had a more positive sum waveform when compared to their multisensory waveform, while the opposite was true for the ADHD group, as their multisensory waveform was more positive than their sum waveform. A second time period of 100–120 ms demonstrated differences in brain activity over parietal-occipital regions, where the ADHD group again had much smaller ERPs than the neurotypical controls. The brain activity in response to the multisensory stimulus indicated that there is a difference in overall brain activity in this brain region and latency in those with ADHD. This may be related to the thinner cortical matter present in adults with ADHD, resulting in an attenuated or altered EEG signal when compared to neurotypical ERPs.The electrodes pertaining to brain regions where a group difference was found in the amplitude of the ERPs in those with ADHD coincides with the regions that other research has shown to have altered brain structure in this population. For instance, the parietal and occipital regions of the brain are often thinner in those with ADHD (Durston et al., A potential limitation of this study was the choice to test participants with ADHD while continuing their regular medication regime. This was done intentionally, in order to test adults with ADHD similarly to how they would function on a typical day, which includes taking medication pertaining to their ADHD. This allows for a clearer understanding of how these participants may react within environments that they are normally in, possibly including university or college lectures or common workplace settings. The fact that the response times of those on and off medication were so similar, suggests that medication did not have a significant influence on ADHD response times. With that in mind, future studies comparing adults with ADHD on and off medication may allow for further understanding of whether medication influences response time and multisensory processing.The main question this research investigated was whether those with ADHD processed AV multisensory stimuli similar to neurotypical individuals. Although the cortical activation differed between groups, there was evidence that those with ADHD did have MSI occur, with potentially associated shorter response latencies, which were described previously as a trend. This could indicate behavioral enhancement as a result of ADHD in adults, however the simple nature of the task did not require complex cognitive processing, only recognition, so the task does not allow conclusions to be made about the impact of these faster responses on cognitive function.This is the first study to measure behavioral and neural characteristics of AV multisensory processing in young adults with ADHD. Extensions of this work may have application for the creation of technological supports, to promote optimal integration when in sensory-rich environments, as many work environments are multisensory. This is an important area of inquiry as a significant portion of the adult population has and will have ADHD. Future work should also consider utilizing a more complex task to assess multisensory processing capabilities in those with ADHD as well as conducting longitudinal and cross-sectional studies of the development of multisensory processing in individuals with ADHD.All authors are listed and all have contributed significantly to the manuscript. HM, BM and PY made the conception and design of research and drafted the manuscript. HM performed experiments. HM, JB and AK analyzed data and prepared figures. HM, BM, CG, JB, AK, and PY interpreted results of experiments, edited and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Statistical learning is an implicit process that allows individuals to track and predict incoming events from their environment. Given that information is highly structured over time, events become predictable, allowing these individuals to make better sense of their environment. Among the studies that have examined statistical learning in attention deficit/hyperactivity disorder (ADHD), findings have been mixed. Our goal was to examine whether increased ADHD symptomatology related to decreased auditory and visual statistical learning abilities. To investigate this, we examined the entire range of ADHD symptomatology using a Research Domain Criteria approach with a clinically reliable questionnaire in addition to well-established auditory and visual statistical learning paradigms. Total ADHD symptomatology was not related to auditory and visual statistical learning. An identical pattern emerged when inattention and hyperactivity components were separated, indicating that neither of these distinct behavioral symptoms of ADHD are related to statistical learning abilities. Findings from the current study converge with other studies but go beyond finding a lack of a significant relationship – through Bayesian analyses, these data provide novel evidence directly supporting the hypothesis that ADHD symptomatology and statistical learning are decoupled. This finding held for overall levels of ADHD symptomatology as well as the subdomains of inattention and hyperactivity, suggesting that the ability to pick up on patterns in both auditory and visual domains is intact in ADHD. Future work should consider investigating statistical learning in ADHD across ages and beyond auditory and visual domains. Similar events and pieces of information tend to co-occur reliably within the environment. The ability to identify and predict these statistical relationships in the environment is a process referred to as statistical learning. Extracting such probabilistic information is important for individuals to make sense of their world and has been shown to play a role in both language proficiency  and predStatistical learning has been explored in both children and adults with attention-deficit/hyperactivity disorder (ADHD) to test the hypothesis that impairments in statistical learning may contribute to ADHD symptomatology. Many of these studies have used an implicit sequence learning task known as the serial reaction time task (SRT). During this task, participants are required to respond to a set of stimuli that follows a repeating pattern that they should learn to predict with repeated exposure, assuming their statistical learning skills are intact. However, among the studies using the SRT with individuals with ADHD, findings have been inconsistent, with some studies reporting intact , and othGiven the apparent difficulties with statistical learning in ADHD, we hypothesize that:(1)ADHD symptomatology will be inversely related to auditory and visual statistical learning abilities and,(2)Given that inattention and hyperactivity are characterized as distinct behavioral symptoms in ADHD, we predict that inattention and hyperactivity will uniquely contribute to statistical learning abilities.Mean age = 18.19, SD = 0.74 years, 64.3% female). Participants were recruited based upon the Research Domain Criteria (RDoC) framework in order to cover the entire range of symptomatology  than between words (0.1 or 0.2), where transitional probabilities were calculated as:Participants completed a well-established auditory statistical learning task similar to that described in the study by Immediately following the artificial language exposure, participants completed a two-alternative forced-choice test (2AFC) to assess whether they could identify trained words from the artificial language  used to correct for multiple comparisons. Bayesian linear regressions were also used to examine whether inattention and hyperactivity uniquely contributed to statistical learning.Bivariate Bayesian correlations  were theScores on the ASRS confirmed that the sample spanned nearly the entire range of symptomatology, (0–17 out of a possible 0–18) in line with the RDoC framework. Previous standardization of the ASRS identified scores of 0–10/11–18 as the best split point predicting clinical-level symptoms with a classification accuracy of 96.2%  . The curt(97) = 58.53, p < 0.001, d = 5.82) and visual  = 42.07, p < 0.001, d = 4.14) statistical learning tasks.Above chance performance on statistical learning tasks were examined and found to be reliability above chance for both auditory  = -0.02, p = 0.849, BF10 = 0.129, and r(97) = -0.01 p = 0.908, BF10 = 0.127, respectively). Further, when isolating inattention versus hyperactivity characteristics of ADHD, neither inattention (r(97) = 0.02, p = 0.816, BF10 = 0.130; r(97) = 0.02, p = 0.871, BF10 = 0.128) nor hyperactivity (r(97) = -0.06, p = 0.529, BF10 = 0.154, r(97) = -0.04, p = 0.683, BF10 = 0.137) scores significantly related to auditory or visual statistical learning, respectively  = 0.417, p = 0.660, R2 = 0.01, BF10 = 0.095. When isolated, neither inattention  = 0.66, p = 0.510, sr2 = 0.005, BF10 = 0.218) nor hyperactivity β = -0.005, t(95) = -0.88, p = 0.379, sr2 = 0.008, BF10 = 0.254) were statistically significant in this model. A second multiple regression was performed to determine if inattention and hyperactivity uniquely contributed to visual statistical learning and the overall model was not significant, F = 0.181, p = 0.835, R2 = 0.004, BF10 = 0.078. When isolated, both inattention  = 0.44, p = 0.659, sr2 = 0.002, BF10 = 0.215) and hyperactivity  = -0.58, p = 0.564, sr2 = 0.003, BF10 = 0.229) were not statistically significant in this model.Contrary to our predictions, auditory and visual statistical learning were not significantly related to overall ADHD symptomatology  may occur and when (frontoneocerebellar) that event may occur are both impaired in ADHD, resulting in less accurate expectations about the environment as well as a weakened ability to detect violations and adjust one’s behavior according to these violations (It is notable that despite using a variety of tasks to assess statistical learning, a number of studies have found this ability to be unimpaired in ADHD, suggesting that similar results would emerge if a clinical sample was included in the current study. Despite this, there is evidence to suggest that statistical learning may be impaired in ADHD, including impairments in executive functioning  and abnoolations .Prior studies failing to find a significant link between statistical learning and ADHD symptoms, however, have been limited in that common frequentist hypothesis testing is unable to provide direct support for a null hypothesis. Our Bayesian analyses provide novel evidence directly supporting the hypothesis that ADHD symptomatology and statistical learning are decoupled, at least in individuals who do not have a formal diagnosis of ADHD. This finding held for overall levels of ADHD symptomatology as well as the subdomains of inattention and hyperactivity. These null findings are important for informing future studies that intend to assess if/how this ability relates to ADHD and related symptoms.There are several important considerations for future work. First, future studies exploring the possible relationship between ADHD symptomatology and statistical learning should include child participants to determine whether the relations differ in younger populations. Previous work examining statistical learning in children with ADHD has found this ability to be impaired . FurtherKP and RS both played a significant role in all aspects of this project including the experimental design, entry, analysis, interpretation of data, the drafting and revising of this manuscript, and final approval for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


When engaged in a search task, one needs to arbitrate between exploring and exploiting the environment to optimize the outcome. Many intrinsic, task and environmental factors are known to influence the exploration/exploitation balance. Here, in a non clinical population, we show that the level of inattention (assessed as a trait) is one such factor: children with higher scores on an ADHD (Attention Deficit/Hyperactivity Disorder) questionnaire exhibited longer transitions between consecutively retrieved items, in both a visual and a semantic search task. These more frequent exploration behaviours were associated with differential performance patterns: children with higher levels of ADHD traits performed better in semantic search, while their performance was unaffected in visual search. Our results contribute to the growing literature suggesting that ADHD should not be simply conceived as a pure deficit of attention, but also as a specific cognitive strategy that may prove beneficial in some contexts. Principles of Psychology1  that “We make search in our memory for a forgotten idea just as we rummage our house for a lost object. In both cases we visit what seems to us the probable neighborhood of that which we miss. We turn over the things under which, or within which, or alongside of which, it may possibly be; and if it lies near them, it soon comes to view.” In modern terms, this suggests, on the one hand, that one cognitively represents both spatial and semantic knowledge as maps or networks3. On the other hand, the similarity between spatial foraging and internal cognitive search suggests that search processes are domain general4. It has been suggested that this generality could derive from the reliance on a dynamic balance of attention between exploration/exploitation that is similar in both foraging in physical space and in cognitive space7. In all domains, searches involve trade-offs between exploiting known possibilities and exploring for better opportunities elsewhere. Furthermore, there is evidence that the similarity between external and internal search processes is a consequence of evolutionary homology and not the result of convergent evolution8. Evidence from neuroscience, genetics and cognitive disorders substantiate the claim that molecular and neural mechanisms that evolved for the purpose of arbitrating between exploration and exploitation in the spatial domain have been recycled in later species for the control of attention4 . Indeed, similar dopaminergic circuits are both implicated in the regulation of goal-directed behaviour and attention, across species10.Searching for definite items in a rich environment is a fundamental behaviour in most animal species. Seemingly unrelated activities (such as foraging in the wild or looking for relevant references to include in a bibliography), all share an underlying structure: one has to navigate a predefined space so as to find items that match a definite category. Interest for this analogy is not recent in psychology and William James wrote in his 11. A deficit of this ability is the core symptom of Attention Deficit/Hyperactivity disorder (ADHD)13, a mental disorder characterized by a reduced ability to focus and sustain attention, and by an excessive level of activity. Dopamine deficit is currently the leading theory for explaining ADHD14, as notably, behaviours associated with ADHD have been correlated with specific polymorphisms alleles coding for dopaminergic proteins17. Individuals diagnosed with ADHD also show elevated levels of the dopamine transporter, responsible for moving dopamine out of the synaptic cleft18. Lower levels of dopamine in the synapse, arguably contributes to the inability to focus, as well as to behaviours that appear to be related to novelty-seeking20. In addition, the reference drug for treating ADHD, methylphenidate, increases synaptically released dopamine21. ADHD and especially the hyperactive subtype is also associated with extreme novelty seeking22, and genes implicated in the dopaminergic pathways, associated with ADHD23  are more frequent in populations that have a history of migration24. Together, this evidence has led to the notion that ADHD might be part of an adaptation to a threatening and food scarce environment our ancestors lived in25. In the present environment, studies have shown a correlation between the number of regions visited when free-viewing a visual scene with curiosity as a personality trait26 and also ADHD27. Together, this suggests that attention deficits in ADHD would co-vary with a bias towards exploration in the regulation of the exploration/exploitation trade-off, and explain some behavioural patterns of activity found in ADHD.Consequently, in humans, being able to regulate one’s attention is closely related to behavioral control28, as most of the traits are found at a variable degree in the general population. Thus we predicted that children with lower abilities to sustain attention would adopt search strategies that favour exploration over exploitation in search tasks. Following Hills et al. (2008)4, we further inquire whether this preference would generalize across external and internal search domains, and to that end, we relied on two classical neurospychological search tasks tapping selectively into each domain. We tested a population of 87 neurotypical children whose general behaviour and attention profile were evaluated on the ADHD rating scale29. For the external search we used the bells test30, where participants are asked to find as many silhouette drawings of bells among a set of distractors, in a limited space - an A4 paper sheet - and time - 2 minutes. For the internal search we used a semantic fluency task, where participants are required to search in memory for words corresponding to a semantic category  and name them aloud. We analyzed both tasks as foraging in visual and semantic spaces7. Recently, measures of semantic similarity by means of word-embedding methods have been tested in semantic verbal fluency32. They stand out as one the most objective methods to measure a semantic distance between productions in semantic fluency tasks, and comparable to euclidean distance in visual search. We predicted that children with higher scores on the ADHD-rs should produce more long distance ‘jumps’ between consecutive items, as a consequence of more explorative traits , with a mean ADHD-rs score of 13.8 (SD = 13.33) and a median of 9. ADHD-rs evaluate 18 behaviours (9 items related to inattention and 9 related to hyperactivity/impulsivity). Note that the ADHD-rs is here used only as a means to evaluate the cognitive dimension that is at the core of the disorder. It has no clinical intent or application. The research was approved by the local ethics committee (comité de protection des personnes d’Ile de France) and conducted according to the Declaration of Helsinki. Informed consent was obtained from parents and teachers of all participants.Participants were 87 children , 36 girls/51 boys, all in fourth grade) from 4 different schools in Saint-Malo, Britany, France. All children were native French speakers. For each child, parents and teachers in their respective classes filled the ADHD-rChildren performed individually two tasks: a visual and a semantic search task in one session. The order of assessment was random. They were informed that they would perform two exercises concerning their attention. In the analyses, we used the teacher’s ADHD scores, on the basis that they had a better overall frame of reference, yet we used parents’ scores as a control and excluded 3 children for whom the parents’ scores was greater than teachers scores by more than one standard deviation.30. The test stimulus was an A4 paper sheet containing black on white silhouettes of common objects (see Appendix 1), with bells designed as targets. 35 targets were distributed equally in 7 columns, each column having the same number of targets (N = 5) and distractors (N = 40) with a balanced number of targets in each quadrants. Children were instructed to cross-out as many bells as they could in a two minutes interval. While participants performed the task, an experimenter registered on the scoring sheet the order of the bells found. Participants started from a bell in the upper left corner , where words or phrases from the vocabulary are mapped to vectors of real numbers. This method involves a mathematical embedding from a space with one dimension per word to a continuous vector space and has been demonstrated to be a relevant method to measure semantic distance between words35. We performed the analysis using the Python implementation of word2vec in the Gensim package36. This method gives a vectorial representation of words in 300 dimensions, and the cosine of two vectors provides a measure of the semantic similarity of two words, ranging from 0 (no similarity) to 1 . We based our analyses on (1 - similarity) to obtain a measure of distance, as with visual search , with the lme4 package for mixed models37, the lmerTest package to perform likelihood ratio tests38 and the lqmm package for quantile regressions39. Raw data, translation of the lists of words and analyses scripts are available on the Open Science Framework at https://osf.io/2n4q9/.We ran generalized mixed models with performance in the search tasks as dependant variables and ADHD score as independent variable and subject as random factor. To statistically test differences in long distance of distributions we used two methods: First, we performed mixed effect quantile regressions from quantile 0.5 to 0.95 by steps of 0.05, on the distributions of raw distances, with the ADHD score as predictor and participants as grouping factor. Second, we splitted participants in highs and lows with respect to the median (m = 9) of the population scores, and for each quantile, from 0.5 to 0.99 in steps of 0.01, of the global distribution of distances, we applied a Poisson regression on the number of distances above said quantile for each child, with group (high or low ADHD score) as a predictor. We tested significance by means of bootstrapping (N = 100) with a cluster-based significance level of 0.05. We performed statistical analysis using SD = 3.0) bells within two minutes. No difference were found depending on ADHD score (p > 0.7). We computed the distances between consecutive bells in each participant’s search path. Search paths of children with higher ADHD ratings were more variable and contained more long distances: we found a positive and significant effect of the ADHD score on both the mean  and the standard deviation  of distances. Inspection of the distribution of these distances  for the quantiles 0.6, 0.8, 0.9 and 0.95  animals within two minutes. Scores on the ADHD-rs were positively correlated with higher production , but also with higher performance , as previously reported within the non-clinical population8. They also repeated themselves more often: in a Poisson regression on the count of immediate repetitions with ADHD score and performance as predictors, we found significant and positive effects of both the ADHD score , meaning that the increased number of repetitions is present even while controlling for number of items produced. There was no effect of ADHD ratings on distant repetitions (p > 0.2).Children named on average 26.9 . This is coherent with the notion that when searching for animals, children behave as explorers, so that more distant positions in semantic space yield longer travel times.Next, as a test of the relation between the corpus-based semantic distances and children’s behaviour, we asked whether Inter item Response Time (IRT) and semantic distances were correlated. We found . However, we found a significant and a positive effect on the standard deviation  of the distances, meaning that the variability of the search paths increased with increasing ADHD ratings. Inspection of Fig. SD = 0.006, IC-95 = , p = 0.03–see Supplementary Table Next, for each child we computed the mean of the distances in her/his search path, as well as the standard deviation. By means of linear regressions with the ADHD score as predictor, we found that the mean distance did not depend on ADHD traits . The linear regression between the two indices controlled for the ADHD score was not significant (p > 0.3).40. Importantly, we found these effects in the tails of the distributions and not at their peaks, suggesting that the differences between children stem from more frequent long jumps with higher ADHD score, rather than from a global search process modification: while all children use a similar base local search strategy, children with higher scores on the ADHD-rs inserted more often jumps to distant positions in the search space. Thus, their tuning of the exploration/exploitation ratio is biased in favor of exploration, without decrement in performance. Our results are also coherent with previous studies on each search task. Performance in visual search is lower in children with ADHD42. In semantic search, the number of words cited is higher in participants with ADHD44. Yet, interestingly, when the fluency is phonological their performance is lower46 which agrees with the notion that phonological search requires more executive control than semantic search47. This difference of performance in different searches might comes from the nature of the environment. Indeed, in our visual search task, targets were uniformly distributed, while targets in our semantic search are plausibly more sparsely and unevenly distributed. We speculate that this distribution could have a greater homology with distributions of targets in natural foraging environments.In this study we found that for two simple search tasks, one external and internal, children who scored higher on the ADHD-rs produced more long distances and were also more variable than those who scored lower on the ADHD-rs. These results are in line with the literature on variability in ADHD25. The diversity of natural search environments might have promoted the emergence of a diverse set of search strategies, contributing to the relatively high prevalence of ADHD traits48. Following these lines, our results contribute to the mounting evidence that lower inhibition in ADHD might enhance creativity50, as a result of more exploratory behaviours. Here we should note that, in the visual search task, all children explored the same visual stimulus while the semantic space that was the base of the semantic search was idiosyncratic. Further research is needed to investigate the role of this distinction in potentiating the benefit of more exploratory search strategies. It is of prime importance to note, however, that our children participants were all healthy children, and that those who scored high on the ADHD-scale were not clinical ADHD. Thus, in clinical populations, some other clinical dimensions of ADHD such as motivation or vigilance disorder53 might come into play and mitigate the benefits of the exploratory strategy found in our results.Along this line, we would suggest that the bias in favor of exploration is a latent cognitive trait that is diversely expressed depending on the task context. Thus, the same exploration bias may have differential impacts on performances depending on search domains, and could even become beneficial as in our semantic task. This is coherent with the evolutionary interpretation of ADHD according to which impulsive and unstable behaviours while maladaptive in contemporary contexts, may have been more often beneficial in ancestral contexts8. The group level consistency of these exploring/inattentive phenotypes suggests some corresponding genotypes especially for genes coding for dopaminergic systems. Further studies might explore the link between exploring and inattentive behaviours and the variability in the genes coding for the dopaminergic system. In addition it might be of interest to take into account the intra-individual variability of behavior, as it is a consistent feature of ADHD and also represents a crucial point in natural selection by determining the behaviour of prey and predators54.Though we did not directly measure dopamine, our results provide insight for the proposal that search processes and regulation of attention are goal directed behaviours that share a general cognitive basis that depends on dopaminergic pathways55, might be caused by periods of rapid shifts in mental contents, also named “transitive states” by James1, during which introspection is difficult. Blanks would thus not be true episodes of empty thoughts, but by-products of explorative attentional strategies in mental space. Further research is thus needed to investigate this hypothetical functional role of mental states subjectively experienced as blanks.Turning now to the subjective component of ADHD, we suggest that jumps in searches could be the overt manifestations of attentional lapses. Indeed, we propose that blank thoughts, during which no mental content can be reported and that we previously identified as more frequent in ADHDappendix and supplementary materials


Introduction: Autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD) are the two most common neurodevelopmental disorders observed in childhood. The DSM-5 accepts a combined diagnosis of ADHD and ASD, while the DSM-IV did not. The aim of this study was to identify and evaluate the adaptive profile of children and adolescents with a diagnosis of comorbid ADHD and ASD, in comparison with adaptive functioning in subjects with a diagnosis of only ASD or ADHD.Materials and Methods: Ninety-one children , aging from 3.1 to 13.4 years (mean age: 8.3 ± 7.2), who met the criteria for a diagnosis of ASD and/or ADHD were enrolled. A neuropsychological evaluation involving cognitive and adaptive assessment was conducted using the Autism Diagnostic Observation Schedule – Second Edition (ADOS-2), the Conners’ Parent Rating Scale – Revised: Long Version (CPRS-R), the Wechsler Intelligence Scale – Fourth Edition or the Griffiths Mental Developmental Scales – Extended Revised, the Vineland Adaptive Behaviour Scale – Second Edition (VABS-II).Conclusion: As to the adaptive skills in the three groups evaluated, a worse general profile was ascertained in the ASD and in ASD plus ADHD groups in comparison with respect to the ADHD-only group. With VABS-II evaluation, we found significant differences among the three groups across all domains and combined scores: Communication , Socialization , Daily Living Skills , Motor , and Adaptive behavior composite [ABC] . Implications of neurodevelopmental double diagnosis such as ASD plus ADHD are discussed. Autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD) are the two most common neurodevelopmental disorders observed in childhood , 2. AccoAttention-deficit/hyperactivity disorder (ADHD) is one of the most common mental disorders affecting children. Symptoms of ADHD include inattention, hyperactivity, and impulsivity. Current estimated prevalence is 5% of children and 2.5% in adults. ADHD is often first identified in school-aged children when it leads to disruption in the classroom and/or difficulties with school duties. It is more common among boys than girls (DSM-5) .Impairment of social competences in neurodevelopmental disorders is common and needs to be thoroughly addressed. In recent years, there has been increasing interest in the diagnostic overlap and similarities between ADHD and ASD –11. EvidAs to adaptive functions, some studies found that children with ASD+ADHD showed a more severe impairment in adaptive functioning and a poorer quality of life than children with ASD alone  while otA specific social-communication core deficit, associated with restricted and repetitive behaviors (RRBs) is the hallmark of ASD. In contrast to the DSM-IV APA 1994), the DSM-5 994, the .Children with ADHD frequently display peculiar social difficulties. Social competences in ADHD are thought to be related to self-regulation difficulties, low social skills adaptive level, and attentional issues, which can impact the overall ability to process social information. Children diagnosed with predominantly inattentive ADHD (PI) are more passive, less aggressive, less assertive, and less knowledgeable of appropriate social behavior than those diagnosed with combined ADHD (CB). Children with PI are more likely than typical children to be socially neglected, whereas children with CB are more likely to be socially rejected . ChildreIn research on interventions, children with ASD+ADHD undergoing social skills training failed to improve, as opposed to children with ASD only and children with ASD and anxiety disorder .The main aim of this study was to identify and evaluate the adaptive functions of children and adolescents with a diagnosis of ASD+ADHD, in comparison with adaptive functioning in subjects with a diagnosis of ASD or ADHD.This cross-sectional study included 91 children , ranging from 3.1 to 13.4 years (mean age: 8.3 ± 7.2), who met the criteria for a diagnosis of ASD and/or ADHD Table 1.All children underwent a full clinical evaluation, including medical history, clinical observations, and assessment. Diagnosis of ASD was based on DSM-5 criteria. Impairments in intentional communication, eye contact engagement, shared attention behavior, use of gestures, and restrictive and repetitive behaviors were assessed and detailed. Diagnoses were also confirmed by using the Autism Diagnostic Observation Schedule – Second Edition (ADOS-2) .Diagnosis of ADHD was based on DSM-5 criteria. Overall, clinical criteria evaluation contributed to defining the diagnostic classification. In addition to the ADHD features of hyperactivity, impulsivity, and/or inattentive problems, relevant social deficits – if present – were also thoroughly described. We also used the Conners’ Parent Rating Scale – Revised: Long Version to confirm the ADHD diagnoses n = 25 (CPRS-R) (Conners 2001).Exclusion criteria included (1) genetic and neurodevelopmental disorders of known etiology , (2) serious chronic diseases, (3) significant sensory or motor impairment, (4) presence of seizures, and (5) use of psychoactive drugs as possibly interfering with the clinical profileAccording to the diagnostic criteria, 31 children have been identified as ASD only (ASD group), 25 as ADHD only (ADHD group), and 35 as ADHD and ASD (ASD+ADHD group). Characteristics of the groups are described in After complete description of the study to parents and/or guardians, written informed consent for data acquisition and clinical examination was obtained according to procedures approved by the local ethics committee. The research was conducted according to the rules of the Helsinki Declaration regarding good clinical practice and ethics.The study was approved by our local ethics committee.All children were examined by our research team. The neuropsychological evaluation involving cognitive and adaptive assessment was conducted by means of a diagnostic protocol in order to identify the main clinical and developmental features and to depict a comprehensive profile of the children enrolled in the study. The protocol included the administration of (1) the ADOS-2 for measuring the severity of autistic symptoms  to childThe ADOS-2 was administered by experienced and board certified examiners to determine the severity of ASD symptoms. It is a semi-structured, standardized assessment instrument designed to obtain information about social-communication development and repetitive and restricted interests in children. This tool is considered the gold standard for ASD evaluation and is widely used in clinical practice. The ADOS-2 diagnostic algorithm yields classifications of ASD versus non-ASD children and a calibrated severity score (CSS) for the algorithm total that provides further information, including the severity of the disorder.The CPRS-R is a tool for obtaining parental reports of childhood behavior problems that contains summary scales supporting ADHD diagnosis and quantifying ADH severity. This scale has a seven-factor model composed of the following factors: Cognitive Problems, Oppositional, Hyperactivity–Impulsivity, Anxious–Shy, Perfectionism, Social Problems, and Psychosomatic. It has good internal reliability, high test–retest reliability, and effective discriminatory power. In addition, the CPRS-R includes a corresponding factor structure with the Conners Teacher Rating Scale – Revised and comprehensive symptom coverage for ADHD and related disorders. Three types of ADHD are now recognized: predominantly inattentive (PI), predominantly hyperactive–impulsive (PH) and combined (CB).To evaluate intellectual functioning and determine the IQ, we administered – according to the age of the child – the WISC-IV or the GMDS-ER. Both scales provide a value that represents the subject’s general intellectual ability. These measures are standardized by chronological age, with a mean of 100 ± 15. In this study, we considered IQ indicators to be the Full Scale Intellectual Quotient (FSIQ) for the WISC-IV and the developmental quotient (DQ) for the Griffiths Scales.The VABS-II is a semi-structured parent interview used to obtain parent ratings of children’s adaptive functioning across three domains: Communication, Socialization, and Daily Living skills. Standard scores were obtained for each domain and combined to provide an Adaptive behaviour composite (ABC) standard score. VABS-II scores have a mean of 100 ± 15, with lower scores indicating more severe impairment.Comparisons between groups were examined, as appropriate, by means of one-way analysis of variance (ANOVA) followed by post-hoc Welch two-sample t-test and Tukey contrasts for multiple comparisons of means, as well as by means of Pearson’s chi-squared test, Kruskal-Wallis rank sum test, and Wilcoxon rank sum test with continuity correction. Linear regression model with ABC as outcome variable was used to model several covariates. Variables were entered according to a procedure of forward selection. The first variable entered into the equation was the one with the largest correlation with ABC. The next variables were added according to the largest change in the R 2 statistic, until no more change occurred. The chosen model was the one with the largest R2. Goodness-of-fit statistics are shown: Multiple R 2, Adjusted R 2,R Language and Environment for Statistical Computing program .F-statistics, standard error of the estimate, and p-value. An alpha level of 0.05 was used for all statistical analyses. Results, if not otherwise specified, are given as means ± SDs. All statistical analyses were performed using the 2 = 0.939; p = 0.625). IQ was different among groups . The post-hoc analysis showed that IQ was significantly higher in the ADHD group, compared to the ASD  or to the ASD+ADHD group , while there was no significant difference between the ASD and the ASD+ADHD group .As shown in In terms of parent ratings of children’s adaptive functioning, measured by means of the VABS-II, we found significant differences among the three groups across all domains and combined scores: Communication , Socialization , Daily Living Skills , Motor , and ABC  Table 3.2 = 0.974; p = 0.324).Considering clinical presentations in the two groups presenting ADHD features, in the ADHD-only group we observed 20 CB and 5 PI presentations; in the ASD+ADHD group, 24 CB and 11 PI presentations. The difference between the two groups was not statistically significant . Overall, none of Conners indexes showed relevant differences between the ASD+ADHD and the ADHD-only group Table 2.ADOS-2 total scores were similar in the two groups with ASD features  with no statistically significant differences between the two groups .In a linear regression model, higher VABS-II ABC scores were negatively associated with age and ASD diagnosis, and positively associated with IQ. There was no significant association with gender Table 4.Research focusing on co-occurring ADHD and ASD has been directed primarily on origins and clinical characteristics and with minor effort on interventions. Children with ADHD and ASD experience more difficulty in daily situations as compared to those with only one disorder. Co-occurrence of ADHD and ASD is associated with a lower quality of life and poorer adaptive functioning as compared to children with ASD only . In addiAs to the adaptive skills, in the current study a worse general profile was ascertained in ASD and in ASD+ADHD groups with respect to the ADHD-only group in all VABS-II domains  and ABC. Slightly lower scores not statistically significant were found in the combined group with ASD+ADHD in comparison to ASD group detected with respect to all the VABS-II domains: Communication, Daily Living Skills, and Socialization .Other studies compared adaptive profiles in the three groups of children with ASD, ADHD, and ASD+ADHD and demonstrated the following findings , 20. In Also in the study by Turygin et al. , adaptivAs an additional remark and a future direction in the evaluation of adaptive skills in ASD it is important to note that minimal clinically important differences (MCIDs) on VABS-II scores have not been rigorously established in ASD. To fill that gap a large multisite study has been carried out and lower VABS-II standardized score MCID estimates were observed for younger and more cognitively impaired children. This should be taken in account when evaluating adaptive functions in ASD concomitant to intellectual disability alone or combined with ADHD –42,.There are some limitations in the current study that should be mentioned. Adaptive functions have been detailed in three different clinical groups, but the lack of a typically developing control group is the first limitation to be noted. A second limitation pertains to the relatively small sample size that may have influenced the between-group differences reported as to adaptive and cognitive ability and to the effect of IQ on adaptive function that may be underestimated. A higher number of participants would likely reflect more accurately these differences between the groups. Lastly this is a cross sectional evaluation of the three clinical groups and longitudinal studies of adaptive functioning in ASD+ADHD are strongly needed in order to increase the understanding of the development, change, and stability of symptoms over time and to identify protective and worsening factors of these conditions.Children with ASD+ADHD had a greater treatment need which could imply additional treatments for both school and community services , 43, 44.When symptoms are not managed, they may lead to more severe psychiatric comorbidity as well as poorer school, family, and cognitive outcomes among this population and so specific attention is warranted to readily provide appropriate intervention.The study is carried out in accordance with the recommendations of Good Clinical Practice (GCP) guidelines, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki and its later supplements and local legal requirements. The IRB is the Institutional Review Board, at the University Hospital of Siena approved the study procedure and all study documents.VS contributed to the design of the study, recruit and made multiple assessments of the children. FB, MS and AP contributed to the evaluation and recruitment of the children. LG participated in the design and the statistical analysis of the study. RC contributed in writing of the article and revised the methods of recruitment and selections of the study groups.No funding were received for this research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.