Individuals with autism spectrum disorder (ASD) frequently demonstrate symptoms of attention-deficit/hyperactivity disorder (ADHD). Previous findings in children with ASD have suggested that these symptoms are associated with an impairment in executive function (EF) abilities. However, studies rarely considered this association within a single framework that controls for other related factors such as Theory of Mind (ToM) abilities and ASD symptoms.We used structural equation modeling to explore the relations among EF, ToM, and symptoms of ASD and ADHD, using data from a population-based sample of 100 adolescents with ASD and full-scale IQ ≥ 50  cohort). The study used a multi-measure and multi-informant approach, where performance of inhibition, planning, switching, and working memory tasks indexed EF and performance on tasks involving mentalizing indexed ToM. Measures of ASD and ADHD symptoms included parent and teacher reports and direct observation of the children. Shared source of symptom reporting was accounted for with a parental rating latent factor indexed by symptom measures reported by parents.Impairments in EF abilities were specifically associated with ADHD symptoms while impaired ToM was specifically associated with ASD symptoms, when accounting for the associations of each cognitive domain with the other factors. ASD and ADHD symptom latent factors were also correlated, but this association became nonsignificant once the shared source of reporting from parents was accounted for and within a model that also controlled for the correlated pathway between EF and ToM factors. The specific relations between the cognitive domains and behavioral symptoms remained even after controlling for IQ.In this ASD sample, symptoms of ADHD and ASD are underpinned by separate cognitive domains. The association between EF and ToM impairments is a likely partial explanation for the co-occurrence of ADHD symptoms in ASD, but the role of shared reporting effects is also important and supports the inclusion of independent informants and objective measures in future research.The online version of this article (10.1186/s13229-017-0177-1) contains supplementary material, which is available to authorized users. Autism spectrum disorder (ASD) is a condition affecting 1–2% children worldwide –5 comparTwo disorders can co-occur beyond chance level due to artifactual or non-artifactual reasons , 14. In In the context of cognitive function, impairments of theory of mind (ToM) and executive function (EF) cognitive domains are often reported in children with ASD e.g., , 16–19).–19.16–19every EF subdomain xibility , 42 thatxibility . Finallyxibility . Thus, pxibility –46) or bxibility –49), whiWe explore the associations among EF, ToM, and symptom domains of ASD and ADHD and address the above challenges in the present study. In line with a recent approach , we inveN = 131 (77%) had full-scale intelligent quotient (FSIQ) ≥ 50 and were followed up at the age of 14–16 years (wave 2). Of these 131 individuals, 19 declined to take part, 11 could not be contacted and one stated interest but could not take part before the end of the study. Thus, in total, 100 adolescents  participated in the current study. The children did not differ from those who did not take part, although eligible (n = 31), on baseline measures of IQ, the ADOS-G and ADI-R total scores, the Social Responsiveness Scale . The follow-up was conducted in two sessions completed on average in 29 days  with 94 participants completing the follow-up session within 2 × SD days from the mean. Tests were divided equally between 2 days, with IQ tests reserved for the first day of testing, in case of participant drop-out. For each testing day, a task order was fixed that enabled presentation of activities to be balanced  and accommodate any constraints . Half of the participants received the tasks in reverse order with some adjustment to account for task constraints. The study was approved by the South-East London Research Ethics Committee (05/MRE01/67). Informed consent was given by the parents and by the participant if their level of understanding was sufficient.The Special Needs and Autism Project (SNAP) is a population-based cohort of people with ASD who were first ascertained and characterized at the age of 10–12 years . The chle  total sle   are listed in Additional file The Wechsler Abbreviated Scale of Intelligence  was choADI-R, and (3) parent ratings on the SRS.ASD symptoms were indexed by (1) total algorithm score on the ADOS-G, (2) total algorithm score on the ADHD symptoms were measured using (1) parent and teacher ratings on the hyperactivity domain of the SDQ, (2) parent report of inattention and hyperactivity/impulsivity symptoms frequency and their impact on everyday functioning on the Profile of Neuropsychiatric Symptoms , and (3The EF measures included in this study were (1) Opposite Worlds, part of the Test of Everyday Attention for Children  and (2) The ToM measures included in this study were (1) a combined False-Belief story (“The Chocolate Story”), a test of first- and second-order false-belief understanding , 67; (2)2 statistics. A two-factor structure fits the EF and ToM factors better and was chosen. To improve the “purity” of the factors, indicators that cross-loaded, for example, significantly loaded to both the EF and ToM factors, or to the factor not expected a priori, and those or with factor loadings ≤ 0.4 were excluded in the first instance.Data preparation and descriptive analyses were undertaken in STATA 11 . Raw datIn step 2, we built the SEM model 1  than a one-factor model . ToM and EF were correlated . Inspection of this model revealed that several tasks cross-loaded on the “other” neurocognitive domain , but the correlation between EF and ToM was reduced from .62 to .40.The EFA of the ToM and EF indicators better fitted a two-factor . Critically, paths between EF and ADHD , ToM and ASD , EF and ToM , and ASD and ADHD  were significant. Those between EF and ASD  and ToM and ADHD  were not.Model 1 . The paths of interest between EF and ADHD symptoms  and ToM and ASD symptoms remained , and the paths between EF and ASD and ToM and ADHD were nonsignificant. The correlation between ASD and ADHD became nonsignificant , indicating that the correlation between these symptom domains was partially accounted for by the shared variance of the parent ratings.Accounting for the shared parental information resulted in model 2 that approached the threshold of acceptability . The removal of a second nonsignificant path between EF and ASD factors also did not significantly change the last model fit . The final removal of the nonsignificant path between ToM and ADHD also did not worsen the model fit . This model showed that EF impairments were associated with increased ADHD symptoms , ToM impairments were associated with ASD symptoms , and EF and ToM were correlated .The final model Fig.  was deri2[95] = 138.7, p = .002; CFI = .91; TLI = .88; RMSEA = .07; AIC = 6811.5; BIC = 6960; Additional file β = .40, p = .002) and ToM and ASD , while controlling for the association between higher IQ and less impairment in EF  and ToM .By regressing EF, ToM, ASD, and ADHD in full model 2 on FSIQ and removing nonsignificant paths, we arrived at the final parsimonious model  and between ToM and observed ASD symptoms , while controlling for the association between IQ to EF  and ToM impairments . The model structure was also preserved when children whose two testing days were separated by more than the 2 × SD days of the mean were excluded from the analyses  have more severe ASD traits relative to those with ASD alone, judged from parent-rated questionnaires such as the SRS , 47, 49.Indeed, the final SEM model showed that information obtained from the same source  on ASD and ADHD traits moderated their correlation. That is, ratings by the same informant on different measures are more highly correlated than those from different informants. There are multiple possible explanations for this finding. Informants, in this case, parents, may have a specific response style, such as a tendency to rate all behaviors as high or low, that influences their responses across measures . FurtherIn line with the comorbidity literature, our model showed that artifactual and non-artifactual mechanisms could explain the increased co-occurrence of ADHD traits in the ASD children population. Firstly, ADHD traits might be reported at increased rates, presumably in those with more severe ASD, due to shared reporting source effects from parents. These cases, which do not express true comorbidity, one hopes are few and can be differentiated from true comorbidity cases during clinical observations, corroborated by non-parental source of information. Aside from this artifactual reason, our model provides preliminary supports for the cognitive mechanisms underlying the true comorbidity of ADHD traits in children with ASD. We have shown in this study that the cognitive correlates of the ASD and ADHD traits in children are dissociable, which supports the additive model for the co-occurrence of ADHD traits on ASD symptoms. Furthermore, from our model, the co-occurrence of the separable ASD and ADHD traits could be explained partially by the moderate correlation between EF and ToM impairments (.56), indicating that individuals with both ASD and ADHD symptoms constitute those who are “doubly hit” by both EF and ToM impairments.Due to the limitations posed by the available data, the support our model gives to the additive model is preliminary. To satisfactorily adhere to the additive comorbidity model, we believe that the ADHD traits found in our ASD population must be phenomenologically equivalent, both in presentations and their associations to the cognitive factors, with symptoms found in the pure ADHD population. While previous findings suggest that ADHD symptoms in clinical ASD and ADHD populations have similar presentations  wiThe inclusion of a well-characterized sample of children with ASD is an advantage for this study. Although the sample is not ASD population representative due to the exclusion of individuals with IQ below 50, it is still population-based and is free from clinical sampling biases. In fact, the comparisons we made between eligible individuals who did and did not take part in the study suggested that the study participants had representative ASD and ADHD traits for ASD children with IQ of 50 and above, therefore substantively extending previous neurocognitive findings in this topic which typically excluded children with learning disability. The inclusion of multiple measures from multiple informants gives an advantage over the typical approach of using single measures. The study includes multiple measures of ADHD psychopathology including the SDQ, a well-recognized screening instrument for children and adolescents, and the diagnostic instrument CAPA. Furthermore, the ADOS and the ADI-R, used to index autistic traits in the model, are considered “gold-standard” research instruments for assessing ASD .One limitation of the study is that factors were modeled with EF and ToM predicting the ADHD and ASD symptoms, in line with the idea that EF and ToM deficits are endophenotypes that mediate the link between genes and behavioral symptoms (see  for alteThis study adds to the growing literature that explores the cognitive underpinnings of ADHD symptoms in the ASD population. We found that there is a specific association between EF and ADHD symptoms that remained even after controlling for their associations with variations in ToM abilities and ASD symptoms, which supports the additive hypothesis of ADHD symptoms of ASD in the comorbid cases. Within the clinical context, this finding improves our understanding of how impairments in distinct cognitive domains contribute to the phenotypic variations of ASD, which often include additional presentation of ADHD. Our findings also suggest similarities in the cognitive correlates of ADHD symptoms in ASD as in pure ADHD , 39, alt


Pharmacological and behavioral therapies have limited impact on the distinct neurocognitive impairments associated with ADHD, and existing cognitive training programs have shown limited efficacy. This proof-of-concept study assessed treatment acceptability and explored outcomes for a novel digital treatment targeting cognitive processes implicated in ADHD.Participants included 40 children with ADHD and 40 children without ADHD. Following psychiatric screening, ADHD ratings, and baseline neuropsychological measures, participants completed 28-days of at-home treatment. Neuropsychological assessment was repeated at end-of-study along with treatment satisfaction measures.Eighty-four percent of treatment sessions were completed and ratings showed strong intervention appeal. Significant improvements were observed on a computerized attention task for the ADHD group and a highly impaired ADHD High Severity subgroup. There was no change for the non-ADHD group. Spatial working memory also improved for the ADHD group and the ADHD High Severity subgroup.Findings provide preliminary support that this treatment may improve attention, working memory, and inhibition in children with ADHD. Future research requires larger-scale randomized controlled trials that also evaluate treatment impact on functional impairments.NCT01943539ClinicalTrials.gov  Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity that are elevated relative to developmental level and pervasive across settings and over time, often persisting from childhood into adulthood . AlthougCurrent evidence-based treatments for ADHD include medication and behavioral therapies involving the patient and/or family members , 7. DespGiven the chronicity of ADHD and the challenges of achieving sustained treatment efficacy over time, the potential for directly impacting ADHD impairments through direct cognitive training has garnered widespread interest . Indeed,Neuroracer, to measure and, over time, reduce susceptibility to cognitive interference among a population of older adults. Results of the study indicated that interference susceptibility was reduced and demonstrated that cognitive benefits transferred to performance on untrained tests of attention, impulsivity, and multitasking [Cognitive neuroscience research has the potential to inform the development of novel interventions for specific disorders, such as ADHD, by linking implicated brain systems with specifically-targeted digital interventions, which can be optimally designed to operate within game interfaces . In a seitasking . In addiProject: EVO (EVO) is a digital treatment based on the Neuroracer model that deploys the interference-based cognitive control-targeting mechanics in a high-quality action video game-like interface. The intervention was developed using high-quality graphics and reward loops designed to be engaging for children. Additionally, the intervention embeds algorithms that continuously adapt to cognitive control ability and provide feedback on progress and compliance.Given that these cognitive processes–difficulty with sustained attention, impulsivity, and working memory–are similarly implicated in ADHD, the current study was designed to directly explore the feasibility of using an advanced version of the digital interference treatment with this population. Designed to achieve acceptability in this population, as well as to enable at-home engagement and high-compliance with the treatment regimen, A multi-site research program was undertaken to examine the cognitive mechanics of the EVO platform across ADHD and non-ADHD children to assess feasibility in multiple pediatric populations. Here, we report on the feasibility, safety, and effects of the EVO adaptive treatment as an at-home, non-physician-administered intervention in this population. Specifically, the objectives were: 1) To demonstrate treatment acceptability in an at-home intervention protocol, specifically addressing compliance with the protocol and qualitative feedback at the end of the study period; and 2) To explore whether participants demonstrate improvements in cognitive function  following the intervention period.M = 10.3 years) and 40 age-matched children without ADHD or any psychiatric disorder (M = 10.5 years)  between January 2014 and August 2014. Data collection was completed on-site. Participants included 40 children with a confirmed diagnosis of ADHD (5 years) . EligiblA subset of participants from the ADHD group was subsequently selected via post-hoc statistical analyses to comprise the ADHD High Severity group. As described further below and characterized in MINI International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) [INI-KID) . The MINADHD-Rating Scale-4thedition (ADHD-RS-IV) [D-RS-IV) . The ADHColumbia Suicide Severity Rating Scale (C-SSRS) [(C-SSRS) . The C-SAdverse events. Parents were instructed to report any adverse events by phone to the study staff during the study period, and any adverse events that were spontaneously reported during study visits were recorded.Compliance. The number of treatment sessions completed at home was divided by the number of prescribed sessions to calculate the proportion of required intervention sessions completed for each participant.Questionnaire on intervention appeal. Questionnaires regarding treatment experience and satisfaction were administered at Screening (Day 0) and Post-Intervention (Day 28) visits and were used to assess product acceptance and tolerability. Parents completed a 14-item paper questionnaire about their child’s experience, and study participants responded to 16 questions that were administered orally by study staff. Both Questionnaires were a mix of Likert scales, multiple choice, and open-ended questions. Descriptive statistics were calculated for Likert items while frequency of responses was totaled for multiple choice and open-ended questions.Test of Variables of Attention  [d’ (sensitivity index) and includes scores typically ranging from -10 to 10. The threshold for typical performance on the API is set at zero, with most typically developing individuals performing above zero . The API score was used as the primary TOVA outcome for this study. In addition, all of the TOVA standardized scores were also examined as well as Exgaussian TAU, which is an experimental measure that is sensitive to the long reaction times that are observed among individuals with ADHD.rsion 8) . The TOVBehavior Rating Inventory of Executive Function (BRIEF)–Parent Form [ent Form . The BRICambridge Neuropsychological Test Automated Battery (CANTAB) is a computer-based cognitive assessment that measures neuropsychological functioning [ctioning , 30. TheThe study involved three phases: a screening visit, an intervention period, and a post-intervention visit . The scrDuring the intervention period (Days 1–27) participants were instructed to use the intervention at-home for approximately 30–45 minutes a day, 5 days a week. Each daily treatment period was accomplished by completing 7 consecutive EVO sessions, which consisted of either a four-minute dual-task intervention segment or an automatically triggered ten-minute dual-task evaluation segment that was used to recalibrate the cognitive skills difficulty for the next level. On each day of the intervention, EVO counts the number of sessions as they are completed and limits use for the remainder of any given day following 7 completed sessions. Compliance with treatment was monitored electronically and the parents of participants were notified by email if there was failure to use the intervention over a 48-hour period. Parents were also given a reminder phone call on day 21 to notify them that their child was entering the final week of the study.Participants returned to the clinic on Day 28 for a final evaluation. Post-intervention data that were collected included the cognitive assessments  and additional EVO cognitive metric and interaction data, as well as parent-completed questionnaires.Project: EVO platform was designed to incorporate the fundamental features of Neuroracer [Project: EVO employs a perceptual discrimination attention/memory task as well as a continuous visuomotor “driving” task. Performance is assessed on each task in isolation and when performed together to calculate a performance index for each individual user. A personalized multi-task intervention regimen is automatically configured and delivered to the user, and is optimized adaptively across both tasks to constantly increase multi-task performance. As patients/participants proceed through the treatment, periodic re-calibration occurs to maintain an optimal difficulty level. In addition, the mechanics are coded into a proprietary framework that utilizes real-time and between-session adaptation in order to tailor the treatment specifically to an individual automatically, without the need for any configuration or tuning by a clinician, thus allowing it to be deployed entirely remotely in the patient’s home. This tailoring ensures that patients are consistently performing at a predefined level of difficulty that is challenging but also tolerable because of inherently rewarding videogame-like interaction mechanics.The uroracer  into a sIntervention safety and acceptability were examined by analyzing EVO data on compliance with intervention use, adverse events, and parent and child satisfaction ratings.Analyses were also conducted to examine all cognitive/behavioral outcome measures that were administered in the pre- and post-intervention visits. Analyses were run separately for the ADHD and non-ADHD groups. The means of the pre-test scores were compared to the means of the post-test scores using a 2-tailed paired T-test. For the main neurocognitive outcome, TOVA API, a Shapiro-Wilk test for normality was performed. If the distribution of scores was found to deviate from normality at an alpha of less than 0.05, an equivalent Wilcoxon sign rank test was performed instead of a T-test. Due to the exploratory nature of these analyses, all results less than an alpha of 0.05 were considered statistically significant. For completeness we also report if the alpha levels meet significance under a Bonferroni correction within a statistical family. The outcome measures from the TOVA, CANTAB Spatial Working Memory, CANTAB Rapid Visual Processing, CANTAB Delayed Match to Sample, and BRIEF each measure distinct aspects of executive function and each will be treated as independent statistical families for Bonferroni adjustments. All analyses were conducted in the open source statistical package, R (v. 3.3.1). Only participants who completed the at-home period and completed the final assessments were included in the analysis, therefore no missing data was present. Completing the at-home period was defined by completion of 50% of the required treatment sessions. Treatment session data was uploaded electronically through WI-FI and processed through a cloud-based Application Programming Interface (API) to create a daily report to clinical site staff on participant treatment compliance. Clinical staff were instructed to reach out to the participants if there was no documented usage of the intervention for more than 24 hours. Participants that were dropped or did not complete the study were replaced and thus only participants without protocol deviations  were included in analyses. Four participants from the non-ADHD group were dropped from the study for poor compliance, which represents a replacement rate of 5% of the total study final sample size. An Intent To Treat (ITT) analysis was not considered necessary due to the low drop rate.In addition to these planned analyses, post-hoc analyses were also conducted to examine outcomes for the subset of the ADHD group with most impairment as defined by greater functional deficits and lower attentional performance. A cut-point analysis was performed  to find Given the exploratory nature of the study, the sample size was chosen to be sufficiently large to detect significant within subject effects of at least 0.45 (Cohen’s D) for a 2-Tailed test, at anlpha-0.05 with a power of 80%.A total of 87 participants were screened for the study and a total of 3 participants were screen fails. During the intervention period, four participants from the non-ADHD group were dropped from the study for non-compliance with the at-home intervention use. These participants were replaced by recruiting additional participants. All of the ADHD group participants were compliant with the intervention and none of these participants were replaced during the study. A total of 40 ADHD and 40 non-ADHD participants completed the study. See Across all participants, 84% of all prescribed in-home sessions were completed. This is equal to approximately 10.9 hours of total intervention time per child across the approximately 28-day intervention period. The ADHD group completed an average of 81% (10.8 hours of intervention), the ADHD High Severity subgroup completed an average of 86% (11.1 hours of intervention), while the non-ADHD group completed an average of 87% (11.5 hours of intervention) of the required at-home sessions. There were no significant differences found between the ADHD and non-ADHD groups (p = 0.20), the ADHD and ADHD High Severity subgroup (p = 0.38), or the non-ADHD and the ADHD High Severity subgroup (p = 0.30). Compliance was calculated based on number of exact days at home with the intervention, which varied slightly between groups based on each child’s exit visit.A total of 9 adverse events were reported over all study phases; however, none of those adverse events were judged by the PIs and site staff as related to EVO . None ofM = 6.93; ADHD High Severity subgroup M = 7.1; non-ADHD M = 6.85). Parent ratings of their child’s enjoyment showed a similar pattern of overall enjoyment . See Results from the questionnaire on intervention appeal showed an average participant-reported enjoyment rating for EVO treatment of 6.9 on a 1–10 scale  = 0.35, mean of differences = -1.43, 95%CI-Lower = -2.75, 95%CI-Upper = -0.12). In addition, a larger effect of the intervention was observed for the subset of ADHD participants with greater functional deficits and lower attentional performance  = 0.71, mean of differences = -3.05, 95%CI-Lower = -4.96, 95%CI-Upper = -1.15). There was no change in API scores for the non-ADHD group  = 0.17, mean of differences = -0.39, 95%CI-Lower = -1.16, 95%CI-Upper = 0.37). See Figs Significant improvements were observed on the TOVA primary composite measure, the Attention Performance Index (API) in the ADHD group  = 0.65, mean of differences = -13.16, 95%CI-Lower = -22.19, 95%CI-Upper = —4.13), and Reaction Time Variability (Variance) Standard Score  = 0.62, mean of differences = -26.42, 95%CI-Lower = -45.42 95%CI-Upper = -7.43). Both the ADHD group  = 0.38, mean of differences = 53.27, 95%CI-Lower = 3.34, 95%CI-Upper = 46.02) and the non-ADHD group  = 0.32, mean of differences = 40.27, 95%CI-Lower = 0.32, 95%CI-Upper = 33.33) showed a decline in Omission Error Standard Score . D-prime, a measure of signal detection sensitivity, remained relatively constant in both the ADHD group (p = 0.718) and the ADHD High Severity subgroup (p = 0.406). Analyses of the ExGaussian Tau, an experimental measure derived from the TOVA that may be a sensitive metric for attentional challenges, indicated significant improvement for both the ADHD group  = 0.34, mean of differences = 30.56, 95%CI-Lower = 1.7, 95%CI-Upper = 59.43) and the ADHD High Severity subgroup  = 0.69, mean of differences = 64.77, 95%CI-Lower = 22.85, 95%CI-Upper = 106.7) A complete table listing of TOVA standardized score results with Bonferroni corrections reported is found in Several other standardized score measurements from the TOVA were examined. The ADHD High Severity subgroup, but not the full ADHD group, showed significant improvements on two indicators of ADHD severity: Reaction Time Mean Standard Score  on 8 of 12 variables within the CANTAB Spatial Working Memory (SWM) test, 3 of 10 variables within the Rapid Visual Processing (RVP) test, and 0 of 16 within the Delayed Match to Sample (DMS) test. Figs  on 8 of N = 40, z = 1.81, p = 0.07, Effect Size (r) = 0.29, mean of differences = 2.48, 95%CI-Lower = 0.0, 95%CI-Upper = 5.5)  = 0.51, mean of differences = 3.23, 95%CI-Lower = 1, 95%CI-Upper = 7.5) and Inhibition  = 0.47, mean of differences = 5.68, 95%CI-Lower = 1, 95%CI-Upper = 15.5) improved significantly. The Working Memory and Inhibit scores did not change significantly for the non-ADHD group. In addition, the BRIEF summary scores  did not change significantly over time for any of the groups.On the Working Memory subscale, percentile scores for the ADHD group improved non-significantly but a trend-level effect was observed  . For theProject: EVO was well-tolerated by children with ADHD, was feasibly deployed in the home setting over a treatment period of four weeks, and resulted in positive ratings of acceptability by both parents and children. Notably, there was high compliance and no dropouts in the ADHD cohort. The lack of dropouts was particularly notable considering the time burden required, the fact that no physician or clinician was directly involved in the treatment protocol, and documented dropout rates of >10% in traditional treatment trials as well as real-world settings [The primary aim of this study was to explore the feasibility of using an at-home digital intervention with children diagnosed with ADHD. settings , 32. Givsettings , 34, yetProject: EVO was developed, participants who received the digital intervention demonstrated improvements in attentional functioning and spatial working memory as measured by accepted computerized tests over the one-month intervention period. These improvements were observed on TOVA’s composite Attention Performance Index and hallmark sub-component metrics such as reaction time and reaction time variability (variance), as well as a range of metrics on the CANTAB spatial working memory test. Interestingly, the ADHD groups’ improved Attention Performance index and maintenance of D-prime, despite a decrease in the omission error standard score, may imply attentional improvements that accompany and are not compromised by a shift to a more cautious/withholding response profile.Findings from the current study provide preliminary support that this digital therapy intervention may be effective for improving attention, working memory, and inhibition in pediatric ADHD, especially among children with greater symptom severity and impaired attention. As shown in prior work with Neuroracer, the platform upon which Results of the study build upon those reported for other computerized cognitive training programs that have demonstrated positive outcomes on executive tasks that are similar to trained tasks but have shown limited generalization to other tasks , 36. In Project: EVO is designed to appeal to children through a consumer-grade videogame-like format and a user-friendly at-home intervention that can be self-administered, both of which may enhance motivation and acceptability in real-world treatment deployment. Indeed, qualitative ratings provided by parents suggest that the vast majority describe the intervention as having value, and a majority of parents believed it was worthwhile enough to report the desire for access to the intervention after the study period. Importantly, despite 80% of parents indicating that the intervention was somewhat or very worthwhile, 40% of parents did not indicate a desire to continue the intervention. These parent ratings may be influenced by children’s perceptions that the intervention was challenging. As a therapeutic product, EVO attempts to balance children’s experience of both fun and challenge with effective clinical outcomes. Overall, these satisfaction findings are still considered to be very good and are consistent with prior research that parent acceptability of ADHD treatment is influenced by multiple factors [Additionally and uniquely,  factors .Project: EVO was developed to address the underlying areas of neurocognitive functioning that are impacted in ADHD. In keeping with the guidelines outlined by the National Institutes of Mental Health experimental medicine initiative, treatments that directly address the underlying disease mechanism in psychiatric conditions are greatly needed [Results of the current study are particularly important given that medication management, the mostly commonly utilized treatment for ADHD, has a very significant effect on core symptoms of ADHD but only equivocal effects on specific executive functions –40. In ay needed . AlthougA key part of the current proof-of-concept study was to gain an understanding of how clinically-relevant outcomes may be assessed using measures that have demonstrated utility in characterizing functioning in individuals with ADHD. For example, the BRIEF parent-report provides important clinical information about functional deficits associated with ADHD, and has been correlated with both neuroanatomy and performance on working memory measures , 45. AltSeveral study limitations are important to note. The positive performance trajectories of children with ADHD and non-ADHD participants without ADHD were objectively demonstrated via several standardized outcome measures; however, next studies will need to include additional measures of real-world outcomes in order to determine if the cognitive improvements translate to everyday functioning. In addition, because the current study was an open-label, proof-of-concept trial, it is possible that any improvements observed via parent report were influenced by parent expectations of benefit that would be received from the intervention. However, research on cognitive training methods suggests a much smaller magnitude of improvement , and theAlthough medication is the front line treatment for children with ADHD, there remains a need for the development of alternative intervention modalities to optimize care for these patients. Delivery of a digital treatment intervention on a mobile device may increase access to treatment, reduce cost of care, and increase compliance by making the treatment enjoyable and comfortable for use in an at-home setting. Future research on Project: EVO will require larger-scale randomized controlled trials to better examine the efficacy of this digital treatment for children with ADHD. These trials will benefit from including outcomes to evaluate the impact of treatment not only on executive functioning tasks and ADHD symptoms but also on the functional impairments  that impact these children in their day-to-day lives. Additional studies that examine the amount of therapy necessary to produce a meaningful effect  will also be important to identify the most optimal delivery of this intervention.S1 Checklist(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S1 Protocol(PDF)Click here for additional data file.


Wheat is globally one of the most important crops. With the current human population growth rate, there is an increasing need to raise wheat productivity by means of plant breeding, along with development of more efficient and sustainable agricultural systems. Damage by pathogens and pests, in combination with adverse climate effects, need to be counteracted by incorporating new germplasm that makes wheat more resistant/tolerant to such stress factors. Rye has been used as a source for improved resistance to pathogens and pests in wheat during more than 50 years. With new devastating stem and yellow rust pathotypes invading wheat at large acreage globally, along with new biotypes of pest insects, there is renewed interest in using rye as a source of resistance.Currently the proportion of wheat cultivars with rye chromatin varies between countries, with examples of up to 34%. There is mainly one rye source, Petkus, that has been widely exploited and that has contributed considerably to raise yields and increase disease resistance in wheat. Successively, the multiple disease resistances conferred by this source has been overcome by new pathotypes of leaf rust, yellow rust, stem rust and powdery mildew. However, there are several other rye sources reported to make wheat more resistant to various biotic constraints when their rye chromatin has been transferred to wheat. There is also development of knowledge on how to produce new rye translocation, substitution and addition lines. Here we compile information that may facilitate decision making for wheat breeders aiming to transfer resistance to biotic constraints from rye to elite wheat germplasm. Triticum aestivum L.) provides about 20% of the calories in the diet of the human population. Augmenting its productivity is a global task of paramount importance. The demand for this crop is increasing at a higher rate than its production, and at the same time there is also a great need for producing it without compromising the environment. Plant breeding, in combination with environmentally friendly production systems with increased efficiency are required to meet the demands [Wheat . Genes from rye have been incorporated in wheat in the form of substitution and translocation lines. Indeed, several studies show chromosome 1R to contribute a yield advantage in wheat ..Secale cTriticosecale Wittmack) is attributed to Rimpau in 1888, and thereafter more efforts were put into producing wheat-rye hybrids. The advent of colchicine treatment and tissue culture at the beginning of the twentieth century greatly facilitated the production of triticales [The first attempts to hybridize wheat and rye can be traced back to the experiments conducted by Stephen Wilson, presented in 1873 . Althougiticales .Historically, mainly four rye sources have been used to incorporate rye chromatin in wheat, deployed as (1B)1R substitution or 1BL.1RS and 1AL.1RS translocation lines. The first and most widely deployed source is 1R from Petkus rye. Genotypes with 1BL.1RS translocations were first developed in Germany by Riebesel via extensive crossings between wheat and rye, and such a translocation from Petkus became ancestor of wheat cultivars released in Western Europe, Russia, Mexico, Chile and other countries. From triticale, it is possible to develop wheat germplasm with chromosomes or chromosome arms exchanged for rye chromatin. The other three historic sources for cultivar development were of that kind, developed in Japan, Germany and USA .http://www.ars-grin.gov/cgi-bin/npgs/pvp/pvplist.pl?) and Schlegel’s compilation, the percentage of commercial varieties carrying rye chromatin is about 15%. These examples demonstrate how the current importance of rye introgressions in wheat varies between countries.Between 1960 and 1990, several hundreds of cultivars with (1B)1R substitution, or 1BL.1RS and 1AL.1RS translocations were released. At the International Maize and Wheat Improvement Center (CIMMYT), 60% of the wheat descendants were 1BL.1RS genotypes during the 1990’s . In ChinPartly due to new pathotypes of stem and yellow rusts invading wheat at large scale, in Africa, northern Europe and China, there is renewed interest in using rye as a source for new resistance genes in wheat. Here we compile historic and more recent information on resistance to biotic stresses transferred from rye to wheat.T. aestivum (2n=6x=42), originates from the Near East. It is composed of three genomes  from three diploid ancestors. Donors of the A and D genomes are relatives of Triticum urartu Tumanian ex Gandilyan and Aegilops tauschii Coss., respectively. The origin of the B genome is not completely clarified, but certain evidence points out Aegilops speltoides Tausch as the relative of the donor. Hexaploid wheat originates from the hybridization of Triticum turgidum L. (AABB) and A. taushii (DD) relatives. Despite its polyploid nature, wheat shows a diploid-like behavior with preferential pairing between homologous chromosomes during meiosis [Wheat,  meiosis –12.S. cereale (2n=2x=14), is a diploid species that also originates from the Near East [Rye, ear East . The chrle 2n=2x=, is a diThe allopolyploid nature of wheat makes it highly tolerant to modifications in its genetic composition. The homoeologous pairing between rye and wheat allows the introduction of desirable agronomic characteristics in wheat from rye such as resistance to certain pests and diseases and tolerance to various abiotic stresses. Additionally, the buffering capability of wheat for tolerating important modifications in its genome has allowed the development of different genetic stocks consisting of monosomic, telocentric, deletion and nullisomic lines. These types of plant material have played a significant role in genetic research, for instance in the determination of physical locations for various molecular markers and genes , 16.The transfer of a target chromosome to be incorporated in wheat can be done by selecting an adequate aneuploid wheat line and cross it with rye or a previously developed amphiploid, in this particular case triticale. Wheat-alien substitution lines are frequently used as bridges to produce wheat-alien translocation lines , 16, 17.Another strategy to obtain translocation lines is to recover spontaneous wheat-rye translocations that occur due to the centromeric breakage and fusion of the chromosome arms. Chromosomes tend to break at the centromeres during meiotic metaphase I forming telocentrics. Different telocentrics may fuse again, and thereby exchange chromosome segments , 18.Inducing random translocations by irradiation methods in the absence of homoeologous pairing of substitution lines is yet another strategy, although this method can be laborious and cause several deleterious effects . The winIt is also possible to obtain translocation lines with the procedure described by Lapitan et al. , in whicph1b mutant of wheat. The functional Ph1 allele in chromosome 5B inhibits homoeologous pairing between wheat and alien chromosomes whereas the mutant allows pairing and recombination between homoeologues. Thus it is possible to reduce the amount of genetic material introduced from the alien species [Another strategy is by using a  species , 17. How species .ph1 mutants. When applying optimal concentrations of okadaic acid, homoeologous pairing can take place even in the presence of the Ph1 allele [Yet another strategy to induce homoeologous pairing between rye and wheat chromosomes is to expose tillers to okadaic acid before cells enter into meiosis phase. This will induce early condensation of chromatin, which is associated with the phenotype of 1 allele .Kr alleles which make them easily crossable with rye, something which has been used in more recent efforts to create new 1BL.1RS translocation lines [Many Chinese wheat cultivars have on lines , 26.Rye is well documented as a rich source of resistance to pests and pathogens in wheat. Most of its desirable characteristics have been found in chromosome 1R. Nonetheless, resistance is conferred to wheat from the incorporation of other rye chromosomes as well. However, we found no reports on this from chromosome 7R and just one report, on aphid resistance, from chromosome 5R . One advPuccinia triticina Erikss.), yellow rust (Puccinia striiformis var. striiformis Westend), stem rust (Puccinia graminis Pers. f. sp. tritici Erikss. and E. Henn.), and powdery mildew  (Table Various rye sources incorporated in wheat have been reported to confer resistance to leaf rust (l) Table .Table 1RLr26, Yr9, Sr31 and Pm8 resistance genes for leaf rust, yellow rust, stem rust and powdery mildew, respectively. Unfortunately, diseases are able to overcome major race-specific resistance genes like these. However, one remarkable case is the resistance gene Sr31 from Petkus rye that remained effective against stem rust for more than 30 years. When found defeated first in Uganda in 1999 [Chromosome 1R from Petkus rye has been the most deployed of the rye resistance sources over the years since the 1960’s. This has conferred resistance to several important diseases of wheat. It carries  in 1999 , this po in 1999 .Sr1RSAmigo and powdery mildew resistance gene Pm17, allelic to Pm8, and Imperial rye provides stem rust resistance gene Sr50/SrR. In China there are many recent attempts to transfer new yellow rust resistance from rye sources, such as that from dwarf rye R12 with temporary gene designation YrR212, a 1BL.1RS translocation giving recessive yellow rust resistance from the 1BL.1RS source SW1862 and Aigan rye contributing a non-designated YR gene. Also in China, there are now breeding lines and cultivars with 1RS derived from Petkus rye but with other alleles than Yr9 and Pm8, temporarily designated as YrCn17 and PmCn17. Since rye is out-crossing, there may be such within-cultivar allelic variation [Apart from Petkus rye and the wheat cultivars with the 1BL.1RS translocation derived from this source, like Kavkaz and Veery, there are several other 1R sources of resistance to diseases. Insave rye deployed as 1AL.1RS chromatin in Amigo wheat carry stem rust resistance gene ariation  , Diuraphis noxia (Mordvilko), Rhopalosiphum padi L. and Sitobion avenae (F.); the cecidomyid Mayetiola destructor (Say); the nematodes Heterodera avenae (Wollenweber) and Heterodera filipjevi (Madzhidov) Stelter; and the mite Aceria tosichell Keifer  1R, 3DL.3RS and (5D) 5R, but so far without gene designations [Another important example of insect resistance from rye is the gene in wheat . Only DnD. noxia , 45. Resgnations .ph1 mutants were exploited by Lukaszewski [R. padi and S. avenae. It is not known as yet whether resistance is conferred by the same or different rye genes. One line with 1RS from Amigo wheat was resistant to S. avenae both at seedling and adult plant stage.Centromeric breakage-fusion and the utilization of aszewski , 46–50, aszewski  and Zhanaszewski  to produCmc3 in 1AL.1RS, gives resistance to the mite A. tosichell. Furthermore, resistance to the cecidomyid M. destructor has been found in 2RL and 6RL and gene designations have been made as H21 and H25, respectively. Resistance to the nematodes H. filipjevi and H. avenae has been reported in 6R wheat derives (Table Another rye gene in Amigo wheat, Depending on the wheat genetic background, the rye source and the type of abiotic stress factors, studies have shown that rye transferred into wheat may have both positive and negative effects on wheat performance. Hoffman  and WainKim et al.  tested dAdditional traits that can potentially be exploited are aluminum and acid soil tolerance. It is known that rye possesses tolerance to these soil conditions in chromosomes 3R, 4R and 6R. However, presence of gene suppressors in the wheat genome may hamper full expression of these traits . FurtherLr34 and Lr67 leaf rust resistance genes provide pleiotropic effects on powdery mildew, yellow rust and stem rust [Plant breeders are continuously trying to find new sources for resistance that can be transferred into elite wheat germplasm. To find and transfer resistance genes that are effective against a wide range of strains of the key pathogen is ideal. Furthermore, it is desirable that those durable resistance genes are also effective against other pathogens, and other pests. Genes giving these plant characteristics have only been found in hexaploid wheat so far. For instance tem rust –64. Howe


Cortical neuronal migration and formation of filamentous actin cytoskeleton, needed for development, normal cell growth and differentiation, are regulated by the cyclin-dependent kinase 5 (Cdk5). Attention deficit hyperactivity disorder (ADHD) is associated with delayed maturation of the brain and hence we hypothesized that cdk5 may have a role in ADHD. Eight functional CDK5 gene variants were analyzed in 848 Indo-Caucasoid individuals including 217 families with ADHD probands and 250 healthy volunteers. Only three variants, rs2069454, rs2069456 and rs2069459, predicted to affect transcription, were found to be bimorphic. Significant difference in rs2069456 “AC” genotype frequency was noticed in the probands, more specifically in the males. Family based analysis revealed over transmission of rs2069454 “C” and rs2069456 “A” to the probands. Quantitative trait analysis exhibited association of haplotypes with inattention, domain specific impulsivity, and behavioral problem, though no significant contribution was noticed on the age of onset of ADHD. Gene variants also showed significant association with cognitive function and co-morbidity. Probands having rs2069459 “TT” showed betterment during follow up. It may be inferred from this pilot study that CDK5 may affect ADHD etiology, possibly by attenuating synaptic neurotransmission and could be a useful target for therapeutic intervention. Major symptoms of the disorder include age inappropriate inattention, hyperactivity and/or impulsivity2. Though mostly detected in school-going children, ADHD is also diagnosed in adults3. Cognitive problems, as a result of poor information processing, have also been reported in ADHD patients4. Further, a number of psychiatric disorders co-occur with ADHD2. Among them, oppositional defiant disorder (ODD) and learning difficulties (LD) are the most common co-morbid conditions amongst Indian5 as well as Caucasoid2 populations. Long term follow up of ADHD probands revealed personality disorder, substance abuse and/or criminal behaviors in adults6, indicating persistence of traits. Thus, early recognition of the condition becomes necessary for proper intervention7.Attention deficit hyperactivity disorder (ADHD) is a developmental disorder often characterized by dysfunction of the synaptic system8. During resting phase or excited stage, ADHD children showed brain activity similar to younger normal children10. Specific maturational delay in connection between the default mode network and task positive networks was also reported11. Abnormal circuitry propagation or atypical development was also evidenced14. Imaging study documented slower cortical development as a strong basis of ADHD15. Pattern of development of the cortical thickness was comparable in both ADHD and healthy children, but a significant difference in the initiation time15, with almost a 2–5 years delay in the cortical points , was noticed as compared to normal healthy brain. However, development in primary motor cortex peak thickness was not that delayed15. Diffusion tensor imaging revealed decreased neural branching in children with ADHD16. A difference in the developmental trajectory of the caudate was also observed17. A meta-analytic study showed hypo-activation of the fronto-parietal network regulating attention, while somato-motor network exhibited hyper-activation18. Thus, the balance between cortical thickening during early childhood and cortical thinning during pubertal stage may be disrupted in ADHD patients15. This may eventually lead to an altered over all brain activity and expression of ADHD associated symptoms. Siblings of individuals with ADHD also exhibited disruption in white matter, thereby indicating a strong familial basis19.Whether ADHD is caused by delayed or atypical maturation of the brain, as compared to the normal brain, remained a matter of conjecture till date20, since absence of CDK5 leads to lethality21. Two homologous non-cyclin activators, p35 (CDK5R1) and p39 (CDK5R2), aid in the function of Cdk5 and their differential expressions in mice during development indicate individualized roles in the adult brain too22. Phosphorylation of double cornitine (DCX), crucial for neuronal migration in the cortex, is mediated by Cdk5. A mutation at the 297th serine residue of DCX led to loss of function of the protein as compared to pharmacological inhibition of Cdk523. Cdk5 mediated phosphorylation of Focal Adhesion Kinase (FAK) is also crucial for neuronal migration through regulation of FORK, a microtubule important for nuclear translocation24. Further, Cdk5 induced activation of Wiskott-Aldrich syndrome family member 1 proteins in the neurons was reported to play major roles in the formation of filamentous actin cytoskeleton and therefore, needed for development as well as normal cell growth and differentiation25. Maintenance of neuron-astrocyte integrity in the developing brain through connexin was also reported to be regulated by Cdk526.The Cyclin-dependent kinase 5 (Cdk5) is a proline-directed serine/threonine kinase crucial for normal development27, a major trait observed in ADHD probands. Neuroanatomical analysis revealed delayed maturation in the cortical regions of the brain of ADHD probands15. Gene expression database (BGEE) exhibited Cdk5expression in several cortical regions including the frontal cortex, dorsolateral prefrontal cortex and caudate nucleus during the developmental stages (ST1). On the contrary, the primary motor cortex, which showed early maturation in ADHD probands as compared to typically developing children15, was devoid of Cdk5 expression during infancy, pubertal or adolescent stages . On the basis of various neurobiological functions and localization of Cdk5, we hypothesized that aberrant Cdk5 expression during the developmental period may interfere with cortical maturation observed in ADHD probands, thus affecting the etiology of ADHD. Since one of the easiest ways to identify gene function is by looking into functional genetic polymorphisms, we analyzed genomic DNA samples of 848 individuals, including unrelated nuclear families with ADHD probands and ethnically matched controls, to identify contribution of CDK5 gene variants in the etiology of ADHD. Association of different gene variants with endophenotypes of the probands including, sex, age of onset, inattention, hyperactivity, impulsivity, behavioral problem, co-morbid disorders and executive deficit, were also analyzed in order to find out contribution of gene variants on these traits. Finally probands were re-assessed to find out association of the variants with persistence of the disease severity.CDK5R1 knockout mice  were reported to exhibit hyperactivityThree genomic regions of CDK5 were analyzed by DNA sequencing. Exon 2, containing the binding site, and its flanking regions were analyzed in 100 samples and no bimorhic site was detected in a stretch of 801 bp. Hence, this site was not analyzed any further. Two other sites analyzed were a stretch of 868 bp, including the exon 6 containing the active site and the exon 10 with its flanking regions (780 bp) covering rs2069459 already studied in relation to different diseases including malignancy. These last two stretches were analyzed in all the samples by DNA sequencing to detect frequencies of eight reported SNPs (NCBI).In silico analysis showed that all three may affect transcription (ST2). Other five variants, rs2069453, rs1057766, rs2069460, rs11541602 and rs2069455, though may change Cdk5 activity, were found to be monomorphic in this population (ST2). Statistical analysis was performed for these three bimorphic variants only. The data obtained after 1000 permutation were presented and thus hold true for multiple correction.Out of eight SNPs, only three, rs2069454 (M1), rs2069456 (M2) and rs2069459 (M3), were found to be bimorphic in the studied population. Control samples deviated marginally (0.02) from the Hardy-Weinberg equilibrium (HWE) for M2. Further analysis revealed that only the male control individuals deviated from the HWE. All other sites followed the HWE (P > 0.05).No statistically significant difference was observed for M1 . “AC” genotype frequency was higher in the parents of probands also .M2 revealed a bias in occurrence of the “AC” genotype in the probands  as opposed to male controls. The “C-T” haplotype formed by M2-M3 also showed a trend for association . M1-M3 “C-G” haplotype revealed statistically significant association , which was principally due to significantly lower occurrence in the male probands . The C-C-G haplotype formed by the three markers showed lower occurrence in the probands , which was statistically significant for the male probands . However, power of all these analyses was <50% thus indicating necessity for further validation. The parental population failed to show any significant difference in the occurrence of haplotypes (data not presented for brevity).A number of haplotypes showed significant difference in occurrence (ST4). M1-M2 “C-C” haplotype was absent in the probands and stratified analysis showed a marginally significant difference for the male individuals (χ2 > 0.10). The female probands also showed positive LD between these two sites; however, the LOD score was less indicating absence of true LD.The three SNPs residing at a distance of 373 basses M1-M2) and 1686 bases (M2-M3) revealed lack of LD in the control as well as parental populations , which was basically maternal in nature . No significant haplotypic transmission bias was found from either parent excepting for a preferential maternal transmission of G-A and C-A of M1 and M2  (ST5). Haplotypes with M3 “T” also showed a trend for higher transmission {χ2 = 5.01, P = 0.08, Power 47% (ST5)}.Marginal over transmission of M2 “A” was noticed from the parents  from mothers below 30 years of age (Mean age 24.12 ± 5.26) while no bias was found for mothers on or above 30 years of age (Mean age 33.15 ± 5.32).Stratified analysis based on maternal age during the birth of the proband Table  revealedNone of the markers showed any significant association with the age of onset of the disorder as well as disease severity measured by CPRS-R (data not presented).2 = 6.58, P = 0.01), while no association was found for the same trait measured through CPRS-R. M2 “CC” genotype also exhibited a trend of association with higher IA score . The same allele as a part of the G-C haplotype, formed by M1-M2, also exhibited a positive effect on IA  revealed that M2 “C” allele had positive effect on DSM-IV-TR IA score  while no such effect was observed for the “C” allele. A trend for negative impact of M3 “T”  was also noticed on Inter-Personal Impulsivity (Int-Per-Imp) measured through Tsukayama Scale of Impulsivity (TSI). Haplotype analysis demonstrated negative impact of M1-M2 “C-A” and M1-M2-M3 “C-A-T” on DSM-IV-TR Imp (Table Average impulsivity (Avg-Imp) score was negatively affected by the M2 “CC” genotype . M1 “C” documented lowering effect on the trait score . M2 failed to exhibit any independent contribution. Haplotypes C-A (M1-M2) and A-T (M2-M3) negatively affected the trait score , Short attention span (SAS) or Erratic organizational capability (EOC) (Data not presented). However, performance of probands while playing computerized games was significantly affected by different genotypes Table . M1 “C” 2 = 16.5, P = 0.001, Power 96%). Significant difference was also noticed for M2 due to higher occurrence of “CC”  in the LD group .In the studied ADHD population, 38% probands did not exhibit any co-morbidity (NONE), while 27% presented with ODD/CD, 10% had learning difficulty, 3% was with MD and 22% was under combined group exhibiting more than one co-morbid disorder. Significant association with all the three variants was detected Table . M1 “GC”2 = 5.87, p = 0.02) in disease severity, as measured through CPRS-R ADHD index after 3 years  and Exon 10  revealed presence of three bimorphic variants in the studied Indian population.This pioneering investigation in the field of ADHD showed significant contribution of three 29. Our observation on rs2069454 “C” indicated that lower Bpr and Imp of probands born to younger mothers (<30 years) could be, at least partially, due to the CDK5 rs2069454 “C” variant. On the other hand, the “G” allele, which may lower transcriptional efficiency, as part of haplotype G-C (M1-M2) showed association with increase in trait score for IA and haplotype G-A-T (M1-M2-M3) showed association with low trait score for Int-Per-Imp and BPr. “GG” genotype frequency was low in subjects with co-morbid ODD/CD with concomitantly higher frequency of the “GC” genotype. From these observations it could be speculated that rs2069454 (M1) has a dimorphic role in ADHD associated traits which needs further in depth analysis in large number of samples, since stratification of subjects based on co-morbid disorders lowered the number of subjects in each group.rs2069454 (M1) “C” was identified as a transcriptional activator and frequency of this allele was very low in the studied population. The “C” allele, as part of haplotype also was scanty in the probands. On the other hand, a nominal transmission bias was observed for this allele from mothers below the age of 30 years as opposed to those more than 30 years of age during the birth of the child. This allele, as part of the haplotype “C-A” (M1-M2), was associated with low trait score for BPr and DSM-IV-TR-Imp. An earlier study reported association of higher maternal age (mean 33.3 ± 5.0 years) with oppositional behavior score and HA/Imp score of ADHD probandsIn silico analysis suggested that in presence of the “C” allele, Cdk5 transcription could be lowered due to the absence of binding to transcription factor HSF. The present observation thus creates room for further research to identify how a reduction in Cdk5 level impairs attention to affect the etiology of ADHD.rs2069456 (M2) “AC” heterozygous genotype frequency was significantly higher in families with ADHD probands while a parental transmission bias was noticed for the “A” allele, which was chiefly maternal in nature. Haplotypes showing higher occurrence also had the M2 “A” as their part. QTA exhibited significantly low score for Int-Per-Imp and Bpr in presence of the “A” allele. On the other hand, the “C” allele and “CC” genotype positively influenced the DSM-IV-IA score and thus may be associated with higher IA. As part of haplotypes also, the “C” allele showed association with IA. in silico analysis. Population based analysis showed higher occurrence of the G-G haplotype (M1-M3) in the probands though the difference was statistically insignificant. On the other hand, the C-G haplotype (M1-M3) exhibited statistically significant higher occurrence in the control population and thus the “G” allele could be considered as a protective factor towards ADHD. In contrast to that, frequency of the “T” allele was significantly higher in ADHD probands with co-morbid LD. As part of various haplotypes, the “T” allele showed association with low trait scores for HA, Imp, and Bpr. ADHD probands with “T” allele or “TT” genotype showed significant improvement during follow up as compared to those with “GG”. It could be hypothesized from the above data that the “T” allele may have a role in the etiology of ADHD and co-morbid LD, which merits further investigation.rs2069459 (M3) was found to be a potential splice regulator, though detailed information could be not be obtained by 26, Cdk5 regulates the exocytosis process of synaptic vesicles through phosphorylation of synapsin I, Munc18, and P/Q subtype voltage-dependent calcium channel30. Cdk5 negatively regulates postsynaptic signaling of striatal DA and thus could be speculated to have a role in post-synaptic receptor regulation31. Blocking of DA reabsorption in the axon terminal is induced by chronic cocaine addiction due to activation of delta-Fos B transcription factor, an upstream regulator of Cdk5. Moreover, elevated level of Cdk5 after chronic cocaine exposure may lead to altered signaling through DA receptors to produce adoptive behavioral response32. Cdk5 also modulates cognition as a part of the penumbra of function. Epigenetic modulation of CDK5 was proposed to lead to differential gene expression in the nucleus accumbens ultimately changing stress related and drug abusive behaviors33. In experimental animals, differential Cdk5 expression was observed under chronic and acutely stressed conditions34. The present study also revealed differential role of CDK5 variants in modulating ADHD associated traits and thus warrants further in depth analysis on the contribution of Cdk5 in the etiology of the disorder.Apart from its various roles in developmentin silico functional analysis to identify the role of the gene variants. rs2069454 (M1) “C” was identified as a transcriptional activator by influencing binding with NKX2. rs2069456 (M2) “A” allele was also predicted to act as a transcriptional activator during stress by HSF binding. Earlier study showed that Cdk5 may act as a negative regulator of post-synaptic striatal DA31 and presynaptic DA release35. Through DARPP32 phosphorylation also, Cdk5 may attenuate DA signaling36. Image analysis of ADHD patient’s brain showed hypo-activation of the fronto-parietal network18. We hypothesize that CDK5 haplotypes C-A-T and G-A-T, which was predicted to increase transcription, may inhibit DA transmission leading to reduced cortical excitement  due to disproportionate dendritic spine density in the CA region of the hippocampus42 and generally manifested at an early age2. However, quantitative trait analysis on CDK5 variants failed to exhibit any significant effect of gender and age on the trait scores.ADHD was reported to have a male biasness27. Whether this difference in the level of HA is due to differences between species or is due to inclusion of only limited number of gene variants warrants further in depth analysis on CDK5 to identify its actual role.Allelic variants of the studied markers, M1 “G”, M2 “C” and M3 “T”, as part of haplotypes showed association with reduction in HA score. On the other hand, in a previous study on CDK5R1 knockout mice, with non functional Cdk5, HA comparable to ADHD was documented43 and thus could be taken into account while considering relevance of the study. Further, this novel study on CDK5 revealed significant association of three functional gene variants with ADHD, which is quite intriguing based on the localization and role of CDK5 during the developmental period. Previous investigators suggested that CDK5 regulate vesicular transmission, post receptor DA signaling, dendritic spine formation45, etc. thus regulating neurotransmission. Our study for the first time revealed that CDK5 may affect, (1) trait scores, (2) subtypes, (3) co-morbidity, and (4) long term outcome of ADHD probands. Thus, this novel study on limited number of CDK5 variants indicate a possible role of gene variants in the etiology of ADHD which merits further validation in a large cohort of subjects belonging to different ethnic groups and functional validation of gene variants.Major limitations of the present study are (1) limited number of samples, (2) association of only few variants, and (3) lack of functional validation of the studied SNPs. However, the studied sample size met the criteria for being statistically significantn = 217; mean age 8.10 ± 3.33; sex ratio M:F 8.43:1) were recruited based on the Diagnostic and Statistical Manual for Mental Disorders-IV-text revised (DSM-IV-TR) criteria2. ADHD probands were assessed by the Conners’ Parent Rating Scale-Revised (CPRS-R)46 to measure the presence and severity of symptoms. Intelligence/developmental quotient were determined by the Wechsler’s Intelligence Scale for children47 for probands above five years and Developmental Screening Test for children below 5 years48 respectively. Out of 217 probands, 174 were complete parent-proband trios, 33 had only one parent while 10 were affected probands only. Majority of the probands belonged to the combined subtype (73.0%) while hyperactive/impulsive (12%) and inattentive (15%) subtypes were only limited.Unrelated nuclear families with ADHD probands  were recruited for the study. Informed written consent was obtained for participation in the study. The study protocol was approved by the Human Ethics Committee of Manovikas Kendra (02–110414) which follows the guidelines of the Indian Council of Medical Research. All methods were performed in accordance with the relevant guidelines and regulations of the Committee.Ethnically matched control subjects were evaluated using the DSM-IV-TR criteria for ADHD, hypothyroidism, intelligence/developmental quotient (>80) as well as for any psychiatric disorder running in the family and those without any abnormality  and fragile-X syndrome, were excluded.2. Tsukayama Scale of Impulsivity (TSI)49 was used to measure domain specific impulsivities, like inter-personal impulsivity. Behavioral problem and disease severity were measured through CPRS46.Inattention, hyperactivity, impulsivity were assessed through two different testing tools. For inattention and hyperactivity, traits were measured through DSM-IV-TR and CPRS where as impulsivity was measured though DSM-IV-TRAge of onset was considered as the time when the symptoms were disruptive enough to call for a remediation. Sex and age were considered as phenotypic covariates.Co-morbid conditions were assessed using the DSM-IV-TR criteria. To investigate association between a variant and co-morbidity, probands were classified into four categories; without any co-morbidity (NONE), with co-morbid behavioral problem (ODD + CD), with co-morbid learning difficulty (LD) and with more than one co-morbidity (Combined). Probands were also classified based on the most prominent phenotypes as inattentive (IAS), hyperactive/impulsive (HAS) and combined exhibiting both hyperactivity/impulsivity as well as inattentiveness (CS).50. Additionally, structured questionnaire designed from DSM-IV-TR and CPRS was used to measure Short Attention Span (SAS) and Erratic Organizational Capability (EOC) in the probands as detailed earlier40. Computerized games were used to find out the effect sizes of the polymorphisms on the performance40. 100 probands were called for participating in the game, few could not come due to health issues, few due to personal reasons, while few were reported to leave the station. Finally only 25 probands turned out to participate in the assessment using computerized games.Barkley’s Deficit in Executive Functioning-Child & Adolescent (BDEF-CA) scale was used to assess the executive impairment in ADHD probandsn/T0 .CPRS-R was used for reassessment of ADHD associated traits after 3 years. Improvement index was calculated as 1 − T51. Oligonucleotides designed using the Primer3 program (www.bioinformatics.nl/primer3plus/) were used for PCR amplification in ABI Gene Amplifier #9700 PCR system. SNPs were analyzed by sequencing of the PCR amplicon in Applied Biosystems 3130 Genetic analyzer using Big Dye v 3.1 chemistry and Sequencing Analysis Software, v 5.2. Detailed protocol is given in supplementary material (ST2).Online program F-SNP (compbio.cs.queensu.ca/F-SNP) was used to analyze functional roles of the selected variants. NCBI-CCDS was used to identify functional regions of CDK5. Peripheral blood leukocytes were processed for extraction of genomic DNAhttp://ihg.gsf.de/cgi-bin/hw/hwa1.pl-hwe). Unphased version 3.1.752 was used for population- and family-based analysis. Association with haplotypes was also analyzed using Unphased52. All the results were obtained following 1000 permutation, which takes care of the multiple corrections. For genetic association test, power of the significant results was calculated through Piface, v 1.7653.Hardy-Weinberg equilibrium (HWE) was analyzed using the online software  considering data for the wild type allele in case with respect to control for population based study, and transmitted to non transmitted in family based study.Odds Ratio (OR) was calculated online (http://www.uccs.edu/~lbecker/).Association of alleles or haplotypes with age of onset, ADHD associated traits, and ADHD index obtained during follow up were analyzed by quantitative trait analysis (QTA) through Unphased version 3.1.7. 1000 times permutation was used for corrections for multiple testing. To calculate the effect size correlation for the difference in mean scores obtained through computerized games and different genotypes, Cohen’s d was calculated online (Supplementary Dataset 5


Delay discounting—often referred to as hyperbolic discounting in the financial literature—is defined by a consistent preference for smaller, immediate rewards over larger, delayed rewards, and by failure of future consequences to curtail current consummatory behaviors. Previous research demonstrates (1) excessive delay discounting among individuals with attention-deficit/hyperactivity disorder (ADHD), (2) common neural substrates of delay discounting and hyperactive-impulsive symptoms of ADHD, and (3) associations between delay discounting and both debt burden and high interest rate borrowing. This study extends prior research by examining associations between ADHD symptoms, delay discounting, and an array of previously unevaluated financial outcomes among 544 individuals (mean age 35 years). Controlling for age, income, sex, education, and substance use, ADHD symptoms were associated with delay discounting, late credit card payments, credit card balances, use of pawn services, personal debt, and employment histories (less time spent at more jobs). Consistent with neural models of reward processing and associative learning, more of these relations were attributable to hyperactive-impulsive symptoms than inattentive symptoms. Implications for financial decision-making and directions for future research are discussed. By definition, attention-deficit/hyperactivity disorder (ADHD) is an impairing psychiatric condition.  Those wiIt was once thought that ADHD was a disorder of childhood and adolescence, but research now indicates continued impairment into adulthood. In fact, adults who were diagnosed with ADHD as children report high rates of school drop-out, underemployment, compromised job performance, difficulty maintaining employment, and problems sustaining friendships.  Even morAlthough understudied to date, impulsive and risky decision making among adults with ADHD also manifest in adverse financial outcomes. Adults with ADHD incur more auto loans, have more difficulty paying bills, and open fewer savings accounts than their peers.  In additNeural processing of monetary incentives—including valuations of financial outcomes and decisions about short-term vs. long-term gains and losses—is subserved by functional interactions between the midbrain (mesolimbic) dopamine (DA) system and both the prefrontal and orbitofrontal cortices. Among healthy controls, choices to receive long-term over immediate rewards are associated with frontal activation, whereas choices to receive immediate over long-term rewards are associated with mesolimbic activation.  In additAbnormalities in mesolimbic responding to incentives, and deficiencies in top-down neural control over subcortically-mediated impulses, are hallmarks of the hyperactive-impulsive and combined presentations of ADHD.  Those wiOutside the laboratory, delay discounting manifests in failure of temporally deferred consequences to curtail present behavior, since long-term consequences are also discounted. Accordingly, delay discounting is often referred to as ‘present bias.’ Those who exhibit present bias tend toward impulsive consummatory behaviors, with minimal or no consideration of future consequences—core features of the hyperactive-impulsive and combined presentations of ADHD. . PerhapsThis discussion suggests that financial outcomes of those with ADHD may be affected more adversely than limited data collected to date indicate. We hypothesize that delay discounting—referred to as hyperbolic discounting in the economics literature—will be reflected in associations between ADHD symptoms and previously unevaluated aspects of financial wellbeing. In theory, impulsive consummatory behaviors characteristic of delay discounting should lead to low savings and high borrowing ,27, inclWe evaluate correspondences between ADHD symptoms, measures of delay discounting, and several financial outcomes, including credit card use , amount of savings, and use of extremely high interest rate borrowing . Well replicated findings of preference for immediate over long-term rewards suggest that hyperactive-impulsive symptoms of ADHD should predict present bias, more credit card use with less favorable terms, less savings, and more use of extremely high interest lending. As described below, we controlled statistically for a number of possible confounds, including age, education level, income, sex, and substance use. The latter exerts strong effects on financial outcomes, and is associated with both delay discounting and ADHD (see above).SD = 2.44; range = 2.82–90.48; median = 7.25; excluding the extreme 5% had no effect on the results). Consistent with prevailing MTurk compensation norms, we paid respondents $0.50 for participating. Data quality from MTurk compare favorably with American college samples. [Procedures were reviewed and approved by the University of Florida Institutional Review Board. Informed consent was obtained online, before participants answered survey questions. All 544 participants  were recruited through Amazon’s Mechanical Turk (MTurk), an online labor market comprised of over 100,000 workers in over 100 countries. MTurk is commonly used by social scientists, who post surveys and experiments for workers to choose from. Participation was restricted to respondents located in the U.S. (91.2% native English speakers). Consistent with standard recommendations  we also samples.  Further samples. ,32The following demographic information was collected: (1) age (years); (2) sex; (3) English as native language ; (4) level of education ; (5) annual income ; (6) currently employed ; (7) longest time with a single employer (years); and (8) number of different employers in the past five years.Diagnostic and Statistical Manual of Mental Disorders—fifth edition. [Respondents were asked to endorse or reject, as self-descriptors, each of the 18 symptoms of ADHD in the edition. Present bias was evaluated by asking participants which option they preferred given the following choices: (1) $120 in 1 week vs. $120 in 1 year; (2) $120 in 1 week vs. $137 in 1 year; (3) $120 in 1 week vs. $154 in 1 year; (4) $120 in 1 week vs. $171 in 1 year; (5) $120 in 1 week vs. $189 in 1 year; (6) $120 in 1 week vs. $206 in 1 year; (7) $120 in 1 week vs. $223 in 1 year; and (8) $120 in 1 week vs. $240 in 1 year. Present bias scores (1–8) were assigned based on the highest among these options in which respondents chose the smaller present reward rather than the larger future reward. Similar measures are common in the literature. Convergent validity of associations between impulsivity and financial outcomes was evaluated using the Self Control Scale (SCS).  InternalSubstance use was evaluated using the National Institute on Drug Abuse Quick Screen.  The measFinancial outcomes were assessed with a series of questions including (1) do you have a credit card ; (2) if so, how often are you late on credit card payments ; (3) do you carry a credit card balance ; (4) if so, at what interest rate ; (5) what fraction of your income do you save ; (6) have you used payday loans in the past five years ; (7) if so, what is the average loan amount ; (8) have you used pawn shop services in the past five years ; (9) if so, what is the total value of items you’ve pawned in the past five years ; and (10) what is your approximate current debt in dollars?Demographic characteristics and variable correlations are presented in We executed a series of multiple linear regressions (MLRs) to assess relations between ADHD scores and employment histories, financial outcomes, use of extremely-high interest rate borrowing, self-control, and present bias, controlling for age, income, sex, education, and substance use. Language was not covaried given that the vast majority of participants were native English speakers. In MLR, coefficients for each variable reflect their significance when entered into the regression equation last. They therefore assess independent effects of each variable, over-and-above all others in the equation. Thus, there was no need for stepwise entry, which is appropriate for variable sets. .In the first set of MLRs, ADHD total scores were evaluated. Results are presented in self-control, which accounted for variance in both hyperactive/impulsive symptoms (b = -.406), and inattentive symptoms (b = -.131).Next, we re-ran the MLRs with hyperactive/impulsive and inattentive symptoms entered separately. Results are presented in We evaluated correspondences between self-reported ADHD symptoms, delay/hyperbolic discounting, and financial outcomes among a large sample of adults, by assessing both hyperactive-impulsive and inattentive symptoms, present bias, savings, debt, use of very high interest rate borrowing, and late payments—controlling for a host of extraneous influences. Debt burden, late credit card payments, and high interest rate borrowing  were associated with one another. Many of these outcomes were also associated with present bias and hyperactive-impulsive symptoms of ADHD, although effect sizes were generally modest. Nevertheless, findings are likely meaningful given that (1) they were significant over-and-above effects of substance use, income, and education—all of which are associated strongly with both ADHD and delay discounting; and (2) they were more specific to hyperactive-impulsive symptoms, consistent with hypotheses derived from neural accounts of both ADHD and delay discounting.F = 3.27, p < .04, even though neither hyperactivity-impulsivity nor inattention provided independent prediction, both bs≤.08, both ps≥.12. Thus, although relations between financial outcomes and ADHD were specific to hyperactive-impulsive symptoms, relations between present bias and ADHD were not.In contrast to specific associations between financial outcomes and hyperactive-impulsive symptoms, present bias was associated with total ADHD scores, but not independently with either inattentive or hyperactive-impulsive symptoms. This remained the case when covariates were removed from the model. With only hyperactive-impulsive and inattentive symptoms entered as predictors of present bias, the regression equation was significant, r = -.53, p < .001) derived from hyperactive-impulsive and inattentive symptoms, both bs≤-.13, both ps≤.001. Collectively, differential associations among variables indicate that our measures of ADHD symptoms, present bias, and self-control were not redundant.In contrast, the strong relation between ADHD symptoms and self-control scores  of delay-discounting in ADHD. [The effect size we observed for the relation between present bias and ADHD symptoms was small, in ADHD.  There arThe biggest limitations of this study stem from convenience sampling using very brief measures of ADHD, present bias, and financial outcomes. All data we collected were self-report, and therefore may suffer from systematic response biases and halo effects. Anonymity softens such effects, and many participants reported significant substance use, which suggests a reasonable level of candor. Nevertheless, future research should include more detailed assessments of ADHD symptoms, more extensive indices of delay/hyperbolic discounting, and more objective measures of financial outcomes. Indeed, only 248 of 409 participants who carried credit card balances were able to report their interest rates. Given issues with inattention among those with ADHD, such lack of awareness of financial details is more likely, which may have resulted in under-reporting and smaller effect sizes.These limitations notwithstanding, our findings suggest that relations between hyperactivity-impulsivity and important financial outcomes generalize to broader samples. We view our results as an important step toward improving our understanding of sources of delay discounting and their implications for financial wellbeing. Our findings connect hyperactive-impulsive ADHD symptoms—which mark pursuit of short-term rewards and associated delay discounting—to real financial decisions and outcomes. Furthermore, our results demonstrate that hyperactivity-impulsivity—not inattention—is the main driver of these effects.


N = 476) and their siblings (N = 337) on health-related quality of life (HRQoL) and happiness was quantified using multiple standard measures . In the analysis, careful statistical adjustments were made to ensure a like-for-like comparison of ADHD families with two different control groups. We controlled for carers’ ADHD symptoms, their employment and relationship status and siblings’ ADHD symptoms. ADHD was associated with a significant deficit in the patient’s HRQoL (with a CHU-9D score of around 6 % lower). Children with ADHD also have less sleep and were less happy with their family and their lives overall. No consistent decrement to the HRQoL of the siblings was identified across the models, except that related to their own conduct problems. The siblings do, however, report lower happiness with life overall and with their family, even when controlling for the siblings own ADHD symptoms. We also find evidence of elevated bullying between siblings in families with a child with ADHD. Overall, the current results suggest that the reduction in quality of life caused by ADHD is experienced both by the child with ADHD and their siblings.Childhood attention-deficit/hyperactivity disorder (ADHD) has been associated with reduced health and well-being of patients and their families. The authors undertook a large UK survey-based observational study of the burden associated with childhood ADHD. The impact of ADHD on both the patient (The online version of this article (doi:10.1007/s00787-016-0841-6) contains supplementary material, which is available to authorized users. Attention deficit-hyperactivity disorder (ADHD) is a common childhood onset neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention and/or hyperactivity and impulsivity that can continue throughout life. A recent systematic review of prevalence rates, using the DSM-IV diagnostic criteria, gave estimates between 5.9 and 7.1 % of children worldwide; with males more likely than females to meet the criteria for an overall diagnosis of ADHD and for each of the three DSM-IV subtypes . ChildreQ’, or utility,For policy makers there is increasing interest in knowing whether these health and quality of life burdens can be identified using preference based measures of health-related quality of life (HRQoL), like the EuroQol-5D (EQ-5D) , 18. TheThe aim of this study was to examine the impact of ADHD on health (particularly as measured by utility) and well-being outcomes for patients themselves and their siblings through comparison with carefully matched control groups. In addressing this we give careful attention to the fact that ADHD tends to cluster in families , 27 and The study obtained ethical approval from the Sheffield Ethics Research Committee, research governance was approved in each research site, and written consent was obtained from all participants.A large cross-section, observational survey was conducted across 15 ADHD centres/clinics in England and Scotland  from December 2010 to September 2012. Families were invited to participate in the study if they had a child (or children), aged 6–18, with a current diagnosis of ADHD and attending one of the ADHD clinics. The children had all received a clinical diagnosis of ADHD although centres/clinics varied in their diagnostic protocols and instruments used. This sample was representative of a typical UK ADHD-clinic population receiving treatment. The sampling frame covered a wide geographical area and included both specialist mental health services (CAMHS) and specialist paediatric clinics. Data relating to parents or carers and siblings living with the child with ADHD were also collected. Children with a formal diagnosis of CD were excluded to maintain a tight focus within the study on ADHD and remove its confounding effect on the impact of ADHD. The combination of ADHD and CD may be aetiologically distinct from ADHD alone .There were two different control groups. The first control group was taken from wave 1 of the Youth Panel 2009–2010) of ‘Understanding Society: The UK’s Household Longitudinal Survey’ (USoc), a multi-topic household survey in the UK, conducted by the Institute for Social and Economic Research (ISER) . This of009–2010 N = 136).Five hundred and forty-nine families with a child with ADHD consented to the study. Of these no information was collected on 4 families, and only medical information on the children with ADHD was collected for 90 families. Questionnaire data was collected on 455 ADHD-group families. Of these 394 had 1 child aged 6–18 diagnosed with ADHD, 51 had 2 children diagnosed with ADHD, 8 had 3, 1 had 4 and 1 had 5. Sufficient questionnaire data was collected on 476 of these 529 children (90 %) for them to be included in the analysis. Of the 455 ADHD-group families there were a total of 392 eligible siblings  of which sufficient questionnaire data was collected on 337 (86 %). Data on 123 control families were collected from the SYC. Of these families 61 had 1 child, 51 had 2 children, 10 had 3 children and 1 had 4 children. Some questionnaire data was collected on 196 of these 197 eligible children. Only those children with siblings in the eligible age group were compared to the siblings in the ADHD family group , with the response options of never; not much (1–3 times in the last 6 months); quite a lot (more than 4 times in the last 6 months); and a lot (a few times every week). The same questions were asked for how often the child performs those acts towards their siblings. These questions were also taken from Understanding Society.Parents/carers reported the typical bed time and getting up time of their children with ADHD, and all children from the SYC-control group.The Adult Self Report Scale  was comParents completed the SDQ , a behavunobservable differences between the families with a child with ADHD and those without.Since there were considerable difference in the background characteristics of the ADHD-family group and the control groups  is treated as a dependent variable and modeled as a function of child and family characteristics. We adopt a model that is suitable to each outcome measure in question, with consideration given to the nature and distribution of the outcome measure. Life and family happiness, and hours of sleep are treated as cardinal and analysed using linear models (OLS). Responses to bullying questions and EQ-5D-Y are analysed using ordered logit models. The bounded nature of the CHU-9D utility instruments which cannot go above one at full health and the positive skew of the data with many values at full health make it suitable for analysis with the tobit model . Robust The background characteristics of the children with ADHD, their siblings and the respective controls are shown in Table These differences in initial carer characteristics, particularly between SYC-control and the ADHD-family group, highlight the importance of the matching procedure and the need to include covariates within the regression models. The matching process prunes or drops cases were a good match cannot be found and then applies weights to the control group to create a new weighted-control group which is more similar to the ADHD-family group in terms of covariates. For the SYC comparison this resulted in dropping 37 ADHD family group children and 12 SYC children, leaving a sample of 184 SYC and 439 ADHD-family group children. For the USoc comparison of just the 10–15-year olds, the matching resulted in dropping 730 of the 4,234 USoc children and none of the ADHD-family group leaving a sample of 3,504 and 307, respectively. For the SYC comparison of siblings the matching resulted in dropping 39 ADHD family group children and 3 from the SYC leaving a sample size of 298 and 133, respectively. For the USoc comparison the matching resulted in dropping 271 of the 3,477 children from the USoc and 1 of the 178 children from SYC leaving a sample of 3,206 and 177, respectively.Table When compared with SYC controls, children diagnosed with ADHD have significantly poorer HRQoL across all measures Table . We founWe also considered predictors of individual item levels within the utility instruments, the CHU-9D and the EQ-5D-Y, using ordered logit models (see on-line supplement Table S.2 and S.3). This found that the children with ADHD reported significantly poorer outcomes in problems with school work, feeling annoyed, having had problems sleeping the previous night, daily routine, and joining in with activities, but not in feeling sad, feeling worried, being in pain or feeling tired. For the EQ-5D-Y items the children with ADHD report more problems with washing/dressing, usual activities, pain/discomfort and feeling worried/sad/unhappy, but no differences in mobility.Children with ADHD report 52 min less sleep per night than their matched SYC-controls in the model with standard controls. Including the primary carer ADHD screen as a control reduces this to 44 min difference, and further including employment, and relationship status (column 3) reduces this further to about 39 min difference. Where the primary carer is cohabiting this results in the child getting 26 min more sleep per night compared with having a primary carer who is not in a co-habiting relationship. Those children with primary carers who screen positive for possible ADHD have about 22–27 min less sleep per night.Comparisons to the SYC-control and to the USoc control  we see that each additional point on the 0–10 scale lowers the CHU-9 by 0.010 and the VAS by 1.620. Both control groups find a strong negative impact of own conduct problems on happiness with life. Only the USoc comparison also finds a negative impact of hyperactivity symptoms. The USoc comparison also finds a significantly negative relationship between own conduct problems and happiness with family. However, the direction of causality is not clear in these relationships.Table After making careful adjustments to ensure an appropriate comparison, ADHD was found to be associated with a substantial reduction in the quality of life of patients. Patients, who were being treated for their ADHD, still experienced lower health, lower subjective well-being, less sleep and elevated bullying compared with children who did not have ADHD. This is consistent with previous studies that have identified a health and well-being loss from childhood ADHD –7, 9–14.The utility scores for children with ADHD that are currently being treated are 0.06  lower based on the CHU-9D than children without ADHD. This difference is substantial relative to the typical minimum important difference in utility scores  and remaChildren with ADHD give a poorer evaluation of their family and of their life overall (about a third of a SD) and clearly experience lower subjective well-being than children without ADHD. There is little evidence on the impact of other health conditions on children available to compare to these findings, but this magnitude of effect is strong relative to the impact of family circumstances .Children with ADHD sleep significantly less than children without ADHD. Sleep deficit is one outcome in which our controls play an important role. The raw mean difference between the children with ADHD and their SYC-controls is about 1 h , making direct comparison problematic, this does offer some support for validation for the use of the CHU-9D in terms of the magnitude of utility loss in children with ADHD within the current treatment provision. One of the problems with the CHU-9D is the high number of missing values in the CHU-9D for the item on schooling  which may have arisen due to the child being currently excluded or not attending school and therefore not being clear how to respond to the question about ‘today’ if they did no school or homework ‘today’. This is a shame since if the child is currently not attending school, but they are of school age, then their school related problems are considerable. However, re-running the analysis with different assumptions made for this response makes minimal difference to our conclusion.A key strength of the study is the ability to control for a broad range of household characteristics and the carers’ ADHD screen to account for the clustering of ADHD across families. Surprisingly, adjustments made within the matching process and the regression analysis had only minimal impact upon the differences between the ADHD-family group and the control group. The raw mean difference in CHU-9D, for example, is about 6 %  drives the impact on health and quality of life of both the child with ADHD and their siblings. To explore this would require a consistent diagnosis of comorbidities across the sample which is not available here.A further limitation with the study is that we do not have data on all siblings within the ADHD family group. There may be selection into participating in the study which could be related to general compliance of the child, or to those who feel aggrieved at living with a child with ADHD. However, most eligible siblings did complete the questionnaires .The study unavoidably focuses the children on living with an ADHD or living with a sibling with ADHD. In answering questions such as happiness with their family siblings are likely to give greater attention to aspects of living with a sibling with ADHD than if they answered questions with no framing towards ADHD. Furthermore, children may have perceived an incentive to overstate their problems.We rely on the assumption that children in the control groups do not have ADHD themselves or a sibling with ADHD (diagnosed or undiagnosed). For the SYC-control group the self-reported presence of a child with ADHD was an exclusion criteria. The USoc control group is a representative sample of a households in the UK, hence potentially includes families with children with ADHD. This is likely to result in a slight underestimation of the overall impact of having ADHD, or having a sibling with ADHD, but since the prevalence is likely to be around 4 %  this shoChildren with ADHD, treated within the UK, experienced a lower level of HRQoL than children without ADHD. This was the case even after controlling for differences in household characteristics and a control to capture possible ADHD in parents/carers. Using the pediatric utility instrument, the CHU-9D, we find a decrement in utility of around 6 %. This implies a substantial unmet health need, despite receiving current treatment. Both children with ADHD and their siblings reported that they were substantially less happy with their family and less happy with life overall. Both groups also suffer elevated levels of intra-family bullying.Supplementary Tables (DOCX 49 kb)Below is the link to the electronic supplementary material.