The Strengths and Difficulties Questionnaire (SDQ) is widely used to assess child and adolescent mental health problems. However, the factor structure of the SDQ is subject to debate and there is limited evidence investigating measurement equivalence invariance (ME/I) between treatment groups, informants, and across time.A randomised controlled trial (RCT) recruited 250 participants (6–17 years) who had been referred for an attention deficit hyperactivity disorder (ADHD) assessment. Participants and their clinician either received or did not receive a QbTest report . Parents and teachers completed the SDQ at baseline and 6-months later. This study aimed to understand the factor structure of the SDQ in a clinic referred ADHD sample, and validate the scale as a screening/diagnostic aide and as a measure of treatment outcome both in clinical and research settings. Exploratory Structural Equation Modelling (ESEM) was performed to examine the factor structure, and ME/I was assessed between treatment groups, informants, and time points. The criterion validity of the SDQ predictive algorithm for ADHD was compared with clinician and research diagnoses using logistic regression and tests of diagnostic accuracy.A 5-factor structure provided the best fit with strong factorial invariance between treatment groups and across time points, but not across informants (parent and teacher ratings). SDQ ratings of ‘probable’ hyperactivity disorder were good predictors of clinical  and research diagnoses of ADHD , and research diagnoses of Hyperkinetic disorder . Further examination of the SDQ hyperactivity ‘probable’ rating showed good specificity (84.5%-74.5%) but poor sensitivity (45.0–42.5%) for ADHD.The findings indicate the SDQ is a valid outcome measure for use in RCTs and clinical settings. However, care should be taken when using the SDQ predictive algorithm to screen for ADHD in clinically referred samples. The Strengths and Difficulties Questionnaire (SDQ) is a brief, 25-item, measure of behavioural and emotional difficulties that can be used to assess mental health problems in children and young people aged 4–17 years , and hasThe SDQ has five scales scored 0–10); emotional problems, conduct problems, hyperactivity, peer problems and pro-social scale. The scales are combined  into a ‘total difficulties’ score 0–40). Research on the factor structure of the SDQ has found inconsistent factor structures with the number of factors ranging from 3–5 [0. Resear; emotionThe majority of studies exploring the factor structure of the SDQ have been conducted using general population samples , 14–16 wFurthermore, there is a lack of research on the utility of the SDQ for predicting specific disorders such as ADHD. Across disorders, the SDQ has been shown to correctly identify 81–91% of children with a psychiatric diagnosis in a referred sample . Of the The SDQ has been frequently used in research trials to compare outcomes between two groups and across time points. It was recently used as a measure in a multi-centre RCT  for children and young people referred to clinics for an assessment of attention deficit hyperactivity disorder (ADHD) , 25. HowIt is important to compare SDQ scores between the experimental intervention and control groups such as in the AQUA-Trial to assess the measurement equivalence/invariance (ME/I) of the SDQ measures between groups so as to ensure the SDQ measures the same latent constructs in the same way. Previous evidence of the SDQ ME/I have been reported by gender, ethnicity groups, geographical regions, age and school year groups , 14–16, Among studies investigating the factor structure of the SDQ, Exploratory Factor Analysis (EFA) and especially Confirmatory Factor Analysis (CFA), have been used as the analytical methods. In CFA modelling, each item is generally allowed to load on one factor and all non-target loadings are constrained to zero. In applied research, it is generally justifiable by theory and/or item contents that item(s) can cross-load on different latent factors , 10, 14.Given the SDQ is frequently used as a screening and outcome measurement tool for ADHD in clinic and research settings, there is a need to further understand the factor structure and the accuracy of the parent and teacher completed SDQ in detecting ADHD in a clinic-referred sample. This study primarily aimed to understand the factor structure of the SDQ in this sample, as well as validate the scale as a screening/diagnostic tool, and as a measure of treatment outcome both in clinical and research settings. Using ESEM and logistic regression, this study aimed to investigate, 1) the factor structure of the SDQ in a clinic sample of children referred for an ADHD assessment who participated in the AQUA-Trial, 2) the measurement invariance between parent and teacher informants, and 3) the measurement invariance between the two treatment groups of the AQUA-Trial across follow-up time points (baseline and six months) and 4) the diagnostic accuracy of the SDQ to detect ADHD in a clinic referred sample.n = 250 children participating in the AQUA-Trial [https://clinicaltrials.gov/ct2/show/NCT02209116), it was also later registered with the ISRCTN . The QbTest is a computerised assessment of ADHD, comprising of a Continuous Performance Test with infrared camera to track motion during the test. The QbTest report presents the performance of an individual child against a norm-referenced database and indicates the severity of inattention, impulsivity and hyperactivity. Participants and their assessing clinician were randomised to either immediately receive the QbTest report (QbOpen group) or the report was withheld (QbBlind group). Both groups were followed for six-months from first appointment (baseline).The sample were UA-Trial . The AQUUA-Trial , 25. TheEligible participants were children aged 6–17 years referred for their first ADHD assessment to a CAMHS or community paediatric clinic. Exclusion criteria were previous or current ADHD diagnosis or assessment, being non-fluent in English, and suspected moderate/severe intellectual disability. Informed consent was obtained from all individual participants included in the study. When the child was under 16-years-old, parents provided written consent for their child’s participation, verbal or written assent was also gained from the young person. When the child was 16-years-old or over, both the parent and young person provided written consent. Ethical approval was granted by Coventry and Warwick Research Ethics Committee (Ref: 14/WM/0166) and research and development (R&D) permissions were granted at each participating Trust. The research has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Outcome assessors were blind to group allocation throughout the trial. Details on the trial protocol and its primary outcome have been published .The parent and teacher rated SDQ consists of 25-items that are rated on a 3-point Likert scale , with a mixture of positive and negatively phrased items. The 25 items are designed to be divided between five sub-scales. Each of the five sub-scales comprises of five questions each. These five sub-scales and their items are listed below:Emotional Symptoms—5 items = “complains of headache/stomach ache”; “Many worries, often seems worried”; “Often unhappy, down-hearted or tearful”; “Nervous or clingy in new situations, easily loses confidence”; and “Many fears, easily scared”.Conduct Problems—5 items = “Often has temper tantrums or hot tempers”; “Generally obedient, usually does what adults request'”; “Often fights with other children or bullies them”; “Often lies or cheats”; and “Steals from home, school or elsewhere”.Hyperactivity-Inattention—5 items = “Restless, overactive, cannot stay still for long”; “Constantly fidgeting or squirming”; “Easily distracted, concentration wanders”; “Thinks things out before acting”; and “Sees tasks through to the end, good attention span'”.Peer Problems—5 items = “Rather solitary, tends to play alone”; “Has at least one good friend”; “Generally liked by other children”; “Picked on or bullied by other children”; and “Gets on better with adults than with other children”.Pro-Social Behaviour—5 items = “Considerate of other people’s feelings”; “Shares readily with other children ”;”Helpful if someone is hurt, upset or feeling ill"; "Kind to younger children"; and "Often volunteers to help others . The items for pro-social behaviours are not included in the total difficulties score . IndividThe standard SDQ can be supplemented with a brief impact supplement which assesses the impact of the child’s difficulties in terms of distress, social impairment, burden and chronicity . The SDQhttp://www.sdqinfo.com/c4.html) by collating information on symptoms and impact from multiple informants [The SDQ diagnostic prediction algorithm generates ‘unlikely’, ‘possible’, or ‘probable’ ratings for conduct, emotional, hyperactivity and any psychiatric disorders (formants . SDQs weDevelopment and Well Being Assessment (DAWBA) Children were assigned psychiatric diagnosis based on the Development and Well Being Assessment (DAWBA [t (DAWBA ). The DAt , non-normed fit index (NNFI) and Root Mean Square Error of Approximation (RMSEA) along with χgood fit . Stepwisormality , 36 wherormality , 37, witormality , the Mpl measure . SpecifiTo test the criteria-related validity of the SDQ, separate logistic regressions using STATA 14 were conducted to investigate whether the SDQ can predict ADHD/hyperkinetic diagnosis made by 1) independent research criteria for ADHD based on the DAWBA-derived diagnosis (DSM-IV/V), 2) independent research criteria for HKD based on the DAWBA-derived diagnosis ICD-10, and 3) clinician rated diagnosis of ADHD. In order to further explore the SDQ’s validity as a screening tool tests of diagnostic accuracy were conducted on the SDQ predictive algorithm for hyperactivity. For the purpose of analysis, the three resulting scores  was reduced to two variables: ‘probable’ hyperactivity SDQ predictions were counted as positive for ADHD and ‘possible’ and ‘unlikely’ as negative for ADHD .In total, 250 participants were consented, randomised and received the intervention (QbTest with the report either disclosed or withheld). Of these 123 were in the intervention arm (QbOpen) and 127 in the control arm (QbBlind). The participants’ background and demographic characteristics were similar between arms see . Table 1In order to test the proposed factor structures, modelling fitting results with factor structures ranging from 3–7 for baseline and follow-up data were obtained see . Given tTo further explore this, we investigated the factor loading pattern of the different structures. Meaningful loadings were assessed using the criteria of 0.32 ("poor"), 0.45 ("fair"), 0.55 ("good"), 0.63 ("very good"), and 0.71 ("excellent") . The loaFor teacher data , items fFactor loadings for individual items show that some items had large cross-loadings. Although most items loaded onto the primary factor above 0.40, several items also loaded onto alternative factors greater than 0.30, and/or there was not a difference of 0.20 between factor loadings. This was true for 7 items for the parent see  and 7 itSignificant correlations between factors are presented in For the ESEM modelling, we collated data from both treatment groups and sequentially ran ME/I test models of: configural invariance, loading invariance and item threshold invariance. The model fit indices for each ME/I model are presented in Next, we tested the longitudinal measurement invariance between the two treatment groups across time points for parent and teacher data. The various ME/I model fitting data for parent and teacher data are presented in The longitudinal measurement invariance between parent and teacher data were then compared across time points. The results are presented in To test the criterion validity of the SDQ, we investigated the association between the SDQ diagnostic algorithm and the child’s diagnosis assigned by the clinician and the DAWBA. The SDQ algorithm predicted that a hyperactivity disorder was probable in 35% (79/228), possible in 59% (135/228) and unlikely in 6% (14/228) of the sample. The sensitivity/specificity and positive/negative predictive value of the SDQ ‘probable’ ratings against clinician and independent research diagnoses (DAWBA) are presented in Given that the SDQ is internationally a widely used clinical and research tool to aid the diagnostic assessment of ADHD we aimed to investigate the factor structure and the accuracy of the SDQ in detecting ADHD in a clinic-referred sample using novel and vigorous ESEM techniques, alongside logistic regression and tests of diagnostic accuracy. The findings revealed that a 5-factor structure  for both parent and teacher data was the best fit. This 5-factor structure showed strong factorial invariance across time points (baseline and follow up) indicating the validity of the 5-factor structure as an outcome measure. However, strong measurement invariance was not evidenced between parent and teacher scores, indicating parents and teacher data measure the same construct but in slightly different way. To our knowledge this is the first time cross-time measurement invariance or treatment group invariance, and informant (parent/teacher) invariance across time has been investigated for the SDQ. Furthermore, we showed that scores on the SDQ hyperactivity scale were associated with a research and clinical diagnosis of ADHD in a referred sample. However, further analysis on sensitivity/specificity demonstrated that the SDQ predictive algorithm was not sensitive to ADHD.Model fitting information comparing models with 3–7 factors showed that the 5-factor model remained superior over time for both parent and teacher data. Uniquely, this is the first study to show that this 5-factor model demonstrated strong factorial measurement invariance across time points (baseline and follow-up) and between treatment groups (QbOpen and QbBlind) across time points. In addition, generally poor correlations were found between the five factors suggesting that the factors are broadly measuring independent concepts. With regard to evidence for broader ‘internalising’ and ‘externalising’ factors, poor (but positive) correlations were found between the emotional and peer-problem factors  for both parent and teacher data (correlations ranged from .237-.324), and poor-to-fair correlations between conduct and hyperactivity  were found for teacher data (correlations ranged from .331–4.81). However, the weak nature of these correlations combined with model fitting data suggest that a 3-factor model does not best fit the data. The confirmation of a 5-factor structure for the SDQ is consistent with findings from some studies using community samples –15 and sAlthough our findings evidenced a 5-factor structure consistent with the factors proposed by Goodman  and GoodFor teacher data, several other items cross-loaded onto the conduct scale, including the hyperactivity item ‘reflective’, the peer problems items ‘popular’ and ‘bullied’. Although the item ‘generally liked’ positively associated with conduct problems, it is important to note that the direction of scoring of this question is reversed so that scoring “very true” on this item would be a score of 0 to represent no/few issues with peer problems i.e. a positive association between peer relationship problems and conduct problems.Given that ADHD is a highly heterogeneous condition, with individuals showing different degrees of symptom severity and impairment, as well as different patterns of symptoms, there is a need to better understand the diversity and commonality between symptoms to define more homogenous ADHD phenotypes .The finding that positive (but weak) correlations between the internalising factors of peer problems and emotional problems as well as various cross loadings for items of hyperactivity, conduct, emotion and pro-social and peer problems suggest that although each are distinct symptom domains in ADHD, they are not fully separable, suggesting some convergence on the casual pathways . The croMeasurement invariance between treatment groups across time periods is necessary to be able to meaningfully compare outcomes between two treatment groups. The strong factorial invariance found between the two treatment groups at and across measurement time points and the weak factorial invariance between teacher and parents ratings, indicate that comparing the observed score between treatment groups  and time-points is valid. The untenable invariant threshold model between teacher and parents indicated there are systematic response differences towards the same child’s behaviour between teachers and parents , indicathttp://www.sdqinfo.com/c4.html) but without the third informant (self-report data), as such this may have weakened the predictive validity of our results and greater predictive validity may be found with a complete informant data set. This is perhaps particularly important for ADHD where self-report data is important. Furthermore, given the complexity of ADHD, the condition should never be diagnosed based on the result of one assessment tool [In contrast to previous studies, our findings showed mixed support for the utility of the SDQ to predict ADHD , 24, 27.ent tool .Our findings are strengthened by the use of the novel ESEM approach, which combines both the benefits of EFA and CFA ,44. A liIn conclusion, results of an ESEM approach showed that a 5-factor structure best fitted parent and teacher rated SDQs for a sample of children and young people referred to specialist services for an ADHD assessment. The 5-factor structure showed strong factorial measurement invariance across treatment groups and time points. Therefore, the factor structure of the SDQ should be considered a valid and robust outcome measure for future research studies and to inform clinical judgement of patient symptoms/improvement. Strong measurement invariance was not observed between parent and teacher ratings indicating differences in parent and teachers rating scores. Although ‘probable’ scores on the SDQ hyperactivity scale were a good predictor of receiving a clinical and research diagnosis of ADHD, the poor sensitivity of these scores for ADHD indicate that care should be taken when using the SDQ predictive algorithm to screen for ADHD.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


R2 = 0.26–1.69%), but not with anxiety. After accounting for a general psychopathology factor, on which all symptoms loaded positively , an association with specific hyperactivity/impulsivity remained significant. ADHD PRS explained ~ 1%  of the variance in the general psychopathology factor and ~ 0.50%  in specific hyperactivity/impulsivity. Our results suggest that common genetic risk variants associated with ADHD, and captured by PRS, also influence a general genetic liability towards broad childhood psychopathology in the general population, in addition to a specific association with hyperactivity/impulsivity symptoms.Common genetic risk variants have been implicated in the etiology of clinical attention-deficit/hyperactivity disorder (ADHD) diagnoses and symptoms in the general population. However, given the extensive comorbidity across ADHD and other psychiatric conditions, the extent to which genetic variants associated with ADHD also influence broader psychopathology dimensions remains unclear. The aim of this study was to evaluate the associations between ADHD polygenic risk scores (PRS) and a broad range of childhood psychiatric symptoms, and to quantify the extent to which such associations can be attributed to a general factor of childhood psychopathology. We derived ADHD PRS for 13,457 children aged 9 or 12 from the Child and Adolescent Twin Study in Sweden, using results from an independent meta-analysis of genome-wide association studies of ADHD diagnosis and symptoms. We estimated associations between ADHD PRS, a general psychopathology factor, and several dimensions of neurodevelopmental, externalizing, and internalizing symptoms, using structural equation modeling. Higher ADHD PRS were statistically significantly associated with elevated neurodevelopmental, externalizing, and depressive symptoms ( More = 0.20)) .rg range 0.54–87) [rg range 0.31–41) [rg range 0.46–74) [rg range 0.45–58), and depression (rg range 0.34–77) [In addition to evidence of a strong genetic liability, it is well known that ADHD is highly comorbid with other psychiatric conditions . One imp0.54–87) , learnin0.31–41) –14, oppo0.46–74) –17, anxi0.34–77) . Molecul0.34–77)  have bee0.34–77) –22. Mixe0.34–77) –28.Based on the extensive phenotypic and genetic overlap among psychiatric disorders, it has been suggested that comorbidity may be attributed to a general factor of psychopathology that increases risk for virtually all prevalent psychiatric conditions , 30. TwiThese findings suggest that the co-occurrence of childhood psychiatric conditions is, at least in part, due to shared common genetic risk variants. Given the extensive comorbidity in ADHD and shared genetic risks with other psychopathology, it can thus be hypothesized that a proportion of the genetic risk variants associated with ADHD in recent GWAS  might noThe Child and Adolescent Twin Study in Sweden (CATSS) is an ongoing study targeting all 9-year-old (born after June 1995) and 12-year-old (born before July 1995) twins born in Sweden since July 1992. Parents were contacted for a telephone interview on the twins’ 9th or 12th birthdays. The overall response rate in CATSS is 80% . AnalyseA total of 11,551 CATSS twins were genotyped using the Illumina Infinium PsychArray-24 BeadChip. Prior to analysis, stringent quality control (QC) procedures were performed on the genotyped markers and individuals using standardized procedures. After QC, 561,187 genotyped SNPs and 11,081 samples were retained. Genotypes for another 2495 monozygotic (MZ) twins were imputed from their genotyped co-twin, resulting in a sample size of 13,576 samples with genotype data. Details of the QC protocol, imputation, and principal components extraction are presented in supplementary note z scores, effective sample size and allele frequency in the discovery GWAS [p value thresholds (0.00001 ≤ PT ≤ 1). Indels, multi-allelic and symmetric/ambiguous SNPs were excluded. Autosomal SNPs with a minor allele frequency (MAF) ≥ 0.05 and good imputation quality (INFO score) ≥ 0.8 were clumped  using PLINK.v.1.9 [PT ≤ 0.50 for the main analysis [p value thresholds are presented in the ADHD polygenic risk scores (PRS) were generated in CATSS based on summary statistics from what should theoretically be the most powerful discovery sample available : a meta-analysis of the largest GWAS of clinically diagnosed ADHD  [ery GWAS . After eNK.v.1.9 . RetaineNK.v.1.9 , 40. In analysis . SensitiChildhood psychiatric symptoms were assessed using the Autism-Tics, ADHD, and Other Comorbidities inventory (A-TAC). A-TAC is a 96-items questionnaire corresponding to DSM-IV definitions of childhood psychiatric disorders. Questions assess lifetime symptoms in relation to same-age peers . We seleAll scales were rated according to three response categories: “no” (coded 0), “yes, to some extent” (coded 1), and “yes” (coded 2). As the A-TAC internalizing scales, SCARED, and SMFQ are not directly comparable measures, we used a split-sample approach based on the available internalizing assessments. The final sample sizes with genotype and phenotype data were 6603  for the A-TAC subsample, and 6854  in the SMFQ/SCARED subsample. Based on previous simulation studies, this sample size is more than adequate for estimating complex structural equation models (SEM) and PRS associations , 53.We estimated associations between ADHD PRS and ADHD symptom dimensions  and related neurodevelopmental , externalizing , and internalizing  symptom dimensions, using confirmatory factor analysis and regression analyses implemented via SEM. Path diagrams of the models are presented by subsample in Fig. We first fitted a correlated factors model where symptoms from each subscale were set to load onto a corresponding single latent trait factor. All the latent trait factors were allowed to correlate. In the A-TAC subsample, a correlated factors model with eight latent trait factors was fitted, corresponding to symptoms dimensions of IA, H/I, ASD, LD, ODD, CD, DEP, and ANX. In the SMFQ/SCARED subsample, we fitted a correlated factors model with 12 latent trait factors, including the first six factors outlined above, one DEP factor measured via SMFQ, and five latent anxiety factors corresponding to the SCARED subscales of PD, GAD, SAD, SA, and SP. Previous studies of the SCARED have shown that this five-factor structure has the best psychometric properties for the questionaire –56.Second, we fitted a general factor (or bifactor) model, which in addition to the latent trait factors, included a general psychopathology factor. The general factor model quantifies the extent to which covariance among symptom dimensions reflects both a general factor , and a number of specific latent trait factors (on which only a subset of the symptoms load) , 58. CorIn both models, the latent factors were regressed on ADHD PRS using SEM, with sex, age and the first six PCs (to account for population stratification) included as covariates. We evaluated whether the models provided a good fit to the underlying data using the comparative fit index, and the root mean square error of approximation (RMSEA) . A likelp value thresholds (0.00001 ≤ PT ≤ 1).We performed several sensitivity analyses to test the robustness of results from the general factor model . Similar correlations were found in the SMFQ/SCARED subsample across neurodevelopmental, externalizing and the depression latent factors , whereas correlations with the latent anxiety factors were generally weaker . Notably, SP showed a particularly weak correlation with H/I (r = 0.11), and overall lower correlations with all latent factors . Correlations across the latent trait factors, before and after regression of PRS and covariates, are presented in supplementary Figures The correlated factors model fit the data well in both subsamples (CFI > 0.94 and RMSEA < 0.02)  Table 11. All syβ = 0.06, p = 0.011). Standardized regression results for PRS p value threshold ≤ 0.5 are reported in Table p value thresholds in supplementary Figures In both subsamples, higher ADHD PRS were statistically significantly associated with higher symptom levels in all latent neurodevelopment, externalizing, and depression factors, after adjusting for covariates. ADHD PRS was not statistically significantly associated with any of the latent anxiety factors, with the exception of the latent PD factor . Furthermore, omitting the general psychopathology factor resulted in a statistically significant decrease in model fit Table . Standarβ = 0.09–10, p < 0.0001), explaining ~ 1% of the variance in the general factor after adjusting for covariates. After accounting for covariance across all symptoms via the general psychopathology factor, the association between ADHD PRS and the specific latent H/I factor remained significant in both subsamples , explaining 0.37–69% of the variance in the specific H/I factor after adjusting for covariates. In the SMFQ/SCARED subsample, we also observed a significant negative association between ADHD PRS and the specific latent SP factor . Standardized regression results for ADHD PRS p value threshold ≤ 0.5 are reported in Table p value thresholds in supplementary figures In both subsamples, higher ADHD PRS were significantly associated with the general psychopathology factor (R2 = 1.00–14%: H/I R2 = 0.81%), than in females .After excluding ADHD cases, ADHD PRS remained statistically significantly associated with the general psychopathology factor and the specific H/I factor in both subsamples. Results excluding one MZ twin per pair did not differ markedly from the main analyses. Analyses testing for sex differences in the association between ADHD PRS and the latent factors showed a similar pattern of results as the main analyses, although PRS associations were generally stronger in males  in an independent population-based sample, but also with a range of childhood neurodevelopmental (R2 = 0.40–0.53%), externalizing (R2 = 0.41–1.19%), and to a lesser extent, internalizing symptom dimensions (R2 = 0–0.41%). Importantly, when modeling the shared variance across these symptom dimensions, we found that the associations were largely accounted for by a general childhood psychopathology factor. The significant association between ADHD PRS and a general psychopathology factor (R2 = 0.86–1.06%) suggests that a considerable portion of the genetic variants associated with ADHD, that are captured by PRS and shared with other measures, reflect a non-specific genetic liability toward broad childhood psychopathology.Results from this study show that common GWAS variants that increase the risk for ADHD as captured by PRS, are not only associated with ADHD symptoms (R2 = 0.37–0.69%). About 2/3 of the association between ADHD PRS and H/I could be attributed to general variance shared across childhood psychopathology symptoms, and ~ 1/3 to variance specific to hyperactivity/impulsivity. These finding were robust across both subsamples and provide important molecular genetic confirmation of results from previous twin studies, showing that whilst a substantial proportion of genetic influences on ADHD symptoms are shared with a general psychopathology factor, there are also ADHD-specific genetic influences [Beyond the association between ADHD PRS and a general psychopathology factor, results also showed a unique association between ADHD PRS and specific H/I (fluences , 32, 33.In contrast, there was no significant association between ADHD PRS and specific inattention after accounting for covariance across all symptom dimensions via the general factor. There are several potential reasons for the differential pattern of ADHD PRS association across ADHD symptom dimensions; it is possible that hyperactive/impulsive symptoms are stronger drivers of ADHD diagnosis, leading to an overrepresentation of combined and primarily hyperactive/impulsive ADHD cases in the clinical discovery GWAS. The lack of association between ADHD PRS and specific inattention is likely also explained by the fact that the majority of the inattentive symptoms loaded very strongly onto the general psychopathology factor, leaving little variance in the specific inattention factor. This may suggest that inattention is phenotypically and genetically more closely linked to a general liability for childhood psychopathology, or it may reflect measurement properties of the A-TAC. To disentangle these explanations, results would need to be replicated in a different sample using other measures and/or raters.In the correlated factors model, we found significant ADHD PRS associations with neurodevelopmental, externalizing, and depression symptom dimensions, but not with anxiety. Anxiety symptoms also showed the weakest loadings onto a general factor, both when measured by A-TAC and SCARED. These results suggest that anxiety may be less genetically associated with ADHD in childhood, as compared with other psychopathology dimensions. Such a conclusion is generally consistent with findings from twin and molecular studies . NonetheAnalyses stratified by sex generally showed stronger PRS associations in males relative to females, particularly for associations with the general psychopathology factor and specific hyperactivity/impulsivity. Although this could reflect sex-specific genetic differences, it is more likely to be explained by lower levels of hyperactivity/impulsivity, neurodevelopmental, and externalizing symptoms in females .Finally, by demonstrating that PRS derived from a GWAS meta-analysis of clinically diagnosed and population trait ADHD are also linked to a wide range of childhood psychopathology problems in the general population, this study further highlights the utility of recently developed multivariate GWAS methods –67. PossResults from this study must be interpreted in the context of the study limitations. First, we relied on parent ratings of childhood psychiatric symptoms, which may inflate cross-trait covariance and possibly lead to overestimation of the general psychopathology factor . AlthougResults from this study indicate that genetic risk variants associated with ADHD and captured by PRS also influence a more a general genetic liability toward broad childhood psychopathology. Beyond contributing to shared genetic liability, ADHD PRS also seem to capture genetic risk with stronger and/or unique effects on hyperactivity/impulsivity. Our findings emphasize the utility of adopting a more dimensional, multivariate framework, and the need to account for the inter-related nature of psychiatric conditions when studying the genetic architecture of childhood psychopathology .Supplementary Materials


This study analyzes the relation between attention-deficit hyperactivity disorder (ADHD) and later-life labor market outcomes in the United States and whether these relationships are mediated by educational attainment. To overcome endogeneity concerns in the estimation of these relationships, we exploit the polygenic risk score (PRS) for ADHD in a cohort where the diagnosis of and treatment for ADHD were generally not available. We find that an increase in the PRS for ADHD reduces the likelihood of employment, individual income, and household wealth. Moreover, it increases the likelihood of receiving social security disability benefits, unemployment or worker compensation, and other governmental transfers. We provide evidence that educational attainment mediates these relationships to a considerable extent (14–58%). Attention-deficit hyperactivity disorder (ADHD) is a neurobehavioral developmental disorder that is characterized by inattention, hyperactivity (restlessness), disruptive behavior, and impulsivity . A recenThe impairments in problem solving, planning, and understanding the actions of others have led most ADHD studies to focus on the influence of ADHD on school performance. For example, studies using a sibling fixed-effects model have shown that having ADHD symptoms is negatively associated with test scores and educational attainment , 12. TheOne of the primary challenges in assessing the relation between ADHD and labor market outcomes is to deal adequately with endogeneity, particularly the measurement error in ADHD and the mutual causality between the manifestation of ADHD symptoms and labor market outcomes. Regarding measurement error, most studies have generally relied on a survey-based dichotomous measure of ADHD diagnoses (yes/no) and the age of ADHD diagnoses , 11. NevStudies relying on self-reported ADHD symptoms or diagnoses may also suffer from reverse causality, meaning that labor market experiences may influence the manifestation and reporting of ADHD symptoms. For example, Fletcher  draws onTo deal with the above-described endogeneity concerns, we exploit recent advances in unraveling the genetic architecture of ADHD. The heritability of ADHD is in the range of 70–80% , meaningThis paper investigates the association between ADHD and later-life labor market outcomes using the PRS for ADHD. The PRS for ADHD materializes at conception, and hence we circumvent the measurement issues around the diagnosis of ADHD as well as issues of reverse causality because labor market outcomes cannot change an individual’s value of the PRS for ADHD. Moreover, we draw upon a representative sample of individuals between 50 and 65 years of age (and their spouses) from the Health and Retirement Study, a cohort where the diagnosis of and treatment for ADHD were generally not available. As such, the sample allows for estimations of later-life labor market outcomes that are less biased by time-trends related to diagnoses and treatments of ADHD.Our approach relates to studies using sibling-fixed effects. However, sibling fixed-effects control for the unmeasured time-invariant genetic and environmental factors. Moreover, sibling fixed-effects do not parse out the relative effects of genes and the environment. With a higher prevalence of ADHD among boys than among girls , siblingOur results are generally in line with the study by Fletcher  on the rTo investigate the relation between ADHD and later-life labor market outcomes, we draw upon longitudinal data from the Health and Retirement Study (HRS). The HRS is an ongoing representative panel of Americans aged 50 and over and their spouses. In this study, we use the PRS for ADHD released in April 2018. This PRS for ADHD is based on the GWAS on ADHD by Demontis et al. . We mergIn line with previous studies on ADHD and labor market outcomes , 15, ourOur main explanatory variable is the PRS for ADHD. A PRS is a weighted sum of genetic variants, and the weights are proportional to the estimated effect size of the genetic variant on the outcome of interest in a GWAS . In our Consistent with much of the literature examining the associations between health and labor market outcomes, and given the non-time varying measure of the polygenic ADHD score, we use random-effects panel regression. Mediation is assessed using the “difference-in-coefficient” approach . This ap2The results in Table p-values < 0.001) and meaningful in terms of effect size because the percentage of the relationship between the PRS for ADHD and labor market outcomes mediated by educational attainment (the indirect effect as percentage of the direct effect of the PRS for ADHD on the outcomes) ranges from 13.92%  to 57.62% (receiving unemployment or worker compensation).4Table We performed additional analyses to assess the robustness of our findings. First, given the higher prevalence of ADHD among males compared to females , there iSecond, although its sampling strategy  makes the HRS an appropriate data set to study later-life labor market outcomes, labor-market decisions at these ages are also intertwined with the decision of when to retire. Therefore, we repeated the analyses in (i) the subsample of individual-wave observations with age below 60, and (ii) the subsample of individual-wave observations with age between 50 and 55. For individuals in these age categories, we expect the decision to retire to be less of a confounding factor in our analyses. The direct effect estimates are available in Table The present study contributes to the emerging stream of literature showing the value of using genetic information to understand the determinants of later-life labor market outcomes , 2. We fThe present study contributes to an emerging stream of studies incorporating genetic information in micro-economic models . We noteNevertheless, the present study contributes to the literature by highlighting the negative effect of ADHD on labor market outcomes among individuals for whom treatment for ADHD was generally not available, and the considerable mediating effect through educational attainment in this relationship. These results raise the question of whether it may be worthwhile to genetically screen for ADHD at a very young age. It is one of the promises of “genoeconomics” to identify possibilities for targeted interventions by giving genetic information about children to parents to create a developmental environment that is most likely to cultivate the children’s abilities . TestingHowever, these benefits must be weighted against the disadvantages of genetic screening. First, before one should start with using the PRS for ADHD as a screening instrument, further research on what exactly makes those with a high genetic propensity for ADHD have relatively low educational attainment is needed. Second, the manifestation of ADHD is not solely determined by genes. Hence, a diagnosis of ADHD based on genes only may result in misclassification. Another possible consequence may be that either private insurers would not insure such individuals, thereby increasing burden on the government to cover such costs. Alternatively, those with a genetic predisposition for ADHD may purchase unemployment insurance, which also may not be insured as someone’s genetic make-up is not the result of random or qausi-random environmental circumstances beyond someone’s control. As such, the burden on governmental programs may increase due the non-insurability of labor market outcomes of individuals with a higher genetic predisposition for ADHD. Clearly, careful ethical consideration of the desirability of genetic screening in the context of ADHD is utmost needed.


Objective: To determine whether the acute cerebral hemodynamic responses to oxygen inhalation are impacted by race or acclimation to high altitude.Methods: Three groups of young healthy males, who were Tibetans  with lifelong exposure to high altitude, and Han Chinese (lowlanders) with five-year  and three-day  exposures, participated in the study at an altitude of 3658 m. Cerebral blood flow velocity (CBFV) was recorded for three minutes prior to and during pure oxygen inhalation (2 L/min), respectively, using a transcranial color-coded duplex (TCCD) sonography at the middle cerebral artery (MCA). The blood draw and simultaneous monitoring of blood pressure (BP), heart rate (HR), and finger arterial oxygen saturation (SaO2) were also performed.Results: Values are Mean ± SEM. The three groups had similar demographic characteristics and HR responses, with the group differences (P < 0.05) found in hemoglobin concentration , baseline BPs and HR as expected. Both the Tibetans and Han-5yr groups presented blunted BP responses to O2-inhalation when compared to the Han-3d group; more interestingly, the Tibetans showed significantly reduced responses compared with Han-5yr and Han-3d in CBFV, cerebral oxygen delivery (COD), and pulsatility index (PI) as assessed by Δ%CBFV/ΔSaO2 , Δ%COD/ΔSaO2 , and Δ%PI , respectively.Conclusion: These findings provide evidence on the Tibetans trait of a distinct cerebral hemodynamic regulatory pattern to keep more stable cerebral blood flow (CBF), oxygen delivery, and pulsatility in response to oxygen inhalation as compared with Han Chinese, which is likely due to a genetic adaptation to altitude. Previous studies indicate that Tibetans have lower resting blood pressure (BP), heart rate (HR), hemoglobin concentration (Hb) and cerebral blood flow (CBF), but higher resting ventilation and augmented acute hypoxic ventilatory responses in comparison with the immigrant lowlanders . Althoug2-inhalation will provide foundations for optimizing the therapeutic potential of this intervention. Such data may also further our knowledge and understanding concerning the oxygen-related cerebral hemodynamic responses, years of acclimation in lowlanders, and genetic adaptation in Tibetans.Our previous cross-sectional study indicates that Tibetans may have distinct cerebral hemodynamic responses to the long-term (> one year) altitude exposure  when compared with Han Chinese . Additio2-inhalation between Tibetans and immigrant lowlanders with short- or long-term  high-altitude exposure. A secondary aim was to provide insight into the specific racial trait to high-altitude adaptation as suggested by our previous findings ; (ii) Han immigrants to high altitude for exactly five years , who were already acclimated to the altitude; and (iii) Han newcomers who were recently exposed to the high altitude for three days . All data were collected at Lhasa (3658 m). This study was approved by the ethics committees of Tangdu hospital affiliated to Fourth Military Medical University and General Hospital of Tibet Military Area Command, which was performed in accordance with the guidelines of the Declaration of Helsinki and Belmont Report.A total of fifteen Tibetans and thirty-one Han Chinese signed informed consent to participate in the study. The Tibetan subjects were born and raised at the Qinghai-Tibetan Plateau (3240∼4507 m), while the Han subjects were born and raised close to sea level (≤ 400 m). Their ancestries were confirmed using family history questionnaires. None of the subjects had traveled to other altitudes within one year before participation. Participants were divided into three groups: (i) Tibetan natives with lifelong exposure to high altitude . Subjects were asked to remain still, silent and awake throughout the test to optimize physiological stabilization and data collection.All subjects were young (17–27 years) and healthy as screened by medical history questionnaire and standard physical examination. None of the subjects had any known cerebrovascular, cardiovascular, or pulmonary disorders, nor did they have high-altitude illnesses, e.g., acute or chronic mountain sickness, high-altitude cerebral or pulmonary edema, as confirmed according to the international consensus statement . No subj2) was measured simultaneously using a finger pulse oximeter . All of these parameters were measured three times with the averaged values used for final data analysis. Venous blood sample was drawn to assess hematocrit (Hct) and Hb.During each stage, brachial systolic and diastolic BP (SBP and DBP) and HR were measured with an interval of ∼30 s using an electronic sphygmomanometer . Arterial blood oxygen saturation  with a 1.8–3.4 MHz sector array probe (M3S-D) was used to record the CBFV . With th2 inhalation through a nasal cannula at 2 L/min. This is a routine dosage for low-flow oxygen therapy in clinics (2 from 21% (air) to approximately 28–30% in the present test (The CBFV was recorded continuously for three minutes at the MCA prior to and during continuous pure O clinics  and coulent test . All the2) was calculated as: CaO2 (ml O2/dL) = 1.36 (ml O2/g Hb) × Hb (g/dL) × SaO2 (%)/100, with the small quantity of dissolved oxygen  not included in the estimation  can be calculated from the product of CBF and CaO2.The peak systolic velocity (PSV), end diastolic velocity (EDV), time-averaged maximum velocity (defined as CBFV), resistive index (RI), and pulsatility index (PI) were obtained semi-automatically , where ttimation . To estipost hoc pairwise group comparisons. The data distribution was assessed by the Shapiro–Wilk test. Homogeneity of variances was assessed by Levene’s test of homogeneity of variance. The repeated-measures ANOVA was performed to compare the physiological parameters pre and post O2-inhalation with the group differences examined in the responses (interaction effects). The potential impacts of baseline values on the hemodynamic responses to O2-inhalation were tested using correlation analysis between baseline and response variables, with no any significance observed. All data are presented as mean ± SEM, with P < 0.05 considered statistically significant.Statistical analysis was performed with the SPSS 19.0 software . One-way ANOVA was used to compare the basic variables among the three groups, with the LSD test used to make 2 to a stable level within four minutes. The body weight and body mass index of Tibetans and Han-5yr groups were comparable but both little lower than the Han-3d. The Tibetans had lower levels of Hct and Hb than the Han-5yr group, but slightly higher Hct and Hb than the Han-3d group. The Han-5yr group was approximately three to four years older than the other groups as expected, since most of the Han subjects were soldiers from sea-level with older ones staying longer at altitude. There were also no differences in the TCCD settings between groups.2 was comparable between the Tibetans and Han-5yr groups, and lowest in the Han-3d group. BP (more significant for DBP) was generally lowest in the Tibetans and not different between Han groups. The CaO2 was significantly different between the three groups, with the Han-5yr being the highest and the Han-3d being the lowest. The baseline HR and CBFV showed no differences between the Tibetans and Han-5yr, but were highest in the Han-3d. Although the CVRi showed no significant differences between the three groups, the PI and RI were significantly higher in the Tibetans than in the Han-5yr and Han-3d groups, with no differences found between Han groups.The baseline physiological values are presented in 2-inhalation are presented in 2, CaO2, CVRi, RI, and PI increased, along with the decrease of BPs, HR, and CBFV in all groups . The Tibetans and Han-5yr groups demonstrated reduced magnitudes of changes in SBP and MAP than Han-3d group in response to O2-inhalation . No significant differences were found for all measured systemic responses to O2-inhalation between the Tibetans and Han-5yr groups . The Tibetans showed trends of smaller responses in CBFV  and RI , but significantly smaller responses in COD  and PI  compared to Han-5yr group; Moreover, the COD response in Han-5yr was smaller than in Han-3d , with the PI and RI responses showed no significant differences between the two Han groups  and Δ%COD/ΔSaO2 , than both the Han-5yr and Han-3d groups  the Tibetans and the acclimated Han immigrants showed blunted systemic hemodynamic responses to O2-inhalation compared with Han newcomers at altitude; (ii) the Tibetans also presented a blunted the COD response to O2-inhalation as compared to either acclimated Han immigrants or Han newcomers at altitude; (iii) when normalized by ΔSaO2, both CBF and COD responses were blunted in the Tibetans compared with both Han groups; (iv) the increase of CBF pulsatility to O2-inhalation was dampened in the Tibetans as compared to both Han groups.To the best of our knowledge, this is the first study to explore the impact of race  on cerebral hemodynamic responses to OThe resting hemodynamic characteristics of the Tibetans were basically consistent with previous reports, in slightly lower BP , lower H2 under the hypobaric hypoxic environment at altitude.The resting hemodynamic traits of Han Chinese groups with acute and chronic exposures to altitude agree with previous reports , which s2-inhalation at altitude were basically opposing responses as observed during acute exposure to hypoxia (2 and O2) between the Sherpas (a branch of Tibetans) and acclimated lowlanders as reported previously , COD, and PI responses found different between the two races. The Tibetans showed generally blunted CBF, oxygen delivery, and pulsatility responses to O2-inhalation than the acclimated Han immigrants at altitude, which would be more obvious when normalized by ΔSaO2. The blunted COD response is in line with our previous finding on a blunted total COD change of Tibetans as compared to Han Chinese for long-term altitude exposure from sea-level (0% vs. 10%)  . The colvs. 10%) . This ravs. 10%) . In addidiseases , or oxygdiseases . Similar2-inhalation in the Han-5yr compared with Han-3d indicates lowlanders likely blunt their CBF and oxygen delivery responses but with pulsatility response unaffected after acclimation.Additionally, the decreased responses of CBFV, CVRi, and COD, but similar RI and PI responses to O2-inhalation. This design enabled a comparison between genetic adaptation and years of acclimation at altitude. In addition, all the subjects were young and healthy with very similar backgrounds and demographic features except for the high-altitude experience; and their group differences were representative in hemoglobin and baseline physiological values at altitude, which are consistent with previous reports  or PaCO2 limited our interpretation of CBFV response  in the cerebral hemodynamic responses to O2-inhalation at altitude (There are several limitations that should be mentioned. Firstly, we used the CBFV at MCA to estimate the CBF, which did not consider the potential vasoconstrictive effect of Od gasses , it stilresponse . Howevermigrants , suggestmigrants  while thaltitude . Finally2-inhalation when compared with both Han newcomers and acclimated Han immigrants at altitude. Our results suggest the Tibetans possess a distinct cerebral hemodynamic regulatory pattern to keep more stable CBF, oxygen delivery, and pulsatility in response to acute variations of oxygen environment than lowlanders, as a result of altitude adaptation for thousands of years.The Tibetans demonstrated a racial trait of blunted cerebral hemodynamic responses to OC-YX conducted the data analysis, interpretation, and wrote the manuscript. L-HR helped with subjects’ enrollment and data collection. JS and AK helped with data analysis, interpretation, and manuscript preparation. PZ and HW helped with data collection and manuscript preparation. JL designed the study, and performed the data collection, analysis, interpretation, and manuscript preparation. All authors edited and revised the manuscript and approved final submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Attention deficit/hyperactivity disorder (ADHD) has been frequently reported as co-occurring with autism spectrum disorder (ASD). However, ASD-comorbid ADHD is difficult to diagnose since clinically significant symptoms are similar in both disorders. Therefore, we propose a classification method of differentially recognizing the ASD-comorbid condition in ADHD children. The classification method was investigated based on functional brain imaging measured by near-infrared spectroscopy (NIRS) during a go/no-go task. Optimization and cross-validation of the classification method was carried out in medicated-naïve and methylphenidate (MPH) administered ADHD and ASD-comorbid ADHD children  to select robust parameters and cut-off thresholds. The parameters could be defined as either single or averaged multi-channel task-evoked activations under an administration condition . The ADHD children were distinguished by significantly high MPH-evoked activation in the right hemisphere near the midline vertex. The ASD-comorbid ADHD children tended to have low activation responses in all regions. High specificity , sensitivity (93 ± 7.3%), and accuracy (82 ± 1.6%) were obtained using the activation of oxygenated-hemoglobin concentration change in right middle frontal, angular, and precentral gyri under MPH medication. Therefore, the significantly differing MPH-evoked responses are potentially effective features and as supporting differential diagnostic tools. Neurodevelopmental disorders are characterized as neurological conditions with impaired development in behavior, language, learning, motor, and social functions with an early onset of symptoms in infancy, childhood, or adolescence . For neaThe recently published DSM-5 started to adopt comorbidity between ASD and ADHD . Not onlPrior to DSM-5, translating the symptomatic features to ADHD/ASD diagnosis was not easy. It has been reported that there are five times the number of discrepancies in the evaluation of ADHD prevalence between DSM-IV and the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) . The diaPast 20 years have witnessed significant advancements in neuroimaging technology . AccordiThe differences between typically developing (TD) control and ADHD patients  were observed using fNIRS measurement of task-related performances such as inhibition control , attentik-means clustering; 83.3% accuracy) between young ASD and TD children. ASD children presented a weak efficient network between the right PFC and other regions  compared to TD children while watching a cartoon. In resting state, ASD children presented weaker bilateral functional connectivity and stronger fluctuation magnitude (oxygenated and deoxygenated hemoglobin) than TD children  non-social deficiencies, (2) atypical connectivity, and (3) social deficiencies . Variouschildren . ASD-TD In our recent study, we reported the significant difference in neurofunctional pathology for inhibition control in ADHD and ASD-comorbid ADHD children evaluated using fNIRS . The ADHt(30) = 2.08, p < 0.05, Cohen’s d = 0.77] than those of the ADHD group (92.8 ± 12.9). The FS-IQ of all subjects were assessed using the Wechsler Intelligence Scale of Children Third (WISC-III) or Forth (WISC-IV). All subjects provided oral consent, and written consent was obtained from the parents of all subjects according to the latest version of the Declaration of Helsinki. The study was approved by the Ethics Committees of Jichi Medical University Hospital and the International University of Health and Welfare. The collaboration among Jichi Medical University Hospital, the International University of Health and Welfare, and Hitachi, Ltd. was reviewed by an internal board at Central Research Laboratory, Hitachi, Ltd. Technical problem  unexpectedly occurred and affected data  were excluded in the further analysis.The dataset used in this study was obtained from the experimental data previously reported by The experiment was designed in a randomized, double-blind, placebo-controlled, and crossover study. MPH (18 mg) and placebo were administered in a pseudo-randomized order across subjects on different measurement days (at least 4 days apart). fNIRS measurements were conducted twice; before and after MPH or placebo administrations on a measurement day. fNIRS measurement involved using a multichannel system  with dual wavelengths (695 and 830 nm) and a 10-Hz sampling rate. A 3×5 probe plane incorporated eight sources and seven detectors resulting in 22 channels. Two probe planes were placed on the head following the positioning manner as described elsewhere . The proParents evaluated their children on a Japanese version of the ADHD Rating Scale-IV (ADHD-RS-IV)  before at-test) in each condition .There were five features extracted from behavioral performance data – (1) accuracy of go response during the baseline period , (2) accuracy of go response during the stimulus period, (3) accuracy of no-go response during the stimulus period , (4) response time of correct go response during the baseline period, and (5) response time of correct go response during the stimulus period. The differences between the ADHD and ASD-comorbid ADHD groups were then statistically evaluated  based on the modified Beer-Lambert law (k), which was determined by the optimization of the rejection accuracy between visual examination and algorithm application. The optimum k was found to be 3 resulting in 96.1% rejection accuracy. We eliminated channel-wise data having more than two trial signals rejected. The noise-free trial signals were then fitted by the averaged amplitude of the 10-s baseline for each trial, channel, and signal type .Signal preprocessing was carried out on the MATLAB-based software Platform for Optical Topography Analysis Tools  . The optbert law . Forty-fbert law . This alO2Hb and ΔCHHb from 4 s after onset to the end of stimulus across trials. Brain activation was initially observed from group analyses (ADHD and ASD-comorbid ADHD) at the first measurement (before any administrations), after MPH administration, and after placebo administration as the exploratory data analysis (one-sample t-test). The differences between the ADHD and ASD-comorbid ADHD groups were then statistically evaluated (two-sample t-test).Our previous results indicated the prominent roles of the right MFG and IFG in the GNG task . TherefoO2Hb and ΔCHHb under each administration condition). Significant differences may occur in more than one channel-wise activation. Therefore, classification optimization included multiple channel selection. To avoid spurious optimization in channel selection, the significant channels were categorized on the basis of brain macroanatomy. If a channel was located between two or more regions, the spatial grouping would be determined by the higher region probability  and estimated group classification areas. Linear and quadratic discriminants and SVM operations were carried out in the two-axis feature thresholds, similar to OR and AND operations. There was no overlapping channels or groups in the two-axis feature thresholds. To confirm the robustness of feature performance, we conducted leave-one-out cross validation with 32 iterations . The optimum result was determined by high averaged specificity (true ADHD) – sensitivity (true ASD-comorbid ADHD) in the training data and high accuracy in the test data.This optimization was conducted in six operations, i.e., simple, OR, AND, linear discriminant, quadratic discriminant, and SVM. The simple operation classified subjects based on the one-axis feature threshold (single or multiple channels/groups) in receiver operating characteristic (ROC) analysis. The OR and AND operations categorized subjects using the two-axis feature thresholds (single or multiple channels) with different operation between thresholds . Figure The significantly differed behavioral performance data  between ADHD and ASD-comorbid ADHD groups were also optimized for diagnostic features using similar operations. Accuracy and response time features were classified separately without any combination across features. The combination for an axis may only occur within a feature – for example, the average of go response accuracy during baseline and stimulus periods. The diagnostic performances using brain and behavioral features were compared to assess the feature efficacy.t-test) between ADHD and ASD-comorbid ADHD groups in any behavioral performance features. Multivariate ANOVA  and post hoc analysis were further performed showing the significances of performance features and the insignificances of group and administration conditions. The accuracy of go response was increased  in the stimulus period in parallel with slower reaction time  compared to the baseline period. This may suggest that the behavioral performance was likely influenced by the task paradigm per se rather than group or administration condition.O2Hb and ΔCHHb shown in HHb  in the right MFG but no significant ΔCO2Hb changes on the first day of pre-administration. However, the ASD-comorbid ADHD group showed significant increase of ΔCO2Hb  in the right IFG, MFG, superior temporal gyrus (STG), and precentral gyrus (PrCG). The decrease of ΔCHHb  was observed in the right PrCG. There were no significant differences  between ADHD and ASD-comorbid ADHD groups in both ΔCO2Hb and ΔCHHb activations. After MPH administration (O2Hb  and decreases in ΔCHHb  in substantial areas of the right MFG/IFG and parts of the PrCG, postcentral gyrus (PoCG), supramarginal gyrus (SMG), angular gyrus (ANG), STG, and middle temporal gyrus (MTG). The ASD-comorbid ADHD group responded differently to MPH administration by presenting significant decreases in ΔCO2Hb  in the right PrCG. This group comparison suggested that MPH resulted in significant increases in ΔCO2Hb  and decreases in ΔCHHb  in the ADHD group. The ΔCO2Hb apparently increased in most of the right MFG extending to the right PrCG, SMG, and ANG, while a significant decrease in ΔCHHb was observed in a single channel (38) of the right PoCG. In post-placebo administration (O2Hb  in the right MFG. The ASD-comorbid ADHD group also presented significant increases in ΔCO2Hb in the right MFG  and ANG  without observed ΔCHHb significances. The differences of ΔCO2Hb and ΔCHHb activations were insignificantly found in this inter-group comparison. O2Hb and ΔCHHb activations for each group, administration condition, and region.According to the activation patterns of ΔCstration , the ADHstration , the ADHHHb activation presented a single significant channel. The simple operation can be solely carried out under these conditions. Meanwhile, ΔCO2Hb activation significantly differed in the broad area around the midline vertex. Seven significant channels  and right SMG-ANG  significantly presented the highest specificity (94 ± 3.4%). The highest sensitivity (100%) was offered by four combinations of feature-operation (peration . FurtherO2Hb activations in right MFG-ANG vs. right PrCG). By using all optimization operations, the classification spaces were also incorporated in the activation coordinate (N = 21) was presented by the SVM operation (black-line) whereas the OR and AND operations  performed the complete classification of ASD-comorbid ADHD group . The activation differences were observed further in the subject-average ΔCO2Hb and ΔCHHb waveforms of the ADHD and ASD-comorbid ADHD groups in corresponding channels  compared to the ASD-comorbid ADHD group (magenta-plots). The intra-group difference on ΔCHHb (blue- and cyan-plots) was insignificantly distinguished.ordinate . The higTo the best of our knowledge, this is the first study showing classification based on inhibitory responses in ADHD and ASD-comorbid ADHD children since the comorbidity of ADHD and ASD has not been recognized until recently. We found distinct MPH-evoked response during the GNG task between ADHD and ASD-comorbid ADHD children. MPH medication reduced the activation in ASD-comorbid ADHD children, while ADHD children experienced positive neuromodulation after MPH administration in major areas of the right hemisphere. The applicability of MPH-evoked response as a differential group feature was optimized and cross-validated. High classification accuracy  in both training and test datasets suggested the advantages of our current fNIRS, inhibitory paradigm, and analysis. We believe that our findings will likely enable recognition of ASD comorbidity in ADHD children. This is one step further in the development of a clinically differential diagnostic tool that supports the standard symptom-based examination.The activation in several brain regions, including the bilateral ventrolateral prefrontal cortex (VLPFC) and dorsolateral prefrontal cortex (DLPFC) (or IFG and MFG), SMA, anterior cingulate gyrus, inferior parietal and temporal lobes, caudate nucleus, and cerebellum, has been observed during performance of the GNG task . The rigO2Hb activation. ASD patients have also been reported as having low activation compared to TD controls in the bilateral DLPFC, left VLPFC, left premotor area, left pre-supplementary motor area, and frontal pole during the inhibitory stop-signal task . HoweverThe comparison of behavioral performance among TD controls, ADHD and ASD patients had been previously investigated. The inconsistencies were observed by either significant or null differences of inhibitory control performance. TD controls showed higher accuracy response , faster t(30) = -2.38, p < 0.05]. Similar to previous results, only functional imaging results presented group-related activation patterns. This might suggest that the group differences were more feasibly observed by functional brain features than behavioral performances.ASD treatment focused on co-morbidity impairment  rather than the core of ASD phenotypes . Due to O2Hb activation in ADHD children during the GNG task . Therefore, the optimum feature was obtained from the characteristics of ΔCO2Hb activation after MPH intake. We conducted leave-one-out cross-validation analysis to confirm the channel selections, cut-off thresholds, and suitability of optimization operations  in assessing new samples. Without categorizing channels according to spatial-related groups, classification performance improved  to distinguish between the ADHD and ASD-comorbid ADHD groups. For further confirmation, we evaluated classification performance using the three-axis feature thresholds (each region as an axis) for linear discriminant, quadratic discriminant, and SVM operations . HoweverWe encountered three limitations in this study. First, the sample number was limited and imbalanced. The obtained feature should be validated in new datasets with a larger sample number to anticipate the inflated group variances as the sample number increases. However, given the absence of neuroimaging-based classification involving ASD-comorbid ADHD children, this study should provide an initial step in encouraging further studies. Second, as we mentioned above, the relationships among behavioral performance, personal traits, symptomal-disorder severity level, and brain activation are still unknown. However, it is necessary to interpret individual neuropathophysiology in ADHD and ASD-comorbid ADHD children. Future research may address this issue by using multiple factor analysis. Third, the detailed mechanism and pharmacological effect on brain activation in ASD-comorbid ADHD children are still unknown. Further investigation is required to interpret the divergence of MPH-evoked response in both groups. The evaluation of medication efficacy over time should be addressed to assess neuromodulation and neuroadaptability to medication.O2Hb activation which ADHD children presenting increased activation; yet, ASD-comorbid children presented hypoactivation in the right hemisphere. This suggests the feasibility of implementing fNIRS measurement, the GNG task, and the current features as clinically differential diagnostic biomarkers.We investigated the effective biomarkers as features to differentially distinguish between ADHD and ASD-comorbid ADHD children. The characteristics of features were optimized and cross-validated. The most optimum feature was selected on the basis of distinct MPH-evoked response on ΔCYM, MN, TI, and TT designed and performed the experiments. SS conceived the presented idea and performed the computation analysis. MK, TT, and ID verified the analysis. YM, AM, TY, and ID supervised the finding of this work. All authors discussed the results and contributed to the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.