Increased Intra-Subject Variability (ISV) is a candidate endophenotype of ADHD. ISV’s relationship with response speed is highly relevant for ADHD as patients are highly variable but typically no slower than controls. This brief report addresses the relationship between variability and speed by employing dimensional analyses for differentiated performance measures, with a particular focus on the ex-Gaussian measures, across relevant ADHD studies and in young healthy adults (N = 70). For both patients with ADHD and healthy adults, we found that reaction time standard deviation and mean reaction time were strongly correlated, thus failing to dissociate, but ex-Gaussian tau (τ) shared only little variance with Gaussian mu (μ), thus dissociating slow responses (τ) from response speed or—if given—slow responding (μ). Our results highlight the utility of employing the ex-Gaussian measures to disentangle ISV and speed, particularly for ADHD data as patients make more slow responses but are not overall slower than typical controls. Intra-Subject Variability (ISV) refers to short-term within-person variations in performance. Increased ISV of reaction times (RTs) is among the most robust findings and is a candidate endophenotype for Attention Deficit Hyperactivity Disorder . With r2 + τ2  linear trends, (non-) periodic fluctuations] and is therefore a sensitive but non-specific indicator of ISV . The sam2 + τ2 .In patients with ADHD, elevated τ is reported alongside normal or even lower μ see Table 1.ADHD studies have shown that increased ISV in patients is attributable primarily to τ rather than σ Table 1 Principal Components Analysis for Young Healthy Adults). We address the dimensional relationship between differentiated RT ISV and speed measures across pertinent ADHD studies , or were reported using a “less than” value  rather than a precise value, and were thereby excluded from the analyses. Finally, as bivariate scatterplots revealed that the scores for some variables of the Leth-Steensen et al. study  and ERP study (n-back task with three levels (0-back/1-back/2-back) was administered in two runs. Participants responded to series of letters presented for 1000ms with average stimulus onset intervals of 2s on average, with targets occurring in 25% of the total trials [see Saville et al. for details; 2/(n − 1)); i = trial number; n = number of trials), σ, τ, and spectral power. Variability was studied in the frequency domain through the computation of spectral power by resampling the residualized RT time series to a frequency of 1 Hz through linear interpolation and by Fast Fourier Transformations (FFT) separately for each participant and each block. FFT scores, derived in bins of 0.004 Hz across the range of 0–0.25Hz, were pooled across bins 0–0.1Hz given as this spectral range is sensitive to increases in ISV in ADHD , and task/stimuli-related effects such as working memory load (0-back/1-back/2-back), trial-type (targets/non-targets) and run (first/second). This step was conducted to “extract” endogenous variability, or Type I (except time-on-task effects) and II ISV . The “re in ADHD . MRT andz-transformation, a Principal Component Analysis (PCA) with varimax rotation was run on the following 9 variables: RTSD, CSD, FFT < 0.1 Hz, τ, σ, μ, MRT, omission errors and incorrect responses. The PCA fulfilled assumptions of sampling adequacy (Kaiser-Meyer-Olkin test: KMO = .753) and sphericity (Bartlett’s test: χ²(36) = 1,013.02, p <.001). A scree plot confirmed that three factors, with eigenvalues greater than 1, were retainable.After averaging scores across by task block and removing the COMT genotype-related variances through r = .94r= .99r = −.06r = .96r = .25r = .18With regard to dimensional analyses across the pertinent ADHD studies in For young healthy adults, the PCA revealed that ISV, speed and accuracy split into three separate components see Table 2.r = .74). Strong to moderate associations of RTSD  and MRT  with the ex-Gaussian measures support the algebraic dependency of these measures. Importantly, τ and μ shared a comparatively weaker association (r = .28) and even loaded highly on separate components of ISV and response speed, respectively. As a control, the exclusion of the ex-Gaussian measures from the PCA resulted in ISV and response speed measures loading highly on a single component.Pearson’s correlations suggest that dimensional patterns in healthy adults are overall consistent with those found across ADHD studies. A positive association was present between RTSD and MRT (butsee below). This highlights that measures accounting for only those group differences that cannot already be explained by group differences in MRT such as “Coefficient of Variance” (CV = RTSD ÷ MRT), convey an unclear concept of variability and are thus unsuitable for ISV research in ADHD.To begin with, strong associations were found between RTSD and MRT, both across pertinent ADHD studies Table 1 Despite the close relationship between RTSD and MRT or their positive associations with the ex-Gaussian ISV measures, it is noteworthy that τ shared only little variance with Gaussian μ in data of ADHD and healthy populations. These patterns point to a dissociation of slow responses (τ) from response speed or slowing (μ). In fact, in the PCA comprising of data for young healthy adults, τ and μ loaded highly on two separate components—ISV and response speed, respectively. Upon the exclusion of the ex-Gaussian measures from the PCA however, ISV and response speed measures loaded on a single factor; thus similar to a previous ADHD familial study . As suchThe weak association found between τ with μ in both, general and clinical populations is overall in line with the patterns of patients with ADHD having elevated τ but normal or even smaller μ Table 1 The associations of the classical ISV and response speed measures, RTSD and MRT, with the ex-Gaussian ISV measures, τ and σ, are in line with the algebraic dependency of these measures. If RTs were normally distributed, μ and σ would be equal to MRT and RTSD, respectively. However, the typical rightward skew of RT distributions, with τ modeling this ex-Gaussian portion contributes substantially to MRT as well as RTSD and thus also to its correlations with these measures. The near-perfect correlation of τ with RTSD in, both, the ADHD and healthy sample data suggest that these values may be interchangeable. This result is not surprising since the rightward skew of RT histograms, captured by τ and emerging due to the speed of a response having a physiological limit but the slowness of a response being largely uncapped, is sensitive to individual differences in variability .The dissociation of σ from all other ISV measures in the PCA results for young healthy adults is striking and supports the idea that differentiated ISV measures reflect different facets of variability. In ADHD literature, elevated ISV is typically characterized by elevated τ but a few studies have also found σ to be elevated see Table 1.Finally, in the context that omission errors, similar to τ , may alsA limitation of the present study is its moderate sample size for the PCA. Yet, a subject-to-item ratio above the recommended 5:1   and the The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee, School of Psychology, Bangor University, Bangor, UK. The patients/participants provided their written informed consent to participate in this study.GS, CS, and CK contributed conception and design of the study. GS, BF, and CS organized the database. GS performed the statistical analyses. GS wrote the first draft of the manuscript with support from CK. All authors contributed to the article and approved the submitted version.The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the funding programme Open Access Publishing.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.


To investigate the relation between cardiac vagal activity (CVA), a measure of autonomic nervous system (ANS) flexibility, and self-reported emotion regulation (ER) difficulties in adolescents with attention-deficit/hyperactivity disorder (ADHD) and controls.n=34) and controls (n = 33). Multiple linear regression analyses investigated the relation between CVA, as indexed by high frequency heart rate variability (HF-HRV), and ER difficulties as assessed by the Difficulties in Emotion Regulation Scale (DERS). Supplemental analyses were performed in ADHD and control groups separately. Analyses assessed effects of body mass index (BMI), physical activity levels, and HF peak as a surrogate of respiration on CVA.The sample comprised 11–17-year-old adolescents with ADHD (Lower CVA was associated with ER difficulties, and specifically with limited access to effective ER strategies. When investigating the relation between CVA and ER in the ADHD and control groups separately, there was a tendency of lower CVA predicting limited access to effective ER strategies in the ADHD group, and not in the control group.The results suggest that lower CVA, i.e., reduced ANS flexibility, in adolescents with ADHD and controls is associated with self-reported ER difficulties, and specifically with limited access to effective ER strategies. There was a tendency for lower CVA to predict limited ER strategies only in the adolescents with ADHD and not controls. Individuals with attention-deficit/hyperactivity disorder (ADHD) are characterized by difficulties with self-regulation , includiEmotions—multifaceted processes involving changes in experiential, behavioral, and physiological responses —are not In ADHD, ER difficulties are shown to have a negative influence on well-being and self-esteem, peer relationships, school performance , and socIn the current study, we investigated the association between CVA and self-reported ER difficulties in adolescents with ADHD, and without ADHD (i.e. control group). We applied a naturalistic study design by including control participants with mental health disorders that often are comorbid to ADHD, such as anxiety disorders and Tourette Syndrome (TS). We hypothesized that lower CVA would be associated with ER difficulties, and expected this effect to be more prominent in adolescents with ADHD. We further explored the association between CVA and specific aspects of ER: acceptance of emotional responses, goal-directed behavior, and impulse control in relation to emotions, emotional awareness, access to effective ER strategies, and emotional clarity. We hypothesized that ER strategies would be the domain most closely associated with CVA, as CVA has been found to associate with neural activity during reappraisal and response modulation . This suThe current study is a cross-section of a follow-up project on ER in children with ADHD  is a 36A Norwegian translation of the DERS was used  Althoughn=3, Controls: n=3) had one through five missing item scores, in total 14 missing scores.In the current sample, six participants (ADHD: 2 (kg/m2). BMI could not be calculated for four participants , as they declined to get weighed or due to technical errors.Engagement in physical activity was assessed during the K-SADS, as the adolescents reported in “Other activities; Sport and Exercise”. Physical activity levels were scored on a six-point Likert-type scale from 0 (“zero times a week”) to 5 (“seven times or more a week”), as for Question 1 of the Physical Activity Questionnaire for Adolescents . The PAn=17, of 19) conducted a washout period of at least 24 h. This equals minimum five half-lives of the stimulant medication . CVA data were log-transformed to approximate a normal distribution . We perfp =.007 (.05/7). In follow-up regression analyses, an interaction term of ADHD*CVA  was added as a predictor to test for interaction effects between ADHD and CVA on ER difficulties . There were no group differences in CVA power spectrum values (There were no differences in age nor gender between the ADHD and control group Table 1.p=.004) in multiple linear regression analyses.To test the effect of CVA on ER difficulties, we conducted multiple linear regression analyses Table 3,The aim of the present study was to investigate whether CVA, an index of autonomic flexibility, was associated with self-reported ER difficulties in adolescents with ADHD and controls. Results showed that, as expected, lower CVA significantly predicted higher STRATEGIES scores. There was also a tendency for lower CVA to predict higher DERS TOTAL scores. This indicates that level of CVA reflects the ability to use effective strategies for ER. Results also showed that the ADHD diagnosis associated with higher DERS scores, and thus higher ER difficulties, except for in relation to lack of emotional awareness. However, no interaction effects between level of CVA and ADHD on the DERS scores were found. When analyzing the association between CVA and DERS separately in the diagnostic groups, we found that lower CVA was associated with higher STRATEGIES scores in the ADHD group and not in the controls. Thus, despite the non-significant interaction effect between CVA and ADHD, there was a tendency for CVA to be associated with less access to effective ER strategies specifically in the ADHD group.Our results suggest that level of CVA is important for the use of ER strategies perceived as effective by the individual experiencing and expressing the emotions. The STRATEGIES subscale measures the flexible use of situationally appropriate strategies . This fiIn our study, the small sample size, and as such low statistical power, may restrict the detection of interaction effects between ADHD and level of CVA on DERS scores. Despite this restriction, when looking into the relation between CVA and ER for the ADHD and control groups separately, we found a significant effect of CVA on ER  only in the adolescents with ADHD. This indicates that despite a lack of a significant interaction effect, there is a tendency of lower CVA to associate with higher ER difficulties specifically in the ADHD group. Since there are few studies of CVA and ER in ADHD, it is still not clear whether lower CVA predicts ER difficulties specifically in ADHD. As far as we know, only one previous study has found parent-reported emotion dysregulation to be associated with lower CVA in children and adolescents with ADHD . FurtherPrevious studies of CVA and DERS in young, healthy adults have found CVA to be associated with less emotional impulse control and emotional clarity , and lesOur results, indicating that lower CVA and thus inflexibility of the ANS is associated with limited access to ER strategies among adolescents with ADHD and controls, might have important theoretical and clinical implications. Our study contributes to the understanding of which specific domains of ER are related to CVA, which could support theoretical models of the relation between neural and psychological processes. Further, this might lead to increased focus on biofeedback treatment, which has been suggested to increase ER abilities [e.g. ]. Also, The current study has several strengths and limitations. One strength is that we have included possible confounders on CVA such as physical activity levels , BMI 5454, and HDatasets are available on request. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.This study was carried out in accordance with the recommendations of Regional Committee for Medical Research Ethics of Western Norway, study number 2014/1304. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Regional Committee for Medical Research Ethics of Western Norway.LS and KP designed the study. LS, SA, EK, and HE were involved in data collection. EK and LS, and BO performed data acquisition and analyses. EK and LS wrote the first draft, and JK, OF, BO, HE, and KP contributed to manuscript editing. All authors have read and approved the final manuscript.This work was supported by grants from the Research Council of Norway (190544/H110), the Western Norway Health Authority  to KP and the National Norwegian ADHD network to LS.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.


Although childhood-onset psychiatric disorders are often considered as distinct and separate from each other, they frequently co-occur, with partial overlapping symptomatology. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) commonly co-occur with each other and with other mental disorders, particularly disruptive behavior disorders, oppositional defiant disorder/conduct disorder (ODD/CD). Whether these associated comorbidities represent a spectrum of distinct clinical phenotypes is matter of research. The aim of our study was to describe the clinical phenotypes of youths with ADHD with and without ASD and/or ODD/CD, based on neuropsychological and psychopathological variables. One-hundred fifty-one participants with ADHD were prospectively recruited and assigned to four clinical groups, and assessed by means of parent-reported questionnaires, the child behavior checklist and the behavior rating inventory of executive functions. The ADHD alone group presented a greater impairment in metacognitive executive functions, ADHD+ASD patients presented higher internalizing problems and deficits in Shifting tasks, and ADHD+ODD/CD subjects presented emotional-behavioral dysregulation. Moreover, ADHD+ASD+ODD/CD individuals exhibited greater internalizing and externalizing problems, and specific neuropsychological impairments in the domains of emotional regulation. Our study supports the need to implement the evaluation of the psychopathological and neuropsychological functioning profiles, and to characterize specific endophenotypes for a finely customized establishment of treatment strategies. Attention deficit and hyperactivity disorder (ADHD) is one of the most frequent reasons for consultation in the context of mental health services for minors, causing significant impairment in various life contexts from childhood to adolescence and adulthood ,2,3. DesThe impact of comorbidities in ADHD patients can be substantial. For instance, the severity of ADHD symptomatology is further worsened, in terms of emotional and behavioral problems, when ASD phenotype overlaps along with its adaptive impairment ,10,11,12Recent studies have raised the question of whether ADHD with associated comorbidities is merely characterized by an increased symptoms variety with enhanced impairment severity, or it represents a spectrum of distinct phenotypes . InteresEF deficits in children with ADHD and/or ASD feature some shared characteristics, as shown by a recent meta-analysis by Craig and colleagues , includiOn the other hand, EF deficits have also been identified in children with ODD/CD, and they partially overlap with those typically found in ADHD patients ,25. SomeOnly a few studies have investigated EF profiles among young people with ADHD and comorbid ASD and/or ODD/CD. A recent study by Leno et al.  aimed atBased on these findings, we hypothesize that ADHD alone and ADHD with associated comorbidities represent distinct phenotypes, with suitable clusters of psychopathological and neuropsychological features. These psychopathological and neuropsychological indices may be useful not only for differentiating clinical subtypes of patients, but also for identifying specific intervention pathways. To test this hypothesis, the main aim of our preliminary study is to describe the clinical phenotypes of minors with ADHD in its “pure” presentation and with psychiatric comorbidities (ASD and/or ODD/CD), with respect to psychopathological and neuropsychological variables, to highlight similarities and differences among the groups. This study represents one of few literature reports that evaluates clinical phenotypes of children with ADHD+ASD+ODD/CD comorbidity. Furthermore, to the best of our knowledge, this is the first study assessing EF in such a cohort through parental reports, by means of BRIEF-2 questionnaire designed to explore EF of youths in daily contexts, related to everyday life behaviors. Furthermore, the BRIEF-2 inventory finely captures the multidimensional structure of EF, and particularly the distinction between cold (metacognition) and hot  dimensions.Therefore, we assessed psychopathological profiles using a dimensional approach for identifying phenotypic domains, representing meaningful variations across multiple domains of behaviors, with two broader dimensions, internalizing and externalizing behaviors ,37,38.Our study prospectively included 151 drug-naïve participants  recruited from March 2019 to December 2019 in the Department of Child and Adolescent Psychiatry of our third level hospital with a nation-wide catchment. The investigation was carried out in accordance with the latest version of the Declaration of Helsinki.The diagnoses, based on DSM-5 diagnostic criteria, were made using historical information, and a structured clinical interview, the Italian version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL) , adminisThe Italian version of the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV)  was alsoPatients were included whether they received a diagnosis of ADHD with or without comorbid ASD, ODD/CD, or both. Other associated psychiatric conditions, such as anxiety and mood disorders and learning disabilities, were accepted but not primarily considered for analytical purposes. Exclusion criteria were as follows: a total WISC-IV IQ score lower than 70 points; younger than 6 years old or older than 18 years old; use of psychoactive medications; neurologic impairments or neurodegenerative conditions. All recruited patients met inclusion criteria and agreed to participate in the study after informed consent was obtained by parents. We identified four clinical groups in our sample: ADHD alone group, including 64 participants ; comorbid ADHD+ASD group (hereinafter referred to as ADHD+ASD group), including 19 participants  who did not fulfill ODD/CD diagnostic criteria; comorbid ADHD+ODD/CD group (hereinafter referred to as ADHD+ODD/CD group), including 43 participants  who did not fulfill ASD diagnostic criteria; comorbid ADHD+ASD+ODD/CD group (hereinafter referred to as ADHD+ASD+ODD/CD group), including 25 participants  who fulfilled diagnostic criteria for both ODD/CD and ASD. These groups could present other associated psychiatric conditions, i.e., anxiety and mood disorders or learning disabilities. All patients were assessed with the Italian version of the child behavior checklist for ages 6 to 18 years (CBCL-6/18) , a 118-iThe parents of all patients were asked to fill in the Italian version of the BRIEF-2 inventory . The ins®  and RStudio®  software. For each clinical variable with a continuous distribution, outliers were defined as observations lying outside the range between (first quartile − 1.5 * interquartile range) and (third quartile + 1.5 * interquartile range) and removed. For each BRIEF-2 subscale-related variable, observations were removed if the corresponding values for either the infrequency or the inconsistency scale were higher than the 99th percentile of normalized data. Statistical analyses were performed by means of MatLab2 test was used to detect significant differences  for clinical and demographic categorial variables, such as gender and clinical comorbidities. When more than 20% of observations had expected frequencies less than 5, Fisher’s exact test was performed. Analyses of variance (ANOVA) was conducted to assess significant differences  between clinical and demographic variables with continuous distribution. Homogeneity of variances across groups was checked using Levene’s test before applying ANOVA; all variables showed equal variances in the different groups , so that variance homogeneity assumption was reliably satisfied. A Tukey post-hoc test was utilized whenever the ANOVA led to a statistically significant result in order to retrieve significant comparisons between variables. The χFinally, we performed a principal component analysis (PCA) with Promax oblique rotation to empirically derive variables that were subsequently compared between clinical groups. All BRIEF-2 and CBCL-6/18 subscale variables were used to conduct the PCA—specifically, the inhibit, self-monitor, emotional control, shift, initiate, working memory, plan/organize, task-monitor, and organization of materials scales of former; and the anxious/depressed, withdrawn/depressed, social problems, somatic complaints and thought problems, attention problems, rule-breaking behavior, and aggressive behavior symptoms scales of the latter. A scree-plot was used to determine the appropriate number of components. Thus, a number (greater than 1) of components was extracted, and component scores for each participant were computed using regression approach. An ANOVA with Tukey’s post-hoc test was then conducted to assess significant differences between groups in the continuous data of the three components identified through the PCA. As shown in Only one outlier was removed from the analysis in the somatic complaints subscale of the CBCL questionnaire, belonging to the ADHD+ASD+ODD/CD group. No outliers were identified and removed based on interquartile range for all the other CBCL variables. Scores obtained by the four clinical groups and ANOVA statistics in the CBCL are illustrated in As for the syndromes scale B, a signAs for the DSM-oriented scales C, the ADFinally, significant differences emerged in the sluggish cognitive tempo scale between ADHD alone (62.72 ± 9.13) and ADHD+ODD/CD groups (57.88 ± 7.26), and in the obsessive-compulsive problems scale between ADHD+ASD+ODD/CD (64.13 ± 10.63), and both ADHD alone (56.69 ± 6.91) and ADHD+ODD/CD groups (56.33 ± 7.02).No outliers were identified and removed based on BRIEF-2 variables’ interquartile ranges. Profiles of the scores obtained by the four clinical groups in the BRIEF-2 scales and ANOVA statistics are shown in As for the BRIEF-2 subscales B, inhibiThe PCA we performed led to a three-component solution explaining 59% of variance. As shown in As illustrated in The main aim of the present study was to identify possible phenotypes of patients with ADHD, ASD, and ODD/CD, based on EF and psychopathological domains, in order to better characterize their diagnostic frameworks. To the best of our knowledge, this is the first study assessing EF through parental reports, by means of the BRIEF-2 questionnaire, in a clinical sample of youth with comorbid conditions.The BRIEF-2 questionnaire provides a structured assessment of EF behaviors in everyday life environments, by means of a multi-comprehensive assessment of different aspects of child behavior. A large number of world-wide clinical trials and case-control studies are indeed available to derive specific normative data based on age and gender. For these reasons, we chose the BRIEF-2 questionnaire as a valuable instrument for the assessment of EF in our sample.The four groups in our sample were similar in terms of age, gender, and cognitive abilities. Our analyses demonstrated in the ADHD alone group a specific pattern of impairment in attentive and metacognitive domains, as revealed by CBCL questionnaire and BRIEF-2 inventory , with greater impairment in the domains of the so-called “cold” EF. These features appear more evident in the ADHD alone group when compared to those associated with comorbid conditions, which seems to support the executive dysfunction theory of ADHD ,44,45. OAs expected, when ADHD is comorbid with ODD/CD, it is associated not only with attention problems , but also with rule breaking behavior . ExternaThe PCA supports these findings. Indeed, the ADHD alone group is associated with the attention problems of CBCL, and with the cognitive regulation-related subscales  of BRIEF-2. The triple comorbidity ADHD+ASD+ODD/CD is associated with both RC2 and RC3. However, the RC2  is prevalently associated with both ADHD+ASD and ADHD+ASD+ODD/CD, with a prevalent contribution of the ASD component. On the other hand, the RC3  is prevalently associated with the ADHD+ASD+ODD/CD and ADHD+ODD/CD, with a prevalent contribution of the ODD/CD component.While the profile of the ADHD+ODD/CD is consistent with previous findings regarding the increased severity of both externalizing symptoms and emotional dysregulation ,47,48, tIn summary, our study highlighted that, compared to ADHD alone, comorbid ODD/CD and ASD worsen externalizing and internalizing symptoms’ severity, and the neuropsychological profile, but with different patterns. ADHD alone appears to be characterized by a more specific impairment of metacognitive (“cold”) functioning and attentive capacities. On the other hand, comorbid ODD/CD seems more significantly related to “hot” executive dysfunctions and emotional self-regulation. Less consistent findings have been found regarding the implications of ASD comorbidity, namely, a more selective impairment in shifting, possibly related to their low cognitive flexibility.Our data are in line with novel approaches to the classical categorical nosographic entities, using dimensional psychopathological and neuropsychological tools, in a transdiagnostic fashion, possibly with supporting evidence from biological markers, to understand the common patterns of neural dysfunction that link apparently different disorders, including ADHD and ASD . Within Our study shows, however, a number of limitations. Importantly, a marked discrepancy in the sizes of the four groups, particularly between the ADHD alone group and those associated with comorbid ASD. Furthermore, these groups were not recruited through an age-matching protocol design, though no significant difference in age emerged when comparing them by means of statistical tests. Additionally, we did not include a control group of healthy children. Therefore, future studies with larger age- and sex-matched samples and healthy controls could be carried out to support our results. Moreover, an ecological parent report-based measure of EF was used in the present study. It should be noted that low correlations have been reported between performance measures and behavioral rating scales of EF in ADHD and ASD research; however, task-based measures capture only limited facets of the EF system in a limited temporal span, while neglecting the integrated multidimensional decisional process related to and based on a priority-based strategic analysis performed by the individual, which is what daily life situations often require. From this perspective, ecological measures are important to foresee the severity of dysfunction in daily life situations experienced by children at home and school, which are important settings wherein parents and teachers assessing the essential expressions of executive functioning may provide a valuable amount of information which could help clinicians in their measurements.Another limitation of our study is that it did not include the “limited prosocial emotion” specifier. Further research could be performed, including using more participants showing high levels of callous-unemotional traits, in order to possibly identify further phenotypes of ADHD with comorbid disruptive behavior disorders. Similarly, we could not ascertain the presence of subthreshold symptoms within the different clinical domains explored by the K-SADS interview and the ADOS-2 observation; subthreshold symptoms could be usefully considered in a multidimensional perspective in future studies. Unfortunately, we could not provide teacher-rated measures of ADHD symptoms to compare parent reports, though educational observations were performed for all patients. Finally, we did not correct our tests for multiple comparisons, as all comparisons we performed would not survive such correction with a statistical threshold below 0.001. Nonetheless, given the exploratory nature of our study and the relatively limited sample size, we believed it more convenient to keep all statistically significant comparisons while waiting for future larger studies aimed at corroborating—or invalidating—our results.Our study provides a further contribution to a better understanding of the clinical manifestations of ADHD and comorbidities with ASD and/or ODD/CD, and suggests that specific comorbidities might help in the selection of the treatment strategies. Our findings support the need to associate, within the clinical assessment and diagnostic framework, the evaluation of psychiatric comorbidities and neuropsychological functioning, with more thoroughly characterized specific clinical phenotypes, i.e., by means of standardized neuropsychological tests in clinical settings.


Participants were required to respond to whether the presented stimulus was at the same location as that presented two trials earlier. The ADHD-trait group showed significantly slower response times than the control group in the spatial 2-back task. In terms of spectrum, the ADHD-trait group showed significantly reduced theta power than the control group. In contrast, the ADHD-trait group exhibited an increased alpha power compared to the control group with the 250–1000 ms interval after stimulus onset. In terms of the PLV, the ADHD-trait group showed significantly weaker theta phase synchrony and fewer connection numbers in frontal-occipital areas than the control group. In terms of the theta brain network, the ADHD-trait group showed a significantly lower clustering coefficient and longer characteristic path length than the control group for the theta band. The present results indicate that college students with ADHD traits have deficits in spatial working memory and that these abnormal activities in neural oscillation, functional connectivity, and the network may contribute to spatial working memory deficits.This study investigated deficits of spatial working memory in college students with attention-deficit/hyperactivity disorder (ADHD) traits using event-related potentials (ERPs) and the spatial 2-back task. We also computed sensory-level activity using EEG data and investigated theta and alpha neural oscillations, phase-locking values (PLV), and brain networks. Based on the scores from the Adult ADHD Self-Report Scale (ASRS) and Conners’ Adult ADHD Rating Scales (CAARS), an ADHD-trait group ( Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders and is observed in around 3–5% of school-aged children . HoweverTo date, no diagnostic biological markers of ADHD have been reported, despite various attempts to investigate its biological mechanisms . RecentlPatients with ADHD have deficits in several cognitive areas, including attention, memory, and executive function. Of these, the deficit in working memory has received much attention . This isn-back tasks or paradigms to investigate working memory levels, such as Sternberg and delayed-response sample tasks, have reported inconsistent results with either a decrease or increase in theta and alpha oscillations  in healthy subjects observed the connectivity between frontotemporal, central-parietal, and occipital areas during working memory tasks , and smaThe severity of symptoms, the existence of comorbid disorders, and medication in adult ADHD patients are known to affect performance on working memory tasks, neuronal oscillations, and brain functional connectivity related to working memory. Thus, it has been suggested that studies should be conducted with non-clinical individuals with ADHD traits instead of with ADHD patients . MoreoveTherefore, the present study investigated the spatial working memory of non-clinical college students with ADHD traits using ERPs and spatial 2-back task. Graph theory were applied to examine the functional connectivity networks in different frequency bands. Specifically, while performing the spatial 2-back task: (1) theta and alpha bands were divided to investigate changes in neural oscillations and functional connectivity; and (2) clustering coefficient and characteristic path lengths were examined to determine whether college students with ADHD traits exhibited different network functions from those of the normal control group.n = 40, 7 males and 33 females). Students with scores <3 on part A of the ASRS, <16 total points on the ASRS, and T scores <37 on the ADHD index of the CAARS, were included in the normal control group  (see Supplementary file 1 for details). The internal consistency of the CAARS had a Cronbach’s alpha in the range of 0.74–0.95 (t(79) = 0.53, ns]), educational level  = −0.57, ns]), or IQ scores  = −0.57, ns]).Details of the participants’ screening procedures are described in the previous study by our research group . Student.74–0.95 , while t.74–0.95 . The twoThe Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV), non-patient version (SCID-NP)  was admiA spatial 2-back task was used to evaluate spatial working memory. This task consisted of two conditions: under the congruent condition, the locations of the current stimulus and stimulus presented two trials earlier were the same; under the incongruent condition, the locations of the current stimulus was different from that of the stimulus presented two trials earlier. A total of 360 trials  were randomly administered in two blocks. Participants were required to respond as fast and accurately as possible to the congruent stimuli by pressing a button with the index finger but to ignore the incongruent stimuli.E-Prime software  was used to administer the spatial 2-back task. A crosshair was displayed for 1500 ms as a fixation point, then the stimulus was presented for 500 ms, and the inter-stimulus interval was 2500 ms. The distance between the participant and the computer monitor was 80 cm. We ensured that the distance for all participants was same by fixing the chair’s position. Before the experimental session, a block of 20 trials was administered to ensure that participants understood the instructions. The two types of conditions and the procedure for the stimulus presentation are illustrated in Electroencephalography was measured using 64-channel Geodesic Sensor Net connected to a 64-channel, high input impedance amplifier  in a shielded and soundproofed experimental room. All electrodes were referenced to Cz, and impedance was maintained lower than 50 kΩ . EEG actIn this study, event-related spectral perturbation (ERSP) was calculated to observe the power change over time during the spatial 2-back task. The spectral power of each band was calculated using a fast Fourier transform (FFT) along with a Hanning window. We averaged the results for two distinct frequency bands  in four time-intervals . We selected 20 non-adjacent electrodes  out of 64 electrodes based on previous studies . ERSP waSingle-trial signals were first transformed into narrowband signals in the theta (4–7 Hz) and alpha (8–12 Hz) bands through bandpass filtering. Then, the phase was extracted through Hilbert-transformation. The synchronization between each pair of selected channels was estimated in terms of phase-locking value (PLV) . To idenThe spatial pattern of inter-regional phase synchrony can be visualized based on graph theory. A network is composed of several nodes, connected to each other by edges. For statistical comparison of network characteristics, the clustering coefficient and characteristic path length were calculated using equations used in previous studies . To detet-test. Response times on the spatial 2-back task were analyzed with an independent t-test, and accuracy was evaluated with a mixed-design analysis of variance (ANOVA) with stimulus condition (congruent and incongruent) as the within-subject factors, and group (ADHD-trait and control) as the between-subjects factor. Only correct responses were included in the behavioral analysis.Demographic characteristics and scores on the ASRS and CAARS were analyzed using an independent Possible correlations between EEG measures  and behavioral measures, and ADHD symptoms, were explored within each group using the Pearson correlation coefficient.p < 0.001).The demographic characteristics and scores on the ASRS and CAARS of the control and ADHD-trait groups are presented in F = 13.15, p < 0.001, F = 74.12, p < 0.001, F = 0.22, ns, The mean response times and accuracy data for the two groups are presented in F = 30.62, p = 0.001, F = 52.27, p = 0.001, F = 16.10, p = 0.001, F = 29.90, p = 0.001, With the theta band, we found the main effects of group, time interval and AOIs see . The ADHF = 2.55, p = 0.05, F = 2.81, p = 0.05, F = 2.91, p = 0.05, Analysis of the alpha band revealed the main effects of group, time interval, and AOIs see . The ADHr = −0.31, p < 0.05] and parietal  areas. With the alpha band, both groups showed no significant correlation between the alpha power and the behavioral response time.The correlation between frequency band activity and behavioral measures was investigated within each group. Correlation analyses were applied only to frequency bands and behavioral response time that showed significant differences between the two groups. In the 250–500 ms interval in the control group, but not in the ADHD-trait group, a negative correlation between theta power and behavioral response time was found in the central  at the parietal area, and between the theta power and the impulsivity/emotional lability score on the CAARS  at the occipital area. Additionally, a negative correlation between the theta power and the self-concept score on the CAARS  was found at 750–1000 ms in the occipital area. With the alpha band, a negative correlation between the alpha power and the self-concept score on the CAARS  was observed at 500–750 ms in the occipital area.The correlation between frequency band activity and symptom severity scores was investigated in the ADHD-trait group. During the 0–250 ms interval, a negative correlation was found between the theta power and the self-concept score on the CAARS , whereas no correlation was found between theta band characteristic path length and ADHD symptoms.The small-world network characteristics were evaluated using two representative graph theoretical measures: clustering coefficient and characteristic path length. F = 1.46, ns, F = 0.02, ns, This study used ERPs and a spatial 2-back task to investigate whether abnormalities in neural activity and functional brain networks affect spatial working memory in college students with ADHD traits. Compared to the control group, the ADHD-trait group exhibited significantly longer response times to the congruent stimuli on the spatial 2-back task. In response accuracy, no significant difference was observed between ADHD-trait and control groups. These results are consistent with previous studies examining working memory in patients with ADHD  and suggThe ADHD-trait group showed significantly decreased theta power compared to the control group during the spatial working memory task. In addition, an increase in theta power was observed in the occipital area with the 0–250 ms interval and in the central area with the 250–1000 ms interval, which is consistent with previous studies on ADHD patients . Previoun-back task compared to a normal control group. In the case of an alpha band, the ADHD-trait group showed significantly increased alpha power compared to the control group with 250–1000 ms post stimulus. In particular, differences between the ADHD-trait and control groups were observed at the frontal and occipital areas, which is consistent with some previous studies on ADHD patients. For example, We investigated functional connectivity and phase synchronization of theta and alpha bands to identify interactions between various brain regions during the performance of spatial working memory tasks. In the theta band PLV (TPLV), the ADHD-trait group showed significantly decreased functional connectivity across the anterior-posterior areas with 0–500 ms post-stimulus compared to the control group, as well as significantly fewer TPLV connections. Theta phase synchronization has been observed to be more spread across the brain than other frequencies . AccordiThis study investigated the functional separation and integration of the brain for efficient information processing using graph theory. One of the parameters of the network is the clustering coefficient that quantifies the functional segregation of the network, which is a measure of the local structure in the brain, indicating the proportion of neighboring nodes that are interconnected . This meWith the theta band, the ADHD-trait group showed a reduced clustering coefficient compared to the control group and increased characteristic path length with the 0–500 ms interval. Although studies on the functional brain network during a working memory task in patients with ADHD have not yet been reported, these results are consistent with the results of previous studies that reported abnormal functional brain networks during resting state and interference control tasks. For example, some studies investigating the characteristics of functional brain networks at resting state reported that patients with ADHD have higher local connectivity and lower global connectivity compared to the control group . It has We investigated the relationship between EEG measures and ADHD symptoms in the ADHD-trait group. A significant negative correlation was observed between theta power, alpha power and CAARS self-concept and impulsivity/emotional lability scale scores. Some previous studies have suggested that the ratio of two frequencies, such as the theta/beta ratio, controls some symptoms, such as impulsivity . Other sn-back task is useful for investigating working memory, it is limited to distinguish between information encoding and retrieval . The patients/participants provided their written informed consent to participate in this study.K-MJ contributed to experimental design, data processing, analysis of results, and writing of manuscript. D-WK supervised the data analysis part of the experiment. K-MJ, M-SK, and D-WK revised the manuscript and carried out literature research. M-SK supervised aspects of the systematic literature review, preparation of the manuscript, revision, editing, and final intellectual content. All authors contributed to the article and approved the submitted version.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.


PARK2 locus that have been previously associated with ADHD. Human-derived fibroblasts (HDF) were cultured and human-induced pluripotent stem cells (hiPSC) were reprogrammed and differentiated into dopaminergic neuronal cells (mDANs). A series of assays in baseline condition and in different stress paradigms ) focusing on mitochondrial function and energy metabolism  abundance) were performed and changes in mitochondrial network morphology evaluated. We found changes in PARK2 CNV deletion and duplication carriers with ADHD in PARK2 gene and protein expression, ATP production and basal oxygen consumption rates compared to healthy and ADHD wildtype control cell lines, partly differing between HDF and mDANs and to some extent enhanced in stress paradigms. The generation of ROS was not influenced by the genotype. Our preliminary work suggests an energy impairment in HDF and mDAN cells of PARK2 CNV deletion and duplication carriers with ADHD. The energy impairment could be associated with the role of PARK2 dysregulation in mitochondrial dynamics.The main goal of the present study was the identification of cellular phenotypes in attention-deficit-/hyperactivity disorder (ADHD) patient-derived cellular models from carriers of rare copy number variants (CNVs) in the  PARK2 (=PRKN) genetic locus, implicating this target as a candidate gene for ADHD [Attention-deficit/hyperactivity disorder (ADHD) is a very heterogeneous disorder, with a broad spectrum of type and severity of symptoms that interfere with personal functioning and negatively impacts social and occupational activities ,2. The gfor ADHD . PARK2 -derived neuronal cells [PARK2 CNVs associated with ADHD in human-derived cellular models.The role of al cells ,23 in thPARK2 CNVs (deletion and duplication) in comparison to healthy and ADHD wildtype (WT) controls. We conducted experiments focusing on mitochondrial function and energy metabolism. The MQC system is responsible for basal and normal cellular functions but also plays an important role after cellular homeostasis disturbances [PARK2 expression [In this study, we investigated two different cell models; human fibroblast cell lines (HDF) and as well preliminary data from hiPSC-derived midbrain-derived dopaminergic neurons (mDANs). Cells were derived from adult ADHD patients carrying urbances . Thus, wpression . We alsopression . FinallyPARK2 CNV duplication risk-carrier = PARK2CNV_DUP/ADHD, one adult ADHD PARK2 CNV deletion risk-carrier = PARK2CNV_DEL_A/ADHD and one healthy CNV non-risk carrier = WT_A/HEALTHY, see PARK2 risk-CNV was confirmed by Illumina Infinium Omni2.5-8 bead array analysis.Patients were recruited in 2013 at the Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital of Würzburg, Germany within a previously published sample  see Tab. The hea2) and fibroblast cultures were generated after standard procedure  under local anesthesia  using a skin puncher  was used to reprogram the fibroblasts into hiPSCs following the manufacturer’s protocol . Embryoid body (EB) assay was performed as well as immunostaining and PCR for pluripotency markers in hiPSC and EBs, see PARK2 genetic mutation are commonly causal for familial Parkinson’s disease and here neurodegeneration in dopaminergic cells plays an important role [Dopaminergic differentiation was chosen because ant role . On the ant role . In addiant role . The neuant role  as descrant role . For bas® Red CMXRos  and imaged with Zeiss Axio Observer.Z1 microscope with ApoTome function . Images were analyzed by a semi-automated analysis with Fiji ImageJ software as described elsewhere [For immunofluorescence assays cells were grown on Matrigel-coated coverslips. For the analysis of mitochondrial network morphology, fibroblast cells were grown on coverslips and mitochondria were stained using 400 nM MitoTrackerlsewhere . Shape dGenomic DNA from cell lines was extracted with the DNeasy kit  and analyzed on an Illumina Infinium Omni2.5-8 bead array at the Institute of Human Genetics, LIFE&BRAIN, University of Bonn  following the manufacturer’s protocol  according to manufacturer’s instructions. RNA quality and absence of gDNA contamination was measured using the Standard Sensitivity RNA Analysis Kit with Fragment Analyzer . Because of the low gene expression of Protein concentration from whole cell lysate was determined with ADV02 assay  and absorbance was measured at 600 nm wavelength by the Infinite M200 PRO microplate reader . PARK2 protein levels were measured with Human Parkin SimpleStep (Enzyme-linked Immunosorbent Assay) ELISA Kit  following the manufacturer’s instructions  for 24 h. For experimental procedures cultured cells from the same line were randomly assigned to one of the treatment groups. Cells were used in similar passage numbers . Standard curves were rendered by using CurveExpert 1.4 (https://curveexpert.software.informer.com/1.4/).For each experiment we report the single values of each individual replicate that were also used for statistical analysis. Data were analyzed using SPSS . Data were tested for normal distribution by Kolmogorov-Smirnov and Shapiro-Wilk test and parametric or non-parametric tests were applied appropriately. Exploratory one-way ANOVA or two-way ANOVA as well as repeated measures ANOVA  were used and Kruskal-Wallis test or Mann-Whitney test were performed. If statistical analysis showed significant difference, post-hoc testing was performed (Tukey HD test). Additionally, effect size was calculated . Correlation between values obtained from HDF and values obtained from mDANs was calculated by bivariate correlation and Pearson’s r reported. The level of significance was set at PARK2 CNV status was confirmed by an Illumina Infinium Omni2.5-8 bead array that showed PARK2 CNVs, spanning exon 2 of transcript NM_004562  to allow a diagnosis of PD. There were no significant differences between the rols see .PARK2, we assessed whether the presence of a PARK2 CNV might exert downstream effects on biological processes regulated by the PARK2-PINK1 interaction [Given the known physiological role of eraction . Two maieraction ,35. = 59.715, p ≤ 0.0001, ηp2 = 0.416; FF: F = 16.147, p ≤ 0.000, ηp2 = 0.246) and the genotype  = 40.067, p ≤ 0.0001, ηp2 = 0.488; FF: F = 13.713, p ≤ 0.000, ηp2 = 0.246), and for the interaction between the two variables  = 35.441, p ≤ 0.0001, ηp2 = 0.458; FF: F = 22.492, p ≤ 0.0001, ηp2 = 0.349). For both AR and FF, the main difference was between PARK2 CNV duplication/ADHD versus WT/healthy and WT/ADHD control (Tukey HSD p ≤ 0.0001) and PARK2 CNV deletion-carrier/ADHD (Tukey HSD p ≤ 0.0001). Our findings suggest that the PARK2 CNV duplication carrier/ADHD cells with and without nutrient deprivation might exhibit a pronounced elongated mitochondrial shape and tubular branching compared to PARK2 CNV deletion carrier/ADHD and WTs with and without ADHD  a significant effect of treatment (nutrient deprivation) could be seen for both the indices (AR: FADHD see A–C.PARK2 gene expression in fibroblasts due to their genotype and/or after nutrient deprivation (starvation)   . PARK2 g studies  and a prut ADHD) . Nutrienanalysis .We further assessed Parkin protein levels both in fibroblasts and dopaminergic neuron lines in baseline conditions, after 24 h of nutrient deprivation and after 24 h treatment with 10 µM carbonyl cyanide 3-chlorophenylhydrazone (CCCP).PARK2 gene expression levels, as well the PARK2 duplication as the PARK2 deletion carriers showed reduced PARK2 protein levels compared to the wildtype  = 11.082, p ≤ 0.0001, ηp2 = 0.451) and significant effect of treatment  = 13.810, p ≤ 0.0001, ηp2 = 0.506) but not a significant interaction between the two fixed factors. Post-hoc comparisons using the Tukey HSD on the treatment effect indicated that the treatment difference was mainly driven by CCCP treatment vs. baseline and vs. starvation (both Tukey HSD p ≤ 0.0001) that induced a decrease of PARK2 protein levels. In regard of the genotype, not only fibroblasts lines derived from PARK2 deletion carrier but also PARK2 duplication carrier showed lower levels of PARK2 protein when compared to WT (both Tukey HSD p ≤ 0.0001).Fibroblast cell lines were first explored separately (regarding duplication vs. deletion vs. wildtype) but similar to the wildtype A. An expwildtype B. The an = 4.819, p = 0.024, ηp2 = 0.682). The wildtype healthy control mDAN line showed increased PARK2 protein levels in comparison to as well PARK2CNV_DUP/ADHD as PARK2CNV_DEL_A/ADHD at baseline  = 11.462, p < 0.0001, ηp2 = 0.343) and treatment  = 43.465, p < 0.0001, ηp2 = 0.664), as well as an interaction effect between the two variables  = 4.666, p = 0.003, ηp2 = 0.298) (p < 0.0001). Genotype differences were mainly observed between the PARK2 CNV deletion carrier/ADHD and WT/Healthy and WT/ADHD (Tukey HSD p < 0.0001), and to a lesser extent between the PARK2 CNV duplication and WT/Healthy and WT/ADHD (Tukey HSD p = 0.064). In both cases it appeared that ADHD/PARK2 CNV carrier cells showed lower levels of ATP compared to WT healthy and ADHD controls, both in basal conditions and after 24-h starvation. However, CCCP treatments appeared to affect all genotypes equally  but similar to the other experiments, as well the PARK2 duplication as the PARK2 deletion carriers showed reduced ATP levels/fold changes compared to the wildtype in baseline and after starvation stress. After CCCP treatment the different cell lines showed similar fold changes in comparison to the healthy control at baseline A. In HDF= 0.298) C. Both s equally A,B.p < 0.0001, p < 0.0001) and PARK2 CNV duplication/ADHD compared to WT healthy control (Tukey HSD p = 0.004) (see The mDANs showed lower levels of ATP from cultures subjected to starvation compared to baseline conditions (Tukey HSD 004) see C.PARK2 duplication as well as the deletion carriers showed an effect in the same direction, we analyzed the data from the HDF first separately and then together to increase the statistical power. The time course recording was arbitrarily divided into three bins of 30 min each  to avoid multiple testing and because repeated measured ANOVA was technically not possible to conduct with all the measured time points. Student’s tests were run for each interval to determine if there were differences between the genotypes in baseline OCR. Statistical analysis showed significant differences between genotypes both under baseline conditions  in the HDF and mDANs under baseline conditions, 24-h starvation stress, and 24-h CCCP treatment A–C and anditions A,D and anditions B,E. HDF ions see A and aftions see B,E. We cment see C,F. Our  = 71.024, p < 0.0001) could be seen, but no interaction between time and genotype. This suggests that the fluorescence signal declines over time, possibly because of the utilization of the reagent, which was unaffected by genotype. This finding was similar in the samples subjected to 24-h starvation  = 411,536, p < 0.0001), but in this case the decline was higher in healthy and ADHD wildtype control lines, potentially because of the earlier reported elevated OCR compared to the PARK2 CNV deletion and duplication carriers  = 88.790, p < 0.0001).To determine if with time the recorded fluorescence would decrease, we calculated an exploratory repeated measures ANOVA on RFU recorded in the intervals. In the baseline conditions, a significant effect of time  = 55.208, p < 0.0001, ηp2 = 0.902) but not of genotype  = 2.183, p = 0.165, ηp2 = 0.154), and also no interaction effect  = 0.768, p = 0.485, ηp2 = 0.114). The main differences for treatment were found between baseline and starvation conditions (Tukey HSD p < 0.0001), and baseline and CCCP treatment (Tukey HSD p < 0.0001). A higher amount of ROS could be observed after starvation  and CCCP treatment  compared to baseline conditions . We did not find a significant difference in ROS response between starvation and CCCP treatment (Tukey HSD p = 0.274). In the mDANs, PARK2 CNV carriers seemed to display a lower amount of ROS in comparison to the healthy WT. Both starvation and CCCP treatment increased the ROS amount in all three mDAN cell lines  and reactive nitrogen species (RNS), which under normal conditions are attenuated by the redox scavenger system . We therPARK2 CNV deletion and duplication carrier cells to nutrient deprivation stress which has also been described in other Parkin-or PINK1-deficient models [Our results, at least in part, could reflect dysregulated mitophagy (via PARK2/PRKN and PINK1). It has been shown that parkin is transported to the mitochondria which have an impaired energy metabolism, where it ubiquitinates mitofusin (Mfn) to mark them for degradation and mitophagy . If parkt models ,47.PARK2 CNV deletion or duplication cells, suggesting there could be alternative compensatory pathways regarding mitochondrial or metabolic stress. Recent studies give evidence of oxidative and nitrosative stress markers being altered in ADHD compared to healthy controls, as reviewed by Lopresti and colleagues [The regulation of oxidative stress, measured by the level of ROS, was not altered in our ADHD lleagues . Despitelleagues , reportelleagues . HoweverPARK2 CNV carriers might have an energy impairment. This impairment could be due to the diverse role of PARK2 in mitochondrial dynamics, potentially leading to the disruption of normal brain plasticity and cellular resilience [Our work suggests that ADHD silience . Severalsilience ,51,52,53PARK2 CNVs represent a rare variation found just in a small subset of ADHD patients. Furthermore, we did not conduct electrophysiological experiments yet to investigate basal neuronal functions besides the ability to produce dopamine. Additionally, we did not in detail investigate the CNV effect on neuronal function and survival which will be done in future studies.The results of our study should be considered under several limitations. First, the number of biological repeats (patients and controls) was low, especially in the mDAN, and we did not use isogenic controls. Second, although the models presented in this study are one of the few available cellular models for studying ADHD, The data that support these findings are available from the corresponding author, S.K.-S, upon reasonable request. Human subject data will be deidentified to protect confidentiality.


Recent estimates suggest that there is a substantial number of Japanese students with developmental disabilities. This study aimed to examine potential associations between autistic, autistic subcomponents, and attention deficit/hyperactivity disorder (ADHD) traits with student performance (as measured by presenteeism) and class attendance among Japanese university students. Participants comprised 721 students from different regions of Japan who completed a self-administered internet survey. Autistic and ADHD traits were measured using an abridged version of the autism spectrum quotient (AQ-Short) and adult ADHD self-report scale (ASRS). Presenteeism, which is an indicator of student performance, was assessed using the modified World Health Organization Health and Work Performance Questionnaire. Class attendance during the past year was self-reported by participants. Students with high levels of autistic traits and high levels of ADHD traits were significantly more likely to report poor student performance . Regarding autistic trait subcomponents, students with high levels of preference for routine  and high levels of difficulties with social skills  were also significantly more likely to report poor student performance. There were borderline significant associations between traits of attention-switching difficulties and poor student performance . Regarding ADHD trait subcomponents, students with high levels of inattention  were also significantly more likely to report poor student performance. Students with both high levels of autistic traits and high levels of ADHD traits were more likely to report poor student performance than those with high levels of only one trait type. There were, however, no statistically significant associations between these traits and low class attendance risk. Sickness presenteeism was significantly associated with high levels of both autistic traits and ADHD traits among Japanese university students.  At present, in Japan, almost all (97.1%) the developmental disabilities among university students comprise autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD) (1). Both these neurodevelopmental disorders are well known to cause severe impairment and morbidity (2). On the other hand, it is well known that the early detection of mental health problems enables early intervention and support, which may improve health and prevent further mental deterioration (3).Recent estimates suggest that the number of students diagnosed with or suspected of having developmental disabilities is substantial in Japan (4), while students with ASD will exhibit high levels of behavioral and emotional difficulties at school, including attention difficulties. One study reported high rates of academic underachievement in students with ASD, with an intelligence quotient (IQ) within the average range  (5). We hypothesized that students with ASD, therefore, would exhibit poor student performance owing to difficulties caused by their ASD symptoms and would subsequently show poor academic achievement. A sociodemographic gradient may also exist, as some research suggests that ASDs are more prevalent among less-educated adults (6), and that autistic traits are generally more prevalent among those of lower socioeconomic status (SES) (7). These findings suggest that autistic traits are probably concentrated among low SES groups because individuals with high autistic traits will exhibit poor student performance as a result of difficulties caused by their ASD symptoms.Auditory filtering difficulties, sensory under-responsiveness, and sensory seeking are associated with academic underachievement among students with ASD in mainstream classrooms (8). For example, it has been shown that university students with ADHD symptoms tend to exhibit low academic achievement, as they find it difficult to concentrate on their studies and complete assignments (10). Furthermore, a previous study of workplace costs found that ADHD was associated with a 4–5% reduction in work performance and the relative odds of sickness absence were doubled (11). Adult ADHD is a common disorder in the US civilian labor force and is associated with substantial losses in work time (12). These findings suggest that individuals with high levels of ADHD traits will exhibit poor student performance owing to difficulties caused by ADHD symptoms, not because of low ability.ADHD symptoms have many negative life impacts and university students with these issues are known to have a lower quality of life than their peers without ADHD (13). Initial reports of presenteeism were based on studies of workers; however, subsequent reports indicate that presenteeism also occurs among students (15). Some research has also demonstrated a relationship between poor work performance (sickness presenteeism) and sickness absence (18), including one study from Japan where poor work performance (more sickness presenteeism) was related to higher absence rates due to mental health issues (19). This suggests that poor student performance may lead to low class attendance, and therefore, that individuals with high levels of autistic and ADHD traits are more likely to demonstrate lower class attendance rates.Presenteeism is an indicator of student performance. Presenteeism is a tendency for individuals to go to work despite suboptimal functioning because of illness or other medical conditions (9), the simultaneous evaluation of both autistic traits and ADHD traits is rare. In addition, most studies on developmental disability traits and poor student performance have utilized academic achievement as an indicator of student performance (9). When excluding the effect of ability, academic achievement tends to be affected by student performance factors, such as presenteeism. Gender differences have also been shown to be important considerations within Japanese academic settings (20). As such, the current study examined potential associations between autistic traits, autistic trait subcomponents, and ADHD traits with poor student performance (sickness presenteeism), and class attendance rates among Japanese university students. Findings from the current study will provide useful data to help devise appropriate student support services and policies.Although research has examined some associations between student performance and developmental disability traits (autistic traits and ADHD traits) with typical intelligence Participants comprised Japanese university students who completed a self-administered internet-based questionnaire survey in December 2016. The sample was drawn from a pool of individuals who had previously registered with a Japanese online survey company , comprising approximately 1.2 million registered members across all Japanese prefectures. The survey company guarantees the most recent internet user representation, and groups are not biased toward specific attributes. A total of 128,807 registered university students were selected randomly and invited by email to participate in the study. In the first email, screening was conducted to confirm that the respondent was a university student. In the second email, a greeting and a questionnaire were sent to respondents who had confirmed that they were university students in the screening. The greeting explained that the study theme was developmental disability traits among university students. The survey was closed when 1,030 students had completed the questionnaire. The company provides 103% of the desired sample size. The company was asked to provide 1,000 samples, and the ratio of each course was 1:6:1:2 for junior college (2-year university course), 4-year university course, 6-year university course, and graduate students. At the close of the survey, screening emails had been sent to 73,603 registered students, and questionnaire emails sent to 2,314 respondents who answered the screening. The only inclusion criterion was that participants had to be currently enrolled in either a 4- or a 6-year university course. When junior college students and graduate students were excluded from the total of 1,030, the final analysis target was 721. The questionnaire was configured to finish automatically when all responses had been completed.A total of 721 students aged 18–30 years completed the survey. The sociodemographic characteristics assessed were age, gender, length of study (4- or 6-year university course), faculty, and leisure-time physical activity (no or yes). Other characteristics assessed were skipping breakfast (yes or no) and living arrangements . The students’ field of study was classified into four groups: science and engineering , medicine , literature , and others.(21). It is based on the AQ questionnaire created by Baron-Cohen et al. (22) and is used worldwide. AQ-Short total scores have been shown to differentiate accurately between participants and individuals clinically diagnosed with Asperger’s syndrome. The distribution of the AQ-Short scores is approximately normal across the Dutch general population and English student population (21).The abridged version of the autism spectrum quotient (AQ-Short) is a self-administered test that measures the degree to which an adult with typical intelligence has traits associated with ASD (21). The social behavior subcomponent further comprises four lower-order subcomponents assessing difficulties with imagination , a preference for routine , difficulties with social skills , and attention-switching difficulties  (21). Participants respond to statements using a 4-point Likert scale: 1 = definitely agree; 2 = slightly agree; 3 = slightly disagree; and 4 = definitely disagree. Scoring is reversed for items on which an “agree” response reflects the presence of a characteristic of autism. Scores are summed; a minimum AQ-Short score of 28 indicates no autistic traits and a maximum score of 112 indicates a full autistic phenotype. The accuracy of the AQ-Short in differentiating individuals with ASD from controls was evaluated using receiver operating characteristic curve analysis. The area under the curve (AUC) was 0.97. The cutoff was ≥ 70, and the sensitivity and specificity were 0.94 and 0.91, respectively (21). We used a Japanese version of the AQ-Short form (7), with all 28 items needing to be answered for the AQ-Short data to be considered acceptable.AQ-Short subcomponent scores were used as a measure of specific autistic traits and comprise two higher-order subcomponents assessing a fascination for numbers and patterns  and social behavioral difficulties  p < 0.001; ASD sample: r = 0.95, p < 0.001) (21). Second, participants are burdened with 50 questions in the AQ questionnaire, compared with 28 questions in the AQ-Short questionnaire. Third, there are several short versions of the AQ. However, the AQ-Short contains a subcomponent to measure the autistic phenotype “a fascination for numbers and patterns,” which was uniquely identified in our previous study as a positive aspect on which workers of greater SES scored highly (7).The AQ-Short was used instead of the original AQ for the following reasons: first, the correlations between the AQ and AQ-Short were very high and significant in all samples , was administered to determine participants’ ADHD traits. The ASRS Symptom Checklist comprises 18 DSM-IV adult ADHD criteria (24), while the ASRS Screener (a short form of the ASRS) comprises 6 of the 18 ASRS questions found to be most predictive of symptoms consistent with ADHD. The 6 questions represent the 4 symptoms of inattention and 2 symptoms of impulsivity/hyperactivity that characterize ADHD (23). The ASRS Screener items assessing inattention  and impulsivity/hyperactivity  were defined as ASRS subcomponents in this study. Each question asks how often a particular symptom has occurred over the past 6 months, with responses rated on a 0–4 scale: never (0), rarely (1), sometimes (2), often (3), and very often (4). Higher scores indicate a higher level of ADHD traits. We utilized a Japanese version of the ASRS for this study (25), and all 6 items had to be answered for the ASRS data to be considered complete. Data obtained using the DSM-IV ASRS screening scale can also be used to determine ADHD traits according to the DSM-5 criteria (2), by applying scoring rules developed to update the DSM-IV ASRS screening scale to the DSM-5 version of the screening scale (27). The DSM-IV ASRS scale can therefore be used as a practical screening tool for the DSM-5 adult ADHD, despite several of the screening scale questions not necessarily reflecting DSM-5 symptoms : sensitivity, 84.2%; specificity, 89.5%; AUC, 0.87) (26). This results in a minimum score of 0 and a maximum score of 20.The adult ADHD self-report scale (ASRS), which was developed in conjunction with the World Health Organization (WHO) and the Workgroup on Adult ADHD (30), a self-reporting instrument designed to estimate the workplace costs of health problems in terms of self-reported reduced job performance (sickness presenteeism). Illness in the workplace can result in lost productivity, while “presenteeism” refers to productivity decreases among employees who are present but functioning at suboptimal capacity owing to illness or other medical conditions (29). Although there are various definitions of the term “presenteeism,” all pertain to being physically present at work. There is also a Japanese version of the HPQ short form (31).Student performance was assessed using the modified WHO Health and Work Performance Questionnaire (WHO-HPQ) (30). The modified HPQ measures presenteeism using the following question: “On a scale from 0 to 10, where 0 is the worst student performance and 10 is the best student performance, how would you rate your overall studying performance on the days you studied during the past four weeks?” A presenteeism score is obtained by multiplying the respondent’s response to the question by 10. Presenteeism has a lower bound of 0  and an upper bound of 100 (no lack of performance during time on the job). A low presenteeism score indicates a low level of performance. In this study, poor student performance was defined as having a presenteeism score ≤ 40 (32).We utilized a version of the HPQ short form modified for its suitability among student populations. Presenteeism can be calculated as the difference between actual and potential performance of self over the past 28 days “Of the number of days that you were expected to attend during the past year, what percentage of days did you attend?” The number of class hours in Japanese universities varies greatly according to the field of study. For this reason, in this study, the group with the lowest quartile defined as “low attendance” had a percentage of attendance of below 80%.The percentage of attendance was measured using a self-administered questionnaire comprising the following question:Categorical variables were presented as percentages. AQ-Short scores, AQ-Short subcomponent scores, and ASRS scores were used to indicate the trait of developmental disability. For categorical variables, the chi-squared -test was used to compare subjects with poor student performance or low class attendance to those without poor student performance or low class attendance. Spearman correlation analysis was used to evaluate the relationship between the score quartiles of developmental disability traits, poor student performance category score, and low attendance category score, and to identify the collinearity between the score quartiles of developmental disability traits. Ordinal numbers 1–4 were assigned quartile categories of each developmental disability trait. Logistic regression analysis was undertaken to estimate the odds ratio (OR) and 95% confidence interval (95% CI) for poor student performance and low attendance, using the score quartiles of developmental disability traits. Only the ADHD trait subcomponent “Hyperactivity–Impulsivity” was analyzed in tertiles because it could not be divided into quartiles from the distribution. Goodness of fit of the logistic model was assessed using the Hosmer–Lemeshow test.p-values of < 0.05 were considered statistically significant. All analyses were conducted using SPSS .Univariate analysis was first performed, with multivariate models then adjusted for age, gender, length of study (4- or 6-year university course), field of study , leisure-time physical activity (no vs. yes), skipping breakfast (yes vs. no), and living arrangement . Autistic traits, autistic trait subcomponents, ADHD traits, and ADHD trait subcomponents as the traits of developmental disability comprised the exposure variables of interest. In the first model, autistic traits and ADHD traits were entered simultaneously. In the second model, the five autistic trait subcomponents and ADHD traits were entered simultaneously, and stepwise multivariate logistic regression was conducted. In the third model, autistic traits and ADHD trait subcomponents were entered simultaneously. To examine combinations when scores on one or both autistic traits and ADHD traits were high, logistic regression analysis was used to estimate the OR and 95% CI for poor student performance and low attendance, using the “high” score and “low” score on the developmental disability traits. The 4th quartile (highest) of the developmental disability traits was defined as “high” and the 1st quartile (lowest) to the 3rd quartile of these was defined as “low.” Two-tailed Ethical approval was obtained from the International University of Health and Welfare Ethics Committee (No. 16-Ig-62) and the Kitasato University School of Medicine Ethics Committee (No. B16-160). The study was conducted in accordance with the standards specified in the 1964 Declaration of Helsinki. Participant responses implied voluntary consent for participation.Participant demographics are displayed in The simultaneous analysis of autistic traits and ADHD traits in a multivariate adjusted model revealed that students with high levels of autistic and ADHD traits were significantly more likely to report poor student performance . The simultaneous analysis of autistic trait subcomponents and ADHD traits revealed that poor student performance (sickness presenteeism) was statistically associated with Routine traits and Social skills traits, and weakly associated with Switching traits. However, there were no demonstrable associations between autistic traits or ADHD traits and low class attendance risk. These findings suggest that high levels of autistic and ADHD traits probably affect student performance, but are less influential on class attendance.The current study revealed statistically significant associations between autistic or ADHD traits and poor student performance. Although, to the best of our knowledge, no previous reports demonstrate an association between autistic traits and poor student performance, some research has documented an association between ASD and both academic achievement and driving. There are some parallels with our current study of students’ academic achievement, as driving can also be viewed as a “performance” task, of sorts.(33), suggesting that students with ASD are underperforming relative to their ability level. Students with ASD are also reported to experience high levels of behavioral and emotional difficulties at school, such as anxiety and depression (5). Individuals with high autistic traits with or without ASD diagnosis are at a high risk of associated psychiatric disorders, particularly depression and anxiety (36). In terms of driving, individuals with ASD have been shown to underperform in their driving ability and are involved in more traffic accidents than drivers without ASD, although individuals with ASD outperform on aspects related to rule-following (39). These findings when viewed in the context of our own results suggest that individuals with ASD/high levels of autistic traits probably struggle to concentrate and to calm their emotions and behaviors during class. From another perspective, one of the characteristics of ASD is the presence of restricted, repetitive behaviors, interests, or activities (2). Therefore, it is difficult for students with ASD/high levels of autistic traits to follow university rules set by others, resulting in poor student performance. The current study also suggests that difficulties due to high levels of autistic traits probably manifest in poor student performance, which subsequently leads to poor academic achievement, as students are unable to demonstrate their true abilities. Poor academic achievement in the current research was affected not only by ability but also by poor student performance , suggesting that high autistic traits or ASD might be generally more prevalent among less-educated people (7).Previous reports have shown that the academic achievement of students with ASD is often lower than would be expected based on their IQ levels alone (11). In the present study, we used the WHO-HPQ to assess student performance. Previous studies on students have revealed that the ADHD traits of hyperactivity, impulsivity, and inattention can affect school performance (41). Several studies have also reported an association between ADHD traits and student academic achievement, for example, individuals who exhibit symptoms of inattention, hyperactivity, and impulsivity (with or without ADHD diagnosis) also suffer poor academic and educational outcomes (42). One previous study even found that the association between ADHD and academic underachievement persisted after adjusting for IQ (43). This suggests that academic achievement is affected by aspects of student performance, such as presenteeism, rather than being a direct reflection of academic ability. Lower than expected academic achievement in students who have sufficient ability may indicate mental stress and a tendency to depression. Evidence shows that individuals with ADHD or probable ADHD are at a high risk of associated psychiatric disorders, such as depression and social anxiety (46). Social anxiety also has a substantial effect on failure to complete school, and increases the risk of examination failure (47).Our results, demonstrating an association between ADHD traits and poor student performance, are congruent with other research, including a previous study of employees that showed that ADHD is associated with a 4–5% reduction in work performance as measured by the WHO-HPQ (49). Interpersonal relationships are important for performance in practical classes at university. However, students often study alone, so social skills traits may have little effect on performance. Individuals with ASD tend to perform lower than their typically developing peers on reading comprehension (50). Taken together with our results, these findings suggest that individuals with high levels of autistic traits may prefer routine to avoid reading comprehension, and as a result, do not improve their reading skills and consequently exhibit poor student performance. Our previous study uniquely showed that workers of higher SES scored higher on the Numbers/Patterns trait, although workers of lower SES had significantly higher autistic trait scores than their respective counterparts (7). Baron-Cohen et al. (51) found that mathematical talent is linked to autism. The autistic traits indicated by Baron-Cohen et al. resemble the Numbers/Patterns trait. These findings suggest that the Numbers/Patterns trait may not be associated with poor student performance because it has positive aspects. We found that the Imagination trait was not independently associated with poor student performance. Individuals who score highly on the Imagination trait find it difficult to understand another person’s point of view. However, individuals need other people . This suggests that the lack of association between Imagination trait scores and poor student performance reflects the fact that students often study alone.Analyses of the five autistic trait subcomponents, ADHD, and poor student performance showed that scores on all autistic trait subcomponents except two (Numbers/Patterns and Imagination) were associated with poor student performance. Of the five subcomponents, there was a strong effect size for Routine traits, but weak effect sizes for Social skills and Switching traits. We assumed that student performance would be strongly associated with the Social skills trait, resulting in lower academic achievement, based on previous reports that have shown that improved social skills lead to improved academic achievement (53).Analyses of the two ADHD trait subcomponents and poor student performance showed that scores on the Inattention trait were associated with poor student performance. However, we found that the Hyperactivity–Impulsivity trait was not independently associated with poor student performance. This seems to support previous study findings that hyperactivity/impulsivity symptoms present in childhood decline with age, whereas inattention symptoms persist into adulthood, and most adults with ADHD present with prominent symptoms of inattention (55). Our results are consistent with previous reports that individuals with both ASD and ADHD have higher rates of other comorbid symptoms than individuals with either ASD or ADHD alone (57).The analysis of a combination of autistic trait scores and ADHD trait scores and poor student performance showed that participants with either “high” autistic traits or “high” ADHD trait scores were significantly more likely to report poor student performance, and that participants with both “high” autistic traits and ADHD trait scores were more likely to report poor student performance than those with only “high” scores on one trait type. DSM-5 no longer prohibits the co-diagnosis of ASD and ADHD, and recent studies indicate that the co-occurrence of ADHD and autistic symptoms is common (11), which contrasts with our findings whereby high levels of autistic or ADHD traits affect student performance, but do not affect class attendance, suggesting that poor student performance owing to these traits does not necessarily lead to low class attendance. Our outcome measure of class attendance included not only absences from class owing to illness but also cases where students skipped class. In previous studies on children or students aged 5–17 years, individuals with ASD or ADHD were more likely to have shown chronic school absenteeism, school dropout, or school refusal behavior than individuals without these conditions (60). These previous studies are inconsistent with our results. This may be because our participants were university students, and therefore highly motivated to study compared with the samples of children or students aged 5–17 years in the previous studies.In the current study, we found no statistically significant association between low class attendance risk and autistic traits, autistic trait subcomponents, or ADHD traits. A previous study on workers revealed that ADHD was associated with reduced work performance and the relative odds of sickness absence were 2.1 (61) and DSM-5 ADHD (3.55%) (62). When limited to university students, the prevalence of ASD without co-occurring intellectual disability is 1–2% (63), and approximately 2–8% of students show clinically significant levels of ADHD (64). The Japan Student Services Organization reported that only 0.2% of college students in Japan have developmental disabilities, 56.7% have ASD, 25.2% have ADHD, and 14.7% have two or more concurrent developmental disabilities (65). These estimates are based only on students with a certificate of disability or those whose disability was revealed by a medical examination, which may indicate that the number of students with developmental disabilities has been underestimated. There are several possible reasons for the high proportion of students with developmental disabilities in this study compared with previous reports. First, some individuals have high levels of autistic traits/ADHD traits, but do not have an ASD/ADHD diagnosis. Second, many participants in this study with high levels of autistic traits and/or ADHD traits may have participated because they were interested in exploring their own high levels of autistic/ADHD traits. Additionally, participants were university students and so belonged to a rather high SES group. More individuals with high autistic traits and/or ADHD traits may have been attracted to the study because of social class bias. Owing to the presence of this bias, high SES is a reported sociodemographic risk factor for ASD (67). For instance, children with autism whose mothers are more highly educated may have had more opportunity to participate in early interventions than children whose mothers have had fewer years of education (67).About half of our participants had autistic trait scores above the cutoff and 18% of participants had ADHD trait scores above the cutoff. These proportions are high compared with the prevalence of both previously reported ASD (0.98% to 2.2%) (69).There were a few potential limitations of the current study that are worth considering. First, our questionnaire may be less appropriate for participants with a low IQ, as it required a certain degree of reading comprehension skills. The participants in our study, therefore, presumably comprised those with typical intelligence levels. Second, as we used an online questionnaire, the sample may have been biased toward individuals who owned a computer and were more familiar with internet use. Third, the current study utilized self-reporting measures, which may exhibit an inherent degree of recall error and bias (particularly regarding class attendance), although this methodology is by no means uncommon in psychological research The implications of our study are that the students with high levels of autistic and ADHD traits were significantly more likely to report poor student performance, although there was no association between autistic traits or ADHD traits and low class attendance risk. Attendance and absence are easy to distinguish objectively, but poor student performance is harder to determine. It is important to notice poor student performance early and creating a suitable learning environment will improve their performance. It helps students attain an academic achievement that better reflects their ability.Overall, the current study suggests that poor student performance as measured by sickness presenteeism was significantly associated with high levels of both autistic traits and ADHD traits. Low class attendance risk was not statistically associated with either autistic or ADHD traits, however. The findings revealed that while high levels of autistic and ADHD traits affect student performance, class attendance rates are less affected, at least in Japan.NoneThis work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI [grant number JP16K09105 (Chief: Dr. Tomoko Suzuki)] and IUHW Research Grants (Chief: Dr. Tomoko Suzuki), Japan.The authors would like to acknowledge Dr. Baron-Cohen’s permission to create our Japanese version of the AQ-Short questionnaire. We thank Dr. Derek R. Smith for his helpful advice. We would also like to express our sincere gratitude to all participants for their involvement in our research. We also thank those who cooperated with us during data collection and entry.TS conceived the study, designed the protocol, and enrolled participants, and was responsible for the statistical analysis and drafting of the manuscript. SI and TS were responsible for the organization of the entire cohort and participated in data collection. KW was the advisor for the whole study and completed the manuscript. BM, NN, and SY gave valuable advice and revised the manuscript for important intellectual content. All the authors were involved in the data interpretation and contributed to preparing the manuscript.Ethical approval was obtained from the International University of Health and Welfare Ethics Committee (No. 16-Ig-62) and the Kitasato University School of Medicine Ethics Committee (No. B16-160).Supplementary Material 1-5.Click here for additional data file.