The ADHD group  had a significantly higher preference for same-sex or either-sex partners; and higher rates of electronic sexual exchanges, masturbation, and sexually transmitted diseases. They were more adventurous in sexual interests and practices and substantially less satisfied with their partners, both sexually and generally. Within the ADHD group, significant sex differences emerged: females had younger onset of sexual activities, used contraception less frequently, had more sexual partners and practiced more infidelity. Sexual interests differed between the sexes, but females more commonly acted on them, whereas males did not. Findings suggest both sexes engage in risky sexual behaviors, perhaps driven by impulsivity, but risk is substantially greater for females with ADHD.Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity and impulsivity. A dearth of studies have investigated psychosexuality in this population, often with few  participants. We recruited individuals with and without ADHD via an anonymous online survey distributed electronically by ADHD support organizations and social media. We investigated sexual history; interests and practices; and relationships. Of 1392 respondents, we classified an ‘ADHD’ group ( Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder characterized by pervasive behavioral symptoms of hyperactivity, inattentiveness and impulsivity that have been present for at least 6 months and adversely impact daily functioning and development . It is aEarly studies noted an association between poor relationship satisfaction, a high incidence of divorce among participants with diagnosed ADHD and risky sexual behavior in young adulthood ,4,5. ComMore recent studies comparing young people and adults with ADHD with typically developing controls have found a range of poor psychosexual outcomes for those with ADHD ,9,10. ThIn a recent systematic literature review, Soldati et al.  found thSome studies have compared male and female sexual functioning. Compared with controls, both males and females with ADHD have been found to engage in risky sexual behavior that carries an increased risk of developing STIs , to haveHowever, most studies into sexual behavior of people with ADHD have focused on males, either exclusively or as the majority proportion of participants. The number of participants is often low ,10. HencOne of the main advantages of the current survey is that we measured both relationship satisfaction and sexual satisfaction, and importantly explored the association between them within the context of ADHD symptom severity and other potentially confounding variables. To the best of our knowledge, this has not been done before, and it adds a novel approach to the analysis of the data.Following the methodology that Vowels and Mark  recommenIs ADHD associated with sexual orientation; sexual interests and practices; and riskier sexual behavior?Are there differences between ADHD and other participants in the motivation behind extra-partner affairs?Does an ADHD diagnosis reduce the association between relationship satisfaction and sexual satisfaction in one’s current relationship?To what extent do ADHD symptom severity and associated clinical conditions adversely affect the extent of sexual satisfaction in relationships?The research questions were as follows:n = 619), the United Kingdom (n = 556), USA (n = 126), Denmark (n = 40), Turkey (n = 22) and other countries (n = 103). Nearly all participants specified their gender , classifying themselves as female , male  and non-binary . Seventy-five (5.2%) participants responded that their current gender did not match their sex assigned at birth, the majority of whom  were in the non-binary group. All of the (non-binary) participants were excluded from the current analyses and will be presented in a separate article. This left a sample of 1392 participants, of whom 1368 (98.3%) gave their age. The mean ages for females and males were 38.1  and 41.4 , respectively. Males were older .There was a total of 1466 participants from Canada (A 34-item questionnaire was developed for the survey consisting of four separate parts (available by contacting the lead author). There was no corroborative information collected to support the answers from the questionnaire for pragmatic reasons and issues of confidentiality .This asked for background information . Participants were not asked about other medications, since the primary focus of the study was on ADHD.For clinical diagnosis, the participants were asked from a pre-determined list ‘Has a health practitioner ever diagnosed you with any of the following conditions :’  oral, anal and vaginal intercourse; genital stimulation; and the use of adult toys regardless of whether orgasm/ejaculation was reached.A ‘romantic relationship’ was defined as two people who are emotionally close. The reader was informed that terms often used to describe this type of relationship are boyfriend, girlfriend, partner and/or spouse. Casual sexual encounters or ‘friends with benefits’ were not included in this definition.Some questions asked about behavior ‘in a typical month’. These questions referred to experiences that occurred before the COVID-19 pandemic when social distancing measures were not in force.This included items about age of the participant when they first engaged in consensual sexual activity, number of sexual partners over their lifetime, extra-partner sexual engagement whilst in a committed relationship, factors that motivated the participant to engage in extra-partner relationships, use of contraception and STIs.This focused on practices associated with pornography, masturbation, electronic exchanges of a sexual nature and specific sexual interests and consensual activities in which they have engaged.The survey was designed using the ‘SurveyMonkey’ website in the English language. Draft formats of the survey were sent to collaborating ADHD organizations  and the Danish ADHD Association) for review and feedback. This led to several revisions. In an introduction to the survey, readers were informed that the purpose of the survey, which took around 20 min to complete, was to better understand the sexual interests and practices of adults with and without ADHD. To be eligible, participants had to be 18 years or older. They were informed that their responses were anonymous and not linked to any personally identifying information. Participation was voluntary, and if they chose to participate, they were informed they had the option of only responding to questions they felt comfortable answering. Other than age , there followed 32 questions presented in either a closed option format  or in an open option format . At the end of the survey, participants had the opportunity to add written comments.Thus, a convenience sample was obtained via this anonymous online survey; links to the survey were shared and distributed electronically by the ADHD organizations and through social media. The survey was open for three months until 12 January 2021, and the data were exported into SPSS for analysis.t-test) and number of lifetime sexual partners (Mann–Whitney U test due to skewed data). Chi-square tests were used for categorical variables. Phi was used to measure effect size for 2 × 2 contingency tables. Cramer’s V was used to measure the respective effect sizes with tables larger than 2 × 2.Means and standard deviations are provided for continuous variables, including age .Most studies on the association between relationship satisfaction and sexual satisfaction have suggested that the link is bidirectional, but there is evidence from longitudinal research for directionality and that relationship satisfaction is a better predictor of sexual satisfaction than vice versa . We usedn = 20).Among the 541 ADHD participants, 46 (8.5%) had comorbid ‘ASD’. Of the 66 participants with ‘ASD’, over two thirds (69.7%) also claimed an ADHD diagnosis. For this reason, only the findings for the ADHD group are specifically provided in the current study. The sample of ASD participants without ADHD was too small for any meaningful independent analysis  and data from all other participants, labelled the ‘Other’ group . There was no significant age difference between the ADHD (38.8 years) and Other (39.6 years) participants. Having made these adjustments, the participants in the two groups were compared for further analysis. These were the ‘ADHD’ group (n = 538), and it was 9.54  for the Other participants. This difference between the two groups was significant .Out of the 1373 participants who completed the ASRS, the mean score among the ADHD group was 15.88 . Females  had a significantly higher total symptom score than males ; . Both age and sex were related to the severity of symptoms. Age correlated negatively (younger age more symptoms) with total ADHD symptoms  claimed that they were currently taking prescribed ADHD medication. All but six (2.1%) of those 286 participants reported an ADHD diagnosis. When focusing only on the ADHD-diagnosed group, 280 (51.8%) reported being currently prescribed ADHD medication and regularly taking it. This medication group is included in Three hierarchical regression models see  were conOut of 1371 participants who gave a specific gender preference , 974 (71.0%) had a sexual preference for the opposite sex. A further 18 participants  claimed that none of the categories provided applied.Participants were asked about their relationship with their current partner (for those who had one). n = 987, p < 0.001). The correlation was similar for females and males, 0.468 and 0.493, respectively. However, the correlation was significantly lower  in the ADHD group  than the non-ADHD group , accounting for 15.9% and 25.9% of the respective associations. There was a significant relationship between current relationship satisfaction and sexual intimacy satisfaction for the whole sample . Relationship satisfaction was added in Step 2. The results are provided for the final model in This section focused on the age of the participant during their first sexual relationship, the number of lifetime sexual partners, extra-partner sexual affairs, and sexual intercourse without use of contraception. Female participants with a history of ADHD had sex at a significantly younger age (16.3 years) than Other female participants (17.1 years), but the difference was less than one year. No significant difference was found between the two groups for males. As far as the number of sexual partners are concerned, the distribution of each group of participants was highly skewed with a large range, which was 0 to 400 (median = 10) for females and 1 to 1000 (median = 9) for males. Fewer than 4% of the participants in both groups reported never having had a sexual relationship . Of those who had had a sexual relationship, for females, the lifetime median for sexual partners was significantly higher than that of the Other participants . For males, there was no significant difference in the lifetime number of sexual partners, the median being nine. 2 = 8.18, df = 1, p < 0.01, Phi = 0.113). No significant differences emerged for males. Participants were asked about the frequency they had sexual intercourse without contraception . The six categories  were combined into two groups .2 = 25.9, df = 1, p < 0.001, Phi = 0.144). As far as ADHD for the total sample is concerned, there was no significant overall difference between the ADHD (31.2%) and Other participants (29.2%).For the 1256 participants who answered the question regarding the use of contraceptives, 377 (30.0%) fell in the high-risk group. There was an overall significant difference in high-risk-taking between males and females: 39.2% of the males and 25.2% of the females were classified as high risk-takers . No significant difference was found for males with or without ADHD. This suggests that only females with ADHD were at increased risk of regularly failing to use a contraceptive during sexual intercourse .However, in a separate analysis for females and males with ADHD, as shown in We enquired about the motivation for infidelity from those participants who had owned purposefully engaging in sexual activities outside of their current or past romantic relationships. Options were selected from a predetermined list of 20 items see . We used discriminant function analysis to determine which of the variables in Out of 1392 participants, 296 (21.3%) reported a history of one or more STIs, the most common being chlamydia (9.5%), followed by ‘genital/anal warts’ (6.6%) and ‘genital herpes’ (5.2%).2 = 4.1, df = 1, p < 0.05, Phi = 0.061]. The proportions were similar for females and males.In the ADHD group, 132 (24.4%) reported one or more STI, in contrast to 164 (19.3%) among the Other participants. This difference was significant  of the ADHD and 477 (66.4%) of the Other participants said they enjoyed watching pornography. This difference is not significant, nor was there any significant difference in the frequency they watch it.Most individuals watched pornography on their own, 334 (92.2%) of the ADHD and 499 (89.7%) of the Other group, and there was no significant difference between the two groups.2 = 11.6, df = 3, p < 0.01, Cramer’s V = 0.112).On a four-point scale , participants with ADHD reported to masturbate significantly more often than the Other group when watching pornography , followed by Step 2 (adding ADHD). The results are shown in The four dependent variables explained 7.6% (low effect size) of the variance in frequency of masturbation; a younger age, having ADHD and being a male were the best predictors. In a separate analysis, adding the severity of the ADHD symptoms to the model in Step 2 did not add significantly to the variance in masturbation.2 = 8.22, df = 1, p < 0.01; Phi = 0.076).The participants were asked about sexual encounters on electronic devices, which included phone sex , cybersex , sexting , sending and receiving sex images (both requested and unrequested) and intentionally seeking sexual encounters on dating apps. Out of the 1392 participants who responded, 273 (50.5%) of the ADHD and 363 (42.7%) of the Other participants claimed one or more of the eight items. The difference was significant ; cybersex ; sexting ; and intentionally seeking sexual encounters on dating apps .Significant differences between the two groups emerged. ADHD participants had a significantly greater number of exchanges than the Other participants: for phone sex ’. The participants were asked ’Have you ever engaged in the following consensual sexual activities ?’. n = 907, p < 0.001) and males . The corresponding correlations for ADHD and Other participants were 0.695 and 0.619 (p < 0.001), respectively. The size of the correlation was significantly larger  for females with ADHD  than for Other female participants . In contrast, the correlations for males with ADHD  compared to the Other male participants  were very similar. The findings suggest a stronger association between the sexual interest and practice scores among females than males with ADHD. This suggests that females with ADHD are relatively more likely than males with ADHD to practice what they would like to do in terms of adventurous sexual activities.There was a significant linear relationship between the total mean sexual interest and practice scores both for females  claimed one or more clinical condition, which exceeds that of worldwide general population . We deliConsistently with previous studies , there wOf the ADHD-diagnosed group, over half reported being currently prescribed ADHD medication and taking it regularly. A further 13.1% reported being prescribed ADHD medication but not taking it daily. Regular use of ADHD medication was not associated with the severity of ADHD symptoms. The most likely reason for this is that medication moderates symptom severity .Consistently with the findings of Hertz et al. , compareSimilar findings have been reported for people with ASD, which also included non-binary participants . In the The current findings corroborate another online survey that reported females with ADHD engaged in consensual sexual intercourse at a younger age, yet there was no significant difference for males . In bothn = 17,898) with age and sex-matched controls . They found 30% and 41% reductions in STIs with short-term and long-term use of medication, respectively [n = 7500) and controls (n = 3020), Hua and colleagues [In line with previous research, the current survey found that more of the ADHD than Other participants had engaged in risky behavior. The former had a greater number of sexual partners; were more likely to participate in infidelity and unsafe sex without use of contraception; and had more sexual infections ,8,9,10. ectively . Applyinlleagues  found thWith respect to sexual interests and practices, we found a close overall linear relationship between the two, accounting for between one third and half of the joint variance. Interestingly, the strongest relationship was among females with ADHD, accounting for 51.1% of the shared variance, in contrast to 34.0% of females without an ADHD diagnosis. Consistent with this finding, the structure matrix loadings in the discriminant function analysis between ADHD and Other participants showed a similar pattern across interests and practices to that found among the male participants. Despite the robust sex difference, both female and male ADHD participants reported significantly more adventurous sexual interests and sexual practices than the Other participants. Females reported to have experienced their sexual interests . By contrast, males reported specific interests but were less likely to engage in them. This suggests that females with ADHD are more likely to act out their sexual interests than males. This may reflect that females are more likely to act impulsively on interests and ideations; they may engage in promiscuous behavior in an attempt to gain friendships and/or social capital and in personal relationships may comply with requests that they may normally reject . PsychoeCompared with Other participants, there was a significantly lower association between sexual satisfaction and relationship satisfaction among the ADHD participants, and lower sexual and relationship satisfaction overall. Bijlenga et al.  reportedWe enquired about the motivations for infidelity for participants who had admitted to purposefully engaging in sexual activities outside of their current or past romantic relationships. The most common reasons were poor impulse-control, alcohol disinhibition, sensation-seeking behaviors and feeling misunderstood (medium effect size). These four motivational factors are best described as increased ‘risk factors’ for infidelity  among people with ADHD. Whilst poor impulse-control is a key diagnostic feature of ADHD symptomatology , alcoholThe main strengths of the current survey were the large samples size, the large number of female participants and the focus on the relationship between current sexual satisfaction and relationship satisfaction after controlling for the effects of age, natal sex and severity of ADHD symptoms.The main limitation is the self-reported nature of the information provided, which could not be corroborated, including the diagnostic data and the selective nature of the participants. In addition, surveys, whether administered in written form or online, lack the flexibility and potential depth of information that can be elicited from face-to-face interviews. In future research, it might be helpful to ask about other relevant mental health conditions, such as eating and sleeping disorders, and prescribed medication, apart from those specific to ADHD. In addition, the current study was cross-sectional, testing participants only at one time-point and just following the international COVID-19 pandemic and the serious restrictions on social opportunities and behavior. Our survey specifically addressed this by stating, ‘Some of the questions ask about behavior “in a typical month”, these questions refer to experiences that occurred before the COVID-19 pandemic when social distancing measures were not in force.’ However, we acknowledge that this may have affected memory recall.It is unclear whether the specific female difference found in the current and Hertz et al.  studies Another limitation is the general measures of relationship satisfaction and sexual satisfaction, which only provide an overall level of satisfaction rather than the range of different components that are likely to reflect the overall scores. For example, we did not focus specifically on sexual dysfunction, which has been found to be more prevalent among people with ADHD diagnosis and symptomatology ,10.The findings suggest that both sexes engage in risky sexual behaviors. However, the risk appears to be substantially greater for females with ADHD. Thus, these findings underscore the need for greater recognition and support for females who present with ADHD symptoms clinically. Using a harm-reduction model, early assessment and intervention of ADHD symptoms may extenuate the noted risks associated with impulse control and psychosexuality. Protective factors such as the education system may also play an important role through regular psychometric screening of ADHD symptoms in youth. As with all early recognition programs, the process that follows must allow for accessible psychological evaluations and responsive treatment by qualified health professionals. The development of an interdisciplinary harm reduction program may also benefit youth with ADHD symptoms through tiered interventions and psychosocial peer support.In likeness to our recommendation for earlier intervention, similar harm-reduction principles can be applied within the scope of psychosexual health and safety. As poor impulse control is reportedly the most significant factor as to whether ADHD respondents engage in risky sexual behaviors, having readily accessible contraceptives can promote safety and sexual health. What is challenging about managing ADHD is that the problem may not occur due to a lack of self-discipline, but rather, as a means to use executive functioning abilities. Individuals with ADHD who have adequate self-awareness and self-regulation skills will still struggle to overcome executive functioning deficits compared to their neurotypical peers. For this reason, it may be helpful to think of ADHD as a disorder of performance; one may have the knowledge and skills to govern themselves, but a limited capacity to follow through consistently. It is imperative in a clinical setting to engage patients in an ongoing dialogue; including sexuality and sexual activity in an open manner may help to reduce the risk of negative outcomes.


Over the last fifty years extraordinary scientific progress has been attention-deficit/hyperactivity disorder (ADHD) science on a number of levels. First, finding the best way to characterize the condition. Second, understanding of why some are affected by the condition and others in terms of originating environmental and genetic risk, underlying mediating biological and cognitive processes and moderating environments. Third, the awareness of the breadth of ADHD's impact on people's lives   of the ADHD child is the site of the dysfunction that causes ADHD? However, it's become increasingly clear from the start of the 21st century that this is a profound mischaracterisation of ADHD. Furthermore, the question which derives from it is a scientific form of a “red herring” , and brain markers derived from functional structural imaging techniques . In addition to such markers from “within” the individual, they examined variations in environmental exposures to social privation, nicotine, and alcohol use during pregnancy and early maltreatment. While emphasizing that intervention biomarker studies are in their infancy, the way the authors highlight this approach's potential to facilitate better treatment will motivate the field to make a significant investment in biomarkers research.In the first paper in the special issue, Haavik discusses how variations in an individual's genome could, in the future, guide drug therapy. This proposal was built on a review of recent developments in ADHD genetics. While recognizing that the large-scale routine implementation of these approaches remains a considerable way off, he argues for a gradual strategy starting with rare ADHD syndromes with highly penetrant risk genes and well-defined neurometabolic aberrations. For example, he cited the work of Fernandez-Castillo that showed the deficiency of L-2- hydroxyglutarate dehydrogenase (L2HGDH), X-linked creatine transporter deficiency, and HUWE1 mutations presenting ADHD symptoms. Similarly, recently a group of researchers found a single homozygous locus on chromosome 18 affecting N-cadherin function shared by affected individuals in Bedouin pedigree in the south of Israel, diagnosed with ADHD according to the DSM-5 and display severe hyperactive behavior  and associated impairment variations . It has been argued that the patterns of co-occurring difficulties, rather than the core symptoms themselves, create much of the impairment and distress associated with ADHD . The authors describe the range of medications used in a large sample of children with ADHD and/or ASD. Children with ADHD and autism combined had higher rates of anxiety, depression, and ODD than those with just autism or ADHD alone. ADHD or the ADHD + ASD groups took more psychotropic medications than the ASD group. While the ADHD with co-morbid ASD reported the highest rate of non-stimulants, antipsychotics, antidepressants. The second paper in this sub-section, by Yin et al., focused on a feature that occurs so commonly with ADHD that some have called for it to be considered part of the broader ADHD phenotype—sleep disorders. In a convenience sample of 100 unmedicated children the authors looked at the relationship between ADHD and sleep problems as rated by questionnaires. Like previous studies, the authors found a robust link between these sets of difficulties and a strong linear relationship between the severity of ADHD symptoms and sleep problems. In a manner worthy of appreciation, the researchers attached the raw data as a supplementary data. This study reminds us that we should consider individual differences in sleep problems when developing treatment plans.The first paper in the Special Issue to address this issue of the treatment of co-occurring conditions is by Ganjeh et al.) takes a novel approach focusing on developing adolescent mental health problems. It is a large naturalistic longitudinal population-based cohort study looking for the relationship between adolescents' mental health disorders and earlier childhood physical activity. It poses the question of whether physical activity can be protective in relation to later mental health problems. The findings were counterintuitive. Childhood physical activity levels were related to lower levels of ADHD symptoms later on in life during adolescence, extending to a more general pattern of mental health outcomes. The authors argue that giving children the opportunity for physical activity could be valuable clinically as an adjunct to other interventions. We would encourage the authors to explore this hypothesis systematically eventually in RCTs. The final study by Korfmacher et al., compared a form of self-management training against neurofeedback in people with ADHD. They found that both treatment options improved core ADHD symptoms. However, only the self-management treatment resulted in self-concept and quality of life improvements. Thus, they emphasized the added value of self-management training over neurofeedback in the treatment of ADHD.The third study in this sub-section (In this special issue, we bring two under-researched aspects of ADHD translational science together—the need for more precise targeting of specific features and characteristics and, at the same time the need to broaden the focus away from core symptoms to other aspects of functioning. These separate ambitions should jointly inform the next steps of treatment development in ADHD.ES-B and SZ wrote the final version. All authors contributed to the writing of the manuscript. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with increased risk for poor educational attainment and compromised social integration. Currently, clinical diagnosis rarely occurs before school-age, despite behavioral signs of ADHD in very early childhood. There is no known brain biomarker for ADHD risk in children ages 2–3 years-old.The current study aimed to investigate the functional connectivity (FC) associated with ADHD risk in 70 children aged 2.5 and 3.5 years via functional near-infrared spectroscopy (fNIRS) in bilateral frontal and parietal cortices; regions involved in attentional and goal-directed cognition. Children were instructed to passively watch videos for approximately 5 min. Risk for ADHD in each child was assessed via maternal symptoms of ADHD, and brain data was evaluated for FC.Higher risk for maternal ADHD was associated with lower FC in a left-sided parieto-frontal network. Further, the interaction between sex and risk for ADHD was significant, where FC reduction in a widespread bilateral parieto-frontal network was associated with higher risk in male, but not female, participants.These findings suggest functional organization differences in the parietal–frontal network in toddlers at risk for ADHD; potentially advancing the understanding of the neural mechanisms underlying the development of ADHD. Attention deficit hyperactivity disorder (ADHD) is a common disorder affecting 7–11% of US children  and affeA wide variety of treatments for ADHD exist, though the long-term efficacy of these treatments is limited. 40–50% of individuals diagnosed with ADHD in childhood continue to meet criteria for ADHD and require treatment in early adulthood , 13. MorOne potential avenue for furthering our understanding of ADHD’s ontogeny is to examine resting-state functional connectivity (FC). FC is the temporal correlation of spatially distant neurophysiological events . A numbFurther, there are notable sex differences in ADHD with respect to prevalence rates , 24 and Because of methodological constraints, little is known of the neural ontogeny of ADHD. Common task-based neuroimaging methods used on adults ) are often challenging for children under 5 years old, primarily due to very young children moving too much to yield good data , 29–31. In this context, the current study aimed to investigate the neural profile associated with risk factors for developing ADHD at the age of 2–3 years-old. fNIRS was used to assess FC between lateral frontal and parietal cortices during passive viewing of video clips. Risk for ADHD was determined via maternal adult symptoms of ADHD, because previous research has shown that maternal ADHD is more predictive of childhood ADHD symptomology than paternal ADHD . We hypoN = 37, female = 21) and 3.5-years-old  were recruited. All participants’ age fell within ±6 weeks of the target ages of 30 (2.5 years) or 42 (3.5 years) months. The current sample size is justifiable based on previous work with functional connectivity, fNIRS, and very young children .The ADHD self-report screening scale (ASRS v1.1) , 39 was Parents also completed a short temperament questionnaire specifically designed for children aged 1–3 years old (Early Childhood Behavior Questionnaire [ECBQ] – Short Form ). TemperN = 23) and 54 cm (N = 47) hat to account for head size differences across these two age groups. Among the children that used the 52 cm hat, 12 were 2.5 year old and 11 were 3.5 year old. Participants were placed between 63.5 and 65 cm from a computer monitor. Videos played on a 530 mm × 330 mm monitor with a resolution of 1280 × 960 pixels.The experimental data were recorded using a continuous NIRS imaging system  at a sampling rate of 25 Hz. The data were measured simultaneously at two wavelengths (690 nm and 830 nm). The system automatically adjusted light intensity to provide optimal gain. The optodes were placed over the dorsolateral prefrontal and parietal cortex . The disA session required two trained researchers: one to administer the passive watching paradigm and one to run the fNIRS machine. A blackout curtain was then positioned to cover half of the opening between the parental seating area and the child testing area to reduce artifact due to lighting. One researcher was stationed behind an occluder, where the fNIRS acquisition computer and machine were located behind the child. The other researcher sat off to the right side of the child, outside of their peripheral vision, to redirect them to the screen if they looked away and to provide a whispered reminder to “be quiet and calm” and “watch the video”.We used a passive watching paradigm because it has been suggested that passively viewing a video is roughly analogous to rest in young children . In the No child received all video clips. A white central fixation was presented on a black screen between videos, and recordings from this time were not included in the concatenated data analyses. This task resulted in an accumulated recording of 4 min for each participant while viewing sessions lasting between 270 and 450 s. Given that an intertrial central fixation was used to reorient children to the screen and transition to the next video segment, some individual variability in the overall recording session duration did occur. While using this passive viewing paradigm, it is important to note that collecting resting-state measures in very young children has been challenging .2  and HbR  concentration changes using the modified Beer–Lambert law  nor frequency of sex . Information about the number of parents in the household , highest level of parental education, household income per year, number of siblings in the home, and childcare currently being utilized for the participant during the day is provided in The two groups of children of 2.5 and 3.5 years of age did not significantly differ in their ADHD risk scores . Parents reported less fear and greater inhibitory control in older children compared to younger children. However, these differences were not retained after applying a Bonferroni correction for multiple comparisons . Rosch However, it is important to note that the sex-by-ADHD risk interaction findings reported in the current study are not inconsistent with previous studies (see ). A receWhile not a main question of the current study, the main effect of sex was also investigated given the report of sex differences in FC using resting-state functional MRI data in typically developing children . In a saThe current data should be considered in light of some limitations. First, our sample size is modest. However, given the importance of early development and the difficulty in obtaining neuroimaging data in very young children, even a modest sample is informative. Second, the current sample is predominantly homogenous along demographics dimensions , which potentially reduces the generalizability of the findings. Such limited variability reduces the likelihood that the current findings can be attributed to demographic variability; however, the role of demographic variables will need to be examined explicitly in future research.Third, the current study did not comprehensively or directly assess the risk for ADHD and therefore, the current findings should be interpreted cautiously. Maternal ADHD symptoms were selected as a risk proxy as they have been previously used as a contributing factor to childhood ADHD symptomology . AdditioLast, the cross-sectional nature of the study is a limitation. Not all children in our risk group will go on to get a diagnosis of ADHD e.g., , 61). De. De61]).Overall, ADHD risk in the current study is associated with FC changes in male toddlers. The data suggest that male children, but not female children, as young as 2.5 years-old may show altered FC profiles in the attentional network in association with ADHD risk. These data suggest the use of fNIRS in very young children may be useful in providing information about early risk for psychopathology. Neuroimaging work in very young children can and should inform future work on developmental trajectories to psychiatric diagnoses, including ADHD, clinical assessment, and intervention.


Attention deficit/hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders in childhood. In Switzerland, the complex diagnosis and treatment are being carried out by adolescent−/child psychiatrists, and pediatricians. Guidelines recommend a multimodal therapy for patients with ADHD. However, it has been questioned whether health professionals follow this approach or favor drug therapy. This study aims to provide insights into the practice of pediatricians in Switzerland regarding diagnosis and treatment of ADHD and their perceptions of these processes.An online survey (self-report) about current practices of diagnosis and management as well as challenges regarding ADHD was distributed to office-based pediatricians in Switzerland. One hundred fifty-one pediatricians participated. Results show that therapy options were almost always discussed with parents and older children. Exchange with parents (81%) and level of child’s suffering (97%) were central when selecting therapy options.Therapies about which pediatricians informed most often were: pharmacological therapy, psychotherapy, and multimodal therapy. Challenges voiced were the subjectivity of diagnostic criteria and dependence on third parties, low availability of psychotherapy, and a rather negative public attitude towards ADHD. Needs that were expressed were further education for all professionals, support for coordination with specialists and schools as well as improvement of information on ADHD.Pediatricians do consider a multimodal approach when treating ADHD and take the families` and children’s opinions into account. Improvements of the availability of child and youth psychotherapy, the strengthening of the interprofessional cooperation with therapists and schools, and efforts to increase public knowledge about ADHD are proposed. Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood and youth with a worldwide prevalence between 5 and 8% , 2. ADHDIn practice, the diagnosis and management of ADHD is complex and involves various difficulties. First, there is no single, specific etiology that is able to explain the emergence of ADHD, and second, no biomarkers exist that can help to clearly establish the diagnosis , 11. HenAccording to international guidelines, the diagnosis should be based on a comprehensive and structured exploration of the patient and his/her caregivers, including possibly other reference persons  in order to assess the frequency and extent of the symptoms and associated impairments in different situations and areas of life as well as comorbid conditions. Questionnaires and psychological tests can assist in establishing a diagnosis in the context of a professional evaluation, which should furthermore be based on a thorough medical history of the child and at least one reference person , as well as an assessment of the child including observations and psychological tests .Previous studies examined the diagnostic process of ADHD in children and reported that clinicians perceive the diagnostic assessment of ADHD as challenging , 17, 19.Regarding primary care, concerns about the quality of ADHD diagnoses have been raised due to various barriers, such as constraints with diagnosis and treatment, the time-consuming interprofessional work with other specialists, teachers, school social workers and parents, the lack of reimbursement, misconceptions of ADHD among physicians, limited use of diagnostic instruments and consideration of comorbid conditions . However.For the treatment of patients diagnosed with ADHD, the guidelines  recommen who received an MPH treatment within 1 year increased from around 1.5 to 2.4% from 2006 to 2010 [Despite the fact that a multimodal therapy approach for ADHD is considered the “gold standard”, it is unclear to what extent this approach is followed in daily routine. Raising numbers of prescribed pharmacological treatments for ADHD –23 have  to 2010 . Analyse to 2010  from 200 to 2010  from 200 to 2010 . A study to 2010 .Although various studies have described an increase in drug therapies for ADHD compared to other therapeutic measures, reasons for the increase are not entirely clear. Research in social science has indicated that the prevalence of ADHD diagnosis and medical treatment has been influenced by various social factors, such as performance pressure in the school environment , gender How do pediatricians structure the diagnostic procedure of ADHD ?How do pediatricians approach ADHD treatment and more specifically: do pediatricians apply the recommended multimodal approach?What challenges do pediatricians experience when diagnosing and treating ADHS and what suggestions do they have to improve the management of ADHD?Building on these recommendations and studies on the practice of ADHD diagnosis and treatment, our study aims to examine Swiss pediatricians’ current practice as well as their perspectives on perceived barriers and facilitators to follow guidelines. Due to the limited number of child and adolescent psychiatrists, pediatricians play a central role in managing ADHD in Switzerland. We therefore aimed at investigating Swiss pediatricians` attitudes and practices regarding ADHD management. The specific study questions wereThis first national study of ADHD practices presents comprehensive data regarding the management of children and adolescents with ADHD in pediatric settings in Switzerland.n = 1620, hospital and office-based pediatricians, about 80% of pediatricians of Switzerland) to an online survey . Other specializations were (child and adolescent) psychiatry and psychotherapy (n = 22) and others or unknown specialization (n = 7). For the purpose of this study, only the results of the pediatricians are presented in the following. Analyses consist of frequency distributions and Chi-Square tests.Of the 1620 pediatricians who were invited, the starting page of the online questionnaire was visited by 200 participants, out of whom 49 (24.50%) aborted the questionnaire after the first seven questions (16% of the questionnaire) and were excluded from the analyses. Thus, the analysis is based on 151 questionnaires , which corresponds to a response rate of 9.3%. Of the 151 questionnaires, 112 participants (74%) completed the German version, 35 participants (23%) the French version and four participants the Italian version (3%). The total of 151 participants consisted of 88 women (58%) and 59 men (39%) as well as four participants with unknown gender (3%). On average, participants had 17 years of work experience (range 4 to 35 years). The majority of pediatricians reported working in private practice (54.1%), followed by collective practice (27%), cantonal hospital (8.2%), university hospital (4.9%), and others (5.8%). Most of the participants were pediatricians  and the answers to the question how often information is provided about specific forms of therapy was converted to a dichotomous variable  versus .M = 1.77, SD = 0.87, range = 5–1) and teachers/school  were rated the most often, while school psychological service  and the child itself  were rated less often  consultations to establish or rule out the diagnosis with parents and teachers; thereof on average of two  with the child, when they generally perform the diagnostic workup themselves. The question about the use of ADHD screening instruments for diagnosis (see Table Pediatricians reported to plan about an average of three (p = .057), but women tended to give more frequent information on family therapy than men (p = .012).With regard to the question, what kind of information pediatricians provide to the families when ADHD has been diagnosed in their child, both general information on ADHD and specific information  about coaching possibilities, patient organizations, drug treatment and occupational therapy options were reported to be provided by more than 50% of the pediatricians (see Table p = .177) nor to sex (p = .148).When asked how often they inform about different therapy options, the majority (70–90%) reported to always or often inform about pharmacological therapy, psychotherapy, and multimodal therapy , in total, 220 answers were given. Pediatricians most frequently pointed out difficulties in establishing a diagnosis (n = 44), including the lack of clarity and completeness of the criteria, variability of symptoms depending on the child, making the right diagnosis, danger of overlooking co-morbidities, assessment of the disposition - environmental influences. Also, very frequent (n = 37), the acceptance by the family, involvement of the family, sometimes difficult family constellations or differing opinions in the family regarding treatment were mentioned as challenges. Similarly often were mentioned, the cooperation and involvement of school and teachers (n = 17), negative attitudes towards ADHD and explicit negative reactions, for example about the use of ADHD-specific drugs (n = 16), do justice to the child (n = 15), difficulties in multidisciplinary  (n = 12) and good, close supervision during the therapy (n = 11), including individual adaptation of the therapy to the child, or finding the right therapy for the individual child and assessment of side effects compared to the benefits.In the following, the answers to questions with an open-ended response format are presented categorized into thematic groups. The question “In your opinion, what are the three biggest challenges in the diagnosis and treatment of ADHD?” , in total, 205 answers were given.The question “Current guidelines provide for multimodal therapy. In your view, what are the three biggest challenges in implementing the multimodal therapy concept?” (see Table n = 57), such as the lack of cooperation and coordination between the professionals involved and the families. Further, the very time-consuming organization, the question of the lead of the organization and the lack of communication between the actors was mentioned. Regarding structural problems, n = 9 of the participating pediatricians pointed out the lack of reimbursement options. Frequently, pediatricians pointed out low availability of specialists and therapy places (n = 33), particularly the low availability of child and adolescent psychiatrists. In addition to structural/organizational reasons and the lack of professionals, reasons related to the family were often mentioned (n = 77). In the context of children and families, their therapeutic adherence and compliance (n = 48) was frequently reported as a therapeutic challenge, further the high demand on time and the general lack of capacity on the part of the families (n = 16) and lack of motivation (n = 5) was mentioned. Some pointed out lack of support and implementation of the concept in the school (n = 9) and finally bad or wrong information about the treatment concept, lack of insight and prejudice against medication (n = 6) was reported.Various reasons were put forward as challenges in the implementation of a multimodal therapy concept: Structural problems were mentioned most frequently . Pediatricians expressed a need for further training in diagnosing ADHD (n = 22) and better support in terms of coordination and cooperation with other specialists (n = 7), in particular with teachers and school social workers. More coaching and further training for school professionals was also proposed (n = 6), but also the creation of an exchange platform in the sense of a peer consulting  (n = 3). Finally, stronger efforts were proposed to improve information on ADHD and its treatment in treatment settings (n = 5)  as well as in public .The question “In which areas would you like more support/further training?”  and improvements of reimbursement consequently was frequently stated by our sample and in the literature , 35, 39.This first national study on ADHD management presents comprehensive data regarding the management of children and adolescents with ADHD in pediatric outpatient settings in Switzerland. Due to the low response rate, the results should be interpreted with caution, as selection effects may have influenced the results. We most probably reached highly motivated professionals, and pediatricians who treat ADHD in their practice, which already is a selected group. Additionally, all results are based on self-reported data. A non-participation analysis for assessing the reasons of renouncing participation in the survey could not be performed. However, as pediatricians are known to have limited time and resources available, a low response rate was expected.Most pediatricians not only invest adequate time in a careful diagnosis of ADHD, but they also follow current diagnostic guidelines by considering a multi-modal approach when treating ADHD and show a high involvement of the family and the child in the choice of therapy options. However, they highlight further potential in the treatment of ADHD by improving the availability of psychotherapy, the cooperation with other specialists, such as teachers and school social workers, and public information on ADHD in Switzerland. Furthermore, better reimbursement of therapies and efforts in the context of interprofessional cooperation with therapists and schools were proposed. Various efforts directed at physicians involved in the care for ADHD patients, therapists, politicians, schools, and families emerged to be relevant 1.) to inseminate sufficient know-how about diagnostics and treatment to primary care providers, 2.) to install adequate reimbursement tools for covering interprofessional work for patients with ADHD, 3.) to improve the availability of non-pharmaceutical therapies and support for ADHD in order to support a sustainable adherence with the recommended guidelines.


Further, ADHD traits and the volumetric correlates  were each negatively and positively correlated with N-back performance. Together, these findings confirm volumetric deficits in children with more prominent ADHD traits. Highly heritable in both girls and boys and potentially more significant in boys than in girls, the structural deficits underlie diminished capacity in working memory and potentially other cognitive deficits in ADHD.Previous research has demonstrated reduction in cortical and subcortical, including basal ganglia (BG), gray matter volumes (GMV) in individuals with attention deficit hyperactivity disorder (ADHD), a neurodevelopmental condition that is more prevalent in males than in females. However, the volumetric deficits vary across studies. Whether volumetric reductions are more significant in males than females; to what extent these neural markers are heritable and relate to cognitive dysfunction in ADHD remain unclear. To address these questions, we followed published routines and performed voxel-based morphometry analysis of a data set  curated from the Adolescent Brain Cognition Development project, a population-based study of typically developing children. Of the sample, 634 and 2,826 were identified as monozygotic twins and dizygotic twins/siblings, respectively. In linear regressions, a cluster in the hypothalamus showed larger GMV, and bilateral caudate and putamen, lateral orbitofrontal and occipital cortex showed smaller GMVs, in correlation with higher ADHD scores in girls and boys combined. When examined separately, boys relative to girls showed more widespread (including BG) and stronger associations between GMV deficits and ADHD scores. ADHD traits and the volumetric correlates demonstrated heritability estimates ( ADHD is associated with inattention, impulsivity, hyperactivity, and an array of cognitive deficits with varying degrees of severity5. For instance, as compared to healthy controls, individuals with ADHD showed lower capacity of working memory9, a key cognitive function that is closely associated with fluid intelligence12. Extensive research has focused on identifying the neural markers of ADHD, and many studies demonstrated gray matter volume (GMV) reduction in cortical and subcortical structures, including the basal ganglia (BG), in adults and children with ADHD14.Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, with a prevalence rate of approximately 7% in children and 4.4% in adults15. Other studies identified GMV reduction more specifically in the BG17, and the volumetric deficits appeared to normalize with age and treatment with stimulant medications17. A meta-analysis of 11 studies comprising 320 ADHD cases and 288 controls showed GMV reduction in the right globus pallidus and putamen, as well as bilateral caudate in children with ADHD, and GMV reduction in the anterior cingulate cortex (ACC) in adults with ADHD18. A higher percentage of treated relative to non-treated participants were associated with fewer deficits, suggesting a remediating effect of the medications. The ENIGMA ADHD Working Group reported smaller volumes of the accumbens, amygdala, caudate, putamen, and hippocampus in ADHD cases at 4–63 years of age, relative to controls19. In particular, the effect sizes were highest in children, whereas case–control differences were not present in adults. Thus, it appears that regional volume reduction is more prominent in children than in adults with ADHD and neural development and/or chronic treatment with medications may account for the differences.A number of meta-analyses aimed to investigate the consistency of the volumetric reductions in ADHD. ADHD relative to typically developing children showed global volumetric reductions, with the cerebellum, splenium of the corpus callosum, and right caudate showing the most significant differences29 with ADHD but did not necessarily implicate the BG. To confirm BG volumetric reduction as a neural marker of ADHD would require the investigation of a large sample of children within a limited age range with the absence of the effects of prolonged medications.On the other hand, many other studies have described smaller volumes in the frontal cortex, temporal cortex, insula, and cerebellum in children or adults31. Earlier studies have suggested that boys with ADHD showed lower GMVs in the ACC and BG, whereas girls with ADHD exhibited higher GMV in the ACC and no difference in the BG, as compared to typically developing children34. Therefore, it would be critical to consider sex differences in examining volumetric reduction in the BG and other cortical and subcortical structures as neural markers of ADHD.Previous work has also studied sex differences in ADHD traits and GMVs in association with ADHD. ADHD is more commonly observed in boys than girls35, with 60–80% of variance in ADHD risk attributed to genetic effects41. Previous work has also suggested the heritability of the volumetric markers of ADHD. Alterations in total and BG GMV have been observed not only in individuals with ADHD but also in their unaffected siblings and relatives, suggesting genetic and shared environmental risks43. Individuals with ADHD showed reduction in total and regional cortical GMVs, and their unaffected siblings showed intermediate GMVs between individuals with ADHD and healthy controls44. Indeed, recent studies have aimed specifically to characterize the molecular genetic bases of the volumetric features of ADHD48. Studies have reported evidence of global pleiotropy for variants affecting ADHD risk for total intracranial and subcortical regional volumes49. Another ENIGMA study also showed shared genetic heritability with brain structural correlates but did not report the exact heritability estimates of ADHD or of the structural correlates50.ADHD is highly heritable51, suggesting unique environmental influences. Further, genetic and environmental risk factors may contribute to distinct regional volume losses52, and the sex differences in ADHD symptoms may result from the different genetic and environmental influences on brain structures53.In addition, affected twins showed significantly smaller caudate than their unaffected co-twins54, and to inform longitudinal research of later releases of the ABCD data.The present study aimed to examine sex- shared and specific volumetric markers of ADHD traits for the whole brain. Because of the focus of previous studies on the BG, we also performed region-of-interest (ROI) analyses specifically of the caudate, putamen, and pallidum. Using a large data set of children (9–10 years) of the Adolescent Brain Cognition Development (ABCD) study, we employed voxel-based morphometry to identify GMV correlates of ADHD in girls and boys combined as well as separately. We evaluated how the volumetric deficits contribute to cognitive dysfunction, as evaluated in an N-back task, and assessed sex differences in the relationships between volumetric and cognitive deficits. Finally, we estimated the heritability of the neural and behavioral markers and examined sex differences in the heritability estimates. The overall goal was to characterize structural brain alterations during an early and prodromal stage of ADHD, as with many of the ENIGMA studies of ADHDVoxel-based morphometry). Thus, the current sample consisted of 11,502 subjects . Of the 11,502 children, 634 and 2,826 were identified as monozygotic twins (MZ) and dizygotic twins/siblings (DZ), respectively. The ABCD data were collected from 21 research sites across the country. Children have been participating at baseline and follow-up assessments, which will continue over a period of 10 years. The consortium workgroups established standardized assessments of physical and mental health, neurocognition, substance use, culture, and environment, as well as multimodal structural and functional brain imaging and bioassay protocols. Structural magnetic resonance imaging (MRI) data were acquired using an optimized protocol for 3 T machines, including Siemens Prisma, GE 750 and Philips, with voxel size 1 mm isotropic55.The ABCD Release 2.0 cohort comprised 11,601 children; however, 99 subjects were not included in the current study because of questionable image quality or poor image segmentation  and normed by sex, age, informant, and ethnicity59. Seven ADHD items in the CBCL DSM-oriented scale were used in the ABCD study: #cbcl_q04_p - Fails to finish things they start; #cbcl_q08_p - Can't concentrate, and can't pay attention for long; #cbcl_q10_p - Can't sit still, restless, or hyperactive; #cbcl_q41_p - Impulsive or acts without thinking; #cbcl_q78_p - Inattentive or easily distracted; #cbcl_q93_p - Talks too much; and #cbcl_q104_p - Unusually loud. Each item’s response was rated on a scale of 0 = Not True, 1 = Somewhat or Sometimes True, and 2 = Very True or Often True. The sum of raw scores was transformed to normalized T score (range 50–70) based on the percentiles of national normative sample of nonreferred populations60. Investigators have used different CBCL cutoff scores for a diagnosis of ADHD58. Here, for instance, with a cutoff of 65 (ADHD T score), 451 (7.5%) boys and 272 (5%) girls would be considered to have a diagnosis of ADHD. With a cutoff of 70 or the 98th percentile of national normative samples,193 (3.2%) boys and 75 (1.4%) girls would be considered to have a diagnosis of ADHD.The children were assessed with the ABCD Parent Child Behavior Checklist (CBCL) from the Achenbach System of Empirically Based Assessment61. Briefly, there were four blocks each with 2- and 0- back conditions in each run, with a total of 2 runs. Participants were required to respond to a set of stimuli of emotional faces or places. Each block consisted of 10 trials (2.5 s each) and 4 fixation trials (15 s each). At each trial, participants were instructed to respond to whether the picture was a “match” or “no-match” of a pre-specified target (0-back) or the stimulus shown two trials back (2-back). Here, we employed the accuracy rate of 2-back trials as a measure of the capacity of working memory.Participants performed an N-Back task62, as well as an average T1 anatomical template for visualization63. A cohort of 1000 children (500 girls) was selected from the ABCD dataset according to the following procedure. We generated 10,000 random samples with 1000 children  and selected the one with age and scan site distributions closest to those of the entire cohort. We used SPM Segment to generate the individual’s tissue maps and the TOM8 Toolbox (http://dbm.neuro.uni-jena.de/software/tom) to create the population TPMs and T1 anatomical template, controlling for the effects of age and sex64. DARTEL templates were constructed using utilities available in SPM. This involved creating gray (rp1) and white (rp2) matter segments after affine registration followed by DARTEL nonlinear image registration, whereby all selected images were iteratively aligned with a template generated from their own mean, and finally normalized to the MNI space (ICBM template).In order to perform voxel-based morphometry with appropriate templates, we constructed customized tissue probability maps (TPMs) and DARTEL templateshttp://dbm.neuro.uni-jena.de/vbm/). The details of VBM analysis have been described in our previous studies66. The VBM analysis identifies differences in the local composition of brain tissues, accounting for large-scale variation in gross anatomy and location. The analysis includes spatially normalizing individuals’ structural images to the same stereotactic space, segmenting the normalized images into distinct brain tissues, and smoothing the gray matter images. We used the raw images to avoid potential interference with the CAT12 preprocessing pipeline. T1-images were first co-registered to the MNI template using a multiple-stage affine transformation during which the 12 parameters were estimated. Co-registration was performed with a coarse affine registration using mean square differences, followed by a fine affine registration using mutual information. Coefficients of the basis functions that minimized the residual squared difference between the individual image and the template were estimated. Our custom TPMs constructed from 1,000 ABCD children were used in the initial affine transformation. T1 images were then preprocessed with spatial-adaptive non-local means denoising filters67 as well as Markov random fields, corrected for intensity bias field and segmented into cerebrospinal fluid, gray, and white matter68. Segmented and the initially registered tissue class maps were normalized using DARTEL62, a fast diffeomorphic image registration algorithm of SPM. As a high-dimensional non-linear spatial normalization method, DARTEL generates mathematically consistent inverse spatial transformations. We used our custom DARTEL template in MNI space, as constructed from 1,000 ABCD children, to drive DARTEL normalization. Skull-stripping and final clean-up were performed with default parameters in the CAT12 to remove remaining meninges and correct for volume effects in some regions. In particular, skull-stripping was performed by refining the probability maps of SPM using adaptive probability region-growing, and the final clean-up routine consisted of morphological, distance and smoothing operations after the final segmentation. Normalized GM maps were modulated to obtain the absolute volume of GM tissue corrected for individual brain sizes. Finally, the GM maps were smoothed by convolving with an isotropic Gaussian kernel (FWHM = 8 mm).We implemented the VBM analysis to quantify regional GMVs with the CAT12 toolbox  per subject was plotted and visually checked (option “Display slices”), and outliers were detected by computing the voxel-wise cross-correlation of GM density across subjects (option “Check sample homogeneity”). A total of 47 subjects presented clearly faulty segmentation of brain tissues and were removed from the group analyses. The faulty segmentation likely resulted from poor contrast or artifact of the structural images or abnormal brain shapes. Additionally, for each subject, pairwise correlations were computed voxel-wise between the subject's GMV and all the other subjects' GMV. The mean correlation represented how similar the subject's GMV was to the rest of the sample. Fifty-two subjects with a mean correlation < 0.70, suggesting a higher variance, were also removed.Quality check of images was performed visually and quantitatively with tools available in the CAT12 toolboxt tests to sex differences in ADHD scores and 2-back accuracy rates. We computed correlations between 2-back accuracy rates and ADHD scores in girls and boys separately. Slope tests were used to assess sex differences in the correlations.In group analyses, we first compared age (in months) between girls and boys. We used two-sample t test with age (in months), total intracranial volume (TIV), ADHD score, study site, and scanner model as covariates. We performed a whole-brain linear regression against the ADHD score in girls and boys combined, as well as in girls and boys separately, with age, TIV, study site, and scanner model as covariates. We also performed the same whole-brain linear regression analyses without including TIV as a covariate. The results were evaluated with a voxel p < 0.05, corrected for family-wise error (FWE) of multiple comparisons, based on Gaussian random field theory as implemented in the SPM. Clusters were overlaid on the custom MRI template obtained from the 1,000 ABCD children. Effect sizes were computed using tools available in the CAT12, by approximating Cohen’s d70 from the t-statistics using the expression 71. The effect sizes of two-sample t tests were computed according to the equivalence 72. Customized computations were implemented and verified with the equivalent effect size (Hedge’s g) calculated using the MES toolbox (https://github.com/hhentschke/measures-of-effect-size-toolbox)73.For the GMV data, we first examined sex differences in the whole-brain GMV using a two-sample p values74. Note that the analysis did not represent “double-dipping”, as the slope tests may confirm or refute sex differences79. This is because the regression maps were identified with a threshold and for example, a cluster showing correlation in boys could show a correlation that just missed the threshold in girls; thus, slope tests were needed to examine whether the correlations were indeed different between the sexes. Sex differences in the extent to which the GMVs correlated with ADHD scores could also be tested in a linear mixed-effects model with an interaction term. We conducted the analyses in SPSS 26.0, with sex, ADHD score, and sex × ADHD score as fixed effects, twin/sibling status as a random effect variable in the model, after controlling for age, race, study site, scanner model, and TIV.For the ROIs identified from linear regressions in girls or boys alone, we tested sex differences in the correlation directly with slope tests, with the same set of covariates and showed two-tailed A total of 1,052 children received psychotropic medications (some multiple medications) at the time of the study . Children receiving stimulant treatment likely had more significant ADHD symptoms and, as expected, demonstrated significantly higher ADHD scores than unmedicated children see “” Section65. The analyses of the GMVs were done for the clusters combined of those identified with positive and negative correlations, respectively, from all subjects and of the “girl-specific” and “boy-specific” correlates.Of the current sample, 634 and 2,826 were identified as monozygotic twins (MZ) and dizygotic twins/siblings (DZ), respectively. Thus, we examined whether the ADHD scores, as well as the volumetric correlates, were more significantly related in MZ, as compared to DZ and unrelated pairs (UR), and in DZ as compared to UR. To this end, we computed the Pearson’s correlations each for the ADHD scores, 2-back accuracy rates as well as the volumetric correlates for the MZ and DZ pairs. For the UR, pairs of children were randomly constructed by shuffling and splitting the sample into halves. This procedure was repeated 100 times, and the mean regression lines were computed. For the correlations in MZ and DZ pairs, 95% confidence intervals (CI) were also calculated. We performed slope tests, pairwise, to examine the differences between MZ, DZ and UR. The details of correlation analyses have been described in one of our previous studies81 for the ADHD scores and 2-back accuracy rates based on univariate ACE models82, with age, race, and study site as covariates. We also estimated univariate ACE models for the volumetric correlates, with scanner model and TIV as additional covariates. Only same-sex DZ pairs were included for the analyses of girls and boys separately. The ACE model decomposes the observed variance into additive genetic factors (A), also known as heritability, shared environmental factors (C), and unique environmental factors (E), in addition to measurement errors83. The correlation between the additive genetic variance is fixed to 1.0 for MZ and 0.5 for DZ. The correlation between the shared environmental variance is set to 1.0 for both MZ and the DZ based on the equal environment assumption. The correlation between the non-shared or unique environmental variance is set to zero. The expected variance-covariance matrices within the MZ and DZ are as follows:a, c, and e represent the path coefficients for the A, C, and E factors, respectively84. The variance of A, C, and E was estimated using the maximum likelihood method on the variance–covariance matrices and 95% CI of A, C, and E were computed: 95% CI = mean ± 1.96 × standard error, with the assumption that the population standard deviation is a known value. Values of χ2/df, root-mean-square error of approximation (RMSEA), and Tucker-Lewis index (TLI) were used as model fit indices. A χ2/df < 2, an RMSEA < 0.06, or a TLI > 0.95 indicates good fit. Genetic variance (a2) < 0.30, 0.30–0.60, and > 0.60 is considered low, moderate, and high, respectively85. We performed two-sample t tests to compare girls and boys in the genetic variance of the ADHD score, sex-specific volumetric correlates, and 2-back accuracy.For girls and boys combined and separately, we used Mplus 8 to compute the genetic influence (heritability estimates), shared environmental influence, and unique environmental influencet tests to examine sex differences.Because of the focus on BG, we also estimated the ACE models of the caudate, putamen and pallidum GMVs with the AAL masks for girls and boys combined and separately, with the same set of covariates. Likewise, we performed two-sample https://abcdstudy.org/. The ABCD data repository grows and changes over time. The data used in this report  came from http://dx.doi.org/10.15154/1503209. All recruitment procedures and informed consent forms, including consent to share de-identified data, were approved by the Institutional Review Board of the University of California San Diego with the study number 160091. Informed consent from parent and assent from child under 18 years old was obtained from all individuals prior to the study.We have obtained permission from the ABCD to use the Open and Restricted Access data for the current study. All methods were performed in accordance with ABCD Bioethics and Medical Oversight Guidelines and Procedures. The ABCD data is publicly available at T and raw scores of girls and boys are shown in Supplementary Fig. t = 2.20, p = 0.028; two-tailed two-sample t test). Boys showed significantly higher ADHD scores than girls . However, boys and girls showed the same range in ADHD scores (50 to 80) with comparable variability (coefficient of variation: 0.111 for boys and 0.097 for girls).The distributions of ADHD t = 6.13, p < 0.001; two-tailed t test with the same covariates). Two-back accuracy rates were significantly and negatively correlated with ADHD scores in boys  and in girls . A slope test showed that boys and girls did not differ significantly in the correlations .Boys showed a significantly higher 2-back accuracy rate than girls , ADHD score, study site, scanner model, and TIV. As shown in Supplementary Fig. p < 0.05, FWE corrected). Girls relative to boys showed higher GMVs in relatively small clusters in the left caudate head , bilateral inferior frontal gyri  and right intraparietal sulcus . The unthresholded map is shown in Supplementary Fig. We first examined sex differences in GMV with a two-sample p < 0.05, FWE corrected). Overall, in boys and girls combined, ADHD scores were positively correlated with a single cluster in the area of the hypothalamus . The inset of Fig. 87. In contrast, higher ADHD scores were associated with the reduction of GMVs in an extensive array of brain regions, including bilateral caudate and putamen, orbitofrontal cortex (OFC), visual cortex, somatomotor cortex, and temporal cortex. The unthresholded map is shown in Supplementary Fig. We examined regional volumetric correlates of ADHD scores in linear regressions for all subjects and girls and boys separately, all with age (in months), study site, scanner model, and TIV as covariates. Brain regions with GMVs in correlation with ADHD scores are shown in Fig. r’s ranging from 0.066 to 0.190, all p’s < 0.001) in linear regressions with the same set of covariates. None of these correlations showed a significant sex difference in slope. These results are shown in Supplementary Table Except for the hypothalamus, 2-back accuracy rate (%) were positively correlated with the volumetric correlates identified of boys or girls or of all three BG subregions  and parietal cortical regions as well as subcortical structures, such as the thalamus. In contrast, girls relative to boys appeared to demonstrate more significant GMV reductions in association with higher ADHD scores in the bilateral inferior/middle temporal cortex, frontopolar cortex, medial occipital cortex, and the precuneus. No voxels showed GMVs in positive correlations with ADHD scores in girls or boys alone. These findings are shown in Fig. p < 0.05 FWE corrected in Supplementary Fig. As some morphometric studies of ADHD also showed imaging findings without specifically accounting for TIV. Thus, we performed additional analyses without including TIV as a covariate and showed the results evaluated at t = 0.250, p = 0.803)  than in girls  for boys’ joint ROIs, with a significant slope difference  than in girls  for boys’ specific joint ROIs, with a significant slope difference , consistent with the slope tests, but not any other ROIs . Note that none of the results on sex differences would be significant with correction for multiple comparisons. The results are summarized in Supplementary Table Supplementary Table F = 2,164.32; p < 0.001, covariance analysis with age, sex, race, study site, and twin status as covariates]. The GMVs (mean ± SD) of the caudate were 0.398 ± 0.046 and 0.393 ± 0.049, of the putamen were 0.482 ± 0.049 and 0.481 ± 0.050, and of the pallidum were 0.531 ± 0.060 and 0.524 ± 0.058 for unmedicated and stimulant-treated children, respectively. Covariance analysis with age, sex, race, study site, twin status, scanner model, and TIV as covariates showed a significant effect of psychostimulant for the GMVs of bilateral caudate , putamen , and pallidum . Thus, psychostimulant-treated children showed higher ADHD scores and relatively smaller BG GMVs as compared to medication-naïve children.A total of 941 (8.2%) children received psychostimulant medications, who relative to unmedicated children showed more significant ADHD symptoms [ADHD score (mean ± SD): 60.70 ± 7.96 versus 52.51 ± 4.78; p < 0.05 FWE corrected). The unthresholded map is shown in Supplementary Fig. In an additional analysis, we excluded all medicated children and performed the same set of regression analyses on ADHD scores . The results showed largely similar volumetric correlates of ADHD scores, though diminished in effect sizes, likely because of the exclusion of the children with more significant ADHD traits  were moderately heritable, GMV correlates (a2 = 0.60–0.79) were highly heritable, and 2-back accuracy rates were near moderately heritable (a2 = 0.30–0.39). The shared-environmental effects on the measures were negligible or weak (c2 = 0.00–0.19). Two-sample t tests showed no significant sex differences in the heritability estimates of ADHD scores, volumetric correlates, or 2-back accuracy rates .Table 88. Two-sample t tests showed that there were no significant differences in the heritability of caudate, putamen, or pallidum GMVs between girls and boys .The model fits and ACE estimation for the GMVs of BG subregions are shown in Supplementary Table 91. More significant and broadly distributed in boys than in girls, the volumetric reductions clearly involved bilateral caudate and putamen. As part of the cognitive control network94, the BG play important roles in executive functions such as attention, memory, response inhibition, and set-shifting, known to be impaired in ADHD96. Indeed, we showed that ADHD scores and bilateral caudate GMVs were negatively and postively, respectively, correlated with 2-back accuracy in both girls and boys. Both ADHD traits and the volumetric markers are heritable. The heritability of ADHD scores, volumetric markers, and working memory were similar between the sexes. Together, the current findings confirm BG volumetric reduction in children with ADHD traits, overall more significant GMV reductions in boys than in girls, and the roles of caudate GMV reduction in working memory dysfunction. We have also observed a cluster in the hypothalamus that showed GMV in positive correlation with ADHD score across all subjects but not in boys or girls alone. With future releases of the ABCD data, it would be of tremendous interest and translational significance to investigate how these structural brain markers predict the development of full-blown ADHD and comorbidities in these children. We discussed the main findings below.We observed negative correlations between the ADHD scores and GMVs in a wide array of brain regions in both boys and girls, in line with previous findings99. However, it remains unclear whether the neuroendocrine abnormalities reflect experienced stress or the pathophysiological process of ADHD per se100. A recent imaging study implicated hypothalamic dysfunction during post-error slowing in the stop signal task, a deficit correlated with the inattention score, in children with ADHD101. In broad consistency, earlier volumetric studies have associated smaller hypothalamus GMV with behavioral inhibition traits in children65 and young adults102. The functional implication of enlarged hypothalamus in children with higher ADHD traits, if replicated, remains to be investigated.We observed a cluster in the hypothalamus that showed GMV in positive correlation with ADHD scores across all subjects but not in boys or in girls alone examined at the same threshold. The hypothalamus is known for its role in motivated behavior and stress response. Studies have reported altered activity of the hypothalamus-pituitary axis, as evidenced in the abnormal diurnal rhythm of cortisol secretion and cortisol response to stress, in children with ADHD27, and volumetric deficits of the OFC appear consistent with affective dysfunction in some patients with ADHD89. On the other hand, few extant VBM studies reported OFC GMV reduction in ADHD103. Children in the ABCD cohort were of 9 to 10 years of age, younger than the subject population reported in most of the earlier studies, which have suggested age-related changes in the volumetric correlates of ADHD104. For instance, machine learning demonstrated that although structural MRI data can significantly separate ADHD from control participants for both children and adults, prediction performance and effect sizes were better for the child than the adult samples105. It thus remains to be seen whether OFC volumetric deficits may perhaps normalize with age but would continue to manifest in individuals with higher emotional impulsivity.Most notable among the cortical regions showing significant GMV reductions in link with ADHD scores in boys are bilateral, including both medial and lateral, OFC. The OFC and orbito-frontal-limbic circuit have been implicated in emotional impulsivity107 and in adolescents with ADHD24. Other studies showed that the baseline GMV of the occipital cortex predicted inattention symptoms in a 2-year follow-up and was associated with the genetic risk for ADHD108. The GMV of the occipital cortex may predict stimulant treatment response in ADHD109. Another study of college students associated greater impulsivity scores to higher volumes in the occipital cortex specifically in the region that represents the peripheral visual field110. Together, prior literature provides evidence implicating occipital cortical GMV in ADHD traits, in accord with the visual cortical dysfunction in link with attention deficits in ADHD111.In girls and boys combined as well as in boys alone, the occipital cortex showed diminished GMV in relation to ADHD traits, consistent with earlier reports of occipital cortical GMV reduction in children with ADHD with a mean age of around 10 years112. In contrast, although girls relative to boys appeared to show more significant deficits in the inferior/middle temporal cortex and the precuneus, the differences did not show significantly in slope tests. In boys relative to girls, more widespread deficits were found in the OFC, frontoparietal regions, including the ACC, and subcortical structures such as the thalamus and BG, which are the core structures of the cognitive control network114. These sex differences are confirmed by slope tests but not by the interaction effects with correction for multiple comparisons, and the slope tests restricted to the BG nuclei did not demonstrate significant sex differences117. These findings are broadly consistent with those reported from the ENIGMA studies of ADHD118 and those implicating a link between caudate nucleus and deficits of executive function in ADHD111. On the other hand, although the findings together are commensurate with the proposition of frontal-striatal network dysfunction in ADHD, evidence also suggests the roles of other cortical structures, including the occipital cortical regions, in the etiological processes of attention deficits in ADHD111. Our findings enrich the theory by identifying the structural deficits of these regions in ADHD and provide a potential explanation of more significant ADHD symptoms in boys. Note that boys and girls did not demonstrate significant differences in the correlations of ADHD scores or volumetric correlates with 2-back accuracy, or in the heritability of the volumetric markers, ADHD score, or 2-back accuracy. We speculate that these volumetric correlates may more broadly represent neural markers of sex differences in clinical manifestations, including greater impulsivity in boys than in girls with ADHD121, which should be explored further. Studies investigating the heritability, particularly those involving the computation of polygenic risk scores, of both neural and behavioral markers would help addressing the inter-relationships between gene, brain, and behavior in ADHD122.The volumetric changes associated with ADHD scores are more remarkable and broadly distributed in boys, consistent with a higher incidence and greater severity of ADHD in boys than in girls123. Further, lesions of the putamen have been associated with the development of secondary ADHD125. In contrast, the GMV reduction of the precuneus remains strongly associated with ADHD scores in medication-naïve girls, suggesting potentially distinct volumetric markers of ADHD traits in the female sex.As expected, children medicated with stimulants showed higher ADHD scores than those who were medication naïve. We demonstrated in medication-naïve children similar albeit slightly weaker volumetric reductions in association with ADHD traits. In particular, the volumetric reductions in the putamen and pallidum were no longer significant, suggesting that the lentiform nucleus may play a unique role in the pathogenesis of more significant forms of ADHD. Indeed, studies focusing on subcortical structures have typically reported putamen volumetric deficits in ADHDa2 = 0.59, all subjects), suggesting that variance in the ADHD symptoms can largely be accounted for by genetic factors128. In accord with previous findings42, the volumetric correlates were also highly heritable, with a2 = 0.60 for the hypothalamus and a2 ~ 0.70 for the “negative” correlates. On the other hand, studies have also supported the roles of environmental factors in the development of ADHD130. For instance, ADHD has been associated with low socio-economic status131. In particular, with e2 at 0.41 across all subjects, 0.46 in girls, and 0.44 in boys, unique environmental factors modestly explained the variance in ADHD symptoms, in accord with previous reports132. An earlier longitudinal study showed that as the ADHD symptoms evolved from childhood to adolescence, the unique environmental influences may result from a socialization process dominated by interpersonal relationships with parents, teachers, and peers36.ADHD traits in children were near highly heritable , largely in line with earlier reports that genetic variation accounted for 43–85% of the variance in the BG subregional GMVs in children135. Other studies with ~ 200 twin pairs showed comparable heritability for BG GMVs in young and old adults (a2 = 0.65–0.88)138. Thus, the heritability of BG GMVs appears to be stable across age. Two-back accuracy rate was moderately heritable , though not to the extent observed in young adults 139. The trend of increased heritability for working memory across age has also been observed for other cognitive functions140.The GMVs of the BG subregions  were strongly influenced by genetics (141. Another study likewise found no evidence for sex differences in the genetic influences on externalizing disorders, including ADHD, conduct disorder, and oppositional defiant disorder in adolescents142. Given that ADHD appears to be more prevalent in the male sex, the sex difference may manifest in other behavioral or structural or functional neural markers of ADHD. Identifying the sex-related markers would help in investigating sex-specific genetic polymorphisms of ADHD143.We did not observe sex differences in the heritability of ADHD traits, volumetric markers, or working memory, in accord with an earlier work144 and conduct disorders145. We did not control for these and many other clinical variables in data analyses; thus, the impact of comorbidities on the current findings remains to be clarified, as demonstrated in the ENIGMA studies of ADHD148. Second, the correlations between ADHD scores and GMVs are relatively weak, which may reflect the non-clinical populations of the ABCD cohort. On the other hand, it is worth noting that the effect sizes were within the range reported of volumetric markers of ADHD cases19. Third, pubertal maturation may contribute to sex differences in brain development149. The ABCD study includes pubertal survey and provides an opportunity for future work to investigate the effects pubertal age on brain development150. Fourth, we acknowledge the limitations of twin design analyses, including the difficulty in detecting shared environmental effects and inflated estimates of heritability151. Fifth, these are cross-sectional findings; with release of more and more follow-up data, investigators would be able to examine how the volumetric deficits evolve along with clinical manifestations of ADHD into late adolescence. Finally, whereas the current work focuses on quantifying GMVs, previous studies have investigated other morphometric measures, including cortical thickness and surface area, to better understand cerebral structural alterations in ADHD54.A number of limitations need to be considered. First, ADHD is known to be comorbid with many behavioral conditions, including depressiveTo conclude, we confirmed cerebral, including basal ganglia, volumetric deficits of ADHD traits in children and potential sex differences in these structural alterations in relation to ADHD traits. ADHD traits and their volumetric markers are highly heritable with no evidence of sex differences in heritability. These findings would inform future research of the neural markers of ADHD and of the clinical trajectories and efficacy of treatment as the ABCD children are followed through late adolescence.Supplementary Information.


The prevalence of adult Attention Deficit and Hyperactivity Disorder (ADHD) has been investigated in the general population by multiple studies. However, few studies have focused on identifying its prevalence in the military population, particularly among military parents of children with ADHD.The aim of our study was to screen for adult ADHD among military parents of ADHD children followed-up at the child and adolescent psychiatry department in the Military Hospital of Instruction of Tunis, Tunisia.th Diagnostic and Statistical Manual ADHD criteria and the Conners Comprehensive Behavior Rating Scale. Whereas adult ADHD was screened for using the Adult ADHD Self-Report Scale-V1.1.This prospective study was carried among military parents  of ADHD children. Children were diagnosed with ADHD based on the 5Fifteen children and twenty-nine parents were included in the study: sixteen of the parents were military members and thirteen were civilian spouses. Eight (50%) of the sixteen military parents, and four  of the civilian spouses were screened positive for ADHD. Whereas 73% of these children had at least one parent screened positive for ADHD, and 53% had at least one military parent screened positive for ADHD. These results suggest a high prevalence of adult ADHD among this population.ADHD occurs in childhood and may persist into adulthood. The findings of this study indicate that ADHD symptoms are not limited to the youth and are common in military population. Implications on screening, management, preventive measures and research should be discussed.