Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder which typically presents in childhood. This diagnosis may often be overlooked in adulthood, particularly in psychiatric populations. The Adult ADHD Self-Report Scale (ASRS) is an internationally used and reliable screener; however, studies investigating its use in African populations are limited.To investigate the application of the ASRS in a South African setting.A patient population in Port Elizabeth, South Africa, was identified as representing a developing or low- and middle-income country population.A convenience sample of acutely presenting psychiatric participants admitted for stabilisation was used. Fieldworkers administered the ASRS; collected information relating to demographics, differential diagnoses, substance use disorder (SUD) presence and substance consumption; and prescribed medication relating to current or historical treatment of ADHD.The study sample included 30 participants, with black people representing the majority of participants. Adult ADHD Self-Report Scale completion revealed the rate of ADHD within the study population to be 43.3%, a contrast to the initially presumed prevalence of 6.7% which was based on reported methylphenidate therapy. A difference in SUD prevalence was identified between subjects screening positively and negatively for ADHD with a greater tendency towards SUDs seen for ASRS-positive individuals. Significant differences were identified in relation to cannabis- and polysubstance use for ASRS-positive individuals.Despite limitations related to the sample used and challenges in ASRS administration, investigation findings support recommendations for ADHD screening inclusion in acute inpatient settings in South Africa and ASRS translation into indigenous African languages. Emotions are not necessarily more intensely felt in persons with ADHD but are more quickly displayed and thus seem more marked. They are less moderated by executive self-regulation.Attention-deficit/hyperactivity disorder (ADHD) is defined as a neurodevelopmental disorder which is characterised by inattention, hyperactivity, impulsiveness and poor organisation.Diagnostic and Statistical Manual of Mental Disorders (DSM-5).10 A key factor in its diagnosis is the degree to which the core symptoms of hyperactivity, impulsiveness and/or inattention result in functional impairment in different environments and domains.3 In adolescence and adulthood, hyperactive behaviours tend to decline, but impulsiveness- and/or inattention-related impairment(s) can impact adulthood contextual domains such as household management and work performance.12Commonly conceptualised as a childhood condition which is ‘outgrown’ during adolescence, ADHD has been described in adult populations for several decades, but its persistence in adulthood was only officially recognised in the fifth edition of the 13 Studies by Mohr et al.14 and Corbisiero et al.15 indicated high rates of comorbidity of ADHD with other psychiatric conditions. Of notable consideration is the high rate of comorbidity between ADHD and substance use disorders (SUD), with literature indicating that risk of development of one or more SUDs in adult ADHD is twice as high as that seen for the general population.16 Additionally, the presence of ADHD has been reported to modulate the course of SUDs, with its presence being associated with earlier age of onset, more rapid induction of SUD, more severe presentation of SUD and poorer remission rates.16 However, despite this known association, distinguishing ADHD as a co-occurring disorder in SUD subjects presents its own challenges given that each condition can impact the other’s presentation, prognosis and severity.17The recognition of adult ADHD in psychiatric populations is often underreported. The diagnosis may be obscured by high rates of psychiatric comorbidity.18 Furthermore, symptoms may vary among patients, evolve over time and mimic symptoms of other disorders.20 Additional challenges in ADHD research which predated DSM-5 were as follows:These examples of adult ADHD recognition challenges are emphasised when considering that characteristics often accompanying the disorder were not explicitly associated with ADHD diagnostic criteria prior to DSM-5 publication.the criterion that clinically significant ADHD symptomology should have been present from childhood, prior to the age of 7 years22problems with retrospective corroboration of symptoms in adults.The net result of these diagnostic limitations was to make the diagnosis of ADHD in adults contentious. Therefore, the disorder is often under-diagnosed and under-recognised in adults, emphasising the need for a valid screening instrument.1 (together with its earlier editions) and the International Classification of Diseases (ICD-10)23 were the diagnostic manuals available for the diagnosis of ADHD at the time the study was conducted.3 It is acknowledged that ICD-11 has since been released. For older adolescents and adults, the DSM-5 requires at least five symptoms of hyperactivity-impulsiveness, inattention or both. It also calls for strong evidence that the symptoms interfere with, or reduce the quality of social, academic or occupational functioning. The predominantly hyperactive-impulsive, predominantly inattentive and combined subtypes are maintained, but are now called presentations, because their poor temporal stability has resulted in their validity being questioned.25 To facilitate ease of application of these diagnostic criteria, a number of screening tools based on their listed symptom categories and descriptions have been developed. These include Conners Comprehensive Behaviour Rating Scales (CBRS),26 Conners Adult ADHD Diagnostic Interview (CAADID),27 Mini International Psychiatric Interview (MINI PLUS) ADHD module,28 Brown ADD Scale (BADDS) Diagnostic Form, Wender Utah Rating Scale (WURS)10 and the World Health Organisation’s (WHO) Adult ADHD Self-Report Scale (ASRS).29The DSM-530Self-report measures are frequently used to confirm ADHD symptoms and levels of impairment in adolescents and adults. There is strong evidence that adults are reliable reporters of current ADHD symptoms and that adults’ self-ratings and informant ratings are highly correlated.22 required adult subjects to complete two questionnaires. Each one related to either childhood or current ADHD symptoms, with a parent and partner completing similar corresponding questionnaires. The subjects were not drawn from an ADHD population, nor were they assessed for ADHD.22 When the completed questionnaires were compared and assessed, statistically significant correlations were found.22 These results suggest that, when questions hold a degree of specificity, adult self-assessment of ADHD symptoms can be considered accurate.22To investigate the validity of adult self-report questionnaires with respect to the factor involving corroboration of both retrospectively reported childhood symptoms and current ADHD symptoms, the study by Murphy and Schachar27 in 2005 and is a reliable and valid self-administered screener for evaluating current ADHD symptoms in adolescents and adults.32 The ASRS v1.1 consists of 18 questions (nine questions probe for inattention and another nine questions probe hyperactivity and/or impulsiveness) based on the criteria used for diagnosing ADHD in the DSM-IV (Text Revision). Six of these items – the ASRS Part A (ASRS-A) – have been found to be most predictive of symptoms consistent with a diagnosis of ADHD.35The Adult ADHD Self-Report Scale Version 1.1 (ASRS v1.1) was developed jointly by the WHO and Kessler et al.36 The investigation by Kessler et al.29 reported very good reproducibility of overall clinical evaluations for use of the six-question screener, with the full evaluation enabling refinement of the clinical classification and showing good correlation with clinician-rated symptom severity. The ASRS sensitivity within the survey population used by Kessler and colleagues was determined to be 68.7%, with a specificity of 99.5%, yielding total classification accuracy of 97.9%.10 However, it is noted that specificity may be lower in clinical samples with high rates of psychiatric comorbidities, with a substance using sample yielding a higher sensitivity of 87.5% but a lower specificity of 68.6%.10 This underpins the need to ensure that proper diagnostic assessment is completed following screening activities.The design of the ASRS allows for collection of ADHD symptoms in the context of adulthood by relating symptoms to adult situations such as work, tasks or projects instead of play or school activities.37 According to Yeh et al.,36 the investigation into the use of the Chinese version of the ASRS in Taiwanese adults was thought to be the first study to examine the psychometric properties of this tool outside of the United States. The Chinese version was found to be reliable and valid.36 An investigation into the use of the Korean version of the ASRS showed that the tool displayed good reliability and validity.37This screening tool has been translated into many different European and Asian languages and is widely used internationally.38 with superficial assessment of the rates of the condition reported for both Africa and the Middle East in the metaregression analysis conducted by Polanczyk et al.39 supporting this notion. Although the rates of ADHD reported for these locations were comparatively lower than those for other geographic areas, the authors stated that this finding should be interpreted carefully given that the number of available studies for inclusion in their review was limited in comparison to other geographic areas.39 This study did, however, determine that the reported variability in prevalence rates across the world was mainly because of the methodological differences in the analysed studies.39 Thus, there is scope for the application of a standardised screening tool within African populations. Such a screening tool should be appropriately translated to facilitate understanding and allow for comparison with international prevalence rates.Research into the use of the ASRS in African populations is, however, limited. It has not yet been translated into African languages. Some schools of thought hypothesise that the diagnosis of ADHD is a construct of modern Western culture,40 and the operating characteristics were improved. The new screening scale is short, is easily scored, detects the majority of cases and has a high predictive value.The ASRS has been updated for DSM-5 criteria by Ustun et al.,The primary aim of the study was to investigate the use of the ASRS in a South African setting.The objectives of the study were as follows:to screen for ADHD using the ASRS in a psychiatric patient population in the public health sectorto administer the ASRS using psychiatrically trained nurses working within the selected environmentto establish the potential prevalence of ADHD in the study populationto identify substance use pattern differences between groups defined by the ADHD screening results and the presence of EF and emotional control (EC) deficitsA patient population in Port Elizabeth, South Africa, was identified representing a developing or low- and middle-income country (LMIC) patient population. This population was used to perform a preliminary investigation into the use and application of the ASRS within a South African context. The setting was an acute psychiatric treatment unit that is utilised for the stabilisation of psychiatric patients presenting with acute mental illness .Consultation with physicians in the facility indicated that routine screening for psychiatric conditions such as mood disorders and schizophrenia in subjects was established; however, screening for the presence of ADHD was generally not conducted. Given the high rate of comorbidity seen in psychiatric populations and the potential for identification of previously undiagnosed ADHD in adults, the unit was considered to be an appropriate setting for conducting the study.A convenience sample was used with a target for screening of 30 patients. The sample size was not statistically determined, but was applied because of the study being exploratory in nature with limited resources available. Three psychiatrically trained nurses were utilised as fieldworkers. Selection of the nurses was performed in consultation with the facility physicians. Exclusion criteria were participants who did not complete primary school education and those with a history of head injuries. Training was provided to fieldworkers during April 2016. Each nurse screened 10 patients, with data collection being completed between July and December 2016.Although the ASRS is designed for self-administration, a decision was taken for the screening tool to be administered to aid understanding of possible language or cultural peculiarities which were thought to provide challenges with translation and interpretation of the type of information required from the participants. The data collection duration was not preset prior to initiation of the study for the following considerations:Administration time required for each screening could not be accurately determined based on the training provided to the representative fieldworker.Data collection was performed within the facility during the participants’ inpatient admission, thus limiting the time available for identification of suitable participants and administration of the data collection tools.Use of private consultation areas was required, the availability of which was limited.Fieldworkers had to perform the screening during hours in which they were not on duty.The ASRS was used with another data collection tool suitable for use in psychiatric patients, which included details on demographic information and substance use and consumption. The data collection pack consisted of the following sections and summarised detail regarding the particular information gathered within each section:Demographic information: Introducing questions including the differential diagnosis, endorsement of the presence or absence of SUDs upon admission as per DSM-IV classification, general demographical considerations, level of education and employment status.ASRS v1.1 (expanded): Questions 1 through 18 examine ADHD-associated symptoms as per DSM-IV, questions 19 through 32 examine EF and EC and questions 33 and 34 examine impulsiveness as per DSM-5.Substance consumption information: A comprehensive list of various intoxicating substances to examine historical or present (active within the month preceding admission) consumption of substances by patients and duration(s) of long-term substance use where present.ADHD treatment details (if applicable): To assess for current or historical use of ADHD-indicated medication.It was estimated that administration of the data collection pack would require a period of 1 hour per interviewed participant.Data were analysed using Statistica v. 12 (Dell Software). Analysis of variance (ANOVA) models were employed to determine differences in age, gender, inattentive and hyperactive/impulsive scores, EFs, EC and age when substance consumption started. The results were analysed as a function of ASRS results (ASRS positive vs. ASRS-negative). Fisher’s exact test was used for 2 × 2 contingency tables . Differences in other categorical data  were established by chi-square test.The research proposal wherein the procedures for collection of data from human participants were described was in accordance with the ethical standards of the Research Ethics Committee (Human) of Nelson Mandela University and with the 1964 Helsinki declaration and its later amendments (H14-HEA-PHA-081). Permission to perform the study at the selected unit was provided by the hospital manager. The study fieldworkers selected candidates for the investigation and informed consent was obtained from participants prior to administration of the data collection tools. The informed consent procedure allowed for patients to endorse if they granted permission for the results of the screening to be shared with their attending health-care team with consideration for potential modification of the respective treatment plan(s); however, follow-up related to this aspect was not undertaken within the study and has not been reported upon.n = 30). The ethnicity of participants varied with the highest ethnic representation being black people (46.7%), followed by mixed race people (23.3%) and white people (3.3%). The remaining participants did not clearly endorse ethnicity (26.7%).A total of 30 questionnaires were completed. All participants were South African, with two-thirds of the participants being men  and 13.3% were English speaking . More than half the participants were unemployed  and 50.0% did not complete the final year of their secondary education (grade 12).Half of the participants indicated isiXhosa as their home language, 36.7% were Afrikaans speaking . The admission diagnosis was not stated for 40.0% of participants . In some instances, conditions such as bipolar disorder and substance-induced psychosis were the considered differential diagnoses.The diagnoses endorsed upon admission varied; refer to Challenges with administration of the ASRS were reported by the fieldworkers because of difficulty in interpretation of concepts therein described in the English language. As a result, the estimated interview duration of 1 h was found to be conservative, and the data collection process was more time consuming than initially anticipated.Cronbach’s α was calculated for both the ASRS-A (first six questions) and the full scale ASRS (18 questions) for the present sample. Cronbach’s α for the ASRS-A was 0.72, which is an acceptable value, while for the ASRS-18 it was 0.93, which is considered excellent. The internal consistency, which is a measurement of scale reliability, is adequate for the ASRS-A and excellent for the full-scale ASRS for the sample under investigation.n = 30). This increased to 43.3% upon completion of the 18-question screening tool (ASRS-18) (n = 13). It is noted that 6.7% of participants indicated current or previous pharmacotherapy with methylphenidate, possibly signifying previous ADHD diagnosis . The predominantly inattentive presentation was most commonly identified within the ASRS-positive group .n = 20), compared to 13.80 ± 8.40 for females (n = 10) (p = 0.013). Hyperactive/impulsive symptoms were also more severe for male participants, but the difference between genders was not significant (p = 0.101). The ASRS-positive group showed significantly increased severity of hyperactive/impulsive and inattention symptoms of ADHD, as well as problems with EF and EC than the ASRS-negative group.Analysis of ADHD symptom score differences between genders revealed that inattention symptoms were significantly more severe in male participants, with the mean score for inattention being 21.00 ± 6.29 out of a possible 36 (n = 30), whereas diagnosis of a current SUD was endorsed for 73.3% of participants . Thus, 92.3% of the ASRS-positive group had one or more SUDs , while 58.8% of the ASRS-negative group were identified as having SUDs . Substances were divided into alcohol, cannabis and street drug use (cocaine and methamphetamine) and comparisons were made between the ASRS-positive and ASRS-negative groups for patients with a current SUD. The number of ASRS-positive participants endorsed for substance use of each substance division was greater than the representation for ASRS-negative participants; however, no significant differences were found for use of alcohol and street drugs. Significant differences were identified for cannabis and polysubstance use .Considerations for the presence of SUDs in participants were based on the stated diagnosis (where this was available); endorsement of one or more SUDs being present by the indication of alcohol, cannabis and/or drug abuse/dependence (as per DSM-IV); and assessment of the reported substance consumption obtained with completion of the data collection pack. Previous or ongoing substance consumption was reported by 93.3% of participants  in our study sample, with an increase to 43.3% (n = 13) with completion of the full ASRS. Both participants with a history of methylphenidate therapy were within the ASRS-positive group. Although the ADHD diagnosis was not verified by a suitably qualified physician for the purpose of this investigation, these rates are reflective of the assertion that historical diagnostic shortcomings and an array of other potential diagnoses could impair identification of ADHD. The need for further exploration of ADHD being an undetected condition in existing psychiatric adult populations is emphasised by the estimation that most adults with ADHD will experience one or more co-morbid psychiatric disorders during their lifetime and that high comorbidity rates are a distinct feature in adults and children with ADHD.41This study showed that there is merit in investigating the possibility of underlying ADHD in psychiatric populations. Using the endorsement of either current or historical methylphenidate use, the presumed initial rate of ADHD in the study population was 6.7%, which is in line with the estimated prevalence rate of 1.0% – 7.3% in adults39 This finding supports the need for appropriate translation of the ASRS to better facilitate investigation of this disorder in African populations.Considering participant ethnicity in relation to ASRS-positive participants, 6 out of the 10 ASRS-A participants were black people, 3 were mixed race people and one was unknown. Following the completion of the 18-question screener, the demographics were 7 black people, 4 mixed race people, one white person and one unknown. Thus, more than half of the participants in whom a diagnosis of ADHD was suspected were black people, which concurs with literature reports of inter-racial variance in ADHD prevalence rates being more dependent on the application of suitable screening tools than any distinct cultural differences.10 The identified presentations of the disorder were skewed towards the inattentive subtype, which is consistent with the study by Cumyn et al.41 wherein this was the predominant presentation; however, the literature suggests that the majority of cases are the combined presentation.42 The combined presentation is thought to affect both genders equally, whereas the inattentive presentation is more common in women.42 The predominantly hyperactive/impulsive subtype is considered the rarest presentation.42The gender distribution among the ASRS-positive group was consistent with the existing literature of higher ADHD prevalence rates in males compared to females. It has been noted, however, that variability in the prevalence between the genders tends to reduce with age as more women become diagnosed in adulthood because of the impairment arising from the internalised symptoms which may have been overlooked in childhood.44 The executive control and cortico-cerebellar networks coordinate executive functioning, which can be described as planning, goal-directed behaviour, inhibition, working memory and the flexible adaptation to context. These networks are underactivated and have lower internal functional connectivity in individuals with ADHD compared with individuals without the disorder.43 Persons with ADHD show impairments in judgement, organisation, planning and decision-making as well as in behavioural disinhibition and cognitive flexibility.46 EC deficits include irritability, hot temper, low frustration tolerance, impatience and sudden unpredictable shifts towards negative emotions such as anger, dysphoria and sadness, occurring in an intensity that is considered inappropriate in relation to the situational context, age and developmental stage.48 It predicts poorer social outcome and peer rejection.49 With reference to Barkley’s model,50 impairments in EF and EC can be explained by a core inhibition deficit; insufficient emotional regulation is considered to represent one consequence of deficient executive inhibitory control. The consequences of impaired response inhibition associated with ADHD to emotional competence include increased emotional reactivity, decreased frustration tolerance and diminished ability to self-regulate emotions. Both motivational and executive processes are likely involved in emotional responding.48The significant differences identified between the ASRS-positive and ASRS-negative groups for EF and EC are consistent with literature stating that such deficits are strongly associated with ADHD.51 It is notable that these consistencies were identified between studies conducted within South Africa for both cannabis- and polysubstance use, with specific consideration given to the association with cannabis given that this would appear to contrast against the assumption that ADHD would have a greater association with stimulant abuse.51 It was considered by Fabricius et al.51 that this may be related to easy accessibility of cannabis in South Africa. The association with polysubstance use could be interpreted as being consistent with the literature stating that ADHD is associated with a more severe presentation of SUD.16It was reported that trends identified for cannabis- and polysubstance use were significantly different between the ASRS-positive and negative groups. These findings were consistent with the results reported in the study by Fabricius et al.This study provided evidence within an African setting which concurs with existing literature regarding the association between mental illness, ADHD and substance use. Given the previously stated lack of data on ADHD from Africa, this investigation contributes to the greater body of literature specifically pertaining to ADHD, but also its relation to substance use and co-morbid psychiatric conditions. The determination of Cronbach’s α in such a setting is valuable for guiding the use of the ASRS within similar study populations and thus aiding further efforts towards gathering the much needed data on this condition in Africa. These study strengths are, however, impacted by a number of limitations which are discussed below:The sample size was small, and thus, the generalisability and reliability of the study are limited and would require reproduction on a larger scale in order to appropriately identify trends.A convenience sample was used, and thus, a selection bias existed in the study population. These results cannot be applied to the general population and can only be considered for potential generalisation in similar clinical settings.Despite investigations that support the validity of self-report questionnaires, recall bias in this specific patient population may have resulted in skewing of the results which impairs the study reliability and generalisability.As the participants were sourced from a clinical setting, the nature and severity of the current illness(es) experienced by the participants may have interfered with the integrity of data collection tools, resulting in comorbidity bias. Additionally, the provisional or differential diagnoses upon admission were not available for all participants, reducing the potential for analysis directed by confirmed diagnostic information.The English version of the ASRS was used for a population with varying home languages. The specific limitation with respect to the sample was the lack of a validated format of the ASRS in Xhosa. Thus, there is the risk of inappropriate interpretation of the data collection tool questions which may have skewed the results in different possible ways, although the Cronbach’s α indicated that the instrument was reliable for the sample.Despite the fieldworkers being psychiatrically trained and thus ideal for interactions with subjects within the study sample, the previously stated limitations relating to the study sample and language barriers were apparent with respect to appropriate interpretation and administration of the ASRS. Such challenges could be mitigated by the presence of a psychometrist; however, the availability of such professionals in developing areas may be limited, and as such, it is imperative that similar investigations in such populations are undertaken by fieldworkers with psychometric assessment experience and/or training. Despite the mentioned obstacles, the statistics done on the responses of the present sample showed that the instrument (ASRS) was suitable for the population under investigation as it showed to be reliable .Despite the stated limitations, this study provides evidence to support a recommendation to include ADHD screening in acute inpatient settings in the South African context. It is noted that generalisability of the study is limited, and, thus, a further recommendation would include continued investigation into use of the ASRS on a larger scale with improved study design and controls, such as ensuring appropriate diagnostic assessment for ASRS-positive participants to allow for a true indication of the prevalence of ADHD in the selected population. However, application of the ASRS on its own may be insufficient. The diversity of culture and language in South Africa creates a need for appropriate translation and validation of this instrument into indigenous South African languages so as to facilitate improved completion and/or administration of the tool across different ethnic groups.n = 30), supporting the recommendation that ADHD screening be included within the standard assessment in such populations. Substance consumption and use was widespread within the study population, with significant differences being identified between the ASRS-positive and ASRS-negative groups for cannabis- and polysubstance use. A significant increase in severity of executive functioning and EC problems was also associated with the ADHD-positive group which was consistent with the available literature indicating the association between such deficits and ADHD.Although challenges related to language and duration of the assessment, the target number of screenings was completed within a culturally diverse sample of psychiatric patients. Determination of Cronbach’s α for both the ASRS-A and ASRS-18 within the study population revealed the internal consistency to be adequate for the ASRS-A and excellent for the full scale ASRS. The potential prevalence of ADHD within the study population was 33.3% (ASRS-A) and 43.3% (ASRS-18), whereas the presumed initial prevalence estimation was only 6.7% (


Smaller subcortical volumes were associated with more severe social deficits on the SCQ in ASD and ADHD, and less severe deficits in OCD. On the RMET, larger amygdala/hippocampal volumes were associated with fewer deficits across groups. Overall, patterns of associations were similar in ASD and ADHD, supporting a common underlying biology and the blurring of the diagnostic boundaries between these disorders.Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD) have been associated with difficulties recognizing and responding to social cues. Neuroimaging studies have begun to map the social brain; however, the specific neural substrates contributing to social deficits in neurodevelopmental disorders remain unclear. Three hundred and twelve children underwent structural magnetic resonance imaging of the brain . Their social deficits were quantified on the Social Communication Questionnaire (SCQ) and the Reading the Mind in the Eyes Test (RMET). Multivariable regression models were used to examine the structural neuroimaging correlates of social deficits, with both a region of interest and a whole-brain vertex-wise approach. For the region of interest analysis, social brain regions were grouped into three networks: (1) lateral mentalization , (2) frontal cognitive , and (3) subcortical affective  regions. Overall, social communication deficits on the SCQ were associated with thinner cortices in the left lateral regions and the right insula, and decreased volume in the ventral striatum, across diagnostic groups ( Increasingly, recognition of the overlapping and related nature of both the symptoms and the biology of different neurodevelopmental disorders8 has led to a call for research that spans diagnostic boundaries, and focuses instead on dimensions of psychopathology9.Social deficits are a defining feature of autism spectrum disorder (ASD), but also frequently affect children with attention-deficit/ hyperactivity disorder (ADHD)11 and reviews of the literature14  are thought to reflect the underlying cortical microstructure, involving the number and organization of cortical neurons, neuronal dendritic arborization, the number and size of glial cells, and to some extent the maturation of the adjacent white matter37. Cortical thickness, including the timing and rate of cortical thinning, has been a major area of study across neurodevelopmental disorders, particularly in ASD24.Cortical gray matter volume is a product of cortical thickness and surface area. Recent work has provided evidence that cortical thickness and cortical surface area measurements are under distinct genetic influences38, executive function in ADHD39, and reward processing in OCD40), one hypothesis is that the neutral substrates of sociality will also differ by diagnosis . An alternative hypothesis is that the brain regions associated with social deficits will span diagnostic boundaries. In children with ASD, ADHD, and OCD, for example, white matter fractional anisotropy correlated with adaptive functioning abilities, irrespective of diagnosis8. Neuroimaging analyses comparing the structural neuroanatomical correlates of social deficits across children with ASD, ADHD, or OCD have not yet been performed.The association between brain  and behavior  was the focus of the following study, in efforts to identify and compare the neural substrates of sociality across disorders. Given that the specific behavioral dimensions that contribute social impairments may vary across neurodevelopmental disorders 42, the Kiddie-Schedule for Affective Disorders and Schizophrenia43, the Parent Interview for Child Symptoms44, and the Children’s Yale-Brown Obsessive-Compulsive Scale45). Research ethics board approval was obtained at each institution.Participants were recruited via the Province of Ontario Neurodevelopmental Disorders Network, across four Centers in Ontario, Canada . Controls were recruited through advertising in public transit, in hospitals, and on social media. Inclusion criteria were age <18 years, and a clinical diagnosis of ADHD, ASD, or OCD. Controls had no developmental diagnosis, and no first-degree family history of such. Participants were recruited into the study based on their primary psychiatric diagnosis; potential comorbid symptoms/traits were captured on symptom surveys (described below). Standardized research assessments confirmed the primary clinical diagnosis using established metrics  is a validated social perception task, in which study participants are asked to label the emotion/mental state in a still image of human eyes47. This metric quantifies social perception abilities. Participants in this study completed the child version of the RMET, which included at total of 28 items48. For simplicity and congruence with the SCQ, we examined the number of incorrect RMET items. Therefore, for both measures, higher scores indicate greater social deficits.The Social Communication Questionnaire (SCQ) is an established 39-item measure that quantifies risk for ASD through parent/caregiver report on social abilities and behaviorsT-scores. T-scores >65 are suggestive of elevated symptomatology49. To quantify OCD symptoms, the Toronto Obsessive-Compulsive scale was used. This is a parent report measure, where items are scored −3 to +3; total scores >0 discriminated OCD cases from controls in a community sample50.To characterize the study sample, ADHD symptoms were quantified across groups using the Child Behavior Checklist (CBCL) ADHD subscale. This is a parent report measure that yields age- and sex-normed All structural MRI data were collected at the Hospital for Sick Children in Toronto, between June 2012 and July 2017. The majority of scans (74%) took place on the 3-Tesla Siemens Trio TIM; a hardware upgrade to the Siemens Prisma scanner took place in June of 2015 .51 The CIVET pipeline applies a non-uniformity correction on the images52 followed by stereotaxic registration to the Montreal Neurologic Institute (MNI ICBM152) template (non-linear sixth-generation target)54. Next, brains were masked, extracted, and classified into gray matter, white matter, and cerebrospinal fluid, which were used to generate gray and white matter surfaces59. A surface-diffusion kernel was applied60, and regions were registered to the automated anatomical labeling atlas63. Cortical thickness measurements were taken from the distance between the two smoothed surfaces65. Quality assurance was assessed at the time of the scan for motion artifact, and was analyzed through the CIVET quality control (QC) analysis pipeline. Scans that were flagged on the QC analysis were manually reviewed for quality, and potentially excluded. Thickness measurements from the automated anatomical labeling atlas regions were grouped into regions of interest to reflect the three primary social neural networks14 Statistical analyses were performed using SAS (University Edition) version 9.4, and R version 3.5.1.3) for subcortical structures, treating diagnosis  as a categorical predictor variable, while also including age, sex, and hardware upgrade as covariates to account for their effects.As a preliminary analysis, we initially tested for diagnostic differences in brain structure for the combined social regions of interest, as well as mean whole-brain cortical thickness and volume. For this, we used a linear regression model to estimate the least-squares (LS) mean cortical thickness (in mm) by region, or mean volume  are presented; ORs >1 indicate greater deficits with increasing size, whereas ORs <1 indicate fewer social deficits with increasing size. We included age, sex, hardware upgrade, and diagnosis as covariates, while also testing for interactions between thickness/volume and diagnosis. Where the thickness/volume-by-diagnosis interaction term was non-significant (p > 0.05), the interaction term was dropped from the model and ORs were presented across all groups combined. Where the interaction term was significant, ORs were presented for the diagnostic groups separately. Logistic regression analysis was chosen given that scores on the SCQ and RMET have a ceiling/floor, and follow a binomial distribution. Results are presented without correction for multiple comparisons in order to convey overall patterns; associations that remained significant after a false discovery rate (FDR) correction71 are indicated in the text/tables.Next, we used a logistic regression model to examine the proportion of items selected on the social measures  as the dependent variable, predicted by each millimeter increase in cortical thickness, or 0.1 cmq < 0.05) are presented graphically. The proportion of significant voxels, and the number of significant peaks  per region of interest are also presented.As a secondary analysis, the association between cortical thickness and social behavior was examined using a whole-brain vertex-wise approach, with the same logistic regression models applied to 70,000 vertices across the entire brain. ORs for vertices that remained significant after an FDR correction  were excluded due to low quality. Demographic data for all remaining participants are presented in Table F = 4.10, p = 0.007, non-significant after FDR correction). On pairwise comparisons, the OCD group had larger mean hippocampal volumes. Also adjusting for whole-brain volume yielded a less significant main effect of diagnosis on hippocampal volume ; other structures remained non-significant.The multivariable predicted LS means for the regions of interest for each diagnostic group are presented in Supplementary Table p < 0.008 for all region-by-diagnosis interaction terms) . In ADHD and ASD, thinner cortical and smaller subcortical structures were associated with increased social deficits in a similar pattern. Findings remained significant after FDR correction  Table . Distincs) Table . In the on Table . The discits OR >.0. In ADp = 0.02 and 0.04) and the right insula (p = 0.02), although findings became non-significant after FDR correction. For these regions, interaction terms were kept in the model to convey patterns, however. Specifically, the ASD group showed a significant association (thinner cortex = greater deficits) and other groups were non-significant (Table p < 0.0001), right amygdala volume (p = 0.003), and hippocampal volumes (p = 0.001) were associated with fewer social deficits across groups. As a sensitivity analysis, we also adjusted for IQ in the model, given data showing that RMET scores vary with cognition1. Adjusting for IQ resulted in a similar pattern of associations  age-by-region interaction. Specifically, the association between right lateral region thickness and SCQ score was more pronounced at younger ages (age 7 OR: 0.17 (0.10–0.30)), and was less significant in older children (age 11 OR: 0.39 (0.29–0.53), age 15 OR: 0.89 (0.57–1.35)). Several other regions showed a similar pattern, although the effect was less pronounced and would become non-significant after a correction for multiple comparisons . There were no significant age-by-region interactions for analyses involving the RMET. For the right lateral region, we then also tested for a three-way interaction term “age-by-region-by-diagnosis,” to determine whether the age effects on the brain–behavior relationship varied across diagnostic groups. There was a nominally significant three-way interaction term (p = 0.05). ASD and ADHD had more significant associations between brain and behavior on the SCQ at younger ages (OR age 7 for ASD: 0.20 (0.10–0.40); OR age 7 ADHD: 0.24 (0.08–0.70); OR age 15 ASD: 1.81 (1.07–3.06); OR age 15 ADHD: 0.94 (0.23–3.82)), whereas the opposite pattern was observed for the OCD and control groups (OR age 7 control: 0.24 (0.03–1.80), OR age 7 OCD: 0.50 (0.02–14.2); OR age 15 control: 0.02 (0.01–0.4), OR age 15 OCD (0.12 (0.03–0.45)).As an exploratory analysis, we also examined for age effects on the association between brain region and social deficits by checking for age-by-region interaction terms for the regions of interest. On the SCQ, the right lateral region showed a highly significant  modify the severity of social deficits in ASD and ADHD72. We therefore examined for associations between OXTR genotype and brain structure in brain regions previously found to be affected by OXTR genotype in control populations  regions)80. Exploratory analyses , with the exception of the left insula, in ADHD .There were 100 participants with ASD, and 51 participants with ADHD who also had genotype data available regarding single-nucleotide polymorphisms (SNPs) in the In this study, we examined and compared the structural neuroimaging correlates of social abilities across a large sample of children and youth with different neurodevelopmental disorders. Overall data suggest similarities in the biological substrates of social communication abilities in ASD and ADHD; both had widely distributed associations between brain structure and SCQ score, involving multiple subcortical and cortical regions. Exploratory analyses identified age-associated difference in this effect in the right lateral regions, which were congruent in ASD and ADHD as well. For social perception on the RMET, all four groups showed a similar pattern of associations involving subcortical regions, while participants with ASD also had significant findings in the right insula and the right lateral regions. Across all diagnostic groups, associations were highly heterogeneous.82. For example, diffusion tensor imaging has shown a lack of diagnostic differences in fractional anisotropy of white matter tracts between ASD and ADHD, while OCD appeared distinct8. Latent class analysis of symptom surveys and cognitive profiles suggest overlapping cognitive deficits in both disorders81. Patterns of social perception deficits were strikingly similar in ASD and ADHD as well1. Rare copy number variants affecting many of the same genes have been shown to contribute to risk for both ASD and ADHD83.Our results are consistent with emerging data suggesting ASD and ADHD may be overlapping conditions, with related neuropathology and symptomatology84. In this study, we used the SCQ, which has been found to be a better screening instrument for ASD compared to the SRS85, and is not affected by IQ like other measures86. Despite different measures, our results using the SCQ  are broadly consistent with several studies examining structural neuroimaging correlates of the SRS/ADI-R in control participants89 and in ASD90. The direction of our results are inconsistent with two larger studies, the first using the SRS in a large population of typically developing in 6–10 year olds24, and the second using the Autism Diagnostic Observation Schedule in ASD across the lifespan29. Difference in results may be explained in part by true biological differences in the neural substrates of these measures, heterogeneity across disorders and populations, variability in the age ranges of included participants, use of models that may or may not account for comorbid symptomatology/differences in IQ, and variation in the measurement aspects of the underlying social construct in different disorders.Previous studies examining the association between brain structure and social deficits have yielded inconsistent results. Most studies have used the Social Responsiveness Scale (SRS) or subscales of the ADI-R to quantify social communication deficits, primarily in control populations. Recent work suggests the SRS may be highly sensitive to non-specific behavioral problems, reflecting general levels of impairment more so than social deficits per se94, although findings may defer in ASD95. Our results for the RMET highlight subcortical structures as being important across disorders; children with ASD also had associations in the lateral mentalization regions and in the insula. It is possible that this finding is reflective of more immature or inefficient social perception abilities in children with ASD, leading them to rely on additional cortical circuits to make social evaluations96.Functional and structural neuroimaging work has associated RMET performance with activation/size of the inferior frontal gyrus, middle/posterior temporal regions, and the amygdala/hippocampus in typically developing adults98. In control participants, completion of the RMET was associated with activation in the posterior superior temporal sulcus across age ranges, but younger children  also activated the prefrontal cortex, the inferior frontal gyrus, and the temporal pole during this task as well96. Given that the direction of the association between cortical thickness and general intelligence may change over childhood (from a negative correlation in younger children to a positive correlation in older children)99, and that neurodevelopmental disorders such as ASD and ADHD are associated with aberrant cortical maturation18, further research is merited in order to characterize disorder-specific longitudinal changes in the neural substrates of social deficits.Exploratory analyses identified age-associated differences in the association between right lateral region thickness and SCQ score; data also suggested that these age-associated differences may vary by diagnostic group. Existing literature supports the hypothesis that the neural substrates of social abilities may change over the course of development. For example, previous longitudinal work has shown that the patterns of functional MRI (fMRI) activation during facial/emotion recognition tasks change with age, and in particular, after pubertylarger subcortical structures being associated with greater social impairments. Previous research has found frontal–subcortical hyperactivity during processing of emotional stimuli in OCD on fMRI100. Therefore, it is possible that the social deficits associated with increased subcortical volumes reflect aberrant processing of social–emotional information in OCD. Consistent with this hypothesis, the distinct OCD pattern in our data persisted even after adjusting for severity of OCD symptoms. The OCD group also showed strong lateralization effects with respect to the association between social abilities and insula thickness, consistent with previous data in control populations70. Overall, despite overlapping symptomatology with other disorders , the pattern of brain–behavior associations involving the OCD group appeared quite distinct.The pattern involving OCD on the SCQ is notably different with p = 0.09) in 107 pediatric OCD patients compared with 210 controls21. This stands in contrast to adult populations, where smaller hippocampi were found21. Notably, across other social brain regions of interest, there were no significant structural differences in thickness or volume between the four diagnostic groups. Meta-analyses comparing over 1000 participants with ASD20, or ADHD23, to control participants have found smaller subcortical volumes in both ASD and ADHD. Cortical differences have varied by study, and by age, for both disorders, however20. These results further highlight a lack of biological differences between ASD and ADHD in spite of differences in mean scores on the SCQ and RMET.The structural finding of nominally larger hippocampal volumes in OCD is consistent with data from a recent large meta-analysis showing a trend towards larger hippocampi (OXTR) corresponded with differences in structural neuroanatomy101. Specifically, different OXTR rs2254298 and rs53576 genotypes corresponded with differences in amygdala volumes80, and OXTR rs2254298 genotype was associated with differences in thickness of the right insula and dorsal anterior cingulate as well101. Our exploratory analyses did not reproduce these findings in children with neurodevelopmental disorders, although rs53576 genotype was associated with thickness in the left insula in the ADHD group. Despite this, overall trends suggested a similar direction of gene–brain associations in ASD and ADHD.Oxytocin is a pituitary neuropeptide known to affect social behavior. A few small studies, in typically developing adult cohorts, have examined whether common genetic differences in the oxytocin receptor gene ; diagnostic differences in the measurement aspects of this measure were not the focus of the paper but could also contribute to results. SCQ scores were right skewed in controls, ADHD, and OCD, but left skewed in ASD, raising the possibility of influential outliers driving results. Case-wise diagnostics were conducted and repeat analyses excluding 13 potential influential outliers based on difchi-squared values yielded the same pattern of findings (note no dfbetas were >2). Restricting the range of SCQ scores to 0–15 resulted in similar patterns as well, although less significant results. The group with ASD tended to be higher functioning; results may be less generalizable to a more severely affected population. Also the oxytocin results should be considered exploratory in view of the small sample size and the multiple testing.Overall, this study revealed that the structural neuroimaging correlates of social communication and social perception deficits vary across neurodevelopmental disorders. There were more similarities than differences between ASD and ADHD, even on an ASD-specific outcome measure. Data support a common underlying biology and the blurring of the diagnostic boundaries between these two disorders.SUPPLEMENTARY INFORMATION


This study aimed to examine the resumption of attention-deficit hyperactivity disorder (ADHD) prescriptions in early adulthood in young people whose ADHD prescriptions stopped in adolescence. Whilst prescribing studies indicate that the proportion of those with ADHD stopping treatment in late adolescence remains in excess of the proportion expected to be symptom free, very few studies have examined patterns of resumption amongst young adults previously prescribed medication. Primary care records from the UK Clinical Practice Research Datalink from 2008 to 2013 were used to examine the outcome of resumption of ADHD prescriptions from age 20 years in a sample of cases with ADHD whose prescriptions stopped aged 14–18 years. A Cox regression model was fitted to explore variables that could theoretically be associated with resumption of prescriptions. Of 1440 cases, 109 (7.6%) had their ADHD prescriptions resumed. Characteristics associated with an increased probability of resumption included female gender, learning disability, referral to adult mental health services, and prescription of antipsychotic medication. In this study, only a small proportion of adolescents who stopped ADHD medication subsequently resumed their prescriptions in primary care. Those that did resume were a more complex group. As many vulnerable individuals with ongoing ADHD symptoms may not have the resources required to surmount the barriers to re-enter services, the implication is that not all those who could benefit from resuming medication are able to do so. The findings raise questions around whether current care models are flexible enough and whether primary care services are adequately supported in managing this group. Attention-deficit hyperactivity disorder (ADHD) is now accepted to be a condition which can persist throughout life. Follow-up studies suggest that up to three-quarters of young people diagnosed as children continue to experience symptoms into adulthood, even if they no longer meet full diagnostic criteria . These sConsequently, much recent research has focussed on ADHD management during the transition to adulthood, and in particular on the prescription of ADHD medication. In studies from the UK, USA and Europe –9, the pStopping medication can have positive outcomes for many young people, but for others, cessation may be detrimental and destabilising at a vulnerable life stage. Arguably, we would therefore expect to see a prescribing pattern whereby some adolescents with continuing symptoms stop medication but later seek to restart treatment as young adults. Guidance from the UK National Institute for Health and Care Excellence (NICE) now recognises that people with ADHD previously treated as children may return to services after the age of 18 years, seeking support and treatment for continuing ADHD symptoms .This group of young adults who do not undergo transition into an adult service for their ADHD but then seek to re-enter services and potentially resume treatment have been little studied to date. Therefore, there is a gap in knowledge about the scale and needs of this group, which is important for planning services and training. Very few studies have examined the patterns of resumption amongst young people previously prescribed medication. Notably, whilst the USA-based large multimodal treatment of attention-deficit hyperactivity disorder (MTA) trial reported on ‘stopping and restarting’ patterns in adolescents, it has not published data on resumption after the age of 18 years . In the We aimed to explore resumption of ADHD medication prescriptions in early adulthood (from age 20 onwards) amongst young people whose prescriptions stopped in adolescence aged 14–18 years) using primary care prescribing data from the UK Clinical Practice Research Datalink from 2005 to 2013. The age range of 14–18 years for cessation was chosen as it corresponds to adolescence and the period of preparation for transition to adult health-care services; this is the time at which young people are most likely to cease their ADHD medication  years us. We studWhat proportion of young people who have previously been prescribed ADHD medication and stopped resume prescriptions in early adulthood, and what is the probability of resuming medication from age 20 years onwards?What background characteristics are associated with resumption?Our chief research questions were:This study was a secondary analysis of data from the Clinical Practice Research Datalink (CPRD), a large UK clinical database run by the Medicines and Healthcare Products Regulatory Agency (MHRA). The primary care section of the database contains the records of over 11 million patients and is contributed to by more than 670 GP practices across the UK, covering over 6% of the population . CPRD usN = 9390) with a recorded diagnosis of ADHD using NHS coding schemes ; with no further ADHD prescribing recorded until the age of 20 years if at all.Have at least 1 year’s worth of follow-up data in the CPRD from the age of 20 years.To be included in this analysis, patients had to:Resumption was defined as having at least one recorded prescription of ADHD medication at the age of 20 years or over. We reported the percentage of included cases who resumed prescriptions and the age of restarting, and the percentage of those who resumed prescriptions by age at stopping. The percentage of those who resumed medication was then reported by key characteristics . We also reported the prescription of non-ADHD psychotropic medication in individuals who did and did not resume ADHD prescriptions and used the Chi squared test to compare the proportions.p value less than 0.05 were then included in a multivariable (adjusted) Cox regression model, with the same tests of the proportional hazards assumption applied. The proportional hazards assumption was checked by examining Schoenfeld residuals and plotting Nelson–Aalen cumulative hazard estimates. Statistical analyses were carried out using Stata software (version 14) [We then performed survival analysis to examine the time to resumption of ADHD prescriptions. The observation period for the survival analysis began from the year of the case’s 20th birthday and continued until a new prescription for an ADHD medication was recorded (resumption), until records for the patient were no longer available or the study period ended (31 December 2013)—i.e., they were censored. The median time to resumption was reported and the distribution of time to resumption was summarised using the Kaplan–Meier estimator. A Cox regression model was fitted to explore variables that could theoretically be associated with resumption of prescription, based on the previous descriptive analysis. These included gender, referral to adult psychiatry, non-ADHD psychotropic primary care prescribing aged 18 and under or aged 19 and over, and recorded psychiatric comorbidities, as well as age at stopping medication. Each variable was initially examined in an unadjusted Cox regression model, reporting hazard ratios. Predictor variables with a sion 14) .There were 1440 young people in the analysis who met our inclusion criteria. Of these, 1293 were male (89.8%), 395 (27.4%) had a prescription for a non-ADHD psychotropic medication at any point, 348 (24.2%) had a recorded diagnosis of another psychiatric disorder (excluding learning disability), and 64 (4.4%) had a recorded learning disability.In our sample, 109 participants  had their prescriptions resumed at the age of 20 years or over. Of the 147 females in the sample, 17 (11.6%) resumed medication, compared to 92 of 1293 males (7.1%).Of the 1440 participants, 427 (29.7%) stopped medication at age 16 years, which was the most common age for cessation, although 372 participants (25.8%) stopped medication at 17 years and 319 (22.2%) at 18 years.Table Once prescriptions were resumed, the median duration of persistence was 1 year (interquartile range 1–4 years) with the range from less than a year to 5 years; cases still prescribed medication at 6 years or more post-resumption were censored see “”.Overall, the later the young person stopped medication, the more likely they were to restart prescriptions in early adulthood. Only 6 (5.3%) of the 113 who stopped medication at age 14 years resumed medication, compared to 42 (13.2%) of the 319 who stopped medication aged 18 years  was 4.6% (95% CI 3.5–5.8%); at 3 years (by the age of 22), this had risen to 7.6% (95% CI 6.3–9.2%) and at 5 years (by age 24) this was 9.7% (95% CI 7.7–12.0%).The probability of resumption rose most steeply in the first 2 years of observation from age 20 years. When fitting the Cox regression model, there was evidence of violation of the proportional hazards assumption for some variables when examining resumption over the full follow-up period see “”. Table In the adjusted model, female gender remained associated with an increased probability of resumption, as did a recorded referral to adult mental health services and having a recorded learning disability diagnosis. Prescription of antipsychotic medication was also strongly associated with resumption. Those that were older at the time of stopping medication also appeared to be more likely to restart within this time period.As shown in Table International studies have demonstrated that the majority of young people with ADHD stop medication by the age of 18 years –9. Our fThe low prevalence of resumption in our study is in line with earlier work by Wong et al. which examined prescribing between 1999 and 2006 . Our morWe found that the younger the age at stopping, the less likely cases were to resume their prescriptions. Continuing to take medication into late adolescence is logically likely to be a marker of severity of symptoms, although notably the age of stopping prescriptions was associated with resumption in our Cox regression even after adjusting for psychiatric comorbidities (often related to severity). It is possible that the young people stopping medication earlier in adolescence are those who experienced remission of their ADHD, and who consequently underwent a planned cessation of medication. Correspondingly, those who stopped later around the age of leaving child services may have encountered the ‘twilight zone’ of transition described by Young et al. , and theWe found that female gender was also associated with an increased probability of resumption. It is feasible that gender differences in help-seeking behaviour exist which could influence re-engagement with services ; howeverOverall, a higher proportion of those restarting medication had a psychiatric comorbidity recorded at some point, with the most common being anxiety and depression, but none of the comorbid conditions were significant predictors of resumption in our adjusted Cox model. It is worth noting that comorbid conditions recorded in primary care may also be mislabelled; Asherson et al. have argued that lack of recognition of the clinical presentation of ADHD in adulthood may lead to undertreatment, or to substitution of other psychotropic medication for ADHD medication if the symptoms are mistaken for the signs of other common psychiatric disorders .A study of adults with a new diagnosis of ADHD in the CPRD noted high rates of primary care prescribing of non-ADHD psychotropic medication in this group, with 16% being prescribed antipsychotic medication . In our This is the only study we are aware of to explore resumption of ADHD prescribing in early adulthood in UK primary care since the introduction of the 2008 NICE Guidance . It usesThe low proportion of cases with a recorded secondary care referral also suggests that referrals were under-recorded or perhaps recorded as free text ; this consideration does limit any conclusions we can draw about the level of oversight and specialist support for resumption .This study examines only medication prescribing. There is no way of knowing whether or not the cases included in this study were actually taking their medication as prescribed, and whether they stopped medication when prescribing ceased or at a much earlier point. Non-adherence is estimated to be widespread in ADHD, and is also influenced by factors such as socioeconomic status, beliefs and attitudes and of course adverse effects and medication effectiveness, which we were unable to take into account when considering differences between those who did and did not resume their prescriptions , 33.Unfortunately, our exploration of resumption in this respect was also limited by the absence of data on symptoms and severity of ADHD recorded in CPRD. Even so, we were able to examine age at stopping, recorded psychiatric comorbidities and non-ADHD psychotropic prescribing, all of which provide information characterising who did and did not resume prescriptions and can direct future research.The low prevalence of prescription resumption in our study is perhaps surprising, given that we know that the rate of prescription cessation remains greater than the rate of symptom decline , 6–9. ThOur research therefore adds support to the case for designing and commissioning more flexible models of prescribing support in ADHD, with easier exit and entry points. Brinkman et al.’s survey of participants in the MTA trial reported that many adolescents recognised the need for medication only after stopping, and suggested that a ‘start–stop–start’ pattern with the attendant flexibility may be their preferred model . Early fHowever, alongside a need for flexibility, our findings also paint a picture of greater complexity in those who resume prescriptions, contributing to the debate about where ADHD is best managed for adults with the condition. As argued by Coghill, symptom monitoring, monitoring of impairment and functioning and monitoring of comorbid conditions are skilled and time consuming tasks which may be best suited to specialist care .NICE guidance, and consensus statements outline recommendations on prescribing in ADHD, but research suggests implementation and service provision is highly variable , 30, 34.There is also a need to develop and evaluate models of managing ADHD in transition and early adulthood which address the tensions between prescribing for a potentially complex patient group, and a more flexible approach to medication management. In particular, findings from the MTA study and CATCh-uS suggest that the ‘stop–start’ pattern of medication is likely to be influenced by the patient’s current circumstances and context , 34; futThis study suggests that most of those who stop medication in adolescence do not have their prescriptions resumed in early adulthood. Those that could benefit from resuming their medication as adults need to be able to do so in a safe and stable manner in order to experience improved outcomes. Our findings raise questions around whether current models of care and prescribing are suitable and flexible enough for young adults with ADHD, and whether professionals in primary care are adequately supported by specialist services in managing this group. As a result, there is a need for studies to examine the barriers to implementation of guidance, and for evaluation to develop and refine feasible and acceptable models within an environment of resources constraints.


The multi-relaxation time lattice Boltzmann method (MRT-LBM) has an excellent performance in dealing with the complex flow in many different areas. According to the specific behavior of the fluids, it also has some shortcomings when applied to some special flow like as the non-Newtonian flow. In Cement-3D printing, the fluids always exhibit according to the yielding behavior. When using the standard MRT-LBM, the simulation maybe divergent. In order to solve the problem, this work presents an improved MRT-LBM considering the non-Newtonian effect as a special forcing term to ensure the stable and accurate simulation. Finally, the Poiseuille flow was used to validate the feasibility of the proposed method. It haIn recent years, lattice Boltzmann method (LBM) has developed into a more popular method for fluid simulation. It can be applied in flow simulation with complex fluid types or various structures ,11,12. ACompared with power-law fluids, the researches on yielding fluids are insufficient. Although some researchers proposed improved methods for Bingham fluids, they are not well suited for Herschel–Bulkley fluids. The power-index has an important impact on the simulation. In this work, an improved method was proposed for substituting the non-Newtonian influence of a Herschel–Bulkley fluid into external forcing term. Then cement paste flow in 3-D printing was numerically simulated using the proposed method. Finally, the improved method was validated using a Poiseuille flow. aτ is the shearing stress, K is the viscosity coefficient, τ0 is the initial yielding stress, and n is the power-law index. When n = 1, the fluids will be Bingham fluids; otherwise, it corresponds to Herschel–Bulkley fluids. The kinematic viscosity ν can be calculated using:Common yielding fluids mainly include Bingham fluids and Herschel–Bulkley fluids. They can be expressed using a universal equation:ρ is the density, and m is used to control the increase of stress. When m approaches zero, the equation will become the constitutive model of power-law fluids.In 1997, Papanastasiou and Boudouvis proposed a modified equation to simplify the constitutive model :(3)νap=r is the displacement vector, ie is the discrete velocity, F′ is an external force, and δt is the time step size, which is always set as δt = 1. The second item on the right side relates to the external force.Standard MRT-LBM in two-dimensional space (D2Q9 model) with a forcing term can be expressed as :(4)f, the strain rate tensor is described as:Then, the second invariant of strain rate tensor can be acquired using Equation (11) as:With Equation (12), the shearing rate can be calculated as:αβσ can be described as:αβδ is a Kronecker delta, K is the viscosity coefficient, and P is the pressure. In the following section, the forcing term will be used to explain the non-Newtonian effect. For standard MRT-LBM with external force, the forcing term is expressed as [According to the equality of two different expressions in Reference , then suessed as :(15)F′=MFurther, the item αu and βu are the components of u, and μ is the dynamic viscosity. The relation between kinematic viscosity and dynamic viscosity is ν = μ/ρ. Also, the above equation can also be described using the following equation, which includes the forcing term:The Navier–Stokes equation would be expanded by Chapman–Enskog at incompressible limit as:F in Equation (18) can be obtained as:Combining Equations (3) and (14), with Equation (17), the force related term s8 for next iteration could be calculated. The specific programming process based on software MATLAB 2014a  can be conducted according to the following steps:(1)Conduct the physical transformation according to the dimension theory. The Reynolds number is taken as the key criteria, and then the other parameters used in simulation can be obtained.(2)8s.Set the simulation domain and initial conditions, especially the initial value of the factor (3)Calculate the equilibrium distribution function according to Equation (5).(4)ϱ = Mf, eqϱ = eqMf, and the force term is mainly used to explain the non-Newtonian effect of yielding fluids. The calculations can be seen in Equations (15), (16), and (19).Conduct the collision step, where the specific function is shown as:(5)s8 will be updated.Calculate the strain rate tensor and shearing rate by using Equations (11)–(13), then the kinetic viscosity can be obtained using Equation (3) and the relaxation factor (6)Conduct the streaming step, where the streaming function is shown as:(7)Process the boundary condition, where the non-equilibrium bounce-back scheme is taken as the boundary method.(8)Conduct the next calculation from Step (3).(9)Calculate the density and velocity, where the corresponding equations are:Thus, the non-Newtonian effect described as a special external force will be achieved. Combining the shearing rate obtained from Equation (13) with Equation (3), the parameter Many experiments have been done to determine the detailed types and proportions of materials. The specific components are ordinary Portland cement, water, polycarboxylate water reducing agent, modified rod soil, and cellulose ether. The corresponding proportion were 1:0.4:1.5‰:0.4‰:1.2‰. The rheological equation for mixture fluids can be obtained using a viscometer, which is described as:With Equation (23), it can be found that the mixture fluids were Herschel–Bulkley fluids. It belongs to typical yielding non-Newtonian fluids.In the Cement-3D printing experiment, the structure of the printing head is the key technology. According to repeated testing, the single screw extruder was taken as the printing head. The detailed structure is shown as The specific meanings and values of the key parameters have been listed in The lattice number was set as 480 × 160. The rotation speed of the screw was 40 r/min. The diameter of the screw was designed to be 50 mm. The velocity was only set at the upper surface in u at different directions are given in u was near the inner wall of the barrel and the smallest velocity u was near the outer wall of the screw. In u was near the center line of the channel and the smallest velocity u was at the wall of swirling leaves.In addition, velocity components were also analyzed. The velocity distributions of the component v was at the midway between the barrel and the screw. Then the velocity v was always 0 along the center line of channel. In v near the outer wall of the screw was approximately 0 and it fluctuated near the wall of the swirling leaves. It was bigger when the position was closer to the inner wall of the barrel.It can be found from H represents the distance between the two parallel plates, and τy is a critical point, which is equal to:The obtained results demonstrate the stability of the new, improved MRT-LBM. To ensure accuracy, we took the theoretical results of Poiseuille flow to compare with numerical results of the proposed method. The theoretical solution of yielding fluids is shown as:re is:We consider that the pivotal factors mainly included initial yielding stress, power-index, and lattice number. The accuracy will be discussed using the result of the relative error. The equation of the error n was set as 0.5, 1, 2, where these values correspond to shear-thinning fluids, Bingham fluids, and shear-thickening fluids, respectively. For different power indexes, the effect of lattice numbers and initial yielding stresses were explored. To avoid the effect of other factors, we set the pressure gradient −5, viscosity coefficient ρ = 1, and distance H = 1. Lattice numbers were set in the range of 50–250 and the initial yielding stresses were selected as 1.0 × 10−6, 2.5 × 10−6, and 3.5 × 10−6.The power index n = 0.5 and lattice number was 200. It can be seen that the numerical velocities were consistent with the theoretical velocities at different initial yielding stresses. In In n = 1, it corresponds to a special kind of yielding fluids named Bingham fluids, and the velocities of the simulation and theory at three different initial yielding stresses are shown in When the power index n = 2, it belongs to shear-thickening fluids. The velocities of simulation and theory at three different initial yielding stresses are shown in When the power index In In u, the biggest velocity in two directions occurred at the position of the inner wall of the barrel and the center of the channel, respectively. The smallest velocity in two directions occurs at the position of the outer wall of screw and the wall of swirling leaves, respectively. For the velocity component v, it was 0 when the position was at the center of the channel and the outer wall of the screw. In order to prove the stability and accuracy of the method, numerical solutions of the Poiseuille flow were compared with theoretical solutions. The numerical results were consistent with the theoretical results. For the cases of different initial yielding stresses and power-indexes, the relative errors were small enough to verify the feasibility of the improved method.The non-Newtonian effect will result in a poor stability and accuracy when using standard LBM in the numerical simulation of yielding fluids. To improve the situation, a modified multi-relaxation-time lattice Boltzmann method was proposed. The non-Newtonian part was modeled with a special forcing term. Then the improved method was applied in a simulation of mixed paste in the extruder of Cement-3D printing. Stable streamlines and velocity distributions were obtained. For the velocity component


Years of research have added to our understanding of Attention Deficit Hyperactivity Disorder (ADHD). None-the-less there is still much that is poorly understood. There is a need for, and ongoing interest in, developing a deeper understanding of this disorder to optimally identify risk and better inform treatment. Here, we present a compilation of findings examining ADHD both behaviorally and using neurophysiologic markers. Drawing on early work of McIntosh and co-investigators, we examined response to sensory challenge in children with ADHD, measuring HPA activity and electrodermal response (EDR) secondary to sensory stressors. In addition, we have examined the relationship between these physiologic measures, and reports of behavioral sensory over-responsivity and anxiety. Findings suggest that sensory responsivity differentiates among children with ADHD and warrants consideration. We link these findings with research conducted both prior to and after our own work and emphasize that there a growing knowledge supporting a relationship between ADHD and sensory over-responsivity, but more research is needed. Given the call from the National Institute of Health to move toward a more dimensional diagnostic process for mental health concerns, and away from the more routine categorical diagnostic process, we suggest sensory over-responsivity as a dimension in the diagnostic process for children with ADHD. Attention Deficit Hyperactivity Disorder (ADHD), is a neurodevelopmental disorder characterized by ongoing patterns of inattention and/or hyperactivity and impulsivity that impacts an individual’s functioning across multiple environments. Despite being the most commonly diagnosed mental disorder in children in the United States , ADHD reResearchers have sought to understand the complex features more fully associated with childhood ADHD and have begun to examine qualitative and quantitative variables that impacted the expression, or predicted outcomes, of ADHD. Diagnostic moderators such as comorbid anxiety disorder, symptom severity, and child intellectual level were all identified in the ADHD population as part of a large multimodal treatment study . In addiIn parallel to the work described above, but in a separate body of research, investigators began to focus on the presence of atypical sensory responsivity in children with ADHD. While a variety of terms have been used in the literature to reflect atypical sensory responsivity, sensory modulation disorders (SMD) may best describe this group of behaviors as a type underlying sensory processing dysfunction. SMDs are characterized by an inability to respond to environmental stimuli in a way that matches the demands of the stimulus . IndividMultiple investigators have established that children with ADHD have difficulties in sensory processing and regulating emotional responses to sensation based on parent report questionnaires . These sImportantly, while the research published in the first decade of the 21st century expanded the scientific community’s knowledge of ADHD, associated symptoms, and neurological processes, these studies were generally conducted in isolation. That is to say, anxiety in ADHD was studied separately from stress responsivity in ADHD, and separately from sensory responsivity in ADHD. Building from these studies, our work in the Kathryn Lawrence Dragas Sensory Processing and Stress Evaluation (SPASE) lab began a trajectory of work, considering the potential relationships between and among these constructs , using a more dimensional approach. We will review this work, published between 2009 and 2012, in the proceeding section, followed by an update on the current literature to provide an understanding of this area to date.The overall purpose of this review is to summarize the current state of understanding related to ADHD and sensory processing as well as to provide a direction for future research. Ultimately, we will suggest that atypical sensory processing is an important and often overlooked dimension in the diagnosis and classification of ADHD.n = 24) and without ADHD (n = 24)  . SOR in rs = 0.52 to 0.68, p ≤ 0.01; The Revised Children’s Manifest Anxiety Scale RCMAS; , a behavOur findings in this study supported the work of n = 48) based on their response to sensory challenge, measuring sensory responsivity, electrodermal response (EDR) to sensory challenge, and cortisol at baseline and following the sensory challenge  . As befoThe Sensory Challenge Protocol provided the sensory stressor and we examined EDR and cortisol measures to address our question. EDR reflects the sympathetic nervous system reaction to sensation, the stressor in this study. Our protocol included a 3-min baseline and ongoing EDR measurement during the sensory challenge protocol and for a 3-min recovery at the end of the sensory challenge protocol. EDR measurement used procedures we had successfully used in other studies , and folUsing stepwise discriminative analysis we found that anxiety variables were the strongest predictors, although the overall model was further strengthened using EDR variables. The combination of these variables allowed us to correctly classify 85.6% of our total sample into groups that included ADHDs, ADHDt, TYP, and TYPs. We were able to cross validate this discriminative function, correctly classifying nearly 45% of all children. In interpreting these findings we suggested that SOR be considered a steady state characteristic, driving the child’s response to environmental sensation. n = 131) to conduct a path analysis examining the primary SOR model. As reported above, we used the SensOR  and outcome measures . Thus, our path model indicated that the strength of the child’s response to sensory stimuli, as seen using the sensory challenge protocol, mediates the relationship between baseline measures of arousal and attention, and anxiety and sympathetic nervous system recovery. This too supports the primary SOR model proposed by There are of course limitations to this body of work. Our sample sizes were small, with recruitment challenged by our desire to include children with ADHD but not co-morbid behavioral or psychological conditions . These smaller sample sizes limited our ability to distinguish between ADHD presentations , as well as our ability to examine gender differences. Our sample was also self-referred, and inherent in this is potential selection bias. Our work in the SPASE lab has been globally focused on sensory processing, and our interest in sensory processing may have attracted a unique sample of children. An additional study limitation was our use of parent report measures to assess sensory responsivity. While there is support for the validity of parent report , there aResearch examining the constructs of sensory responsiveness, anxiety and physiological stress responsivity have continued to emerge in the ADHD literature, and results have filled in some of the gaps left by our original work. The purpose of this section is to succinctly review recent progress in the area of ADHD and sensory responsivity.n = 37 per group). In addition to finding that children with ADHD had a greater prevalence of atypical sensory modulation and nearly all other sensory processing problems, these authors also found strong correlations between a variety of sensory processing and modulation impairments and inappropriate behavior and learning responses in children with ADHD. Although sensory-based sub-groups were not used in this study, the authors did examine differences in SP scores between ADHD subtypes , but no significant differences were identified. These findings support the work of Multiple studies have been conducted replicating earlier findings that individuals with ADHD have a higher prevalence of atypical sensory responsivity compared to those without ADHD . ShimizuWhile the majority of research investigating atypical sensory processing and ADHD has been conducted in children, n = 234). Similar to what had already been found in diagnostic samples, these investigators found an association between ADHD traits and a higher frequency of sensory difficulties in all sensory systems . In this investigation they found this relationship for both over and under responsivity. A shortcoming of this study was that no statistical methods were used to assess gender differences or potential sensory-based subtypes. Panagiotidi’s work was in part based on earlier work by Similar to the study by Panagiotidi and Colleagues, Another interesting finding from the Ben-Sasson (2014) study was that girls with ADHD appeared to be at a unique risk for having tactile over-responsivity. Previous findings in children had been somewhat mixed, with some investigators indicating that girls with ADHD more frequently show tactile defensiveness , and othGiven that there is over-lap between behaviors associated with poor sensory processing and ADHD, there has been some effort put toward understanding how these behaviors may change or manifest over time and how they might be better distinguished from one another. For instance, Both ADHD and SOR have been shown to have a genetic link. For ADHD heritability is high . There hBrain imaging studies might also offer some insights. While they have produced some inconsistencies for children with ADHD, looking across these investigations and the few available on children with sensory processing disorders uncovers some interesting overlaps. For instance, Owen and colleagues found, in children with sensory processing disorders, decreases of fractional anisotropy in several regions, but of interest is the superior longitudinal fascicle. Individuals with both sensory processing disorder  and ADHDSince our initial work examining links between sensory processing, anxiety, and stress responsivity, no other studies could be identified that have examined these constructs simultaneously in children or adults with ADHD. Researchers of HPA functioning in the ADHD population have continued using a variety of methods for inducing stress and producing variable results e.g., . KamradtFinally, the overlap between symptoms of anxiety and ADHD has been widely studied in recent years; and research suggests that approximately 25% of adults with ADHD also meet the criteria for generalized anxiety disorder (GAD) and that approximately 25% of adults with GAD also meet the criteria for ADHD . In adulOur previous work established a link between SOR and anxiety  in childYears of research have added greatly to our understanding of ADHD. None-the-less there is a need for, and ongoing interest in, developing a deeper understanding of this disorder, to optimally identify risk and better inform treatment. This is consistent with the intent of the Research Domain Criteria (RDoC) delineated by the National Institute of Mental Health (NIMH) which are designed to support a better understanding of mental health and ill-health using multiple dimensions . Here, wOur collected findings are of interest. Not all children with ADHD show SOR, but when they do, the SOR influences other aspects of behavior. In addition, we have documented that the presence of SOR moderates the stress response to sensory challenge for both typical children and children with ADHD. That is when children identified as having SOR they release cortisol in response to threat  regardless of the presence or absence of the ADHD diagnosis. In contrast, children with ADHDt showed a blunted cortisol response, a finding reported by at least some other investigators . FurtherAn important aspect of the co-occurrence of ADHD and sensory responsivity comes in the form of an impact on activity and participation. The three cases presented in our early work  indicateThe RDoC encourages investigators to use multiple methodologies to understand defined constructs, and we have moved toward this goal with our work. The findings reported here reflect parameters most closely related to the arousal and regulatory systems domain of function in the RDoC, although anxiety and perceived threat also reflect the negative valence systems domain. As units of analysis we have used physiologic, behavioral, and informant-report tools that reflect and relate to sensory processing in children with ADHD. Based on our work we have suggested that sensory responsivity may be an important factor to consider beyond the DSM V diagnosis of ADHD presentation.There are several directions that could be taken to develop a dimensional understanding of ADHD. Two of these more closely examine anxiety and SOR in children with ADHD. The first involves more thoroughly embedding our understanding of these features of ADHD within the context of daily life. As noted above, this process has begun. Investigations have been conducted examining the impact of atypical sensory responsivity in children with ADHD on their participation in the activities of daily life. Examining the role played by anxiety has not been undertaken relative to occupation and participation, and links between anxiety, SOR, ADHD, and engagement in the occupations of childhood have yet to be addressed.The second direction involves embedding this work within a neurodevelopmental trajectory. This has been done to some extent in children with SOR and no other diagnosis , and to Overall there is greater depth and breadth in investigations of sensory processing concerns and their relationship with the diagnosis of ASD. This is to be expected as sensory responsivity differences form one component of the DSM V diagnostic criteria for this disorder. This body of literature is vast, and it is beyond the intent of this paper to summarize the work that has been done. However, it may be important to look to that literature and consider its application in understanding the dimension of atypical sensory responsivity in children with ADHD. In doing so we may gain a better understanding of specific neural systems and networks underlying both atypical sensory responsivity and ADHD. In fact, In conclusion, it is time to take a dimensional perspective in understanding ADHD and its various manifestations. There is general agreement that it is, in fact, a dimensional disorder , and thaBoth authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.