HT) and polymorphic information content (PIC) were 0.36 and 0.30 respectively for both landraces and modern cultivars. Linkage disequilibrium for the modern cultivars was higher than for the landraces . Analysis of the genetic structure showed a clear geographical pattern for the landraces, which were clustered into three subpopulations (SPs) representing the western, northern and eastern Mediterranean, whereas the modern cultivars were structured according to the breeding programmes that developed them: CIMMYT/ICARDA, France/Italy, and Balkan/eastern European countries. The modern cultivars showed higher genetic differentiation (GST) and lower gene flow  than the landraces , indicating a better distinction between subpopulations. The maximum gene flow was observed between landraces from the northern Mediterranean SPs and the modern cultivars released mainly by French and Italian breeding programmes.Assessment of genetic diversity and population structure in crops is essential for breeding and germplasm conservation. A collection of 354 bread wheat genotypes, including Mediterranean landraces and modern cultivars representative of the ones most widely grown in the Mediterranean Basin, were characterized with 11196 single nucleotide polymorphism (SNP) markers. Total genetic diversity ( Of the daily intake of humans, wheat provides 19% of the calories and 21% of the protein (http://www.fao.org/faostat). It is generally accepted that to match the global population demand wheat production will need to increase by 1.7% per year by 2050 [Wheat is one of the most widely cultivated crops in the world, covering an area of 219 million ha with a production of nearly 772 million t in 2017  genotypes, the MED6WHEAT IRTA-panel, of which 170 correspond to landraces from 24 Mediterranean countries and 184 to modern varieties cultivated in 19 countries in the region . Accessions were genotyped with 13177 SNPs from the Illumina Infinium 15K Wheat SNP Chip at Trait Genetics GmbH . Markers were ordered according to the SNP map developed by Wang et al. [http://www.fieldgenetics.com). Genetic diversity was estimated as total diversity (HT) [GST), was calculated as GST= DST/HT, where DST is the genetic diversity between populations. DST was calculated as DST= HT—HS, where HS is the mean genetic diversity within populations. Gene flow was estimated as Nm = 0.5(1- GST)/GST according to McDonald and McDermott [Polymorphic information content (PIC) values were calculated following the formula described by Botstein et al.  using thity (HT)  using POity (HT) . The coecDermott .r2) for markers with known map positions using TASSEL 5.0 [P<0.001 with a sliding window of 50 cM. The intra-chromosomal r2 values were plotted against the genetic distance and a LOESS curve was fitted to determine the distance at which the curve intercepts the line of a critical value of r2 in order to estimate how fast the LD decay occurs for each chromosome. The critical value of r2 was determined as the mean r2 for each genome.Linkage disequilibrium (LD) was estimated as the square of marker correlations  and PIC were estimated for each chromosome. For the landraces, HT ranged from 0.39  to 0.25 (4D) (mean 0.36) and PIC ranged from 0.33 (1A) to 0.21 (4D) (mean 0.30). For the modern collection, HT ranged from 0.40  to 0.29 (7D) (mean 0.36) and PIC ranged from 0.33  to 0.25 (7D) (mean 0.30)  . A summar2) for single chromosomes. In the landraces, LD ranged from 0.07 in chromosome 7D to 0.34 in chromosome 1D, with a mean among chromosomes of 0.14. The percentage of locus pairs showing a significant LD at P<0.001 ranged from 16% to 59% for chromosomes 7D and 2D, respectively, with a mean of 33% , with 21% of the locus pairs in LD at P<0.001 .LD was determined . The D genome had the most markers with significant LD at P<0.001 (39%) in the landraces, whereas in the modern cultivars the B genome had the most, with 51% of the locus pairs showing a significant LD at P<0.001  revealed the presence of two distinct subpopulations, one including landraces and the other including modern cultivars (data not shown). On the basis of this result, a subsequent analysis of population structure was performed independently for the landraces and modern cultivars.The first analysis of the population structure of the 354 accessions was carried out using 557 common SNPs markers between landraces and modern cultivars evenly distributed across the genome. The markers were selected according to the distance of the LD decay for each chromosome in order to avoid the use of markers with a significant LD. The Bayesian clustering method using the Evanno test  to inferΔK for the landraces was observed for K = 2 (2861), followed by K = 3 (1031)  to one of the SPs (q>0.7), whereas using a moderate q-value (>0.5) the number of accessions within an SP increased to 144 (85%), leaving 26 as admixed  were lower than those of the accessions from western Mediterranean countries. Also, 40% of the accessions from Tunisia belonged to SP2. Accessions from Portugal and Spain were distributed in SP1 and SP2. As shown in q-values for the most represented SP. The three Portuguese accessions included in SP2 had a mean q-value of 0.592, and the Spanish and Greek accessions showed q-values slightly higher than 0.5 for the two SPs  . Populat3 (1031)  accordin admixed . The westhan 0.8 . SP1 alsthan 0.8 . Finallythan 0.8 . As show two SPs , denotinΔK) for modern cultivars was K = 2 (728) (q >0.7. The first SP (SP4) included 73 cultivars from France, 10 from Italy and 1 from Serbia. The second SP (SP5) included cultivars from eastern Europe, mainly Serbia (21 out of 24). The third SP (SP6) included 33 cultivars from Spain, 15 cultivars from North African countries, 12 from the Middle East and Central Asia (Afghanistan), and finally 4 from northern Mediterranean countries . Finally, 12 cultivars remained as admixed.Following the Evanno test, as previously reported for landraces, the most likely number of structured SPs ( 2 (728) . The fir 2 (728) . A set o 2 (728) , S1 FileThe relationships between landraces and modern cultivars were also analysed by PCoA as a complementary way to visualize their clusters. In agreement with the results shown by STRUCTURE, the first two coordinates of the PCoA clearly separated the landraces from the modern cultivars, and within each group accessions were clustered matching the results of STRUCTURE . LandracHT) ranging from 0.2651 for modern cultivars from the Balkans and eastern Europe (SP5) to 0.3690 for northern Mediterranean landraces (SP2) (DST) was low (0.0706), resulting in a genetic differentiation (GST) among SPs of 0.1838. This means that only about 18% of the variability observed was due to differences between SPs. The estimation of GST also allowed us to estimate the gene flow (Nm) among SPs. The value of this estimate (2.22) indicates a high level of gene exchange, which denotes a low genetic differentiation among the SPs. When analysed by type of accessions, the landraces showed a GST = 0.1198 and an Nm = 3.67, whereas the modern cultivars showed a GST = 0.1673 and an Nm = 2.49, indicating higher gene exchange between the landrace SPs  . The genrace SPs . When corace SPs .To better detail the kinship among accessions, a neighbour-joining tree was built using the common SNPs markers between the landraces and modern cultivars. The dendrogram showed two main clusters with a robust separation between them . Within The first cluster of modern cultivars (M_Q1) included cultivars from France and Italy (SP4), in addition to three landraces: ‘Bahatane’ and ‘TRI17938’ from France and ‘TRI16516’ from Italy. The second cluster of modern cultivars (M_Q2) represented mainly the accessions carrying CIMMYT and ICARDA genetic background (SP6), including cultivars developed in Turkey, Spain, Egypt, Syria, Morocco, Algeria, France, Italy, Afghanistan, Sudan and Tunisia. This cluster also included the French cultivar ‘Boticelli’ classified in SP4 but including 37% of the genetic background from SP6. Finally, the third cluster of modern cultivars (M_Q3) contained the group of all elite cultivars from the Balkan Peninsula and eastern Europe (SP5): Serbia (23), Macedonia (2) and Hungary (1). This cluster also included the Syrian landrace ‘Salamuni-A’, classified as admixed by STRUCTURE.The clustering of landraces suggested a more complex distribution among the SPs according to the higher frequency of admixture revealed by STRUCTURE. The first cluster (LR_Q1) showed three branches, the first one with landraces mainly from the western Mediterranean (SP1), with Morocco as the best-represented country of the branch (45% of the accessions). The second country represented in this branch was Algeria (14%), and the remaining accessions from Cyprus, Egypt, Greece, Iraq, Portugal, Spain, Syria, Tunisia and Turkey were represented in a lower frequency (3%-6%). The second and third branches included mainly eastern Mediterranean landraces, with 81% of the accessions coming from Egypt, Iraq, Jordan, Syria and Turkey. The remaining accessions were from western  and northern  Mediterranean countries.The second cluster (LR_Q2) was represented by landraces from the three SPs in two main branches. The first branch included a division between genotypes from the eastern (SP3) and western (SP1) Mediterranean SPs. The eastern group was represented mainly by Turkish landraces (46%). An additional 27% was represented by landraces from Cyprus, Iraq, Jordan and Lebanon, and finally the remaining cultivars were previously grouped by structure analysis in the northern Mediterranean SP (SP2). The second group of the branch was composed mainly of landraces from the eastern Mediterranean countries Cyprus (10%), Israel (50%), Jordan (20%) and Syria (10%), although the Bayesian clustering determined that their structure was more similar to that of landraces from the western Mediterranean SP. Only one landrace from Morocco was included in this group. The second cluster included landraces from the western and northern Mediterranean SPs. The first branch included most of the accessions from western Mediterranean countries  and one accession from Libya. In this branch, 50% of the accessions showed high levels of admixture. The second branch of the cluster was divided into two groups, the first including mostly landraces classified as western Mediterranean by STRUCTURE. The group included cultivars from Algeria, Greece, Italy, Morocco and Turkey. The second group was mainly composed of northern Mediterranean landraces (71%) from Albania, France, Italy, Macedonia and Serbia. Although included by structure analysis in this SP, Portugal, Spain and Tunisia from the western Mediterranean and Lebanon from the eastern Mediterranean were also included in this group.Finally, the remaining landraces were included in four branches (LR_Q3) of the main clusters examined above, with 76% corresponding to northern Mediterranean countries . Within these branches, five modern cultivars were also included: the Spanish cultivars ‘Montcada’ and ‘Montserrat’, the Turkish cultivars ‘Ata 81’ and ‘Cumhuriyet 75’, and the Serbian cultivar ‘KG 100’.th century as a consequence of the introduction of high-yielding improved semi-dwarf cultivars. Several studies, reviewed in Lopes et al. [Genetic diversity is essential for plant breeding because it provides new knowledge for improving cultivars. In wheat, the genetic diversity was narrowed down during the second half of the 20s et al. , have cos et al.  reporteds et al.  the moniAmong the 11196 polymorphic markers in the MED6WHEAT panel with a known genetic position according to the map of Wang et al. , the B gThe average PIC value for both the landraces and the modern cultivars was 0.30. This value is in agreement with previous studies using bi-allelic markers such as SNP or DArT in either common or durum wheat. In common wheat, Lopes et al.  found a Linkage disequilibrium is defined as the non-random association of alleles at different loci and decays rapidly with genetic distance. Thus, determining the LD decay over physical and genetic distance within a panel of genotypes is an important step for determining the resolution and marker density required for association studies. Moreover, LD is influenced by population structure due to stratification and unequal distribution of alleles within groups of genotypes, which can result in false associations .r2 calculated for intra-chromosomal loci was 0.12 and 0.18 with 32% and 46% of the locus pairs in LD for landraces and modern cultivars, respectively. It is well known that crops gradually lose their genetic variability through domestication and breeding, resulting in more uniform cultivars, reducing the recombination rate and affecting LD [r2 for both landraces and modern cultivars. Similar results were reported by Chao et al. [In the current study, the mean cting LD , 41. Theo et al.  and Lopeo et al. , who expq-value (>0.7), suggesting high levels of admixture among SPs, whereas for the modern cultivars 79% of the accessions showed a strong q-value. Oliveira et al. [The first attempt to dissect the genetic structure of the MED6WHEAT panel showed a clear separation based on historical breeding periods, i.e. landraces vs modern cultivars. Thus, for subsequent analysis of population structure, independent analyses were carried out for the two groups of germplasm. According to a Bayesian-based analysis, without a priori assignment of accessions to populations, the landraces showed a geographic structure according to the eastern and northern zones of the Mediterranean Basin, whereas accessions classified as western Mediterranean showed a high level of admixture. This classification denoted a migration from the centre of wheat domestication in the Fertile Crescent to the west of the Mediterranean Basin, as reported by Moragues et al.  and Soria et al.  suggestea et al.  proposedBased on the defined SPs, the results of PCoA and neighbour-joining were in agreement with those reported by STRUCTURE, showing first a robust separation between the landraces and modern cultivars, and within these main clusters a separation into three genetic SPs.The origin of the axes in the PCoA showed a mixture between landraces and modern cultivars, as also reported by Oliveira et al. . LandracAccording to the admixture revealed by STRUCTURE analysis, the modern cultivars showed well-defined clusters with differentiation among SPs in the neighbour-joining tree, whereas for the landraces the branches included accessions from different SPs. Only four genotypes from a given SP were misclassified within the clusters of modern cultivars. Within M_Q1, formed mainly by French and Italian cultivars, two French and one Italian landraces were also included. Cluster M_Q2, which grouped most cultivars carrying CIMMYT and ICARDA genetic background, also included the French cultivar ‘Boticelli’, which, although belonging to SP4, has an important genetic background (37%) from SP6. Cluster M_Q3 included a landrace from Syria, classified as admixed, with 30% of its genetic background from the northern Europe SP. The presence of landraces within modern cultivars was reported in a global durum wheat panel by Kabbaj et al. , who conWhen landraces were analysed by hierarchical clustering, a higher level of admixed genotypes was found on the basis of the STRUCTURE classification. Although the main groups within the clusters were formed mostly by members of specific SPs, a discrepancy between the classifying methods was observed among groups of the same cluster. As reported for clusters LR_Q1 and LR_Q2, some of the landraces misclassified by STRUCTURE according to their country of origin or with a high level of admixture were grouped by neighbour-joining into clusters containing accessions from the same geographical region. In LR_Q3, five modern cultivars were also grouped with landraces, probably due to the presence in their pedigree of genetic background from landraces or closely related accessions. However, the closed pedigree of most commercial cultivars did not allow us to clarify this.These results highlight the importance of using different approaches to determine the genetic structure of a germplasm collection. Although the different methods are coincident for the genotypes with strong genetic membership to a given group, they are useful to complement the information provided when accessions show large admixture or gene flow among different geographical regions, as in the case of Mediterranean landraces.Genetic differentiation and gene flow provide information about population differentiation. Gene flow homogenizes populations by genetically decreasing variance among populations and increasing variance within populations. In our study, the analysis of genetic differentiation and gene flow indicated that the majority of the genetic variation was explained by differences among cultivars within genetic SPs. Differentiation of modern cultivar SPs was higher than that of landrace SPs, indicating a lower level of gene exchange among cultivars from different origins. These results are in agreement with population structure and neighbour-joining clustering, in which a higher level of admixture was found for landraces from different geographical regions. Accordingly, it has also been suggested that the low genetic differentiation among SPs is due to seed exchange by farmers, mainly influenced by geographic distances , 50. WheThe current study aimed to explore the presence of genetic and geographic structures in a collection of bread wheat landraces and modern cultivars representing the variability existing for the species in the Mediterranean Basin. The results demonstrated the usefulness of the methodologies employed for achieving this goal. The structure for landraces showed a geographical pattern with different levels of admixture, mainly justified by physical distances between the territories where they were collected, whereas the structure for modern cultivars reflected differences and similarities between the genetic pools managed by the breeding programmes operating in the region.The results reported in the current study may be of special interest for driving the development of new cultivars with desirable traits for the climatic conditions of the Mediterranean Basin, and for identifying useful molecular markers through genome-wide association studies to assist breeding programmes.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(TIF)Click here for additional data file.


In spite of its importance for education, the relationship between learning behaviors (LB), attention deficit hyperactivity disorder symptoms (ADHD) and executive functioning (EF) in children with autism spectrum disorder (ASD) has hardly been explored. The first objective of the present study was to compare children with ASD without intellectual disability and children with typical development (TD) on ADHD symptoms and learning behaviors: Motivation/competence, attitude toward learning, persistence on the task, and strategy/flexibility. The second objective was to analyze the mediator role of behavioral regulation and metacognition components of EF between ADHD symptoms and learning behaviors in children with ASD. Participants were 89 children between 7 and 11 years old, 52 with ASD and 37 with TD, matched on age and intelligence. Their teachers filled out questionnaires assessing executive functioning as well as learning behaviors. Parents and teachers reported on inattention and hyperactivity/impulsivity behaviors. Compared to children with TD, children with ASD presented significantly more ADHD symptoms and poorer learning behaviors. In addition, there were significant mediation effects of the behavioral regulation index (BRI) and metacognition index (MI) of EF, indicating that both are part of the route through which ADHD symptoms impact to learning behaviors of children with ASD. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties in social communication and interaction and restrictive, repetitive, and stereotyped patterns of behavior, activities, and interests . ASD hasThe body of literature on academic skills in children with autism is extensive, with the reviews highlighting the variability in the academic performance across the spectrum and in the different academic areas . HoweverThe understanding of the factors that can be related to the educational results of students with ASD is limited. Although the associations among IQ, academic progress, and response to students’ intervention is significant , the acaStudents exhibiting adaptive learning behaviors participate and dedicate efforts to activities, they collaborate in class and show positive attitudes toward learning. Evidence from research in children with ASD indicates that they show a unique constellation of characteristics that represent challenges to learning. For example, children with ASD have difficulties to engage with or respond to their environment , they shChildren with ASD and ADHD symptoms also show a weak profile on EF  a wide rThe deficits in executive functions (EF) showed by children with ASD that in addition present ADHD are more remarkable even than those of children with ASD, mainly in verbal working memory tasks . The finIn summary, literature shows that children with ASD have problems with school adjustment and academic performance, possibly related to social and communication difficulties and rigid thinking and behavior. Previous findings also point out the ADHD symptoms ,26,27 anThe goal of the present study was to examine LB in children with ASD and the relationships between ADHD symptoms, LB, and EF. By analyzing the relationship between these three variables in a mediation frame-work we intent to extend the previous findings on the negative influence of the symptoms of inattention and hyperactivity/impulsivity in the development of children with ASD. The focus is to understand whether there is a link between LB and ADHD symptoms in children with ASD, by assessing whether this relationship is assembled through the EF.The following two questions were asked in this empirical study: 1) Does the level of development of LB and the symptoms of ADHD differ between children with ASD and children with TD? The hypothesis is that the children with ASD will have more ADHD symptomatology and lower scores than TD children on all the LB: Motivation, Attitude toward learning, Persistence on the task, Strategies/Flexibility; and 2) Are ADHD symptoms, the EF and the LB correlated in children with ASD, with the EF in a mediating role between the ADHD symptoms and LB? The prediction is that LB, ADHD symptoms and EF will maintain association with each other. Moreover it is supposed that both EF components, behavioral regulation and metacognitive components, will mediate in the relationship between ADHD symptoms and all the LB.Participants in the present study were 89 children with ages between 7 and 11 years old, enrolled in schools in the Valencian Community. They all had an intellectual capacity (IQ) equal to or above an average score of 70 measured by the K-BIT which includes verbal and non-verbal measures , and theThe participants with ASD had received a clinical diagnosis in centers specialized in the assessment and treatment of autism and other neurodevelopmental disorders in the Valencian Community . To confirm the diagnosis, cut-off scores were used that are recommended on the Social Communication Questionnaire (SCQ),   and on tThe children with TD were enrolled in the same schools as the children with ASD. They had no history of psychopathology or referral to children’s mental health units (USMI), according to information from the school records. The exclusion criteria for all the participants in this study were rated through an extensive prior anamnesis performed with the families. They included neurological or genetic diseases, brain lesions, visual, auditory, or motor sensorial deficits, and an intelligence coefficient below 80.The teacher version of the BRIEF was administered. It consists of 86 items that assess the child’s EF through the observation of his/her behavior in the school context. The items are scored on a Likert-type scale with three levels , and they are grouped in 8 scales that make up two general indices. The behavioral regulation index (BRI) assesses the child’s capacity to change his/her affective state and modulate his/her emotions and behavior by using appropriate self-control. It includes the subscales of inhibition, shift and emotional control. The metacognitive index (MI) includes the subscales of initiative, working memory, planning/organization, organization of materials, and monitoring. This index reflects the child’s ability to manage tasks cognitively and supervise their performance.The direct scores can be transformed into T scores, with higher scores indicating worse executive functioning. The test-retest reliability of the teacher version of the BRIEF ranged between 0.86 and 0.92 on the Spanish adaptation . These vThe instrument used was the LBS, a 29-item, standardized questionnaire for teachers. The purpose of the test is to assess behaviors related to the student’s effective learning in the past two months. The items on the questionnaire, positive and negative, are rated on a Likert-type scale with 3 response options . The LBS offers 4 dimensions and a total score: Motivation/Competence, which includes behaviors related to the anticipation of success; Attitude toward learning, which measures the willingness to be involved in learning activities; Persistence/attention, which rates persistence on the task and concentration until finishing it; and Learning strategy/flexibility, which evaluates the general way of approaching tasks. High scores indicate good learning behavior. The direct scores are transformed into normalized T scores , based on the standardization of a representative sample of 1,500 students with ages between 5 and 17 years. The internal consistency coefficients are high for the total score  and for the subscales  . In our An 18-item rating scale of behaviors was used in an interview with the parents and teachers. Nine items describe inattention behaviors, and the other nine describe hyperactivity/impulsivity behaviors, as described in the DSM-5 criteria for ADHD. The confirmation ANCOVAs showed statistical significance on all the variables (p < .001): Motivation toward competence, attitudes toward learning, persistence on the task and strategy/flexibility. ASD group showed lower scores than TD group  = .37, Froup see .p = .147, η2P = .025; Attitude p = .267, η2P = .014; Persistence p = .457, η2P = .007; Strategy p = .266, η2P = .015; Total LBS p = .400, η2P = .008).Additionally, we introduce the ADHD symptoms as a covariate in the MANCOVA in order to see the generic effect upon the group differences in the LBS. In this case the differences between both groups became not significant , the MI (p < .001), attitude (p = .005), persistence (p < .001), and strategy (p = .001). Likewise, significant associations were observed between the BRI and the subscales of the LBS, motivation (p = .001), attitude (p < .001), persistence (p < .001), and strategy (p < .001), and between the MI and motivation (p < .001), attitude (p < .001), persistence (p < .001), and strategy (p = .001).In the ASD group, the ADHD symptoms were significantly correlated with the BRI  as mediator variables in the ASD group.Mediation analyses were performed using the PROCESS program of mediation, moderation, and conditional analysis , in ordebootstrapping procedure for a sample of 10,000 and a 95% confidence interval (CI), was statistically significant, CI . Similar results were observed when the direct effect of the ADHD symptoms on persistence in learning was evaluated (B), controlling for the BRI and the MI. Statistically significant indirect effects were found with the mediator MI, CI , and with the two mediators, BRI and MI, together, CI . By contrast, p = .046), so that the mediation was partial. The analysis of the covariate used indicated that the sex did not have a significant impact .Children with ASD are increasingly being included in ordinary classrooms in Spain, following the tendency of other European countries and the United States. Consequently, schools face the challenge of providing appropriate services to respond to the needs of students with this neurodevelopmental disorder. Given the many challenges faced by children with autism in the school setting, it is important to understand the factors that underlie to their classroom school functioning. The present study represents an attempt to collaborate in advancing in this direction, completing the contribution of perspectives focused on school grades or standardized test scores with the analysis of LB in children with ASD.Confirming the first hypothesis, teachers evaluated children with ASD as having a lower level that the children with TD in all learning behaviors which allow students to get educational goals in the classroom. According to their teachers, the children with ASD showed typical behaviors of insufficient motivation, such as less willingness to complete tasks and little perseverance, giving up when encountering difficulties without making an effort to overcome them. Likewise, the teachers reported that the children with ASD had a more negative attitude toward learning, which was shown, for example, in their resistance to taking on a new task. Finally, but not less important, the children with ASD adopted peculiar and inflexible procedures in performing tasks, which materialized as difficulties in shifting from one task to another, modifying a procedure according to a new objective, or trying to find new solutions to problems. On the whole, this learning style is framed in the constellation of restrictive behaviors and interests that are part of the definition of the disorder in the DSM- 5 .Importantly, our findings extend previous work supporting that children with autism have impairments in significant components of school success, including behavioral school engagement, school liking, emotional school engagement , 23, 22.Likewise, the comparison of ADHD symptoms in the group of children with ASD and the group with TD yielded the expected results, coinciding with empirical studies that show the presence of ADHD symptoms in children with ASD ,62,63. TIn order to advance the understanding of the manifestations of ADHD symptoms and EF in LB of students with ASD, the second question of this study addressed the analysis of the relationships among all these variables. On the one hand, the results supported significant and negative associations between ADHD symptoms and LB in children with ASD. Only the relationship between motivation and ADHD symptoms did not achieve statistical significance, although it was close, which suggests that curiosity about learning activities does not have a strong relationship with inattention or hyperactivity/ impulsivity behaviors in students with ASD. On the other hand, consistent with the literature, a significant relationship between ADHD symptoms and EF  was founThe idea that EF deficits could be related to the LB of children with ASD is justified when considering the contextual demands of the school setting. Therefore, it is interesting to explore what specific facets of EF, behavioral regulation, and metacognition, were mediating the relationships between ADHD symptoms and different LB. A strong point of this empirical study, which to our knowledge has not been addressed until now, consisted of the analysis of the possible mediator role of EF in the LB of children with ASD, that is, in the attitudes and behaviors that allow them to participate in and benefit from instruction in class. The results of the multiple mediation analysis carried out were coherent with the prediction. Specifically, the metacognitive processes, that is, the amalgam of working memory, planning, initiative, organization of materials, and supervision skills, mediated the relationship between the ADHD symptoms and the attitude toward learning. Along the same lines, the metacognitive processes were also a significant mediator of persistence on the task. In other words, metacognitive executive functions explained the attitudes toward learning and the persistence on the task, above and beyond the dimensions of inattention/disorganization and hyperactivity/impulsivity. In addition, the results of the multiple mediation analysis showed the importance of the behavioral regulation index. However, there was only partial mediation between ADHD symptoms and the strategy/flexibility learning. Therefore, success on non-routine problem-solving tasks that require flexible thinking and the generation of new problem-solving strategies depends, at least partly, on skills such as inhibition, shifting, and emotional control. Precisely, as has been noted, more than 50% of children and adolescents with ASD have clinically significant cognitive flexibility deficits in the BRIEF . These iIn summary, the findings showed significant mediation effects of the BRI index and MI index of EF, suggesting that both are part of the path (mechanisms) through which ADHD symptoms affect LB in children with ASD. In other words, these impairments are not only a function of diagnosis of ASD but are also a consequence of the combination of the ASD and ADHD characteristics. There seems to be a unique contribution of specific aspects of EF in predicting different learning behaviors of children with ASD, with metacognition being more related to attitudes toward learning and persistence to task, and behavioral regulation being more related to strategy/flexibility. Thus, the appropriate development of the metacognitive processes of the EF may promote the tendency towards being proactive, as well as abilities to stay on task, ignore distractions, and persist despite the difficulties. On the other hand, the executive functioning behaviors of behavioral regulation might play an essential role in the child's capacity to select coping strategies, and to develop strategies effectively to a specific learning situation. In fact, behavioral regulation has previously been revealed as a salient index of self-regulation for both emotional and behavioral school engagement .Our study has some limitations that must be mentioned. The measures of EF and the learning behaviors used in the current study relied primarily on teacher reports, that may have been negatively influenced by knowing the ASD diagnosis. However, we trust that the knowledge of the difficulties that ASD implies and its consideration as a “special educational needs” in the education system might have avoided to a large extent the possible negative bias in the teacher’s assessment.Although the information was provided by informants who are very familiar with the behavior of the individuals in relevant contexts for academic and social learning, it would have been desirable to complement this information with observations made in the classroom and/or home. Results may have been different if observational or performance measures related to a learning behavior and executive functions were utilized. In fact, the agreement between neuropsychological tasks of EF and parents and teacher’s ratings using the BRIEF shows moderate correlation values between these two different assessments procedures ,66. As tAdditional measures of student academic performance were also not included and future research is needed to document the relations between the EF, ADHD symptoms, learning behaviors and additional measures of classroom academic performance. The small sample size, particularly of the TD group, and the major sampling bias towards males in the ASD sample represent a limitation of the study. The results cannot be generalized to girls with ASD either, given the low number of female participants. This limitation is not unique to our study, however, it must be a goal for future research on ASD as findings so far are scarce, suggesting a relative weakness for females compared to males diagnosed with ASD on executive function and daily living skills . These dIn addition, the transactional processes that take place in the classroom are difficult to rate and are not captured in our study. For example, it would also have been important to explore other factors associated with a positive atmosphere in the classroom, such as the teacher’s degree of closeness  and the Despite these limitations, the current study makes an important contribution to our knowledge about implications of executive functions in learning behaviors of children with ASD, underlining the importance for designing interventions that fit their special educational needs. Efforts to increase EF in students with ASD in educational settings could be fundamental to promote LB such as motivation / competence, attitudes towards learning, persistence on tasks and strategy flexibility. There are some positive data in this direction. For example, a recent work demonstrated that the application of the Cogmed Working Memory Training improved the attention, impulsivity, emotional reactivity and academic achievement in children with ASD and comorbid ADHD .However, interventions in the common environments of children with ASD and ADHD have more guarantees of effectiveness and generalization of skills than those implemented in a clinical context. As experts point out, the problem of ADHD is not a problem of lack of knowledge but rather of the application of what is known in the context and at the right moment, due to a EF deficit . In the S1 File(XLS)Click here for additional data file.


Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) share high rates of comorbidity, with the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition now acknowledging the comorbid diagnosis of ASD and ADHD. Although structural abnormalities in the prefrontal cortex, cerebellum, and basal ganglia occur in both ASD and ADHD, no structural studies have focused exclusively on patients with comorbid ASD and ADHD. We thus aimed to clarify the structural features and developmental changes in patients with comorbid ASD and ADHD in a relatively large sample from two sites. Ninety-two patients were age-matched to 141 typically developing (TD) controls (age range: 5–16 years) and assessed for volumetric characteristics using structural magnetic resonance imaging (i.e. surface-based morphometry). While there were no significant differences in prefrontal cortex, cerebellum, and basal ganglia volumes, patients with ASD and ADHD exhibited significantly lower left postcentral gyrus volumes than TD controls. We observed significantly lower postcentral gyrus volumes exclusively in children and preadolescents, and not in adolescents. Our findings suggest that abnormal somatosensory, attributed to delayed maturation of the left postcentral gyrus, leads to the core symptoms experienced by patients with comorbid ASD and ADHD. Attention-deficit/hyperactivity disorder (ADHD), another neurodevelopmental disorder, is likewise characterized by age-inappropriate inattention, hyperactivity, and impulsivity1. The prevalence of both disorders is high, with recent reports indicating that ASD is found in 1.5%2 of the population, while ADHD is found in 7.2%3. ASD and ADHD are more common in boys than in girls, with a male-to-female ratio of about 3–4:15. Individuals with ASD or ADHD have difficulties in daily life and often develop a variety of comorbidities such as oppositional defiant disorder, conduct disorder, depression, and anxiety disorder, especially during adolescence or later1. It is therefore critical to diagnose and treat these patients early, to prevent the development of secondary psychiatric problems.Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairments, communication deficits, restricted interests, and repetitive behaviours6. Previous studies performed in accordance with the DSM-IV, therefore, did not recognize the occurrence of comorbid ADHD and ASD, leading to ADHD traits in patients with ASD being largely ignored and such patients diagnosed with ASD only. However, clinical symptoms of inattention and hyperactivity often occur in individuals with ASD, and children and adolescents with ADHD often exhibit social interaction deficits7, with 28% of patients diagnosed with ASD reported to likely also have comorbid ADHD8 and up to 70% of patients with ADHD exhibiting ASD symptoms9.The diagnostic criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) did not allow for a simultaneous diagnosis of ASD and ADHD in the same individual10. For example, one study reported that children with ADHD and ASD exhibited greater attention deficits than children with ASD alone11. Another study reported that children with ASD and ADHD had greater impairments in adaptive functioning and a poorer health-related quality of life than children with ASD or ADHD alone12. In both ASD and ADHD, effective use of stimulant medications can reduce ADHD symptoms, including hyperactivity and inattentiveness, while the behavioural symptoms of ASD remain unchanged13. These findings provide further evidence that ADHD and ASD are two distinct disorders. As a result of this distinction between ASD and ADHD, their comorbid diagnosis is now allowed by the DSM-51. Despite high comorbidity and greater impairment experienced by individuals with both conditions rather than a single diagnosis, brain imaging studies of individuals with co-occurring ADHD and ASD are limited and the underlying pathophysiology remains unclear.Multiple studies have also suggested that ASD and ADHD are in fact distinct conditions14. While ASD is often associated with brain overgrowth in early childhood and adolescence15, individuals with ADHD often exhibit smaller brain volumes16. The prefrontal cortex and basal ganglia (e.g. the caudate) exhibit increased volumes in ASD19, and the same regions exhibit decreased volumes in individuals with ADHD21. In contrast, the cerebellum and the corpus callosum exhibit decreased volumes in both ASD and ADHD24. Taken together, these studies indicate that the cerebellum and corpus callosum are affected in similar ways in ASD and ADHD, while findings on total brain, prefrontal cortex, and basal ganglia volumes have shown opposite patterns in the two diseases. How brain volumes are affected in patients with comorbid ASD and ADHD remains unclear.Prior brain imaging studies of individuals with ASD or ADHD have revealed some of the pathophysiology as well as biomarkers for these diagnoses. In particular, structural magnetic resonance imaging (MRI) studies have revealed widespread changes in the volume of the cerebral cortex in ASD or ADHD, although these findings are somewhat inconsistentAlthough many structural studies of individuals with ASD or ADHD have been reported, as described above, none have so far focused on patients with both ASD and ADHD. Due to this paucity of research, the structural and developmental features of patients with this comorbidity are still a mystery. The purpose of the present study was thus to clarify the structural features and developmental changes that occur in patients with both ASD and ADHD by examining a relatively large structural MRI sample collected across two study sites.The protocol for the present study was approved by the Ethics Committee of the University of Fukui (Assurance no. 20110104) and the Institutional Review Board of Osaka University Hospital (No. 12168-9). All experimental procedures were conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Clinical Studies of the Ministry of Health, Labour, and Welfare of Japan. After a complete explanation of the study, all participants and their parent(s) provided written informed consent and assent for participation. This study has been registered with the University Hospital Medical Information Network (UMIN000025830).1. Participants’ intellect was assessed via the Wechsler Intelligence Scale for Children-Forth or -Third Edition (WISC-IV or WISC-III)25. Parents were asked to complete the ADHD Rating Scale-IV (ADHD-RS) to evaluate their child’s inattention and hyperactivity/impulsivity symptoms26. The Autism-Spectrum Quotient (AQ) Children’s Version was used at the University of Fukui27, and the Social Communication Questionnaire (SCQ) was used at Osaka University to evaluate ASD traits28. Handedness was assessed using the Edinburgh Handedness Inventory29. Exclusion criteria for both groups included contraindications for MRI, a Full-scale Intelligence Quotient (FSIQ) score < 70, a history of severe head trauma, major physical or neurological illnesses, and drug or substance abuse. The non-autistic and non-ADHD status of TD participants was confirmed using AQ or SCQ and ADHD-RS, respectively.In total, this study included 233 participants, 92 boys with ASD and ADHD (ASD + ADHD) and 141 typically developing (TD) boys. Participants with ASD + ADHD were recruited at the University of Fukui or at Osaka University, Japan. TD children were recruited from the local community and assessed to ensure that none had developmental delays, received any special support education, or had a history of epilepsy or other psychiatric disorders. The diagnoses of ASD and ADHD were based on the DSM-52, 256 × 256 matrix size, 172 slices, voxel dimensions = 1.0 × 1.0 × 1.0 mm3). At Osaka University, two kinds of 3-T MR scanners  were used for high-resolution T1-weighted anatomical MRI .MRI was performed at both the University of Fukui and Osaka University. At the University of Fukui, a 3-T MR scanner  was used for high-resolution T1-weighted anatomical MRI  was used. As in our previous work31, pre-processing of anatomical data was performed using the following steps: (1) motion correction and non-uniformity correction, (2) automatic Talairach transformation, (3) intensity normalization, (4) skull strip and segmentation of the subcortical white and grey matter, (5) tessellation of the white and grey matter, (6) surface smoothing and inflation, (7) topology correction, and (8) parcellation. Cortical reconstructions were reviewed for quality and corrected by trained experts, as necessary. Volumes were calculated across 68 regions using the Desikan–Killiany atlas template32. Motion artefacts and automated segmentation results were reviewed for data quality by two trained experts (Y.M. and M.J.)33. Cortical thickness and surface areas were also investigated, using similar methods.A vertex model was used to measure the participants’ brain volumes across several thousand cortical folding patterns within a brain surface curvature templatet-test for numerical variables and chi-square tests for categorical variables. All statistical tests were two-tailed; p-values < 0.05 were considered statistically significant.Demographic data are expressed as means ± standard deviation (SD). All clinical values were compared between groups using Welch’s p < 0.05 with false discovery rate (FDR) correction for multiple comparisons.For brain imaging analyses, total intracranial, grey matter, cortical, subcortical grey matter, and cerebellar cortex volumes were compared via analyses of covariances (ANCOVAs) (ASD + ADHD versus TD) that included age, FSIQ, and scan site (i.e. MRI machine) as covariates in the model to control for potential confounding effects. Next, we compared the basal ganglia  and calculated cortical volumes, cortical thickness, and surface areas across the 68 regions of interest (ROIs) using the Desikan–Killiany atlas template and ANCOVAs with age, FSIQ, scan site (MRI machine), and total intracranial volume as covariates. The statistical threshold was set to 34 children of 5–9 years of age , preadolescents of 10–12 years of age , and adolescents of 13–18 years of age . We compared the volumes of ASD + ADHD-related regions which showed significant differences in the primary analysis, within each age group, between the participants with ASD + ADHD and TD controls using ANCOVAs with FSIQ, scan site (MRI machine), and total intracranial volume as covariates, to investigate the developmental changes. All statistical analyses were performed using the Statistical Package for the Social Sciences 25 software .We then separated participants into three groups according to their age, as has been done previously:p < 0.01; Table Participants’ demographic and clinical characteristics are presented in Table p = 0.018, FDR-corrected) , there was no significant difference between patients and controls in the adolescent age group (p = 0.171)  Fig. , 4S, 5S.In the present study, we investigated the structural features of the brain in children with ASD + ADHD using relatively large participant pools from two study sites. While we found no significant differences in prefrontal cortex, cerebellum, or basal ganglia volumes, patients with ASD + ADHD exhibited significantly smaller left postcentral gyrus than TD controls. Additionally, in patients with ASD + ADHD, the volume of the left postcentral gyrus was only significantly smaller in children and preadolescents, not in adolescents.35. On the other hand, another study reported that 29 children and adolescents with ADHD exhibited reduced volume in the postcentral gyrus compared to 29 TD controls36. This finding survived familywise error corrections. Thus, although prior reports point to structural abnormalities in the postcentral gyrus in ASD and ADHD, findings have been inconsistent. This may be due to the small sample sizes and differences in statistical thresholds used. In the present study, however, we used a relatively large sample size across two recruitment sites, and we report findings after multiple corrections. Our study may thus be able to add more valid evidence to the earlier reported inconsistent findings. In addition, the postcentral gyrus, which is the location of the primary somatosensory cortex, is involved in somatosensory functions. Sensory problems are core symptoms of ASD and are listed in the ASD diagnostic criteria of the DSM-51. Interestingly, children with comorbid ASD and ADHD reportedly experience more sensory processing problems than those with either ASD or ADHD alone37, and postcentral gyrus abnormalities may further reflect this increased risk for sensory processing deficits. Furthermore, sensory processing abnormalities contribute to social, communication, and repetitive behaviour deficits, which are the core features of ASD38 and are also associated with attentional deficits39. Therefore, in children with comorbid ASD and ADHD, sensory problems may stem from postcentral gyrus abnormalities, which in turn may lead to core ASD and ADHD symptoms .Few postcentral gyrus volume abnormalities have been previously reported in patients with ASD or ADHD. One study that included only 15 children and adolescents with ADHD and 15 aged-matched participants with ASD revealed increased postcentral gyrus volumes in these patients relative to the TD controls. However, this finding did not survive a correction for multiple comparisons42. These changes in symptoms may result from improvements in postcentral gyrus function with development. Additionally, longitudinal studies of brain development in ADHD have reported delayed cortical development44. Similarly, as suggested by the present study, brain structure maturation may also be delayed in patients with ASD + ADHD.Critically, developmental trajectories may also be different in patients with ASD and ADHD comorbidity. In the present study, the left postcentral gyrus volume was significantly lower in children and preadolescents with ASD + ADHD than in TD, but there was no difference in adolescents. This finding suggests that developmental delays in the left postcentral gyrus may improve with age. Although ASD and ADHD are lifelong disorders, their features can change with development, and a substantial proportion of individuals with ADHD and a smaller proportion of individuals with ASD exhibit improved symptoms with age46. This suggests that the primary pathophysiology in ADHD + ASD may differ from that in ASD or ADHD alone. For instance, in an fMRI study using a temporal discounting task, which assesses a key aspect of reward-related decision making, patients with comorbid ASD and ADHD exhibited more pronounced differences in brain-behaviour associations compared to TD controls and those with ASD or ADHD alone. This finding suggests that ASD and ADHD are neither phenocopies nor additive pathologies, but that comorbid ASD and ADHD represents a distinct neurofunctional pathology altogether47. Furthermore, response rates to methylphenidate in individuals with ASD + ADHD differ from those of children with ADHD alone. The National Institute of Mental Health Collaborative Multisite Multimodal Treatment Study of Children with ADHD reported response rates of 70–80%, as compared to the 49% reported in the Research Units of Paediatric Psychopharmacology Autism Network methylphenidate trial49. Methylphenidate acts to increase dopamine and noradrenaline concentrations by inhibiting their reuptake in the prefrontal cortex and reward system, both of which exhibit abnormalities in patients with ADHD. The less pronounced effect of methylphenidate in patients with ASD + ADHD may thus be due to the attention-deficit symptoms in this comorbid condition being related to the somatosensory gyrus rather than to action in the prefrontal cortex or reward system as in ADHD alone. Despite similar symptoms, the regions causing the symptoms, that is, the underlying brain morphology, may differ. It is thus possible that a diagnosis based solely on symptoms is not always in line with the relevant pathophysiology, and symptomatic diagnoses do not often lead to effective therapy. On the other hand, therapy based on pathogenesis, not on a symptomatic diagnosis, may lead to better outcomes. In cases where structural MRI shows abnormalities in the postcentral gyrus rather than the prefrontal cortex and the reward systems, as seen in this study, the effect of methylphenidate may be weaker. Such considerations may be helpful for the selection of medication in the context of prior therapeutic strategies.Interestingly, we found no significant differences in total brain, cerebellar, prefrontal cortex, corpus callosum, or basal ganglia volumes, although these are areas in which abnormalities are often reported in both ASD and ADHD50. Using this model, researchers and clinicians are expected to identify syndromes based on their pathophysiology and thus ultimately improve treatment outcomes for psychiatric diseases. Imaging studies may further help clarify the underlying neuronal mechanisms of psychiatric disorders and thus provide new frameworks for their classification. This may ultimately lead to the development of treatment strategies that consider underlying pathophysiology. Using this model, the field’s future understanding of conditions of the brain will undoubtedly improve, as will the precision of their treatment.The National Institute of Mental Health in the United States launched the Research Domain Criteria (RDoC) project as an answer to the problem that a diagnosis based on symptoms does not always lead to effective therapy. This project sought to create a framework for research on pathophysiology, especially in the fields of neuroscience and genomics51.While our findings offer significant benefits to the field, our study has some limitations. First, it is limited by its inclusion of only patients with comorbid ASD and ADHD. To further investigate the more exact, detailed progress of these diseases, both combined and in isolation, additional longitudinal studies are required. Second, the present study does not include female patients with ASD + ADHD, and our findings can therefore not be generalized to all cases of ASD + ADHD. Third, some patients with ASD + ADHD in this study were under medication, for example with methylphenidate, which may have affected their brain structureIn the present study, we assessed the unique brain structure features of patients with ASD + ADHD. This approach, to the best of our knowledge, has not been used elsewhere, and the relatively large sample size used here presents a significant advantage. Patients with ASD + ADHD exhibited significantly decreased left postcentral gyrus volumes compared to TD controls; however, the left postcentral gyrus volume in patients with ASD + ADHD was only significantly smaller in children and preadolescents, not in adolescents. These findings suggest that the pathophysiology of ASD + ADHD may be primarily related to somatosensory deficits and delayed maturation of the left postcentral gyrus. Our results improve the field’s current understanding of the neurobiological mechanisms underlying comorbid ASD and ADHD and may lead to the development of novel treatment strategies that consider the relevant pathophysiology.Supplementary Materials


Secale cereale var. Multicaule Polish: Krzyca), and to compare these parameters with open-pollinated and hybrid cultivars of common rye. The following determinations were made: the morphological and mechanical properties of grain, milling energy and the protein, starch, ash and free phenolic content of the analyzed flours, their amylograph characteristics and antioxidant potential. It was found that primitive rye has shorter kernels, lower thousand-kernel weight and a higher contribution of redness in color compared with common rye. In primitive rye grain rupture force was determined at 68.9 N and rupture energy at 35.6 mJ. Flours made from primitive rye grain have a higher content of ash and free phenolic compounds, lower starch content and similar antioxidant potential relative to common rye flours. The results of the amylograph test revealed that primitive rye flours were characterized by high baking quality. The primitive rye flours can be alternative ingredients for bread making and provide health advantage such as higher content of phenolic compounds. However, further research is needed to analyze variations in the properties of primitive rye grain and flour resulting from changes in environmental and climatic conditions.The consumers interest in organic food and farmers’ search for cultivars with increased usefulness for organic farming have contributed to the revival of ancient cereal species and their launch onto the food market. In view of the above, the aim of this study was to determine the physicochemical properties, antioxidant potential and baking quality of grain and flour of primitive rye ( Sustainable production and consumption plays a very important role in the food industry. The growing demand for food leads to agricultural intensification and increased use of chemicals  which exert negative effects on the environment. The popularity of organic food is on the rise due to growing levels of consumer awareness about the environmental impacts of agricultural production. Consumers are also becoming increasingly health-conscious and seek organic foods that deliver health benefits  can be grown under difficult soil conditions. Its’ Polish  and Czech and Slovak (Kribica) names denote a spark, and they were coined in an era of fire-fallow cultivation when arable fields were created by burning forests  than open-pollinated and hybrid rye cultivars for breadmaking.Rye bread is a rich source of biologically active compounds, including antioxidants and dietary fiber, mainly arabinoxylans , but cv. Lesan, showed lower milling yields. The baking quality, nutritional value and health-promoting properties of primitive rye flour have been investigated by Konopka et al.  genotype Krzyca.According to the results of investigations, the demand for organic foods is on the rise , and to compare these parameters with open-pollinated and hybrid cultivars of common rye . These studies can contribute to the popularization of primitive rye genotype Krzyca.The aim of this study was to determine the physicochemical properties, antioxidant potential and baking quality of grain and flour of primitive rye . The evaluated genotype was obtained by breeding grain from the collection of the Plant Breeding and Acclimatization Institute—National Research Institute in Radzików, Poland. Two organically grown cultivars of common rye , including open-pollinated rye cv. Dańkowskie Złote and hybrid rye cv. KWS Bono, were used as the reference standards. Open-pollinated rye cv. Dańkowskie Złote was placed on the Polish National List of Agricultural Plant Varieties (COBORU) in 1968 and remains the oldest registered rye variety, which best testifies to its unique properties. Hybrid rye cv. KWS Bono was registered in Poland in 2014. Primitive rye and both common rye cultivars were grown in an organic farm in Godki near Olsztyn in north-eastern Poland .The experiment was performed on organically grown grain of primitive rye  and force at the end of compression (Fe). Rupture energy (Erc) and total compression energy (Et) were calculated . The sifting roller was removed from the mill to obtain middlings . Kernel samples (125 g) from each rye genotype/cultivar were weighed to the nearest 10 mg on the WLC 2/A1 electronic scale  and milled in the laboratory. Twenty-five samples of each genotype/cultivar were milled. The specific milling energy Er (kJ/kg) was calculated with the following formula: Er = (Ec − Es)/mg, where: Ec—total energy consumed by the mill; Es—energy required for initiating the motion of ground particles ; mg—mass of milled sample (kg). Fifteen replicates of middlings from each rye cultivar were separated with a sieve shaker  equipped with sieve 200 μm openings and a collection pan. The sample was shaken for 10 min (vibration amplitude of 1.5 mm). The fraction that passed through the 200 µm sieve was regarded as extracted flour. Flour yield was determined as the percentage of straight-grade flour.Two types of flour were produced in the milling test: extracted flour and wholemeal flour. The moisture content of grain was brought up to approximately 15% (on a wet basis), and grain was ground in the Quadrumat Junior roller mill (Brabenderdi) and volume fraction (φi): davg = SUM(φi ·di). The size of flour particles d(0.1), d(0.5) and d(0.9) (µm) corresponding to the maximum diameter of 10%, 50% and 90% of flour particles, respectively, was determined. The span of volume-based size distribution was determined as follows: SPAN = (d(0.9) − d(0.1))/d(0.5).The size distribution of flour particles was determined by laser diffraction analysis (LDA) using the Malvern Mastersizer 2000 analyzer  according to the AACC 55-40-01 . The ariThe flours were evaluated for ash content  for 15 min at 1400 rpm and a temperature of 22 °C. The supernatant was separated in an Eppendorf shaker (type 5810R) for 10 min at 10,000 rpm and a temperature of 22 °C.The content of free phenolic compounds was determined spectrophotometrically with the Folin–Ciocalteau reagent (Merck) according to Konopka et al. . The col−1 in methanol), and the mixture was shaken and incubated in the dark at room temperature for 30 min. Absorbance was measured at 517 nm against methanol, using FLUOstar Omega multi-mode microplate reader . Antioxidant capacity was determined based on a curve of % DPPH·scavenging activity of different Trolox (Sigma Aldrich) concentrations in methanol and expressed as µmol Trolox per 100 g of flour dry mass.The antioxidant potential of flour samples was determined in the DPPH  radical scavenging assay according to Konopka et al. . The extp ≤ 0.05). The data were also subjected to Pearson correlation coefficient analysis. The results were regarded as statistically significant at p ≤ 0.05.The results were processed statistically using Statistica for Windows v. 10 software (StatSoft Inc.). The data were analyzed by one-way analysis of variance (ANOVA). The significance of differences between means was determined by Tukey’s test  and cv. KWS Bono (hybrid cultivar) are presented in Table In the color analysis, the average value L* of primitive rye grain (51.3) was significantly higher than in hybrid rye (47.3) and lower than in open-pollinated rye (54.3). The grain of primitive rye was characterized by a higher contribution of redness (a* = 5.24) in comparison with the grain of common rye cv. Dańkowskie Złote (a* = 4.24) and KWS Bono (a* = 3.29), but it did not differ significantly from the reference rye in the value of the b* (19.04). According to Zykin et al. , the cole, Erc and Ee) did not differ significantly across the analyzed cultivars. The average specific milling energy of primitive rye grain (95.3 kJ kg−1) was approximately 11% higher relative to the open-pollinated cultivar and approximately 29% lower relative to the hybrid cultivar. Milling energy is determined by the milling method and the properties of grain, mainly moisture content, kernel size and hardness and the degree of milling  to 133.6 kJ kg−1 (grain with 18% moisture content).The mechanical properties of cereal grain play a very important role in the milling process  and extracted flour (5.91%) from primitive rye grain was significantly higher in comparison with the corresponding flours from open-pollinated rye and was similar to hybrid rye flours. The protein content of primitive rye flours was typical of rye flour  has shorter kernels, lower thousand-kernel weight and a higher contribution of redness in color compared with common rye grain. Primitive rye grain is suitable for milling. Its mechanical properties, milling energy, flour yield and the granulometric composition of flours are typical of common rye grain. Both extracted and wholemeal flours of primitive rye grain contain more ash and less starch than common rye flours. The results of the amylograph test revealed that primitive rye flours were characterized by high baking quality. Primitive rye flours contain more free phenolic compounds than common rye flours, and their antioxidant potential is comparable with that of the reference flours. All of the analyzed wholemeal flours were characterized by higher DPPH radical scavenging activity than extracted flours.A thorough knowledge of the attributes of primitive rye grain is essential for obtaining food products of the highest quality. The results of this study indicate that primitive rye . However, further research is needed to analyze variations in the properties of primitive rye grain and flour resulting from changes in environmental and climatic conditions.


Mage = 22.14 years, SDage = 4.82 years) who completed five response time tasks and self-reported symptoms of ADHD using the Conners’ Adult ADHD Rating Scale was used. Principal components analysis (PCA) on these response time measures  produced two variability factors (labelled response selection and selective attention). To understand the genetic drivers of IIRTV we performed a genome-wide association analysis (GWAS) on these PCA-derived indices of IIRTV. For the selective attention variability factor, we identified one single-nucleotide polymorphism (SNP) attaining genome-wide significance; rs62182100 in the HDAC4 gene located on chromosome 2q37. A bootstrapping mediation analysis demonstrated that the selective attention variability factor mediated the relationship between rs62182100 and self-reported ADHD symptoms. Our findings provide the first evidence of a genome-wide significant SNP association with IIRTV and support the potential utility of IIRTV as a valid endophenotype for ADHD symptoms. However, limitations of this study suggest that these observations should be interpreted with caution until replication samples become available.Intra-individual response time variability (IIRTV) is proposed as a viable endophenotype for many psychiatric disorders, particularly attention-deficit hyperactivity disorder (ADHD). Here we assessed whether IIRTV was associated with common DNA variation genome-wide and whether IIRTV mediated the relationship between any associated loci and self-reported ADHD symptoms. A final data set from 857 Australian young adults (489 females and 368 males;  In ADHD, increased IIRTV has been suggested as a leading endophenotype that is able to index the underlying genetic risk for the disorder10. Several behavioural genetic studies have revealed the heritable nature of intra-individual variability, driven largely by additive genetic influences12. Rather than simply representing a non-specific marker of brain dysfunction, IIRTV has been associated with neurobiological networks of attention15. Indeed, neuroimaging studies in both adolescents and adults suggest that IIRTV is related to task-dependent activations of the anterior cingulate and medial prefrontal cortex, dorsolateral prefrontal cortex, and posterior parietal cortex16. Collectively, these findings have led to the proposition that IIRTV arises from the involvement of two processing streams; a top-down attentional control process whereby IIRTV results from spontaneous fluctuations in attentional demand, or a bottom-up process wherein IIRTV develops due to disruptions of the “default mode” network of the brain, comprising regions that are typically deactivated during goal-orientated response time (RT) tasks18.A hallmark of neurocognitive disturbance in many neurological and psychiatric disorders is an increase in intra-individual variability in response time (IIRTV). This increase has been observed across a range of cognitive tasks and disorders, including strokeDAT1, DRD4, SLC6A2 and ADRA2A in cohorts of individuals with and without ADHD22 the additive genetic influences of IIRTV have yet to be explored in a genome-wide manner.Despite growing support for both a genetic and neurobiological basis of IIRTV, its specific genetic architecture remains largely unknown. Although a small number of candidate gene studies have identified associations with catecholamine system genes such as The aim of the present study was to perform a genome-wide association study (GWAS) of IIRTV in a general population sample of young adults. Specifically, we explored IIRTV across a range of neurocognitive measures of RT performance, and conducted principal component analyses (PCAs) to reduce the dimensionality of the RT data into two components, representative of response selection and selective attention, respectively. We then performed a genome-wide association analysis on these PCA-derived indices of IIRTV to identify novel sources of genetic variation contributing to IIRTV. Finally, we explored the association between our PCA-derived indices of IIRTV and self-reported ADHD symptoms, and examined whether the former mediated the relationship between individual genetic variants associated with IIRTV and self-reported ratings of ADHD symptoms. Using this methodology, we identify the first genetic locus associated with IIRTV at a genome-wide level.23. Ethical approvals were granted by both the University of Queensland and Monash University ethics committees. All participants provided informed consent before completing a battery of five cognitive RT tasks and self-reporting ADHD symptoms using the long version of the Conners’ Adult ADHD Rating Scales (CAARS-S:L)24. Saliva samples for genetic analysis were obtained from participants using Oragene kits , and DNA was isolated from those according to standard protocols.One thousand two hundred and ninety-six control participants with no self-reported personal history of psychiatric or neurological disorders were recruited for the present study. Participants were recruited from Melbourne and Brisbane, Australia. To control for genetic variation amongst ethnic populations all participants were of European ancestry as determined by self-report of the ancestry of all four grandparents25, where participants provided either a ‘left’ or ‘right’ response to a target arrow located centrally on a computer screen, while discounting four flanking distractors ; (4) a spatial competition task, which required participants to indicate the orientation of a target letter (an upright or inverter letter ‘T’), while ignoring competing distractors  locations from amongst rival distractors , which provides a measure of response-time variability that controls for differences in the baseline speeds of processing14. Full details of the above tasks have previously been reported22.For all cognitive tasks, stimuli were presented on an 85 Hz, 12 × 16-inch cathode ray tube monitor positioned at a viewing distance of 65 cm  away from the participant. Participants were instructed to align their visual gaze onto a centrally positioned fixation cross (+) and to make their responses using a standard computer keyboard. Five cognitive tasks were completed in counterbalanced order: (1) an Eriksen flanker taskors Fig. ; (2) a cors Fig. ; (3) a sors Fig. ; (5) a Pors Fig. . Across 2, age × sex, testing site, and the top two eigenvectors for the underlying population sub-structure. A genome-wide significance threshold of 5 × 10−8 was adopted to adjust for multiple comparisons. Linkage disequilibrium (LD) analysis was subsequently performed for our lead SNP using PLINK v1.929, and were plotted using Haploview 4.130A GWAS was performed to test for an association between our PCA-derived indices of IIRTV and individual genetic variants (imputed SNPs) using a linear regression model that assumed an additive model of genetic inheritance. Regression models were adjusted for age, sex, age31. To assess the influence of IIRTV as an endophenotype of ADHD symptoms we utilised the Preacher and Hayes32 bootstrapping macro in SPSS to bootstrap the sampling distribution of the indirect effect, where the indirect effect corresponds to a reduction in the strength of the gene/ADHD symptom association that is attributed to IIRTV. Bootstrapping estimates were based on 100,000 bootstrap samples.In the current study, we tested a mediational model of an endophenotype, which assumes that the causal path from gene to disorder/trait passes through an endophenotypeMage = 22.14 years, SDage = 4.82 years).A stringent quality control process was conducted on all task and genotypic data (see Supplementary Information). After quality control, the remaining sample comprised of 857 subjects  and 37.2% and 19.38% (Brisbane) of the variance, respectively. Oblimin rotation35 was performed and revealed the presence of a simple structure, with our measure of ICV showing strong loadings and all task variables loading substantially to either one of two components (similarly for our measure of SD RT).For the SD and ICV RT data derived for each of the five response time tasks, PCA was performed separately across each testing site (Melbourne and Brisbane). Given the intrinsic correlation between measures of SD and ICV RT data, the analyses and results are presented for ICV only . For each PCA by site, the correlation matrices consisted of multiple coefficients above 0.3. Bartlett’s test of sphericity22.ICV from the Flanker, Go and Stop tasks loaded very strongly on component 1 (hereafter factor 1), while ICV from the Competition and Cueing tasks loaded very strongly on component 2  IV Inattention; DSM IV Hyperactivity/Impulsivity; and ADHD Index) as assessed by the CAARS  IV Inattention; DSM IV Hyperactivity/Impulsivity; and ADHD Index) as assessedby the CAARS : rs62182100 within the HDAC4 gene . The imputation quality (r2) for rs62182100 is 0.51382—meeting the recommended INFO imputation quality score standard of 0.3 suggested to define sufficiently good imputation quality38, with a minor allele frequency of 0.05525. Comparable results were found for the measure of SD of RT , we identified one imputed SNP that attained genome-wide significance  are open access and available under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License.GWAS summary statistics for this study . This indicates more significant associations between genetic variants and IIRTV than expected by chance alone. The inflation factor of the test statistic for ICV factor 2 (λ = 1.01) revealed minimal inflation, suggesting little influence of population stratification or other systematic biases. LD analysis of our genome-wide significant SNP (rs62182100) identified three surrounding variants  with our lead SNP . As can be seen from Fig. To examine the addictive effect of the association between the 24. The indirect effect of rs62182100 on the different self-reported ADHD symptom measures via factor 2 ICV are summarised in Table We also assessed the influence of IIRTV as an endophenotype of ADHD symptomatology by evaluating whether any of the PCA-derived indices of IIRTV mediated an association between genetic variation in the genome-wide significant SNP rs62182100) and measures of self-reported ADHD symptoms 82100 and12. Here we have identified the first genetic locus associated with increased IIRTV through genome-wide analysis in a sample of healthy young adults. Further, we have also demonstrated that IIRTV mediates the relationship between the identified genetic variants present in the HDAC4 gene and self-reported symptoms of ADHD.Increased intra-individual response time variability is characteristically reported across a range of heritable neurological and psychiatric disorders. In disorders of attention, particularly ADHD, an increase in IIRTV is thought to be endophenotypic, indexing underlying genetic riskHDAC4 gene and IIRTV. The HDAC4 gene belongs to a class of histone deacetylase (HDAC) genes and encodes HDAC439, which has been implicated in the indirect inhibition of DNA transcription40 through modulation of promoter activity41. HDACs play a pivotal role in the regulation of transcription factors by moderating their access to DNA41. Animal studies have previously implicated HDACs in the modulation of memory43, learning, and synaptic plasticity44, and pharmacological research has demonstrated that the administration of HDAC inhibitors lead to improvements in learning and memory45. Importantly, HDAC4 is strongly expressed in the brain, being broadly expressed in the cortex and hippocampus46. Selective loss of HDAC4 in the brain has been shown to result in impairments in spatial learning and long-term synaptic plasticity and spatial learning44. Although no study to date has directly implicated HDAC4 in specific attentional processes, and given our lead SNP (rs62182100) lies within this gene, the relevant activity of this family of enzymes warrants further investigation within the context of attentional processes.This study reports for the first time a genetic association between DNA variation in the 49. Second, the observation that IIRTV mediates the relationship between DNA variation in the HDAC4 gene and ADHD symptoms in healthy young adults, provides critical support for a mediational model of an endophenotype, under which genetic liability for a trait  passes through the endophenotype31. Third, the fact that we identified our association with the “selective attention” and not “response selection” IIRTV factor is interesting. The cognitive tasks loading on the selective attention factor require participants to filter distractors and focus on task-relevant stimuli, which maps conceptually to subjective reports of “distractibility” in ADHD.The results of this study are noteworthy for a number of reasons. First, to our knowledge it is the first study to employ a genome-wide approach to isolate the genetic substrates of IIRTV. Previous studies, including our own, have performed surveys of a limited number of candidate genes with mixed results50)—will be needed to advance the field. One could argue that if IIRTV is an endophenotype for ADHD then genes linked to this trait should emerge as risk genes for ADHD more broadly. However, a simple look-up in the latest ADHD GWAS meta-analysis performed by the Psychiatric Genomics Consortium and Danish IPSYCH group51 found no evidence of association with either rs62182100 or the three SNPs in LD  within HDAC4. Also, although one other study52 has recently reported cross-disorder evidence of association (p = 7.65 × 10−6) for rs3791556 located within the HDAC4 gene and five major psychiatric disorders (including ADHD) the associated SNP (rs3791556) is not in LD with the SNP associated with IIRTV in this study. Interestingly, HDAC4 has been implicated in functional genetic pathway analyses, with the class of histone methylation processes  reportedly showing the strongest association of genetic expression related to the adult disorders 53. These observations suggest that our genome-wide findings should be interpreted with caution until replication samples become available. Indeed, we also note that we previously published a candidate gene association with IIRTV in a subset of the current sample  and found an association with the SNPs in the ADRA2A gene and ICV factor 1 (response selection) (rs1800544 and rs602618)22. We do not, however find any consistent evidence for this association in the current genome-wide analysis in the sample of 857 young adults .Nonetheless, there are a number of limitations to this study. First, our study is underpowered to detect reliable genome-wide discovery signals and lacked a replication sample. We suggest that a consortium approach to cognitive genetics—much like that used for imaging genetics (e.g., ENIGMAHDAC4 gene and to mediate the relationship between this genetic variant of the HDAC4 gene and self-reported ADHD symptoms. Although these data provide further support for the primacy of a response time variability endophenotype for ADHD symptoms, they require replication in larger cohorts.In summary IIRTV captured by PCA-derived indices of response time variability were found to be associated with a genetic variation in the Supplementary Material


Early recognition, identification and treatment of children with attention deficit hyperactivity disorder (ADHD) can reduce detrimental outcomes and redirect their developmental trajectory. We aimed to describe variations in age of ADHD diagnosis and stimulant prescribing among general practitioner practices in a nationwide network and identify child, parental, household and general practice factors that might account for these variations. Cross-sectional study of children aged under 19 years registered within a general practice in the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network in 2016, RCGP RSC has a household key allowing parent and child details to be linked. Data from 158 general practices and 353 774 children under 19 were included. The mean age of first ADHD diagnosis was 10.5 years  and the mean percentage of children with ADHD prescribed stimulant medications among RCGP RSC practices was 41.2% (95% CI 38.7 to 43.6). There was wide inter-practice variation in the prevalence of diagnosis of ADHD, the age of diagnosis and stimulant prescribing. ADHD diagnosis is more likely to be made later in households with a greater number of children and with a larger age difference between adults and children. Stimulant prescribing for children with ADHD was higher in less deprived practices. Older parents and families with more children fail to recognise ADHD and may need more support. Practices in areas of higher socio-economic status are associated with greater prescribing of stimulants for children with ADHD. Attention deficit hyperactivity disorder (ADHD) is the most common psychiatric disorder in childhood and adolescents.Early recognition of this condition is critical as diagnosis and resultant intervention that addresses the wide range of personal, social, educational and occupational needs of children with ADHD can redirect the developmental trajectory of children with this condition.There is evidence that the threshold for ADHD diagnosis and the prescribing of stimulant medications may be influenced by demographic characteristics of the child,We carried out this study to describe variations in the average age of ADHD diagnosis and prescribing of stimulant medications among general practices who are members of the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) networkWe used information from the RCGP RSC sentinel network database. At the time of this study it hosted a pseudonymised dataset from a nationally representative sample of just under 2 million people registered with a network practices.UK general practice is suitable for this type of study because it has a registration-based system with patients registered with a single practice. Practices have been computerised since the late 1990s, with pay-for-performance (P4P) introduced in 2004.We used coded data reported in online 10.1136/bmjebm-2018-111133.supp1Supplementary dataWe used Read codes, and also some CMR brand specific proprietary drug dictionary codes, reported in online 10.1136/bmjebm-2018-111133.supp2Supplementary dataWe extracted demographic information about children with ADHD including their age, sex, ethnicity and socio-economic deprivation as indicated by their index of multiple deprivation (IMD) score.An average demographic profile of children with ADHD for each practice was compiled including their mean age, the proportion of female children with ADHD and the proportion of children from the lowest IMD quintile. Racial and ethnic disparities between non-white and white populations has also previously been shown in ADHD diagnosis and treatment,The rate of hyperkinetic conduct disorders (HKCD) for each practice was also calculated separately.Between August 1st and August 31st 2017 we extracted information about the general characteristics of RCGP RSC practices from summary statistics extracted for each practice, from individual practice websites, the NHS choices website and the General Medical Council (GMC) register.We collected information about the quality of care provided by each practice from the National General Practice Profiles published by Public Health England, including the total achievement score in the P4P scheme, called the Quality and Outcomes Framework (QOF), as well as the total scores in the clinical and public health management domains of the QOF.We used a household identifier in each medical record to extract information about the average profile of households in each practice including the proportion of single adult households, the average number of children under 19 years in each household, and the average age difference between adults and children in each household.We report descriptive statistics generally reporting mean for normally distributed data, but mode, median and mean where there is a skewed distribution of data.We used a Shapiro-Wilk testMultiple linear regression was then performed to examine the effect of child, general practice and household factors on the percentage of children with ADHD prescribed stimulant medications. The residuals from a multivariate regression of mean age of ADHD on study covariates was positively skewed, so we employed a log transformation of mean age of ADHD.The regression coefficients from this log-level regression was used to calculate an effect of a unit change in the study co-variates with a percentage change in the average ADHD diagnosis age between practices.All statistical analysis was undertaken using R release V.3.5,Data from 158 general practices and 353 774 children under 19 years of age in the RCGP RSC national sentinel practice network were included in the study. 82.9% (131/158) practices were located in an urban location. The mean number of GPs per practice was 7.5  and the mean number of years since GPs had qualified from medical school was 19.7 years. Each GP within the practice had a mean of more than two specialist qualifications in addition to their medical qualificationsThe GP practices within the network achieved a mean 97.7% (95% CI 97.1 to 98.4) total P4P/QOF score overall . This compares with a mean total P4P/QOF score of 95.5% (95% CI 95.3 to 95.6) median=97.7, mode=100.0 for all practices in England.Information from a total of 803 218 households in the RCGP RSC network showed 23.2% included a child under 19 years of age between January 1st and December 31st 2016. This compares with national census data for 2015 of 29% of households with dependent children under 19 years of age. On average 31.0% (95% CI 29.6 to 32.4) of households with children were occupied by a single adult over 18 years of age. This compares with national census data for 2015 of 25% of single parent households. In households with children, there was an average of just under two children under 19 years of age. This compares with the national census data for 2015 that showed that 40% of families had two dependent children and 15% of families had three or more dependent children Within households in the RCGP RSC network the average age difference between adults and children was 36 years (95% CI 35.7 to 36.3).3470 children with a coded diagnosis of ADHD within the RCGP RSC network were included in the study. Of these, 649  were female and 316  were of non-white ethnicity. There was wide variation in the rate of ADHD diagnosis between practices within the RCGP RSC network from 0.01 to 3.2 per 100 children (inter-quartile range 0.62). See Only 26 children with HKCD were recorded in the RCGP RSC network.The mean age of first diagnosis with ADHD was 10.5 years . There was also wide inter-practice variation in the age of first diagnosis of ADHD with the diagnosis being made anywhere between 5 and 17 years of age across RCGP RSC practices.The mean percentage of children with ADHD prescribed stimulant medications among RCGP RSC practices was 41.2% (95% CI 38.7 to 43.6). There was wide inter-practice variation in prescribing with RCGP practices prescribing stimulant medications for between 5.6% and 77.3% of their patients under 18 years with ADHD.Multiple regression analysis showed that characteristics of the households in which children with ADHD are raised was a strong predictor of the mean age that children are diagnosed in practices see .For every unit increase in the average number of children in the household, there was a 27.6% increase of in ADHD diagnosis age within the practice. Equally, for every unit increase in age difference within practice households, there was a 3.8% increase in ADHD diagnosis age within the practice.Larger practices with less registered children were also more likely to make ADHD diagnosis at an older age, although the magnitude of these effects was much less than the characteristics of the households.Other characteristics of the general practice including the urban/rural location, the proportion of children in the lowest socio-economic quintile, the qualification of GPs or indicators of the practice clinical care did not significantly affect the inter-practice variations in the age of ADHD diagnosis.The number of children with ADHD in a practice or the number of children with HKCD did not significantly affect the inter-practice variations in the age of ADHD diagnosis.Using data from a nationally representative network of general practices, we found wide inter-practice variation in the prevalence of recorded diagnosis of ADHD.While most children were diagnosed with ADHD by the age of 10, we also found widespread variations with practices making a diagnosis of ADHD anywhere between 5 and 17 years of age. ADHD diagnosis was more likely to be made later in households with a larger number of children and wider age gaps between adults and children in the households, suggesting that the diagnosis was more likely to be made in families with older parents. Larger practices with less registered children were also more likely to make diagnosis of ADHD at a later age.Stimulant prescribing for children with ADHD also showed widespread inter-practice variation, with more affluent practices more likely to prescribe stimulant medications for their children with ADHD.There are a number of strengths of our study including its large sample size of 158 practices from a nationally representative sentinel network, which allows us to examine the impact of a key primary care determinants on the age of ADHD diagnosis, a relatively under-researched area.However, our study has a number of limitations including the observational and cross-sectional nature of the study design which does not allow us to draw causal inferences from our results.There are a number of limitations to our study related to the use of routinely collected data, including the quality of data in primary care records and data about practice characteristics in publicly available sources.Data from the RCGP RSC sentinel network has been previously appraised and published.Assessment of exposure to stimulant medications was not undertaken in this study, thus while we found inter-practice variations in stimulant prescribing practices, we cannot make conclusions about variation in stimulant exposure between practices. This is important as it has been suggested that exposure to stimulant medication, both early initiation of therapy and chronic exposure are important adverse childhood outcomes.We know that the RCGP RSC household key under-estimates household size.The enumeration of practice structural variables for this study relied on the extraction of publicly available data about practices from their own websites, NHS choices and the medical register held by the GMC. The quality of this information, especially its completeness, consistency, timeliness and accuracy has not been validated in published data, and given the number of practices involved in this study we did not attempt to validate the accuracy with individual practice staff.The outcomes of age of ADHD diagnosis and stimulant prescribing may also be affected by differences in behaviours of individual GPs in practices, however it was not possible to test any clustering of outcomes by individual GPs as there is insufficient details to consistently document activity by GPs in our dataset.Lastly, we have not included data from child and adolescent mental health services where the majority of diagnoses of ADHD are made. Thus, there is a risk that the diagnosis from secondary care may be missed or there may be a delay in the recording of diagnosis in the primary care record.The ADHD prevalence from this study of 1.0%,  is similar to previous studies in the UK,Previous studies have found variations in the prevalence of ADHD in community samples and variations in the age of ADHD diagnosis.Sayal and colleagues suggested that parental recognition of problems and views that their child has hyperactivity were associated with greater severity of symptoms. In their sample they found that the majority of parents discuss their concerns with professionals based in education services and few had consulted primary care for these problems.In contrast stimulant prescribing for children with ADHD was significantly determined by the proportion of children in the practice in the lowest IMD quintile. This is similar to other studies which have found that socio-economic status is an important determinant of stimulant prescribing,We have shown that the composition of households is important in the recognition of ADHD. Households with a greater number of children may hinder the parental recognition of an over-active child. The larger age gap between adults and children within households hints at another possible explanation which revolves around the greater experience and situation handling skills of the caregivers, which may result in greater absorption of over-active behaviours and a later ADHD diagnosis in these households.This would suggest that older parents with more children would benefit from greater support to recognise the symptoms of ADHD and present their children to healthcare services for earlier assessment.The finding that larger practices with fewer registered children diagnosed children with ADHD at a later age may suggest that GPs in these practices have less confidence in making the diagnosis early and a higher threshold for symptoms to be initially presented in these practices. It could also reflect poorer links with specialist education services or child mental health services for definitive diagnosis in some practices and is an area for further study.Previous research suggests that parental factors are critical to recognition of ADHD, and our research on households hints at how parental recognition may be influenced by the age and experience of the caregiver.However, further research is required to understand the barriers and facilitators to early parental recognition, especially the role of statutory services including school health services which are often the first point of call for parents with symptomatic children.Early recognition, identification  and treatment of attention deficit hyperactivity disorder (ADHD) can alter the trajectory of this condition.However, the factors which influence variations in ADHD diagnosis within primary care in the UK are not described.This study describes the variation in age of ADHD diagnosis and stimulant prescribing for children with ADHD among a nationally representative sample of English general practices.Family factors were significantly predictive of age of ADHD diagnosis in the practice. For every unit increase in the average number of children in the household, there was a 27.6% increase in ADHD diagnosis age within the practice. Equally, for every percentage increase in age difference within practice households, there was a 3.8% increase in ADHD diagnosis age within the practice.General practice factors were also significantly predictors of an older age of ADHD diagnosis in general practices, although the magnitude of these effects was much less than the characteristics of the households.General practice factors such as a decrease in the proportion of children from the lowest index of multiple deprivation (IMD) quintile in the practice were also significantly associated with increased stimulant prescribing for children with ADHD. For every unit increase in proportion of children in the lowest IMD quintile in the practice, there was 0.1575 less prescriptions for stimulant medication for children with ADHD.Patient factors were not significantly predictive of age of ADHD diagnosis or stimulant prescribing for children with ADHD.Health system factors resulting in wide inter-practice variation in the age of ADHD diagnosis and stimulant prescribing could be identified and addressed to facilitate earlier diagnosis of these children.Families with older parents and parents with more children could be offered greater support to recognise the symptoms of ADHD and present their children to healthcare services for earlier assessment.