The Holistic Evidence and Design (HEAD) study of U.K. primary schools sought to isolate the impact of the physical design of classrooms on the learning progress of pupils aged from 5 to 11 years (U.S. kindergarten to fifth grade). One hundred fifty-three classrooms were assessed and links made to the learning of the 3,766 pupils in them. Through multilevel modeling, the role of physical design was isolated from the influences of the pupils’ characteristics. This article presents analyses for the three main subjects assessed, namely, reading, writing, and math. Variations in the importance of the physical design parameters are revealed for the learning of each subject. In addition to some common factors, such as lighting, a heavy salience for Individualization in relation to math becomes apparent and the importance emerges of Connection for reading and of Links to Nature for writing. Possible explanations are suggested. These results provide a stimulus for additional finesse in practice and for further investigation by researchers. The Holistic Evidence and Design (HEAD) project investigated how the built environment within and around classrooms in U.K. primary schools influences learning rates in children. Building on a comprehensive Environment-Behavior (E-B) model , and infWithin the Naturalness principle, much research has been carried out about optimum: lighting levels , acoustiBeyond these basic “comfort” requirements, to actively learn within an environment, further features are required. It has been shown that to engender the concentration essential for learning, a child needs to be actively engaged with the learning process. The third principle of Level of Stimulation aims to create measures that put a scale on the visual stimulation of a classroom. This is done through two measures: Color and Complexity. The scientific research into color is extensive and color can affect children’s moods, mental clarity, and energy levels . The meaTo summarize, the three design principles were split into 10 classroom parameters as follows:Naturalness: Light, Sound, Temperature, Air Quality, Links to NatureIndividualization: Ownership, Flexibility, ConnectionLevel of Stimulation: Complexity, Color.More detailed descriptions of the 10 built environmental parameters of Light, Sound, Temperature, Air Quality, Links to Nature, Ownership, Flexibility, Complexity, and Color are given in could be thought to impact the learning of pupils. In this article, we aim to investigate how the elements of the built classroom environment aid or hinder learning progress for each of three separate subjects, namely, reading, writing, and math. Each of these subjects could require different levels of a whole range of pupil skills such as focused concentration, creativity, and problem solving and so may call for different optimal physical learning environments.It can be seen that all of the above factors Although we focus on the role of the physical features of a large sample of classrooms, we also address pupil characteristics and teacher effects to control out their influences through a range of related measures, twinned with multilevel statistical modeling.A description of the data collection and research methodology is contained in the next section of this article. This is followed by the results from the subject investigations and then the discussion, and conclusion.In the U.K. primary school, pupils are based predominantly in one classroom for their learning over a given academic year. U.K. primary school buildings reflect the mild winters and summers that occur. They are heated through the winter season but mostly naturally ventilated in the summer months.2; an average total floor area of 3,700 m2; and an average pupil count of 400. Three of the Blackpool schools were older and built between 1900 and 1920 and four were modern being built between 1970 and 2006. The Hampshire schools had a mix of rural, suburban, and urban sites, typically with ample outdoor space and recreation areas. They had an average site area of 22,900 m2; an average total floor area of 1,840 m2; and an average pupil count of 280. One Hampshire school was built in approximately 1880, six were built between 1950 and 1979, and three were built after 1990. The Ealing (outer London) schools were mostly within high population density areas and were large multiform urban schools. They had an average site area of 11,700 m2; an average total floor area of 3,050 m2; and an average pupil count of 450. Four of the schools were built between 1900 and 1921, three built in the 1950s and 1960s, and three built after 1980. More detail of the building characteristics is given in The strategy for the data collection was to achieve a diverse data set, so maximizing the opportunities to explore the impacts of variations in various parameters. Thus, data were collected from 153 classrooms in 27 primary schools in the United Kingdom. The researchers worked with three Local Authority councils (LAs) from three areas with very different characteristics: Blackpool on the coast of northwest England, Hampshire in the south of England, and the outer London borough of Ealing. The building ages and characteristics were chosen to reflect different building regulations from the Victorian era through the 20th century. The Blackpool schools were in a mix of urban and suburban areas with predominantly compact sites having an average site area of 10,600 mThe architectural data collected about the schools consisted of two surveys: a whole school survey taking measures of shared spaces, for example, libraries, assembly halls, gyms, outdoor areas; and a detailed classroom survey for each class. The classroom survey measured both architectural measures such as size of windows, placement of doors, and Interactive whiteboard (IWB), and soft features such as the quality of blinds and number of learning zones. A list of features was assessed in each classroom to provide a database of measurements of the physical environment. Measurement elements were chosen using a hypothesis-led procedure based on the three design principles. It became apparent through preliminary bivariate analyses (see below) that the Level of Stimulation factors were curvilinear so that, for example, with the parameter Complexity neither very sparse/plain rooms nor very cluttered/messy rooms were optimal for learning. Consequently, the scale for Complexity had both very low and very high levels of Complexity scoring low, but intermediate rooms, with a balance of interesting but more ordered features scoring high. For each of the factors in Blackpool school and pupil data were collected in the pilot study in the academic year 2011-2012. Hampshire and Ealing data were collected for the academic year 2012-2013. In this study, data from all three areas were combined.Data were collected about each of the 3,766 pupils covering the age range from Year 1 (age 5 years) to Year 6 (age 11 years), and critically, including the classroom in which they resided. The spread of pupils across the three locations was 715 from Blackpool; 1,535 from Hampshire; and 1,480 from Ealing. Fifty percent of the sample were female and the spread across Years 1 to 6 was, respectively, 12% in Year 1, 16% in Year 2, 20% in Year 3, 17% in Year 4, 19% in Year 5, and 16% in Year 6.We were modeling both pupil-level progress and classroom effects, so it was important to include in the model any pupil factors that could confound the latter effects. Thus, data were collected as to whether the child was in any of the three special categories of having free school meals , having English as an additional language , or having special educational needs . Because of their large impact on educational attainment and linkage to additional funding, data for these three categories are routinely collected for each child in English schools. The representation of these categories in the sample was, respectively, 21%, 23%, and 18%.Pupil age was another important factor to include as pupils learn at different rates, but in the United Kingdom, their age determines which year group they are in, and pupils in different year groups learn at different rates. In this research, we use both “actual-age” (in months), which determines the pupil’s year group, and “age-in-year” (the number of months the child is past their birthday at the start of the academic year), which is how old each pupil is relative to other pupils in that child’s class.reading start. The same is done for the Writing Progress and Math Progress models. A second explanatory variable was calculated from the start grade of each child by relating it to how far ahead or behind a pupil was compared with other pupils in the same year group. The second pupil measure calculated was the start grade group-mean-centered on year group, and for the Reading Progress model, for example, this was termed reading start-on-age.In educational research, another key predictor of both progress and attainment for each pupil is the level from which that pupil is starting, or start level. In this investigation, the start level is grand-mean-centered on the whole data set to form a pupil variable that is related to how far through the primary school curriculum the pupil is. For the Reading Progress model, this is termed Finally, attendance information was gathered and pupils were removed from the data set if they started in a classroom more than 1 week after the start of the first term. A separate investigation into the attendance rates showed, after removing those children starting late, that progress rates on average did not differ significantly for pupils with poorer attendance. Consequently, attendance rate was not used as a pupil variable in the final model.The subject progress measure was used as the dependent variable in the regression analyses . Thus, data were collected for each pupil for reading, writing, and math improvement over 1 year. For each pupil, the start level at the beginning of the year and the final end level were provided. This was converted to a linear scale using the U.K. National Curriculum (NC) points system, which is widely recognized and is detailed in Purely as an aid for interpreting results, there was a final step in creating the pupil measures used in the study. The progress measures  were grand-mean-centered, and the progress measures and each of the pupil variables  were also divided by their respective standard deviations. Each of the variables consequently has a mean of 0 and a standard deviation of 1. This last step “normalizes” the data so that the magnitudes of the calculated regression coefficients are directly comparable with each other, and a coefficient of one indicates a magnitude of 1 standard deviation.shared by groups (classes) of pupils, and the individual characteristics of the pupils themselves. This creates the conditions to support a type of regression analysis called multilevel analysis, which allows the various influences on the pupils’ academic progress to be isolated. This procedure is detailed in the following section and In summary, this rich data set concerns both the features of classrooms, overall progress in learning (the mean of progress in the three subjects). These same 10 parameters were found to have slightly different, but still significant, individual correlations with each of the different subject progress measures . MLM analysis allows modeling of the variance–covariance matrix from the data directly so that the normal requirement of homogeneity of variance across the whole data set can be dropped .1The structure of the MLM used for this study was a two-level model where pupils at Level 1 were nested within classrooms at Level 2.nested is used as each child learns in only one classroom during the year. MLM analysis also allows unexplained variance to be identified at each of the model levels. Therefore, for example, relevant aspects attaching to the pupils that were not captured by the individual pupil measures used  remain in the unexplained variance at Level 1. Similarly, although we were not explicitly able to model any teacher-related measures, it can be seen that these important elements are compartmented in the unexplained variance at Level 2. A specialist modeling software package MLwiN . This type of step-up procedure for the model was used because each of the explanatory variables to be added was independent of each other, namely, gender, age, and the key pupil metrics of FSM, ESL, and SEN.There was deemed to be a significant change where the p < .05 (two-tailed). Although all were individually significant, the 10 Level 2 variables did exhibit some unavoidable intercorrelations. Because of this what is known as a top-down approach was used when adding these variables where all the 10 parameters are added together. The fitted model therefore showed the combined effect of all these factors, before each factor was removed to test for its individual significance in the overall model . Where the presence of the parameter significantly improved the model, it was retained; if not, then it was left out. Once all of the parameters that were not significant had been removed, a further procedure was carried out by adding back in each of the rejected parameters. This last step is important as the classroom parameters, because of their intercorrelation, had an impact on each other. A higher p value limit was allowed in the final test as both the bivariate analysis and the individual modeling results had already shown the significance of each individual classroom parameter at the higher level.The next part of the process involved adding the classroom explanatory variables at Level 2. Each environmental factor was tested individually by creating a model with just this environmental factor, and there was deemed to be a significant change where the ll model . As eachThe results of this series of multilevel modeling exercises for the separate subjects are shown in For the Level 1 explanatory variables, on average pupils make less progress as their grades improve as all three  have negative correlations with their respective progress. The start-on-age variables are also negatively correlated showing that children who start ahead of their age group also make less progress. For the other pupil-level variables, pupils with SENs and pupils from poorer backgrounds (FSM pupils) both make significantly less progress than average. For reading progress, the older children in their year group make more progress as shown with the positive correlation with age-in-year. Pupils who have ESL/EAL do significantly better in writing and math progress. Girls make more progress in writing and boys make more progress in math.At Level 2, the classroom parameters significant across all the three subjects were Light and Flexibility. Flexibility had a particularly large correlation with math progress (coefficient = 0.197). The Level of Stimulation factors of Color and Complexity are also both significant in the Reading and Writing Progress models. Connection is significant in the reading progress model, Links-to-Nature is significant in the Writing Progress model, and Ownership is significant in the Math Progress model.The two-level nature of the MLM allows variability in pupil progress to be divided between the pupil level and the classroom level. The three subject models all start out approximately equally with roughly 67% of the variance allotted to the pupil level and 33% to the classroom level. As described above, explanatory factors are introduced to fit the regression model initially at the pupil level and then at the classroom level. The amount of the variability that is then explained by the significant factors at each level is called the Proportion Reduction in Variance (PRV). For the Reading Progress model, an approximately equal amount of variance is explained by the pupil factors (18.6%) and the classroom factors (18.9%). At the pupil level, some of the factors that are significant are the pupil start grade, whether they have SEN or are registered as having FSM and also if they are old compared with their class (age-in-year). For the Writing and Math Progress models, the PRV explained by the pupil factors is smaller at 16.9% and 13.5%, respectively, and the PRV explained by the classroom factors is larger at 22.4% and 23.4%, respectively.An improvement statistic can be found by setting all nonclassroom variables in the model at their average value and calculating the classroom effect on the progress measure for each classroom. By comparing the range of the most effective classroom minus the least effective classroom, as a percentage of the total range of the pupil progress score, gives the improvement percentage due to just the classroom parameters. The improvement statistic shows that on average the classroom factors have an effect of approximately 10% of the total variability in pupil progress for each subject  were both significant. These two factors relate to the visual environment of the classroom. The color elements were initially rated with pale and white colors rated low and vivid (saturated) colors rated high. However, as already mentioned, wall and display colors were subsequently found to be curvilinear meaning that the optimum level for learning was in the middle of the ranges. learning as well. Color is widely accepted to stimulate the brain, which has consequential effects on moods, mental clarity, and energy levels . This seems to explain why wider corridors, which are accounted for in the model, had a positive impact on reading progress. It also implies that “corridor libraries” could be considered as a positive design feature.There are then three factors that each emerges as significant in only one of the individual subject models and not in the other two. In just the Reading Progress model, Connection was found to be significant. The Connection factor was a measure of the width and way finding features of the corridor spaces. A small investigation was done and found that of the 27 schools in the study, 11 of the schools had libraries in corridors or atrium spaces. These have been termed In just the Writing Progress model, Links-to-Nature was found to be significant. The measure of Links-to-Nature described in this study is a measure of natural elements in the classroom (wooden furniture and plants), views of nature from the windows, and whether there is direct access to an outdoor learning zone from the classroom. In their classroom studies, Studies on the effects of nature or natural views on learning have covered many possible influences. Within this research, improved levels of progress owing to Links to Nature were only found to be correlated with writing and not with either reading or math. This would indicate that the particular measure of “links-to-nature” developed during this study is possibly more related to the “creative” nature of writing rather than the more general “restorative” effect, described by In the Math Progress model, Ownership emerges as significant. The Ownership factor measures how well the classroom reflects its use by pupils; does the room have pupils’ work on the walls, do the pupils have their own lockers or coat pegs, and is the furniture ergonomic for the size of the children. It can be seen that all of the factors significant in the Math Progress model (beyond the Light factor) are from the Individualization principle. Individualization is made up of measures for Flexibility and Ownership and was designed to reflect how well the space was adapted for the use of particular children.Progress in math is associated with factors that are well accepted such as cognitive ability and understanding of the subject, and teacher quality, and availability of appropriate resources. Unlike other subjects, math is also associated with factors that are less well understood, such as societal expectations  and testPupils from the age of 5 years spend over 6 hr every day of the week within the school environment. Apart from their home, they spend more time in class than anywhere else. Their primary school journey takes them from vulnerable infants to independent learners and thinkers over a period of 6 years. Classrooms need to materially cater to them over this journey. Within the framework of (Level of) Stimulation, Individualization, and Naturalness (the SIN factors), an investigation has been undertaken of how the differing elements of the classroom environment impact on pupils’ learning as measured by progress in reading, writing, and math.It is notable that generally, the individual correlations between the 10 design parameters and learning progress are relatively small. It is a feature of this study that it has successfully isolated the influence of these multiple factors in holistic, naturalistic environments and revealed how there are typically multiple factors at play. That said, if resolved successfully, the return can be great, as although the individual correlations are small, the combined impacts on learning have been modeled as being substantial. For each of the different subject models, the aspects of the classroom environment taken together explained approximately 10% of the variability in the pupil performance. These are big effects and can be compared with the place where a class learns about all three subjects, the findings of this study highlight some subject-specific variations in the optimal characteristics of the physical space provided, that can be taken into account. Our results show both reading and writing performances are particularly affected by the Level of Stimulation parameters. The biggest impact from classroom design is in math progress, where the Individualization of the classroom to the child appears to be of paramount importance. There could also be possible, tentative, implications for secondary school design, where subject-specialist classrooms are more common.Although most primary school classrooms will probably remain The focus of the results is on the ambient environmental conditions delivered over a whole year; however, these findings could be used in practice by teachers as they move from one subject to the next and by designers in terms of creating opportunities for dynamically configurable spaces. The factors involved are generally highly practicable to achieve, and in many cases, improvements would not be expensive.The study by the nature of its design has strengths, such as the large sample size and detailed level of granularity, at the classroom level. But it also has limitations. These relate mainly to practical limits on the collection of data. Information gained was limited to English schools and of course the application of the results beyond this context should be approached with care. There are also data limitations on the multiple aspects known to be important for learning progress e.g., . For exaevidence, and so a raised awareness that the issues identified in this study do impact on learning. Until now, most of these issues seem to have occupied a blind spot for practitioners, and so have been unaddressed and unmanaged, hence the variability found in these factors. Once they come into focus, rapid and effective action is entirely possible and likely. For researchers, these broad findings provide a foundation to investigate further the underlying reasons for the identified subject differences so that these can be explored and explained further.From the researchers initial experience, what seems to make the difference to practitioners is


Background: The human factors are of great importance, especially Motorcycle Rider Behavior Questionnaire (MRBQ) and attention deficit hyperactivity disorder (ADHD) in motorbike riders in road traffic injuries. This study aimed to predict MRBQ score by ADHD score and the underlying predictors by the logistic quantile regression (LQR), as a new strategy.Methods: In this cross-sectional study, 311 motorbike riders were randomly sampled by a clustering method in Bukan, northwest of Iran. The data were collected by MRBQ and ADHD standard surveys. To assess the relationship at all levels of MRBQ distribution, LQR in 5th, 25th, 50th, 75th and 95th quantiles of MRBQ score was utilized to assess the predictability of ADHDscore and its subscales in addition to the underlying predictors of MRBQ score. To do this, an unadjusted and as well as adjusted 4-step hierarchical modeling was used.Results: Almost in all quantiles of MRBQ scores, direct and significant relationships were observed between MRBQ score and ADHD score and its subscales . Besides, the driving period  and hour driving  also came to be the significant predictors of MRBQ score.Conclusion: ADHD score and driving parameters can be taken into the consideration when planning actions on the motorcycle rider behaviors at all levels of the MRBQ. Iran has less than 1% of world’s population whilst it has more than 2.5% of world traffic accidents.4 Although, in recent years, the mortalities by road traffic injury have decreased in Iran, still it has been ranked as the third cause of mortality after cardiovascular disease and stroke.5 The reports showed that death rate from road accidents in Iran is 20 times higher than the global average.6Road traffic injury is a global problem. Based on a report by the World Health Organization (WHO), road traffic injuries kill 1.27 million individuals annually and 20-50 million people are injured subsequently.7 in a way that, compared with car drivers. They have 8, 4 and 2 times higher risk of death, the risk of injury and risk of having an accident by the pedestrians respectively.8 A motorcycle has 9.3 times more possibilities of an accident than the cars.9 Compared to high-income countries. In low- and middle-income countries, the great parts of the population are pedestrians, bicycle and motorcycle riders and 90% of happened mortalities from traffic accidents in these countries.10 In Iran, more than 51% of traffic accidents are involving motorcyclists.11Motorcycle drivers are among vulnerable groups in road accidents,13 Among the human agents, cognitive attention, as one of the most important aspects, is the main causes of traffic accidents in a way that it comprises 20%-50% of accidents.14 In this regard, one factor that contributes to road traffic injuries and accidents are attention deficit hyperactivity disorder (ADHD) which is a developmental chronic nervous disorder. Those individuals with this disorder experience significant difficulties in various aspects to their lives. As a factor, motorcyclist driving behavior is the last link to the chain of human causal and psychological factors to the accident.16 When examining the undeniable role of humans in the chain of events leading to the accident, the identification of this factor can be one valuable action in traffic safety.17 The first study about hyperactive drivers and road safety was carried out by Weiss et al.18 The landmark of the studies about ADHD and road safety, was taken place by Barkley et al.19 They showed that drivers with ADHD had three to 4 times higher odds of an accident than drivers without ADHD.19 Sadeghi-Bazargani et al studied the relationship between motorcyclists’ behavior and ADHD with motorcycle traffic injuries by common binary logistic model. They showed that riding behavioral scale and ADHD subscale B scored by age, educational degree, and the reason for motorcycle riding could be considered as potential determinants of motorcycle injuries.20According to the WHO, road deaths comprise 25% of all deaths caused by injuries. Human agent is the main cause in 60% of vehicle accidents.21 All previously analyzed data done on MRBQ were based on generalized linear models (GLMs).23 which did not take into the account the limitation of the bounded nature of variable, and may lead to bias in findings.24 Specific statistical methods are required to comprehensively address the causes and risk factors of major road traffic accidents and their consequences. Therefore, the present investigation aimed to utilize logistic quintile regression (LQR) to investigate the predictors of motorcycle behaviors (assessed by MRBQ). Unlike partial/average partial/average partial/average view of the relationship that classical statistical models present and as an advantage, this model provides a description of the relationship at different points of the outcome. Using various quantiles in response instead of just mean response and implementing the bounded nature of the outcome, the LQR could provide more comprehensive projections of risk factors of MRBQ.Considering the importance of human health in medical and epidemiologic studies, the accuracy of the results is more important, therefore; those statistical models that have the minimum bias and error should be utilized. Applying statistical models without this criterion may not be tailored to such data and may lead to bias in results and decision making.A total of 311 Iranian motorcycle ridermen recruited in this cross-sectional study based upon a cluster sampling scheme in Bukan, northwest Iran in 2016. Bukan is located in west Azerbaijan province. The population of this city consists of 224 628 persons according to the general census of Iranian statistical center in 2011. Bukan city was divided into 14 homogeneous clusters, and then 7 clusters were randomly chosen. Afterward, enough samples were collected in each cluster to achieve the determined sample size. Data were collected through referring homes and motorcycles shops. Some adaptations were done with sampling design and sample selection for feasibility to perform sampling. The inclusion criteria were used motorcycle (at least 3 times per month), age +15 years, residing in Bukan and being conscious and alert when filling out the questionnaire. The exclusion criteria were a lack of motivation to participate and to complete the questionnaires in a self-descriptive manner.20 on the main outcome of this study, the relation between MRBQ and ADHD. Considering 95% confidence level and 80% power, the sample was estimated to be 227 subjects according to odds ratio (OR) about 1.4 as the effect size. Taking into account the cluster design, the sample size was increased to 296 cases by a design effect of 1.3 and then increased to 311 for more precision.20The study size was determined using primary information obtained from the study by Sadeghi-Bazargani et alThe study main variables included Motorcycle Rider Behavior Questionnaire (MRBQ) as the outcome and ADHD as the predictor of MRBQ. Data were collected in a self-descriptive manner using MRBQ (with 48-items) and Conner’s short-form ADHD questionnaires to assess the motorcycle riders’ behaviors and ADHDs respectively.25 and developed by Ozkan et al.26 In this study the internal consistency reliability, as assessed by Cronbach’s alpha, was supported for MRBQ (α = 0.896). The respondents were asked to report the frequency of their behaviors during last year by selecting one of the 5 points scales . The MRBQ score was computed by summing over the items. The score ranged over 0-192 where in the higher scores indicate the less attention to the traffic rules.In the present investigation, the MRBQ was utilized to assess motorcycle riders’ behavior as the outcome variable. MRBQ was first built in 2007 by Elliott et al27 In this research, the internal consistency reliability was supported for ADHD scale (α = 0.891) and for all ADHD subscales (0.643-0.899). ADHD has 4 subscales; subscale A measuring inattention (I1 +I9 +I13 +I14 +I19 +I21 +I26 +I29 +I30), subscale B measuring hyperactivity, impulsivity (I2 + I4 + I6 + I8 + I16 + I18 + I22 +I25 +I27), subscale C (A +B), and subscale D measuring ADHD index (I3 +I5 +I7 +I10 +I11 +I12 +I15 +I17 +I20 +I23 +I24 +I28). The symptomatology of the scale is based on the DSMIV , which the diagnostic criteria for ADHD are similar to those in the DSMV.28 Rating scales will ask the respondents to score behaviors on a 4-point frequency scale ranging from 0 = never/rarely to 3 = very often. ADHD scale and all subscales scores were computed by summing over the items . The higher the score, the more severe the symptom is.The ADHD questionnaire was also translated, and the validity and reliability were assessed and confirmed in a study by Sadeghi-Bazargani et al.The predictors of MRBQ in this study were considered to be: age, marital status, educational level, job status, income level, house price level, car price level, hygiene cost level, motorcycling aim, using helmet, having driving license, driving license period, driving period, hour driving, number of days used, sub-accident, vehicle type, cell phone answering.To completely take into the account the above-mentioned predictors, both trend effect, and effect compared to a reference category were considered as the LQR models. When a complete set of quintiles showed a significant relationship with outcome variable MRBQ, then the effect considered to be significant. The missing values were deleted listwise and since they were less than 5%, the effect of missing values was ignorable.Data were summarized and presented using the mean (SD) or the median (percentile 25 - percentile 75) along with (Minimum-Maximum) for numeric and the frequency (percent) for categorical variables, respectively.The data on MRBQ outcome (ranged over 0-192) was projected by LQR via the following equation:Logit(MRBQ) = log[(MRBQ + Ɛ)/(192 – MRBQ + Ɛ)]We considered Ɛ = 0.001. And finally our model was:logit(MRBQ)(P) = βP,0 + βP,1ADHDQmin = -0.001 and ymax= 192.001 and the 5 quantiles of P = 0.05, 0.25, 0.5, 0.75 and 0.95 were considered. Furthermore, the standard error and P values were estimated using 1000 bootstrap samples. The model parameters were estimated using lqreg package in Stata, utilizing the codes lqreg, lqregpred and lqregplot and then the bootstrap standard errors are estimated.29 Based on LQR model and considering P < 0.1 in the univariate analyses for all quantiles, the variables were chosen to enter into the multivariate model which includes age, income, marital status, car price, using a helmet, years of driving records, days driving, driving hours, answering a cell phone, number of cars, ADHD and ADHD subscales. In the multivariate modeling strategy, 4 models were fitted taking considering P < 0.05. In model 1, the MRBQ was modeled with significant background variables in the univariate analyses. In the model 2, the ADHD score was added in the model 1. In model 3, the subscales of BSS and ASS of ADHD were added in the model1. Finally, in the model 4, the subscale DSS was added in the model 1. In each model confidence interval and P values were computed for P = 0.05, 0.25, 0.50, 0.75, and 0.95. In addition to the analyses done by indicator categorical variables, trend analyses were done by directly entering the ordinal categorical variables to the models. All analyses were performed using STATA 14 software .ADHD is a predictor of MRBQ in this model. The bounds were set as yA total of 311 subjects participated in this study. A number of 104 persons had age between 25 to 28 years. 21.22%, 45.34% and 33.34% of samples had primary, diploma and university degree, respectively. Most of the subjects (81.03%) had not a driving license. 71. 38% had more than 2 years of driving record, and 77.49% had not an accident in their driving lifetime. Other information on background variables are presented in The summarized measure of MRBQ outcome and the main predictor, i.e. ADHD and its subscales are presented in P < 0.1 in the univariate analyses for all quantiles of MRBQ score, the significant variables went back to ADHD score, and all ADHD subscales score beside the marital status, using a helmet, years of driving records, driving hours, days driving, cell phone answering. Furthermore, ADHD all its subscales scores besides the age, income, car price, years of driving records, driving hours, and the number of cars, were significant in the trend analyses . The participants were free to participate in the study, and the obtained information was just used for scientific purposes, and privacy was preserved meanwhile. All participants filled and signed the informed consent and assent. For the illiterate people, the informed consent form was read by the researcher or someone to whom he/she trusts. Then instead of signing, fingerprints were taken from participants.The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.All authors read and approved the final manuscript. MAJ and HSB conceived of the study and participated in the design and data collection. MAJ and MB participated in the data analyses and MS preparation.We would like to appreciate Research Deputy of Tabriz University of Medical Sciences who supported our study.


Attention Deficit Hyperactivity Disorder (ADHD) is the most common neuropsychiatric disorder in childhood and is characterized by a delay of cortical maturation in frontal regions. In order to investigate interference control, which is a key function of frontal areas, a functional MRI study was conducted on 17 ADHD boys and 17 typically developing (TD) boys, while solving the multi source interference task (MSIT). This task consists of two conditions, a “congruent condition” and an “incongruent condition”. The latter requires to inhibit information that interferes with task-relevant stimuli. Behavioral results showed that ADHD subjects committed more errors than TD children. In addition, TD children presented a larger MSIT effect -a greater difference in reaction times between the incongruent and the congruent conditions- than ADHD children. Associated to the MSIT effect, neuroimaging results showed a significant enhancement in the activation of the right lateral prefrontal cortex (rlPFC) in ADHD than in TD subjects. Finally, ADHD subjects presented greater functional connectivity between rlPFC and bilateral orbitofrontal cortex than the TD group. This difference in connectivity correlated with worse performance in both groups. Our results could reflect a compensatory strategy of ADHD children resulting from their effort to maintain an adequate performance during MSIT. While the incidence of this condition is greater among children in pre-school and school age, the disorder can persist into adolescence and adulthood in 30% to 60% of the cases4. During childhood, one of the main consequences of the syndrome is that children show poor school performance. During adolescence and adulthood, problems such as decreased self-esteem, social isolation, increased risk of accidents and psychiatric comorbidity become manifest, which are important in the context of public health7. Currently, it is posited that a good therapeutic outcome depends largely on early and appropriate treatment8, but also on a correct diagnosis and management of comorbidities that are usually associated with ADHD and that are often much more disruptive than ADHD itself11. Mainly due to the complexity of the clinical and biological features of this disorder, its diagnosis remains controversial12. In this context, it is increasingly necessary to study more closely the neurobiological mechanisms underlying ADHD.Attention Deficit Hyperactivity Disorder (ADHD) is the most common neuropsychiatric disorder in childhood13. Brain areas such as the insula and striatum are affected15, even though the alterations in the prefrontal cortex might be the most prominent ones17. It has also been shown that the degree of maturation delay in the right prefrontal region correlates with the severity of adult inattention symptoms18. Already in 1997, Barkley proposed that ADHD is caused by abnormalities in the prefrontal cortex and its connections to the striatum, resulting in severe alterations in inhibitory behavior19. Behavioral inhibition, also called interference control, includes self-directed responses that result from competing events and responses21. Although interference control is a relevant cognitive process in development, little is known about its functional maturation22. For example, some studies have found an increase in dorsolateral prefrontal activity associated with higher interference control23, while others have found a decrease in this activity parallel to improvement in performance during development25.In the last years, structural MRI studies have shown that ADHD children present a delay in their cortical maturation28. This task has been paramount in the characterization of cognitive control and inhibition impairment in neuropsychiatry diseases29. Nonetheless, some authors indicated that other alterations  present in ADHD children may adversely affect the interpretation of the poor performance in this type of task27. In order to tackle this problem, recent studies have used the Multi-Source Interference Task (MSIT), which was designed as a block-formatted functional neuroimaging task and has shown to be effective to study response inhibition and attentional control31. Specifically, the MSIT presents 3 digits on a computer screen. Participants have to report the identity of the number that is different from the other two numbers by pressing a button on a response-pad. In the congruent condition (CC), the distracters are zeroes and the target number  is always placed congruently with its position on the response-pad. In the incongruent condition (InC), the distracters are other non-zero numbers, and the target number  is never placed congruently with its positions on the response-pad , while they were solving the MSIT Table . AdditioF4,29 = 29.8, p < 0.001). Even though the ADHD children committed more mistakes , there was no interaction between condition and diagnosis . Considering only correct responses, both groups demonstrated equivalent significant increases in reaction time (RT) during InC as compared to CC . Interestingly, the MSIT-effect , was greater in TD than in ADHD group .Both groups demonstrated a significant accuracy decrease during the InC as compared to the CC . Using the same analysis described for accuracy above, we observed that control subjects trend to decrease their RTs as the congruent block elapses . In contrast, ADHD subjects do not show this trend  for ADHD and TD children. For cluster detection threshold, we used a z value of 2.3; and, for cluster correction threshold, p < 0.05. In our task and MRI configuration, this setting has an adequate rate of false positive results . Neuroimaging results showed a greater activation of the right medial and inferior frontal gyrus for the ADHD group , a psychophysiological interaction (PPI) analysis was performed using the main cluster of the ADHD > TD MSIT-effect as seed. To avoid circular inference, per each subject we used the result of the contrast excluding the subject’s image (see methods for more details). This analysis showed that the ADHD group presented a significantly greater functional connectivity with bilateral orbitofrontal cortex (OFC) and striatum as compared to the TD group  Fig. , Table 3A correlation analysis between performance during the incongruent condition and PPI connectivity between rlPFC (rMFG and rIFG) and OFC showed that the connectivity between these brain regions is positively correlated with a worse performance in both groups Table . Additio36. In spite of large studies and meta-analyses of behavioral studies discussing the relative importance of executive function as a key marker for diagnosing ADHD39, inhibition impairment is consistently found in ADHD children. In fact, evidence of impoverished inhibition skills in ADHD comes from studies that have used motor inhibition tasks42, delayed response tasks and interference tasks44 among others. Poor behavioral inhibition is also evident in deficient performances in tasks that demand the detection of ongoing modifications of rules and the implementation of different strategies in order to respond properly, like the Wisconsin Card Sorting Test45. Although current evidence remains controversial, it suggests that ADHD children have problems of response perseveration in a similar manner as those of patients with frontal lobe damage46. In accordance, we found that in the first five trials of each block, TD children increased their accuracy in a steeper slope than ADHD children  increases with age, evidencing a more prominent process of monitoring performance49. On the other hand, the activity in dlPFC related to inhibitory control seems to decrease with age50. This change can be interpreted as a reduction in effort whereas a more efficient cortical processing is carried out in order to solve the task22. Interestingly, the MSIT produces reliable and robust activation of both dACC and dlPFC as well as the rest of the cingulo-fronto-parietal attention network31. Although our results did not directly measure developmental alterations, the use of this task gives the opportunity to study the different components of the frontal circuit related to interference control.Current evidence shows that ADHD children present a delay in cortical development53, diffuse axonal injury54, multiple sclerosis56 and schizophrenia57. The structural maturation delay of the dlPFC has also been related to persistent inattention symptoms in adults18. Thus, this functional alteration could be a marker of symptoms in adults, and can be related to other problems such as social anxiety disorder4.When we compared the brain activation patterns elicited by the MSIT-effect between ADHD and TD groups, differences were significant only for the ADHD > TD contrast Fig. . These d60. Dorsolateral prefrontal cortex encodes different task rules and participates in behavioral change when the environment requires to change a strategy63. It has also been shown that the connectivity with ventromedial prefrontal cortex and OFC is important to implement flexible changes64. Thus, these areas participate in integrating different information in order to flexibly adapt our behaviors67. For example, dlPFC -as part of the frontoparietal control network - changes its connectivity with other brain networks depending on the requirements of a specific task69. In our contextual functional connectivity analysis we found that ADHD children present more connectivity between rlPFC and OFC during MSIT-effect than TD children. This result is mainly due to a modulation of the connectivity in TD children  are related to inattention and response variability in ADHD children72. Interestingly, prior work in adults with ADHD shows alterations in the OFC activation during reward task and risky decision making73. Thus, the altered pattern of OFC connectivity can also be related with a more impulsive response29.Interestingly, frontal structures have been related to flexible behavioral change and task switching74. Thus, it is important to carry out similar experiments in pharmacologically naïve patients or their relatives to weigh the relative influence of medication on these findings.Finally, taken together these results show that ADHD children have a deficit in interference control that is partly compensated with higher activity in the rlPFC. This activity seems to be modulated by contextual functional connectivity with OFC. In the same line, the recruitment of prefrontal resources as part of a compensation mechanism behavior could reduce the availability of such higher-order prefrontal resources to solve other tasks. It should be considered that these compensatory brain activations involve an additional metabolic cost, which could lead to shorter periods of focused attention, reduced ability to inhibit stimuli interfering, longer reaction times and a generally impoverished behavioral performance. Despite the fact that ADHD children who participate in our experiment did not take medication 24 hours previous the MRI session, it is impossible to rule out an effect of medication75, because by themselves behavioral studies are blind to possible underlying brain compensatory mechanisms that, in our study offer an explanation of why the ADHD group maintains an overall similar behavioral performance to the TD group.The use of neuroimaging techniques in conjunction with paradigms from neuropsychology, highlight the importance of the complementary use of these approaches76 and DSM-IV. All participants were Chilean, native Spanish speakers, had normal or corrected to normal vision, no color-vision deficiency and were right handed according to the Edinburgh Inventory77. They had an average or higher IQ assessed by WISC-R78, and agreed to be examined to rule out any morbidity79.Forty male children aged 10 to 12 years, 20 ADHD-combined type and 20 age and IQ-matched typically developing children, participated in the study  is always placed congruently with its position on the pad. In the incongruent condition, the distracters are other numbers , and the target number  is never placed congruently with its position on the response pad t number , 2, or 3Data were tested for normality with the Kolmogorov-Smirnoff test. When data did not meet the normal distribution assumption, nonparametric tests were used. Accuracy and reaction time (RT) were analyzed with mixed ANOVA, using condition (CC or InC) and diagnosis (ADHD or control) as factors. Differences of RT for MSIT-effect  between groups were analyzed with the Wilcoxon test. Changes of the RT for MSIT-effect during congruent blocks were analyzed with Spearman correlation analysis, and differences between groups were analyzed with Wilcoxon signed-rank test. Finally, mixed linear model over single-trial was used to complement the Spearman correlation analysis, using trial number, group and the interaction between them as regressors. Errors were clustered using subject as a grouping factor.Images were acquired at the Department of Radiology and TAC of the Clínica Alemana de Santiago, in a GE HDX 1.5 T MRI gradient 33 mT/M scanner. A volumetric 3D sagittal T1-weighted SPGR sequence was used for acquiring structural data, with TE 4.7, TR 14.5 ms, TI 500 ms, FOV 25 cm, matrix size 320 × 256, flip angle 12°, slice thickness 1.2 mm, 166 slices per volume, NEX 1, 63.84 KHz bandwidth, 6.5 min total time. A T2-weighted sequence was used for acquiring functional data, with 43 slices per volume (covering the whole brain), TR 2516 ms, TE 35 ms, FOV 24 cm, flip angle 90°, matrix size 64 × 64, slice thickness 3 mm, 63.8 KHz bandwidth, Ten blocks of 24 trials each one  were presented to the subjects within 7 minutes and 27 seconds on the screen. The block duration for both conditions was 44.7 seconds, and then the General Linear Model was fitted using these values. Data sets from 34 of our 40 participants met criteria for high quality and scan stability with minimum motion correction (<3 mm displacement in any direction) and were subsequently included in the fMRI analysis.http://www.fmrib.ox.ac.uk/fsl/index.html). The first five volumes from the time series of each run were discarded to allow the hemodynamic response to stabilize. Data preprocessing involved the following steps: motion correction (MCFLIRT), brain extraction (BET), spatial smoothing with a 8-mm FWHM Gaussian kernel, and high pass temporal filtering using Gaussian-weighted least-squares straight line fitting with sigma = 100.0 s, and pre-whitening. The BOLD response was modeled using a separate explanatory variable (EV) for each task condition (congruent and incongruent). Then the block design was convolved with a double gamma function to produce an expected BOLD response. The temporal derivative of this time-course was then included in the model for each EV to capture any unexpected temporal shifting, and motion correction parameters were also included in the design as additional nuisance regressors. Data for each condition were fitted to the general linear model. Estimated beta maps for contrasts were normalized to MNI152 standard space for each participant for subsequent group comparisons using FLIRT in two stages. First, functional images were aligned with the high-resolution T1 using a six-degrees-of- freedom rigid-body warp. Then, the T1 was registered to the standard MNI atlas with a 12-degrees-of-freedom affine transformation. Second-level activation maps were calculated with FSL using mixed-effect model (FLAME1 + 2).Functional and structural imaging data were preprocessed and analyzed using the FMRIB Software Library , which showed significant differences in MSIT-effect (InC > CC) between both groups. The seed was defined per each subject used the following procedure. We calculated the significant cluster of the MSIT-effect (ADHD > TD) using the entire sample excluding the subject’s imaging. Then, the seed was defined as the conjunction between the MSIT-effect cluster and the anatomically defined lateral prefrontal cortex . Thus, we avoided any circular inference in the results. We then generated a demeaned BOLD time-course regressor from the seed as well as an interaction term with the demeaned psychological regressor (double gamma convolved incongruent conditions time-course) to generate PPI terms. The main effect of the psychological regressor was also included in the GLM. For the PPI time-courses, we subtracted the global mean  from the individual time-course of each region investigated, to avoid positive results due to global correlations.We also conducted psycho-physiological interaction (PPI) analyseset al., 2016 methodology82. The permutation test demonstrated a false positive rate < 0.05 .All results reported were based on an initial uncorrected voxel-level threshold of Z > 2.3 for the cluster detection. Then we corrected the whole brain using the cluster level using p < 0.05. In order to avoid false positive results, al the parameters used in our analysis were tested using the Eklund Supplementary Information


Alterations in temporal processing may represent a primary cause of key symptoms in ADHD. This study is aimed at investigating the nature of time-processing alterations in ADHD and assessing the possible utility of testing time estimation for clinical diagnostics. Retrospective verbal time estimation in the range of several minutes was examined in 50 boys with ADHD and 53 boys with other mental disorders. All participants (age 7–16) attended an outpatient clinic for ADHD diagnostics. The diagnostic assessment included the WISC-IV. Subjects with ADHD made longer and less accurate duration estimates than the clinical control group. The ADHD group showed a specific WISC-IV profile with processing speed deficits. In the ADHD group there was a correlation between processing speed and quality of time estimation that was not observed in the comparison group: higher processing speed indices were related to more accurate duration estimates. The findings provide support for the presence of a faster internal clock in subjects with ADHD and lend further support to the existence of a specific WISC-IV profile in subjects with ADHD. The results show that analyzing WISC-IV profiles and time estimation tasks are useful differential diagnosis tools, particularly when it comes to distinguishing between “real ADHD” and pseudo-ADHD. According to the triple pathway model , deficitVerbal time estimation tasks may be conducted with prospective or retrospective estimation. In a prospective estimation task, subjects know in advance that a duration judgment will be required. In a retrospective estimation task, subjects are asked for a judgment without prior warning subsequent to duration presentation . Task deStudies with prospective verbal time estimation , 23, 30 With regard to clinical samples and the consequences of ADHD in daily life, two neglected but possibly important factors were addressed in the present study. Firstly, most earlier studies compared subjects with ADHD to control subjects without any remarkable psychiatric symptoms. However, the challenge in clinical diagnostics is to distinguish ADHD sensu stricto from other syndromes with partially similar symptoms. If tests of time perception were indeed instrumental in diagnostics , it woulSecondly, time estimation tasks were designed to be more relevant to situations in daily life . These tasks lasted several minutes, whereas most earlier studies had used durations of less than 1 min. This study is the first to use retrospective verbal time estimation tasks in the range of several minutes and the first to employ a combination of time estimation and intelligence diagnostics for profiling children with ADHD and clinical controls that require careful differential diagnostics.A total of 113 boys between 7 and 16 years of age participated in this study. All subjects attended the outpatient clinic of a psychiatric hospital for children and adolescents for diagnostic assessment. They all were in suspicion of ADHD because parents and/or teachers reported symptoms of ADHD. Male gender and a diagnosed psychiatric disorder (after diagnostic assessment) were both key inclusion criteria for this study. Total IQs less than 85, any kind of medication and autism spectrum disorder, psychotic disorder, acute and posttraumatic stress disorder, dissociative disorder, eating disorder, or substance-related disorder diagnoses were all key exclusion criteria. Eight participants with a total IQ below 85 were excluded from study after diagnostic assessment. One additional participant was excluded because no psychiatric disorder was diagnosed and another participant was excluded as an outlier as his estimates deviated more than 2 standard deviations from the rest of the sample. The remaining 103 subjects were assigned to the ADHD group or the clinical control group.Before the beginning of the study, children and their parents had been informed in writing about the study. Informed consent was obtained from all participants.DISYPS-II that measured symptoms of ADHD, depression disorder, and anxiety disorder. The ADHD rating scales were also completed by teachers if the parents agreed. All subjects were tested on the WISC-IV. School reports were also reviewed. Subjects completed a range of other questionnaires in addition to this standardized assessment , specific assessment , or underwent additional examinations such as EEGs or complete blood count, if such procedures were deemed necessary.The diagnostic assessment comprised at least four sessions including a semi-structured diagnostic and clinical interview with the child and at least one parent. The interviews were conducted by experienced clinical psychologists or psychiatrists. The psychiatrists conducted physical and neurological examinations for all subjects. The parents completed several rating scales of the The results from all diagnostic assessments were evaluated by a multidisciplinary team. Members of the team included experienced psychiatrists, psychologists, psychotherapists, and qualified social workers. Most of the evaluators were blinded to the aim of the study and the results of the verbal estimation task.r = .97. The reliability coefficients of the four index scores range from r = .87 to r = .94.The German version of the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV)  was usedα = 0.70 to α = 0.90. The ADHD rating scale includes 20 items describing symptom criteria according to ICD-10 and DSM-IV as well as additional items assessing symptom onset, symptom duration and pervasiveness. The rating scale includes 9 items that assess the severity of inattention according to ICD-10 and DSM-IV. The severity of hyperactivity is assessed by 7 items. Since the conceptualization of items is not consistent between DSM-IV and ICD-10, the diverging items are enclosed (symptom of being “on the go” from DSM-IV and symptom of a persistent pattern of excessive motor activity from ICD-10). There is an extra item for the symptom of subjective feelings of restlessness. The severity of impulsivity is assessed by the 4 items according to ICD-10 that include the symptom of talking excessively. There is empirical evidence for the validity of the three-factor solution comprising inattention, hyperactivity and impulsivity separately . The results of the verbal time estimation tasks, WISC-IV and DISYPS-II are shown in Table The subtest p < 0.001). Figure p < 0.003). Separate analysis of subtypes showed that subjects with ADHD-C  and subjects with ADHD-PI  showed significantly longer estimates than controls . Likewise, accuracy scores differed significantly from controls in the combined presentation  as well as in the predominantly inattentive presentation  . Between ADHD subtypes, there were no significant differences .Subjects with ADHD gave significantly higher time estimates than subjects with other mental disorders p < 0.00. Figure F = 28.51, p < 0.001]. There are no differences between the two groups in terms of full-scale IQ, verbal comprehension, perceptual reasoning or working memory. Separate analysis of processing speed in subtypes showed that both ADHD-C  and ADHD-PI  showed significantly lower processing speed indices than controls . The difference between ADHD subtypes with lower processing speed in ADHD-PI was also significant .The results of the WISC-IV showed a significant difference in processing speed between groups. Subjects with ADHD had lower processing speed indices than subjects with other mental disorders  as well as higher scores on the inattention  and hyperactivity subscales . Teachers’ ratings also yielded higher ADHD total scores  and higher scores on the inattention subscale  for the ADHD group. No significant differences were measured between the two groups on the impulsivity subscale or in the total scores for depression and anxiety.Ratings by parents in the ADHD group yielded higher ADHD total scores compared to the clinical control group [r = 0.32; p = 0.018), but not in the ADHD group .A possible effect of age on any of the reported effects was assessed by means of ANCOVA. There were no contributions of age to any of the observed group differences, except for the ratings by parents on the inattention subscale. These rating scores increased with higher age of children in the control group (Retrospective time estimates of subjects with ADHD were significantly longer and less accurate than estimates made by patients with other psychiatric disorders. The WISC-IV results showed a specific profile for the ADHD group, which had significantly lower processing speed indices compared to the clinical control group. No differences were observed between groups for the other index scores or full-scale IQ. In terms of the ADHD group, there were correlations between processing speed index and systematic error as well as between processing speed index and accuracy score: subjects with higher scores in processing speed provided estimates of relevant time durations that were shorter and more precise.The findings of the present study have implications for diagnostic assessment in clinical practice: administering the WISC-IV and an additional time estimation task provides useful information for differential diagnostics, in particular in terms of distinguishing true cases of ADHD from pseudo-ADHD. Since there is no single instrument that can provide a definitive ADHD diagnosis, several methods should be used within the diagnostic process. Rating scales have a prominent role in this process and are reliable in terms of differentiating between subjects with ADHD and subjects without psychiatric disorders. However, there are often discrepancies among ratings of child psychopathology provided by parents, children and teachers . DistingThe results provide further support for the existence of a specific WISC-IV profile for subjects with ADHD. A performance profile with a weakness in processing speed occurs not only compared to non-clinical controls , the curADHD is associated with deficits in a variety of executive functions . AlteratThe findings of the present study indicate that alterations in retrospective time estimation are more likely based on pacemaker speed or reference memory. There are different, partially incompatible psychological and neurological conceptualizations of temporal processing . Most psInvestigating the interdependence of time perception and processing speed and their underlying factors is difficult since both processes are highly complex. Processing speed in the WISC-IV involves, for example, mental speed, reaction time, choice reaction time, visual scanning, learning new material, recall abilities and motor speed . Processtemporal g. Findings for time-processing alterations in children with ADHD correspond with frequent reports from parents that these children have a poor perception of time and time-related problems in everyday activities [temporal g may manifest itself the form of more self-control and a greater appreciation of delayed rewards, better planning and organization skills and thus improved performance in everyday life.Processing temporal information is crucial for performing effectively in everyday life, in particular in terms of predicting, anticipating, and responding efficiently in a variety of situations, for example, when it comes to being on time for an appointment, planning in an appropriate amount of time in which to complete homework assignments or scheduling various other daily tasks. Rammsayer and Brandler  assumed tivities . Mioni etivities  showed ttivities  and temptivities . This mativities . A fastetivities , which mtivities . Immediativities . ImproviThis study has some limitations. The small sample size precludes a serious statistical analysis of subgroups. Future research should investigate retrospective verbal time estimation in homogeneous subject samples with “pure” depressive disorder, anxiety disorder, or conduct disorder and in different presentations of ADHD. The findings of the present study are restricted to male subjects. Barkley et al.  did not In conclusion, the findings of the present study confirm deficits in time perception and processing speed in children and adolescents with ADHD. Taking these parameters into account is likely to enhance the validity of diagnostic assessments. Based on the present findings, the analysis of WISC-IV profiles in combination with retrospective time estimation testing is advisable. Likewise, the awareness of alterations in temporal processing in children with ADHD among parents and teachers will help to improve everyday life for those affected.


ADHD) is associated with brain functional connectivity (FC) abnormalities.Recent evidence suggests that attention‐deficit hyperactivity disorder  group of 25 boys and an age/IQ‐matched group of 42 drug naive boys newly diagnosed with ADHD (ADHD group). Functional connectivity was estimated while both groups performed a low‐demand reference task and the A‐X version of the continuous performance task (CPT A‐X).In this study, we use steady‐state visually evoked potential event‐related partial coherence as a measure of brain TD and ADHD groups exhibited similar prefrontal FC increases prior to the appearance of the target in the reference task, these groups demonstrated significant FC differences in the interval preceding the appearance of the target in the CPT A‐X task. Specifically, the ADHD group exhibited robust prefrontal and parieto‐frontal FC increases that were not apparent in the TD group.While the ADHD group are discussed in the context of inadequate suppression of cortical networks that may interfere with task performance.The FC differences observed in the  The fMRI‐RSFC is derived from the correlations in the time‐varying BOLD signal between various brain sites during a no‐task or resting state. Regions exhibiting a high correlation (positive or negative) are deemed to have a functional relationship  studies have also indicated evidence of reduced inhibition of the DMN during cognitive tasks in ADHD. Helps, James, Debener, Karl, and Sonuga‐Barke , Helps, Although studies of FC using fMRI provide important insights into dysfunctional connectivity in ADHD, there is an important limitation to this technique that needs to be acknowledged. fMRI, as an indirect measure of brain activity, measures magnetic changes associated with fluctuations in the fMRI blood oxygen level‐dependent (BOLD) signal, rather than neural activity itself  for the investigation of transient changes in connectivity that may occur during cognitive processing. In addition to traditional methods of EEG coherence, the measurement of changes in phase coupling between brain sites during cognitive processes can provide valuable information regarding the timing of communication between these regions  to derive a measure of the SSVEP phase coherence between scalp recording sites. The SSVEP‐ERPC provides a measure of the degree to which the SSVEP phase differences between electrode pairs remain stable across trials once the common contribution of the stimulus eliciting the SSVEP has been removed . Functional connectivity in the ADHD group is compared with patterns of connectivity in an IQ‐matched and age‐matched typically developing group (TD group) while performing the A‐X variant of a continuous performance task (CPT A‐X). The CPT A‐X is a neuropsychological test that has been found to be highly sensitive to attentional disturbance in ADHD . Specifically, in the TD group we would expect that FC associated with TINs, such as the DMN, should be reduced in the CPT A‐X task compared to TIN activity during the performance of a less demanding reference task. By comparison, in the ADHD group, we expect to see less attenuation of TIN FC during the CPT A‐X task when compared to FC in the reference task.22.1SD = 1.74 years) and a mean IQ of 110.96 (SD = 6.02). The ADHD group consisted of 42 males with a mean age of 10.04 years (SD = 2.00 years) and a mean IQ of 107.62 (SD = 9.48). One‐way ANOVA confirmed that the control and ADHD groups were not significantly different in either age   = 2.687, p > .05) or IQ   = 2.497, p > .05). The ADHD group comprised stimulant drug naive participants who met eight or more DSM‐IV criteria for ADHD and were newly diagnosed. All ADHD participants were recruited through the Royal Children's Hospital, Melbourne, while control participants were recruited through advertisements placed in the wider community. The study was approved by the Human Research Ethics Committee of Swinburne University as well as the Australian National Health and Medical Research Council Twin Registry.The TD group consisted of 25 males with a mean age of 10.83 years . Any responses outside the “correct” time intervals were defined as errors of commission, or false alarms, while failure to respond in the correct interval was defined as an error of omission.The cognitive tasks were presented on a computer monitor. Each letter and number subtended a horizontal and vertical angle of approximately 1.0° when viewed by subjects from a fixed distance of 1.3 m. The stimulus used to evoke the SSVEP was a spatially diffuse 13‐Hz sinusoidal flicker subtending a horizontal angle of 160° and a vertical angle of 90°, which was superimposed on the visual fields. This flicker was present throughout the task and special goggles enabled subjects to simultaneously view the cognitive task and the sinusoidal flicker. The modulation depth of the stimulus when viewed against the background was 45%.2.3Brain electrical activity was recorded from 64 scalp sites that included all international 10–20 positions, with additional sites located midway between 10–20 locations. The specific locations of the recording sites have been previously described  interchangeably.For the reference task, FC was determined during the 7.0 s interval that comprised an initial 2.0 s period where the number “4” was displayed followed by a 1.5 s blank screen that was in turn followed by the 2.0 s appearance of the “5” followed by another 1.5 s blank screen. The equivalent FC was calculated for the “A”, “blank”, “X”, blank, intervals of the CPT A‐X. Only FC data associated with correct Ref and CPT A‐X trials was used.2.5t‐test for each point in time for all of the 2016 distinct electrode pairs while participants performed the CPT A‐X task. We then calculated the number of electrode pairs where the magnitude of Student's t |t| is equal to or exceeds a specified threshold level and displayed the electrode pairs where the |t| threshold was equaled or exceeded. For this unpaired comparison comprising 67 participants in total (df = 65), we used a threshold |t| value of |t| ≥ 3.45 corresponding to p ≤ .001.To examine the differences in FC between the TD and ADHD groups, we conducted an independent Student's meanRef), we calculated the one‐sample Student's t‐test for each point in time for all of the 2016 distinct electrode pairs. We then calculated the number of electrode pairs where the magnitude of Student's t |t| is equal to or exceeds a specified threshold level and displayed the electrode pairs where the |t| threshold was equaled or exceeded. For the TD group, we select the threshold to be |t| ≥ 2.78 which corresponds to p ≤ .01 for 25 participants and a Z‐score of Z ≥ 0.56. Positive and negative t‐values corresponding to the positive or negative threshold are displayed separately in terms of the number and location of electrode pairs. Graphs illustrating the number of electrode pairs where t ≥ threshold (positive deviations) are illustrated in red while the converse  are illustrated in blue and such graphs are termed Student t‐frequency curves.To examine the difference in FC during the reference or the CPT A‐X task compared to the mean of the reference task , the number of electrode pairs exceeding the threshold would have been influenced by the number of participants in the ADHD group as well as the strength of the effect. In this case, using the same statistical criterion where the numbers in each group differ will inflate the number of electrode pairs satisfying the statistical criteria in the larger group even if the effect size is the same or even smaller in the ADHD group. We have therefore selected a |t| threshold in the ADHD group that corresponds to the same effect size in the TD group. In this case, a Z‐score of Z = 0.56 for the ADHD group corresponds to a Student's t‐threshold |t| = 3.63, and with 42 participants in the ADHD group, this in turn corresponds to p = .0009.In this study, we set the threshold |t| threshold exceeded for the independent samples t‐test. The permutation test used was based on the approach first described by Blair and Karniski  that enabled us to determine the exact probability of observing the given Nt0 if the null hypothesis was correct. Where the permutation test is conducted at multiple points in time, we apply a Bonferroni correction based on the number of permutation tests conducted. A Bonferroni corrected p‐value based on a p = .05 is quoted and in the situation where a single comparison condition of p < .05 applies but the corrected p value for significance is not reached, then the comparison is deemed not significant and labeled NS.A permutation test was used to determine the statistical significance of the number of comparisons where the relevant |Karniski  and is bt| threshold value was exceeded for the one‐sample t‐tests described above. This was determined separately for the positive and negative Student's t‐values at each of the points in time illustrated in Figs t‐tests comparing FC during either the reference of CPT A‐X task with the meanRef that are equal to or exceed the nominated threshold |t| value, designated here by the symbol Nt0. We then recalculated the number of comparisons exceeding the nominated threshold Nti after swapping the meanRef and relevant task (reference or CPT A‐X) FC values in a random subset of the TD or ADHD group where Nti is the number exceeding the threshold for the ith random subset. This process was repeated 10,000 times producing a distribution 10,000 of Nti values  that enabled us to determine the exact probability of observing the given Nt0 in either the positive or negative direction if the null hypothesis was correct. Where the permutation test is conducted at multiple points in time, we apply a Bonferroni correction as previously described.For specific points in time, a permutation test was also used to determine the statistical significance of the number of comparisons where the relevant |p) of observing a given number of comparisons where the |t| threshold is exceeded takes into account the multiple comparisons made (2016) as well as the correlation between electrode pairs.It should be noted that this permutation test estimation of the probability  a threshold value corresponding to p = .01 at each point in time for the TD group. The corresponding r‐threshold for the ADHD group is also set at |r| ≥ 0.5 which corresponds to p ≤ .0007. Figures 7 and 8 comprise plots illustrating the temporal variation in the number of FC measures correlated with processing speed exceeds the r‐threshold are termed “correlation frequency curves”. In displaying the correlation frequency curve for both groups using the same r‐threshold, as opposed to matched p‐values, we avoid the situation where the effect strengths in the ADHD group may appear inflated because of the larger ADHD group. As with the Student's t‐frequency curve, a permutation test was used to determine the statistical significance of the number of correlations between RT and FC that exceeded the r‐threshold value of |r| = 0.5. We briefly describe the permutation test used to test the correlation between RT and FC. For any given point in time for the TD or ADHD group correlation frequency curve, the number FC–RT correlations equal to or exceeding the |r| = 0.5 threshold is determined and designated as Nr0. The individual reaction times for all participants in either the TD or ADHD group are then randomized so that any given FC and RT measure are unlikely to be associated with the same individual. The number of FC–RT correlations satisfying the threshold condition is then calculated (Nri) and the process is repeated 10,000 times . This enabled us to determine the probability of observing the Nr0 correlations satisfying the threshold condition on the assumption that the null hypothesis applies. Where the permutation test is conducted at multiple points in time, we apply a Bonferroni correction as previously described.To examine the relationship between reaction time (RT) and FC, the linear correlation between individual mean CPT A‐X RT and FC was calculated for each point in time for the ADHD and TD groups. This yielded 2016 time series illustrating the correlation between FC and RT across the whole sample. To explore temporal variation in the strength of the correlation between processing speed and FC for the TD group, we determined the number of electrode pairs where the magnitude of the correlation coefficient r exceeds 0.5,   = 4.357, p = .041). This a common observation  was significantly lower than meanRef according to a paired Student's t‐test. The red lines illustrate the equivalent situation where FC was significantly larger than meanRef.The maps illustrated in Figs t‐tests exceeding the threshold value are listed above each map. Where the permutation test indicates the number of both positive and negative t‐tests are significant at the same point in time, then a separate p‐value is listed for the positive and negative values.The results of the permutation tests used to determine the statistical significance of the peak number of The 7.0 s interval comprising the appearance of the number “4” followed by the blank and then the target number “5” are associated with significant changes in FC for both groups. During the appearance of the “4”, we observe a nonsignificant tendency to a reduction in parieto‐frontal FC in the TD group Fig. A and a sRef‐Blank interval. While smaller and restricted to frontal and prefrontal sites, we also observe a significant FC increase in the ADHD group during the Ref‐Blank interval . Figure The approach used to examine changes in FC during the reference task was also used to examine FC changes during the CPT A‐X task. In this case, we examined the CPT A‐X FC with respect to the time averaged reference task  Fig. B in the 4We found a number of significant FC differences between the TD group and ADHD group. However, as the methodology used in this study may be unfamiliar, we start this section with a restatement of some of the major findings to orient the reader before discussing the significance of the results in more detail.AX Blank period, and at the transition from the “X” to the following blank period  TINs that interfere with task performance and are thus suppressed during task execution. In light of this, we will refer to this parietal‐frontal FC component as DMN‐like FC.While the behavior of 4.2A‐X Blank and the Ref‐Blank periods. While there were a number of topographic and timing differences in FC when comparing control and ADHD groups, this discussion section will focus on two principal differences.In comparing the FC changes in the ADHD group with those of the TD group, we will focus on the changes during the Ref Blank  while the ADHD group inappropriately engages the DMN in the more demanding CPT A‐X task. This suggestion is consistent with a large body of evidence pointing to reduced suppression of DMN activity in attentional tasks in ADHD and a positive correlation between reduced DMN suppression and distractibility in ADHD . This is consistent with the observations of van Leeuwen et al.  while the internally generated cortical information flows or top‐down processes are mediated by synchronous oscillations in the 10–20 Hz range (Fries, top‐down processes as opposed to fMRI findings that reflect very low‐frequency processes.Our methodology may influence the findings in two important ways. Firstly, the higher temporal resolution (Silberstein, e Fries, . Our sel4.2.4We have observed differences in brain FC between the control and ADHD groups. Our findings are consistent with the notion of ADHD being associated with TIN abnormalities. Specifically, our findings are consistent with the possibility of TINs in general or the DMN in particular being abnormally activated in the presence visual attention levels that were found to suppress such networks in the TD group. To the extent that our observations reflect DMN activity, then our findings call into question the simple “failure to inhibit the default mode network” hypothesis of ADHD. This is suggested in light of the fact that the TD group exhibited high levels of default mode network activity during the low‐demand reference task while the ADHD group exhibited low levels of default mode network activity during this task.The authors declare that they have no conflict of interest.


Desmoid-type fibromatosis is a benign fibrous neoplasia originating from connective tissue, fascial planes, and musculoaponeurotic structures of the muscles. Currently, there is no evidence-based treatment approach available for desmoid fibromatosis. In this article, a case of a patient in the pediatric age affected by desmoid fibromatosis localized in the orbit is presented. The aim of the article is to describe this unusual and rare location for the desmoid fibromatosis and outline the principle phases in the decision-making process and the therapeutic alternatives for a patient affected by desmoid fibromatosis.  The protocol of this review included study objectives, search strategy, and selection criteria. The primary end point of this study was to analyze the head and neck desmoid fibromatosis. The secondary end point was to identify the available therapies and assess their specific indications.  The mean age of patients was 18.9 years ranging from 0 to 66, and 52% were female. A bimodal age distribution was observed, and two age peaks were identified: 0–14 years (57%) and 28–42 years (18%). The most common involved areas were the mandible (25%) followed by the neck (21%). In 86% of the cases, the treatment was the surgical resection of the disease, and only in 5% of the cases, the surgical resection was followed by adjuvant radiotherapy.  The orbital location is extremely rare, especially in the pediatric population. The management of desmoid fibromatosis is based on the function preservation and the maintenance of a good quality of life, but in case of symptomatic patients or aggressive course of the disease or risk of functional damages, the surgical approach may be considered. Therapeutic alternatives to surgical resection are radiotherapy and systemic therapy. CTNNB1 (the gene that codes for beta-catenin) [Desmoid-type fibromatosis is a benign fibrous neoplasia originating from connective tissue, fascial planes, and musculoaponeurotic structures of the muscles . This tucatenin)  with p.Tcatenin) , 14. Descatenin) , 12. Acccatenin) . Abdomincatenin) . Predilecatenin) , 15. Heacatenin) . In the catenin) , 17. Thecatenin) . The patcatenin) . These tcatenin) , 18. Clicatenin)  and a vacatenin) . Currentcatenin) . In the catenin) , 19, andcatenin) .In this article, a case of a patient in the pediatric age affected by desmoid fibromatosis localized in the orbit is presented. The aim of the article is to describe this unusual and rare location for the desmoid fibromatosis and outline the principle phases in the decision-making process and the therapeutic alternatives for a patient affected by desmoid fibromatosis.The protocol of this review included study objectives, search strategy, and selection criteria. The primary end point of this study was to analyze the head and neck desmoid fibromatosis. The secondary end point was to identify the available therapies and assess their specific indications. Retrieval of studies was performed through MEDLINE on PubMed using keywords “extra-abdominal desmoid fibromatosis” and “head and neck desmoid fibromatosis” as free text word and/or combined with “therapy” and “treatment.” Initial searches were performed in March 2016, and only studies published as full papers in English from 1978 to 2016 in peer review journals were selected. Selection criteria were study design and the presence of sufficient data about sex, age, disease location, and treatment.After initial screening, 128 studies were identified, but the subsequent evaluation for eligibility has caused the exclusion of 22 studies because they were published only in abstract form, they were not in English, or they were not providing information about one or more of the requested areas . In total, 106 studies were considered adequate for this review.The mean age of patients was 18.9 years ranging from 0 to 66, and 52% were female. A bimodal age distribution was observed, and two age peaks were identified: 0–14 years (57%) and 28–42 years (18%). The most common involved areas were the mandible (25%) followed by the neck (21%). In 86% of the cases, the treatment was the surgical resection of the disease, and only in 5% of the cases, the surgical resection was followed by adjuvant radiotherapy.A male Senegalese ten-month-old baby was referred to our department due to the presence of a painful and firm mass at the level of the right orbital area of substantial size. This neoplasm was first noticed three months after the birth as a swelling with exophthalmos and had also resulted in the progressive loss of visual acuity. In the following months, the neoplasm had met with a rapid expansive and infiltrative growth . Radiogrβ-1 (CTNNB1) gene in the tumor was analyzed by Sanger sequencing, which revealed the S45F somatic mutation , long-term cosmetic and functional morbidity, and secondary malignancy . Thus, tIn adult patients, therapeutic alternatives to surgical resection in case of nonresectable lesions and in situations where surgical treatment would lead to functional impairment are radiotherapy and systemic therapy , 20, butRadiotherapy should be recommended for patients with nonradical tumor resection after primary surgery or at first recurrence and for function preservation. Furthermore, radiotherapy as adjuvant treatment improves local tumor control and should be preferred to radiotherapy at recurrence .Currently, there are also different pharmacological options available: antihormonal therapies (tamoxifen), nonsteroidal anti-inflammatory drugs (NSAIDs), low-dose chemotherapy (methotrexate plus vinblastine), tyrosine kinase inhibitors (imatinib or sorafenib), and full-dose chemotherapy using anthracycline-based regimens, including liposomal doxorubicin . In a peDesmoid-type fibromatosis is a benign fibrous neoplasia which can involve the head and neck district in the pediatric population causing considerable esthetic and functional deficits and affecting the patients' quality of life. The orbital location is extremely rare, especially in the pediatric population, and due to the infiltrative growth, it can involve the adjacent anatomical structures and cause the loss of visual acuity. The management of desmoid fibromatosis is based on the function preservation and the maintenance of a good quality of life, and for these reasons, the watch-and-wait policy is currently agreed as the initial strategy. In the pediatric population, the use of different pharmacological options should be considered in case of symptomatic patients or aggressive course of the disease or risk of functional damages. The surgical approach should be avoided if possible. A therapeutic alternative to systemic therapy and surgical resection is radiotherapy. However, radiotherapy is considered not indicated in the pediatric population and in the orbital location.