This study investigated foundation phase teachers’ perceptions of Attention Deficit Hyperactivity Disorder (ADHD). The teachers’ views on the aetiology, appropriate interventions and incidence rates of ADHD were examined. A total of 130 foundation phase teachers from mainstream private and public schools completed a self-developed questionnaire that had been piloted by the researchers. Both descriptive and inferential statistics were used to analyse the data, specifically to determine whether there were differences in responses between public and private school teachers. Thematic content analysis was used to identify the themes that emerged from the open-ended questions. It was found that the teachers had a limited understanding of ADHD, in terms of what it is as well as the aetiology. In addition, it emerged that medication was the preferred method of intervention despite the participants’ awareness of alternative intervention methods. A comparison of the private and public school teachers’ results indicated no significant difference in their perceptions regarding the aetiology, interventions or incidence rates of ADHD. As one of the most publicized conditions affecting children over the past two decades , there iWhile there are numerous studies on teachers’ perceptions of ADHD it is important to note that there are a number of aspects that influence perception, such as stigma, experience, knowledge and prior learning. The current study focused primarily on teachers’ understanding and knowledge of ADHD. There is no documented research on private and public school teachers’ perceptions of ADHD in South Africa. A number of studies have reported that teachers’ perceptions of the incidence of ADHD in their classrooms are considerably higher than the existent prevalent rate of this condition amongst children ,13,14,15Holz and Lessing This study examined South African foundation phase teachers’ perceptions surrounding ADHD since the symptoms of ADHD usually have their onset during this phase of children’s schooling Teachers’ thoughts on the incidence rate, aetiology and possible interventions for children presenting with ADHD were explored. While international studies have explored similar aspects of ADHD it has been demonstrated that culture plays a role in the individuals’ perception of ADHD . ConsequThe essential futures of ADHD as stated in the DSM-5 points to a pattern of inattention, impulsivity and hyperactivity which is persistent and present in two or more settings . In relaADHD is divided into three subtypes according to the main features of each type, namely ADHD: Predominantly Inattentive Type; ADHD: Predominantly Hyperactive-Impulsive Type; and ADHD: Combined type. Kern and Seabi  found thAccording to Mowbray  One cannot negate the biological cause of ADHD, however the environment and various interactions with others does affect the manner in which ADHD is displayed. So while the disorder and etiology thereof is a distinct entity, a number of other factors exacerbate or mimic the symptoms of ADHD. The stress resulting from the excessive demands of modern day schooling, unemployment, marital problems, substance abuse, general abuse and neglect from parents causes children to display the symptom of inattention in ADHD ,26. ConsIn the South African context it is important to consider both intrinsic and extrinsic factors in the development of ADHD. Some of the most prominent extrinsic factors include social-economic barriers, inflexible curricula, inappropriate and inadequate provision of support services and lack of parental recognition and involvement. Language and communication difficulties, health difficulties, sensory impairments and intellectual and learning difficulties are some of the most prominent intrinsic factors .Researchers have found a number of causes for the appearance of attention difficulties. A child who is hearing impaired may appear to be dreamy and lacking concentration . This ch▪Medical Interventions which are central nervous system stimulants.▪Behavioural Interventions, and▪Academic Interventions.While no quick treatment exists for ADHD, behaviour can be managed through the use of an educational programme that fits the child’s specific needs and medication, if the parents and doctors think that it will be beneficial . DuPual Medical interventions usually involve the use of central nervous system stimulants. Research into the efficacy of stimulant medication found that between 70% and 90% of children treated with medication responded positively while the remainder of the children displayed no response or the ADHD symptom worsened ,38. Non-Behavioural interventions for ADHD are divided into two distinct categories. The first category refers to changing antecedent events which focus on changing behaviour prior to the specific behavior requiring change. Examples of these interventions include posting rules, modifying assignments and peer tutoring. This is supported by Grandy and McLaughlin  who refeThe second behavioural intervention uses the cognitive-behavioural approach. This refers to consequent events which employ both positive and negative consequences for a specific behaviour. Examples include star charts, time out and privileges. These interventions specifically focus on the child’s thinking processes and aim to encourage children to problem-solve using an appropriate strategy while simultaneously weighing up the consequences of their actions. Academic interventions entail the offering of academic support to learners diagnosed with ADHD. Examples include peer tutoring and individualized direct instruction . PreviouOn the other hand, Evans, Schultz and Sadler  includedDespite having knowledge of the various intervention methods available, studies show that teachers prefer medication as an intervention strategy since they regard it to be more effective and timely ,14. GlasIt is evident from the literature that ADHD is prominent in all communities and cultures. While the cause is influenced by biological, psychological as well as social systems, the prevalent interventions do not necessarily address these various systems. The understanding of both the causes and interventions are informed by the model from which ADHD is understood. While there are a number of distinct models upon which an understanding of ADHD can be based, research shows that teachers are more prone to favouring the medical model to address the symptoms and favour medication to alleviate the symptoms of ADHD due to the fast acting nature of the medication . MowbrayIn contrast to the medical model, a broader socio-ecological model, such as the eco-systemic model acknowledges that interactions between an individual and various systems may either hinder or augment their development ,43. ApplThe present study aimed to (a) establish what teachers understood about ADHD, (b) establish the perceived and actual incidence rate of ADHD, (c) identify what teachers perceived to be the aetiology of ADHD and (d) ascertain teachers’ perceptions on the possible interventions for learners presenting with ADHD. Both private and public school teachers’ views in relation to the above research questions were explored.n = 106) indicated that their schools were located within an urban setting, while 8% (n = 10) of the respondents, reported that their schools were either township schools or inner city schools. In South Africa, a township refers to an under-developed urban living area on the outskirts of towns or cities, primarily reserved for people of colour during the apartheid era. While the highest number of teachers, (n = 56), indicated that they had access to educational psychologists, the public school teachers (n = 38), indicated more access to educational psychologists in comparison to their private school counterparts (n = 18). This is an unexpected finding and is unique to this study as private schools are better funded and resourced. Thirteen teachers indicated that they had access to clinical psychologists.The present research study was conducted at fifteen mainstream primary schools in the Johannesburg East, West and North Districts of the Gauteng region of South Africa. Amongst these, eight were private and seven were public schools. Of the total number of participants, 84% , while the least number of participants, , were within the youngest age range, 20–25 years. The overall mean age of the sample was 36–40 years of age. The participants in the current study comprised of 129 females and one male whose ages ranged between 20 and over 50 years. The majority of the participants were aged over 50 years  of the participants indicated that they had not attended any training course on ADHD while, 46% (n = 70) indicated that they had undergone in-service training relating to ADHD. Of the 130 participants, 50 (38%) were employed at private schools, while 80 (62%) were employed at public schools. Twenty-five participants (19%) reported that they were completing their postgraduate studies. Fifty-four percent , written consent was obtained from the Gauteng Department of Education and from the principals of the fifteen schools via individual meetings with the first author. Teachers were requested, in writing to participate in the study. They were informed, through the use of an information sheet that participation in the study was completely voluntary and that anonymity and confidentiality would be ensured. No identifying information was requested on the questionnaire.t-test analysis was used to ascertain the differences in responses between the public and private school teachers’ perceptions of ADHD. Having been coded and entered into a spreadsheet, the quantitative data was analysed Statistical Analysis Software (SAS).Both qualitative and quantitative data was gathered. The quantitative data was analysed using descriptive statistics employing frequency analysis, based on the aims and research questions of the study. A The open-ended questions were analyzed using thematic content analysis which consists of “burrowing through written records in order to discover their characteristics” .A number of international studies which investigated teachers’ perceptions of ADHD have used self-developed instruments ,18,23. TThis self-developed questionnaire  demonstrThe questionnaire was two-fold in nature, consisting of both open and closed-ended questions, with the knowledge and attitudes about ADHD being evaluated through the use of closed-ended questions employing a Likert scale ranging from Strongly Disagree to Strongly Agree. However the results have been analyzed and interpreted using a three point scale; Agree, Neither Agree or Disagree and Disagree; where the Strongly Agree and Agree results have been combined, as well as the Strongly Disagree and Disagree results. This was done to facilitate the presentation and interpretation of the results. “Do you have learners who have been medically diagnosed as having ADHD in your class?”, and “What is their treatment plan?”, were presented. Question 16 asked “what is your understanding of the term Attention Deficit Hyperactivity Disorder?” aimed at gauging teachers’ overall understanding of ADHD. Questions 17–18 were used to evaluate the teachers’ knowledge regarding the diagnostic criteria for ADHD and were based on the diagnostic criteria specified in the DSM-IV as the study was conducted prior to the publication of the DSM-5. Questions 19–22 evaluated the teachers’ perceptions regarding the causes and interventions for ADHD. Question 19 and 21 were formulated based on the literature presented on perceived and actual causes and interventions for ADHD. Questions 1 to 13 explored the demographic background of the participants and were used in exploring the similarities and differences between private and public schools teachers’ perceptions of ADHD. This section included amongst others, questions around gender, age, qualifications, teaching experience, type of school and number of learners in a class. Questions 14 and 15 examined both the actual and perceived incidence rates of ADHD in the teachers’ respective classrooms along with exploring the actual treatment plans for children diagnosed with ADHD as well as the teachers’ thoughts on the efficacy of various treatment options. Questions such as n = 12) than in private school teachers . This may be linked to the class size where the mean class size of the public school teachers were 30–34 learners per class in comparison to the private school teachers where the mean class size was 21–25 learners. The results of the study indicated that the teachers’ understanding of ADHD focused on the type of behaviour that the child exhibited. Specifically, the behaviours highlighted by the teachers were an inability to sit still, remain focused, complete work and sustain concentration. While these features are not considered diagnostic they are highlighted by Sadock and Sadock  and O’Nen = 16) of all of the teachers alluded to the fact that ADHD is a neurological condition. Twenty percent (n = 10) of the private school teachers referred to the neurology of the condition in comparison to the 8% (n = 6) of public school teachers. The discrepancy between the knowledge of private and public school teachers may be as a result of their course attendance, where 55% (n = 28) of private school, and 40% (n = 32) of public school teachers attended courses related to ADHD. The result seems to indicate that private school teachers have a better understanding of ADHD, possibly due to them attending ADHD courses, in comparison to their public school counterparts.While the neurology of ADHD has been emphasized by numerous authors ,24,25,48n = 100) of the teachers agreed that a child must be inattentive and or hyperactive/impulsive for a diagnosis of ADHD to be made. This is consistent with Amod, Vorster and Lazarus’ [Lazarus’  findingsLazarus’ . n = 66) of the teachers agree with this criterion, where 60% (n = 30) of the private school teachers and 45% (n = 36) of the public school teachers showed an agreement with this criterion. Again course or workshop attendance (45% of public school and 55% of private school teachers) may account for the variability in their responses.However, a different picture emerges with reference to the criterion that states that the behaviour must have occurred before the age of 7 years. Overall, only 51%  and physiological factors . One of the respondents noted that ADHD is a “neurologically based disorder associated with chemical imbalances in the brain affecting temperament and behaviour and performance at school” which is “usually inherited”. These results are consistent with previous research which indicates that ADHD has a neurological basis [n = 16) of the teachers alluding to the neurological basis of ADHD with reference to the diagnostic criteria. Responses relating to the causes of ADHD can be classified into five themes as can be viewed in al basis ,24, and al basis ,50. Howen = 38) of private school teachers and 70% (n = 56) of public school teachers indicating a positive response in this regard. While this result is consistent with teachers in America and Australia [Teachers seem to perceive poor diet as the primary cause of ADHD, with 76%  of the teachers implicated barriers to learning as a causal factor while only 30% (n = 39) drew a link between emotional distress and the causality of ADHD, this despite emotional distress merely being a trigger to inattention and distractibility [n = 55) of the teachers indicated that “inadequate” parenting might cause ADHD which is lower than the 80% of teachers in Sri Lanka who held a similar belief [t = 0.250, p = 0.028 < 0.05, with public school teachers receiving higher scores than private school teachers.Forty two percent  of the teachers regarded Ritalin as the most effective modality for treating ADHD. They indicated that “children are able to achieve academically”, it is effective “almost immediately”, and learners are able to “focus better and complete tasks timeously”. One of the teachers indicated that the children were “motivated to work, join groups, finish work and could write on lines”. As presented in n = 33) of private school teachers and 44% (n = 35) of public school teachers indicating that they believe Ritalin to be an effective treatment for ADHD  of the teachers highlighted play therapy, and 22% (n = 29) counselling, as effective treatment modalities for ADHD. This, despite the finding that only 30% of the teachers specified that ADHD is caused by emotional distress. Eighty three percent of the teachers noted that a neurologist or an educational psychologist should assess children with ADHD. This high percentage of referrals to a neurologist is in contrast to the finding that only 56% of teachers reported that ADHD is in fact caused by neurological factors. This frequency of choice of a neurologist for referral was similar between private school teachers (86%) and public school teachers (81%). Twenty six percent (n = 118) of the teachers employed a system of rewards which can be linked to Grandy and McLaughlin [The intervention strategies chosen by teachers did not focus on a single strategy but instead on multiple interventions. They selected educator interventions, such as moving the child around the classroom, breaking assignments up into parts and ensuring that they have the attention of the child before giving instructions. An additional aspect of the educator interventions was that of behaviour modification. Ninety percent (Laughlin  behaviouBased on the findings it appears that teachers’ understanding of ADHD is based primarily on the medical model as their focus appears to be on the child and not on other systemic factors that may result in the ADHD behavior. However their perceptions regarding the etiology of ADHD takes on a more systemic view as, factors such as parental style, and diet are alluded to. With regards to appropriate interventions, again a more systemic view was adopted by the teachers who amongst other interventions, attempted to adjust the environment to suit the particular child’s learning needs.Several limitations of this study need to be acknowledged. First, given the small convenience sample size which mainly consisted of females, the results cannot be generalized to the wider teaching population. Moreover male participants may have held different perceptions regarding the various aspects of ADHD investigated. Furthermore, only schools in the Johannesburg East, West and North Districts were targeted which would also impact on the external validity of the results. In addition a 5-point Likert scale was used as opposed to a 4-point scale which may have resulted in a greater number of neutral responses. The results of this study imply that teachers do not make a distinction between inattention and ADHD. This means inattentiveness, possibly caused by other factors, is identified by teachers as ADHD without any of the other diagnostic criteria being applied. Therefore, their understanding of the cause of ADHD includes physiological, dietary, parental, emotional and other barriers to learning as factors that may cause the disorder. Consequently, teacher training programmes, both pre- and in-service, on ADHD should include a focus on the causes of the disorder. Moreover, teachers need to be adequately trained to correctly screen learners with ADHD and implement the necessary intervention strategies to assist their learners. The identification process needs to address the possible causes of inattention and hyperactivity in children, over and above ADHD. These may include systemic factors such as emotional distress resulting from abuse or neglect, health difficulties, language difficulties and poverty. This training could be provided through collaboration between government departments and health practitioners, such as neurologists, pediatricians and psychologists, and could take on the form of pamphlets, educational talks and workshops, as well as personal communication. Given the range of systemic factors that could be implicated in the onset of the ADHD behaviours, particularly in the South African context, providing teachers with training to assist these learners is also paramount, both to the successful implementation of inclusive education, as well as the integration and education of these learners in positive learning environments. Moldavsky and Sayal  found thStudies have found a correlation between teacher knowledge and student exposure . This imGiven the findings in the current study, teachers’ understanding of ADHD appear to be limited to the behaviours that are displayed by learners such as an inability to sit still, remain focused, complete work and sustain concentration. While teachers acknowledge the physiological and neurological basis of the disorder, they perceive emotional upset, poor diet, inadequate parenting and other barriers to learning as additional causal factors.Medication is viewed as the primary intervention strategy as it is considered to be fast acting and the most effective, this despite the environmental factors cited by the teachers as causal factors related to ADHD. A comparison of the results indicates no significant difference in private and public school teachers’ perceptions regarding the cause, interventions or incidence rate of ADHD.


Attention deficit hyperactivity disorder (ADHD) is the most common behavioral disorders in children and recent studies reported a relationship between low levels of Vitamin D and incidence of ADHD.The aim of this study was to investigate the association between vitamin D deficiency and attention deficit hyperactivity disorder (ADHD). Also, to study the impact and role of vitamin D on the development of ADH in children.This is a case-control study which was conducted in children below 18 years of age from June 2011 to May 2013 at the School Health and Primary Health care Clinics, Qatar.The study was based on 1,331 cases and 1,331 controls. The data collection instrument included socio-demographic & clinical data, physician diagnosis family history, BMI, and serum 25(OH) vitamin D, calcium, albumin, billirubin, magnesium, calcium, cholesterol, urea, triglyceride and phosphorus. Descriptive and univariate statistical analysis were performed.Of the total number of 3470 children surveyed, 1331 of ADHD and 1,331 of healthy children gave their consent to participate in this study. The mean age  for ADHD versus control children was 10.63±3.4 vs. 10.77±3.4. Overweight (7.7% vs 9.4%) and obesity (4.6% vs 7.7%) were significantly lower in ADHD children compared to their counterparts (P=0.001). Vitamin D deficiency was considerably higher in ADHD children compared to healthy children. The mean value of vitamin D in ADHD children was much lower than the normal value and there was a significant difference found in the mean values of vitamin D between ADHD (16.6±7.8 with median 16) and control children (23.5±9.9) (p<0.0001) and with median 23 (p = 0.006). Mean values of Calcium and phosphorous were significantly higher in control compared to ADHD children (p<0.001). 1331 of all ADHD children had 19.1% had severe vitamin D deficiency (< 10 ng/ml), 44.9% has moderate insufficient levels (between 10-20 ng/ml), 27.3% has mild insufficient levels (between 20-30 ng/ml) and only 8.1% of ADHD had sufficient serum vitamin D levels (>30 ng/ml). Multivariate logistic regression analysis revealed that household income, poor relationship between parents, mothers’ occupation, consanguinity, BMI in percentiles, low duration of time under sun light, physical activity, low serum calcium level and low vitamin D level were considered as the main risk factors associated with the ADHD after adjusting for age, gender and other variables.The study showed that vitamin D deficiency was higher in ADHD children compared to healthy children. Supplementing infants with vitamin D might be a safe and effective strategy for reducing the risk of ADHD, but, further genomic and some other test and relevant studies need to be done. Attention deficit hyperactivity disorder (ADHD) is one of the most common disorders in school aged children . ADHD haFurther, children with ADHD may have functional impairment across multiple settings including home, school and social relationships ; 2008. AVitamin D deficiency is a major health problem and has been noticed in many parts of the world . It is nThe association between Vitamin D deficiency and ADHD in young children has never been reported in the literature. To the best of our knowledge, this is the first study to investigate the association between circulating levels of vitamin D and ADHD and associated risk factors.This is a case-control study matched by age and nationality which was designed to investigate association between vitamin D and ADHD in children below 18 years of age. The survey was conducted in the period from June 2011 to May 2013.The International Review Board’s (IRB) approval was provided by the Research Ethics Committee, Hamad Medical Corporation, with the ethical standards laid down in the 1964 Declaration of Helsinki.The sampling plan was stratified with proportional allocation. Stratification allowed both urban and semi-urban areas to be proportionally represented. The list of names of government schools was obtained from the Office of Director of General Education, Ministry of Education. Government schools were segregated according to gender. There were 94,985 children enrolled in government and private primary schools for boys and girls and 38,622 children enrolled in government and private intermediate or preparatory schools for boys and girls and further 34,132 children enrolled in government and private secondary schools for boys and girls in Qatar. To fulfill the objective of the current study, the study was based on 1331 cases and 1331 controls.The diagnosis of ADHD was based on physician-diagnosis . The Conners’ teacher scale was used to screen ADHD symptoms among children  and teacControl subjects aged below 18 years were identified from community as a healthy and if not ever been diagnosed as ADHD or if they never if they never used any inhaler or ADHD medication. This group involved a random sample of 1,835 healthy subjects who visited the PHC Centers for any reason other than acute or chronic disease, and only 1,331 subjects included due to the either refusal of the mother or difficulty in drawing blood from very uncooperative subjects; with a response rate (72.5%).All government schools are financed and operated by the government of Qatar. Since education in government schools is free, schools are overcrowded compared to private schools. Students in government schools are provided books and transport from school, but not meals. All government schools are segregated according to gender. Schools have daily classes for five days and the schools have two breaks for 15 minutes. Most of the schools have regular parent teachers meeting, but coordination of parents with teachers is poor in all aspects.The Conners’ teacher scale was used to screen ADH symptoms among children. Teacher rating scales  are alsoThe Conners’ teacher scale contained 10 items for each of which the teacher is asked to indicate the degree of applicability to the child being assessed: not at all (score 0), just a little (score 1), much (score 2), very much (score 3). If the score of the child was from 0 up to 9 and from 10 up to 14, the child was classified as having mild and moderate ADHD respectively. The child who scored 15 or greater in Conners’ teacher scale is considered to have high score for ADHD; which might reflect the presence of ADHD. The literate parent of the children completed the questionnaire. Non-Qataris are expatriates who came to Qatar for employment, mostly from Middle East and Far East Asian countries. In this study, we have included non-Qatari children from Arab countries only. The relationship of the consanguineous parents was recorded. Consanguinity was defined as marriages between relatives, either first or second cousin.severe vitamin D deficiency, 25(OH)D <10 ng/ml; 2) moderate deficiency, 25(OH)D 10-19 ng/ml; 3) mild deficiency, 25(OH)D 20-29 ng/ml; and normal/optimal level is between 30-80 ng/ml 18-19,24-25]. According to the recommendations of other studies , a vitamin D metabolite, was measured using a commercially available kit . The treated samples were then assayed using competitive binding radioimmunassay (RIA) technique. Subjects were classified into four categories: 1)  studies , we cateth percentile was considered normal weight, 85th-95th percentile as overweight and >95th percentile as obese. Physical activity was measured as any body movement like walking, running, gardening etc for more than 30 minutes a day.Body mass index (BMI) was calculated as the weight in kilograms  divided by height in meters squared. BMI <85t test was used to ascertain the significance of differences between mean values of two continuous variables and confirmed by non-parametric Mann-Whitney test. A chi-square analysis was performed to test for differences in the proportion of categorical variables between two or more groups. Kruskal-Wallis one-way analysis of variance was used for comparison of several group means. Multiple Logistic regression analysis using the forward inclusion and backward deletion method was used to asses the relationship between dependent (ADHD symptoms) and independent variables and to adjust for potential confounders and orders the importance of risk factors (determinant) for the ADHD. The level p<0.05 was considered as the cut-off value for significance.The student In many developed and developing countries, the majority of children who have ADHD are undiagnosed and there is limited recognition of child mental health problems in primary care. Teachers and parents may be well placed to identify unrecognized children and to facilitate their referral to medical health care services. Vitamin D deficiency and ADHD in young children has never been previously reported in the literature. Perhaps, this is the first study investigating an association between circulating levels of vitamin D and ADHD. Besides, it showed that there is a relationship between vitamin D deficiency calcium; phosphorous; magnesium; and BMI among ADHD children. Overall, there is no study performed indicate association between Vitamin D deficiency and ADHD in young children. It has been only some about 30 years since the first reports suggesting that functions of vitamin D extended well beyond its classical role in systemic calcium homeostasis . More reA causal relationship between micronutrient deficiencies and suboptimal brain function would have major public health implications. Large segments of the world (including the U.S.) population, particularly the poor, are known to be undernourished for a number of micronutrients . A majorAdditionally, the purpose of this study was to compare long-term school outcomes  for children with ADHD versus those without ADHD . Median The correct recognition of ADHD has international public health implications . This highlights the need to improve the ability of professionals to identify which children might benefit from further assessment. Recognition of ADHD, even if accurate, may be harmful through labeling, stigmatization, scapegoating, and effects on self-perception. It has been argued that such over-medicalisation enables both adults and children to avoid taking responsibility for a range of behaviours . CurrentFurthermore, several biological and epidemiological evidence strongly suggests that brain development and critical brain functions can be effected by vitamin D inadequacy related to cognitive or behavioral endpoints . AlthougOver the last decade, the association of vitamin D with neuro-psychiatric diseases conditions has been the focus of interest of multiple studies including those on multiple sclerosis schizophrenia , ParkinsStrengths of our study include large sample size, as case-control designed, age, gender and ethnicity - matched, and physician-diagnosed ADHD and mental health illnesses. There are few limitations in our study including lack of data on oral intake of vitamin D and sun exposure, and the number of different types of ADHD was relatively small.. The observation of low level of vitamin D in young children could be attributed to various factors such as social customs, neglecting sunlight, dietary, and the breast feeding without any vitamin D supplement. Since vitamin D intake was found very poor in children, Vitamin D supplement to infants might be an effective strategy for reducing the risk of ADHD occurrence in children.In Conclusion vitamin D deficiency was greater in ADHD children compared to healthy children. Also, ADHD was also associated with a higher rate of severe Vitamin D deficiencyStudies suggest convincing biological or behavioral evidence linking vitamin D deficiency to brain dysfunction. Currently, data is lacking regarding the association between vitamin D and ADHD in human beings.The association between Vitamin D deficiency and ADHD in young children is never been reported in the literature. To the best of authors’ knowledge, this is the first study to investigate the association between circulating levels of vitamin D and ADHD. The present study revealed that vitamin D deficiency was greater in ADHD children compared to healthy children. Supplementing infants with vitamin D might be an effective strategy for reducing the risk of ADHD occurrence.


Objective: The aim of this study was to assess and compare the effect of two neurofeedback protocols (SMR/theta and beta/theta) on ADHD symptoms, selective attention and EEG  parameters in children with ADHD. Method: The sample consisted of 16 children  with ADHD-combined type (ADHD-C). All of children used methylphenidate (MPH) during the study. The neurofeedback training consisted of two phases of 15 sessions, each lasting 45 minutes. In the first phase, participants were trained to enhance sensorimotor rhythm (12-15 Hz) and reduce theta activity (4-8 Hz) at C4 and in the second phase; they had to increase beta (15-18 Hz) and reduce theta activity at C3. Assessments consisted of d2 attention endurance test, ADHD rating scale (parent form) at three time periods: before, middle and the end of the training. EEG signals were recorded just before and after the training. Result: Based on parents’ reports, inattention after beta/theta training, and hyperactivity/impulsivity were improved after the end of the training. All subscales of d2 test were improved except for the difference between maximum and minimum responses. However, EEG analysis showed no significant differences. Conclusion: Neurofeedback in conjunction with Methylphenidate may cause further improvement in ADHD symptoms reported by parents and selective attention without long-term impact on EEG patterns. However, determining the exact relationship between EEG parameters, neurofeedback protocols and ADHD symptoms remain unclear. ADHD is considered as a widespread, durable, and neurodevelopmental disorder , 2. It aAlso, the analysis of electroencephalogram (EEG) signals, as an informative quantitative method, has revealed that EEG abnormalities in children with ADHD  and 20 mIn this context, we investigated the double impact of SMR/beta neurofeedback linked methylphenidate on the performance of children with AHDH in an overall measure for selective attention (d2-test) and ADHD symptoms. Neurofeedback TrainingNeurofeedback is considered as an operant conditioning of neural oscillations, in which the brain is trained to gain control over specific EEG parameters by real-time visual or auditory feedback. The desired brain activity is reinforced and undesired brain activity is inhibited. Several studies supported that neurofeedback training is a promising treatment for different disorders, especially for ADHD . In thisThe aim of the first section was to train the participants to improve the amplitude of SMR 12-15Hz) and reduce the amplitude of theta waves (4-7 Hz) on C4. In the second section, the participants were trained to increase their beta Hz and re and dimiThe training program was conducted opened eyes with reference placed on the near earlobe using automatic adjustment reward thresholds: 80% and 20% for reward and inhibit bands, respectively. Participants had to maintain the desired activity for two milliseconds then were reinforced by auditory or visual feedback. When they achieved the determined goal, the threshold became more difficult.In this study, 16 children with ADHD combined type (ADHD- C), 3 girls and 13 boys, comprised the sample size. These participants received MPH and neurofeedback training (NFT). All of them fulfilled DSM-IV criteria for ADHD diagnosis by a child and adolescent psychiatrist which was confirmed using the ADHD rating scale. The parents of participants completed the consent form.Inclusion criteria included: 1) age 9-15 years (to eliminate the influence of development on selective attention); 2) IQ > 90 (based on Rayven test); 3) a diagnosis of ADHD combined type (based on DSM IV criteria). Exclusion criteria were the lake of the following conditions: 1) diagnosis axis I disorders; 2) neuropsychiatric disorder; 3) neurologic disorder; 4) convulsion background. None of the participants had experienced cognitive training before: all of them were taking MPH. Assessment included behavioral and cognition at three times: before the start of the training, between the two phases of training, and after the end of the study. EEG analysis was performed before and after the study. Behavioral assessment was performed by ADHD rating scale, and D2 test was used to assess selective attention. D2 Test Concentration Endurance Test (d2 Test) was developed in Germany in 1962 and was introduced as a reliable and valid measure to estimate selective attention . BagheriADHD Rating Scale This scale includes 18 items to measure inattention, hyperactivity/impulsivity symptoms. The score range is from 0 to 54, a high score indicating more intensive ADHD symptoms. Faries et al., (2001) showed acceptable level of test- retest and inter-rater reliability, convergent validity and internal consistency for this scale for assessing ADHD symptoms. Moreover, the scores of this scale are comparable to scores of other validated scales like Conners questionnaire .This scale was completed by parents of each participant before the beginning of the training, between the two protocols and after the end of the training. In addition to measuring the IQ of the participants, we used Raven progressive matrices test for adults. Participants who were above the medium entered the study. EEG Recording Electroencephalogram signal indicates the brain electrical activity and gives us useful information about functional status of the brain and its structural pattern. EEG was recorded using Digital EEG SD-C24 from 7 channels based on 10-20 international system . Limits Data AnalysisData were analyzed using the Friedman test and repeated measures ANOVA, followed by Bonferroni test for pair wise comparison of related means scores between the three times of the assessment. Given the lack of error covariance matrix and normality assumptions for d2 scores, Freidman test was used to detect the differences for the test. The statistical analysis was conducted by SPSS (V.21), and p<0.05 value was considered significant.The statistical analysis of EEG power spectral was performed using independent t-test (parametric test) or independent Mann-Whitney (non- parametric test) according to data distribution. Kolmograph-Smirnov test was utilized to evaluate the normality of the data, and the features  with normal distribution were examined via independent t-test. Also, the features with non-normal distribution were examined via independent Mann-Whitney test. The features were considered significantly different at the level of P < 0.05. Spectral Analysis of EEG SignalPower spectral density (PSD) of EEG signals was computed using the Welch periodogram technique which is based on Fast Fourier transform (FFT). Since the time length of our epochs was 2 seconds, frequency resolution was 0.5 Hz. Five frequency bands were extracted and their power spectral was computed: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), SMR (12-15 Hz), beta1 (15-18 Hz), beta2 (18-34 Hz) and gamma (34-44 Hz).Overall AnalysisDemographic features are briefly illustrated in Means and standard deviations of all pre, middle and post measures in ADHD rating scale were calculated and presented in Behavioral AssessmentADHD rating scale was used to assess the effect of different protocols of neurofeedback on symptoms of ADHD, and results were analyzed using repeated measures ANOVA and Bonferroni tests (p<0.01). The results of descriptive analysis of ADHD rating scale are presented in Given the with-in subject effects and F score  in The paired comparisons between the measures by Bonferroni test are presented in D2 TestGiven the lack of error covariance matrix and normality assumptions for d2 scores, Friedman test was used to detect the differences for the test. Means and standard deviations of the four subscales of d2 test in the three levels of the study were calculated by Friedman test . SignifiEEG Analysis ResultsThe aim of this study was to explore the efficacy and compare both SMR/theta and beta/theta protocols to enhance selective attention and reduce ADHD symptoms; this was done in terms of EEG changes and modification in d2 attention endurance test and symptoms measures. Based on parents’ reports, enhanced attention was observed in ADHD children after beta training . The results of hyperactivity/impulsivity score showed significant improvement from pre- to- post treatment, but not in pre-mid and mid-post treatment. Neurofeedback training also led to improvement in all of subscales of d2 attention endurance test except for SB score in the two phases of the training. Also, analysis of EEG parameters showed no significant differences in EEG power before and after training. It is interesting to note that MPH and neurofeedback training did not affect the power spectral of recorded signals, while d2 test and ADHD rating scale had considerable modifications toward improved performance in participants. While many attempts have been made to evaluate the efficacy of neurofeedback, little information is available about the effect of neurofeedback on EEG parameters. Lubar, 2003  separateSignificant improvement of inattention in the second phase of the training provides a support for the efficacy of beta/theta training for attention enhancement. These results are consistent with previous studies which reported that beta training is associated with attention enhancement , 42, 43.These results can clarify the clinical decisions in ADHD protocol selection and improve sensitivity and specificity of the decisions about the number of sessions in a treatment setting. However, ADHD is a disorder with a spectrum of symptoms from variety of attention problems to hyperactivity/impulsivity, and these results should be interpreted with caution. Absence of an experimental group should be mentioned as a limitation of this study.Small sample size, absence of an active control group and the follow up assessment should be mentioned as the limitations of this study. In this study, our aim was to determine the efficacy of SMR/theta and beta/theta training and compare the results between the two phases of the training. Our study revealed that improvement in behavioral measures, especially attention, can be detected in a shorter period of training; and neurofeedback when combined with MPH can improve selective attention or ADHD symptoms  without long-term impact on EEG signals.However, further studies should to be conducted to examine the long-term effects of neurofeedback training and clarify the relation between EEG patterns and behavioral or cognitive performance in neurofeedback setting.


The central sulcus (CS) divides the primary motor and somatosensory areas, and its three-dimensional (3D) anatomy reveals the structural changes of the sensorimotor regions. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is associated with sensorimotor and executive function deficits. However, it is largely unknown whether the morphology of the CS alters due to inappropriate development in the ADHD brain. Here, we employed the sulcus-based morphometry approach to investigate the 3D morphology of the CS in 42 children whose ages spanned from 8.8 to 13.5 years (21 with ADHD and 21 controls). After automatic labeling of each CS, we computed seven regional shape metrics for each CS, including the global average length, average depth, maximum depth, average span, surface area, average cortical thickness, and local sulcal profile. We found that the average depth and maximum depth of the left CS as well as the average cortical thickness of bilateral CS in the ADHD group were significantly larger than those in the healthy children. Moreover, significant between-group differences in the sulcal profile had been found in middle sections of the CSs bilaterally, and these changes were positively correlated with the hyperactivity-impulsivity scores in the children with ADHD. Altogether, our results provide evidence for the abnormity of the CS anatomical morphology in children with ADHD due to the structural changes in the motor cortex, which significantly contribute to the clinical symptomatology of the disorder. The central sulcus (CS) is one of the most prominent and stable sulci of the human brain, which divides the primary motor and somatosensory areas. The three-dimentional (3D) anatomy of the CS includes motor and sensory maps, somatotopically organized according to Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common childhood-onset neuropsychiatric disorders, characterized by hyperactivity, impulsivity, inattention, or a combination . DeficitSurface-based approaches provide a framework for identifying local changes across the surface anatomy of the sulcus. This is accomplished using 3D surface parameterization following automated sulcal mesh reconstruction, which would enable further shape analysis, such as the encoding of statistical properties in local anatomical variations within individual sulci. Sulcal parameterization is a critical step, and it can create a normalized coordinate system on the CS surfaces that allows to compare the morphological differences at each location of the CS across individuals. The method based on the heat-equation diffusion process along the CS surface  has beenHere, we utilized a surface-based computational approach to investigate the 3D morphology of the CSs in children with ADHD. We first extracted the CS from individual, high-resolution structural MR images, followed by 3D surface reconstruction and parameterization. We then computed several surface metrics  based on the parameterized surface maps. Finally, we statistically analyzed the differences between the children with ADHD and the controls with regard to these metrics.The sample for this study consisted of 21 children with ADHD and 21 demographically group-matched control children, ages from 9 to 15 years old. All of the subjects were strongly right-handed, evaluated using the Edinburgh Handedness Inventory  and had DSM-IV criteria for ADHD combined type, by combining the Conners Rating Scale—Revised-L for both parent and self-reports (T-scores <60 (<1 SD) on all Conners parent and self-reports. For both groups, we included the K-SADS-PL screening questions and supplements to rule out pervasive developmental disorders, substance use and abuse, and posttraumatic stress disorder. Similarly, oppositional defiant disorder with physical aggression (using DSM-IV diagnostic criteria), and all other current Axis I disorders (except for fear of the dark) were exclusionary. Children with any specific learning disorders were also excluded. The basic reading, mathematical reasoning, reading comprehension, and numerical operations subtests of the Wechsler Individual Achievement Test Second Edition  using the Brain Extraction Tool .High-resolution three-dimensional T1-weighted structural MRI data were acquired from each subject using a 3T Philips Achiva TX MR system with a 32-channel phased array head coil . Axial images were acquired using MPRAGE with the following scan parameters: TR = 9.8 ms, TE = 4.6 ms, flip angle = 8°, voxel size = 0.94 mm × 0.94 mm × 1 mm, field of view = 240 mm × 188 mm × 220 mm, SENSE reduction factor = 2.5, 235 contiguous slices. Then, all images were resliced at a 0.94 mm × 0.94 mm × 0.94 mm isotropic resolution. The non-brain tissue was stripped (See ion Tool  provided2 (version 4.3.0) was used to extract the CS for all of the individuals. The whole pipeline is shown in Figure 1. The detailed procedures were as follows.In this study, BrainVISA (BV) softwareTo remove gross differences in brain size and orientation, we normalized all images into Talairach space. First, two professional neuroanatomists manually labeled four points in each axial image, i.e., the anterior commissure, the posterior commissure, an inter-hemispheric point, and a point in the left hemisphere. Then, a transformation based on the two points defined by the anterior and posterior commissures to the Talairach AC/PC referential was computed and applied to the whole image.Figure 1B).The intensities in raw images not only depend on the properties of different tissues but also on the location in the field of view due to the inhomogeneity of magnetic field. Thus, we first corrected the spatial bias  in normaFigure 1C. Sulcal structures were then reconstructed as the medial surfaces of two opposing gyral banks, which spanned from the GM/CSF border at the most internal point of the sulcus to the convex hull of the cortex .A 3D mesh of the external surface of the cortex for each hemisphere was computed based on the boundary between GM and CSF, as shown in Figure 1F.We observed some small, accessory sulci on the surfaces of the extracted CSs. They were usually shorter than 5 mm, originating at a right angle from the stem of the main sulcus, and were considered to be random folds . In thisx–y coordinate system for each CS using the sulcal parameterization method based on the heat-equation diffusion process along the CS surface  were extrapolated by solving the heat equation on the surface, and the bottom and top ridges of the sulcal mesh as well as the endpoints of the sulcus where these top and bottom ridges joined behaved as constant heat sources . In this way, the normalized coordinate system was constructed for each CS surface.To compare the morphological differences at each location of the CS across individuals, we need to parameterize each CS to create a normalized coordinate system on the CS surfaces. Our sulcal parameterization included two steps. The first step was to acquire the  surface . Two cooy position that represented the average value of the signed distance of each node on the mesh at the same position y to the inertial plane of the CS. The inertial plane was defined by the barycenter and was reflected in two orthogonal principal orientations of the CS . The sulcal profile was a morphological characteristic to measure shape variations along the superior–inferior direction  of the CS . Then, two stable anatomical landmarks, L1 and L2, relating to the functional primary motor area of the hand, were detected on the CS along the sulcal profile curve according to the literature . The average sulcal depth was calculated as the mean value of the sulcal depths at all positions. The maximum depth was also chosen from all sulcal depths for each subject. The sulcal length was defined as the average length along the exterior and interior sulcal boundaries of CS. The average sulcal span for the CS was defined as an average 3D distance between opposing gyral banks along the normal directions of the medial sulcal mesh  to describe the entire morphometry of the CS surface. The cal mesh . The suldi = (ni - ) u3 where  is the barycenter of the CS mesh and ni = T represents the node of the mesh and where a given sulcus mesh could be defined as the vector of nodes S = (ni)i = 1....N. The centered mesh Sc was defined as (ni - ). We generated the covariance matrix 1, u2, and u3 are the three eigenvectors of A ordered by decreasing eigen-value. Thus, u1 and u2 represent the main orientation of the CS and construct the inertia plane, and u3 reflects the normal direction of the inertial plane  of the CS compared with controls, we performed multiple linear regression analyses at each y position with age and gender as the covariates. Significant differences were evident in the sulcal depth and profile curves. To further estimate the local shape variations on the surface, we compared the between-group differences in the signed distance on the mesh to the inertial plane in a node-by-node manner. Statistical difference maps for the sulcal profiles between the groups are displayed on the template. The false discovery rate (FDR) was used for multiple-comparison corrections.To determine whether the CS depth and profile in the children with ADHD were different along the superior-inferior direction . The t-scores of inattention, hyperactivity–impulsivity, and DSM-IV total in ADHD children were significantly higher when compared to that in controls. Detailed statistics are shown in Table 1.The children with ADHD and typically developing controls had no significant differences in demographic measures  in the left CS compared to the control group. The average cortical thicknesses along the bilateral CS of the ADHD group were significantly larger than those of the control group .Results of statistic analyses in the global measures  of the CS are summarized in y coordinate at 100 successive points and then produced the depth-position profile (DPP) after sulcal parameterization. The between-group differences of the local CS depth, along the superior-inferior direction of the bilateral CS, are shown in the Figures 3A,B. Ps less than 0.05 were shown as red asterisks after FDR correction. Between group comparison of this local measure found that children with ADHD had significantly greater local depth in the middle section  of left side CS.We measured the CS depth along the Figures 3C,D. The two clinical groups showed significant  differences of local CS profiles in large areas of the superior and middle sections  of the left side CS, and in the middle section (y = 46∼52) of the right side CS.A similar analysis was applied to the sulcal profile. Scatter plots are shown in Figure 4). The warm color indicated that these regions on the CS surface were much farther from the inertial plane  in the ADHD group compared with the control group. Moreover, the left CS showed more differences than the right CS.We also performed a node-based analysis to detect between-group differences in the absolute value of the signed distance of each node to the inertial plane on the CS surface. We found that regional shape variations were located in the middle sections of the bilateral CS , when using age and gender as the covariates. No significant associations among other global measures and hyperactivity-impulsivity scores as well as inattention scores were found in the whole study sample. For those regions showing significant between-group differences in node-based analysis, we further evaluated the relationships between the sulcal metrics and hyperactivity-impulsivity as well as inattention scores in the whole study sample. Significant (P < 0.05) negative correlations between the CS profile and the hyperactivity-impulsivity scores were found in several surface clusters on anterior side of the left CS, which were around the “hand knob” area, whereas the regions showing significant positive correlations located in the posterior side of left CS ; posterior: Figures 5B,D). Meanwhile, significant (P < 0.05) negative correlations between the CS profile and the inattention scores were found in clusters on anterior side of the left CS, while the regions showing positive correlations located in the posterior side of left CS .Significant positive associations between the cortical thickness along the bilateral CS and the inattention scores were found in the whole study sample  of the left CS in the ADHD group were significantly larger than those of the typically developing children. These positions of the CS DPP are near the Sylvian fissure. Many published studies contributed to the mapping of the finger-tapping area (25∼47) of the DPP and to the mapping of the oral movement area, involving lip pursing, tongue movement, and smiling (57∼85), of the DPP . The abny to the inertial plane of the CS, which provides more shape information across subjects than the depth curve. In this study, we found that the between-group differences of the sulcal profile along the left CS were located in the middle section, which involved the function of finger tapping as well as oral movement. Subsequently, we further employed node-based analysis on the sulcal surface and located specific regions in the middle section of the bilateral CS showing significant between-group differences. Many studies have suggested that the middle section of the CS is mainly composed of the somatotopic hand area  associated with the attention and inhibition capacities in similar ways. This finding first time placed the left CS into the brain networks (traditionally thought to involve prefrontal lobe, anterior cingulate cortex, striatum, thalamus, etc.  that mayTo build upon this study, several issues need to be addressed. Firstly, it is not yet well known about how sulcal features directly relate to brain function and the functional implications of the findings need therefore to be explored in future studies. Secondly, we included both male and female subjects in both groups. We acknowledge possible gender-related differences of the GM and WM maturation patterns in typically developing children , and posIn summary, we presented a sulcal geometry-based statistical analysis approach to investigating the morphology of the CS in ADHD. Our study demonstrated significant morphological abnormalities in bilateral CS in children with ADHD, which were significantly associated with their clinical symptoms. These findings suggest that morphological alterations of the sensorimotor cortex in children with ADHD could significantly contribute to the anatomical substrates underlying clinical symptomatology of the disorder.SL and XBL designed the research; XBL collected the data; SW, XWL, and QL analyzed the results; SL, SW, and XBL wrote the main manuscript text. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Attention-deficit hyperactivity disorder (ADHD), one of the most common psychiatric disorders, is characterised by unstable response patterns across multiple cognitive domains. However, the neural mechanisms that explain these characteristic features remain unclear. Using a computational multilevel approach, we propose that ADHD is caused by impaired gain modulation in systems that generate this phenotypic increased behavioural variability. Using Marr's three levels of analysis as a heuristic framework, we focus on this variable behaviour, detail how it can be explained algorithmically, and how it might be implemented at a neural level through catecholamine influences on corticostriatal loops. This computational, multilevel, approach to ADHD provides a framework for bridging gaps between descriptions of neuronal activity and behaviour, and provides testable predictions about impaired mechanisms. ADHD is one of the most common psychiatric disorders during childhood, but the neurocognitive mechanisms behind it remain elusive.Behaviourally, ADHD is best characterized by increased variability across multiple cognitive domains and timescales.By using Marr's three levels of analysis, we show how impairments in neural gain can explain ADHD abnormalities, spanning from behaviour to neural activity.On an algorithmic and implementation level, we show how increased variability can be caused by neural gain impairments, and how it can be modelled using reinforcement learning and corticostriatal network models.We furthermore show how these levels can be linked to impairments in catecholamine systems . This is reliably found across many tasks, laboratories, and countries continuous performance task  and more nontarget responses (errors of commission) Simple response tasks, such as the ask CPT, , requireet al.decision temperature parameter  and animal studies that suggest a hypofunction in a DA system in striatal and prefrontal areas in ADHD A source of more direct evidence comes from human In line with the relatively widespread effects of neural gain in the brain Here, we illustrate how lowering neural gain at the neurophysiological (implementation) and algorithmic levels can induce ADHD-like neurocognitive impairments. To understand why the brain uses neural gain modulation to guide behaviour in the first place, we first discuss the importance of balancing between choice stability and choice variability from a theoretical standpoint.So far, we have concluded that a consistent feature of ADHD is an increased variability in behaviour. According to Marr, the first level of analysis should describe the problem a system  faces and how it tries to solve it The dilemma that the brain has to solve arises from acting in environments where different options may change their value for the subject. Agents not only have to exploit the option it estimates as the best, but must also explore the value of alternative options so as to gather more information From both a reinforcement learning and information theoretic perspective, the arbitration between different options is construed as balancing ‘exploitation’ and ‘exploration, information gathering’. This is a hard problem to solve, but there are simple, well-established, methods, such as randomly sampling from one's beliefs, or Thomson sampling The increased variability in ADHD can be seen as altered exploitation–exploration trade-off. In paradigms with no uncertainty, increased exploration makes no sense; by contrast, in a natural environment, the optimal amount of attentional stability, in view of uncertainty, is a matter of degree. Moving to a societal level, increased exploratory behaviour in a proportion of the population may be advantageous. Simulations by Williams and Taylor In summary, the brain has to arbitrate between either exploiting currently preferred options or sampling alternatives and learn from experience. While low exploration in most members of a group ensures stability, a low proportion of people with ADHD allows learning from exploration and, thus, can be evolutionarily beneficial for a group.The second level of Marr asks how a problem is solved. Specifically, it asks for mathematical descriptions of how the system solves its task. In recent years, these approaches have gained increased interest. Bayesian reasoning and reinforcement learning theories in particular have provided biologically useful algorithms that the brain appears to exploit decision temperature parameterτ, a measure of choice stochasticity. It describes to what extent the agent sticks to what it effectively believes to be the best choice. Higher decision temperatures make the agent more likely to choose from options currently estimated to have less-than-maximum values. By contrast, lower temperatures make the agent choose the highest value option more often, thereby avoiding alternatives even if they have almost the same value  signals. However, recent empirical data that addressed learning and decision-making in ADHD demonstrated that ADHD participants are not well characterized by impaired learning, but instead by an increased decision temperature In the context of learning and decision-making, previous theories We can tentatively conclude that increased variability at the algorithmic level is explained by an increased decision temperature in relation to an action selection process. We suggest that this is likely to be underpinned by lowered neural gain, potentially caused by malfunctioning catecholamine systems  and alteThe implementational level asks how the algorithm functions of the second level are realised in neural hardware, that is, in this instance, how the brain circuits instantiate and dynamically select between different options, what structural change is associated with an hypothesised neural gain impairment, and how this is translated into behavioural dysfunctions.et al.Neural models of corticostriatal circuits provide tools to study a catecholaminergic modulation of behavioural selection processes, such as the effects of reduced DA in striatal areas. Since refined maps of DA receptor distributions in the striatum are established, the majority of these models investigate the role of DA The aforementioned models of corticostriatal loops demonstrate that multiple impairments in neural gain  behaviourally?What are the unique features of NA and DA gain impairments behaviourally, algorithmically, and in neural loop models?How can  non-invasive neuroimaging contribute to dissociating different forms of neural gain impairment?Can a neural gain-based classification of subgroups be predictive of pharmacological treatment efficiency? Can the understanding of the associated information processing inform psychotherapy?Despite a likely heterogeneity in ADHD, we propose that neural gain modulation is a consistent impairment across many clinical subgroups. We can now hypothesise that ADHD subgroups may be better delineated by the specific profile of their neural gain impairments. One subgroup might primarily suffer from striatal DA impairment, expressing itself by more reward-related stochasticity and possibly striatal RPE impairments. Another subgroup might lack in frontal NA functioning, which might be expressed by impaired prefrontal signals and altered multiattribute processing. However, to be able to dissociate such subgroups, we need to develop better behavioural tasks and models, further advance computational neuroimaging, and develop neural models that are capable of dissociating different aspects of neural gain (see Outstanding Questions).


Associations between stress reactivity and callous-unemotional traits and internalizing symptoms were also assessed. The ADHD only (n=95) and ADHD+CD (n=107) groups did not differ in baseline cortisol, but the ADHD+CD group showed significantly reduced cortisol stress reactivity relative to the ADHD only group. Regression analyses indicated that ADHD symptom severity predicted reduced baseline cortisol, whereas CD symptom severity predicted increased baseline cortisol  and reduced cortisol stress reactivity . Callous-unemotional traits and internalizing symptoms were not significantly related to baseline or stress-induced cortisol. Impaired cortisol reactivity is hypothesised to reflect fearlessness and is associated with deficient emotion regulation and inhibition of aggressive and antisocial behaviour. Consequently, it may partly explain the greater severity of problems seen in those with comorbid ADHD and CD.Reported findings on cortisol reactivity to stress in young people with ADHD are very variable. This inconsistency may be explained by high rates of comorbidity with Conduct Disorder (CD). The present study examined cortisol responses to a psychosocial stressor in a large sample of adolescent males with ADHD ( •Cortisol stress responses were studied in 202 adolescent males with ADHD.•Males with comorbid CD showed reduced cortisol stress reactivity.•ADHD symptom severity predicted reduced baseline cortisol.•CD symptom severity predicted increased baseline cortisol and reduced cortisol stress reactivity.•Callous-unemotional traits and internalizing symptoms were not related to cortisol. The regAdolescents with ADHD are a heterogeneous population, with 30−50% of children with ADHD in clinical settings also meeting criteria for Conduct Disorder CD; . There iStudies that did assess and control for comorbid DBDs have still obtained mixed results. Some studies found lower baseline cortisol levels in children with ADHD and comorbid ODD/CD, but not in children with non-comorbid ADHD , whereasThis may be due to differences between studies in cortisol measurement techniques or saliva collection protocols. The HPA axis is a dynamic system that not only responds to psychological and physical stress, but also exhibits a marked diurnal rhythm . TherefoEven when cortisol reactivity to a stressor has been investigated, there are inconsistent findings. Reduced cortisol reactivity to stress has been found in children with ADHD and comorbid DBD compared to children with ADHD alone . Other sincreased cortisol activity or reactivity  traits. These traits identify those at greater risk for severe antisocial behaviour  and reduPrevious research has also focused on heterogeneity in cortisol reactivity between ADHD subtypes. The present study aimed to assess cortisol levels at baseline  and in response to stress  structured psychiatric research diagnostic interview using both parents and children as informants . This inn=95) and ADHD with comorbid CD . In addition, 48.1% of the sample met diagnostic criteria for ODD (48.6% for those with ADHD only and 46.5% for those with ADHD+CD). However, the focus of the paper concentrated on the more severe area of conduct disorder.Participants were allocated to two groups according to whether they met DSM-IV diagnostic criteria for Conduct Disorder or not: ADHD only  traits were measured using the Youth Psychopathic traits Inventory YPI; . The CU Cognitive ability was assessed in all participants using the Wechsler Abbreviated Scale of Intelligence WASI;  – 2-subt2.3Participants arrived at our laboratory in the morning and completed a battery of questionnaires and neuropsychological tests including the WASI before lunch. During this time they were asked to provide three baseline cortisol samples approximately 40 min apart from each other. After lunch they were informed that they would be taking part in a competition with an opponent of a similar age with a cash prize for the winner. This procedure is described in detail elsewhere ; briefly2.4A synthetic swab (polyethylene) was placed into the mouth and chewed/sucked on for 60 s, after which it was placed into a plastic sample tube  and stored at −20 °C. Cortisol levels were determined employing a competitive solid phase time-resolved fluorescence immunoassay with fluoromeric end point detection. The intra-assay coefficient of variation was between 4.0% and 6.7%, and the corresponding inter assay coefficients of variation were between 7.1% and 9.0% . Results2.5Participants rated their emotional responses eight times using an adaptation of a clinical self-rating scale . The sca2.6p=0.42 for baseline; p=0.33 for AUCi), so these cases were included in the analyses. However, the main analyses  were rerun without these participants and the results remained the same after removing them.Two participants were excluded because of missing or incomplete DAWBA data. A cortisol baseline measure was calculated from the average of the three cortisol samples taken before the stress phase of the testing day began. The area under the curve with respect to increase (AUCi) was used to quantify cortisol reactivity, making use of the repeated measurements and emphasizing the change over time rather than the starting level . A negatη2; small≥0.01, medium≥0.06, large≥0.14; Between group differences were assessed using ANOVAs, with Greenhouse-Geisser correction applied where assumptions of sphericity were violated. Effect sizes are reported as eta squared . IQ was therefore not included as a covariate in subsequent between-group analyses.The ADHD only and ADHD+CD groups differed significantly in terms of ADHD, ODD and CD symptom severity, as well as CU traits, but there was no difference in emotional symptoms as measured by the SDQ. Although there was a significant group difference in IQ, IQ was not significantly associated with cortisol =56.71, p<0.001, η2=.24, but no effect of group  and no significant group×time interaction , indicating that the stress paradigm induced negative emotions in both groups to an equal extent =2.24, p=0.14, η2=0.01). There was a main effect of time  but no effect of group  and no group×time interaction . However, analysis of the more sensitive AUCi values for the cortisol response showed a significant group difference, , reflecting more pronounced cortisol stress reactivity in the ADHD than the ADHD+CD group.The ADHD and ADHD+CD groups did not differ in baseline cortisol F=6.88, p=0.001, R=0.26). ADHD symptoms were inversely related to baseline cortisol, whereas CD symptoms were positively related to baseline cortisol ; a regression showed that CD severity significantly predicted cortisol reactivity  in baseline cortisol levels. These results are in contrast with some studies reporting lower baseline cortisol levels in children with ADHD and comorbid DBD compared to children with ADHD only , but areWhen looking at the variables continuously, however, both ADHD and CD symptom levels predicted baseline cortisol. CD symptoms positively related to baseline cortisol levels, whereas ADHD symptom severity inversely predicted baseline cortisol. In terms of cortisol stress reactivity, the findings are consistent with previous results showing reduced cortisol stress responses in adolescent males with comorbid ADHD and ODD when compared to those with ADHD alone . The curWe found that CD severity was the only predictor of our cortisol reactivity measure (AUCi); although the CD group had more ADHD symptoms, these did not significantly correlate with the AUCi measure. CU traits did not predict cortisol levels at baseline or stress reactivity. ODD symptoms were inversely correlated with baseline cortisol, but they did not enter the model with ADHD and CD symptoms when using a stepwise regression. The lack of an association between cortisol and CU traits contradicts previous research that found reduced cortisol stress reactivity in ADHD participants with high CU traits , but a phypo-reactivity during stress. This has implications for arousal-based theories of CD behaviour  and the stress phase always occurred in the afternoon, which is the part of the day that is least affected by varying circadian rhythms .We had no ethical permission, nor did we want to insist that participants should come off their medication for a longer period of time; it could have put more severe cases off from taking part in the research. Therefore, although we controlled for the acute side effects of the medication, we could not control the longer term effects of drug use in this study. Further research, that is able to analyse the longer term effects of ADHD medication on stress reactivity is needed.Finally, although the present study shows that males with ADHD and CD show a weaker neuroendocrine response to challenges that involve frustration and provocation than those with ADHD alone, it is not clear to what extent these findings would generalize to other types of stress (although see In conclusion, this study of adolescent boys with ADHD found a significant difference in cortisol reactivity to a psychosocial stressor between participants with ADHD alone and those with ADHD and comorbid CD. Impaired cortisol reactivity is hypothesised to reflect fearlessness and has been associated with deficient emotion regulation and inhibition of aggressive and antisocial behaviour. Consequently, it may partly explain the greater severity of behavioural problems seen in those with comorbid ADHD and CD. Future research should use longitudinal research designs to study the effects of early adversity on the development of externalizing problems and HPA axis functioning in order to identify more optimal strategies for timely intervention.Conceived and designed the experiment: SHMvG, AT, KL, GF. Performed the experiments: CN. Analyzed the data: CN, SHMvG. Wrote the paper: CN, SHMvG, AT, KL, GF.CN, AT, KL, GF and SHMvG report no conflicts of interest.


Background. Attention-deficit hyperactivity disorder (ADHD) is the most commonly diagnosed behavioral problem in children. Global variations in diagnostic criteria and rating scales of ADHD either by DSM-IV or ICD 10 may contribute to variations in its prevalence. Objectives. The study was conducted to estimate the prevalence of ADHD and to determine its risk factors. Methodology. A cross-section comparative study was conducted in a randomly selected four primary schools in Menoufia governorate, Egypt. All children after a valid consent of their parents (N. 1362) were subjected to complete history taking, medical and psychological assessment, and IQ estimation. Their parents and teachers were subjected to the corresponding Arabic forms of Conner's questionnaire. Suspected cases were confirmed and categorized by DSM-IV criteria. The sample was divided into cases and controls to study the risk factors. Results. Prevalence of ADHD was 6.9% and the male : female ratio was 3.5 : 1. The main risk factors were neonatal problems (OR = 4.3), family history of psychiatric and medical illnesses (OR = 3.5 and 2.8), and male gender (OR = 2.9). Conclusion. Prevalence of ADHD among Menoufia school children was 6.9%. Dealing with its risk factors is mandatory for prevention, early management, and better outcome. ADHD is characterized by developmentally inappropriate levels of attention and hyperactivity resulting in functional impairment in academic, family, and social settings . The DSM Arabia) , 10.Rational and aim of the study: there is a lack of studies in Africa, Middle East, Arab world, and Egypt regarding the prevalence and risk factors of ADHD. This work was conducted to determine the prevalence of ADHD and its risk factors among primary school children in Menoufia governorate, Egypt.This study was conducted after its approval by the ethical committee of the faculty of medicine, Menoufia University, Egypt. All participants gave a valid written informed consent after clear explanation of the study aims and techniques.A cross-section comparative study.The study was conducted in four primary schools: two in Menouf city and the others in Monshaat Sultan village, Menouf district. All were selected by a multistage stratified random sampling technique representing both urban and rural communities of Menoufia governorate, Egypt.Family medicine consultants did complete the medical assessments including full history taking and data collection from parents and teachers.Psychologist performed the IQ testing and explained study tools to both parents and teachers.Psychiatric consultant completed the psychiatric assessment and confirmed the diagnosis.Primary school children in Menoufia governorate, Egypt, had the same chance to be randomly selected in the study sample.N. 1362) in the randomly selected four schools representing both urban and rural communities in Menoufia governorate, Egypt. All school children (Exclusion Criteria. They are as follows: children with IQ scores less than 80% (no. 18); a neurological handicap (no. 3), and chronic illness (no. 11).Wechsler intelligence scale for children; Arabic forms of Conner's questionnaires for both parent and teacher; and the DSM-IV criteria for diagnosis and grading of ADHD  were valN. 1362) in presence of their parents were subjected to complete history taking, comprehensive clinical examinations, and psychological assessment by a psychiatrist and IQ testing by a psychologist. Parents and teachers were invited after their consent to fill the corresponding form of Arabic Conner's questionnaire. They were instructed to rate the child's behavior at home and at school during the past six months.The randomly selected children  and the independent variables are age, sex, neonatal problems, and so forth. The purpose was to analyze the effects of the independent variables on the dependent one. All the significant independent variables tested by chi-squared test  and crude odds ratios  were entered in the regression model analysis.Collected data were statistically analyzed utilizing the Statistical Package for the Social Sciences (SPSS) software program version 18, using a personal computer. Qualitative data were expressed as number and percent and tested by the chi-squared test. However the quantitative data were expressed as mean and standard deviation and tested by the  consanguinity, antenatal illness and drug use, and family size greater than four. An unemployed mother in this study was a protective factor, not a risk factor. Other significant risk factors did not reach a statistically significant level after adjusting the odds ratio by logistic regression analysis. These were low maternal education, paternal smoking, an unemployed father, low paternal education, delivery by caesarian section, and disrupted families.The total prevalence of ADHD among basic school children was 6.9% . The mean age of school children having ADHD ± SD was 8.25 ± 1.62. There was a higher prevalence of ADHD in male than female children with male to female ratio of nearly 3.5 : 1. There was no statistically significant difference regarding most demographic data as regards age, mean age of parents, birth order, and residency. The highest prevalence of ADHD was among seven-year-old school children (22.1%). The younger the birth order, the higher the prevalence of ADHD. Males had a higher prevalence than females (10.9% versus 3.5%). The proportion of inattentive type was 31.5% and was slightly higher in females (1 : 1.9), combined type was 48.0% of the total cases and was predominant in males (2.1 : 1). However, the hyperactive-impulsive type represented 20.5% with a male to female ratio of 1.4 : 1. The major risk factors for ADHD using logistic regression analysis were neonatal problems , family history of psychiatric illness, gender, family history of medical illness,New Results of This WorkAn unemployed mother is a protective factor and not a risk factor.Low parental education is not risky for ADHD.Parental use of smoking tobacco is not risky for ADHD.Disrupted families and delivery type (caesarian section) are not risk factors for ADHD.The worldwide prevalence of ADHD among school children is 5–7% . In the The current study Tables  and 5 reThe current study revealed that children having a positive family history of psychiatric and medical illnesses had 3.6 and 2.85 folds increased chance to get ADHD . The current opinion is that ADHD is a mixture of genetics and environmental factors. However the pathophysiology is unclear at this time, although results from various types of neuroimaging techniques suggest there are differences between individuals with and without ADHD in the brain, such as thinner regions of the cortex , 19.The current study  revealedThe study revealed Tables  and 5 thThe current study Tables  and 5 deThe study Tables  and 5 shChronic exposure to smoking in pregnancy has been extensively studied, where an evidence of a dose-response relationship between the numbers of cigarettes smoked and ADHD severity was confirmed. Smoking in pregnancy is known to result in lower birth weight. Also exposure to carbon monoxide or altered placental function could account for development of ADHD . In the P  value < 0.05, crude OR was 1.6 and adjusted OR = 1.1). This was in comparison to a study in Saudi Arabia which reported the prevalence of hyperactivity-impulsivity disorder was found to be significantly higher in boys living with single parents [In the current study Tables  and 5, d parents . Parenta parents .Parental occupation and education level are known to affect the prevalence of ADHD. The current study Tables  and 5 deMany familial factors may influence ADHD, for example, low parental education, stress factors, and a greater risk of childhood adversity. Family environment may influence development of ADHD and related behavioral problems. Childhood neglect, posttraumatic stress disorder, abuse, and institutionalization have a negative effect on brain development . consanguinity, antenatal illness, antenatal drug use, abnormality at birth, large family size, family history of psychiatric or medical illness, and sex. Prevention, early detection, and management of its modifiable risk factors should be undertaken alongside increasing community awareness. Prevalence of ADHD among school children in Menoufia governorate, Egypt, was 6.9%. ADHD is associated with many risk factors either modifiable or nonmodifiable such asLimitations of the StudyDifficulty in recalling the history of antenatal periods and drug names from some illiterate mothers that is overcome by recalling of data from their family folders.Low level of community awareness regarding childhood behavioral problems.Communal misperception of psychiatric conditions.