N = 2003; Mainz-Bingen region; Rhineland-Palatinate; Germany) with baseline assessments prior to school entry and two follow-ups during first grade were analysed. ADH symptoms were assessed by parent and teacher versions of the Strengths and Difficulties Questionnaire. Associations between ASE and scores of the hyperactivity/inattention subscale (range 0–10) were investigated by regression analysis and adjusted for potential confounders and baseline symptoms prior to school entry. In total, 1633 children  were included. There were no relationships between ASE and parent-reported scores of the hyperactivity/inattention subscale prior to school entry and 3 months thereafter. However, at the end of first grade, ASE was negatively associated with the hyperactivity/inattention subscale in parent  and teacher reports . This ASE effect appeared more pronounced in girls than in boys. Young ASE is related to more reported symptoms of ADH at the end of first grade, but not before. The evolvement of this effect during first grade may be a clue to ASE-related stress factors.Young age at school entry (ASE) is related to attention-deficit/hyperactivity disorder in higher grades. The reason for this association is unclear, but medical oversupply and stress-related factors are discussed. We aimed to investigate whether ASE is associated with reported symptoms of attention-deficit/hyperactivity (ADH) already in first grade. Data of a population-based prospective cohort study (The online version contains supplementary material available at 10.1007/s00787-021-01813-7. Numerous international studies have investigated the relationship between young age at school entry (ASE) and attention-deficit/hyperactivity disorder (ADHD). Two systematic reviews showed that the majority of studies provided evidence for an increased risk of the youngest children within a school class to receive an ADHD diagnosis and/or ADHD medication compared to their older classmates , 2. In lIn the literature, two major hypotheses for this relative age effect are discussed. The most common explanation considers the effect as caused by misinterpretation of conspicuous behaviour and unnecessary medical services in relatively younger children . BecAn alternative explanation for the relative age effect assumes a causal biological stress mechanism . AsIn this context, gender differences concerning the association between ASE and ADHD should be considered. It is well known that the prevalence of ADHD is higher in boys than in girls with a ratio of around 3:1 in population-based samples . In clinTo date, neither of the two hypotheses has been confirmed by available studies. Most studies used cross-sectional designs, only teacher reports, or administrative data, which are not helpful to clarify the nature of the association between ASE and ADHD diagnoses. To gain more evidence in this field, we analysed longitudinal data from a cohort study that examined the relationships between the child’s health and developmental status at school entry and long-term health and educational outcomes during primary school . In the ich komme in die Schule [German]: I am starting school) is an ongoing prospective population-based study with a closed cohort design [N = 3683; population) were approached about study participation by public youth health physicians at the PHE and written informed parental consent was obtained. The research protocol was approved by the local ethics committee, the regional school authority, and the state representative for data protection. The sampling strategy, design aspects, response rates, and results on representativeness of the cohort have been described elsewhere [ikidS . By the end of first grade, the cohort comprised 1834 participants . An additional 136 children were removed from the present analysis because they had not complied with the federal cut-off dates for school entry . This exclusion of children who have been deferred the year before  or were too young for school entry  according to legal regulations should prevent bias in the results. Another 65 children were excluded because neither the parents nor the teacher had responded to the questionnaires during first grade. This resulted in a final analysis sample of 1633 children .Data were collected at four-time points: at the PHE during the last preschool year (T0), 6 weeks before school entry (T1), 3 months after school entry (T2), and at the end of first grade (T3). At time points T1–T3, study-specific parental questionnaires were applied to investigate the child`s general and mental health as well as the child’s use of health services. At T3, class teachers completed a questionnaire about each child’s school-related behaviour. The response rate to the parental questionnaires ranged from 72.6% at T1 to 67.1% at T3 and was 82.9% for the teacher questionnaire (T3).ADH symptoms were assessed at time points T1–T3 by parents and at T3 by class teachers using German versions of the Strengths and Difficulties Questionnaire (SDQ) for parents and teachers –26. The In addition to symptoms, the following clinical ADHD indicators were available from study-specific parental questionnaires: at time point T2, parents reported whether their child had a doctor’s diagnosis of ADHD or indications of a concentration disorder or hyperactivity. At T3, parents reported whether their child had a doctor’s diagnosis of ADHD or needed or used ADHD-related diagnostic procedures, whether their child had a prescription for ADHD medication or received such medication, and whether their child had behavioural problems.We also used information from the parental questionnaires on the following potential risk factors for ADHD: gender, socio-economic status, migrant background, and family structure (nuclear family/single-parent family/foster parents/children’s home) . This soDemographic and clinical characteristics were described by appropriate statistical measures . All descriptive statistics were presented for complete cases. Representativeness was assessed by comparing the distribution of important demographic factors between the population and analysis sample.N = 1236, 1127, and 1393 of 1633, respectively). A separate linear regression analysis was carried out with baseline ratings of the hyperactivity/inattention subscale at T1 (prior to school entry) as the dependent variable. In both cases, effect estimates (non-standardized B- and standardized beta-coefficients) and their standard errors (SE) were calculated using different hierarchic models and adjustment sets: Model 1 included only gender. Model 2 included the adjustment set of model 1 plus socio-economic status assessed by Winkler’s index  was defined as the difference between the date of the first day at school and the date of birth. For the primary analysis, a linear mixed model analysis for repeated measures was conducted with ASE as the independent variable and three reports of hyperactivity/inattention subscale as dependent variable: ratings from parents at T2 and T3 and from teachers at T3 were analysed as correlated outcomes (c status ), migranc status , 33, andSecondary analyses were performed to investigate the association between ASE and two relevant ADHD indicators: first, the hyperactivity/inattention subscale was dichotomised as either “no suspected ADHD” (score ≤ 5) or “suspected ADHD”  by applying German population-based reference values . Second,Due to obvious gender differences in the descriptive analysis concerning the relationship between ASE and the frequency of suspected ADHD, we additionally conducted a post hoc analysis. Here, a marginal logistic model for correlated outcomes was performed with (1) ASE as the independent variable, (2) the three dichotomised SDQ hyperactivity/inattention subscales (from parents at T2 and T3 and from teachers at T3) as dependent variables, and (3) an interaction term between ASE and gender. Odds ratios and their 95% confidence intervals were adjusted by the same variable sets as in models 1–4 of the primary analysis. All statistical analyses were carried out using SAS version 9.4.Based on the PHE data, which were available for the entire population, Table The results of the primary analysis are presented in Table The proportion of children with suspected ADHD based on the hyperactivity/inattention subscale (scores higher than 5) at the different assessment points ranged from 11.0 to 14.7% among boys and from 7.4 to 10.2% among girls (parent reports). These proportions are in line with the recently published age- and gender-specific norm values for the German SDQ hyperactivity/inattention subscale , which iThe results of the secondary analysis confirmed the association between ASE and suspected ADHD based on the hyperactivity/inattention subscale. As presented in Table p = 0.64) nor at T3 .The proportion of children with a clinical ADHD indication was somewhat smaller than the proportion of children with suspected ADHD based on the hyperactivity/inattention subscale, especially according to parent-reported indicators at T3 . Also for the clinical indicators, the proportion of children with ADHD indication was higher among boys than among girls. Further results are listed in the Online Resource (Table S1). Regarding the logistic regression analysis using this clinical ADHD indication as the dependent variable, there was no significant association with ASE, neither at T2  as well as 3 months after school entry. In contrast, ASE was not associated with parent-reported ADH symptoms prior to school entry and 3 months after school entry. At the end of first grade, the association between young ASE and more ADH symptoms in the borderline or abnormal range was consistently more pronounced for girls than for boys. The observed effect modification of ASE by gender was significant only in the case of teacher-reported ADH symptoms. Regarding the clinical ADHD indications during first grade, such as the use of ADHD-related diagnostic procedures, a doctor’s diagnosis of ADHD, or related medication, we found no association with ASE.In the light of the pre-specified two hypotheses (oversupply vs. stress-related) and the expectations concerning the presence of associations between ASE and ADHD-related outcomes prior to and after school entry, our results provide some clues supporting the stress-related hypothesis as an explanation for the relative age effect on ADHD-related outcomes. The finding that more ADH symptoms were reported for the younger children than for the older children at the end of first grade, but not yet around school entry, suggests that the increase in symptoms could be a consequence of school entry in a group of susceptible young first graders. Moreover, the fact that the relative age effect occurred not only in teacher reports but also in parent reports of ADH symptoms, makes it less likely that it is based solely on teachers` misinterpretation of immature behaviours. Thus, our results better fit the stress-related hypothesis than the oversupply hypothesis. For the latter, we would expect that the differences in ADH symptoms between younger and older children within a grade should be present and already perceptible around school entry.However, these results of an observational study could not be interpreted as proof of one of the hypotheses. Since it is not possible to conduct a randomized-controlled trial with a control group of the same age that is not enrolled in school, no clear causal inference can be drawn, yet. There are several other conceivable reasons for the absence of an association between ASE and ADH symptoms at the two earlier time points around school entry. One of them could be, for example, that the measure is not sufficiently sensitive for young children, another, that the development of the prefrontal brain regions does not proceed continuously and, therefore, younger and older children do not differ equally in their inattentive, hyperactive, and impulsive behaviour at all points in time. Hence, the present pattern of results cannot definitely decide between the two hypotheses.Notwithstanding these concerns, our results are largely consistent with previous studies on the relationship between ASE and ADH symptoms –7, but mHowever, findings are somewhat mixed with regard to differences between teacher and parent ratings of ADH symptoms. Besides some studies reporting weaker ASE effects for parent ratings compared to teacher ratings , 7, 12 oOur results tentatively suggest a gender-specific ASE effect, which is in line with other studies also showing a more pronounced relative age effect in girls compared to boys , 37, 39,In contrast to many other studies reporting a difference in the risk of ADHD diagnosis and medication between the youngest and oldest children within a school class , 43, 44,Taken these and our findings into consideration, the relative age effect on ADHD-related outcomes likely depends on amplifying and mitigating country-specific contextual factors like school entry policy, early school-related demands, the role of teachers in perceiving ADHD symptoms and counselling parents, as well as ADHD-related health care properties. It is crucial to determine the precise causal mechanism underlying this phenomenon to develop appropriate strategies tackling the undesirable consequences: in case of oversupply, teachers and clinicians should be sensitised to the relative age of a child within his or her class and the associated danger of misjudging immature but age-appropriate behaviour . CliniciThe major strengths and limitations of the ikidS cohort study have been extensively discussed elsewhere . The preAnother limitation concerns the obvious selection of children for school entry due to the flexible school enrolment practice in Rhineland-Palatinate. Here, the PHE serves to assess the school readiness of all preschool children and, among other things, to recommend early or deferred school entry. This usually results in fewer young girls being deferred for 1 year compared to young boys and more of the oldest girls being allowed to start school 1 year earlier compared to the oldest boys. This had effects on our analysis sample showing (1) only a negligible selection among boys, but (2) a slightly stronger selection among girls in the youngest quarter, and (3) a pronounced selection among girls in the oldest quarter. Theoretically, this could create a spurious association between ASE and ADH symptoms if—and only if—older girls with ADH symptoms are generally selected for early school enrolment the year before. In this case, these symptomatic girls would not be part of the class 1 year later and this would lead to the impression that younger girls have more ADH symptoms compared to older girls within the class. In fact, the opposite is more likely: older and therefore developmentally more mature girls with fewer ADH symptoms are expected to be selected for early school enrolment, leading to a larger number of older girls with ADH symptoms within the class. Despite this potential attenuating influence of the school enrolment practice on the ASE effect, we were able to confirm the presence of such an effect in girls. Hence, we believe that the observed ASE effect was not artificially created by selection bias.The youngest children within first grade show more parent- and teacher-reported ADH symptoms compared to their older classmates. This relative age effect is not seen around school entry, which may provide some first indications that, among other explanations, stress-related factors may account for at least some part of the association. Parents, teachers, and clinicians should be sensitised to this relative age effect to avoid oversupply, misdiagnosis and, perhaps, unnecessary ADHD-related medication. The true mechanism of the association should be discovered to enable effective preventive interventions, which should lead to lower rates of ADHD diagnoses and medications in relatively young primary school children.Supplementary Table S1 Frequency of ADHD indications or suspected ADHD based on the different ADHD variables in the analysis sample (DOCX 14 KB)Below is the link to the electronic supplementary material.


Understanding the neural mechanisms of emotional reactivity in Attention-Deficit/Hyperactivity Disorder (ADHD) may help develop more effective treatments that target emotion dysregulation. In adult ADHD, emotion regulation problems cover a range of dimensions, including emotional reactivity (ER). One important process that could underlie an impaired ER in ADHD might be impaired working memory (WM) processing. We recently demonstrated that taxing WM prior to the exposure of emotionally salient stimuli reduced physiological and subjective reactivity to such cues in heavy drinkers, suggesting lasting effects of WM activation on ER. Here, we investigated neural mechanisms that could underlie the interaction between WM and ER in adult ADHD participants. We included 30 male ADHD participants and 30 matched controls. Participants performed a novel functional magnetic resonance imaging paradigm in which active WM-blocks were alternated with passive blocks of negative and neutral images. We demonstrated group-independent significant main effects of negative emotional images on amygdala activation, and WM-load on paracingulate gyrus and dorsolateral prefrontal cortex activation. Contrary to earlier reports in adolescent ADHD, no impairments were found in neural correlates of WM or ER. Moreover, taxing WM did not alter the neural correlates of ER in either ADHD or control participants. While we did find effects on the amygdala, paCG, and dlPFC activation, we did not find interactions between WM and ER, possibly due to the relatively unimpaired ADHD population and a well-matched control group. Whether targeting WM might be effective in participants with ADHD with severe ER impairments remains to be investigated.The online version contains supplementary material available at 10.1007/s11682-021-00532-6. In addition to deficits in attention, hyperactivity, and impulsivity : the threshold, intensity, or duration of affective arousal, which can be measured through the processing of emotionally salient stimuli  was activated stronger in the presence of emotional stimuli during cognitive control tasks  paradigm, interleaving emotional stimuli with WM-load blocks. We expected that ADHD participants, compared to controls, would show higher levels of amygdala activation in response to negative emotional, relative to neutral images. Moreover, participants with ADHD were expected to show decreased dorsolateral PFC (dlPFC) and paracingulate gyrus (paCG) responses to high versus low WM-load tasks. We furthermore expected amygdala reactivity in participants with ADHD to be reduced in response to negative emotional blocks following high WM-load more so than in controls.Thirty adults with ADHD and 30 controls (19–35 years of age) were included in the study. Inclusion criterion for the ADHD group was prior clinical ADHD diagnosis according to the DSM-IV .Data points more than three standard deviations from the mean were removed as outliers . Analyses of task and fMRI data used linear mixed-effects models (lme4 Rv.3.5.3) . Functional scans were acquired using a 2D-GE-EPI sequence . A scan with opposite phase-encoding-direction was used for distortion correction.Participants were scanned on a 3 T whole-body MR system  using a 32-channel receive-only head-coil. T1-weighted (T1w) scans were obtained using a 3D-TFE sequence   analyses used predefined ROIs: Amygdala, dlPFC, and paCG (Supplementary Methods 1.4). The amygdala is a key region in emotional processing  = 1.68, p = 0.19, ∆BIC =  − 3.07), but a main effect of WM-load was found (χ2(1) = 40.29, p < 0.001, ∆BIC = 34.87)  = 6.78, p < 0.01, ∆BIC = 1.36), and that attention reduced after high-load WM-blocks (χ2(1) = 34.87, p < 0.001, ∆BIC = 1.36)  = 3.68, p = 0.05, ∆BIC =  − 1.74), suggesting that participants with ADHD paid more attention to the negative emotional images compared to controls. The validation task showed that negative images were perceived as more negative than the neutral images by both groups (χ2(1) = 143.29, p < 0.001, ∆BIC = 138.55)  = 2.01, p = 0.15, ∆BIC =  − 2.73; all other ∆BIC < -4.74; left dlPFC: χ2(1) = 2.62, p = 0.11, ∆BIC =  − 2.13), but a main effect of WM-load was found (PaCG: χ2(1) = 41.12, p < 0.001, ∆BIC = 36.38; left dlPFC: χ2(1) = 56.35, p < 0.001, ∆BIC = 51.605) = 4.10, p = 0.04, ∆BIC =  − 0.65; all other ∆BIC <  − 3.73), indicating that there was less WM-related right dlPFC activity in participants with ADHD compared to controls, which suggests that WM processes are only limitedly impaired in the investigated ADHD population.In line with the whole brain results, we found no significant interaction between WM-load and group on PaCG activation and left dlPFC (PaCG: χ2(1) = 37.32, p < 0.001, ∆BIC = 45.65), showing higher amygdala activity for negative than neutral images, but this was not moderated by group. We furthermore found no significant three-way interactions between emotional image type, preceding WM-block, and group (χ2(1) = 1.40, p = 0.84, ∆BIC =  − 20.37; all other ∆BIC <  − 5.19), which suggests that taxing WM did not influence ER-related amygdala activation.We assessed the effects of the emotional image type and group on amygdala activity during emotional processing Fig. B. Model 2(1) = 3.66, p = 0.45, ∆BIC =  − 18.10; all other ∆BIC <  − 4); neither did we find a main effect of emotional images (χ2(1) = 0.97, p = 0.32, ∆BIC =  − 4.47), which suggests, in contrast to our hypothesis, paCG activity did not react to emotional salient stimuli, in either the ADHD or control group.Additionally, we assessed the effects of emotional images, the preceding WM-load and group on paCG activity during emotional processing, but found no three- or two-way interactions  would interact on a neural level. We demonstrated a significant main effect of negative emotional images on amygdala activation and a significant main effect of WM-load on activation of the paCG and dlPFC across both groups but did not find strong evidence for group differences. These findings were in line with the WM-task performance, which also did not reveal any group differences. Contrary to our hypothesis, neither amygdala nor paCG activity was reduced in response to negative vs. neutral images after inducing a high WM-load in participants with ADHD or controls.ED in ADHD consist of a complex combination of dimensions, including emotion recognition, ER, and emotional lability. In the current study, however, we did not find amygdala hyperactivity in response to negative emotional stimuli in adults with ADHD. This contrasts earlier findings of amygdala hyperactivity in response to negative emotional faces   activity, we could not replicate these findings using our paradigm. These results might be due to compensatory effects in the adult participants with ADHD. Furthermore, targeting WM might still be effective in individuals with severe impairments in emotion regulation. These findings contribute to the understanding of the neural mechanisms of ER in controls and participants with ADHD.Supplementary file1 (DOCX 851 kb)Below is the link to the electronic supplementary material.


Various factors may have a role in the development of attention deficit hyperactivity disorder (ADHD). Although the specific pathophysiology of this disease is still not entirely understood, it is believed to be caused by a mix of genetic, maternal, dietary, and environmental factors. The effect of these factors can determine the severity of ADHD; for example, some of them are dose-dependent, but there is a typical pattern that all are known to be associated with either early childhood exposure or maternal exposure during pregnancy. Some factors share a similar mechanism of affecting pathways and increasing the risk of ADHD.ADHD is not a disorder that can be detected before symptoms appear in a child, making it more challenging to anticipate even if a child has been exposed to a known trigger. Environmental pollutants were investigated, and it was shown that there was a link between ADHD in childhood and exposure to pollutants throughout childhood or during pregnancy. It is well known that maternal health is a significant factor in the unborn child's development in many health aspects. The central nervous system (CNS) is a primary system that can suffer irreversible damage from health conditions, stress, depression, or specific nutritional deficiency when the pregnant mother is subjected to these conditions. Even though numerous studies have been conducted to investigate the probable causes of ADHD, with some of them having robust findings, no conclusive explanation can be provided to identify a definitive cause or a risk factor. Attention deficit hyperactivity disorder is a neurobiological condition characterized by impaired attention, impulsivity, and hyperactivity that often begins in childhood and frequently persists into adulthood . The ideSome studies have shown that ADHD is associated with various environmental factors. There are primarily prenatal risk factors such as maternal stress, smoking, drinking alcohol in pregnancy, and perinatal factors such as low birth weight and prematurity, environmental toxins , psychosocial conditions , and nutritional deficiencies. However, it has not been proven that the variables are causal, that they are not distributed randomly among the population, or that the connections detected could also be due to confounding variables or selection bias, as ADHD itself could result in increased exposure to certain environmental factors . AdditioIn this traditional review, we will highlight the maternal and environmental factors as well as nutritional deficiencies that could lead to the development of ADHD in children. Identifying these factors will also help focus on prevention and reduce the incidence of ADHD, as it affects many aspects of the lives of children and families and is usually associated with other psychiatric and mental comorbidities.DiscussionIn this section, we summarized the research and focused on the link between certain factors and the risk of ADHD in children, as shown in Figure Exposure to Environmental PollutantsA population-based pregnancy cohort study was conducted by the Norwegian Institute of Public Health. At roughly gestational week 18, blood samples were taken from both parents and the child's umbilical cord at birth to check for concentrations of some organic acids. Perfluorooctanoic acid (PFOA), polyfluoroalkyl substances (PFAS), perfluorodecanoic acid (PFDA), perfluororoundecanoic acid (PFUnDA), and perfluorohexane sulfonate (PFHxS) were measured, and the connection with increased ADHD diagnosis was investigated . These sThe link between prenatal and postnatal exposure to environmental pollutants such as polychlorinated biphenyl-153 (PCB-153), dichlorodiphenyldichloroethylene (p-p'-DDE), and hexachlorobenzene (HCB), and childhood ADHD was investigated by analyzing PCB-153, p-p-p'-DDE, and HCB concentrations in cord blood, maternal blood, and milk from seven European birth cohort studies. They used the specific pharmacokinetics model to calculate persistent organic pollutant (POP) concentrations at birth, and at 3, 6, 12, and 24 months. That was an extensive study with a sample size of 4,437 children on the link between prenatal and postnatal POP exposure and ADHD development in the general population. It has been found that prenatal and early postnatal exposure to PCB-153, p-p'-DDE, and HCB did not raise the risk of ADHD in a sample of 4,437 children from seven European birth cohort studies. Future research should combine psychosocial, genetic, and epigenetic data with information about environmental pollutants to assess if environmental toxicant exposure contributes to ADHD prevalence ) had a significantly elevated risk of ADHD in their offspring compared to mothers who smoked lightly (OR: 1.54). Maternal obesity was related to an increased risk of ADHD  has been extensively linked to preterm delivery and intrauterine growth restriction. PNSE can also be directly assessed using questionnaires, clinical interviews, and biological samples, such as cortisol from maternal saliva, blood, or amniotic fluid, collected prospectively from samples of pregnant women. Prenatal stress alters the development of the amygdala and hippocampus and influences the production and transport of dopamine and the expression of critical receptors. The dopaminergic system serves a variety of activities essential to temperament, including prioritizing stimuli or responses and aiding alternative selection in either a perceptual-cognitive or motor-behavioral setting. The serotonin signaling system also has relevance for temperament, given its association with depression and anxiety associated with negative emotions and has significant consequences for temperament, particularly stress reactivity, fear and anxiety, impulsivity, and attention/regulation ,21.ADHD was found to be substantially related to maternal autoimmune disorders and asthma. Children born to mothers who experienced asthma exacerbations had a significantly increased risk of ADHD (HR: 1.25 [1.08-1.44]), implying that a more severe asthma phenotype results in a higher risk for the offspring. Maternal immunological stimulation may serve as a precursor for neurological diseases via activated microglia. Microglia with altered morphology and density and enhanced microglial gene transcription have been identified in brain samples from individuals with various neurodevelopmental problems. Microglia are macrophages in the CNS that play a vital role in neurogenesis, myelination, synaptic pruning, and brain homeostasis. Microglia that have been activated develop a pro-inflammatory phenotype, typically in reaction to infection and in response to cell injury and death in their environment. Microglia are remarkable in that they begin in the yolk sac and rarely replace peripheral cells during normal development. Thus, early-life microglial perturbations have the potential to program microglia into an activated state, thereby increasing their vulnerability to immunological stimuli later in life. As a result, preconditioning microglia by maternal immune activation may increase susceptibility to neurobehavioral disorders in childhood .The relationship between gestational diabetes (GDM) and ADHD was examined. Hyperglycemia may affect neurodevelopment via oxidative stress, which is linked to poor neurobehavioral outcomes such as motor deficits. It may also affect epigenetic changes in kids, such as the decreased DNA methylation observed in neurodevelopmental disorders. Additionally, hyperglycemia can result in systemic inflammation, as pro-inflammatory cytokines can penetrate the placenta and fetal blood-brain barrier, impairing neurodevelopment. However, women with GDM are more likely to experience many unfavorable obstetric outcomes, including pre-eclampsia, fetal macrosomia, perinatal mortality, cesarean section, instrumental delivery, premature delivery, and neonatal asphyxia, all of which may raise the risk of neurodevelopmental problems .The effect of exposure to neonatal asphyxia results in brain damage. Prior research has demonstrated that hypoxia can create long-term abnormalities of the dopaminergic system and even reduce the expression of the dopamine D2 receptor, hence increasing the risk of ADHD. In addition, current research indicates that hypoxia can change catechol-O-methyltransferase (COMT) gene expression; the COMT gene has been linked to ADHD or ADHD symptoms .The effect of maternal thyroid hormone deficiency during early gestation on the development of ADHD was examined. Children exposed to maternal hypothyroxinemia had higher ADHD scores than non-exposed children. There was no correlation between maternal subclinical hypothyroidism and ADHD scores in children. The fetal brain is dependent primarily on circulating T4 levels for intracellular triiodothyronine supply. Hypothyroxinemia during pregnancy results in irreversible cytoarchitecture defects in the sensorimotor cortex and hippocampus .ADHD symptoms are considered to be caused by dysfunctions in the frontal and prefrontal lobes, which regulate impulsivity and motor activity, as well as in the cortical and subcortical striatal areas, which regulate irrelevant responses and executive function. Also, research demonstrated that children with ADHD had abnormalities in the microstructure of the corpus callosum frontal regions and the white matter connections underlying the primary and somatosensory motor cortices. Additionally, thyroid function is required for the correct development of the monoaminergic and cholinergic neurotransmitter systems, and dysfunctions in these transmitter systems have been associated with attention deficiencies and hyperactivity . Both neA mother's depressive symptoms during pregnancy are linked to ADHD symptoms in children. At the age of three to six years, children of mothers who experienced persistently high depression symptoms throughout pregnancy demonstrated increased levels of ADHD symptoms. The study discovered that increased maternal depressive symptoms following pregnancy were connected to increased child ADHD symptoms. Existing research indicates that increased maternal depressive symptoms and salivary cortisol levels during pregnancy are associated with changes in the offspring's brain structure, functional connections, and right hemisphere cortical thinning. Symptoms of depression during pregnancy have also been associated with increased mRNA levels of placental glucocorticoid and mineralocorticoid receptor genes, implying an increased glucocorticoid sensitivity in the placenta. Emerging evidence suggests that inflammatory markers may be involved, as maternal depressive symptoms and pro- and anti-inflammatory cytokines during pregnancy have been linked. Early pregnancy screening and preventive interventions targeting maternal depressive symptoms may benefit maternal and offspring well-being .According to available literature, both selenium (Se) deficiency and excess may be harmful to health. Currently, the function of maternal Se status in the long-term neurodevelopment of children is largely unknown. The study examined the temporal and dose-response relationships between maternal Se status and the risk of children's neurodevelopmental disorders such as ADHD. Se levels in maternal RBCs were determined in samples taken within 72 hours of delivery. Analyses of the data revealed a positive correlation between maternal Se and ADHD . Even after correcting for relevant covariables, these relationships remained robust, and there was no significant interaction between Se and these covariables. Our data indicate that prenatal exposure to elevated maternal serum Se levels may have a detrimental effect on child neurodevelopment .The Effect of Some Nutritional DeficienciesA study was performed measuring maternal 25-hydroxycholecalciferol (25(OH)D). The mean age at ADHD diagnosis was 7.3 years. The median maternal 25(OH)D level was 29.2 nmol/L. The mean gestational age when samples were taken was 10.7 weeks. This was the first study to link low maternal 25(OH)D levels in early to mid-pregnancy to ADHD in children. Two studies relate low maternal 25(OH)D levels to early pregnancy, supporting this result. Inadequate vitamin D in pregnancy may disrupt fetal programming and expose the child to a suboptimal environment, perhaps contributing to ADHD. Vitamin D affects brain function through controlling calcium signaling, neurotrophic and neuroprotective effects, neuronal differentiation, maturation, and growth. Aberrant attention processing causes alterations in dopamine system ontogeny, novelty-induced hyperlocomotion, and latent inhibition. The risk of ADHD was considerably increased in toddlers with maternal and neonatal vitamin D deficiency ,28.A zinc deficiency effect on the development of ADHD was examined; more than 200 zinc metalloenzymes are needed for neuronal proliferation. Changes in dopamine and serotonin neurotransmitters may explain the link between zinc and ADHD. Zinc is needed for the creation and control of melatonin, which regulates dopamine function, and for converting dietary pyridoxine to pyridoxal phosphate, which converts tryptophan to serotonin. ADHD affects the dopamine and serotonin systems. Zinc may counteract melatonin and serotonin declines and relieve ADHD symptoms. In several countries, blood zinc levels were lower in children with ADHD than in controls, suggesting a link to symptom severity. Zinc tissue concentrations  are lower in children with ADHD than in normal control subjects and compared to population norms .Children born to mothers with iron deficiency anemia diagnosed at 30 weeks or less were more likely to be born preterm  or small for gestational age , whereas children whose mothers were diagnosed with anemia at greater than 30 weeks gestation were more likely to be born post-term and large for gestational age. Anemia identified in 30 weeks or less of pregnancy was linked to a greater incidence of ADHD in offspring. These data show the importance of early iron testing and dietary counseling during prenatal care [A meta-analysis study shows that teens with ADHD had low docosahexaenoic acid (DHA). Lower maternal intake of omega-3 polyunsaturated fatty acids (omega-3 or n-3 PUFAs) during pregnancy is connected to poorer developmental outcomes in the offspring, such as lower scores on fine motor, communication, and social development assessments. Developmentally disabled children have low DHA. Although omega-3 insufficiency may be a cause of ADHD, approximately 20-40% of ADHD patients do not benefit from pharmacotherapies such as methylphenidate and atomoxetine . ChildreADHD and other neurodevelopmental problems have a wide range of consequences for children and their families, yet relatively few studies on the subject are conclusive. Knowing the exact variables and ways of exposure that create ADHD would be excellent for preventing ADHD. There are no definitive therapies available yet, only symptomatic medicines that do not manage symptoms entirely in many cases; therefore, understanding the mechanism of development and risk factors contributing to the development of the condition is essential.More concentrated retrospective research including bigger groups of individuals is required to gain a good understanding of the causative elements rather than just the risk factors that are involved. It is still not fully understood why some children develop ADHD and some do not; even if they were exposed to the same factors, there might be protective variables that need to be researched. In addition, we had difficulties locating much research conducted in the past that investigated the risk factors, and the studies we found had a small number of subjects.


Attention deficit hyperactivity disorder (ADHD) is a common neurobehavioral disorder in childhood. Brain-derived neurotrophic factor (BDNF) is widely distributed in the central nervous system and plays an important role in neural development. Despite several previous studies have examined the association between the Val66Met polymorphism BDNF and ADHD, the results are conflicting.This study aimed to evaluate the association between Val66Met polymorphism and ADHD in case-control and transmission disequilibrium test (TDT) studies using a meta-analysis.Keywords “rs6265” or “Val66Met” and “Attention deficit hyperactivity disorder” were used to search in the PubMed, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure databases before April 2021. Genotype data were extracted to calculate odds ratios (ORs) and 95% confidence intervals (CIs).P = 0.291). Stratified analyses by study design, ethnicity, and sample size further supported that rs6265 was not associated with ADHD.Fifteen studies, comprising of 8,692 samples  and 1,578 families were included and results demonstrated that rs6265 was not associated with susceptibility to ADHD (OR = 0.95, 95% CI: 0.87–1.04, BDNF Val66Met gene is not associated with susceptibility to ADHD.The present study shows that the polymorphism of the  Attention deficit hyperactivity disorder (ADHD) is a common neurobehavioral disorder in childhood , charact(BDNF) gene maps to human chromosome 11p14.1  and transmission disequilibrium test (TDT) studies for the association of rs6265 in We performed a comprehensive search in the PubMed, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure databases to identify all potentially relevant studies using the keywords “rs6265,” “Val66Met,” and “attention deficit hyperactivity disorder.” The literature search was updated on April 30, 2021, and articles in English and Chinese were included for further analysis.BDNF and ADHD risk, (4) clear definition of ADHD, and (5) availability of data necessary for calculating odds ratios (ORs) with 95% confidence intervals (CIs). Reviews, case reports, case-only studies, animal studies, simple commentaries, and studies without sufficient data were excluded. When more than one study had overlapping data, only the study with the largest sample size was selected.The inclusion criteria for the studies were as follows: (1) published in English or Chinese; (2) CC or TDT studies; (3) evaluation of the association between rs6265 in All data were extracted from the original studies by two independent investigators, and disagreements were resolved by discussion. The following information was extracted from the selected studies: authors, publication year, ethnicity and country of the study population, study design, diagnostic criteria for ADHD, sample size, counts of alleles in case and control groups in CC studies, and number of transmitted alleles from heterozygous parents to affected offspring in TDT studies.P-value of the Q statistic test was smaller than 0.10, indicating significant heterogeneity across studies, a random-effects model was used . In the overall meta-analysis, no significant association was observed between rs6265 and ADHD .2 = 27.34, Pheterogeneity = 0.007; TDT: χ2 = 125.97, Pheterogeneity < 0.001). No significant association was observed between rs6265 and ADHD in either subgroup .As shown in 2 = 11.23, Pheterogeneity = 0.129), but still existed in the Caucasian population . Ethnicity-specific meta-analysis indicated no association between rs6265 and ADHD in either the Asian  or Caucasian  populations.Then, the data were stratified according to ethnicity. No heterogeneity was found the Asian population , however the small-sample-size subgroup still demonstrated significant heterogeneity . No significant association was observed between rs6265 and ADHD in either subgroup .Finally, the data were stratified according to sample size. The large sample-size subgroup showed no heterogeneity (χP = 0.041), which may be related to the fact that negative results are not commonly published.Sensitivity analysis was performed by systematically removing a study from the analysis to assess the influence of each study on the pooled OR. This demonstrated relatively robust results for rs6265, with no reverse outcomes. As reflected by the funnel plot  and EggeBDNF gene and ADHD in European and Asian populations , ADHD-I (7–50.4%), ADHD-HI (0–27%). In Chinese population, the proportion of ADHD-C, ADHD-I, and ADHD-HI were 44.4–79.49%, 15.38–50.4%, 4.8–5.31%, respectively; whereas they were 54.0–100%, 7–24.0%, 0–27.0%, respectively, in the European population. Clinical heterogeneity between samples may mask a real association. Although it is currently unclear how clinical heterogeneity may relate to these differences, additional studies on BDNF Val66Met polymorphism on the different subtypes of response to ADHD are still warranted. Another possible underlying explanation relies on the gender imbalance of the samples. The prevalence of ADHD in men is significantly higher than that in women; however, only one study in the Chinese Han population was evaluated the association considering the gender of patients. The role of gender in ADHD susceptibility needs to be investigated in future research.This study aimed to explore the relationship between BDNF Val66Met polymorphism and susceptibility to ADHD and suggested that rs6265 may not contribute to the risk of ADHD.Despite these limitations, this updated meta-analysis integrating CC and TDT studies helped to clarify the association between The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.SM and WC designed the study and did the literature review. SM, SC, YH, and XD did the statistical analysis and prepared the manuscript draft. XL and WC did quality control and contributed to the revisions in depth for the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Comorbidities between Anxiety Disorders, Depressive Disorders or Somatic Symptoms, and Attention Deficit Hyperactivity Disorder (ADHD) can cause variability in the functional impairments faced by young adults. Knowing the possible configurations resulting from these comorbidities is important for a better understanding of the cases, diagnostic processes, and proposed treatments.To verify associations between indicators of the aforementioned mental disorders, and symptoms of inattention or hyperactivity-impulsivity, and functional impairments in different areas of life, related to ADHD.There were 27 participants , with complaints of inattention and hyperactivity-impulsivity compatible with ADHD, screened with ASRS-18 score> 24 and WASI IQ> 79, and assessed by DIVA-2.0 (symptoms of ADHD), ASR-ASEBA , EPF-ADHD . Spearman’s Correlation analysis was performed in the SPSS program (significance p <0.05).Increase in depressive problems associated with increased symptoms of inattention  and hyperactivity-impulsivity . Increased somatic problems associated with increased functional impairment in health . Increase in depressive problems associated with increased losses in the academic , affective , domestic , social , financial  and health .ADHD with comorbidities can have a peculiar clinical evolution with specific characteristics, including diagnosis, management, and response to treatment. These subgroups with different intervention needs demand outlining needs and personalized treatment.No significant relationships.


Attention deficit and hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. Its primary clinical features include symptoms of inattention and hyperactivity/impulsivity, although young people often present with emotional dysregulation, excessive mind-wandering and executive dysfunction. Symptoms of ADHD often persist into adulthood together with high rates of comorbidity and significant psychosocial impairment across the lifespan. Berry, Shaywitz and Shaywitz proposed over 30 years ago that girls with ADHD form a ‘silent minority’ with greater internalized behavior which leads them to be under-identified. Even when referred for clinical assessment, their ADHD symptoms are missed or misdiagnosed for other conditions such as anxiety, depression and personality disorder. This means they will not receive the treatment they need. Compared with controls, they may be especially vulnerable to childhood adversities and health problems and they may cope with these difficulties with dysfunctional strategies (eg. with substance misuse and/or deliberate self-harming behaviours). If we are to enhance long-term outcomes in girls and women with ADHD, healthcare practitioners need to better understand the presentation of ADHD in females, improve detection and assessment of ADHD in order that they may access appropriate treatment. This workshop will focus on the differences in presentation and outcomes between males and females with ADHD.No significant relationships.


Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes – most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613  to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics. Attention-deficit/hyperactivity disorder (ADHD) is estimated to affect 6.1% of children in the U.S. . NeuroimIn this study, we aimed to determine the imaging metrics of brain microstructure, morphology and functional connectivity associated with ADHD diagnosis in a large cross-sectional cohort of preadolescent American children. While prior large-scale studies focused on one set of neuroimaging characteristics  in relation to ADHD diagnosis , we examRRID: SCR_015769) is the largest longitudinal study of neurodevelopment and child health in the United States. Using a school-based recruitment strategy, data from over ten thousand 9–10-year-olds were collected from 21 sites, which included multimodal neuroimaging, and standardized cognitive and clinical assessments , diffusion (dMRI), and functional (fMRI) MRI collected using Siemens Prisma, GE 750 and Philips Achieva and Ingenia 3 T scanners. According to the ABCD Consortium, standardized imaging protocols were used to create harmonized scans across all sites and scanner manufactures.nsortium . Detailsnsortium . The stansortium .” In theMorphometric and macrostructural properties of different cortical regions of interest (ROI) based on the Desikan-Killiany atlas  were extThe dMRI data was collected in the axial plane using a resolution of 1.7 mm in all directions, which is the same as the dMRI data acquisition resolution, and a multiband acceleration factor of 3. Images were corrected for eddy current distortion, head motion, spatial, and intensity distortion correction and gradient non-linearity correction. The dMRI data was aligned with T1w structural images using mutual information after a rough initial alignment with atlas brains. To examine WM microstructural integrity, neurite density (ND) and fiber tract (FT) thickness as well as water diffusion metrics – including fractional anisotropy (FA), mean- (MD), longitudinal- (RD), and axial diffusivity (AD) – were calculated for 35 WM tracts and only ND for 12 subcortical structures from dMRI scans. These metrics from major WM tracts were extracted using AtlasTrack implemented .2 In the ABCD study, a software package called Multi-Modal Processing Stream (MMPS) is used for pre-processing data and incorporates other software packages such as FreeSurfer, AFNI, and FSL. The pre-processing steps include correcting head motion by aligning each frame with the first one, fixing B0 distortions, and resampling the scans to align with each other. The scans are also registered to T1-weighted structural images using mutual information. Linear regression is used to remove quadratic trends, signals that are correlated with estimated motion time courses, and average time courses for WM, ventricles, and the whole brain. Motion regression includes six parameters and their derivatives and squares, and only frames with framewise displacement (FD) less than 0.3 mm are included. Time points with FD more than 0.2 mm are excluded from variance and correlation calculations. The average FD is 0.25 mm. The time courses are filtered between 0.009 and 0.08 Hz. Motion censoring is applied to reduce remaining effects of head motion that survive the pre-analysis regression. Additional censoring is applied based on detecting time points as outliers with respect to spatial variation across the brain to account for lingering effects of head motion. The average correlation between the baseline functional networks according to Gordon parcel atlas  using the stats-package (version 3.6.2).In separate multivariate logistic regression models, we tested the association of different neuroimaging metrics with presence versus absence of ADHD. In each model, we tested the association of one neuroimaging variable with ADHD presence after correction for children’s age, biological sex, race, highest parental education and handedness as covariates. Biological sex, race and highest parental education were assessed by parental reported questionnaires. Handedness was assessed by the Youth Edinburgh Handedness Inventory Short Form. All variables were treated as unordered factors, except of age. The neuroimaging metrics included the averaged FA, ND, MD, RD, FT, and AD of 35 WM tracts and ND of 12 subcortical regions, thickness, and surface areas of 68 cortical regions, the inter- and intra-network correlations of 13 functional networks. To correct for multiple comparisons, we applied false discovery rate separately for each neuroimaging metrics to generate adjusted We trained, finetuned, and compared combinations of six different machine learning classifiers and five feature selection methods to predict the presence versus absence of ADHD. The input included all multimodal MRI metrics with and without the clinical information . Detailed description of the different machine learning algorithms and featured selection methods are in section “2. Machine learning ” and repp-value = 1.574 × 10–9), left rostral-middle-frontal gyrus , left superior frontal gyrus , and right inferior/superior temporal gyrus . An ADHD diagnosis was also associated with reduced cortical volumes , left superior frontal gyrus , right middle-temporal gyrus , and the right inferior-temporal gyrus  regions. No differences in sulcal depths and cortical thickness remained significant after FDR correction .p = 0.229). p = 0.0851).The rate of ADHD diagnosis in both training/cross-validation  and independent validation test  sets were similar , decreased cortical volume and surface area ; and altered connectivity of attention functional networks compared to those without – after correction for children’s age, sex, race/ethnicity, handedness, and highest parental education (as a marker of socioeconomic status). Our findings highlight the neurobiological and connectome mechanism of ADHD among preadolescent children at demographically representative population-level. In addition, we showed the potentials and limitations of machine learning classifiers in combining neuroimaging metrics  to predict ADHD diagnosis. In addition, multimodal framework of our study further highlights the role of frontal lobe cortical regions and associated WM tracts in neurobiology of childhood ADHD .Children with ADHD have been found to have morphological differences in brain structure compared to typically developing children. Our study shows that childhood ADHD is associated with reduced cortical volumes and surface areas in the frontal lobe, cingulate, and temporal lobe. These areas are known to be associated with complex cognitive behaviors such as decision-making, reasoning, personality expression, and maintaining social appropriateness  and visuFurthermore, functional connectivity is also perturbed in individuals with ADHD . In our In contrast to several other studies, we found no significant association between ADHD diagnosis and cortical thickness . Possible explanation could be: both surface area and cortical thickness play a significant mediating role in determining diagnostic differences in volume, with regional brain variation, the contribution of surface area might be bigger than thickness and by using multiple testing correcting the effect of thickness was not statistically significant. Of note, Neuroimaging studies may also facilitate ADHD diagnosis in children. Currently, clinical interviews with children, parents, and caregivers are mainstays of ADHD diagnosis in children . Howeverp = 0.0851). To mitigate the issue of class imbalance in our study (with a 1:3 ratio of children with and without ADHD), we employed the AUC of ROC as the primary measure of performance, as it is not affected by class imbalance, unlike classification accuracy. Prior studies have reported higher accuracy of machine-learning models for prediction of ADHD; however, they have used highly selective ADHD versus control subjects , which was increased to 0.613 (95% CI = 0.580–0.645) by adding clinical variables, although not statistically different (subjects , or lacksubjects . While oThe main strength of our study is large cohort of children from a narrow age-group, who are reflective of demographically diverse U.S. population. Indeed, one of the objectives of ABCD study is to elucidate factors that influence the course of mental illness , which aWe could identify the neurostructural and -functional correlates of ADHD in demographically representative cohort of American children. The comprehensive and convergent results implicated brain regions and networks involved in impulse control, executive function, and concentration in pathogenesis of childhood ADHD. Specifically, our results showed the association of childhood ADHD with frontal lobe cortical volume reduction and lower WM integrity and neuronal density. These morphological and microstructural findings may also explain the observed aberrations of functional connectivity in dorsal attention and default-mode networks among children with ADHD. We also showed limited potentials of machine-learning classifiers in prediction of ADHD diagnosis at general population level. Nevertheless, these neuroimaging correlates can potentially help with diagnosis, treatment monitoring, and response prediction.https://nda.nih.gov/abcd/.Publicly available datasets were analyzed in this study. This data can be found here: the datasets Adolescent Brain Cognitive Development Study: The studies involving human participants were reviewed and approved by the Ethics Committee of ABCD. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.HL implemented the analysis, interpreted the findings, and wrote and revised the manuscript. SP designed the study, interpreted the findings, and supported the writing and revision of the manuscript. SH assisted in data analysis. AMo, SK, AMa, RC, DS, LM, and KK assisted in interpretation the findings and reviewed the manuscript. All authors contributed to the article and approved the submitted version.


Executives functions (EF) are cognitive processes mediating the ability to successfully regulate thoughts and behaviours in order to fulfil a goal. EF impairment has been found both in the Attention Deficit Hyperactivity Disorder (ADHD) and in the Alcohol Use Disorder (AUD). Deficits in EF may have a major impact on patients’ everyday life.The aim of this study was to evaluate EF in a population with a dual diagnosis of Adult ADHD et AUD. We also evaluated the correlation between EF and the dimensions of quality of life.For this observational multicentric study, we included patients with AUD starting long-term residential treatment. We used ASRS v.1.1 to screen for Adult ADHD and DIVA to confirm the diagnosis. We assess EF with BRIEF-A. WHOQoL-BREF was used to evaluate quality of life.Our population consists of 49 patients. Adult ADHD prevalence was 24,49%. Impairment score of all EF explored  was higher in patients with co-occuring Adult ADHD and AUD than in patients without Adult ADHD . We found strong negative correlation between Psychological Health and impairment score of Inhibition , Emotion Control , Self-Monitoring  and planning/Organizing . The other dimensions of quality of life were poorly correlated to EF impairment.The prevalence of Adult ADHD in AUD patients is high. When AUD and Adult ADHD coexist, EF impairment is stronger and quality of life is poorer. Psychological health and EF impairment are strongly associated.


Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder mostly in childhood, which is characterized as inattention, hyperactivity, and inappropriate impulsiveness . ReviewsRecently, neuroimaging techniques have been widely used to elucidate the pathophysiology of ADHD, especially functional magnetic resonance imaging (fMRI) which has made progress to estimate the brain function of ADHD patients due to its high spatial resolution. But the use of resting-state fMRI (rs-fMRI) or task-state fMRI has always been a controversial issue. In fact, the brain consumes a lot of energy at rest, showing spontaneous neural activity, whereas the increase of the brain energy during the task mode is insignificant <5%) [% [5]. MoEarly rs-fMRI ADHD researches focused on aberrant changes of independent brain regions or between different brain regions. Zang et al. found that ADHD patients had decreased amplitude of low-frequency fluctuations (ALFF) in the right inferior frontal cortex, the bilateral cerebellum, and the vermis and increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem . An et aOthers studies pointed that ADHD was characterized as a dysconnectivity syndrome , rather Therefore, the research considered exploring the brain function changes of ADHD groups from the perspective of the whole brain, rather than confined to a single region or predefined network. Using the brain atlas to divide brain regions and constructing whole brain network by the graph theory were the mainstream methods. Namely, the human brain was regarded as a complex network interacting with brain regions, providing a novel way to explore brain functional mechanism from a holistic perspective , 27. TheIn addition, it was important to select different network topology properties to analyze functional network changes, because changes in topological properties of the brain network were related to abnormal cognitive development and many brain diseases –37. NodaBased on the above analysis, we used the AAL_1024 atlas to construct resting-state brain network with high node resolution for detecting more subtle changes of brain regions. And we analyzed the functional brain network based on the graph theory. We attempted to identify changes in functional brain network topology property between two groups at multilevel. Firstly, we analyzed changes of average network property preliminarily. Then, we attempted to find the change of node properties of the ADHD group, especially with the execution control and visual attention-related brain regions. Further, we analyzed the similarities and differences of module compositions between the two groups in detail. Finally, differential network analysis would be conducted to explore the changed network connection of the ADHD group compared with the normal group. We hypothesized that the changes of network topology property were related to the clinical symptoms of ADHD patients. We hoped that this study would provide new insights from the brain network perspective and complement the fewer modularity elaboration at the mesolevel in ADHD brain network research.http://fcon_1000.projects.nitrc.org/indi/adhd200/. The functional images of all participants were scanned by the experimental instrument 3.0 T Siemens Trio scanner. During the experiment, participants did not require performing specific tasks but only needed to relax their minds, close their eyes tightly, and lie flat. Resting-state functional images were obtained by using a gradient-recalled echo-planar imaging (EPI) sequence with scanning parameters (repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, slice thickness = 3.5 mm, field of view (FOV) = 20 cm, and 33 slices per volume); each participant acquired a total of 240 volumes. The high-resolution T1-weighted structure images in the axial orientation were obtained by using a 3D spoiled gradient recalled (SPGR) sequence with scanning parameters .The research data came from Peking University, and detailed description can be downloaded from the following website: In order to obtain good information about the participants, the data were preprocessed (see Data Preprocessing), and data information of participants with large head movements and poor registration results was manually excluded. Finally, 118 participants were selected, including 61 ADHD children  and 57 typically developing control children. The dimensional measures of ADHD symptoms were diagnosed by ADHD Rating Scale (ADHD-RS) IV. All participants met the conditions: (1) right-handed, (2) no history of head injury, (3) no other severe mental illness, and (4) Wechsler Intelligence Scale for Chinese Children-Revised (WISCC-R) scores greater than 80. There was no significant difference between the two groups in gender and age. Psychostimulant medications were forbidden at least 48 hours before scanning. The Research Ethics Review Board of Peking University Institute of Mental Health approved all research. Parents of each participant signed an informed consent form, and all children agreed to participate in the study. The specific age, gender, and ADHD information of participants are shown in http://rfmri.org/dpabi)  in steps of 0.02 to obtain a series of binarized brain networks. Under each threshold, we calculated the average network properties of the two groups and carried out statistical analysis. Then, choosing a reasonable single threshold, we used one-sample p < 0.05, Bonferroni correction). After the threshold of 0.46, there was no significant difference between the two groups. In p < 0.05, Bonferroni correction). The change of the average small-world network property of the two groups with the threshold is shown in In σ and the average node degree of the network 〈k〉 were not less than the natural logarithms of the total number of nodes N, two groups of networks were statistically analyzed to obtain statistical network within group and the difference network between groups. The change of the average node degree of the statistical network of the two groups within the threshold is shown in T > 0.4, the average node degree 〈k〉 was less than the logarithm of the number of network nodes. And it was clearly seen in Figures T > 0.4, λ was greater than 1, indicating an upward trend. In addition, when T was 0.4, the average clustering coefficient and average node degree of the two groups were significantly different in Figures t-test to get the statistical network within group and the two-sample t-test to get the differential network between groups when threshold T = 0.4.Through preliminary analysis of network properties within the overall threshold range, it can be judged that there were differences between the two groups of networks. Under the premise that the small-world property T = 0.4, we performed one-sample t-test on the two groups to obtain the statistical network within group and analyzed the network properties, including node degree, clustering coefficient, shortest path length, betweenness centrality, global efficiency, and local efficiency. The results showed that significant differences were found in the first 50 brain regions of node degree, betweenness centrality, and global efficiency in At the threshold In In In Q was 0.672. Q (-0.5 to 1) was used to measure the effect of modularity division. The larger the value was, the better the modularity division was. There were similarities between modules 1 and 4 of the two groups, and the differences between the two modules were minor.The results of the two groups of network modularity are shown in Module 1 consisted of brain regions involved in visual processing in both groups, including the superior occipital gyrus (SOG), right MOG, right inferior occipital gyrus (IOG), inferior temporal gyrus (ITG), and right cuneus (CUN). Compared with the normal group, module 1 of the ADHD group was smaller and more localized; there were fewer connections with surrounding brain areas, such as the lingual gyrus (LING), parahippocampal gyrus (PHG), and fusiform gyrus (FFG). Module 4 was mainly responsible for the somatosensory, motor, and auditory functions, including the left precentral gyrus (PreCG), postcentral gyrus (PoCG), paracentral gyrus (PCL), superior parietal gyrus (SPG), PCUN, and right supramarginal gyrus (SMG) in both groups.However, there were obvious differences in the topological effects of certain regions and modules. For module 3, the two groups shared brain areas such as the superior frontal gyrus, dorsolateral (SFGdor), superior frontal gyrus, medial (SFGmed), left superior frontal gyrus, medial orbital (ORBsupmed), and MFG. While the ADHD group was only concentrated on the frontal lobe, it was more manifested as a short-distance connection between the frontal lobe. The normal group also included the parietal lobe and temporal lobe, such as the middle temporal gyrus (MTG) and ITG, SMG, and angular gyrus (ANG). And there were obvious long-distance connections between the frontal lobe and temporal and parietal lobe, respectively, in the normal group. For module 2, the normal group was mainly concentrated on the STG and MTG. And the ADHD group was distributed at the ANG, PCUN, median cingulate and paracingulate gyri (DCG), and left MTG. The position of module 2 of the ADHD group was displaced, and the size of the module increased.For module 6, the normal group was concentrated on the prefrontal lobe, including the MFG, inferior frontal gyrus, triangular part (IFGtriang), and inferior frontal gyrus, opercular part (IFGoperc). However, module 6 of the ADHD group was distributed from the frontal lobe to temporal lobe, also including the SMG, right transverse temporal gyrus (TTG), STG, and MTG. And there were obvious long-distance connections between the bilateral temporal lobe in the ADHD group. Modules 7 and 8 of the normal group were mainly distributed at the SMA, thalamus (THA), and pallidum (PAL). Moreover, the newly added modules 7 and 8 of the normal group corresponded to the reorganization and separation of modules 3 and 6 of the ADHD group. In other words, the regions of the ADHD group were assembled more densely, resulting in a reduction in the total number of modules. In general, the modules in the ADHD group seemed to be more localized, resulting in fewer connections between modules.t-test method was used for statistical analysis to obtain the differential network. The results of differential networks between the two groups are shown in This study adopted the AAL_1024 atlas to divide the brain region, constructed the brain network of the ADHD and normal groups by the graph theory, and performed plasticity analysis. We attempted to find the changes in the functional brain network topology of the ADHD and normal groups from multiple levels. From the perspective of network integration and separation at the global level, we found that the clustering coefficient of the ADHD group was significantly reduced and showed stronger small-world properties, indicating that the ADHD group had relatively sparse brain network and weakened functional connection of network. From the contribution of important brain regions at the nodal level, we observed significant differences between groups in nodal properties, especially on the frontal lobe, posterior occipital lobe, SMA, and PCG. The results of modularity at the mesolevel showed that there were short-range overconnections within module and enhanced cohesion especially in the frontal and parietal lobes, long-range underconnections between modules, and weakened coupling especially in the frontal lobe and occipital lobe in the ADHD group. We used rs-fMRI to provide reliable evidence for ADHD brain dysfunction from the perspective of whole organization and to probe large-scale neural communication in the brain from a new perspective.Based on analysis of overall threshold, although brain functional networks exhibited economical small-world topology in both groups in Figures The node degree indicated the interconnection between nodes, and clustering coefficient reflected the relationship between connected nodes becoming neighbors. Both node degree and clustering coefficient were indicators of the local interconnection of the network, clustering coefficient represented the efficiency of specialized processing of local information transmission, and it was considered to be the basis of the cognitive process . The lowThe results of statistical network showed that different regions among two groups were the prefrontal lobe, posterior occipital lobe, SMA, and PCG. For the node degree, it could be found that the ADHD group had no node distribution in the prefrontal lobe, and nodes in the occipital lobe were generally smaller than those of the normal group in In Our results showed that there were significant differences in node degree, betweenness centrality, and global efficiency, especially in the prefrontal lobe, posterior occipital lobe, and SMA. In another study on ADHD children using the AAL_90 atlas , for nodStatistical analysis of differential network manifested that the edges from the frontal lobe to occipital lobe were weakened in the ADHD group compared with the normal group in Resting-state functional brain network was consistently described, indicating the existence of modular organization , 68, 69.In the ADHD group, the bilateral prefrontal lobe modules (module 6) became larger and covered the INS and anterior temporal poles, and these regions were involved in the frontoparietal network (FPAN) . The froIn addition, the ADHD group showed increased modularity in the occipital lobe (module 1), while the motor modules  disappeared. The occipital lobe was mainly involved in visual processing, and the SMA was mainly responsible for the coordination of movement , which mFurthermore, the modularization results of the ADHD group also showed that there were short-range overconnectivity within module and long-range underconnectivity between modules. For example, module 3 in the normal group had a large number of long-range connections from the frontal lobe to occipital lobe, while module 3 in the ADHD group had many short-range connections in the frontal lobe. The results were consistent with the results of a review on ADHD ; the funThis study used the AAL_1024 atlas to construct a functional whole brain network with high nodal resolution of the ADHD group combined with the graph theory and rs-fMRI, to detect more subtle changes in brain regions and differences among groups. And we compared the network topology properties changes of the ADHD group from multiple scales with the normal group. We found that the topological changes of the ADHD group were mainly at the frontoparietal network, bilateral frontal lobe, occipital lobe, PCG, SMA, and PCUN. Overall, changes in topological property of the corresponding brain regions were closely related to the core symptoms of ADHD. Future work can explore how to effectively use brain stimulus technology to regulate the activation and connection of these brain regions, so as to improve the pathological state of ADHD patients.


Several medications used to treat attention deficit hyperactivity disorder (ADHD) have been associated with diminished bone mineral density (BMD) in children. The objective of this study was to determine if evidence exists for a similar association among adults.A retrospective cross-sectional analysis was conducted using data collected by the National Health Nutrition Examination Survey 2013–2018. Data from 7961 individuals aged 18 to 50, 79 of whom were taking medications to treat ADHD. Dual-energy X-ray absorptiometry scans provided measure of body composition. Linear regression models were used to examine associations between ADHD medication use and body composition.P < 0.05) and thoracic spine  (P < 0.05). No difference in BMD was seen in any other skeletal region based on stimulant ADHD medication use (P > 0.05). We found no evidence to suggest that duration of use affected the observed decreases in BMD, P > 0.05.Stimulant ADHD medication usage was found to be associated with decreased BMD in both the skull  (This study using a nationally representative sample assessed whether stimulant medication use in adults with ADHD was associated with decreased BMD. The overall results are inconclusive. Further study is needed to better evaluate if ADHD and/or stimulant medication use is independently associated with bone health. •Stimulant ADHD medication use in adults is associated with decreased bone mineral in the skull and thoracic spine.•No other areas of axial or appendicular skeleton showed significant differences.•There was no dose-response effect between stimulant medication use and bone mineral density.•The overall effect of stimulant medications on adult bone health is unclear. ADHD is primarily diagnosed in childhood and often persists into adulthood . The CenBoth behavioral and pharmacologic therapies are used to treat ADHD, either independently or in tandem. Medications fall into two general categories, stimulant and non-stimulant. Stimulant medications such as methylphenidate, amphetamine and their derivatives are considered first-line treatments, and non-stimulants such as atomoxetine, clonidine and guanfacine are considered second-line . Off-labStimulant ADHD medications, and to a lesser degree atomoxetine, have been associated with pediatric growth restriction . Several22.1We performed a retrospective analysis of data collected from the National Health and Nutrition Examination Survey (NHANES), a series of ongoing cross-sectional health assessments designed to assess the health and nutrition status of non-institutionalized US residents. Each 2-year data collection cycle uses a complex multistage probability cluster design, which oversamples specific populations to obtain both adequate samples for meaningful subgroup analyses as well as more reliable parameter estimates . For thiThe NHANES protocols were approved by the National Center for Health Statistics institutional review board, and written informed consent was obtained from participants 18 years and older. To mitigate the confounding effects of age-related bone loss, only adult individuals aged 18 to 50 years (n = 9559) were considered for inclusion. Consenting participants completed a home-based interview followed by a physical examination and sample collection conducted in a mobile examination center (MEC).2.2Trained interviewers administered home-based health interviews and family questionnaires. Self-reported socio-demographic characteristics included race and ethnicity , age and gender. Interviewers also collected detailed medical history information as well as data regarding prescription medication use. For each prescribed medication, the interviewer recorded the drug name from either the container or a pharmacy printout for verification. The duration and ICD-10-CM code for each medication was also recorded. ICD-10-CM codes were based on participant reported diagnosis, and not directly reported by a health professional or record review. Participants were pooled into two groups based on prescription medication use, those taking medications to treat ADHD, and those not on ADHD medications (controls).During the MEC visit, medical technicians collected biospecimens and conducted physical examinations. Body mass index, blood pressure, and body composition were measured. Body composition was assessed using dual energy X-ray absorptiometry . In addition to soft tissue measurements, DXA scans provided measure of bone mineral content, bone area, and BMD. Only individuals with valid DXA data were included in this study. Thus, pregnant women (n = 198), individuals taller than 77 in (n = 9), individuals over 450 pounds (n = 3), and those declining the scan for other reasons (n = 969) were not included. Additionally, the DXA scan of 16 participants, although completed was deemed invalid for other reasons and thus not included. Combined with the age criteria, this left 7961 participants in our sample. Due to the NHANES design, this sample represents over 120 million non-institutionalized US adults.Serum was assessed for cotinine, a biomarker for recent tobacco use and environmental tobacco smoke exposure. Cotinine is a primary nicotine metabolite with a half-life of approximately 15 to 20 h and has been shown to better correlate with cigarette smoking than self-report . Partici2.3Weighted estimates are provided using 6-year sampling weights to account for unequal selection probabilities among participants and adjustments for differential probabilities, non-coverage, and non-response. Summary statistics for categorical variables include the number of participants as well as the weighted prevalence within each category. Weighted estimates are reported as percentages with 95% Wald confidence intervals (CI). For normally distributed continuous variables, arithmetic means are reported. For skewed continuous data, geometric means are reported. Standard errors were calculated using the Taylor series linearization method. Estimates with standard errors exceeding 30% are noted as unstable.P values less than 0.05 were considered statistically significant. The Bonferroni correction was applied in cases of multiple comparisons. Missing data were treated as missing and no attempts to impute missing data were made. All analyses were conducted using SPSS Complex Samples .Pairwise comparisons were made using Rao-Scott χ2 tests of independence. Sample weighted general linear models (GLMs) were created to assess relationships between ADHD medication use and BMD using age, gender, and smoking status as covariates. Odds ratios (ORs) were estimated using logistic regression models. 3P > 0.05). The sample consisted of 51.4% men  and 48.6% women  with mean ages of 33.8 years (95% CI 33.4–34.2) and 34.4 years (95% CI 34.0–34.8), respectively. Non-Hispanic Whites comprised most of the sample . Hispanics comprised 19.4% . Non-Hispanic Blacks and individuals of other race/ethnic backgrounds including multiracial individuals comprised 12.1%  and 10.3% , respectively. A minority of individuals  were classified as active smokers. Similarly, only a fraction of participants  were found to be taking prescription medications for ADHD. Participant characteristics are reported in A total of 7961 participants between the ages of 18 and 50 years and with valid DXA data were included in our analysis. No temporal differences were noted between the data collection cycles (2013 to 2018) with respect to gender, age, race/ethnicity, income, smoking status, and ADHD medication utilization . Women were more likely to be on some form of medication compared to men . Despite this, men were 3 times more likely than women to be on a stimulant than a non-stimulant . Examinations of race/ethnicity and smoking status revealed higher rates of smoking among ADHD medication users as well as a higher proportion of non-Hispanic Whites taking the medications, both P < 0.001.No difference in age was found between ADHD medication users and controls, P = 0.03 and P < 0.01 respectively). Adjustment for age, gender, and smoking status resulted in estimated marginal means for BMD in the skull of 2.14 g/cm^2 (2.07–2.20) among stimulant users and 2.24 g/cm^2 (2.22–2.25) among individuals not taking ADHD medications. The estimated marginal mean BMD in the thoracic spine of stimulant users was 0.78 g/cm^2 (0.77–0.81) and 0.83 g/cm^2 (0.82–0.83) among individuals not taking ADHD medications. Unadjusted mean BMD values for each assessed skeletal region are reported in P > 0.05). Using age, gender, and smoking status as covariates, GLMs were used to examine differences in the BMD of various skeletal regions between stimulant ADHD medication users and controls see . Models 4Although ADHD is primarily associated with children, up to 50% of children diagnosed with ADHD continue to be significantly impaired in adulthood . As recoThe pathophysiology of these potential bone mass changes is not fully understood. Proposed mechanisms include increased metabolic turnover, appetite suppression resulting in lowered calcium and vitamin D intake, and in sympathetic signaling by decreasing osteoblasts and increasing osteoclasts . In one Stimulant ADHD medication use might affect patients differently based upon their age since adolescent bones are rapidly growing while adult bones are primarily engaged in remodeling. In children, both appendicular and axial skeleton is affected . We, howSome observational studies have suggested a possible protective effect of stimulant ADHD medications against fractures in young adults . WhetherOne of the primary strengths of our study was the use of a large, national database to draw comparisons between regular, long-term ADHD medication use and changes in associated bone density among US adults. Because NHANES data are collected on an annual basis, this will allow researchers to analyze changes within the population over time. Additionally, we used a relatively large sample size that allowed us to adjust for demographic characteristics of age, gender, and smoking status, suggesting that our findings are not due to confounding demographic differences. This paper, however, has significant limitations. This was an observational cross-sectional study; therefore, causality cannot be determined. While we sampled across several NHANES cycles, only 79 subjects were on stimulant medications. Due to our small sample size, we were not able to perform regression models evaluating many of the possible confounders, e.g., diet, physical activity level, sleep, comorbid conditions, other medication usage, substance use, metabolism, and endocrine function. Additionally, the interpretation of our findings is further limited by types of clinical and physical data collected in each 2-year NHANES iteration.5This study provides the first evaluation of the effect of ADHD stimulant medications on BMD using a nationally representative sample. Statistically significant decreases in BMD were noted in the axial skeleton; no dose response effect was observed. There were no significant differences noted in the appendicular skeleton. The significance of our findings is unknown. Further research is needed to adequately assess the effect of stimulant medication and/or ADHD on bone health.The views expressed herein are those of the authors and do not reflect the official policy of the Department of the Army, Department of Defense, or US Government.Michael Jeremy Lawson: Conceptualization, Methodology, Writing - original draft.Thomas A. Beltran: Conceptualization, Data curation, Formal analysis.Carla R. Padilla: Conceptualization, Methodology, Writing – Review & editing.Cristóbal S. Berry-Cabán: Conceptualization, Writing – Review & editing.Y. Sammy Choi: Conceptualization, Supervision, Methodology, Writing – Review & editing.None.


Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in childhood. ADHD is a risk factor for the development of overweight and obesity. One neuropsychological factor that may play a prominent role in the relationship between ADHD and obesity is executive functioning.The aim of this study is to investigate the relationship between comorbid obesity/overweight and cold executive functions, verbal short-term memory, and learning in children with ADHD. This is the first study to examine relationship between verbal short-term memory-learning and obesity in patients with ADHD.This study was conducted with 70 patients with ADHD and 30 healthy controls. In this study, patients diagnosed with ADHD were divided into two groups according to body mass index (BMI) as <85 percentile and ≥85 percentile. Cold executive functions were evaluated by Stroop Test (ST) and Cancellation Test (CT). Serial Digit Learning Test (SDLT) was administered to measure verbal short-term memory and learning capasity. In order to evaluate the severity of ADHD objectively, parents completed the Conners’ Parents Rating Scale-Revised Short Version (CPRS-RS).The ST, SDLT and CT scores were significantly lower in both groups with ADHD than the control group. The CPRS-RS subscale scores were significantly higher in both groups with ADHD than the control group. There was no statistically significant difference in ST, SDLT, CT scores and CPRS-RS subscale scores between the two groups with ADHD.This study show that overweight/obesity comorbid with ADHD was not associated with cold executive functions, verbal short-term memory, learning, or ADHD symptom severity.No significant relationships.