Increasing understanding arises regarding disadvantages of stimulant medication in children with ADHD (Attention-Deficit Hyperactivity Disorder). This review presents scientific findings supporting dietary antioxidant treatment of ADHD and describes substantial alterations in the immune system, epigenetic regulation of gene expression, and oxidative stress regulation in ADHD. As a result, chronic inflammation and oxidative stress could develop, which can lead to ADHD symptoms, for example by chronic T-cell-mediated neuroinflammation, as well as by neuronal oxidative damage and loss of normal cerebral functions. Therefore, modulation of immune system activity and oxidant-antioxidant balance using nutritional approaches might have potential in ADHD treatment. The use of natural antioxidants against oxidative conditions is an emerging field in the management of neurodegenerative diseases. Dietary polyphenols, for example, have antioxidant capacities as well as immunoregulatory effects and, therefore, appear appropriate in ADHD therapy. This review can stimulate the development and investigation of dietary antioxidant treatment in ADHD, which is highly desired. TM), used for the formal diagnosis of ADHD, describes ADHD as “a persistent and pervasive pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development” and resulting in “a negative impact on social and academic/occupational activities”. The symptoms must have an onset before the age of twelve, persist for half a year, and be present in the home and school environment. Symptoms related to the inattentive subtype include difficulties in organizing tasks, often losing things needed for activities, and difficulties in sustaining attention. With regard to the hyperactivity-impulsivity subtype, symptoms include excessive fidgeting with hands or squirming in seat, talking excessively, and having difficulties in waiting for a turn. The combination-type of ADHD contains symptoms from both manifestations [Attention-Deficit Hyperactivity Disorder (ADHD) is a common neurocognitive behavioral disorder with a worldwide prevalence of 5.9–7.1%. It is associated with other psychiatric disorders, such as oppositional defiant disorder (ODD), autism and anxiety . The 5thstations ,4.Despite its high prevalence, no straightforward indication can yet be given about the underlying mechanisms of ADHD due to its complex etiology . EspeciaMoreover, developmental trajectory studies show a delay in cortical maturation in ADHD, most profound in prefrontal regions which are important for attention and motor planning . In addiSLC9A9 gene has been implicated in both ADHD and multiple sclerosis [Generally, a strong genetic contribution (with a heritability ranging from 0.6 to 0.9) is found for ADHD, with several candidate genes, including genes involved in dopamine, noradrenaline, and serotonin transmission and metabolism, but each of these genes individually only slightly increases the risk of ADHD . Genome-clerosis ,19.As an example, genetic variations are seen in ADHD for the dopamine active transporter (DAT) gene. Dopamine is released by activated neurons into the synaptic cleft to function as a neurotransmitter and can subsequently be taken up by DAT, which stops the signal transduction . On averDRD4 (dopamine receptor D4) gene is expressed less abundantly in brain areas involved in attention, such as the anterior cingulate cortex. Consequently the signal is transduced less to the next neuron [Furthermore, dopamine receptors are involved, located on the dendrite of the adjacent neuron and signaling when dopamine is present in the synaptic cleft. In ADHD patients, t neuron .DRD1, DRD2 and DRD5 [NET1), which is responsible for the reuptake of norepinephrine, and the alpha-2A-adrenergic receptor (ADRA2A), which regulates epinephrine release, also potentially play a role [Other genes mentioned in relation to ADHD are and DRD5 . The norAlso related to pregnancy, various factors have been associated with an increased risk on ADHD, such as maternal smoking, low birth weight, preterm birth and small size for gestational age. In addition, it is also increasingly accepted that a robust association exists between ADHD in the offspring and various maternal disorders, such as viral and bacterial infections, allergy and specific autoimmune diseases. Associated pro-inflammatory cytokines could affect neuronal development directly or via epigenetic pathways. Other factors that could mediate the effect between maternal diseases and ADHD are medication, more contact with health care services and genetics . Indeed,The conventional treatment for ADHD consists of behavioral and educational interventions, often combined with psychostimulant medication. The psychostimulants MPH and dextroamphetamine, and non-stimulating prefrontal cortex noradrenaline reuptake inhibitor atomoxetine, are the standard treatments for ADHD. MPH is a central nervous system stimulant as it increases attention and reduces hyperactivity and impulsivity by inhibition of dopamine reuptake in the striatum, without triggering its release . HoweverMoreover, while ADHD medication does reduce symptoms in the majority of patients, most of them experience side effects within the first 6 months of MPH use ,27. A reIncreasing understanding regarding disadvantages of stimulant medication in children stimulates the investigation of alternative therapeutics . DevelopReactive oxygen species (ROS) are oxygen-derived radicals and non-radicals that are oxidising agents and/or easily converted into radicals . SimilarMultiple mechanisms exist to reduce ROS accumulation. First of all, by creating a high oxygen gradient between intracellular and extracellular compartments, oxygen diffuses out of the cell, thereby preventing intracellular ROS formation. In addition, free radicals can be detoxified by non-enzymatic and enzymatic antioxidants . Other m10). In addition, some antioxidants can penetrate membranes easily, including the ability to cross the blood-brain barrier (BBB), while others cannot. Antioxidant enzymes include catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidases (GSH-Px), among others [Different types of antioxidants protect different parts of the body, as some are water soluble , while some are fat soluble , with tyrosine [3-nitrotyrosine is a marker of oxidative damage to proteins, and is formed by a reaction of RNS, such as peroxynitrite  [Of all nucleobases, guanine is most prone to oxidation . When de(8-OHdG) ,57. This(8-OHdG) . When DN(8-OHdG) . Metabol(8-OHdG) .Multiple effects of oxidative stress on the brain can be related to ADHD aetiology . The braS-transferase (GST) and paraoxonase-1 (PON-1).Various studies demonstrated, for example, decreased activity of antioxidant enzymes like CAT in ADHD, as well as increased levels of oxidative stress markers like plasma MDA ,63,64,65Various types of research results point at the role of  immunopathology in ADHD, with indications of chronic neurological inflammation involving cellular immunity, probably requiring a predisposing genetic background ,67,68.The potential involvement of the immune system in ADHD has long been suspected due to the increased prevalence of allergic diseases including atopic dermatitis, asthma and rhinitis in patients with ADHD . AllergiH2 type cytokine secretion, leading to an overproduction of inflammatory cytokines that pass the BBB and activate neuro-immune mechanisms involved in neuronal circuits that relate to behavioral and emotional modulation. Next to elevated cytokine levels, increased stress levels and thereby raised glucocorticoids can interfere with the development of the brain and neurotransmitter systems and increase the risk of developing atopic eczema [Several hypotheses were put forward to explain the association between atopic eczema and ADHD, which could be the result of atopic eczema influencing the development of ADHD, or ADHD increasing the vulnerability to develop atopic eczema. Mutual risk factors connecting both diseases include genetic background and perinatal stress. The underlying mechanism is probably related to an increased secretion of IgE, increased eosinophilic activity and predominantly Tc eczema .No association was found between the severity of IgE-mediated atopy and ADHD scores, based on history of anaphylaxis and skin-prick tests ,74. The H1 mediated cytokines) and schizophrenics (skewing to TH2 mediated cytokines), with somewhat elevated concentrations of TNF-α and reduced levels of IFN-γ [H1 and TH2 cell-mediated disorders, such as ear infections and eczema [ADHD patients are reported to have a cerebrospinal fluid cytokine profile intermediate to that of obsessive-compulsive disorder patients (characterized by a skewing to Tof IFN-γ . Howeverof IFN-γ . Neverthd eczema . In addid eczema . Moreoved eczema  and playd eczema .H1 derived inflammation and a defective regulatory T cell (TReg) immunomodulation. This would lead to an elevation of pro-inflammatory cytokines and reduction of anti-inflammatory IL-10, which has a major role in the pathophysiology of brain white matter damage, as chronic elevation of TNF-α and IL-6 primes cells to undergo apoptosis or necrosis [H1 and TH17 cells and their associated cytokines  can infiltrate into the brain parenchyma through lesioned BBB and have neurotoxic potential by promoting glial activation. The resulting neuro-inflammation could result in cognitive and behavioral impairments [Additionally, cortisol production was found to be reduced in ADHD, which is suggested to be a consequence of early programming of the HPA-axis . Though airments .Reg regulation potentially results in TH2-driven inflammation, making the child “allergy-prone”  by inducing activated B-cells, mast cells and eosinophils [Cellular immunity therefore appears involved as a pathophysiological mechanism in ADHD. Furthermore, a defective Tinophils .During development, cytokines are involved in neuronal proliferation, differentiation, migration and synaptogenesis . AccordiRegs [Commensal microbiota play a crucial role in the development of intestinal immunity, e.g., via the induction of TRegs ,89. ConvRegs ,91,92. If oxidative stress and immunity are involved in ADHD, also dietary micronutrients might be of influence on ADHD . Children with iron deficiencies showed ADHD-related behavior, though serum iron levels were overall not different between ADHD and control subjects ,93,94,95Another trace mineral related to ADHD is magnesium. In 33.6% of the ADHD children a magnesium deficiency in serum was present compared to 15.8% in healthy children. In hair, a 77.6% deficiency rate was found in ADHD children versus 20.7% in healthy children . This isLower plasma zinc levels were found in ADHD patients ,103,104.So far, no differences in plasma selenium levels have been found in ADHD patients ,103. StiCopper concentrations have been found higher in plasma of ADHD patients than in controls. This metal is involved in the metabolism and degradation of catecholamines via dopamine β-hydroxylase and monoamine oxidase , with hiLead is involved in free radical production and affects neurotransmitter generation ,103. HigThere is increasing awareness of the relevance of vitamin D in immune regulation and circulating vitamin D levels were significantly lower in ADHD patients compared to controls ,115. ThiConcentrations of micronutrients, including zinc, iron and copper, can be altered by food additives, and in extreme cases artificial food colorings result in a zinc deficiency ,61. As tTherefore, studies have been performed with elimination diets, in which participants restrict the intake of certain foods such as dairy products, corn, soy and chocolate. Beneficial effects were found in children with ADHD when they were restricted from certain foods and food colorings. Atopic subjects responded more often, which points towards allergic hypersensitivity ,121. MorH1 cells, promoting TH2 effector cell differentiation and enhancing TReg development [Currently, the strongest evidence for an effect of nutrition on ADHD is related to omega-3 fatty acids, like docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) ,123. DHAelopment ,127, by In ADHD patients, lower levels of omega-3 fatty acids were found as well as a higher omega-6/omega-3 (AA/EPA) ratio, and supplementation had a beneficial effect on ADHD symptomatology in various studies ,125,128,Also amino acids are apparently related to ADHD development. In ADHD patients a methionine deficiency had a negative impact on behavior and learning capabilities. Methionine is an essential amino acid required for, among others, GSH production. Since GSH is part of the antioxidant defense, methionine deficiency may create even more oxidative stress .Various data thus indicate the involvement of oxidative changes in ADHD and also cellular immunity is increasingly recognized as a pathophysiological mechanism behind the disorder. There are several ways by which oxidative stress and immune mechanisms could alter neuronal development and/or injure the developing structures, including altered gene expression, direct DNA damage and promotion of cell death, eventually leading to neuronal damage and loss of function. Still, specific immune biomarkers other than antibodies have not been systematically studied in ADHD. In addition, immune and oxidative effects of both standard therapy and nutritional supplementation in ADHD are neglected topics in research.H1 and TH2 cytokine balancing and TReg stimulation is a nutritional target for a number of health conditions, with nutritional supplements including plant sterols, polyphenols, pre- and probiotics, vitamins, minerals and omega-3 fatty acids. Yet, immune and oxidative imbalances linked with ADHD offer potential for nutritional supplementation in ADHD therapy. For example, TPolyphenols, composed of several phenol groups and found abundantly in, e.g., fruits, vegetables, green tea and red wine, are subdivided into different groups, including flavonoids, lignans, stilbenes, hydroxybenzoic acids and hydroxycinnamic acids, and have both antioxidant and immune modulating effects ,134. In The most well-known characteristic of polyphenols is their antioxidant capacity . Polyphe® are examples of effective immunoregulatory and anti-inflammatory polyphenols or polyphenol rich extracts modulating the immune system by influencing both activation and differentiation of multiple immune cell types including TReg cells, TH1 cells, natural killer (NK) cells, dendritic cells (DCs) and macrophages [Epigallocatechin gallate (EGCG), curcumin and Pycnogenolrophages ,137,145.Reg functioning. In vitro assays using naïve T cells from C57BL/6 mice under TH17 differentiation conditions and Baicalin treatment showed a decreased expression of IL-17 [Clostridium or stimulation of Bifidobacterium, Lactobacillus, and Eubacterium species [Mechanisms associated with immune regulation include epigenetic modifications like DNA methylation of regulatory sequences. Polyphenols are able to regulate such epigenetic factors by oxidant and thiol-redox-mediated signaling modulation . In addiof IL-17 . Treatmeof IL-17 , demonstof IL-17 . These f species ,154,155.®, derived from the bark of the French maritime pine (Pinus pinaster Aiton) [® is procyanidin B1 (epicatechin-(4β→8)-catechin) [w/w) procyanidins [The herbal extract Pycnogenolr Aiton) , is compr Aiton) ,156. An atechin) . This exyanidins ,158.®. For example, 46% of catechin metabolites (glucuronides and sulphates) were recovered in urine [® ingestion was detectable in plasma after a single dose of 300 mg and multiple doses of 200 mg although detection times in plasma differ per compound. Metabolite M1 -γ-valerolactone) is formed by bacterial metabolism from catechin [® can be modified during digestion and absorption as well as by the liver [Studies indicate high bioavailability of the individual components of Pycnogenolin urine . Oral Pycatechin ,159. Thihe liver , which c® has multiple pharmacological effects such as antihypertensive, anti-inflammatory and anti-diabetic effects [Pycnogenol effects . Due to  effects . Moreove effects . Additio effects ,157. Thi effects ,160. ® administration caused positive effects on ADHD symptoms compared to placebo. Statistically significant enhanced concentration and reduced hyperactivity, as rated by the Child Attention Problems teacher rating scale, were found after 1 month of Pycnogenol® supplementation as well as improved visual-motoric coordination as rated by psychologists [® and a trend of decreased epinephrine and norepinephrine levels was seen [® increased the activity and expression of SOD, increased GSH levels and decreased GSSG levels, pointing towards less oxidative stress [® administration [® can be beneficial in ADHD because of its direct scavenging ability, chelating activity, stimulation of the DNA repair system and/or combinations thereof [® standardization is limited to total procyanidin content without determination of major polyphenolic constituents and the actual bioavailability of this complex mixture is still unknown [® appears promising for the treatment of ADHD, further research is therefore required.In children, Pycnogenolologists ,140. Thiwas seen ,14,16. Twas seen . Howeverwas seen . In addie stress . Also, astration , while i unknown . Though Other polyphenolic extracts with antioxidant and immune modulating properties have been investigated in the treatment of ADHD, though most studies were relatively small or conducted over a short time span ,167,168.Ginkgo biloba extract, found to improve cerebrovascular blood flow and to affect several central neurotransmitter systems. In a double blind randomized study including fifty ADHD patients, 80–120 mg/day Ginkgo biloba extract (made from the leaves of the Ginkgo tree) for six weeks was much less effective than MPH treatment. Still, Ginkgo caused fewer adverse events than MPH [One example is than MPH . A threethan MPH .Hypericum perforatum extract , containing flavonoids like quercitrin, rutin and quercetin, did not significantly improve ADHD symptoms as compared to placebo in a randomized double-blind trial with 54 patients [Passiflora incarnata (0.4 mg/kg/day pass twice daily), containing the flavonoids luteolin, quercetin and rutin, among other constituents, and MPH (1 mg/kg/day twice daily) performed equally well regarding parent and teacher rating scale scores, with both demonstrating a significant clinical benefit over 8 weeks [patients ,172. In  8 weeks .®, a herbal, polyphenol-rich extract with potent antioxidant and anti-inflammatory properties, which is considered to have therapeutic benefits in ADHD, as it increased antioxidant levels, reduced oxidative damage and improved neurochemical status.Elevated oxidative stress and immune dysfunction, eventually leading to neuronal damage, appear to play a role in the pathophysiological process of neurodevelopmental disorders. Evidence on the association of ADHD with immune and oxidant-antioxidant imbalances offers potential for antioxidant and/or immunomodulatory nutritional supplements  as ADHD therapy. One example is Pycnogenol


M = 11.22, SD = 1.43). Two hundred and fifty six of the children had been diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and 243 were without ADHD. For the analysis of this objective, a causal model was designed to include the following different measures of task-execution: CPT TOVA ; electrical activity (using Q-EEG); and blood-flow oxygenation activity (using nirHEG). The causal model was tested by means of structural equation modeling (SEM). The model that had been constructed was based upon three general assumptions: (1) There are different causal models for children with ADHD and those without ADHD; (2) The activation measures influence students’ executive performance; and (3) There are measurable structural differences between the ADHD and control group models (executive and activation). In general, the results showed that: (a) activation measures influence executive patterns differently, (b) the relationship between activation variables (nirHEG and Q-EEG) depends on the brain zone being studied, (c) both groups showed important differences in variables correlation, with a good fit in each model (with and without ADHD). Lastly, the results were analyzed with a view to the diagnosis procedure. Therefore, we discuss the implications for future research.Attention deficit with, or without, hyperactivity and impulsivity (ADHD) is categorized as neuro-developmental disorder. ADHD is a common disorder in childhood and one of the most frequent conditions affecting school ages. This disorder is characterized by a persistent behavioral pattern associated with inattention, over-activity (or hyperactivity), and difficulty in controlling impulses. Current research suggests the existence of certain patterns of cortical activation and executive control, which could more objectively identify ADHD. Through the use of a risk and resilience model, this research aimed to analyze the interaction between brain activation variables (nirHEG and Q-EEG) and executive variables (Continuous performance test -CPT-) in subjects with ADHD. The study involved 499 children, 175 females (35.1%) and 324 males (64.91%); aged from 6 to 16 years ( Attention deficit with, or without, hyperactivity and impulsivity (ADHD) is one of the disorders that most affects academic performance. Current research suggests the existence of certain patterns of cortical activation and executive control, which could more objectively identify ADHD. To detect these patterns, brain activation variables are recorded in the areas of central and prefrontal cortex through electro-encephalographic techniques such as quantified EEG (Q-EEG) to measure beta-theta electrical activity ratios , as wellOn the other hand, with the publication of the new DSM-5 classification manual , ADHD isAlthough there is a substantial body of symptom-based evidence highlighting the neurologic nature the disorder, the primary causal factors underlying this problem remain unclear to date .Within this context, some investigations point to a delay in myelination formation during brain development , or insuFrom a general perspective, ADHD has been associated with a dysfunction in the central nervous system, characterized by a developmental delay and cortical hypo-activation related to a deficit in the dopaminergic and noradrenergic systems . The norThe above hypothesis is supported by neo-connectionist learning models, which have also linked cortical activation (measured by means of frequency fields) with the cortical areas involved in ADHD . When thConcerning ADHD, a differential pattern of electro-cortical activity has been observed in a state of rest, and it is characterized by increased theta -and decreased beta- activity . This prOn the other hand, increasing cortical activation has been observed in students with ADHD who have had positive responses to intervention, and this has led to a decrease in inattention, impulsivity and hyperactivity according to previous research . For exaOther studies have also found that, by increasing cortical activation with neurofeedback techniques or pharmacological support, individuals with ADHD significantly improved their performance in attention tasks, apparently as a consequence of a decrease in the core symptoms of the disorder . Also, MIn sum, previous research supports the relationship between ADHD symptoms and decreased cortical activation. Nevertheless, although it has been argued that low activation occurs in prefrontal and frontal areas, the specific areas involved in these processes have not been adequately defined . The mosThe difficulties in the detection of specific brain areas have been associated with the presence of differential profiles or presentations in the disorder . Thus, tThe empirical evidence concerning the different categories of symptomatology in ADHD, and their new conceptualization in DSM-5 , makes iIt is important to consider that this disorder not only leads to impairments in the academic context , but alsBy using a risk and resilience model, this research aims to analyze the interaction between brain activation variables and executive function in students with ADHD. For the analysis of this objective, a causal model  was formulated in which different measures were included  we expect to deepen our knowledge of the relationship between activation measures and executive function measures. The SEM designed was fit using two samples of data (control group without ADHD and ADHD group). The first sample (without ADHD) was utilized to fit the model, and the second sample (with ADHD) to analyze the consistency of the data with predictive differences. We also performed multi-group analysis to verify the consistency of the results from both samples, to know which variables differentiate subjects with and without ADHD.Figure 1):Considering the data provided by literature findings, the causal model was tested using structural equation modeling (SEM). This model was built based on three general assumptions (see (1) There are different causal models for children with ADHD and those without ADHD.(2) The activation measures influence a student’s executive performance. Specifically, certain task-execution variables will be related to activation in the left pre-frontal cortex, and others with central zone pre-frontal cortex activation.(3) There are important structural differences between the models for the ADHD and control groups.When estimating the dependent variables of the model (latent variables), we also assume that the measured errors are not inter-correlated in the model, and that there is no relationship between the types of errors committed. Lastly, although previous research indicates reciprocal relationships among the dependent variables measured in this model , in the current investigation it is theoretically unacceptable to expect that reciprocal relationships between causal measures have been observed at a single temporal moment.Our model has two parts: one of measurement, which corresponds to the relationship between the latent variables and their respective observed variables (activation), and a structural part, which involves the relationship between the independent and the dependent variables of the model (execution). The effects of the independent on the dependent variables are indicated with gamma (γ), whereas the relationships among the dependent variables are represented as beta (β).M = 11.22, SD = 1.43). There were 324 males (64.9%) and 175 females (35.1%). As one of the goals of this research was the cross-validation of the study-model developed, the final calibration sample was split into two subgroups . All participants had an IQ higher than 80 .The participants included in the study comprised 499 students aged between 8 and 16 years  clinical diagnosis of Attention Deficit Disorder with Hyperactivity according to the Diagnostic and Statistical Manual of Mental Disorders-IV-R ; (b) symAll healthy controls underwent the same diagnostic assessment to rule out any psychiatric disorders. To ensure the correct assignment of the students to their respective groups, The variables included in the hypothesized model were grouped into two categories: activation measures , and executive measures .The nirHEG , which is not absorbed as much by oxygenated hemoglobin, with infrared light (at 850 nm wavelength), which is less affected by oxygenation . CapillaSD = 20) and used to calibrate all new spectrophotometers , indicating malfunctioning of the ability to increase the nirHEG ratio; that is, the participant is incapable of increasing the ratio and, thereby, brain activation. This apparently indicates a lapse in the attentional process, which, according to Q-EEG , Biocomp 2010 (Developed by The Biofeedback Institute of Los Angeles1) was used to record electrical activity. Q-EEG  is a computerized EEG system, adapted by Activation left cortex was estimated as a latent variable in the SEM from two indicators of activation measures. One of the indicators was nirHEG in Fp1 and the other was Q-EEG in Fp1. Thus, our latent variable takes into account the commonalities between these two ratio-index measures of the of the student’s cortical activation.Activation central cortex was estimated as a latent variable. One of the indicators was nirHEG in FpZ and the other was Q-EEG in Cz. So, our latent variable subsumes the communalities between this two ratio measures indexes of the students’ activation.Test of Variables of Attention  electrode was placed on the right fore-arm and the reference electrodes were placed on the ear lobes. The recording was carried out in a sound-proof and electrically isolated room with low illumination, and the test always at the same time of day (between 4 p.m. and 6 p.m). The Q-EEG was administered to each participant (with their eyes open), and for a maximum duration of 10 min. The nirHEG was administered in the same circumstances of q-EEG. With a measurement of 35 seconds in Fp1 and FpZ duly counterbalancing the order with the characteristics of the band measurement described above. The TOVA measures were standardized, interpreting scores lower than 1.2 standard deviations as negative measures. Lastly, a general executive control index showing recorded readings lower than -1.80 was interpreted as ADHD. For the partial correlations, we took age into account because activation and executive control both tend to decrease with age.The study was conducted in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki), which reflects the ethical principles for research involving humans . All subThe adequacy of the model was analyzed with SEM, using the AMOS.22 program . FirstlyTable 1 shows the descriptive data as well as the two Pearson correlations matrixes corresponding to the Control Group and the ADHD group. Before conducting the statistical analyses, we examined the matrixes with regard to missing data, the presence of outliers, linearity and normality of the data. We examined the data to determine whether any of the variables or subjects presented a significant amount of missing values. Considering the variables with respect to Table 2). Therefore, with normality conditions being met, we decided to fit the model using ML.One of the important assumptions of SEM is that the variables taken must follow a normal distribution. As maximum likelihood (ML) can produce biases when this assumption is violated , we examr > 0.85). The pattern of correlations  was similar both groups.Another important aspect in the initial analysis of the data matrix is to verify that the variables are significantly correlated, although such correlations should not be excessively high , the estimated parameters did not show the expected magnitudes and mathematical sign (consistent with the theory underlying the model), and excessive standard errors were observed (2(28) = 81.11, χ2/df = 2.89, p < 0.001, GFI = 0.939, AGFI = 0.881, TLI = 0.928, CFI = 0.928, RMSEA = 0.089 (0.066–0.111), p = 0.003.In a first assessment of the model , we considered the possibility of including covariance effect between Commissions and RT in the TOVA test (leaving the parameter free) as well as the indirect effect contained in the initially hypothesized model. At the theoretical level, this effect is negative, indicating that a higher number of commissions the response time will be less in TOVA.2(27) = 57.924; χ2/df = 2.145; p ≤ 0.001; GFI = 0.954; AGFI = 0.907; CFI = 0.974; TLI = 0.956; RMSEA = 0.069 (0.044–0.093), p = 0.098], and the improvement over the initial model was statistically significant (Δχ2(1) = 23.192). As expected, the new estimated parameter was statistically significant and negative (r = -0.39). Neither the residuals nor the modification indices recommended carrying out more changes in the model . Table 3 shows the coefficients of the relationships in the measurement model and the structural model, as well as their corresponding estimation errors, critical ratios, and associated probabilities.The results indicated that the fit of the re-specified model was good, . Moreover, assuming the absence of differences in structural weights, no statistically significant differences were found in structural co-variances, structural residuals, and in measurement residuals.In the above analysis, assuming that the unconstrained model is similar in both groups, the results showed statistically significant differences concerning the five criteria examined .2(27) = 98.684; χ2/df = 3.655; p = 0.000; GFI = 0.931; AGFI = 0.860; CFI = 0.973; TLI = 0.954; RMSEA = 0.102(0.081–0.124), p ≤ 0.001]. Considering the criteria used to judge the goodness-of-fit indices, the RMSEA index revealed that the previous model did not optimally represent the relationships observed in the empirical data matrix. After examining the co-variance matrix and the modification indices, we considered including (in our model) the direct effect of the latent variable Activation central cortex on TOVA and D Prime. From a theoretical perspective, the inclusion of this effect seemed to be logical, because D prime is a measure of the quality of concentration obtained from the total number of omission and commission errors. Also, the central cortex area allows which is affected in students with ADHD reflected in a lower quality of the concentration given the higher number of errors. As well as eliminate indirect effect between TOVA commissions and TOVA response time (with a not significant effect p = 0.251). This relationship can be found in students without ADHD, but not in students with ADHD. It is because commissions are related to impulsivity, and RT is related to inattention. Thus, when both variables (impulsivity and RT) are affected, these variables can be clearly distinguished.In the ADHD Group, the goodness-of-fit of the hypothesized model was not adequate , and also that the improvement over the initial model was statistically significant [Δχ2(1) = 31.820]. As expected, this newly estimated parameter was found to be statistically significant and positive (r = 0.27). Neither the residuals, nor the modification indices, indicated that any further changes to the model were necessary .Like inControl Group, statistically and theoretically it seemed appropriate to slightly modify the initial model in the ADHD sample by including the direct effect Table 3. In both samples, the estimated parameters approximated the expected magnitudes and sign, and the standard errors were neither excessively large nor small. In the control Group, with the exception χ2 and its associated probability, the fit-indices indicated that the hypothesized model optimally represented the relationships of in the empirical data matrix. However, the data concerning fit were somewhat lower than in the first analysis. For example, χ2 was higher than the value of the calibration sample . Table 5 shows the coefficients of the relationships in the measurement and structural models, as well as their corresponding estimation errors, critical ratio, and associated probability.The results are presented in Activation left cortex significantly and positively explained TOVA variability (γ = 0.92), TOVA Commissions (γ = 0.79), and TOVA D prime (γ = 0.66). Activation central cortex also positively and significantly explained both TOVA Omissions (γ = 0.94) and TOVA response time (γ = 0.90), both of which are related to attention and concentration. Lastly, as a likely consequence of the re-specification of the with ADHD model, a relationship between TOVA Commissions and TOVA RT was not found.With regard to the predictions of the model, the results obtained in ADHD model are higher than without ADHD sample, except that the relationship between TOVA and IGCE was significantly and positively explained by TOVA variability (β = 0.23), TOVA response time (β = 0.18) and TOVA D prime (β = 0.61). Globally there were small variations that were higher in than the magnitude of the statistics obtained. activation and executive function measures, by examining the relationship between brain activation in selected areas and differences in executive measures. To achieve this aim we employed SEM measures, which also included latent variables such as left and central cortex activation. Although previous studies have analyzed the relationship between activation and execution, SEM has seldom been used in the past. In general, the results showed a different model for ADHD group and control group. So, one conclusion of the study is the presence of a model in which is related in a particular way, the activation in specific areas and the profile of execution of students with ADHD.The current research attempted to deepen our knowledge of the relationship between In general, the data provided by the fit of the model (both in the ADHD and Control groups) provided evidence supporting some of the hypotheses proposed in the model. Therefore, the findings of this study appear to agree with those obtained in previous studies based on more conventional strategies of data analysis, such as hierarchical regression analysis and analysis of variance. The major findings discussed below concern the relationship between activation and execution, and the difference between the ADHD model and the Control model .In this study, it was especially noteworthy that the relationship between activation  and execution was stronger in ADHD subjects than in the control group. The explanation could be that subjects with ADHD show lower cortical activation  and blooMoreover, the relationship between cortical activation and executive function shows differential results depending on the brain area assessed . Regarding left cortical activation, is highlighted the results indicated that differing beta-theta ratios and low blood oxygenation in area Fp1 can be related to hyperactivity and impulsivity symptomatology.Furthermore, when the electric activation shows low levels in Fp1, these data are also supported by nirHEG results and a low performance in TOVA tests. Similarly, when the electrical activation is within normal ranges blood oxygenation and TOVA test results are also normal. While these results have been observed in previous studies analyzing the relationship between Q-EEG and TOVA, and between nirHEG and TOVA , the preOn the other hand, in the case of central activation, this relationship shows lower rates, and although it is observed that those who present low activation levels measured by the beta-theta ratio in Cz, also present a low oxygenation measured by nirHEG in FpZ, as well as a greater number of omission errors and worst response time; the findings do not reach so high interaction as the previous case. In any case, it has to be emphasized that being different points (Cz/FpZ), is logical that correlations decrease slightly in spite of still showing a significant relationship. Furthermore, it is possible that FpZ is also influenced by other variables besides inattention, such as emotion or anxiety control, that many studies have located in Fp2.Firstly, given these results, the relationship between activation and execution seems to be a reliable measure for ADHD symptoms. Secondly, with regard to the differences between models from ADHD group and the control group, could be necessary its incorporation into assessment protocols in order to achieve more reliable and accurate diagnosis. Control group model shows a relationship between commissions and RT. In this sense, it is expected that an increasing of the number of commissions leads, in turn, to a low response time. By contrast, in the case of ADHD, the presence of a high commissions do not lead to a lower RT levels, since this student group also present a deficit in this variable .In the ADHD Group model, it can be observed a relationship between central activation of the cortex and D prime variable offered by TOVA. This fact makes sense, because D prime variable is obtained from the number of omissions and commission errors. Both are produced by a low level of activation in central cortical and left prefrontal brain areas. In this way, ADHD Group showed a greater number of errors both by omission and commission. Nevertheless, subjects from control group do not make omission errors, at least not significantly . FinallyOur results have important implications in ADHD diagnosis. An Activation-Executive diagnosis model was tested to improve the assessment process in ADHD, also explained variables interactions. Moreover, this study lends support to prior studies stating that the prefrontal area is essential in ADHD assessment . This leAlthough the present study has produced interesting results, the implications derived from them should be taken cautiously as some theoretical and methodological limitations can be pointed out.Firstly, it would have been convenient to compare the results obtained by these tests with those provided by other empirically validated tests as SPECT or fMRI, in order to compare the levels of cortical activation through blood flow and their correlations with the values provided by the HEG. Secondly, in future research, it would be appropriate to consider not only the differences between controls and ADHD subjects, but also between the subtypes of the disorder . It would be desirable control variables and problems related to ADHD (such as anxiety or depression) which could affect the obtained results  and specCR, PG-C, MC, DA, and JG-P: Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work. Drafting the work or revising it critically for important intellectual content. Final approval of the version to be published. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.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.


Multiple studies have examined the risk of prenatal antidepressant exposure and risk for autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD), with inconsistent results. Precisely estimating such risk, if any, is of great importance in light of the need to balance such risk with the benefit of depression and anxiety treatment. We developed a method to integrate data from multiple New England health systems, matching offspring and maternal health data in electronic health records to characterize diagnoses and medication exposure. Children with ASD or ADHD were matched 1:3 with children without neurodevelopmental disorders. Association between maternal antidepressant exposure and ASD or ADHD liability was examined using logistic regression, adjusting for potential sociodemographic and psychiatric confounding variables. In new cohorts of 1245 ASD cases and 1701 ADHD cases, along with age-, sex- and socioeconomic status matched controls, neither disorder was significantly associated with prenatal antidepressant exposure in crude or adjusted models . Pre-pregnancy antidepressant exposure significantly increased risk for both disorders. These results suggest that prior reports of association between prenatal antidepressant exposure and neurodevelopmental disease are likely to represent a false-positive finding, which may arise in part through confounding by indication. They further demonstrate the potential to integrate data across electronic health records studies spanning multiple health systems to enable efficient pharmacovigilance investigation. The decision to expose a developing fetus to any medication is a difficult one for parents and clinicians; such a decision may be even more fraught in case of depression and related disorders, where the potential benefits of treatment are still misunderstood by the lay press6 Sociodemographic and clinical data were drawn from three independent EHRs: the Partners HealthCare system, which spans Massachusetts General Hospital (MGH), Brigham and Women's Hospital and Newton-Wellesley Hospital, as well as affiliated outpatient clinics; the Beth Israel Deaconess Medical Center (BIDMC); and the Boston Children's Hospital. Additional maternal and paternal data, as well as confirmation of matching accuracy between mothers and offspring were obtained from the Massachusetts Registry of Vital Records and Statistics.To maximize comparability with prior work, cohort definitions and analytic strategies followed those described in our prior report.11 a scalable computational framework for managing human health data.Specific data drawn from EHRs included sociodemographic data, billing codes, problem lists and medications. These data were managed with the i2b2 server software ,12 this allowed unique individuals to be matched without revealing names or other identifiers outside a given health system. For example, investigators in the Partners HealthCare system never received access to identifiers from the Children's Hospital system, and vice versa. Once the linking was complete, each mother and child was assigned a study-specific identifier and all identifiers were removed from the analysis file.Mothers were identified on the basis of matching child's date of birth and surname, insurance identifiers and hospital encounter date. As a further confirmation of match, and to address cases where children might have different last names or where they might have been removed from maternal custody, Massachusetts state birth certificates were queried for all identified children. Where the mother−child matches could not be confirmed, those pairs were omitted from analysis. Consistent with prior reports, we restricted the analysis to one child per mother, choosing the child with ASD or ADHD when a mother had both a case and control offspring. When two case or two control children were identified from one mother we randomly selected one child for inclusion in the study. Matching across, institutions used a one-way hash of maternal and child identifiers;The Institutional Review Boards of Partners HealthCare, BIDMC and Boston Children's Hospital approved all aspects of this study. Access to Massachusetts birth certificates was approved by the Massachusetts Department of Public Health Institutional Review Board.6 as other reports also suggested.3 Mother−child pairs included in our previous report were excluded.Eligible children for the ASD cohort were identified from the Partners and Boston Children's Hospital EHRs if they met the following criteria: age 2−19 years; at least one ICD-9 code of 299  between 1997 and 2010; delivered at MGH, Brigham and Women's Hospital, Newton-Wellesley Hospital or BIDMC. The ADHD cohort comprised children age 2−19 years with at least one ICD-9 code of 314.x and no ICD-9 code of 299 between 1997 and 2010, delivered at MGH, Brigham and Women's Hospital, Newton-Wellesley Hospital or BIDMC. Prior work demonstrated that these ICD-9 codes have a high sensitivity and specificity for ASD and ADHD versus generally healthy control children  in the Partners Healthcare system based on review of 50 ASD, ADHD and control records by an experienced child neuropsychologist,These children were then matched 1:3 with non-ASD control children delivered at MGH, Brigham and Women's Hospital, Newton-Wellesley Hospital or BIDMC with the same year of birth, birth hospital, sex, insurance type as a proxy for socioeconomic status, race/ethnicity and preterm versus full-term status. Children with any history of ASD, ADHD or intellectual disability  were excluded from the control population. If fewer than three matches could be identified for a case, year of birth was relaxed so that controls were born within 3 years of a given case. Sixty-five ASD cases (2.5%) had no matches among healthy control children and were excluded from primary analysis; their inclusion in exploratory analysis did not change results; 107 ASD cases matched fewer than 3 controls but were included in the analysis with 1 or 2 matched controls. In all, 138 ADHD cases (3.5%) had no matches among healthy control children and were excluded from primary analysis; their inclusion in exploratory analysis did not change results; 317 ADHD cases matched fewer than 3 controls but were included in the analysis with 1 or 2 matched controls.6 Primary analyses examined any exposure during pregnancy  through delivery) and before pregnancy (any time before LMP). Additional exposure windows were calculated based on estimated LMP based on gestational age—this included preconception (3 months before LMP), first trimester (0−90 days following LMP), second trimester (91−180 days following LMP) and third trimester (>180 days after LMP up to delivery). Exposures were identified using e-prescribing data in the EHR, both inpatient and outpatient, which record number of pills, frequency and refill number, allowing calculation of exposure period. Even where the clinician is not the primary prescriber , hospital policy requires reconciliation of medication lists at outpatient visits and hospital admission. The exposure period was truncated when discontinuation was recorded by the clinician. Exposure was defined formally as overlap between a medication prescription window and a given time period  By prior agreement between the Partners HealthCare system and Surescript, medication dispensation data is not allowed to be applied for research purposes and so is unavailable.We used the definitions of exposure applied in prior work.a priori as potential confounding variables and to ensure consistency with our prior report.6 In model 2, presence or absence of maternal major depressive disorder (ICD-9 code of 296.2x or 296.3x) was added to model 1, addressing confounding by indication. Adjusted odds ratios were calculated for both model 1 and 2.Three sets of models were examined for association between individual baseline variables and ASD or ADHD versus control status. In crude analyses, simple conditional logistic regression estimated the odds ratios for disease based on predictor. In model 1, conditional logistic regression models also included gender, race, year of birth, type of maternal insurance (public versus private) and median maternal income derived from census tract; these variables were selected In light of concern for confounding by maternal psychopathology raised in prior reports, proxies for maternal illness severity, including number of psychopharmacologic visits, number of psychotherapy visits, different antidepressant medications in the prior year, and specific psychiatric disorder or comorbidity were also examined in regression models.All analyses used R 3.0.1 .We next examined ASD and ADHD risk associated with antidepressant exposure before and during pregnancy . For botFinally, to elucidate the potential confounding effects of illness severity, In an effort to clarify the risk for neurodevelopmental disorders associated with prenatal antidepressant exposure, we applied a novel methodology to identify a new and independent cohort of ASD cases, another of ADHD cases, and matched cohorts of healthy control children. Data were drawn from three large Boston-based health systems as well as state birth certificate records. For both ASD and ADHD, we find no significant increase in risk associated with prenatal antidepressant exposure. Supporting the possibility that prior reports represent confounding by indication, we do identify risk associated with pre-pregnancy antidepressant exposure, and with prenatal maternal psychotherapy. In other words, the requirement for maternal antidepressant treatment, rather than the medication itself, may be associated with risk for neurodevelopmental disorders in offspring.7 equating antidepressant use with smoking, it is clear that, for some individuals, any risk would be considered to be too great, perhaps because of an unwillingness to recognize the potential neonatal morbidity as well as the known maternal morbidity and mortality associated with depression and related disorders. In our prior report, we found evidence of ADHD risk that persisted despite adjustment for maternal psychopathology. We suggested at that time that, given the risk of that finding representing a false positive, further study was warranted.6 The present results suggest the importance of such follow-up, indicating that ADHD risk is unlikely to be substantially increased by antidepressant exposure. We also note that, unlike ASD where a rodent model initially posited risk,6 the prior probability for association between ADHD and antidepressant exposure would be lower in the absence of a clear mechanism of risk.We recognize that even estimates of risk derived from large cohorts still represent only estimates. So, for example, we can only exclude an increase in odds of ASD >54% with 95% confidence interval. . In all cases, the risk of a prescription for a given medication must be weighed against the potential benefits. In aggregate, the present results should in general increase confidence that any treatment-associated risk is likely to be quite modest. Still, in light of recent media portrayals of depression during pregnancyWe could identify no reason for the discordance between this result and our prior one with regard to ADHD. Of note, the non-antidepressant risk factors for ASD and ADHD  are still noted in the present cohort, suggesting that in other respects it is similar to those in prior descriptions. Likewise, while antidepressant prescribing rates are somewhat lower , the variables associated with such prescriptions remain similar to those in our prior report. Incorporating terms for delivery site, or diagnosis site, did not meaningfully impact results.13 some misclassification of cases may also diminish power.The present study has multiple limitations. First, as already noted, we cannot exclude more modest risk, which would require substantially greater sample sizes. Broader application of the cross-institution approach we describe may represent; first, means of generating such cohorts. Second, there persists some risk for confounding, as any proxy of maternal psychopathology other than direct measurement is imperfect. On the other hand, in light of evidence of efficacy for depression treatment during pregnancy, a randomized trial might be challenged on ethical grounds. Third, there is some risk of misclassification among control subjects, if ASD or ADHD was diagnosed at another institution, which would tend to decrease power to detect true association. The relatively low prevalence of ASD should minimize the impact of such misclassification; in contrast to most prior studies, by spanning the two primary ASD referral centers  in this region, the probability of such misidentification is substantially diminished. Moreover, while we used a case definition previously validated by chart review that included scores from autism diagnostic observation scale,in silico pharmacovigilance studies. Without recruitment of a single individual, existing EHR data facilitated generation of a very large cohort of at-risk patients, matching mothers and offspring. On the other hand, such nonrandomized data presents a high risk for confounding, even when EHRs with more detailed health data are available for analysis. We also note that, while the data generation and analysis required ~4 weeks, interactions with multiple institutional review boards and development of protocols acceptable to these independent bodies required nearly 2 years. Thus, while it is not reflected directly in our results, we would argue that this study also highlights the need for more consistent review policies and protocols across institutions.In the context of prior reports, this study illustrates both the power and the challenge of Taken together, our results in this new cohort provide some clarification of the magnitude of risk reported in prior investigations. Paired with estimates of the benefit of treating depression and related disorders during and after pregnancy, they should aid patients and clinicians in decision-making about this class of psychotropic medications. More broadly, these results illustrate a strategy for linking across health systems to study rarer exposures or outcomes.


Regions with high electrical conductivities in subduction zones have attracted a great deal of attention. Determining the exact origin of these anomalies could provide critical information about the water storage and cycling processes during subduction. Antigorite is the most important hydrous mineral within deep subduction zones. The dehydration of antigorite is believed to cause high-conductivity anomalies. To date, the effects of dehydration on the electrical conductivity of antigorite remain poorly understood. Here, we report new measurements of the electrical conductivity of both natural and hot-pressed antigorite at pressures of 4 and 3 GPa, respectively, and at temperatures reaching 1073 K. We observed significantly enhanced conductivities when the antigorite was heated to temperatures beyond its thermodynamic stability field. Sharp increases in the electrical conductivity occurred at approximately 848 and 898 K following the decomposition of antigorite to forsterite, enstatite and aqueous fluids. High electrical conductivities reaching 1 S/m can be explained by the presence of an interconnected network of conductive aqueous fluids. Based on these results for the electrical conductivity of antigorite, we conclude that high-conductivity regions associated with subduction zones can be attributed to dehydration-induced fluids and the formation of interconnected networks of aqueous fluids during the dehydration of antigorite. Many electromagnetic studies15 have revealed anomalously high electrical conductivities (0.1–1 S/m) in backarc regions as well as in forearc regions in subduction settings 4, contains up to 15–16% (average 13 wt %) water as structural hydroxyl (OH−) groups. The presence of serpentine and other hydrous minerals affects the physical and mechanical properties of a peridotite mantle, such as the seismic velocity, density, and mechanical strength25.Serpentine minerals are the most abundant hydrous minerals in altered ultramafic rocks−4 S/m) at temperatures below 773 K28. These low values cannot effectively explain geophysically observed anomalies consisting of high electrical conductivity values. The dehydration of certain hydrous minerals, including talc, amphibole, lawsonite and chlorite, can significantly enhance the electrical conductivity anomaly32. However, although it constitutes the only stable hydrous phase down to a depth of 200 km, the electrical conductivity of dehydrated antigorite has not yet been investigated. Given that a large amount of water is released during this process, dehydration is expected to impose significant effects on the physical properties of antigorite. Therefore, in this paper, we report the electrical conductivity of antigorite both before and after dehydration and apply the results to an explanation of the high electrical conductivity anomalies observed during geophysical surveys in subduction settings.However, the electrical conductivities of antigorite and other hydrous minerals are relatively low  the released aqueous fluids have high conductivities and (ii) these fluids are interconnected. In our experiments, the electrical conductivity values measured during the final cooling stage subsequent to dehydration were significantly higher than those during the corresponding heating path, and the temperature dependence of conductivity was low during this stage. The sharp increase in the electrical conductivity at approximately 873–1073 K coincides with the dehydration of antigorite to the minerals forsterite and enstatite and aqueous fluids. Therefore, we conclude that the observed high-conductivity anomaly subsequent to dehydration was caused by the presence of interconnected regions of fluids.2+ and Fe3+ ions  rather than protons.Three distinct trends in the activation enthalpy were observed Table . Prior tThe activation enthalpy for conduction subsequent to dehydration was very small (5–40 kJ/mol), likely because the electrical conductivity is controlled by the motion of conductive ions in the aqueous fluids  at the grain boundary plays an important role in forming the connected network. The aqueous fluid forms a connected network affecting the bulk conductivity if the dihedral angle is less than 60°40. In this study, the dihedral angle with the median angle of 52° was observed, suggesting that the aqueous fluids are inter-connected. Given the nature composition of the current samples, the 52° wetting angle is somewhat consistent with the finding of 42° wetting angle on quartzite with saline H2O39.New phases, including forsterite and enstatite, were formed as a result of the dehydration, which was confirmed through back-scattered electron (BSE) images Fig. . FourierOnce the aqueous fluids developed an interconnected network, the electrical conductivity exhibited a weak temperature dependency.29, amphibole30, epidote41, and chlorite32, are less conductive. In contrast, the aqueous fluids released from lawsonite31 and phlogopite42 are highly conductive. These observations reveal that only conductive dehydration-induced aqueous fluids play an important role in modifying the bulk electrical conductivity. Antigorite and chlorite contain up to 13 wt% water, and therefore, interconnected networks of aqueous fluids will form much more easily than within other hydrous minerals. The results from both our study and a previous study31 indicate that antigorite and chlorite, which are typically characterized by high, temperature-independent electrical conductivities, can both form interconnected networks of aqueous fluids. However, such properties have not been observed for other hydrous minerals. Therefore, the observed field anomaly at great depth, which was likely sourced from an interconnected network of aqueous fluids, could not have originated from the dehydration of other hydrous minerals.Many differences are noted among the electrical conductivities of the dehydration-induced aqueous fluids derived from hydrous minerals. The aqueous fluids released from certain hydrous minerals, including talcet al.27 but are higher than those reported by ref.26. This inconsistency is mainly due to the compositions of the samples. Post-dehydration chlorite minerals show high electrical conductivities (close to 1 S/m) attributable to the inclusion of 14% interconnected, conductive, impure magnetite rather than aqueous fluid. The electrical conductivity of interconnected aqueous fluids derived from the dehydration of chlorite is less than 0.01 S/m. Thus, although chlorite is a major host of water within a subducting slab, it is impossible to build a causal relation between the dehydration-induced aqueous fluids and the anomalously high observed electrical conductivity values.Figure 15 and melt10. Regions with relatively strong conductive anomalies in the mantle wedge at depths of ~40–200 km are probably related to the dehydration to the subducting slab. Electrical conductivity models displaying features related to the release of fluids at depth must be consistent with geothermal and petrologic models. The potential for dehydration of a subducting slab is strongly dependent on the geothermal gradient and the stability of key hydrous phases such as antigorite and chlorite  can be attributed to the accumulation of aqueous fluids ite Fig. . Since c9 , the dehydration of antigorite occurs at depths below 150 km38. An MT profile revealed high-conductivity anomalies at depths of ~150–200 km  can be described by an Arrhenius-type relation:(Fig. The electrical conductivities of the natural and hot-pressed antigorite samples were measured at 4 GPa and 3 GPa, respectively, from 523 K to 1073 K. The conductivity values were calculated from the complex impedance spectra of the samples Fig. . The firSupplementary Information


Children with attention-deficit/hyperactivity disorder (ADHD) have some impairment in emotional relationship which can be due to problems in emotional processing. The present study investigated neural correlates of early stages of emotional face processing in this group compared with typically developing children using the Gamma Band Activity (GBA).A total of 19 children diagnosed with ADHD (Combined type) based on DSM-IV classification were compared with 19 typically developing children matched on age, gender, and IQ. The participants performed an emotional face recognition while their brain activities were recorded using an event-related oscillation procedure.The results indicated that ADHD children compared to normal group showed a significant reduction in the gamma band activity, which is thought to reflect early perceptual emotion discrimination for happy and angry emotions (P<0.05).The present study supports the notion that individuals with ADHD have some impairments in early stage of emotion processing which can cause their misinterpretation of emotional faces. A DHD is a common neurodevelopmental disorder characterized by inattentiveness and hyperactivity/impulsivity . IndividHowever, neural studies to investigate these impairments are rare. Results of recent studies revealed that gamma band activity (GBA) in electroencephalography (EEG) as well as Event-Related Potentials (ERP) play an important role in evaluation of higher cognitive processes such as attention, memory, language, and emotion . SeveralAlso results of studies indicate that an increase in gamma band activity at 40 Hz frequency is associated with the tuning of novelty processing by negative emotions . InteresTime course and specific topography of affective gamma band activity modulations during cognitive stimuli demonstrate discrimination between early and late processing stages. According to On the other hand, abnormalities in gamma band response and phase synchronization have been shown to be related to various neurological and psychiatric disorders such as schizophrenia, Alzheimer, and ADHD . Gross eBased on the mentioned studies, gamma band activity could reflect the characteristics of emotional integration or emotional utilization processes in individuals with ADHD. To our knowledge and according to review articles by Nineteen boys, aged between 7 and 11 years (Mean±SD age: 9.21(1.13) years) diagnosed with ADHD were compared with 19 typically developing ones (9.73±1.04 years) matched with age, sex, and years of education. The control group was recruited from elementary schools in Tehran. Children with ADHD were recruited from patients referred to a child and adolescent psychiatric clinic. The individuals with ADHD were diagnosed according to DSM-IV  . The patConners’ Parent Rating Scale-Revised  was administered to the participants to confirm the diagnosis of ADHD and determine severity of their symptoms. If the T-scores of CPRS-R subscales were above 65, the participant was excluded from the normal group. The two groups were right-handed, had corrected to normal visual acuity. Intelligence Quotient (IQ) of all participants were evaluated according to the WISCR IQ test  .A compilation of 6 Caucasian faces  in JPG format expressing happy, angry, sad, and neutral expressions were collected from Cohn Kanade AU-coded Facial Expressions Database . LuminanThe main task included 1 practice and 5 experimental blocks. Each experimental block comprised with 48 trials; 4 emotions  of each faces  that repeated two times. Thus, there were 60 repeats of each expression in a random way. We defined four buttons for each facial expressions procedure  on a joy stick.All participants were invited to the laboratory of EEG recording. The parents completed a consent form before starting the examination. During the EEG session, participants were seated in a comfortable chair in a dimly lit room 60 cm from a 17-inch LG computer screen. The participants were asked to look at the center of the screen during the recordings, if possible without making eye movements, and to blink only during the intervals. To ensure that they attend to stimuli, they were monitored by camera in another control room during task performance. All children were instructed to press on the button for each expression when they recognized the target stimuli during each trial (Stimuli presentation until the end of fixation).Continuous EEG signals were recorded by 64 Ag/ AgCl electrodes mounted in an electrode cap  according to the international 10–20 standard and additional intermediate positions in neuropsychology laboratory of Payam-e Noor University. ASA 4.7.1 software was used for data acquisition. Electrode impedances were maintained below 10 kΩ. The sampling rate was 512 Hz. A 50-Hz notch filter of the recording system, eliminated the line noise during signal acquisition. Event-related oscillations data were analyzed offline using MATLAB R2013a software.Raw data were filtered with a band-pass filter of 0.1 to 80 Hz and referenced to the mastoids average. The eye movement artifacts were canceled using the independent component analysis. In addition, the remaining artifacts with deflection amplitudes of ±100 μV from the baseline were eliminated (primarily through automatic artifact reduction). Artifact-free EEG recordings were then segmented into epochs ranging from 200 ms prestimulus to 800 ms poststimulus. Each channel baseline epoch was corrected by prestimulus average voltage subtraction. Several studies reported that the lower frequencies of Gamma band oscillation (about 40 Hz), modulated with emotional stimuli indicated attentional processing and discrimination of the emotional stimuli .To investigate facial emotion processing based on gamma oscillations, we constructed a new time series by concatenating a specific time window from all clean epochs. Then we estimated the signal power spectrum in frequency band of 35–45 HZ for three different time windows based on literature  and follFrequency-band measures were statistically analyzed using repeated-measure analysis of variance (ANOVA) with the following core factors: facial expression , site (area) , side   as the within subjects factors, and groups (patients and controls) as the between subject factor. Greenhouse Geisser correction was used for the degrees of freedom. In the next step, paired t-test were used to break down between main effects such as site and independent sample t test were used to break down between-subject and interaction effects such as facial expression×group. Throughout the experiment, P<0.05 were considered significant.1, 36=12.36, P=0.001; partial η2=0.155), and a significant main effect of site . Also the results showed a significant interaction effect of site×group , and a significant interaction effect of facial expression×group . In the second (250–500 ms) and third (500–750 ms) time windows, repeated-measure ANOVA revealed only main effect of group which showed smaller gamma band activity in ADHD group compared to healthy control one (P<0.05). We observed no significant main effect or interaction effect in terms of other factors of side, site, face expression, and group (P>0.05) in the second and third time windows. Therefore, our results focused on first time window that indicated early facial emotion processing.We carried out repeated-measure ANOVA  for three time windows separately. In the first time window (0–250 ms), this analyses revealed a significant main effect of group  ,  and anger ,  in ADHD group compared to typically developing children in left and right occipital, respectively  compared to normal group (2.9±0.2) in the first time window. Follow-up paired t test showed greater gamma band activity in occipital site compared to frontal =1.97), central =2.47), and parietal =2.28) areas in ADHD group . Follow-P=0.001) . Also foectively .This study aimed to compare the gamma band oscillations among patients with ADHD and healthy controls during facial emotion recognition. We expected to observe emotion recognition deficits in ADHD children. The results showed a significant (P<0.05) increased gamma band activity within occipital regions during the first time window (0–250 ms) compared to other sites. Also, we observed a significant (P<0.05) reduction in gamma band activity in ADHD children compared to normal group in response to facial expressions within occipital lobe. This study supported our hypothesis that individuals with ADHD were different from typically developing children during facial expression recognition, especially in early stage of facial emotion processing.Consistent with our findings, several studies have shown a similar gamma-band increase in response to emotional pictures with occipital distribution . BalconiAlso, Also, the current results showed a significant diminished evoked gamma band responses only to anger and happiness emotions compared to neutral ones during early stage of processing (time window 0–250 ms) within occipital regions in ADHD group compared to healthy control ones. However, the current findings did not reveal any difference between groups in response to sad faces. Behavioral studies showed that ADHD children have deficits in emotional face recognition, especially negative  ones compared to healthy children . HoweverThus, the current results should be discussed with respect to early visual processing. Unfortunately, the relevant literature is scarce. Inconsistent with current results, Few studies report reduced P100 amplitude in occipital regions in ADHD group , probablThese findings show the important role of occipital gamma oscillations in facial emotion perception. The present study shows that children with ADHD have abnormality in brain function for early stage of emotional face recognition compared to normal children that can be caused by deficits in selective and sustained attention and early visual processing. These findings might provide preliminary evidence for future planning of interventional approaches for children with ADHD.


Objective: Children with attention-deficit/hyperactivity disorder (ADHD) frequently manifest early morning functional (EMF) impairments before school. We conducted a quantitative research survey to assess the impact of these EMF impairments on the family unit .Study Design: We developed an online survey questionnaire to collect data from 300 primary caregivers of children with ADHD and 50 primary caregivers of children who did not have ADHD.Results: Although the ADHD children we surveyed were currently treated with stable doses of stimulants as their primary ADHD medication for at least 3 months, their parents reported high levels of EMF impairments in the child, which had a substantial negative effect on the emotional well-being of parents, on parents' functioning during the early morning routine, and on the level of conflict with siblings. The impact of EMF impairments on family functioning was mediated by the severity of the index child's impairments.Conclusions: EMF impairments exert a pervasive and significantly negative emotional and functional burden on not only the primary caregiver but also on the spouse/partner and siblings. This work suggests that adequate ADHD symptom control during the early morning period may be an unmet need for school-age children with ADHD being treated with stimulants. More work is needed to confirm this finding and determine the degree to which symptom control at other times of day is also an unmet need. Completing these behaviors requires time management, working memory, and self-regulation skills as well as social skills and cooperation that are frequently impaired by ADHD symptoms , which has documented reliability, internal homogeneity, and concurrent validity  to assess the nature and severity of EMF impairments associated with stimulant-treated ADHD and (2) to assess the prevalence, frequency, and impact of EMF impairments on the caregiver, spouse/partner, and siblings of stimulant-treated ADHD children and adolescents. We hypothesized that compared with families not having children with ADHD, those having children with ADHD, who expressed at least mild ADHD symptoms in the morning, would, despite being treated with stimulants, show higher levels of EMF impairments and these impairments would have deleterious effects on the family. We also tested the hypothesis that the impact on the family would be mediated by the severity of the EMF impairments.N = 1,269,000). The Lightspeed GMI panel is constructed so that its consented panel members are generally representative of the U.S. population in terms of age, gender, income, ethnicity, geography, employment, and educational levels in the household . Once that subset was exhausted, survey invitations were sent to the subset of GMI US panelists with children aged 6–17 years . Lightspeed GMI also provided the survey programming and online hosting for the project.Survey participants were 300 caregivers of children diagnosed with ADHD and 50 caregivers of children not diagnosed with ADHD. Potential survey participants were drawn from the Lightspeed GMI US Panel  being self-identified as the primary caregiver of an ADHD child aged 6 to 17 years; (2) the child with ADHD was taking stimulant medication as his or her primary ADHD medication; (3) he or she was taking a stable dose of their primary stimulant medication for 3 months or more; and (4) the caregiver rated that the severity of the child's ADHD symptoms throughout the entire day and during the early morning routine, as two or more on a scale of 1 (no symptoms) to 10 (significant symptoms). We selected ADHD children showing at least mild evidence of ADHD in the morning because our goal was to determine if the expression of ADHD symptoms in the morning was associated with impairments in stimulant-treated children.The study was conducted in April of 2016. Caregiver respondents were blinded as to the research sponsor. The survey was conducted in accordance with and adherence to the Marketing Research Association Code of Standards. Potential respondents were invited to participate through an email message. The survey invitation did not specifically refer to ADHD or/and EMF; so it did not bias participation toward families struggling with this domain of problems. The exact wording of the invitation was, “Today we are conducting a marketing research study concerning healthcare for your family. Your opinions are important to us. Please continue with the survey.” The survey, which was administered as an online questionnaire, required about 20 minutes for completion. It did not contain any option for any of the 18 standard types of PHI data to be collected and thus did not require IRB approval. Surveys were completed voluntarily and anonymously.For the ADHD families, if there was more than one child with ADHD in the household, respondents were instructed to select the ADHD child who had the most severe ADHD symptoms. For the non-ADHD sample, respondents were instructed to select the child whose birthday was next. We refer to these selected children as the “index” children. The questions asked to caregivers of ADHD and non-ADHD youth were slightly different. For example, regarding concerns for safety, the former were asked the following question: “How often do your child's inadequately controlled ADHD symptoms during the early morning routine (before school) cause you concern for their safety and well-being in the home?” and the latter were asked the following question: “How often does your child's behavior during the early morning routine (before school) cause you concern for their safety and well-being in the home?”We compared ADHD and non-ADHD families using logistic regression with ADHD status as the outcome and the demographic and family impact measures as independent variables. We first tested for demographic differences and included significant demographic predictors as covariates in all models. Because the family impact measures are conceptual outcomes, a more standard approach would have been to use these outcomes as dependent variables. However, doing so creates analytic problems due to the extreme nonnormality of the data and the strong assumptions we would need to analyze ordinal data. Logistic regression requires minimal assumptions and provides a valid method of establishing the statistical significance of the association.To test the hypothesis that the impact on the families of ADHD youth was mediated by the severity of the EMF impairments, we included the severity of EMF impairments in the index child as a covariate in models testing for the association of ADHD family status and family impact. Our measure of severity was the answer to the following question: “On a scale from 1 to 10, where 1 means “Mildly Impaired” and 10 means “Very Severely Impaired”, how severe is that child's functional impairment (or difficulty to function) during the early morning routine?.”p = 0.6), or relationship of the caregiver to the child . The index children in the ADHD group were more likely to be male . Thus, all the following analyses are statistically corrected for sex of the index child. Forty-six percent of the ADHD youth were taking an amphetamine formulation and 54% were taking a methylphenidate formulation. Twenty-seven percent had been taking their medication for 3 to 6 months; 21% for 6 months to a year; 21% for one to 2 years; and 31% for more than 2 years.The families with and without ADHD children did not differ in age of the index child . This shows that ADHD children who express some ADHD symptoms in the morning are at risk for EMF impairments. Consistent with this, the median number of school days each week that the child had EMF impairments was greater for youth with ADHD .Overall, 77% of caregivers rated the severity of EMF impairment in their child with ADHD as moderate-to-severe . On the same severity scale from 1 to 10, where 1 means “Mildly Impaired” and 10 means “Very Severely Impaired,” ADHD children were rated as having higher mean levels of EMF impairment compared with controls . We found similar results when assessing the effects of the child's EMF impairments on the spouses/partners of the caregivers. During the early morning period, the caregivers of youth with ADHD reported significantly more conflict with their spouses/partners, more disruption of their spouse's/partner's early morning routine, and that the child's EMF impairments kept the spouses/partners from being on time .Compared with caregivers of youth without ADHD, the caregivers of youth having ADHD were more likely to report that EMF impairments led to more stress from sibling conflict, greater disruption of the child's breakfast, greater disruption of the caregiver's morning routine, and a greater likelihood of being late for their own morning activities .Parents were asked how often EMF impairments caused them concern for the index child's safety and well-being inside and outside the home. The same question was also asked if such ADHD-related EMF of the index child also affected the safety and well-being of the siblings. As p < 0.001), disruptions causing siblings to be late (p < 0.001), disruption of the family breakfast (p < 0.001), and conflict with siblings (p < 0.05). Fifty-two percent of parents with ADHD children reported that sibling complaints about the index child's EMF impairments or disruption of the sibling's morning routine occurred often or very often compared with 18% of control families (p < 0.001).We also assessed the impact of the index child's EMF on the morning routines of their siblings. Compared with the parents not having an ADHD child, those with an ADHD child were more likely to report significant or very significant disruptions of the siblings' morning routines . The exception was the model testing for the impact on parental stress due to conflicts among siblings in the early morning (p = 0.04). To determine if the severity of EMF impairments associated with ADHD was predictive of impact on the family, we conducted regression analyses limited to the ADHD families that predicted the family impact variables from the severity of the ADHD child's EMF impairments. We found significant correlations between the EMF impairment severity of the ADHD child and all the family impact variables .We added the severity of the EMF impairments in the index child as a covariate in all the models testing for the association of ADHD family status and family impact. For all these models except one, the association between ADHD family status and impact on the family lost significance when EMF impairment was a covariate . Thus, the mediation analyses confirm that parents were responding as requested, that is, they were reporting problems in the family caused by the EMF impairments of the index child. These findings show that the adverse family functioning results were not simply due to the presence of ADHD in the child, but were, in fact, associated with the index child's EMF impairments. As further support for this idea, within the ADHD families, we found significant correlations between the EMF impairment severity of the ADHD child and all the family impact variables.Consistent with our hypothesis, mediation analyses showed that the differences in family functioning between the families with and without ADHD were mediated (or could be accounted for) by the severity of EMF impairments in the index child. This finding, to some extent, reflects that nature of the questions about family impact, which asked about the effect of EMF impairments on family functioning . Further evidence for lack of bias regarding EMF impairment is the fact that our 77% rate of caregiver-reported moderate-to-severe EMF impairment severity is similar to the rate of 76% reported by Sallee .Within the constraints of these limitations, our findings show that the primary caregivers of stimulant-treated children and adolescents with ADHD report that inadequately controlled early morning ADHD symptoms and EMF impairments persist despite treatment. EMF impairments exert a pervasive and significantly negative emotional and functional burden, not only on the primary caregiver but also on the spouse/partner and siblings. This work, especially when considered in the context of similar findings by Sallee , suggestWhat are the clinical implications of the fact that stimulant-treated children show evidence of EMF impairments that impact their family? One approach is seen in the data presented. About half the parents indicated that they had woken up their child with ADHD earlier than normal to administer ADHD medication and then let them go back to sleep, so that the medication would provide control in the early morning. Most who used this strategy said it had a very positive or somewhat positive effect. Thus, this is an option clinicians could communicate to parents. The early morning routine also provides a well-defined target for behavioral family therapy or, for adolescents, cognitive behavior therapy. Using organizational charts and reinforcing clearly defined early morning behaviors could alleviate many of these problems. This suggests that psychosocial treatment programs should develop modules aimed at EMF impairments.