Coprococcus and Subdoligranulum), Actinobacteria (genus Collinsella), Bacteroidetes (genus Bacteroides), and Bacteroidota (genus Alistipes) may be possible gut microbiota indicators of ADHD. This type of modeling approach taking microbial genome-environment interactions into account helps us understand the gastrointestinal mechanisms behind ADHD, and establish a path to improve the quality of life of ADHD patients.Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed with hyperactivity, impulsivity, and a lack of attention inconsistent with the patient’s development level. The fact that people with ADHD frequently experience gastrointestinal (GI) dysfunction highlights the possibility that the gut microbiome may play a role in this condition. The proposed research aims to determine a biomarker for ADHD by reconstructing a model of the gut-microbial community. Genome-scale metabolic models (GEM) considering the relationship between gene-protein-reaction associations are used to simulate metabolic activities in organisms of gut. The production rates of dopamine and serotonin precursors and the key short chain fatty acids which affect the health status are determined under three diets  and compared with those of healthy people. Elasticities are calculated to understand the sensitivity of exchange fluxes to changes in diet and bacterial abundance at the species level. The presence of Bacillota (genus  Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed with hyperactivity, impulsivity, and lack of attention inconsistent with the patient’s development level . Today, There is a bidirectional relationship between the brain and the gut, called the gut-brain axis . This axFaecalibacterium prausnitzii, are the major butyrate producers in the human intestine [Negativicutes and Clostridium bacteria [Carbohydrate metabolism in the human gut results in short-chain fatty acid and gas production . Acetatentestine ,18. Propbacteria . SCFAs hPrevotella and Alistipes species as well as Akkermansia muciniphila, a novel mucin-degrading bacterium, have been reported as the most prevalent in ASD children [Bifidobacterium) and metabolites  were also reported in autistic children [Prevotella species and some Coprococcus species. Thus, children with ADHD had lower levels of the genus Prevotella than did controls [Bacteroides spp. and Clostridiae spp. [Faecalibacterium and Ruminococcus and Bifidobacterium genera were positively correlated with total SCFA [The composition of the gut microbiome depends mostly on dietary habits. Drug use, nicotine and alcohol consumption, and stress also alter the gut microbiome . These cchildren ,25,26. Dchildren . Due to children . Short-ccontrols . Some baiae spp. . Faecalital SCFA . High letal SCFA ,29. Proptal SCFA ,30. At pThe lack of deep information on the brain-gut axis for ADHD individuals leads us to investigate gastrointestinal reasons (gut metabolic products) by reconstructing a model of the gut-microbial community with the ultimate aim of determining a biomarker for ADHD. Community growth and elasticities are predicted to understand the sensitivity of exchange fluxes to changes in diet  and bacterial abundance at the species level. This type of modeling approach taking microbial genome–environment interactions into account may help us understand the gastrointestinal mechanisms behind ADHD and offer prebiotic/probiotic treatment options, establishing a path to improve the quality of life of ADHD patients.The work of Aarts et al.  provide −4. The most feasible growth rate for the gut community model is obtained by a tradeoff value of 0.7. During community growth, the lowest possible metabolite intake is aimed at due to the possible lack of resources. IBM CPLEX [Using the bacterial abundance data retrieved from Aarts et al. , we collBM CPLEX  is used iv (in millimoles per gram dry weight per hour) using the stoichiometric matrix S., which has metabolites in its rows and reactions in its columns. Typically, the biomass reaction bmv is normalized so that it will yield 1 g of biomass, which corresponds to a unit 1/h representing the organism’s growth rate. The upper and lower boundaries provide thermodynamic restrictions to the fluxes or limit interactions with the environment, i.e., exchange fluxes. Each organism is typically embedded in an external compartment (the gut lumen) that represents the community environment, in order to depict a community model that contains several species with different abundances (in grams dry weight) for each organism. With the use of MICOM, a flux balance analysis is carried out by adding exchanges for the environment compartment and exchanges between a specific organism and the environment. The input for MICOM is abundances, which it uses to forecast growth rates and fluxes. The gut metabolic community model includes 58 microbial species and 968 exchange metabolites with their exchange reactions showing the overall metabolic capacity of the microbial community.The flux balance analysis is performed by MICOM. By assuming that all fluxes in the biological system are in a steady state and maximizing a biomass reaction that is specific to the organism, flux balance analysis can get approximations of the fluxes for a certain organism. This can be expressed as a constrained linear programming problem for the fluxes, The reconstructed gut community model is introduced to different diets to observe how the bacterial community in the gut will react to different external factors. Western, Atkins’, and Vegan diets are selected as the community growth media. Western Diet represents a typical American diet and is retrieved from the VMH  database  as well Two diversity measures, Alpha and Beta diversities, are here used to characterize communities quantitatively by using the number of species and the number of individuals on any level, sample or community. The first compartment of alpha diversity is the indices of species richness, also known as variety indices. The species richness index is defined as the total number of species, S, in the sample. Among the several indices used to determine diversity in the sample, Chao’s index, Shannon’s index, and Simpson’s index are employed here. The other compartment is called the equability index and shows the relative abundance of different species of a community in terms of their evenness of distribution. The evenness of a community depends on how close the number of individuals of different species is in the community. In the case of dominating species, the evenness index decreases. In order to compare alpha diversity results between groups, two statistical tests are used: F-test, for variance comparison, and the Mann-Whitney U test, to understand how similar the groups are.ijC value. iS and jS given in Equation (1) represent the population of each sample.Beta Diversity is estimated by the Bray-Curtis Dissimilarity Index (Equation (1)), which studies the mutual microbiota of two samples and the population in each sample . The disUnweighted UniFrac Metric measures diversity in a qualitative manner and is based on the phylogenetic tree . MultiplDiversity calculations are done by using the microbiome package of R with raw abundance data retrieved from Aarts et al.  at the sv represents the exchange flux and p represents the parameter.The elasticity coefficient is used to analyze how sensitive exchange fluxes are to changes in exchange flux bounds or changes in bacterial abundance in the selected level. The elasticity coefficient formula (Equation (2)), implemented in the MICOM library, is used where Flux direction is ignored with the absolute value but is defined separately to avoid information loss. The differentiation step size was selected as 0.1 on the log scale, resulting in a 10.5% increase in bacterial abundance in the native scale.Lactobacillus rhamnosus is selected as the probiotic provided to see how much it affects the selected metabolite fluxes and previously mentioned possible biomarkers. The amount of L. rhamnosus is determined with the least squares method, where the relative abundances of each ADHD sample’s gut microbiome are compared to a randomly selected Control sample’s abundance values. The abundance value of Lactobacillus rhamnosus to be added to the community model is calculated as the abundance, which gives the lowest difference between the Control sample and ADHD samples so that the gut microbiome of an ADHD subject becomes closer to the healthy one. In this study, the additional L. rhamnosus abundance is 0.2. After the probiotic addition, the same pipeline is followed for community model building and community growth in different media.Bifidobacterium and Bacteroides have higher abundances in ADHD gut. Since the data of Aarts et al. [Bifidobacterium in ADHD gut is expected to be higher. At the species level, Bifidobacterium adolescentis, Bifidobacterium dentium and Bifidobacterium pseudocatenulatum have higher abundances in ADHD gut , there is no significant difference between the diversity values  of different genders in the same status. Mann-Whitney U test does not find any difference between male and female groups’ species richness c,f,i. SiEvenness is a measure of equality. The value of evenness of a sample refers to the closeness of the number of individuals belonging to a species. Simpson’s Evenness Index is only used for this evaluation j–l. The mwup = 0.49). The Unweighted UniFrac Metric shows how different families can adapt to specific living conditions and how their adaptation differs. In To understand the differences between the two samples, Beta Diversity is employed with three metrics: Bray-Curtis Dissimilarity Index, Unweighted UniFrac Metric, and Weighted UniFrac Metric. Bray-Curtis’s dissimilarity index studies the mutual microbiota of two samples and the population in each sample . The dowAlthough the diversity metrics use different assumptions and formulas for calculations and are affected by changes in the sample , there are no significant differences in the results of the indices used to calculate alpha and beta diversities.Three different diets are applied to the reconstructed gut community model: Western, Atkins’, and Vegan. Western Diet is directly taken from the literature and represents the average American diet. The other two diets are curated from virtual metabolic human data . Atkins’Bacteroides, Bifidobacterium, Coprococcus, Dialister, Collinsella, Dorea, Prevotella, and Ruminococcus, and the species Alistipes putredinis and Subdoligranulum variabile.SCFA production can reduce intestinal inflammation and may help to improve metabolic health . The phyAlistipes putredinis is one of the species coming to the front as important SCFA producers in this study. In addition to more significant acetate production in ADHD subjects, small amounts of isobutyrate, isovalerate, and hydrogen are also produced by Alistipes putredinis. For each sample where acetate is produced (ADHD and Control), the Western Diet gives the lowest acetate export flux while the Vegan Diet gives the highest. The propionate export fluxes are also significant in both ADHD and Control subjects.We compared the flux rates of each metabolite produced and/or consumed in the gut of ADHD subjects with those in healthy humans by flux balance analysis. The following section summarizes our results with significant flux values obtained by the gut community model. Bacteroides coprocola in the ADHD samples is approximately 10 times higher than that of the Control samples. Formate import fluxes are lower while export fluxes are higher in B. uniformis for ADHD samples. In B. vulgatus, ADHD samples export higher amounts of formate. The amount of acetate by B. coprocola exported by the ADHD sample is almost 30 times higher than the neurotypical Control sample. In B. uniformis, ADHD samples mostly import acetate while Control samples export it.Formate production of Bifidobacterium arises some questions as there are contradictory findings in the literature. Acetate has a considerable exchange rate in Bifidobacterium dentium, which is found only in ADHD subjects in the present gut community model. There are lower acetate export fluxes for the ADHD samples in Bifidobacterium pseudocatelunatum. Acetate fluxes mostly show import behavior and all samples that produce lower amounts of acetate are healthy. Acetate import fluxes for ADHD samples are high. The amount of formate produced by Bifidobacterium pseudocatelunatum has one of the highest values in this study, and this bacterium does not consume formate, while export is the only action (no formate import). Our study shows high butyrate, acetate, and formate exchange fluxes (both import and export) by Coprococcus catus, but the flux values of propionate are low and thus not significant in both ADHD and Control subjects. Formate export is mostly done by ADHD samples, and they have a lower relative abundance compared to Control samples. There is no effect of diet (no correlation with diet) on flux values, but the Western Diet shows significantly lower flux values for both ADHD and control cases. Acetate exchange rates, both import and export fluxes, are higher, between pairs, for ADHD samples. C. catus consumes acetate and butyryl–coa to produce acetyl–coa and this process limits butyrate production in the gut since butyryl–coa is also used to synthesize butyrate. C. catus is expected to have a negative effect on butyrate production, since it competes against butyrate-producing organisms for the necessary intermediates. In C. catus, in general, ADHD samples export more butyrate with lower abundance values. In the case of high abundance, export fluxes are almost equal to controls. Butyrate exchange done by another Coprococcus species, C. comes, also shows significant flux values. Acetate import and export fluxes by Coprococcus comes are also significant. The Western Diet results in the highest import and lowest export fluxes for acetate exchange out of the three different diets.The genus Collinsella aerofacians was found in 18 samples out of 20. Acetate, formate, and H2 exchange fluxes are significant compared to the fluxes of other metabolites; however, none of these fluxes outshine each other or are secerned. The SCFAs produced by C. aerofacians show both import and export behavior. H2 is exported only (no import by this bacterium) but export fluxes are mostly low with a few exceptions.In this study, Dialister, the asaccharolytic bacterium Dialister invisus is found in more than half of the ADHD subjects. The only significant fluxes come from acetate exchange, and the acetate export rate is higher for the ADHD sample. This bacterium is usually found in oral flora and in endodontic and periodontal infections [D. invisus in a high number of samples, though no significant metabolite fluxes were observed in this bacterium. In short, ADHD subjects might encounter this bacterium because of another underlying condition in a different part of the body.Under the genus fections ; this miDorea formigeneras. Control samples are prone to import formate. Control samples that export formate have lower reaction rates. Acetate fluxes are higher than formate fluxes in Dorea formigeneras. Acetate is only exported by D. formigeneras. Control samples produce acetate at a higher rate. D. formigeneras is more abundant in Control samples. D. longicatena imports L-tryptophan and exports H2 but fluxes are below 1 mmol/gDCW h. H2 export is an expected outcome of D. longicatena based on the literature, but tryptophan consumption or indole production is not mentioned previously for the gut of ADHD subjects in the literature. Formate and acetate have high exchange fluxes in D. longicatena. Formate export is not observed, and it is only imported by this species. Samples from subjects in their twenties mostly show higher formate import fluxes for Control samples in pairs. Pairs of matching siblings below the age of 20 do not show a correlation with flux. High amounts of acetate are exported by the bacterium D. longicatena as expected, but the acetate flux does not show any relation with age, health status, or diet.Formate and acetate exchange fluxes are also significant in Prevotella copri, Prevotella ruminicola, and Prevotella buccae are expressed in gut samples. Prevotella copri is expressed in five samples, each belonging to a different pair, with only one of them being an ADHD subject. Acetate, formate, and propionate are exported by this species Prevotella copri, but propionate fluxes are not significant. Acetate export by Prevotella copri is higher than formate export, as reported in the literature. Acetate export flux is lowest under the Western Diet and highest under the Vegan Diet. Additionally, for the genus Prevotella, in ADHD subjects, propionate, formate and acetate are also produced by another species, P. ruminicola. Propionate export rates are significant. Both subjects, ADHD and Control, show the highest propionate exchange rate when under the Western Diet. ADHD subjects have lower export fluxes than the Control sample, but subjects are not at the same age nor have the same gender. Acetate exchange is bidirectional for P. ruminicola and exchange fluxes are mostly significant. ADHD samples produce a higher amount of acetate than the Control samples, but these two types of samples belong to different ages and genders.In this study, the species Ruminococcus, Ruminococcus bromii is expressed in 16 samples out of 20, and six of these samples are amongst those diagnosed with ADHD. Formate and acetate exchanges were observed. Formate exchange done by R. bromii is bidirectional and exchange fluxes are low. Acetate is not produced, but only consumed by R. bromii, and the fluxes are significantly high. In terms of gender, female ADHD samples show a higher acetate import rate compared to their Control pairs with one exception. There are not enough pairs to compare male samples. In this study, 11 samples (three pairs), six of them diagnosed with ADHD, show the species R. albus. Formate and acetate are exported with significant fluxes by R. albus. ADHD samples have a higher abundance of this bacterium, as well as higher export fluxes compared to their Control pairs. The difference between ADHD and Control samples is higher for male pairs. Additionally, export fluxes decrease under the influence of the Western Diet. The Atkins’ Diet provides the highest flux values, but these rates are very close to those obtained under the Vegan Diet. There is no visible correlation between flux rates and age. Acetate fluxes are also higher for ADHD samples in pairs. However, there is no correlation between acetate fluxes and dietary habits, age, or gender.Under the genus Subdoligranulum variabile. ADHD samples import more butyrate than the Control sample, which is the opposite for some pairs. In terms of acetate export, ADHD subjects show the lowest flux under the Western Diet effect. S. variabile also imports acetate for ADHD cases and has the highest import flux under Western diet influence.Our results showed a significant butyrate import and acetate export in ADHD subjects with Bacteroides species, are common in the gut population, but some others show sample-specific abundance. The species that appeared in very few samples will be discussed in There are thousands of different species that live in the human gastrointestinal tract. Some species, e.g., those belonging to Recent research revealed that gut bacteria can produce several neuroactive substances, such as gamma-aminobutyric acid (GABA), serotonin, and catecholamines such as dopamine and norepinephrine, or the precursors of these key molecules, which have an impact on ADHD. Theories about the neuroscience of ADHD place much emphasis on abnormalities in the noradrenergic, dopaminergic, serotoninergic, and cholinergic pathways . In the Alistipes putredinis, Bacteroides vulgatus, Bifidobacterium angulatum, Coprococcus catus, Subdoligranulum variabile. Glutamate fluxes also show a correlation with gender or dietary habits. Alistipes putredinis does not produce glutamate; however, it consumes this metabolite at a significant rate. The highest consumption rates are observed under the Western Diet influence with one exception. The flux differences in male pairs are more drastic compared to those in female pairs. Moreover, Bacillus licheniformis, Bacteroides vulgatus, Blautia hydrogenotrophica, Coprococcus catus, and Prevotella ruminicola show higher glutamate import fluxes under the Western Diet influence, whereas Finegoldia magna, Mobiluncus curtisii, Parabacteroides ditasonis, Parabacteroides goldsteinii, and Prevotella copri give the lowest glutamate exchange rates (Western Diet). Except for P. copri, all the species import glutamate, and none of them have insignificant flux rates. Only F. magna has a low glutamate import rate compared to other species. Vegan and Atkins’ diets give very close exchange fluxes for F. magna species. Glutamate exchange held by P. ruminicola is bidirectional, and, while the Western Diet provides the highest import rate, Atkins’ diet gives the highest export rate. In the case of Holdemania filiformis, ADHD samples import more glutamate than the Control sample. C. eutactus only imports glutamate, and female pairs show high differences between ADHD and Control samples compared to male pairs. For Prevotella copri, ADHD and neurotypical Control samples show almost equal glutamate export fluxes. However, as the number of samples including these gut bacteria is very limited, the results should be considered with care, and not taken as conclusive for this kind of rarely-present bacteria.Bacteria and neural tissue follow the same biosynthetic pathway to produce GABA. Glutamate is hydroxylated to GABA in the neuron cytoplasm . GlutamaA. putredinis compared to the Control, and no L-tryptophan export exists. This might result from a tryptophan hydrolysis reaction where tryptophan is hydrolyzed to indole, pyruvate, and ammonium with tryptophanase catalysis. There is no correlation between L-tryptophan import and the three diet types. In terms of gender, the difference between Control and ADHD sample fluxes of tryptophan is more drastic for male pairs than for female pairs. This might partially explain why males show more clear ADHD symptoms than females. ADHD gut also imports L-tryptophan by Bacteroides coprocola at a significantly higher rate. The amount of L-tryptophan imported by ADHD samples by another Bacteroides species, B. uniformis, is more significant compared to Control samples. D. longicatena imports L-tryptophan, but fluxes are below 1 mmol/gDCW h. For D. longicatena, tryptophan consumption or indole production is not mentioned previously for the gut of ADHD subjects in the literature. Only one subject with ADHD  has Bacillus licheniformis (0.12% abundance). There is L-tryptophan import under the Western Diet and export under Atkins’ and Vegan diets by this species, but these fluxes are not significant in amount.ADHD samples show higher L-tryptophan (serotonin precursor) import fluxes by A. putredinis, but flux values are insignificant. Tyrosine exchange only occurs in the import direction but again flux values are very low. Although flux rates are insignificant, phenylalanine and tyrosine import are also observed for Bifidobacterium adolescentis, Bifidobacterium pseudocatelunatum and Coprococcus eutactus, and that is important for model validation. Tyrosine and phenylalanine are also consumed by Subdoligranulum variabile. Both have insignificant flux values (less than 1 mmol/gDCW h), but their consumption information is important. While tyrosine is only imported by this bacterium, phenylalanine shows bidirectional fluxes. S. variabile consumes and produces phenylalanine under the influence of the Atkins’ and Vegan diets, but there is only consumption under the Western Diet.Tyrosine and phenylalanine are both dopamine and noradrenaline precursors ,68. TyroThe fluxes for each reaction in the gut community model were estimated for both ADHD and neurotypical Control cases by the computational pipeline embedded in MICOM, and those with relatively high values are given in Bacteroides uniformis, Bacteroides vulgatus, Coprococcus catus, and Subdoligranulum variabile consistently show higher elasticity variances in ADHD individuals  of exchange fluxes under all the diets are examined, lower elasticity coefficients are obtained in healthy individuals. It has been observed that the elasticity coefficients of the exchange fluxes of amino acid metabolism, minerals and metals such as zinc, cobalt, copper, oxygen and starch (carbon source of the microbiota) are high in ADHD individuals. The slightest change in these will seriously affect the intestinal system of ADHD individuals. In addition, the elasticity coefficients of chloride and proline in healthy individuals are found to be considerably lower than those in ADHD individuals, indicating a flux control. This result shows that the changes in the amounts of chloride and proline have less effect in healthy individuals, but there is no such trend in ADHD individuals.In general, similar elasticity coefficients are obtained between healthy individuals and ADHD cases, but when the standard deviations are examined, lower values are calculated in healthy individuals, which is interpreted as healthy individuals being more resistant to changes and standing firm.Lactobacillus rhamnosus (L. rhamnosus), which is sold as a nutritional supplement and added to a number of foods, including dairy products, is one of the most extensively researched friendly bacteria. L. rhamnosus promotes the growth of good bacteria, such as Bacteroides, Clostridia, and Bifidobacteria, in addition to preventing the colonization of harmful bacteria.Probiotics are described as live microorganisms that provide the host with health benefits when given in sufficient doses. L. rhamnosus helps increase their production. For example, upon probiotic addition to the community model, the ADHD sample starts to consume (import) butyrate at 4.90652 mmole/g DCW/h in Subdoligranulum variabile metabolism. Before the intervention, this particular male ADHD subject was exporting butyrate at a flux rate of 0.95877 mmole/g DCW/h under the Atkins’ Diet. Import rates of acetate and formate in S. variabile increase for most samples (ADHD and Control) for all diets after the L. rhamnosus addition. For Bacteroides uniformis, formate exchange fluxes show minor changes after the intervention. The pairs that export acetate show higher flux values for the sibling with ADHD. Moreover, the number of male samples importing acetate is higher than that of females. Similarly, L. rhamnosus addition to the environment affects the isobutyrate and isovalerate production ability of B. vulgatus. The acetate exchange carried out by Bifidobacterium dentium for ADHD samples is also impacted by the addition of L. rhamnosus. For B. pseudocatelunatum, after L. rhamnosus addition, acetate export flux values of ADHD subjects decrease by 5–15% under the Western Diet. The short-chain fatty acids produced by Coprococcus catus, acetate, butyrate, formate, and propionate, are affected significantly by the addition of L. rhamnosus. These changes in short-chain fatty acid fluxes varies by around 5–10% under the Atkins’ and Vegan diets for ADHD samples, but by 0–5% for Control samples under the Atkins’ diet and by 10–20% under the Vegan diet (mainly for acetate fluxes). Both Control and ADHD samples under the Western Diet show few cases with extreme changes. The other species under the genus Coprococcus is C. comes, which is also affected by probiotic intervention in terms of short-chain fatty acids exchange fluxes. Acetate shows the highest changes in flux values, which are decreased in both ADHD and control cases. All propionate flux values are increased but are still insignificant. On the other hand, L. rhamnosus’s addition to the system had no significant effect on C. eutactus.The exchange (import and export) behaviors of the short chain fatty acids such as acetate, formate, propionate and butyrate are investigated in detail by the gut community model, as Neurotransmitter precursors phenylalanine, glutamate, tryptophan, and tyrosine showed up, but the intervention did not result in any significant changes for these metabolites for any of the microbiota.The fact that people with ADHD frequently experience gastrointestinal (GI) dysfunction, such as constipation, low-grade inflammation, and childhood digestive problems, serves to highlight the possibility that the gut microbiome may play a role in this condition. Children with ADHD have been shown to have adverse reactions to particular meals and food additives, exposure to harmful dietary pollutants, and suboptimal levels of micronutrients such as vital fatty acids, zinc, magnesium, and iron. Food allergies can take the shape of hyperactivity. Given that ADHD children frequently exhibit a behavioral response to food, including real allergies, these two conditions may have similar pathophysiological routes . AlthougDietary interventions can be beneficial for children with ADHD. Thus, in the present study, the effects of the Western, Atkins’ (ketogenic) and Vegan diets on the prevalence of gut microbiota and their metabolites are investigated. In most of the cases examined in the present study, no strong correlation is found between the exchange direction of key metabolites (SCFAs and amino acids etc.) and health status, diets, gender, or age. Inferring with certainty whether a particular microbial profile is linked to ADHD is difficult given the results’ high degree of heterogeneity. The main findings on the key metabolites are summarized in Bifidobacterium and Collinsella), Bacteroidetes (genus Bacteroides and Prevotella), Bacillota (genus Coprococcus and Subdoligranulum), Bacteroidota (genus Alistipes), and Firmicutes . Alistipes is a bile-resistant anaerobic acid-producing indole-positive genus [Alistipes putredinis is succinate, as well as acetic, isobutyric, isovaleric, and propionic acids, which are produced in smaller amounts. Alistipes population found in the human gastrointestinal tract is correlated with fiber and processed food consumption in the diet [A. putredinis [Alistipes genus is also related to fatigue and stress, which are also seen in mood disorders such as depression [Alistipes being an indole-positive organism that effects serotonin availability in a negative way [In the present study, the species contributors to the key SCFAs mainly come from the phyla of Actinobacteria . Lactobacillus brevis and Bifidobacterium dentium  are the most effective species for GABA production in MSG culture [A and GABAB receptors.The gut-brain axis is affected by microbially produced neurotransmitters like GABA or their precursors. Gamma-Aminobutyric Acid (GABA) is an inhibitory neurotransmitter and controls various physiological and psychological processes in brain. Anxiety and depression are related to GABA signalling problems. Glutamate decarboxylase (GAD) and vitamin co-factor pyridoxal phosphate are used to convert glutamate to GABA. Studies show that strains of  culture . Fear anAlistipes putredinis). This might explain why males show more clear ADHD symptoms than females. Most commercial antidepressants focus on raising serotonin levels. Recent studies have shown that the serotonin level in regular mice is significantly high compared to that of germ-free mice, and this might be a result of a host–microbe interaction [Bifidobacterium infantis on serotonin levels of germ-free mice were also observed and an increase in tryptophan in plasma was reported [Lactococcus lactis, Lactobacillus plantarum, and Streptococcus thermophilus can also increase serotonin levels in plasma. However, these organisms are not observed in our gut community model due to their very low abundance in ADHD samples.Serotonin regulates bodily functions such as mood, and is a product of tryptophan, an essential amino acid. In the present study, a comparison based on gender indicates some differences between the healthy and ADHD sample fluxes of tryptophan, which is more significant for male pairs than for female pairs  caused either increases in short chain fatty acid production fluxes or reversed their exchange direction (import/export) for some of the strains in the gut community model, in agreement with the available literature [Lactobacillus rhamnosus GG significantly reduces the signs and symptoms of anxiety and sadness, with a preventive effect on ADHD, and also has a beneficial effect on cognitive function. The children and teenagers who received LGG supplements reported higher health-related quality of life [Our computational study successfully revealed that the probiotic strain terature ,65,118.  of life ,118. NevEach bacterium’s distinct genetic makeup results in the production of distinct sets of metabolites, which interact with host metabolites and additional compounds downstream to form a complex network of host–microbiome interactions. Our findings and those found in the literature suggest that medications that target the gut microbiota specifically may be effective in the treatment of ADHD. As the gut microbiota is an ecosystem, any alterations to one component would probably influence other parts. When designing such treatments, personalized medicine should be used due to the huge individual variation in the human gut microbiome.Bifidobacterium predominates in the gut and, as people mature, its relative abundance slowly declines. Thus, the fact that its abundance was lower in early infancy and rose in early adulthood may be a reflection of delayed gut microbiota maturation in ADHD. An increase in Bifidobacterium species was linked to significantly higher projected dopamine precursor  biosynthesis potential in the gut microbiota of ADHD patients compared to controls [By altering either the metabolic pathways or the expression of the genes encoding the neurotransmitter transporters, the gut microbiome may affect the catecholaminergic neurotransmission system . Early icontrols ,119,120.Coprococcus and Subdoligranulum), Actinobacteria (genus Collinsella), Bacteroidetes (genus Bacteroides), Bacillota (genus Coprococcus and Subdoligranulum), and Bacteroidota (genus Alistipes) may be possible gut microbiota indicators of ADHD, as our case-control study is shown. Alistipes putredinis is especially associated with depression in the literature. Its consumption of high amounts of L-tryptophan is an unexpected behavior for ADHD subjects. In fact, L-tryptophan is a necessary metabolite for serotonin production, and higher consumption in people diagnosed with ADHD may help explain the relationship between depression and ADHD as well as ADHD and low serotonin. However, surely, a single bacterial genus cannot be directly responsible for any neuropsychiatric disorder, but rather they are the product of a complex interaction between numerous bacterial genera. Furthermore, the gut microbiome is not constant, and both internal and external factors can affect how the host responds. Researchers try to find explanations for the inconsistent findings already reported in the literature, and variations in the categorization and diagnosis of these neuropsychiatric illnesses also add to this problem, being another source of confusion in much research. Numerous investigations are likely to be complicated by the heterogeneity in these neuropsychiatric illnesses.The presence of Bacillota (genus


Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children and adolescents. It is characterized by age-inappropriate inattention/impulsiveness and/or hyperactivity symptoms.However, emotional symptoms are frequent in patients with ADHD and may, in some cases, be responsible for a major part of the negative impact on functioning and outcome.It is now well established that a large number of children with ADHD and without any comorbid disorder exhibit symptoms of emotional lability.Recently , given the importance of the impact of emotional symtoms , several authors have argued that emotional intelligence affects health and is essential for success in academics as well as life in general and it is defined as the ability to perceive, appraise, and express emotions; the ability to access and/or generate feelings when they facilitate thought; and the ability to regulate emotions to promote emotional and intellectual growth.•Our research aimed to evaluate and compare the emotional and social functioning of two groups of children, with and without ADHD , aged 6 to 19 .BarOn Emotional Quotient Inventory: Youth Version (BarOn EQ-i:YV™), providing an estimate of their underlying emotional and social intelligence.•One hundred twenty child  were assessed with the •The BarOn EQ-i:YV is specifically designed to assess the coping skills, adaptability, and well-being of children and teenagers.•Children with ADHD and control cases were compared with each other.(p=0.01).•The results showed that the Emotional Quotient (EQ) was significantly lower in the group of children with ADHD intrapersonal skills (p˂0.0010) Adaptability Scale(p=0.005) ; General Mood Scale (p=0.004) and positive impression (p = 0.001) of emotional intelligence between children with ADHD and control cases. Thus, the first group got lower scores than the second one in all aspects.•Also, our results showed that there are statistically significant differences in •ADHD is a disorder that affects the life quality of the person who suffers from it in the personal and social areas. Therefore, the emotional intelligence study in individuals with this diagnosis is important.•And considering the fact that abilities associated with emotional intelligence can be learned and improved, emotional intelligence can be thought as a target for therapy by individualized education for patients with ADHD who have inadequate abilities compared to the healthy population .None Declared


Excessive mind wandering is a common feature of ADHD in adulthood also referred to as mental restlessness and ceaseless mental overactivity. ADHD is a predisposing factor for many psychiatric symptoms, especially negative emotions such as depression and anxiety. Rumination is thought to be a mediator for depression in individuals with ADHD. The above-mentioned mental processes may reduce the ability of individuals with ADHD to be mindful.This study investigates whether the mentation processes  mediate the relationship between ADHD symptoms and anxiety, and depression in adults with ADHD.Medication-free 175 individuals with ADHD who were referred to the Adult Neurodevelopmental Disorders Clinic, Department of Psychiatry, Selçuk University were invited to the study. After initial diagnostic examination including The Structured Clinical Interview for DSM-5 (SCID 5), all participants completed a test battery that included a sociodemographic form, the Adult ADHD Severity Rating Scale, the Hospital Anxiety Depression Scale, the Mind Excessively Wandering Scale, the Ruminative Response Scale, and the Freiburg Mindfulness Inventory. The data of 159 patients whose ADHD diagnosis was confirmed, who did not have any mood episodes and psychotic symptoms at the time of inclusion of the study, and who filled out the forms completely, were included in the analysis. The study was approved by the Selçuk University Local Ethics Committee.Participants ages ranged from 18 to 39 (mean of 22.93 ± 4.36), and 57.2% (n = 91) were women. Also, their 48.6% (n = 77) reported that they alcohol use, and 21.1% (n = 34) had substance use history. According to SCID 5 interview, participants 64.2% had comorbid psychiatric conditions. Pearson correlation analysis revealed that ADHD symptoms, rumination, excessive mind wandering, anxiety, and depression scores were significantly positively correlated with each other, but all were negatively correlated with mindfulness. Linear regression analysis showed mindfulness association with rumination, excessive mind wandering, anxiety, and depression scores, but not with ADHD symptoms. Thereupon, a conducted mediation regression analysis showed that ADHD symptoms indirectly worsened depression and anxiety through increased rumination and excessive mind wandering, and decreased mindfulness ability .Image:Adults with ADHD have distressing mentation patterns which worsened their anxiety and depression. Mindfulness-based cognitive behavioral therapy modalities may help improve excessive mind wandering and rumination in ADHD. Our findings should be warranted in future studies of functional brain connectivity patterns that may serve as a mentation endophenotypes in ADHD.None Declared


Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood neurodevelopmental disorder. Given the profound brain changes that occur during childhood and adolescence, it is important to examine longitudinal changes of both functional and structural brain connectivity across development in ADHD. This study aimed to examine the development of functional and structural connectivity in children with ADHD compared to controls using graph metrics. One hundred and seventy five individuals (91 children with ADHD and 84 non‐ADHD controls) participated in a longitudinal neuroimaging study with up to three waves. Graph metrics were derived from 370 resting state fMRI  and 297 diffusion weighted imaging data  acquired between the ages of 9 and 14. For functional connectivity, children with ADHD  showed lower degree, local efficiency and betweenness centrality predominantly in parietal, temporal and visual cortices and higher degree, local efficiency and betweenness centrality in frontal, parietal, and temporal cortices. For structural connectivity, children with ADHD had lower local efficiency in parietal and temporal cortices and, higher degree and betweenness centrality in frontal, parietal and temporal cortices. Further, differential developmental trajectories of functional and structural connectivity for graph measures were observed in higher‐order cognitive and sensory regions. Our findings show that topology of functional and structural connectomes matures differently between typically developing controls and children with ADHD during childhood and adolescence. Specifically, functional and structural neural circuits associated with sensory and various higher order cognitive functions are altered in children with ADHD. This study explores longitudinal development of both functional and structural connectivity across late childhood and in children with ADHD and typically developing controls. We demonstrated that topology of functional and structural connectomes matures differently between ADHD and controls. Specifically, functional and structural neural circuits associated with sensory and various higher order cognitive functions are altered in children with ADHD. Participants were recruited into the longitudinal neuroimaging project, Neuroimaging of the Children's Attention Project (NICAP) , those with excessive head motion (greater than 0.5 mm of framewise displacement (FD) ) and structural scans with low‐quality DWI data (N = 45 [ADHD = 18]) were excluded. There was no difference between the included and excluded in terms of the age distribution of control or ADHD participants. However, those children with ADHD who were excluded had more severe ADHD symptoms than included ADHD subjects (p < 0.05).After quality control of imaging data (detailed below), functional scans missing a field map  and 297 structural scans  across the three assessment waves  data were acquired using a multi‐band factor of 3 with the following parameters: b = 2800 s/mm2, 63 slices, matrix size = 110 × 100, voxel size = 2.4 mm3, FOV = 260 × 260 mm, bandwidth = 1748 Hz, acquisition time = 3 min 57 s. T1 weighted images were acquired using a multi‐echo magnetization prepared rapid gradient‐echo (MEMPRAGE) sequence along with navigator based prospective motion correction with the parameters: TR = 2530 ms, TE = 1.77, 3.51, 5.32 and 7.2 ms, FOV = 230 × 230 mm, FA = 7°, axial slices = 176, matrix size = 256 × 232, voxel size = 0.9 mm3, acquisition time = 6 min 52 s  scanner session to get familiarized to the MRI environment. Subsequently, MRI scans were acquired using a 3‐Tesla Siemens scanner at a single site. However, waves 1 and 2 were collected on a TIM Trio scanner and wave 3 was collected after an upgrade to a MAGNETOM Prisma scanner . Using a 32‐channel head coil, functional images were acquired using multi‐band accelerated EPI sequences (MB3), with the following parameters: repetition time (TR) = 1500 ms, echo time (TE) = 33 ms, field of view (FOV) = 255 × 255 mm, flip angle (FA) = 85°, 60 axial slices, matrix size = 104 × 104, voxel size = 2.5 mm2.3http://fsl.fmrib.ox.ac.uk/fsl/fslwiki). Standard preprocessing steps such as discarding of four initial volumes to account for initial signal inhomogeneity, motion correction using MCFLIRT (FMRIB's Linear Registration Tool), B0 unwarping, spatial smoothing using 5 mm FWHM, spatial normalization to the MNI template using a 12‐parameter affine transformation and registration of fMRI images to MNI space via high resolution T1 images using FSL FLIRT and FNIRT were undertaken  atlas (360 distinct regions)  is the inverse of the average shortest path connecting all neighbouring nodes of node i, excluding node i. The LE of node i in a network G is measured as Eloc=1N−1∑j≠i∈G1dij, where dij is the shortest path length between node i and node j. Degree sums the number of edges connected to node i. Betweenness Centrality (BC) is calculated based on the shortest paths between pairs of nodes in the network that pass‐through node i. BC is calculated as Bi=∑a≠i≠b∈Gσabiσab, where σab is the number of shortest paths from node a to node b and σabi is the number of shortest paths from node a to b that pass through node i. We also measured global efficiency (GE), which examines the efficiency of information transfer averaged across all nodes. For a network G, global efficiency is calculated as Eglob=1NN−1∑i≠j∈G1dij, where dij is the shortest path length between node i and node j.Graph theoretical measures for functional and structural cortical connectivity were extracted using the Brain Connectivity Toolbox  with more than 2 AIC units less than other nested models were selected as the best‐fitting model  and Rstudio , using the “mgcv” package Wood,  in R Te, which a3No significant effects were identified across any statistical analyses for global efficiency.3.13.1.1Children with ADHD showed lower degree in bilateral inferior parietal cortex, superior temporal gyrus and left visual cortex, but higher degree in the left anterior cingulate, relative to typically developing children Figure . Childre3.1.2Children with ADHD had higher degree in the right dorsal posterior cingulate area (BA 31) and right frontal opercular cortex compared to typically developing children Figure , as well3.23.2.1Children with ADHD showed greater increases in degree, compared to typically developing children, in the right superior temporal gyrus from late childhood to early adolescence. Figure . Further3.2.2Children with ADHD showed a greater increasing trajectory of degree, relative to typically developing children, in the left inferior parietal cortex from late childhood to early adolescence Figure . For LE,4The goal of the current study was to investigate longitudinal changes in the brain's functional and structural topology in children with ADHD. Using graph theory analysis, our results indicate differences in the development of network degree, local efficiency and betweenness centrality predominantly in higher‐order cognitive and sensory regions such as anterior cingulate, posterior cingulate, frontal opercular cortex, superior temporal gyrus, inferior parietal cortex and visual regions in children with ADHD compared to typically developing children.4.1The current findings highlight differential regional organization of functional networks in children with ADHD compared to their typically developing counterparts. Our analysis of functional connectivity showed group differences in anterior cingulate, inferior parietal cortex, and temporal cortex for degree, local efficiency and betweenness centrality. In addition, visual region was found to have an important role in degree where children with ADHD showed lower degree in visual region relative to typically developing children. Regions with consistent differences in degree, local efficiency and betweenness centrality mostly overlapped with the components of DMN, SAL and DAN. Similar brain regions were observed for different graph measures, and this could be either due to the location of these regions or the network measures might be correlated with each other. This suggest that these regions act as network hubs, with a large numbers of connections to other regions in the brain, and falling on the shortest path length between connected pairs of regions in the cortex  used in their study and has highlighted the importance of adequately powered longitudinal data to understand the changes in structural and functional development in children with ADHD , a grant from the Collier Foundation. SS was supported by Postgraduate Research Scholarship from Deakin University (DUPRS).The authors declare no conflict of interest.DATA S1. Supporting information.Click here for additional data file.


Numerous studies point to the comorbidity between gambling disorder (GD) and attention deficit hyperactivity disorder (ADHD). However, there is a lack of research exploring how ADHD symptoms might influence psychological treatment outcomes for GD.Therefore, we aimed to explore differences between patients with GD with and without ADHD symptoms regarding psychopathology, personality, sociodemographic and especially treatment outcome measures.n=170 patients with GD receiving 16 sessions of cognitive behavioral therapy (CBT) in a specialized unit of a public hospital. Multiple self-reported instruments were used to assess GD severity, personality, ADHD and other symptoms and sociodemographic measures prior to treatment.This longitudinal study included A clinical profile characterized by greater GD severity, higher psychopathology and impulsivity, and less adaptive personality features was observed in patients with self-reported ADHD symptoms compared to those without. No significant differences in treatment response (measured by dropout and relapse rates) were reported between the two groups. However, patients with ADHD symptoms described more severe relapses (more money gambled) and GD patients who relapsed scored higher on measures of ADHD, particularly inattention.Individuals with GD and ADHD may experience more severe relapses following treatment, suggesting a need for more vigilant follow-up and interventions for patients with this comorbidity.C. Vintró-Alcaraz: None Declared, G. Mestre-Bach: None Declared, R. Granero: None Declared, M. Gómez-Peña: None Declared, L. Moragas: None Declared, F. Fernández-Aranda Consultant of: Novo Nordisk and editorial honoraria as EIC from Wiley, M. Potenza Consultant of: Opiant Pharmaceuticals, Idorsia Pharmaceuticals, AXA, Game Day Data, Baria-Tek and the Addiction Policy Forum; has been involved in a patent application with Yale University and Novartis; has received research support  from Mohegan Sun Casino and Connecticut Council on Problem Gambling; and has consulted for and/or advised gambling and legal entities on issues related to impulse-control/addictive disorders, S. Jiménez-Murcia: None Declared


We investigated the feasibility and validity of establishing a nationwide e-cohort of individuals with a diagnosis of attention deficit hyperactivity disorder (ADHD) and/or autism spectrum disorder (ASD) for future longitudinal research.Individuals with a childhood diagnosis of ADHD/ASD as recorded on routinely available healthcare datasets were compared with matched controls and a sample of directly assessed individuals with ADHD.This study used data from the Welsh Secure Anonymised Information Linkage Databank in Wales, UK. Routinely collected data from primary care, emergency department and hospital admissions were linked at person level.All individuals in Wales, UK born between 1 January 1991 and 31 December 2000. Individuals with a recorded diagnosis of ADHD and/or ASD by age 18 years were identified using International Classification of Diseases, 10th Revision and National Health Service (NHS) READ codes and matched to 3 controls each and 154 individuals with ADHD recruited from an established research study.Recorded service use for anxiety and depression, alcohol and drug use and self-harm including emergency department use in young adulthood (age 16–25 years).7726 individuals had a recorded diagnosis of ADHD  and 5001 of ASD ; 1.4% and 0.9% of the population, respectively. Cox’s regression analyses showed ADHD was associated with increased risks of anxiety/depression , self-harm , alcohol , drug use  and emergency department service use . Those with ASD were at increased risk of anxiety/depression , self-harm  and drug use  but not alcohol use. The ADHD e-cohort were similar to the directly assessed cohort.Our identification strategy demonstrated the feasibility of establishing a large e-cohort of those with ADHD/ASD with expected patterns of poorer early adult outcomes, demonstrating a valid method of identifying large samples for future longitudinal studies without selective attrition. This study used population wide administrative data from a well-established and comprehensive databank.Data from both primary and secondary care were combined.The study used a directly assessed sample of those with attention deficit hyperactivity disorder to help validate the establishment of the administratively identified sample.Not all individuals had passed through the whole follow-up period at the end of the study.Studies using administrative data is dependent on data having been collected a recorded.Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions that typically onset in early childhood.www.saildatabank.com) that includes health and social care data on every individual. These records include not only secondary care, emergency department contacts and prescription data but also uniquely encompass primary care and household level data. The aim of this study was to assess the feasibility of identifying those within Wales with a diagnosis of ADHD or ASD in childhood using record-linked routinely available healthcare data and investigate the validity of a nationwide e-cohort for future longitudinal clinical, health services, epidemiological and social sciences research. To achieve this aim, we first identified individuals with a childhood record of ADHD and/or ASD using the SAIL record-linked databases and compared them to matched controls for known early adult outcomes (aged 16–25) associated with ADHD and ASD. For those with ADHD, we also compared the e-cohort to a hybrid cohort of individuals with known ADHD diagnoses verified by direct interview.Wales is one of four UK nations with a population of around 3 million and has a number of devolved areas of administration, including health and social care. Wales hosts one of the most comprehensive anonymised record-linked data bases, SAIL (Secure Anonymised Information Linkage) Databank  associated with ADHD and ASD from published literature and cross mapped them to READ codes, a coded thesaurus of clinical terms used in the UK National Health Service. We prepared an initial list of READ codes  and ICD-10 codes and it was shared with general practitioners and mental health clinicians to ensure completeness. This resulted in the identification of additional codes which were added.Individuals with an ICD-10 or READ code from the described list recorded in their GP and/or hospital admission data were identified as having ADHD and/or ASD. The first occurrence of these codes was identified and denoted as age at first available diagnosis. Validation of ADHD codes was also undertaken , whichever came last, to their 25th birthday, end of the study period (31 December 2016), loss to follow-up or death, whichever came first.To establish prevalence rates of ADHD and ASD diagnoses in childhood, all individuals within the SAIL Databank  born from 1 January 1991 to 31 December 2000 were identified  and followed to their 18th birthday.Three controls for each ADHD/ASD participant were identified. Controls were matched on week of birth (±1 year) and sex, inversely matched by ADHD/ASD diagnosis , and required to have as much follow-up data as the cases they were matched with. To maximise the quality of ADHD/ASD incidence data in controls used during matching, controls with more data coverage before the age of 18 were selected first.For comparisons between the electronic cohort and known individuals with ADHD, 579 participants who were given a diagnosis of ADHD using a face-to-face validated diagnostic interview (the Child and Adolescent Psychiatric Assessment)To assess presence of anxiety and depression disorders, a previously validated common mental health disorders measure11All recorded incidents of self-harm were obtained from GP, emergency department and hospital records. As previously described,et al.Alcohol use information was obtained from GP, hospital (inpatient and outpatient) and emergency department datasets, as in John et al,As detailed in John All recorded contacts with hospital emergency departments were obtained from the emergency department dataset.For anxiety/depression, self-harm, alcohol and drug use measures, records from GP, emergency department and hospital inpatient were combined to obtain an overall measure of any occurrence. ICD-10 and READ codes used for each outcome can be found in their respective cited references.To investigate whether individuals with ADHD or ASD were more likely to present with the above outcomes at primary care or hospital services (including emergency department use), occurrences of any of the listed outcomes recorded in the General Practice Database (GPD) were considered as primary care use, while any recorded in the Patient Episode Database for Wales (PEDW) or the Emergency Department Dataset (EPD) were considered to be hospital use. These were dichotomised into the presence of any outcome or not. Additional analysis looked at the presence of self-harm separately in GP and hospital records.Sex and deprivation data were obtained from the Welsh Demographic Service Dataset. Deprivation was assessed using the Welsh Index of Multiple Deprivation (WIMD) obtained for each individual at the start of the follow-up period. The WIMD provides a measure of deprivation by small geographical areas (Lower-layer Super Output Areas) based on seven domains including average income, education, employment and health statistics for individuals in that area. Using the national WIMD cut-offs, quintiles (1–5) of deprivation were defined with level 5 representing the most deprived areas.The age of each participant at the end of the follow-up period was also recorded to control for the variation in dates of birth and therefore length of follow-up.To assess population prevalence, the number of individuals with recorded diagnoses of ADHD and/or ASD was identified within the population comparison sample. This was done separately for males and females.As the study focused on individuals diagnosed with ADHD or ASD in childhood, individuals with a first recorded diagnosis after the age of 18 years and their matched controls were removed from the dataset.Cox’s regression analyses were undertaken to compare the risk of having a recorded incidence of each outcome for those with ADHD/ASD compared with matched controls, with age at end of follow-up as the time variable and presence/absence of ADHD/ASD as the status variable. Following unadjusted analyses, a second model controlled for sex and proportion of available records and a final model additionally controlled for deprivation.Cox’s regression analyses were also undertaken as sensitivity analyses to investigate risk of early adult outcomes for those with ADHD/ASD and matched controls stratified by sex and separately by deprivation quintile.Binomial regression analysis was undertaken to investigate whether, in those with recorded self-harm or accident and emergency department usage, the number of incidents of self-harm and emergency department visits was greater in those with ADHD/ASD compared with matched controls. These models were controlled for sex, age at end of follow-up, proportion of available records and deprivation index.As the SAIL Databank maintains records only from Welsh organisations, individuals who move outside of Wales (and back) or to GPs not covered by the SAIL Databank may not have complete follow-up data. Therefore, analyses were repeated excluding those with <98% total data coverage.Understanding the long-term outcomes for children with ADHD was highlighted as a research priority by families at public engagement events. The aims of the study and relevant outcomes were discussed with stakeholders during the study design stage.et alFor a flow chart of the databases used and the number of participants used at each stage, see Within the e-cohort, a total of 7726 individuals had a recorded diagnosis of ADHD and 5001 of ASD by the age of 18 years see . This reA total of 19 532 individuals were identified as matched controls for ADHD individuals. While matched on age and sex, those with ADHD came from slightly more deprived backgrounds than their matched controls see . The meaControlling for age, those with ADHD had approximately a twofold increase in risk of having a record of anxiety or depression  and a fivefold increase in risk of self-harm . They were significantly more likely to have a record of alcohol  and drug use  and were more likely to have used emergency department services . All associations remained when controlling for sex, record availability and deprivation . For thoA total of 11 427 individuals were identified as matched controls for the 5001 individuals with ASD. Those with ASD came from similar deprivation backgrounds as their matched controls see . The meaWithin the directly assessed sample, 154 individuals who were born between 1991 and 2000 and were included in the matched sample dataset. The directly assessed ADHD cohort were younger at the end of follow-up than those in the e-cohort only  and had an earlier age at ADHD diagnosis . There groups were similar on other demographic measures . The difet alet alThe aims of this study were to establish and validate a nationwide e-cohort of young people in Wales with recorded diagnoses of ADHD and/or ASD. These aims were successfully achieved; lists of relevant ICD-10 and NHS READ codes were created and used to identify an e-cohort of individuals with ADHD and/or ASD. In comparisons between these groups and matched population controls, we observed associations of ADHD or ASD with adverse early adult outcomes in line with research findings from the UK and other countries, validating the identification strategy.The administrative prevalence of individuals with a recorded diagnosis of ADHD and ASD (1.4% and 0.9% of the respectively) in Wales, identified in the SAIL Databank corresponds well with the population prevalence of these disorders from the largest, recent UK epidemiological survey: the 2017 Mental Health of Children and Young People survey27In contrast, population rates of autism have increased over time.et alOur study investigated diagnoses recorded in healthcare. If we use findings from the 2017 England population survey, this suggests that there is neither an overdiagnosis or underdiagnosis of ADHD or ASD in Wales. However, using data from other surveys would suggest that there could be underdiagnosis of ADHD but that administrative ASD rates are in line with nearly all population surveys. In this study, we also identified those with both ADHD and ASD despite codiagnosis being disallowed by ICD and DSM until recently; these are findings that are also observed in the Danish registry  longitudinal follow-up for all individuals still living in Wales, reducing issues associated with selective attrition that can be a disadvantage for longitudinal cohort studies.The verification of ADHD codes using a directly assessed sample as comparison was successful and gives confidence that the e-cohort is similar to those with a known diagnosis. However, there are limitations. This verification process could have been improved by including a directly assessed sample without ADHD to assess the false positive rate; however, this was not available. Similarly, verification of the ASD codes would have been advantageous. However, identifying a sample of individuals of similar prevalence to the most recent population survey in EnglandWhile using a good set of verified codes, as with any data linkage study, we were reliant on the information recorded in the relevant dataset. Therefore, individuals whose ADHD/ASD has not been assessed or diagnosed clinically or where such diagnoses were not accurately recorded would not be included in our sample. This may also be the case for the outcome measures; although we used previously established methods to identify these, if they are not recorded  they would not be represented in our sample. It is also the case that the follow-up period differed between individuals and was not long for the youngest in our cohort, thus potentially underestimating the prevalence of the outcomes. It is worth noting that these limitations are relevant to the matched controls as well as the individuals with recorded ADHD/ASD diagnoses and that the unique availability of records from both primary and secondary care in the SAIL Databank may mitigate reporting issues.Another limitation is that we also restricted the sample to those with a diagnosis of ADHD/ASD by age 18 years, as we were interested in what happens to those with a childhood diagnosis as they grow older. However, we know that many individuals are diagnosed with ADHD and ASD in adulthood  and investigating their difficulties, especially in comparison to those with a childhood diagnosis, would be interesting.Overall, this study has demonstrated the feasibility and validity of an e-cohort of those with ADHD and ASD using health record-linked data in Wales, UK. Our identification strategy resulted in a large sample who could be followed longitudinally into early adulthood with minimal attrition. This type of cohort can be used in future studies, benefitting from the linkage of information from primary and secondary healthcare, social care and educational data available via the SAIL Databank, to further understand the risks and outcomes for those with ADHD and ASD as well as to support planning in health and social care services.


Adolescents with attention-deficit/hyperactivity disorder (ADHD) are at elevated risk of a range of difficulties, among which emotion regulation, peer and co-occurring mental health problems are prominent challenges. To better support adolescents with ADHD, ecologically valid interventions that can be embedded in daily life to target the most proximal antecedents of these challenges are needed. Ecological momentary assessment (EMA) designs are ideally suited to meeting this need.In the mental health in the moment ADHD study, we will use an EMA design to capture the daily life experiences of approximately 120 adolescents aged 11–14 years with a clinical diagnosis of ADHD and the same number of age-matched and gender-matched peers without a diagnosis of ADHD. We will combine this with comprehensive information gathered from online surveys. Analysing the data using techniques such as dynamic structural equation modelling, we will examine, among other research questions, the role of emotion regulation and peer problems in mediating the links between characteristics of ADHD and commonly co-occurring outcomes such as anxiety, depression and conduct problems. The results can help inform interventions to support improved peer functioning and emotion regulation for adolescents with ADHD.This study received a favourable ethical opinion through the National Health Service ethical review board and the University of Edinburgh PPLS Research Ethics panel. The results will be disseminated through journal publications, conferences and seminar presentations and to relevant stakeholders, such as those with ADHD, their families and clinicians. This study will use an ecological momentary assessment (EMA) design to collect high temporal resolution, ecologically value daily life experience data from young people with attention-deficit/hyperactivity disorder.It will use cutting edge statistical analysis techniques to disentangle different aspects of emotional functioning in daily life to identify key intervention targets.A limitation is that data will be collected only over a short period, therefore, developmental changes will not be captured.Further, EMA methods may be vulnerable to low response rates, careless responding or dropout and the reliance on self-reports can be associated with reporting biases.et alAttention-deficit/hyperactivity disorder (ADHD) symptoms may increase the risk of experiencing a range of difficulties in adolescenceDifficulties with emotion regulation among children and adolescents with ADHD are well documented.1Emotion dysregulation often exists alongside and has been proposed to contribute to peer difficulties such as bullying victimisation and perpetration, peer aggression, rejection, dislike and conflict among adolescents with ADHD.9Emotion dysregulation and peer problems are also thought to contribute to the development of co-occurring mental health and behavioural challenges associated with ADHD symptoms. For example, previous research in children and adolescents has found that peer issues and emotion regulation mediate the associations between ADHD symptoms and internalising problems such as anxiety and depression21 22However, the daily life mechanisms underpinning these links and—correspondingly—the most proximal targets for intervention remain poorly understood, particularly in the critical period of adolescence. Ecological momentary assessment (EMA) has proven valuable in child and adolescent mental health research for capturing symptoms and related experiences in near real time and in the flow of daily life and can thus inform more ecologically embedded intervention approaches.24 25There have, however, been only a handful EMA studies of young people with ADHD during the critical period of adolescence.These initial findings point to the value of further exploring, using EMA, the daily life manifestation and impacts of emotion regulation and peer interactions in adolescents with ADHD. For example, as well as seeking to extend these findings to examine a wider range of emotion regulation and peer issues, it will be valuable to examine factors that may impact these domains, their interconnections, and their links to mental health and behavioural outcomes. This will provide illumination on further modifiable targets for intervention, such as physical activity, improved sleep, secure attachment to caregivers and positive teacher–student relationships.We will thus use an EMA design to illuminate how, in the flow of their daily lives, adolescents with ADHD may experience difficulties with emotion regulation and peer problems, how these may contribute to co-occurring mental health and behavioural problems. We will also examine other potential modifiable targets for intervention that may impact emotion regulation, peer problems, mental health and behaviour as well as their links among adolescents with ADHD . Core research questions are:Which aspects of emotion regulation differ between adolescents with and without ADHD, for example, negative or positive emotional lability, emotional reactivity, emotional inertia, emotion regulation strategy use and emotion regulation strategy success?Which aspects of emotion regulation mediate the association between ADHD diagnosis/symptoms and peer problems in daily life?Do emotion regulation and peer issues mediate the links between ADHD diagnosis/symptoms and anxiety, depression and conduct problems?Exploratory analyses may be used to answer a range of additional questions such as exploration of moderation of the links between ADHD diagnosis/symptoms and outcomes to identify other potential intervention targets.Participants are expected to include approximately 120 adolescents aged 11–14 years with a clinical diagnosis of ADHD and 120 matched controls. The clinical sample will be recruited via Child and Adolescent Mental Health Services (CAMHS) in two primary sites in the UK (one in Scotland and one in England) and community partners. In CAMHS, the recruitment procedure will vary from service to service; however, in general participants will be recruited at medication clinics and through ongoing casework.A matched control  will be selected for each participant with a clinical diagnosis of ADHD from a larger pool of participants taking part in adolescent mental health studies (co-)led by the first author. To maximise relevance to community-ascertained samples, minor modifications to the protocol will be made, detailed in the Measures section.Inclusion criteria for the clinical sample will be: aged 11–14 years, a formal clinical diagnosis of ADHD (parent-reported in the case of the youth recruited via the community), the ability to provide informed assent and parental informed consent to participate and the ability to independently complete online surveys and EMA protocols. There will be no explicit exclusion criteria based on co-occurring neurodevelopmental conditions as ADHD commonly co-occurs with other neurodevelopmental conditionsAdolescents with ADHD and parents of adolescents with ADHD are the most important stakeholders in this project. Their views have informed the project design, including the current protocol and will continue to inform the project and the dissemination of its outputs. Young person and parent advisors were recruited via the ADHD charities and organisations with whom our team has collaborative links. Examples of major changes made to the protocol based on their input included extending the inclusion criteria to include lower ages, reducing the length of the surveys, including more positively worded and fewer negatively worded items, removing elements of the project that would have involved location tracking and wearable technology, adding a measure of rejection sensitivity and embedding more fun and educational elements into the study, such as providing ‘ADHD facts’ at the end of each completed EMA survey.Data collection is projected to begin in August 2023 and be completed in 12 months. 34–36EMA surveys. They will be provided with instructions on how to take part and offered on-boarding support via a video call with study staff. Participants will use their own smartphones where possible; however, to avoid selection biases and promote inclusion, study smartphones will be available. An email helpline will also be available for participants to receive help on any issues they encounter.On completion of the online intake survey, adolescent participants will initiate the EMA data collection will take place over a 2-week period and involve five measures per day, all administered via a smartphone application. Four of these measures will be delivered at quasirandom intervals (random within four blocks) and include identical measures of momentary experiences. The fifth will be a brief ‘daily diary’ that captures concepts that vary over slightly longer timescales . All measures will be designed to take less than 2 min as long questionnaires are associated with increased burden and lower compliance and our young person advisory group (YPAG)/parent advisory group (PAG) emphasised the importance of minimising burden.37end-of study survey. Here, they will complete repeat measures of a subset of the questions administered in the original survey, as well as a brief set of questions capturing their participation experiences. Full questionnaires are provided in At the end of the 2-week EMA period, participants will complete an online 10.1136/bmjopen-2023-077222.supp1Supplementary dataThe measures that adolescents will complete over the course of the study are presented in The measures that parents will complete over the course of the study are presented in Prior to the main data collection, the protocol will be piloted in a sample of n=10 adolescents (5 with and five without ADHD). Further modifications may be made to the protocol after piloting.Data will be analysed using techniques such as dynamic structural equation modelling (DSEM).https://osf.io/pvrma/ includes tailored simulations for this study. Taken together, our choice of n=120 per group balances feasibility with statistical precision and includes buffer for missing data and attrition.Our sample size (n=120 adolescents with ADHD and n=120 without) draws on several sample size determination approaches. It is partly based on a resource constraint approach, that is, we will maximise the sample size attainable within the resources available.Ethical review for the collection of the data from the clinically ascertained sample will follow a three-stage process in which sponsorship will be sought from ACCORD, followed by ethical review by the National Health Service ethical review board, and finally, research and development approval. Ethical review for the collection of data based on community and school recruitment will be by the University of Edinburgh PPLS Research Ethics panel. The results will be disseminated through journal publications, conferences and seminar presentations and to relevant stakeholders, such as those with ADHD, their families and clinicians.The current protocol describes the planned design for a combined traditional and EMA survey studyThe study is motivated by a need for new data to inform more ecologically valid interventions that can provide effective improvements to adolescents’ emotional and social functioning in their daily lives.Our findings may be particularly valuable for informing smartphone-based interventions.Our findings can also help accelerate progress in the application of EMA methodologies in populations of adolescents with ADHD. To date, despite their promise, very few such studies have been conducted.et al and Babinski and Welkie56Though a large body of research has investigated optimal design features for EMA in general,


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6–12 years) have been diagnosed with ADHD (2012–2018) This disorder is more likely to occur in males (12%) than females (4.2%). If ADHD goes untreated, there might be problems for individuals in the long run. This research aims to design a collaborative knowledge framework for personalised ADHD treatment recommendations. The first objective is to design a framework and develop a screening tool for doctors, parents, and teachers for observing and recording behavioural symptoms in ADHD children. This screening tool is a combination of doctor-verified criteria and the ADHD standardised screening tool (Vanderbilt). The second objective is to introduce practical algorithms for classifying ADHD types and recommending appropriate individual behavioural therapies and activities. We applied and compared four well-known machine-learning methods for classifying ADHD types. The four algorithms include Decision Tree, Naïve Bayes, neural network, and k-nearest neighbour. Based on this experiment, the Decision Tree algorithm yielded the highest average accuracy, which was 99.60%, with F1 scores equal to or greater than 97% for classifying each type of ADHD. Attention deficit hyperactivity disorder (ADHD) is a complex mental health disorder that affects children aged six to twelve years ,2,3. ADHADHD is a severe neurodevelopmental disorder that has multiple aetiologies, but it is quite complicated to pinpoint a single factor that might be the cause. Mainly, it is caused by a combination of environmental and genetic factors. Environmental factors include lead poisoning from maternal smoking during pregnancy, a less supportive home environment, and recent studies even show that the number of hours of watching television or using other media devices might contribute to environmental factors causing ADHD ,5,8,9. AIf behavioural syndrome goes untreated, there might be problems for individuals in the long run. The patients might be at risk of other health conditions. They would also suffer from decreased social life quality, unsatisfactory academic performance, unpleasant relationships, comorbid psychiatric conditions, and much more. If not treated until adulthood, the symptoms may affect the health and lifestyle of the patients in many aspects. According to research about the persistence rate of ADHD into adulthood, the psychiatric comorbidities of adult ADHD, and the risk of serious adverse outcomes, such as criminality and mortality, ADHD should no longer be viewed only as a disorder primarily affecting the behaviour and learning of children ,18. ADHDChildren with ADHD experience far more obstacles compared to their average counterparts. While many children diagnosed with ADHD receive some special school services to improve their learning environment and experience, this might not always be the case . ConsecuThe primary purpose of this research was to design a collaborative knowledge framework for personalised ADHD treatments. The main objectives included (1) a design of a collaborative framework and a screening tool for ADHD symptoms that could be used by medical professionals, parents, and teachers, and (2) the introduction of a practical algorithm for the classification of the types of ADHD with sets of recommended individual behavioural therapies and activities for different types of ADHD. To find an appropriate classification technique for the types of behavioural syndrome and address the need for such a machine learning algorithm in Thailand, we use machine learning to help with early intervention and learn student behaviour to assess the risk of having ADHD and find a solution; this will help children and those around them to heal and increase their chances of recovering. With the advancement of machine learning, we can classify symptoms more precisely and support large amounts of data, so there are advantages to using machine learning over queries alone.We applied different machine learning algorithms, compared the results, and utilised the best algorithm in our framework. The expected outcome of our proposed framework was to provide an effective way to classify the types of behavioural syndrome and to recommend appropriate treatments and therapies based on the individual’s behaviour.For example, the prevalence of ADHD worldwide ranges between 0.1 and 8.1%. In Thailand, the prevalence is between 4.2 and 8.1% ,24. The This paper is organised as follows: Children are taken to the doctor for the early diagnosis and treatment of ADHD when behaviour resembling ADHD symptoms is observed during the evaluation. For instance, a child is much more mischievous than usual and exhibits emotional outbursts and lacks learning concentration. Hence, ADHD should be diagnosed based on an evaluation of learning, behavioural, and emotional issues. When determining the diagnosis of ADHD, it is critical to consider the following evaluations: 1) an evaluation of the patient’s history and current conditions, 2) an evaluation by the parents and teachers, (3) a psychological assessment, and (4) discussion and treatment recommendations  in the child node to the right of the root node. This infers the 148 entries of 15 at this node condition. If the answer was false (child node left R1) and the value was , there would be 12 entries in the ODD classification. However, if the answer was true and the value was  (child node right R1), there would be 136 entries in the mix-type classification. This assumes that the data meeting this node’s condition is in the ODD and mix-type classifications.(3).Value is used to indicate the class of the predicted activities by the types of ADHD. The activities of the five classes were mix-type (index [0]), non-ADHD (index [1]), ODD (index [2]), hyperactivity (index [3]), and inattention (index [4]).The description for the parameter in the tree graph  is as foWe tested the DT algorithm for processing the recommendations of the activities for ADHD children. The results are shown in In this research, we aimed to overcome the mentioned problems by proposing a methodology and framework that teachers or parents could use to evaluate and screen their children’s behaviour and determine if they were consistent with the types of ADHD. The framework provided recommendations for the appropriate treatments for different types of ADHD children. The expected outcome of our proposed framework was to provide an effective way to screen and classify the types of ADHD and recommend appropriate treatments and therapy based on individual behaviour.The average classification accuracy of 99.60% was achieved by the DT and neural network algorithms. On the other hand, the KNN approach had an average classification accuracy of 98.40%, whereas the Naïve Bay technique had an average classification accuracy of 94.00%. Although the DT and neural network algorithms returned similar accuracy, in this work, we chose the DT algorithm as the main model for the classification of the types of ADHD and recommendations of the activities because the tree structure could be easily explained to all the participants for analysis, change, and future improvement. In addition, the performance, when adding a new data set to the training model, required a shorter time compared to the time required to fine-tune the neural network. The limitation of our research is data collection. During our data collection process, the COVID-19 pandemic was seriously occurring in Thailand and all schools were closed, so it was very difficult to collect data from all participants in the study. This resulted in the small amount of data we used in this work. Also, due to the pandemic situation, we could not directly interview each student to obtain the child’s opinion, which we believe can be valuable and useful for further analysis. Therefore, in our future work, we plan to improve our research by gathering more data from various groups of children such as children in big cities, children in rural areas, etc. Moreover, we will improve our tools, which would better support communications among participants  to share information and follow up cases. In Thailand, the employment of machine learning technology in the children’s health care system is limited. This is especially true for ADHD. Moreover, when a child is found with a condition or a tendency to have ADHD, the child relies on many elements to support the treatment, including access to disease information, medical treatment, specialised doctors related to psychiatry, travelling method to the hospital, and unforeseen expenses incurred by the family.For future work to improve our framework, we would like to generate and expand the data collection and conduct further experiments to design and build more efficient algorithms of the sub-activities based on different types of ADHD. Although the current work could achieve high accuracy for classifying the types of ADHD, some cases would still need to be improved . We also plan to train the model for different scenarios and try to enhance the model’s accuracy and UX/UI design based on feedback from the users .