Despite the increasing body of research on Attention Deficit Hyperactivity Disorder (ADHD), the results of the studies assessing the relationship between executive function deficit and the risk of obesity in people with ADHD are incongruent. Our study aimed to assess the relationship between measures of executive functions and body weight and Body Mass Index (BMI) in children and adolescents with ADHD and control subjects. The study group consisted of 58 subjects aged from 8 to 17 years with ADHD. The Control group consisted of 62 healthy age and sex-matched participants from primary and secondary schools. Weight, height, and BMI measurements were standardized. The Sustained Attention to Response Test (SART) and the Attention Network Test (ANT) were used to assess executive functions. Based on the analysis of the correlation and analysis of moderation, we found that subjects with higher weight in the study group presented a lower efficiency of the inhibition processes and gave more impulsive and incorrect answers. The occurrence of impulsive reactions might contribute to the risk of excessive weight in children and adolescents with ADHD. In recent years, hypotheses regarding the association between attention deficit hyperactivity disorder symptoms and overweight/obesity have been verified. Hypotheses concerned, among others: genetic factors ,2,3,4, eDespite the increasing body of research on ADHD, the results of the studies assessing the relationship between executive function deficit and the risk of obesity in people with ADHD are incongruent. In the study of Graziano et al. , carriedThis study aimed to assess the relationship between measures of executive functions and body weight, and BMI in children and adolescents with ADHD and control subjects. We hypothesized that there is an association between the intensity of ADHD symptoms, such as inattention, impulsivity, and executive function deficits, and excessive weight, which would be of a broader range in the ADHD subgroup compared to healthy control.Several essential features distinguish our work from the published studies. First, we used computerized behavioral procedures known to be valid measures of ADHD-related executive deficits in the Sustained Attention to Response Task (SART)  and theThe clinical group consisted of 60 patients from the outpatient psychiatry clinic, aged from 8 to17 years , from which 58 were qualified for further statistical analysis (two were excluded because of extremely low accuracy in cognitive tasks). The diagnosis was performed according to the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders  for oneControl group consisted of 62 healthy age and sex-matched children and adolescents from primary and secondary schools, aged from 8 to 17 years . The process of control group recruitment was based on a questionnaire filled by the participants’ parents, which provided information about the child’s physical and mental condition. Based on the obtained information, we included participants who did not exhibit any attentional problems, neurological or psychiatric diagnosis, dyslexia, previous brain injury with the loss of consciousness, and had no close family members  with ADHD/ADD diagnosis or any disease that could cause weight gain (same criteria as for the ADHD group).All participants received 150 Polish Zloty (PLN) for the study.Bodyweight was measured with a medical scale (Radwag PUE c/31) with an accuracy of ±100 g. Height was measured using a Harpenden anthropometric instrument with an accuracy of 0.1 cm according to the standard technique ,28 by trThe calendar age at the time of the test was calculated on the basis of the date of examination and the date of birth of the patient. The age was determined in annual intervals.Two cognitive tests were used: the Sustained Attention to Response Test  and theSART  allows ANT , double (two stars), or peripheral . The cues provided participants with information about the temporal and spatial aspects of the upcoming target. The reaction time and accuracy of the reaction were recorded. The effectivity of attentional networks was determined according to standard formulas . t-test with Welch’s correction or the Mann–Whitney test depending on the distribution of analyzed variables. The Pearson correlation coefficient was used to assess the relationship between standardized values of height, weight, and BMI with age. The effect of the cumulated dose of drugs on the risk of overweight and obesity in the study group was assessed using logistic regression analysis. Intergroup comparisons for continuous variables were performed using the Student’s Correlation analysis for cognitive performance indicators was conducted using the Spearman Rank Correlation (Spearman rho) correlation coefficient due to the lack of normal distribution of many variables. Moderation analysis was performed using linear regression. The significance of the effects was determined based on heteroscedasticity-consistent standard errors (HC3). Additionally, following the postulated link between the decreased executive and inhibitory control and obesity in ADHD, we ran a series of moderation analyses. Key performance indices were used as predictors, and their relationship with participants’ weight was moderated by Group (ADHD vs. control). The quality of executive/inhibitory control was captured using three indices considered most valid in the context of ADHD-specific deficits, that is, executive control scores from ANT, and, Coefficient of Variation, and Accuracy in no-go trials from SART. As the cognitive performance scores were not age-referenced, in each of the analyses, they were regressed on age before entering the model. Two strongly skewed indices in SART were normalized using square-root transformation. Interpretation of lower-order effects in regression was facilitated by sum-contrast coding of the grouping variable . Moreover, in the modelling of norm-referenced body mass, centred height was added to the model as a necessary predictor of body weight not taken into account in age and sex referenced norms.p < 0.05. R environment  was usen = 16 (27.6%)  predominantly had the inattentive subtype, and n = 1 (1.7%) boy had predominantly the hyperactive-impulsive subtype. In 43 (74.1%) patients at least one comorbid condition was diagnosed. The most common were oppositional defiant disorder (ODD) . Specific developmental disorders of scholastic skills were found in n = 21 patients (36.2%), tic disorder in n = 2 (3.4%) patients, and conduct disorders in n = 1 patient (1.7%). Boys constituted 79.3% of the ADHD study group (46 out of 58 patients). Forty-one (70.7%) patients  had combined subtype of ADHD, n = 53, 91.4%, including 42 boys). The most frequently used were methylphenidate (MPH) osmotic release oral system (OROS) (74.1% of the sample) and sustained-release (SR) (17.2% of the sample). The average daily dose of methylphenidate OROS was 36.8 mg/day , methylphenidate SR 27.0 mg/day . Adjusted for a holiday break, the average duration of pharmacological treatment for OROS methylphenidate was 1096.9 days , and for methylphenidate SR, the average was 1188 days  (Most of the respondents at the time of study received medication (39 days) .Norm-referenced   standardSignificant differences between the study and the control group were obtained in SART in terms of accuracy in go trials, accuracy in no-go trials (requiring response inhibition), and the coefficient of variation .Significant differences between the study and the control group in ANT were observed in average reaction times, accuracy in both congruent and incongruent trials, and the index of Executive control efficiency .p = 0.042, rho = −0.27)  .The regression analyses did not reveal significant effects of moderation for the executive score in ANT, both for body weight and BMI. In terms of SART, statistically significant results were obtained for: (1) Coefficient of Variation and (2) Accuracy in no-go trials. Both moderation effects were obtained in analyses where norm-referenced standardized body mass was the dependent variable. In the case of the model described in A qualitatively similar effect was obtained in the second analysis, where the more effective inhibition processes represented by a higher accuracy in the no-go trials were accompanied by higher body weight in the control group and lower in the ADHD group.(1)Participants with ADHD made significantly more errors in both ‘go’ trials, which showed their less effective sustained attention, as well as in ‘no-go’ trials, which demonstrated less effective response inhibition and greater impulsivity. These results are consistent with the observations of Johnson et al. , O’Conne(2)Participants with ADHD had significantly higher values of the Coefficient of Variation, which means lower stability of reaction times, thus less stability of attention processes . These In our study, participants with ADHD showed less effective processes of sustained attention, less effective response inhibition, and lower stability of attention compared to the control group. In SART:(1)Participants with ADHD had significantly longer time in response to incongruent stimulus presentation, which indicated less effective inhibition of the attention orienting and greater distractibility. This is consistent with the results of studies of Konrad et al. , Johnson(2)Participants with ADHD had significantly longer total reaction time in the task, which meant less effective attention processes during the whole task. This result is consistent with the results of studies of Johnson et al.  and Krat(3)Participants with ADHD achieved a lower accuracy in congruent trials, which indicated worse stability of attention processes and lower accuracy in incongruent trials, which indicated greater sensitivity to distractors.(4)Participants with ADHD did not differ from the control group in terms of Alerting and Orienting, which indicated a similar level of attention span and similar time to redirect attention to the stimulus . These They responded faster to stimuli and had a lower mean reaction time in response to ‘go’ trials-this difference was not statistically significant. In addition, in our study, participants with ADHD compared to the control group showed pronounced deficits in the executive control, i.e., they were more susceptible to distraction, were characterized by less stable attention processes, and less effective inhibition of the attention orienting. In the ANT:Correlation analysis and moderation analysis were performed to assess the associations. The results obtained in the ADHD group for accuracy in ‘no-go’ trials in SART, requiring response inhibition to stimuli, correlated negatively with standardized body weight, in relation to the standards of Kułaga et al. This indicated that children and adolescents with a higher body weight presented lower efficiency of inhibition processes and provided more incorrect and impulsive responses. A similar conclusion can be formulated based on the results of the moderation analysis-a significant effect was obtained for (1) accuracy in ’no-go’ trials and (2) Coefficient of Variation. Both moderation effects were obtained for analyses where standardized body mass was the dependent variable, which meant that in the study group, better performance in the ‘no go’ trials and greater stability of attention processes were associated with lower standardized body weight.The results of the moderation showed an interesting discrepancy in the models of the relationship between the effectiveness of the inhibition process and weight. The relationship in the control group was significantly different than in the ADHD group. It may suggest that excessive weight in healthy children and adolescents is conditioned by other variables than in children and adolescents with ADHD. There were no statistically significant correlations for the executive score obtained in the ANT and body weight and BMI both in the study group and in the control group. This lack of significant relationships with ANT indices might, to some extent, reflect moderate reliability of network efficiency scores , which,Rapid-response impulsivity, also referred to as response inhibition or impulsive action, is one of the dimensions of impulsivity , which cThe obtained results may suggest that the problem of excessive body weight in children and adolescents with ADHD is more clearly associated with the severity of impulsivity symptoms rather than attention deficits. However, they do not allow for an unambiguous determination whether the body weight increases as a consequence of less effective inhibition, and thus, for example, impulsive food intake, or whether inhibition becomes less effective with increasing body weight, or maybe we are dealing with both phenomena. What is the role of attention deficits?


Attention deficit hyperactivity disorder (ADHD) is one of the most common treatable psychiatric illnesses that affect all age groups from children to adults. Most commonly it is diagnosed in childhood or during teenage years. It can affect the mental and physical health of an individual and disrupt normal academic, career, and social functioning. The quality of life of the individual is affected; thus if diagnosed and treated, the results are good. Obesity and eating disorders are one of the comorbidities associated with ADHD and can lead to various other health problems. This study was done to find out the association between ADHD, obesity, eating disorders, and the effect of medication. We collected data from various studies through multiple electronic databases such as PubMed and Google Scholar. We found 8610 relevant articles and finally narrowed it down to 30 using various criteria. An association was found between ADHD, obesity, and eating disorders, although the mechanism linking ADHD, obesity, and eating disorders still remains unclear according to most studies. Some studies say ADHD medication helps in losing gained weight; some say they do not affect the weight. In today’s world, psychiatric conditions have to be dealt with extreme importance as factors like stress and lifestyle can lead to mental health problems, which in turn can cause various systemic illnesses. These psychiatric conditions are both common in children and adults. Common classification of psychiatric conditions includes anxiety disorders, mood disorders, and personality disorders. Some psychiatric conditions present early in childhood and have the best outcome if diagnosed and treated early. Various studies have been done linking psychiatric conditions with each other, however not many studies have been done linking psychiatric conditions with systemic disorders.Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric issues that affect all age groups including children, adolescents, and adults all over the world . ADHD caObesity is a major health issue that affects all socioeconomic classes across the world . In todaThere have been various studies that show there is a relationship between ADHD and obesity ,7. The sThere are various hypotheses explaining the relationship between ADHD and obesity. Studies have shown that obese individuals can present with ADHD, while individuals with ADHD can be diagnosed with obesity . Some stThus, this study is aimed to link the connection between ADHD, obesity, eating disorders, and how medical management of ADHD alters the course of the above comorbid conditions.MethodWe collected data through electronic databases: PubMed and Google Scholar. The data was collected in September 2020. The keywords used were “ADHD,” “obesity,” “overweight,” “eating disorders,” “psychostimulants,” “amphetamines,” “methylphenidate,” both separately and in combination with each other. We found 8610 articles with the help of the above keywords. The articles were screened by going through the abstracts in comparison to our research question. Inclusion and exclusion criteria were applied, and articles were further narrowed down to the most relevant and updated ones. The inclusion criteria were studies conducted on humans, studies in English, and studies in the last 10 years, relevant to our topic and research questions, peer-reviewed and full texts, or abstracts. The exclusion criteria were gray literature, letter to the editor, editorials, duplicates, overlapping studies, and animal studies.ResultsA total number of 8610 studies were identified from the databases searched. The inclusion criteria were of studies conducted on humans, studies in English, and studies in the last 10 years, relevant to our topic and research questions, peer reviewed and full texts, or abstracts. Based on the inclusion and exclusion criteria, articles were reduced to 355. The articles were screened, and the most relevant and updated studies were further shortlisted to 30. A total of 30 shortlisted articles were studied and analyzed [DiscussionADHD and ObesityObesity is a common risk factor associated with ADHD. Various studies have linked the association between ADHD and obesity. Children diagnosed with ADHD have an increased risk of developing obesity. A study conducted by Goulardins et al. about the relationship between ADHD and obesity suggests that obesity is a common risk factor for ADHD, suggesting that people with ADHD can develop obesity . Many otImpulsivity, which is the main symptom of ADHD, causes weight gain, thus causing obesity. Responses to food cues cause impulsivity, thus causing obesity. Matheson et al. suggested that the main cause of obesity in ADHD is impulsivity . The stuSome studies say gender has a role in determining the association between ADHD and obesity. A cohort study by Castaneda et al. consisting of 1001 subjects showed that female patients with ADHD were associated with childhood and young adulthood obesity. The strongest association was in 10 and 12 years in female children . HoweverMany psychosocial risk factors have been noticed to cause obesity and ADHD. These include physical abuse, parental divorce, prolonged separation of parents, and stress. The research by Pauli-Pott et al. suggests that psychosocial risk factors can cause obesity and ADHD . AnotherADHD, Eating Disorders, and the Effect of MedicationADHD and eating disorders have recently been closely linked together, showing an association between these two. However, the reasons for this connection remain unclear. For instance, researchers believe that the same underlying factors cause ADHD and eating disorders. For example, eating disorders might result from trying to manage ADHD stress. ADHD, obesity, and eating disorders have shared neuropsychological dysfunction. A study by Van der Oord et al. supports this statement by finding an association between obesity and ADHD. Obesity, not alone, but with binge eating, is associated with ADHD . AnotherThe impulsiveness in patients with ADHD leading to eating disorders was found to be more common in obese females when compared to males. A study by Nazar et al. also suggested that the eating disorders associated with ADHD are more commonly noticed in females . The stuPatients with ADHD are also highly likely to experience mood swings and depression. Depression is commonly known to cause eating disorders. Thus, patients with ADHD who suffer from depression have eating disorders. Tong et al. did a study and found the link between ADHD and depression, causing eating disorders .A deficiency in the two neurotransmitters, norepinephrine and dopamine, can lead to an increased desire to overeat, poor self-esteem, inability to follow a meal plan, and inability to accurately judge portion size, and inability to put an end to binge eating. ADHD was the first disorder found to be the result of the deficiency of the above neurotransmitters. ADHD is also found to respond to medications that correct the deficiency of these neurotransmitters. For example, stimulant drugs like amphetamine and methylphenidate are the treatment of choice for ADHD. Simultaneously, these medications can make the patient feel less hungry and make their bodies burn calories faster than usual. These medications are used to help people lose weight and treat binge eating . GranatoHowever, in some studies, a higher frequency of obesity was found associated with methylphenidate use; this may be due to the change in eating habits. Patients taking drugs can eat more frequently at night when the drugs no longer affect the symptoms' level and suppress appetite. A study done by Hanć supports the fact that methylphenidate does not help in weight loss with ADHD . AnotherLimitationsThe mechanism involving the association between ADHD, obesity, eating disorders, and the effect of medication still remains unclear. Various studies also contradict each other of the effect of ADHD medication on obesity. The other limitations of this traditional review were that only studies from the last 10 years were included, and only human studies were included.This study was done to determine how ADHD, obesity, eating disorders, and ADHD medication are connected and how they affect each other. Children diagnosed with ADHD have an increased risk of developing obesity. Impulsivity is the main symptom of ADHD that causes patients to develop obesity. The mechanism involved in linking ADHD and obesity remains unclear. Children diagnosed with ADHD are found to have lesser physical activity and more screen time; thus, having a sedentary lifestyle increases obesity. Gender also plays a role in this association. For example, males tend to be more hyperactive than females, hence losing the gained weight more easily than females. Therefore, females have a higher risk of developing obesity. Various psychosocial factors and a bad childhood also play a role in developing obesity as depressed individuals have erratic eating patterns leading to obesity. The impulsive and inattentive behavior in individuals with ADHD causes them to have erratic eating patterns, leading to obesity. Individuals are more likely to suffer from depression, hence more likely to gain weight. Further research is needed to find out the exact mechanism. More studies involving patients of various age groups, gender, and medication effects also need to be addressed.


High throughput sequencing of bacterial 16S rRNA genes and metagenomes were applied to analyze complex microbial communities inhabiting the human gut and other body sites, and their role in numerous diseases. Studies in animals were important for elucidating the effects of the gut microbiota on the brain and behavior, and the responsible mechanisms. Recent studies in patients have identified bacterial taxa of the gut microbiome possibly impacting different neurological and psychiatric disorders including ADHD. Furthermore, antibiotic treatment of infections globally shape compositions of gut microbiota and might indirectly influence ADHD development. However, published studies revealed still partially incongruent results. Potential reasons for the still ill defined role of gut microbiota in ADHD comprise a) different study designs b) small number of patients c) different age groups analyzed d) inclusion of only treatment naive patients versus patients under medication e) differences of males versus females ratios and f) the heterogenous technology applied for microbiome sequence analysis. Furthermore, the complex interplay between the gut microbiome and individual host genetic factors requires much larger sample sizes and additional patient genome information. Alternative treatment options like probiotics or dietary interventions for ADHD therapy might offer new opportunities to prevent or treat this increasingly common disease. Clearly, further studies are needed to clarify molecular mechanisms imparting the gut brain axis as basis to modify commensal microbiota or their functions to target ADHD. The purpose of this review is to evaluate the most recent literature on the role of the gut microbiome in ADHD. Complex microbial communities inhabiting the human body are a major factor influencing the development of several diseases and are the determinant for functionally important pathogen-host interactions. The analysis field of human microbiome science is currently undergoing a period of exponential increase of knowledge. , 2.A functional link between microorganisms of the gut and the brain has long been postulated, but in recent years, reports on causal effects of the gut microbiota on brain functions and behavior, and the underlying molecular mechanisms of the gut-brain-axis have begun to be elucidated.Animal models implied that stress potentially could directly or indirectly alter the gut microbiome composition and that pathogens of the gastrointestinal tract can affect host behavior. For example, germ-free (GF) mice displayed enhanced hormonal response to stress induced by individual isolation, suggesting that the gut microbes impact the neuroendocrine hypothalamic–pituitary–adrenal (HPA) axis, the central stress response system. Furthermore, GF animals treated with antibiotics showed reduced anxiety-like behavior compared with specific pathogen-free (SPF) controls: GF mice stayed longer in the illuminated compartment of the light–dark box and on the open arms of the elevated plus maze, than their SPF counterparts. In a rat model of anxiety and depression, Schmidtner et al.  could reDue to a lack of suitable animal models and only few studies with still severe technical limitations in humans relatively little is known about the role of gut microbiota in the pathogenesis of ADHD and molecular mechanisms potentially involved have not been defined so far. As of December 2019 and starting in 2017, only 7 studies with relatively limited numbers of patients and controls have been published, which describe associations of microbiome patterns and ADHD in different cohorts applying diverse analytical methods. In the following, the main findings, special aspects and limitations of these studies, which are summarized in In 2017, Aarts et al.  publisheFaecalibacterium in children with ADHD compared to healthy controls. Differences in dietary habits between high-fat Western and Chinese diet might explain deviating microbial patterns in ADHD patients compared to other studies. However, the authors of the study did not include questionnaires to account for nutritional differences.Bypassing the potential interference of ADHD medication on microbial compositions, Jiang et al.  analyzedBacteroides and Neisseria spp. in ADHD children. However, the later did not differ in abundances but only in frequency between the study groups. Prevotella and Parabacteroides showed higher relative abundances in the control group. As a result, Prehn-Kristensen et al. suggest Neisseria and Bacteroides spp. as potential biomarkers for juvenile ADHD. The higher abundances of Bacteroides is in line with findings of Aarts et al.  and 17 male controls (mean age 13.1 years) were analyzed regarding differences of microbial compositions by Prehn Kristensen et al.  and the order Clostridiales (P=0.034). However, they do not provide hypotheses on possible functional interrelationships in ADHD. In addition, Desulfovibrio was also significantly associated with any other psychiatric disorder tested in this study, so enrichment of Desulfovirbio abundance is probably not specific for ADHD. The authors provide an innovative strategy, one of the major drawbacks of the study is probably the limited knowledge about the interaction of the intestinal microbiota and host genetics and the narrow availability of datasets which are mainly based on the co-occurrence of genetic factors of the host and his inhabiting microbes. The study is referring to four different GWASGM studies  were significantly decreased post-micronutrient treatment (from 0.98% to 0.28%) accompanied with an increase of Collinsella abundances. These results are indicating a potential modulatory effect of micronutrients on putative probiotic bacterial species. However, the role of Bificobacteria in ADHD is contradictory and vague. While studies have shown protective effects of Bifidobacterium longum against psychiatric disorders including ADHD  over a period of 10 weeks and fecal samples were collected using OmnigeneGut™ stool collection and stabilization kits (DNA Genotek). Micronutrient supplementation was associated with 50% responder rates, resulting in reduced impairment and improved attention as well as emotional regulation and aggression when compared to the placebo group (29% response). However, ADHD-IV-RS and CGAS metrics did not differ significantly in both groups. Microbiome sequencing based on 16S rDNA amplicons did not indicate changes of microbial alpha-diversity during micronutrient or placebo administration although the micronutrient group showed a higher species richness compared to the control group. On the compositional level, beta diversity did not change considerably in the course of the study within both groups. Also, metabolic capacities of the microbiome bioinformatically predicted from 16S rDNA data did not show significant changes. However, relative amounts of the phylum Actinobacterium  than the controls (mean abundance = 0.02%), whereas members of the genus Lactobacillus were reduced in ADHD. This is in line with the findings of Aarts et al, who found also Bacteroides uniformis and B. ovaus being enriched in ADHD patients. Also, dietary intake varied between both study groups. ADHD-patients had a significantly higher intake of grains versus a lower intake of dairy and vitamin B2. Sutterella stercoricanis abundances correlated with the intake of dairy, nuts/seeds/legumes, ferritin, and magnesium, while Bacteroides uniformis was associated with the consumption of fat and carbohydrates. However, it has to be noted, that species level discrimination using short-read technologies for 16S rDNA-based sequencing is challenging. The genus Bacteroides share very similar 16S rDNA sequences, especially within the sequenced V3 and V4 variable regions. A non-accurate species classification will introduce bias in relative abundances of the respective species and will distort statistical interpretation. The authors did not validate their findings using alternative methods such as species-specific quantification by qPCR. Also, dietary habits were found to be different between ADHD and control groups. This aspect clearly illustrates particular critical issues of association-based microbiome studies. Differentially altered microbial compositions may only reflect variations in dietary intakes or other environmental factors.In a Taiwanese study, the team around Wang et al.  comparedOf special importance, methodological differences in the protocols applied in the published reports might strongly influence the microbiome composition analysis and thus the scientific conclusions. Recapitulating the results from recent studies investigating the role of the human microbiota in ADHD development and its pathophysiology, the majority of studies applied high-throughput 16S rDNA sequencing to investigate differential patterns in the microbiome of ADHD patients compared to healthy cohorts. The heterogeneity in accurate neuropsychiatric diagnosis of ADHD and a vast number of confounding environmental factors which are known to shape microbial compositions in the intestine like nutritional intake, ADHD-targeted or other medication as well as elevated physical activity of ADHD patients compared to controls might impede clear associations of microbial patterns. In addition, methodological obstacles are impeding identification of accurate and reproducible microbial compositions. To date, microbiome sequencing is still lacking standardization and implementation of precise quality controls, which lead to an overall low comparability of microbiome studies . VariatiDespite the fact that ADHD is the most common neurodevelopmental disorder worldwide affecting more than 7% of the children of whom more than half continue to have the disorder in adulthood, relatively little is known on the contribution of microbiome shifts to ADHD predisposition and development so far. Due to high variability in cohorts, important methodological differences of microbiome analysis as well as frequently very low number of included patients  it is too early to define predictive microbiome biomarkers for ADHD or draw conclusions about underlying microbiome-mediated pathomechanisms of the disease. In addition, the analysis of the taxonomic composition of the microbiome alone is a clear limitation and frequently an inappropriate correlate with host phenotype, which might be better predicted by prevalent microbial molecular function or genetic differences on the sub-species level of bacteria. This conception was the basis for the development of the second phase of the human microbiome project, HMP, the Integrative HMP (iHMP or HMP2) , which wAG, JG, and AH contributed conception and design of the study, performed the database searches and wrote parts of the manuscript. All authors contributed to the article and approved the submitted version.Funded by the Deutsche Forschungsgemeinschaft , project ID B13 - TRR221” provided to AG.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.


Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have high rates of co-occurrence and share atypical behavioral characteristics, including sensory symptoms. The present diffusion tensor imaging (DTI) study was conducted to examine whether and how white matter alterations are observed in adult populations with developmental disorders (DD) and to determine how brain–sensory relationships are either shared between or distinct to ASD and ADHD.n = 105, ADHD: n = 55) as well as age- and sex-matched typically developing (TD) participants (n = 58). Voxel-wise fractional anisotropy (FA), mean diffusivity, axial diffusivity, and radial diffusivity (RD) were analyzed using tract-based spatial statistics. The severities of sensory symptoms were assessed using the Adolescent/Adult Sensory Profile (AASP).We collected DTI data from adult population with DD (a primary diagnosis of ASD: Categorical analyses identified voxel clusters showing significant effects of DD on FA and RD in the posterior portion of the corpus callosum and its extension in the right hemisphere. Furthermore, regression analyses using the AASP scores revealed that slopes in relationships of FA or RD with the degree of sensory symptoms were parallel between the two DDs in large parts of the affected corpus callosum regions. A small but significant cluster did exist showing difference in association between an AASP subscale score and RD across ASD and ADHD.Wide age range of the participants may be oversimplified.These results indicate that white matter alteration and their relationships to sensory symptoms are largely shared between ASD and ADHD, with localized abnormalities showing significant between-diagnosis differences within DD. Autism spectrum disorder (ASD) is a developmental disorder characterized by impairment of social interaction and repeated restricted behavior . AttentiNeuroimaging studies contrasting either ASD or ADHD against typically developing people (TD) have shown that both disorders are characterized by atypicality in functional as well as in structural connectivity (reviewed in –13). ThuSensory symptoms include both hyper- and hyposensitivity to textures, smelling, touching, visual, or auditory input (reviewed in ). PractiAs mentioned above, prior studies have examined the brain–symptom relationship across different clinical diagnoses with the perspective of ASD symptoms –16. AlthThe current study tested three hypotheses. First, assuming that different diagnostic groups have different DTI parameters, we examined the effect of a diagnosis of ASD and ADHD on DTI parameters in 218 adults with or without developmental disorders to capture consistencies or inconsistencies in the results of prior studies. Second, we performed dimensional analyses to examine similarities in the brain–sensory symptoms relationship across diagnostic groups with a hypothesis that the brain–sensory relationship is independent of the clinical diagnosis. Finally, we conducted interaction analyses to see distinctions in brain–sensory symptoms relationships between diagnostic groups with an assumption that brain–sensory relationship is influenced by the clinical diagnoses. We selected data with small levels of head motion because it impacts the results of DTI analysis . We usedn = 105) or ADHD (n = 55) and 58 TD participants, matched for age and sex. Clinical participants were recruited from the authors’ outpatient clinic at the Medical Institute of Developmental Disabilities Researches at Showa University, while TD participants were recruited via advertisement or acquaintances of the authors. After a multidisciplinary team, consisting of psychiatrists and psychologists, assessed all participants, clinical diagnosis of ASD and ADHD was made based on DSM-IV-TR. Among the 105 individuals with ASD, we administered the Autism Diagnostic Observation Schedule (ADOS) [n = 101, ADHD: n = 33, TD: n = 58) [n = 86, ADHD: n = 46, TD: n = 18) using Conner's Adult ADHD Rating Scales (CAARS) [n = 105, ADHD: n = 44) were evaluated using either the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) or WAIS-Revised (WAIS-R) [χ2 (2) = 2.10, P = 0.35). Absence of a psychiatric diagnosis in TD participants was confirmed using the Mini-International Neuropsychiatric Interview [n = 62, ADHD: n = 44, TD: n = 38). The AASP is a self-reported questionnaire consisting of 60 items from the following sensory sections, taste/smell processing, movement processing, visual processing, touch processing, activity level, and auditory processing. Participants were asked to respond to each item on a five-point Likert scale from “almost never” to “almost always.” Each item belongs to one of four subscales: Low Registration (hyposensitivity), Sensation Seeking, Sensory Sensitivity (hypersensitivity), and Sensation Avoiding. One participant failed to complete all the items included for Sensation Avoiding. These subscales were contrasted in the three groups using F tests. To correct multiple comparisons, we adopted the Bonferroni method and set the threshold for significance at P < 0.0125 . The Institutional Review Board of Showa University Karasuyama Hospital approved all of the procedures adopted in this study. Written informed consent was obtained from all the participants after fully explaining the purpose of this study. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.We recruited 160 adults with a primary diagnosis of ASD (e (ADOS) , 31 to 8e (ADOS) . To furt (CAARS) . The int(WAIS-R) , 36. As nterview . In the nterview . Each panterview  (ASD: n b = 1000 s/mm2. The directions of gradients were optimized according to a previous study [b0) interspersed throughout the sequence.All magnetic resonance imaging (MRI) data were obtained using a 3 T MR Scanner  with a 12-channel head coil. Diffusion-weighted images were acquired using a single-shot, spin-echo, echo planar imaging sequence. The acquisition parameters were as follows: repetition time = 13,700 ms, echo time = 79 ms, field of view = 200 × 200 mm, matrix size = 100 × 100; 75 contiguous axial slices of 2.0 mm thickness without gap, phase-encoding direction = anterior–posterior, 65 non-collinear motion-probing gradients, and us study . The acqhttps://www.fmrib.ox.ac.uk). DTI data are potentially at risk for a wide variety of artifacts, including motion artifacts and eddy current. Therefore, automatic artifact correction was conducted for all images using DTIPrep [b = 0 image with affine transformation for correcting distortions. We used FSL rmsdiff functions to calculate root-mean-square (RMS) deviation of absolute intervolume displacement with respect to the first image of each run [Images were preprocessed with FSL version 5.0 , mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) images were calculated using the DTIFIT function for all participants. Generated FA images were registered to the Montreal Neurological Institute (MNI) 152 standard space using nonlinear registration. Normalized FA images were averaged to create a mean FA image, which was then thinned to create a mean FA skeleton . Voxel-wF test to examine the main effect of diagnosis on DTI parameters using the FSL randomize tool with age, sex, and motion as nuisance covariates. The contrasts were tested with 5000 permutations. The statistical threshold was defined at P < 0.05, correcting for multiple comparisons by threshold-free cluster enhancement (TFCE). We focused on clusters with a minimum size of 10 voxels. Post hoc pairwise group comparisons were made for clusters with a significant main effect of diagnosis on DTI parameters.We performed an F tests of FA and RD. The nuisance covariates included age, sex, and motion. The analysis was performed independently for all the four AASP subscales. The statistical threshold for significance was defined at P < 0.05, the TFCE corrected, and the spatial extension threshold was set to k > 10 voxels.We performed the dimensional analyses to examine the relationships between sensory symptoms and DTI parameters. In these analyses, we used a vector of AASP subscale scores of all the subjects as an effect of interest. Here, we performed the analysis only for FA and RD maps because a significant main effect of diagnosis was not identified in the MD or AD map see “” sectionF test and repeated for all the four AASP subscales. We adopted a threshold for statistical significance at P < 0.05, corrected for multiple comparisons using TFCE with a minimal number of voxels larger than 10.We examined the interaction of the slope in relationships between the sensory symptoms  and DTI parameters of FA and RD. The variables of interest were the element-wise product of the vector of an AASP subscale and the vector representing diagnostic status: (1) ASD or non-ASD, (2) ADHD or non-ADHD, and (3) TD or non-TD. The three vectors representing the diagnostic status (1–3) were also included in the model. The other nuisance covariates included age, sex, and motion. The analyses were conducted on voxels of the binary mask image identified in the To increase biological homogeneity, we repeated the analyses at cluster level with a narrow age range , focusing on only males. By narrowing the age range, the number of participants was reduced to 161 .F tests showed no significant differences in age, sex, or full-scale IQ (FIQ) among the three groups (P > 0.1). The main effect of diagnosis was observed in all of the four subscales of the AASP. Post hoc tests of Low Registration, Sensory Sensitivity, and Sensation Avoiding showed the same pattern when compared with TD; individuals with ASD and those with ADHD showed significantly higher scores, with no significant difference between these two clinical groups  = 1.185, η2 = 0.0177, P = 0.31 for Fig. F = 0.036, η2 = 0.0088, P = 0.96 for Fig. F = 0.90, η2 = 0.0301, P = 0.41 for Fig. F = 0.22, η2 = 0.0123, P = 0.80 for Fig. The dimensional analyses revealed that RD values in the posterior body of the corpus callosum were negatively associated with Sensation Seeking Table a, b and F, 138 = 1F, 138 = 1F, 138 = 1F, 138 = 1F, 138 = 1F, 138 = 1F test confirmed significant between-group differences in the slopes of the FA-Sensory Sensitivity relationship  = 5.57, η2 = 0.0746, P = 0.005). On the other hand, the analysis with RD showed significant interaction in Sensory Sensitivity score and RD value between ASD and ADHD groups  = 9.61, η2 = 0.159, P = 0.0001). The cluster was located in the right posterior corpus callosum. Other subscales of the AASP did not show any significant results in any DTI parameter.Significant interaction in the Sensory Sensitivity–FA slope was observed between TD and DD groups. People with ASD had negative correlation between Sensory Sensitivity score and FA in the midbody of the corpus callosum, while TD people showed positive correlation Table a, b. An ip  = 1.08, η2 = 0.031, P = 0.34; RD-Sensation Avoiding: F = 0.417, η2 = 0.020, P = 0.66; FA-Sensory Sensitivity: F = 0.84, η2 = 0.033, P = 0.44; FA-Sensation Avoiding: F = 0.83, η2 = 0.032, P = 0.44).As shown in Additional file F = 5.44, η2 = 0.093, P = 0.006). In terms of the interaction between ASD and ADHD groups, the supplementary analysis showed a similar pattern as the primary analysis. RD values from the posterior corpus callosum area were positively correlated with Sensory Sensitivity in individuals with ASD, but negatively associated in individuals with ADHD  = 5.01, η2 = 0.103, P = 0.008).In the supplementary interaction analyses, the pattern of the results remained the same as in the primary analyses. In line with the primary analysis, the slope of FA-Sensory Sensitivity was positive in the TD controls, while it was negative in both the ASD and ADHD groups . In the conventional approach of categorical group comparisons, we used F tests and identified the main effects of diagnosis on FA and RD values in regions around the body and the splenium of the corpus callosum. Post hoc tests revealed that, compared with the TD group, FA values were lower in individuals with ASD and with ADHD, while they were not significantly different from each other. In terms of RD, individuals with ASD and with ADHD had higher values compared with the TD group, again, in the posterior part of the corpus callosum. The dimensional analysis, using the scores of sensory profiles, demonstrated an area in the isthmus of the corpus callosum where the three groups showed comparable relationships between the DTI parameters and sensory problems. In contrast, the interaction analyses showed significant results in a portion of the corpus callosum; the DD groups had a negative association between FA and Sensory Sensitivity, while the TD group showed a positive correlation. The interaction analysis with RD showed that individuals with ASD and those with ADHD had different associations between white matter organization and Sensory Sensitivity. These findings demonstrated both similarities and distinctions in DTI parameters and in relationships with sensory symptoms between the two developmental disorders of ASD and ADHD.The current TBSS study enrolled a relatively large number of participants: 218 adults . These results were not consistent with our expectations. However, they were fully consistent with the results from a study contrasting the scores of a similar measure called Sensory Profile  across pThe dimensional analysis identified the body of the corpus callosum as being associated with sensory symptoms. Given that the region involves fibers connecting sensory areas of both hemispheres , it is rThe analysis showed significant interaction between Sensory Sensitivity and RD values in the posterior portion of the corpus callosum between individuals with ASD and those with ADHD. Among the four subscales, Sensory Sensitivity was the only subscale that showed such interaction. Four domains of AASP differed in interacting principles of neurological thresholds and behavioral responses . SensoryThere are some limitations in the current study. First, the age range of the participants is wide (from 20 to 55 years). Given that few studies have enrolled individuals with ASD in their forties or fifties, the current data might help to address the paucity of data for middle-aged individuals with ASD. However, a wide age range increases the heterogeneity of participants. As we included age as a covariate of nuisance and there was no significant difference in age between groups, the impact of this wide age range was minimized. Furthermore, we conducted supplementary analyses narrowing the age range, which showed a consistent pattern with the results of the primary analyses. Nevertheless, the results should be interpreted with caution, as the age range of the current study is wide. It should be noted that because of the nature of cross-sectional study, it was not possible to assume causal relationships between white matter and sensory symptoms. Future studies covering a wide age range should obtain longitudinal neuroimaging data to evaluate causal relationships between the corpus callosum and sensory symptoms. Besides age, there are some other factors that contribute to the heterogeneity of developmental disorders, such as functionality, comorbidity, and medication. Although we did not include people whose IQ was 75 or below, the inclusion criteria may not ensure biological homogeneity. In terms of comorbidity, we gave a priority to people with minimal comorbidities at recruitment, which is one of the strengths of the current research conducted at a research/clinical center specialized with the developmental disorders. However, formal and research-level diagnostic procedures were not conducted for other mental illnesses because of time and financial constraints. Recruitment of medication naïve participants was ideal to focus on the pathophysiology, which was not practically possible. These factors of origin of heterogeneity are also expected to be addressed in the future study. Second, we included both sexes. As we assumed that sex has an impact on the results , we inclThe current DTI study enrolled adults whose primary diagnosis was ASD or ADHD and compared them to TD people to investigate similarities and differences in white matter organization. We investigated brain–behavior relationships from the perspective of sensory symptoms. This study showed that, in some brain regions, the three groups showed similar relationships of DTI parameters to sensory symptoms,while in other brain regions the groups showed different relationships between the DTI and sensory symptoms. The current study provided insight into similarities and distinctions in the process of development of clinical ASD symptoms and subclinical traits across ASD, ADHD, and TD.Additional file 1:Table S1. Medications. Table S2. Descriptive statistics of the male subsample under 40 years of age. Figure S1. Post-hoc regions of interest analyses using the male-only subsample under 40 years of age. Figure S2. Post-hoc dimensional analyses using the male-only subsample under 40 years of age. Figure S3. Post-hoc regions of interest analyses showing significant interaction in the male-only subsample under 40 years of age.


Attention‐deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder associated with widespread brain morphological abnormalities. Here, we utilized a sulcal pits‐based method to provide new insight into the atypical cortical folding morphology in ADHD.Sulcal pits, the locally deepest points in each fold, were first extracted from magnetic resonance imaging data of 183 boys with ADHD (10.62 ± 1.96 years) and 167 age‐ and gender‐matched typically developing controls (10.70 ± 1.73 years). Then, the geometrical properties of sulcal pits were statistically compared between ADHD and controls.P < .05). We also found that ADHD boys were associated with significantly increased pit depth in the left superior frontal junction, circular insular sulcus, right inferior frontal junction, and bilateral cingulate sulcus, as well as significantly decreased pit depth in the bilateral orbital sulcus .Our results demonstrated that the number of sulcal pits was reduced and confined to the superficial secondary sulci in the ADHD group relative to controls (The experimental findings reveal atypical sulcal anatomy in boys with ADHD and support the feasibility of sulcal pits as anatomic landmarks for disease diagnosis. Sulcal pits analysis revealed decreased pits number in superficial secondary sulci and abnormal pit depth in multiple brain regions in boys with ADHD, providing new insights into this disease. For instance, Brun et al found a reduced pit depth in the left ascending ramus of the Sylvian fissure in children with autistic disorder relative to controls.However, although widespread morphological abnormalities in ADHD have been reported, the effect of this disease on the sulcus has not been fully elucidated. Therefore, the main aim of this study was to use sulcal pits to investigate the atypical cortical folding in ADHD, which may provide new insights into this disease. We first extracted sulcal pits in native space from magnetic resonance imaging (MRI) data of 183 boys with ADHD and 167 age‐ and gender‐matched typically developing controls (TDC) using an adaptive watershed algorithm. Next, we parcellated the white matter surface into several regions based on the group spatial distribution of sulcal pits. Then, the number of pits in deep/shallow folds and pit depth in each area was compared between groups. Finally, we investigated whether the hemispheric asymmetry of sulcal pits could distinguish ADHD patients from normal controls.22.1The MRI data utilized in this study were obtained from the Neuro Bureau ADHD‐200 Preprocessed repository. = 350, 183 ADHD and 167 TDC, ages: 7‐14 years old) was included in the study, with sample characteristics presented in Table Finally, a sample of 350 male right‐handed subjects . This method was not influenced by individual brain size.In this study, we adopted the method proposed by Auzias et al2.4To compare the differences in sulcal pits between groups, we computed the group‐level clusters of pits as presented in Auzias et alWe also generated a symmetric group‐level clusters of pits for asymmetric analyses. The procedure was similar to the above, with the following modifications. First, we performed individual spherical interhemispheric registration to obtain the anatomic correspondences between the left and right cortical surfaces, as introduced in Greve et al2.5k = 2 but found that the primary sulci and superficial sulci could not be well distinguished. Then, we tested k = 3 and it worked out well. The resulting three classes of basins including the deepest basins identified as primary sulci, the shallow basins identified as secondary sulci, and the shallowest basins identified as “dimples”  procedure was used for multiple comparisons corrections (number of tests/clusters = 205 or 104 for the corresponding parcellation). It is worth noting that although ADHD patients have typically lower IQ than normal controls,Statistical analyses were implemented in Python 2.7 using statsmodels 0.9. All statistical tests were two‐tailed. Linear regression models were used to explore the between‐group differences of the pits number in the primary sulci, secondary sulci, dimples, and the entire cortical folds, with the group as a dummy variable and age as a covariate. We also studied the linear effect of age on the pits number using simple regressions. The significance threshold was set at 33.1P = .02). When analyzing the deep early developing sulci, shallow later‐developing secondary sulci, and shallowest dimples separately, we observed that the number of pits in the secondary sulci was significantly reduced in ADHD patients compared with the control group (P = .01), but the number of pits in the deep primary sulci and dimples was not significantly different between groups (P > .05). In addition, we found that age had a significant linear effect on the pits number for all sulcal classes (P < .05).Group comparisons of the number of sulcal pits are shown in Figure 3.2P < .05, FDR corrected) is shown in yellow in Figure P = 8.56e‐4), circular insular sulcus , right inferior frontal junction , and bilateral cingulate sulcus , see Table P = 4.90e‐4 and right: P = 2.73e‐5). Nomenclature of the clusters mainly refers to the existing papers.The 3D distribution of the aberrant clusters in pit depth .The within‐group asymmetries in pit depth for the TDC and ADHD groups are illustrated in Figure 4In this study, we described the first sulcal pits analysis of boys with ADHD and matched controls. Our findings indicated that ADHD boys have a reduced number of sulcal pits compared with TDC boys. We further observed that the difference only occurred in the superficial later‐developing folds but not in the deep early developing folds. Additionally, we found that ADHD subjects accompanied by increased pit depth in several regions, including left SFJ and CIS, right IFJ, and bilateral CS. Meanwhile, reduced pit depth of the bilateral OS was observed in the ADHD group relative to controls.First of all, we found that the number of pits was reduced and confined to the superficial secondary sulci in the ADHD group. These results can also be observed using a different division technique of deep/superficial clusters (see Appendix When investigated at a finer scale, we observed abnormal pit depth in multiple clusters in ADHD, which may indicate important disorganization of the relevant brain regions. Sulcal pit depth is associated with the gyrification process and can serve to describe the morphology of cortical folding.In ADHD, significantly deeper pit depth was found in the left SFJ regions. The SFJ is often called the frontal eye field, which has been shown to play a central role in modulating processing in visual cortex for spatial attention.Additionally, increased pit depth was found in the left CIS region in ADHD boys. According to Lopez‐Larson et al,Our results also revealed a deeper pit depth in the right IFJ in the ADHD group relative to the TDC group. Decreased gray matter volumes and thinning cortical thickness in this region of ADHD patients were observed.We also observed deeper pit depth of the bilateral CS in ADHD boys compared to controls. The observed CS regions were located at the posterior part of the cingulate cortex, which is often found abnormal in ADHD. For example, using magnetoencephalography and functional MRI, Sudre et al confirmed that the posterior cingulate cortex was among the most atypical connectivity regions.By contrast, the bilateral OS was the only region in which the ADHD group showed shallower pit depth. The OS region is one of the earliest developed brain regions and can be identified around 16 weeks of gestation.Some important caveats should be considered in our study. First, the ADHD sample in this study was heterogeneous with two different subtypes, including about a third of the inattentive type and two‐thirds of the combined type. Future study can use a larger sample to compare abnormalities of sulcal pits between different ADHD subtypes. Second, only boys were included in our sample, so additional research is needed to determine whether our results could be generalized to girls with ADHD. Third, the unmatched IQ scores between the ADHD and TDC groups could be considered as bias, in particular concerning a previous study that suggests intelligence can affect the presence of sulcal pits.5Taken together, we conducted the first sulcal pits‐based analyses to reveal cortical morphological abnormalities in boys with ADHD. The results confirmed a distinct pattern of pits number between superficial sulci and deep sulci in ADHD relative to controls. Furthermore, significant between‐group differences of pit depth in several regional clusters were observed. These findings help to increase our understanding of ADHD pathology and support the feasibility of sulcal pits as a potential biomarker in the diagnosis of ADHD.The authors declare no conflict of interest.App S1Click here for additional data file.


This theory elaborates on how a particular trait once favoured in an ancient environment might become maladaptive upon environmental changes. However, given the lack of genomic data available for ADHD, these theories have not been empirically tested. We took advantage of the largest GWAS meta-analysis available for this disorder consisting of over 20,000 individuals diagnosed with ADHD and 35,000 controls, to assess the evolution of ADHD-associated alleles in European populations using archaic, ancient and modern human samples. We also included Approximate Bayesian computation coupled with deep learning analyses and singleton density scores to detect human adaptation. Our analyses indicate that ADHD-associated alleles are enriched in loss of function intolerant genes, supporting the role of selective pressures in this early-onset phenotype. Furthermore, we observed that the frequency of variants associated with ADHD has steadily decreased since Paleolithic times, particularly in Paleolithic European populations compared to samples from the Neolithic Fertile Crescent. We demonstrate this trend cannot be explained by African admixture nor Neanderthal introgression, since introgressed Neanderthal alleles are enriched in ADHD risk variants. All analyses performed support the presence of long-standing selective pressures acting against ADHD-associated alleles until recent times. Overall, our results are compatible with the mismatch theory for ADHD but suggest a much older time frame for the evolution of ADHD-associated alleles compared to previous hypotheses.Attention-deficit/hyperactivity disorder (ADHD) is an impairing neurodevelopmental condition highly prevalent in current populations. Several hypotheses have been proposed to explain this paradox, mainly in the context of the Paleolithic versus Neolithic cultural shift but especially within the framework of the  Its worldwide prevalence is around 5% in children and adolescents and its symptoms often persist into adulthood2. Twin studies have consistently shown high heritability rates of 70–80%3, a considerable fraction of which is explained by single nucleotide polymorphisms (SNPs)4. However, it has not been until recently that genome-wide association studies (GWAS) have reported significant variants associated with ADHD5. In practise, ADHD manifests as a notably impairing disorder with increased risk of psychiatric comorbidity, social dysfunction, injuries from traffic accidents, medical morbidity and premature death. These impairments translate into a reduced quality of life not only to ADHD patients themselves but also to their relatives8.Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterised by inattention, motor hyperactivity and impulsivity9 and yet is so prevalent seems counterintuitive from an evolutionary perspective, as one might expect natural selection to purge the risk alleles from the population10. In this regard, several hypotheses have been put forward to explain the evolutionary persistence of this disorder, each of them emphasising on different aspects of the ADHD phenotype 22. Moreover, even if introgressed sequences of archaic species were subjected to purifying selection shortly after admixture24, the addition of particular introgressed regions could have helped AMH adapt to this new out-of-Africa environment25. In fact, there is now mounting evidence of introgressed variants currently associated with human complex traits that have undergone positive selection in AMH22, including neurological and psychiatric phenotypes22. These results support the mismatch theory by positing that some Neanderthal alleles could have been beneficial for AMH as they moved out of Africa but became detrimental in modern environments.Besides natural selection, a demographic factor that could also explain the observed prevalence of ADHD in current populations is archaic introgression. The sequencing of the Neanderthal9, but current genomic data allows to assess the evidence supporting each proposed model. Here we study the evolutionary nature of ADHD from a genomic perspective by analysing traces of selection in AMH and assessing the role that archaic interbreeding played in the genetic variation associated with this trait. For that purpose, we used ancient samples to analyse the evolution of ADHD-associated genetic load over time, using European present-day genomes as a baseline. Moreover, we also evaluated these theories in the context of the Neolithic transition, considering African admitxure and archaic introgression.Overall, mismatch-related hypotheses in ADHD have been speculative27. In agreement with this hypothesis, a previous study28 described that genetic variants associated with schizophrenia (SCZ) are enriched in mutation-intolerant genes and in regions under strong background selection. Given that ADHD is an early-onset condition, we assessed whether a similar trend could be observed in this phenotype. MAGMA analysis29 statistically supported that LoF-intolerant genes (ExAC30 pLI ≥ 0.9) expressed in brain are enriched in variants associated with ADHD , which is in line with previous results5, thus suggesting that ADHD could be under natural selection pressures. Consistently, a significant enrichment in the heritability from ADHD-associated SNPs located in conserved regions of the genome was also observed in the aforementioned study5.It has been hypothesised that genetic variants associated with early-onset traits under natural selection could be enriched in genes that are intolerant to loss of function mutationsp ≤ 1e-8, the ancestral allele tended to be the risk allele as compared to SNPs that are not associated with ADHD in the GWAS analysis (i.e. those showing a GWAS p ≥ 0.9)  of each SNP. We observed that for statistically significant GWAS SNPs at fADHD, see Methods) and date estimates of ancient European samples from three different datasets  , we observed a significant negative τ between fADHD and sample age  and τNeolithic = −0.1574 (p = 0.034). The Pre-Neolithic dataset (which after filtering consisted of 16 ancient samples) also showed a negative trend although it was not significant . These results support a decrease in the frequency of ADHD alleles over time.Alluding to the most relevant characteristics of each dataset, we refer to these as the Pre-Neolithic35. Hence, we inspected whether the decay trend of current ADHD-risk alleles is expected over time independently of the association of the alleles with the ADHD phenotype. For each of the three datasets, we built a Kendall’s τ null distribution assuming independence of any association between the examined alleles and ADHD by randomising the risk alleles. This approximation is equivalent to examining sets of random SNPs matched to the allele frequencies of the ADHD-associated SNPs. Rejection of the null hypothesis was reported for all three datasets   with those SNPs associated with ASD compared to other tested psychiatric disorders . Similarly, when we compared African and European populations in the 1000 Genomes Phase 3 dataset, African samples also showed higher fADHD. This result is obtained independently of which set of SNPs defined for each of the ancient European datasets is considered . Additionally, the decay of ADHD-associated alleles over time could be explained by successive dilutions from European populations showing an excess of ADHD-protective alleles. Two main ancient migrations have been suggested to have shaped the genetic diversity of European populations: the steppe migration and the farmer Neolithic expansion38. Steppe migration cannot explain the observed sample time trend, as it considers a more recent time period than the one our samples span38. However, the admixture between hunter-gatherers (HG) and Neolithic farmers could explain the observed trend after the Neolithic expansion if HG had a lower burden of ADHD-associated alleles compared to Neolithic people. We observed a significant negative Kendall’s τ correlation between HG and fADHD  in the Neolithic dataset; suggesting that Neolithic-ancestry individuals tend to carry more ADHD-risk alleles than HG. However, since the percentage of HG ancestry increases over the course of the Neolithic period33, sample time could act as a confounder. Therefore, we repeated the analyses controlling for sample time and no statistically significant association between both variables was observed . Taken together, our analyses show that the observed trend between fADHD and sample time cannot be explained by ancestry dilution.Since GWAS measure associations between genotypes and phenotypes in a specific environment and population sample, some studies claim that no out-of-environment nor out-of-sample extrapolation can be performedfADHD and sample age did not consider that the ancient populations might be related over time. We estimated the mean increase in allelic frequency of ADHD alleles per generation including the relationship between ancient populations in the Pre-Neolithic dataset in an Approximate Bayesian Computation coupled to Deep Learning (ABC-DL) using a simple model that considers the average selective pressure acting over all the loci and genetic drift  and supports the existence of a selective pressure towards current protective ADHD alleles from Paleolithic to Neolithic times.When applied to the observed data, the posterior distribution of the trend of the allelic frequency of each ADHD-risk allele per SNP and generation had a mean of −7.72e-5 . This result corresponds to an average decay of 0.12 in the frequency of each ADHD-increasing risk allele since Palaeolithic times until present . This positive correlation indicates that statistically significant p-values tend to be enriched by negative tSDS values, and non-significant p-values by tSDS close to 0. Positive values of tSDS imply that the frequency of the ADHD-risk allele increased in the past, whereas negative tSDS imply that the ADHD-protective allele is increasing in frequency44. Our result supports that polygenic adaptation recently shaped the frequency of alleles that are protective for ADHD in one modern European population.To further evaluate whether selective forces acted on ADHD-associated alleles for the last 2,000–3,000 years, we used the trait Singleton Density Score (tSDS) computed on the UK10K data matched by the ADHD-increasing risk allele45 and current ADHD prevalence could potentially explain Neanderthal genocide  such as weight-related traits, reproductive traits and parental longevity traits5.The exact biological and environmental factors that fuelled the selective pressures over current ADHD-risk alleles through time remain to be elucidated. ADHD is known to be comorbid with many other psychiatric phenotypes such as major depressive disorder or ADSIncreasing knowledge on the biology of the disease and availability of high-quality ancient genomic data will pave the way to new studies on the evolutionary factors shaping the variation of ADHD-risk alleles, whose genetic history has been depicted for the first time in this study.31, 28132 and 18033 European ancient AMH were included in the analyses  were withdrawn from analyses as their sample dates were considered to strongly deviate from the remaining individuals in the dataset.48 with parameters -indep 50 5 2. ADHD-associated variants with an effect size p-value below 0.01 were retained  and once filtered by LD, 3,276, 2,707 and 3,320 SNPs were obtained for the Pre-Neolithic, Near East and Neolithic datasets, respectively. The divergence in the number of SNPs is explained by the difference in the individuals included in the datasets.The number of tagSNPs obtained after the linkage disequilibrium (LD) pruning present in our ancient sequences was enriched by implementing a two-step filtering method. First, individuals with a number of genotype missing genotypes above 50% were removed. After such filtering, 16, 151 and 84 ancient genomes remained in the Pre-Neolithic, Near East and Neolithic datasets, respectively. Then, variants with genotype information below 90% of the remaining individuals were discarded. The resulting variants then underwent LD pruning using PLINK 1.949: 6 east Africans , 7 south-central Africans , and 3 southern Africans (South Africa). Prehistoric African sample dates range from ~8,100 to 400 years ago.Genotypes from 16 ancient African samples were retrieved50 52, major depressive disorder (MDD)53 and schizophrenia (SCZ)54 were retrieved from the Psychiatric Genomics Consortium (https://www.med.unc.edu/pgc/).The effect sizes (log(Odds Ratio) or β) of ADHD association estimated in 8,047,421 SNPs were retrieved from a GWAS meta-analysis that included 11 cohort samples  of European ancestry44 was available through the Dryad Digital Repository44 (Supplementary Table 44.A list of trait singleton density score (tSDS) values calculated using samples from the United Kingdom23  and conducted a MAGMA analysis29; we repeated the MAGMA analysis only using genes expressed in brain (i.e. the threshold to set if a gene was expressed in a given tissue is ≥ 1 FPKM).In order to test whether ADHD-associated variants were enriched in LoF-intolerant genes, we used the list of genes defined as LoF (pLI > 0.9) from ExACfADHD) was computed by combining information from genotype and p-value. This fADHD was calculated for each individual (i) as the average of the set of ADHD-associated SNPs (p < 0.01) with genotype call in that individual (Gi) according to:Gi,j corresponds to the number of copies of the risk allele at locus j in individual i .For each ancient sample, a mean frequency of the number of ADHD-risk alleles  as a proxy. To obtain the chimpanzee allele we used the alignment between panTro4 and hg19 that is publicly available through the UCSC . We downloaded the alignments corresponding to the 22 autosomes present in humans in AXT format. Using a custom Python script, we obtained a FASTA file with the genome of the chimpanzee (panTro4) in the human genome coordinates (hg19). Then we created a BED file that contained the positions of the SNPs and the corresponding chimpanzee allele. We merged this file with the VCF that contains the SNPs being considered, adding the ancestral allele as an extra individual in the last position. In the cases where the result of the alignment was not conclusive, the genotype was labelled as missing. Moreover, if information of ancestral allele was not available, the SNP was not considered.In order to define the ancestral allele of the SNPs used inp ≤ 1e-8 or with a GWAS p ≥ 0.9, suggestive of strong GWAS association with ADHD or with no association, respectively. For each ADHD GWAS-association category, we computed the percentage of SNPs showing the ancestral state as the GWAS-risk ADHD allele, and calculated the odds ratio (OR). Since the power for detecting an association depends on the minor allele frequency (MAF) of each SNP, in order to estimate how likely is to observe such OR by chance controlling for MAF, we classified each SNP by MAF in bins of 0.02. Statistical significance was obtained out of 10,000 resamples. For each resample, we randomly distributed the SNPs of each MAF bin in the two GWAS-statistical significance categories while keeping the proportion of SNPs at each category. We then computed the OR with the resampled dataset and compared with the observed OR.In order to quantify whether ADHD-risk alleles tend to be enriched for the derived or ancestral state, we inferred the ancestral state of 519,911 SNPs that do not contain A/T or C/G alleles and with a GWAS fADHD and sampling age, we computed the Kendall’s τ correlation with the date estimate of the considered samples. We considered Kendall’s τ correlation for these analyses as it allows for a more direct interpretation of the estimated value56. To test whether the estimated correlation between sample age and fADHD is expected under the null hypothesis of independence of the risk allele and the ADHD phenotype , for each SNP we randomly assigned the ADHD increasing risk allele, computed a new fADHD for each ancient individual and estimated the Kendall’s correlation between the new fADHD and the age of the samples. This process was repeated 10,000 times and an empirical p-value was obtained out of this null distribution as the number of times the simulated correlation was greater than the observed one. In the case of the Near East dataset, an additional analysis was conducted to test whether the observed trend could be explained by constant migration from African populations with a putatively lower fADHD than European populations. Taking advantage of the ancient African dataset, which covers similar sampling ages to the one from Near East, we computed a multiple linear regression between fADHD, the continent of origin.In order to assess the relationship between the computed fADHD in each African and European samples from the 1000 Genomes Phase 3 using the SNPs from each ancient dataset. The difference between fADHD was tested by means of a Wilcoxon test as implemented in R.We further computed the 15, Kendall’s correlation was computed between hunter-gatherer ancestry proportions (HG) reported for the Neolithic ancient genomes33 and fADHD. Since both variables covariate with sampling age, we also estimated the partial Kendall’s correlation between HG and fADHD controlling by sampling age using the pcor.test from the ppcor package in R.Aiming to validate some of the mismatch theories  of the ADHD-associated SNP using the test_indep function from the wdm package in R.The Kendall’s fADHD over all loci in ancient samples taking into account the fact that the considered populations are related, we implemented an ABC-DL framework. ABC is a statistical framework for computing the posterior distributions of parameters/models when the likelihood of the data given the parameters is not known but there is a way of generating simulated data using parameter values from prior distributions57. Here, we constructed a frequency-based simulator using the framework proposed by Pickrell and Pritchard58 to explain the change in allelic frequencies under neutrality, we define the evolution of the frequency of the ADHD-risk allele at each locus j  for samples UstIshim, Malta1, Kostenski14, GoyetQ116-1, Villabruna, Loschbour, Vestonice16 and ElMiron in our simulations. We used Eq.  in order to ascertain the most informative summary statistics out of the data. A DL or a set of DLs are trained with a raw representation of the data (raw-SS) to predict as an output the parameter that produced the simulation. During the training phase, raw-SS is non-linearly transformed by the DL to predict the value of the parameter used for the simulation. In a further replication step, new simulations are generated and the DL is used to predict the value of the parameter. This predicted value is then used as summary statistic (SS-DL) in the classical ABC framework. This methodology has been recently applied in population genetics to distinguish among competing demographic models and estimating demographic parameters using whole-genome data61. The 62 using a feedforward architecture of four hidden layers of five neurons each layer. Neurons from the hidden layers used Elliott activation function63 with a slope of 0.1 to prevent signal saturation. The DL was trained using resilient propagation with RPROP- and a Dropout rate of 0.164. Each DL was trained until the error was ≤0.01 or reached 10 000 iterations. The output was from an Elliott activation function and consisted on the value of γ used for the simulation scaled to range between 0 and 1. We used this prediction as SS in the ABC framework. ABC was conducted using the abc R package65 with 1,000,000 new simulations, retaining the 1,000 closest and applying the local linear regression algorithm66.A key point of ABC is the definition of a proper set of non-redundant SS that recapitulates the parameters of interest. However, ascertaining informative non-redundant SS is traditionally difficult. Recently, Bai 44. Following the approach in44, trait-SDS (tSDS) estimates were obtained by matching the SDS value of the ADHD-risk allele. We clustered the tSDS values in bins of 1,000 SNPs according to their GWAS p-value. We computed the Spearman rank correlation between the tSDS values and the p-value of the association with ADHD. In order to assess whether the estimated Spearman rank correlation was expected under the null hypothesis of neutrality, we generated the null distribution out of 1,000 randomisations for each of which we split the genome in bins of 1 Mb, switched the risk allele of the SNPs of each bin with probability 0.5 and computed a new Spearman rank correlation. The resulting distribution was approached to a normal distribution and a one-tail p-value was computed.To identify evidence of recent polygenic adaptation in ADHD during the last 2,000–3,000 years, the SDS statistic estimated for the UK10K data was used. Note this time-span does not correspond to a time-limited phenomenon but to the upper-limit resolution of the applied test23 (Supplementary Table A list of reported tagSNPs of Neanderthal introgressed haplotypes in modern humansSupplementary Information.