Autism spectrum disorder (ASD) often co-occurs with attention deficit/hyperactivity disorder (ADHD). Although methylphenidate (MPH) efficacy and safety are well-demonstrated for ADHD, evidences are scant in the context of ASD. This naturalistic study aimed to analyze long-term MPH efficacy and safety in 40 ADHD children and adolescents with comorbid ASD, comparing them with 40 ones affected by ADHD without ASD. Treatment lasted from 6 to 156 months (longer than 24 months in more than three quarters of patients). Efficacy and safety were measured by clinical global impression and children global assessment scales; influence of intellectual functioning was examined. Comparisons between groups were made by Wilcoxon or Friedmann tests; relationships between functioning scores and other characteristics were analyzed by ordinal logistic and linear regression. Results demonstrated that MPH in patients with ASD was associated with significative reduction of illness severity, clinical improvement and amelioration of global functioning, without significant differences with patients having ADHD without ASD. The trend of reduction of illness severity and increase of global functioning were favorably related with intellectual functioning. No serious adverse events were reported. The findings showed that long-term MPH was effective and well-tolerated in ADHD children and adolescents with comorbid high functioning ASD. Until 2013, autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have been both included in the category of neurodevelopmental disorders ; whereasUntil the fourth revised version of the Diagnostic and statistical manual of mental disorders , this onEpidemiological studies suggested that at least 50% of subjects with ASD receives psychotropic medication during their whole lifespan, and, in pediatric population, the range varies from about 10% to 30% ,30,31,32Indeed, no psychotropic medications have been currently approved by any regulatory agencies for the treatment of ASD core symptom, even if thousands of clinical trials are ongoing ,34. For Among ADHD targeting drugs in ASD , the main evidences support the use of MPH; atomoxetine and α2A receptor agonists (clonidine and guanfacine) should be considered as alternatives among those who are not responsive or intolerant to MPH, even if both showed lower efficacy ,43,44,45Despite the common clinical use of psychotropic medications, several reasons underscore the need for caution and additional evidences; in particular, concerning stimulants’ efficacy and safety in ADHD children and adolescents with comorbid ASD, mixed and not conclusive results have been reported from previous studies.Early studies reported that methylphenidate (MPH) in ASD had modest efficacy, poor tolerability and worsening of behavioral and social problems: Campbell in 1975 noted increased irritability, aggressiveness and stereotypical movements ; case reThese findings were confirmed and enriched by review studies ,60 compaIn 2017, Kim  reportedConsidering the common co-occurrence of ASD and ADHD, the current lack of ASD-targeted medications, the misuse of antipsychotics being weighted by adverse events, as well as the well-demonstrated efficacy and safety of MPH (both short and long-term treatment) in ADHD ,64,65, iWe aimed to assess, in a naturalistic setting, long-term efficacy and safety of MPH, as monotherapy or associated with other psychotropic drugs, in ADHD children and adolescents with comorbid ASD, comparing them with an ADHD control group, homogeneous for age and intellectual functioning.This was a naturalistic study based on a clinical database of Caucasian young people, aged between 6 and 21 years, consecutively referred to Child Neuropsychiatric Unit of the University of Bari during a three years period (March 2017–March 2020). The inclusion criteria were ADHD diagnosis according to DSM-5, pharmacological treatment with immediate release MPH (IR-MPH) or extended release MPH (ER-MPH) and therapy lasting at least six months. The exclusion criteria were syndromic autism and age under 6 years. Given the naturalistic design of the study, patients who already took other pharmacological treatments were not excluded. All the subjects were diagnosed according to clinical judgement from the expert team, composed by child and adolescent neuropsychiatrists and psychologists, specialists in neurodevelopmental disorders. Diagnosis was also supported by standardized diagnostic tools, specific for ADHD and ASD: Revised Conners’ Parent Rating Scale (CPRS-R) , Autism Demographic and clinical characteristic of the two groups, comorbidities and pharmacological treatments beside MPH are described in All the diagnostic and therapeutic procedures, as well as the follow-up and data collection, were part of our standard routine. All subjects and parents received detailed information on the assessment measures and different treatment options and gave their written informed consent to the treatment with MPH. Medication was prescribed according to clinical indications, along with routine community care , as part of a management protocol.The Ethic Committee of the Azienda Ospedaliera-Universitaria Consorziale Policlinico di Bari approved the study (5009/29-07-2016).The research team reviewed medical records of all the patients for the purpose of the study; in particular, the team extrapolated data concerning efficacy and safety measures, such as scores at clinical global impression scale (CGI)  and chilCGI is a brief clinician-rated instrument that consists of three different global measures: severity of illness (CGI-S), global improvement (CGI-I) and efficacy index (CGI-E). CGI-S is rated from 1  to 7 (among the most extremely ill patients). CGI-I is a 7 points scale that requires the clinician to assess how much the patient’s illness has improved or worsened with respect to a baseline state at the beginning of the intervention and is rated from 1  to 7 . CGI-E is a 4×4 rating scale assessing the therapeutic effect of treatment with psychiatric medication and associated side effects. The patient’s response to treatment is combined with any salient side effect on a grid to give an CGI-E. A child with a good response to treatment and no reported side effects would have an CGI-E of 4.00. A patient with no response to treatment or who got worse during treatment, having significant side effects thus requiring a change in treatment, would have an CGI-E of 0.25.C-GAS is a numeric scale used by mental health clinicians to rate the general functioning of child and youths under the age of 18. Scores range from 1 to 100, with higher scores indicating better functioning. Patients with a C-GAS ranging from 70 to 61 struggle in a single area, albeit functioning generally well; patients with a C-GAS ranging from 60 to 51 have variable functioning with sporadic difficulties or symptoms in several, but not all social areas; patients with a C-GAS ranging from 50 to 41 have moderate degree of interference in functioning in most social areas or severe impairment of functioning in one area; patients with a C-GAS ranging from 40 to 31 have major impairment of functioning in several areas; patients with a C-GAS ranging from 30 to 21 are unable to function in almost all areas.Primary outcomes were efficacy and safety data, measured by CGI-S, assessed at the baseline (T0), after 1 month (T1), after 6 months (T6), after 24 months (T24); CGI-I, assessed after 1 month (T1), after 6 months (T6), after 24 months (T24); CGI-E, assessed at the last follow-up and C-GAS—assessed at the baseline (T0) and at the last follow-up. Safety data were analyzed at baseline and at each follow-up. Discontinuation rate was recorded and described.Secondary outcome was the influence of different variables  on efficacy (measured by CGI-S and CGI-I) and global functioning (measured by C-GAS).All data obtained from ASD + ADHD group and ADHD group were compared to each other, henceforth analyzed.p-values were adjusted according to Bonferroni. Quantitative variables were summarized as median, range and interquartile range or 95% confidence interval for the median.Quantitative data, as CGI-S, CGI-I, CGI-E, C-GAS, were not normally distributed, therefore the comparison between disease group was performed with Wilcoxon for independent groups. A test for repeated measures, i.e., Friedman test, was performed separately in each disease group, in order to compare results among follow-ups. Given the multiple test approach of the analysis, 2 test or Fisher’s exact test when appropriate. In order to compare the percentages of side effects, a multiple comparison strategy was applied: the statistical Fisher’s exact test was used, and p-values were adjusted according to a permutation method for the purpose of controlling family wise error rate.Qualitative variables are summarized as count and percentage, comparisons between independent groups were performed by χAn ordinal logistic model was performed to evaluate predictors of changes in CGI-I and CGI-S.The model for CGI-S had ΔCGI-S as dependent variable, defined as the difference obtained subtracting the CGI-S score at T24 from the CGI-S score at T0, classified as neutral (0) and classes of increasing improvement (from −1 to −3).The model for CGI-I had a dependent variable classified as improved if the score decreased, neutral if there was no difference between CGI-I score at T1 and CGI-I score at T24, and not improved if the score increased.The predictors in both models were: age, sex , length of therapy (in months), age of therapy onset, dose of drug, disease group . A backward selection procedure was used for the multivariable model, but, in order for variables to have an effect independently from the disease group, a new model was fitted including this variable; R-square was used as measure of fitting.p-value < 0.05 was used to assess statistical significance.A p-value < 0.1 as threshold to enter the model, in order to choose best predictors.A multiple linear regression was used to evaluate if changes in C-GAS depend on IQ and other possible predictors . A backward selection method was applied with a The software used for the analysis was SAS 9.4 .The starting dose of MPH was 0.3–0.5 mg/kg/day. The dosage could be increased up to 1 mg/kg/day depending upon the subject’s clinical response and tolerability, up to maximum of 60 mg/day. The total dose could be administered in two or three doses/day. After one month of titration, the IR-MPH was generally replaced with ER-MPH. The range of therapy duration was from 6 to 156 months. A total of 18/80 participants (22.5%) of the total sample had a follow-up treatment lower than 24 months, 22/80 participants (27.5%) had a follow-up treatment ranging from 24 months and 36 months, 40/80 participants (50%) had a follow-up treatment higher than 36 months. At the last follow-up, 73/80 participants (91.25%) were treated with ER-MPH (37/40 ASD + ADHD subjects and 36/40 ADHD subjects); 7/80 participants (8.75%) were treated with IR-MPH (3/40 ASD + ADHD subjects and 4/40 ADHD subjects). Characteristics of MPH treatment were described in p < 0.0001) Comparing the ASD + ADHD group with the ADHD group, the difference in CGI-S improvement was not statistically significant in none of the follow-up points. Moreover, 62,2% (23/36) in ASD + ADHD group and 79.5% (31/39) in ADHD group has shown a ΔCGI-S (from T0 to T24) lower or equal to −2, which is a fairly good amelioration, in both groups, without statistically significant difference (p = 0.3255).In ASD + ADHD group the difference in CGI-S from T0 to T24 was statistically different (p = 0.0001). Comparing the ASD + ADHD group with the ADHD group, the difference in CGI-I was not statistically significant in none of the follow-up points. The percentage of responders  was 70.3% (26/37) in ASD + ADHD and 64.1% (25/39) in ADHD patients, but the difference was not statistically significant (p = 0.5699).In ASD + ADHD group the difference in CGI-I from T0 to T24 was statistically different (In ASD + ADHD group the median (95% CI) CGI-E was 2 (1.5–2). Comparing the ASD + ADHD group with the ADHD group, the difference in CGI-E was not statistically significant. CGI measures are described in p < 0.0001) Comparing the ASD + ADHD group with the ADHD group, the difference in C-GAS was not statistically significant at either T0 or last follow-up. C-GAS measures are described in In ASD + ADHD group the difference in C-GAS from T0 to the last follow-up was statistically different , 7.5% mood stabilizers (10% at baseline), 2.5% antidepressants (5% at baseline), 2.5% anxiolytics and 20% melatonin ; in ADHD group, 12.5% of patients assumed antipsychotics , 10% mood stabilizers (12.5% at baseline), 2.5% antidepressants and 22.5% melatonin .No severe adverse events were reported; indeed, among these, no cardiovascular events neither suicidal ideations nor behaviors were seen.p = 0.6256) Comparing ASD + ADHD group with ADHD group, the prevalence of the lack of side effects was not statistically significant , each temporary, in the first days or weeks of treatment; the same side effects were seen in ADHD group .Two ADHD patients with comorbid ASD (5%), both affected by level 3 ASD associated with intellectual disability, after partially responding to the treatment for few weeks, presented worsening of behavior with restlessness and increased stereotypes, that completely resolved after treatment discontinuation. In the ADHD group, one patient (2.5%), affected by anxiety disorder, had to stop the drug, despite it worked on attention, due to the onset of restlessness and a state of “inner tension”, that resolved once stopping the medication. Side effects are described in In ASD + ADHD group, eight patients interrupted MPH treatment (20%) for the following reasons: four patients because of clinical improvement (10%); two patients  because of clinical worsening (5%); one patient because of low compliance to the treatment (2.5%); one patient was lost at follow-up (2.5%).In ADHD group, nine patients interrupted MPH treatment (22.5%) for the following reasons: clinical improvement for six patients (15%); clinical worsening for one patient (2.5%); low compliance to the treatment for one patient (2.5%); one patient was lost at follow-up (2.5%).2 = 0.075, p = 0.7846).The discontinuation rate did not result significantly different (χp = 0.0094), the model resulted statistically significant  and the R-square of the model was 13.2%. The model allows to estimate the probability of each class of improvement as function of IQ  were removed from the model by the selection procedure. The regression for ΔCGI-S was fitted with IQ  = 0.01, on of IQ : the prop = 0.0094) and age of therapy onset  = 0.09, p = 0.021). Coefficients suggest that the elder the patient the lower the probability of the improvement and the earlier starting treatment the higher the probability of improvement. The disease group was not statistically significant  = 0.23, p = 0.8356) and also the other variables were removed from the model because not being statistically significant and having a false increase in R-square measure, which was 9.6% in the final model.The model for CGI-I has shown that statistically significant variables related to changes in clinical improvement were age  = 0.09, p = 0.0003), dose of drug , sex  and age  was found. The results of the regression, having an R-square = 32%, suggested that C-GAS increased in higher IQ and for higher dose, but it decreased in male subjects. The disease group was not statistically significant  and also the other variables  did not result statistically significant. The effect of IQ could be described by a line with the slope obtained by the model that shows how improvement of C-GAS increases when IQ is higher , followed by SSRIs (8%) and SGA (8%) ; data frAs a matter of fact, the history concerning the co-presence of ASD and ADHD, as well as the consequent application of stimulants also for autistic subjects, has been marked by limits and ambivalences that delayed diagnostic and therapeutic progress. Nowadays, in scientific community, the comorbidity between ASD and ADHD is judged as a common feature and stimulants application emerges as a prolific research field. Considering the clinical traits and neurobiological deficits shared by ASD and ADHD, it has been possible to speculate that subjects with ASD may benefit from the same evidence-based pharmacological treatment, successfully used in subjects with ADHD without ASD.The first findings have shown that methylphenidate (MPH) was less efficacious and tolerated in people with ASD, compared to ADHD alone ,48,49,50The present study analyzed long-term MPH efficacy and safety in a naturalistic setting of 40 ADHD patients with comorbid ASD, compared to 40 patients with ADHD. All the participants had a follow-up lasting from 6 to 156 months  of treatment, strictly clinically characterized and monitored.Results suggested that MPH in ADHD children and adolescents with comorbid ASD was associated with significative reduction of illness severity, clinical improvement in the first two years of treatment and amelioration of global functioning until the last follow-up, without significant differences with children and adolescents with ADHD without ASD at one, six and twenty-four months follow-up.The present study also found a higher probability of clinical improvement in subjects from both groups who start an earlier MPH treatment. Furthermore, by analyzing the influence of intellectual functioning, results demonstrated a favorable trend of reduction of severity of illness and amelioration of global functioning associated with higher IQ. These findings are in accordance with previous studies , in whicAs for safety data, treatment with MPH in ASD patients was well-tolerated. There were no serious adverse events. The main adverse events were temporary loss of appetite, abdominal discomfort and headache, without significant differences with ADHD group. Only two ADHD patients with comorbid ASD (5%) presented major adverse events consisting in worsening of behavior with restlessness and increased stereotypes that caused the interruption of treatment and both resolved completely after treatment discontinuation; interestingly, both patients were affected by level 3 ASD associated with intellectual disability.The mean of efficacy index, measured by dividing therapeutic effect score by side effect score, was indicative of a marked therapeutic effect with no significant interference of side effects, or, alternatively, of a minimal therapeutic effect without side effects and there were not significant differences among the two groups. Concerning treatment persistence, previous studies revealed that MPH in ASD patients, compared to other classes of psychotropic drugs, showed a lower rate of treatment discontinuation, with a higher adherence rate in children than in adolescents, that may be explained by children’s greater parent involvement in controlling treatment ,79. In tAlthough not being a focus of this study, our results revealed that co-therapy  did not interfere with response rate to MPH or increased side effects, according with other reports ,62, and,These reassuring findings about MPH efficacy, safety and treatment persistence were in accordance with literature from 2000s ,58,62,80The present study had different strengths: first of all, the sample was phenotypically characterized, throughout intellectual profile, severity specifiers, diagnostic subtypes and comorbidities; patients were strictly monitored and followed at least every six months, the follow-up period being longer than 2 years in over three-quarters, higher than most the other studies; it described both effectiveness of Immediate Release and Extended Release formulation of MPH. Finally, being it a naturalistic study, clinicians could feel confident in titrating medication and adjusting doses as needed, therefore collected data are closer to real-life setting.The limitations concerned the utilization of global efficacy measures, rather than specific ASD and ADHD measures for core symptoms. These aspects, not deeply investigated in this study, could represent a future direction of research about MPH effect in ASD + ADHD patients.Compared with previous studies, the present one showed higher response rate and tolerability of MPH treatment in ASD patients. We hypothesized that this improved stimulants’ profile in ASD may be explained, besides the above-mentioned features associated with naturalistic setting, by the high percentage of subjects with average intellectual functioning in the sample.In conclusion, our study shows that long-term MPH treatment, lasting 24 months on average, is effective and well-tolerated in ADHD children and adolescents with comorbid high-functioning ASD, even in association with other psychotropic drugs. Although long term studies are necessary to confirm these results, we recommend a routine use of MPH in ADHD with comorbid high-functioning ASD.


Attention-deficit/hyperactivity disorder (ADHD) is a developmental condition that often persists into adulthood with extensive negative consequences on quality of life. Despite emerging evidence indicating the genetic basis of ADHD, investigations into the familial expression of latent neurocognitive traits remain limited.n = 20), their unaffected first-degree relatives (n = 20) and typically developing control participants (n = 20), we assessed endophenotypic alterations in the default mode network (DMN) connectivity during resting-state functional magnetic resonance imaging in relation to cognitive performance and clinical symptoms. In an external validation step, we also examined the dimensional nature of this neurocognitive trait in a sample of unrelated healthy young adults (n = 100) from the Human Connectome Project (HCP).In a group of adult ADHD probands (The results illustrated reduced anti-correlations between the posterior cingulate cortex/precuneus and right middle frontal gyrus that was shared between adult ADHD probands and their first-degree relatives, but not with healthy controls. The observed connectivity alterations were linked to higher ADHD symptoms that was mediated by performance in a sustained attention task. Moreover, this brain-based neurocognitive trait dimensionally explained ADHD symptom variability in the HCP sample.Alterations in the default mode connectivity may represent a dimensional endophenotype of ADHD, hence a significant aspect of the neuropathophysiology of this disorder. As such, brain network organisation can potentially be employed as an important neurocognitive trait to enhance statistical power of genetic studies in ADHD and as a surrogate efficacy endpoint in the development of novel pharmaceuticals. Attention-deficit/hyperactivity disorder (ADHD) is a childhood onset neurodevelopmental condition, characterised by clinical symptoms of impulsivity, hyperactivity and inattention as well as emotional dysregulation . Given prior research, we hypothesised that alterations in the connectivity of DMN to the frontal brain regions would be shared between adults with ADHD and their first-degree relatives, which would significantly correlate with deficits in sustained attention performance and severity of ADHD symptoms. Finally, based on recent reports that highlight ADHD symptomatology as dimensional traits across a continuum of normal to abnormal functioning  we aimed to assess the generalisability of our findings by examining the potential utility of this neurocognitive trait in explaining dimensional variation in ADHD symptomatology in a cohort of unrelated healthy young adults.To this end, here we investigated alterations in brain functional network connectivity within the DMN during rs-fMRI and tested the suitability of this measure as a potential neurocognitive endophenotype in a cohort of 20 adult ADHD probands, their 20 unaffected first-degree relatives and 20 typically developing control participants . All individuals gave informed consent prior to their participation. ADHD probands and their first-degree relatives were recruited from the Adult ADHD Research Clinic, Addenbrooke's Hospital, Department of Psychiatry, University of Cambridge, whereas typically developing controls were recruited from the local community.For the patient cohort, the diagnosis of ADHD (DSM-IV Text Revision) was based on a full clinical interview with the patient and an informant who had known the patient since childhood (e.g. parents). The clinical assessment included Barkley Adult ADHD Rating Scale Version IV (BAARS-IV) for childhood and adult symptoms, provided by both the patient and the informant Barkley, . In addis.d. = 10.31, 3/17 female to male ratio), comprising 16 patients with combined type and four with inattentive type; 16 of which were medicated with methylphenidate, while four were not receiving any medication for their ADHD diagnosis. In addition to the patient group, a total of 20 age and gender-matched first-degree relatives  of the ADHD probands and 20 typically developing control participants  were recruited for this study. Neither the first-degree relatives, nor the typically developing control participants showed ADHD symptoms meeting the DSM-IV Text Revision diagnostic threshold for ADHD, or a diagnosis of any other comorbid disorder. With the aim of minimising the potential impact of psychotropic medication on cognitive performance and brain dynamics, ADHD participants were asked to withhold taking their medication 24 h before  using the SPSS Statistics software package (Version 23.0) (https://www.ibm.com/products/spss-statistics). The self-report BAARS-IV current total symptom scores as well as the inattentive and hyper-impulsive sub-scales were also assessed for group differences with the same statistical procedures, correcting for age, gender and NART full scale IQ scores (online Supplementary Table S2).In order to ensure matching of demographic information between groups, age, gender and NART full scale IQ scores were entered into multivariate and univariate analyses of variance (ANOVA) and post-hoc t tests were corrected for multiple comparisons using the Bonferroni method.All participants completed the Rapid Visual Information Processing (RVP) test of sustained attention from the Cambridge Neuropsychological Test Automated Battery (CANTAB) , based on the MATLAB platform (Version 15a) (http://www.mathworks.co.uk/products/matlab/).Participants were scanned using a Siemens MAGNETOM Tim Trio 3T scanner (32-channel head coil) at the Wolfson Brain Imaging Centre, Cambridge. The brain imaging session started with a high resolution T1-weighted, magnetisation-prepared rapid gradient-echo (MPRAGE) structural scan . The echo planar imaging sequence parameters for the 8.73-min eyes-closed resting-state functional data acquisition were as follows: 32 slices in each volume, 3.0 mm slice thickness, 3.0 × 3.0 × 3.0 voxel size, TR = 2000 ms, TE = 30 ms, flip angle = 78 degrees, 262 volumes. The MRI data preprocessing pipeline included slice-time and motion (six degrees of freedom) corrections, co-registration to the high resolution T1-weighted structural image, normalisation to the Montreal Neurological Institute (MNI) space through the unified segmentation–normalisation algorithm  , default mode (n = 419), default network (n = 80)’, three false-positive discovery rate corrected, statistical co-activation maps (P term|activation) were created. The three maps corresponding to the three search terms were binarised, overlapped and masked with an anatomical automated labelling atlas-based PCC/PCUN template obtained from the Wake Forest University PickAtlas toolbox (http://fmri.wfubmc.edu/software/pickatlas)   (https://www.nitrc.org/projects/conn) . Moreover, group-level ANOVA was carried out using F-statistics to examine changes in DMN connectivity (PCC/PCUN seed) across ADHD probands, their first-degree relatives and the typically developing control group. The individual connectivity values (z-scores) extracted from the cluster that showed significant alterations in connectivity to the chosen seed were then entered into a univariate ANCOVA  to further examine the fixed-effects of group membership on DMN connectivity. Post-hoc t tests were carried out to assess pair-wise group differences that were corrected using the Bonferroni method.Following this procedure, seed-based functional connectivity analyses were performed for each subject using the average BOLD signal from the binarised ROI map described above. One-sample z-scores), BAARS-IV current total symptom scores and the number of total hits from the RVP sustained attention task was examined using bivariate non-parametric partial Spearman's correlations . All measures were assessed for normality using the Shapiro–Wilk test and Q–Q plots, and the correlations were Bonferroni corrected for multiple-comparisons. The results were visualised using the Seaborn package . Full details on the HCP dataset, including the acquisition parameters are provided in the literature . We then employed bivariate non-parametric partial Spearman's correlation to assess the link between DMN connectivity and Achenbach Adult Self-Report (ASR) DSM-oriented Attention Deficit/Hyperactivity (AD/H) scale .Subsequently the same data denoising procedures (except for scrubbing) and functional connectivity analyses from the main experimental study was employed for the HCP functional data. A one-sample Finally, the cluster of brain regions showing significant alteration in its connectivity to the chosen seed from the main experiment was decoded for cognitive terms using Neurosynth meta-analysis toolbox  = 14.402, p < 0.001, partial η2 = 0.348)  between ADHD probands and controls  as well as between first-degree relatives and controls ; but there were no significant differences between the ADHD probands and their first-degree relatives   b. Post-h= 0.059] , Fig. 2aF = 6.300, p = 0.003, partial η2 = 0.189), in which both the ADHD probands  and their unaffected first-degree relatives  performed significantly worse than the typically developing control participants, whereas the former two groups showed no significant difference to each other   = 62.328, p < 0.001, partial η2 = 0.698) that diverged away from brain connectivity and sustained attention task performance results  = 8.982, 95% CI (21.061–33.165), p < 0.001, partial η2 = 0.599] and typically developing controls ; however, the latter two groups did not show a significant difference to each other . Similar results were obtained for the inattentive and hyperactive-impulsive sub-scales (online Supplementary Table S2).In contrast, an ANCOVA between groups on the BAARS-IV current symptom scores showed a significant group difference , p = 0.002] , p = 0.006] , p < 0.001] ]. In other words, DMN connectivity was linked to ADHD symptom scores that were approximately 8.1 points higher as mediated by attentional performance.Across all groups, the BAARS-IV current total symptom scores showed a significant correlation with PCC/PCUN to right MFG connectivity [= 0.002] d. Moreov= 0.006] e, which partial rs = 0.341, 95% CI (0.07–0.565), p = 0.019], corrected for age, gender and mean framewise displacement , p = 0.006], whereas the hyperactivity scores showed no significant correlation with DMN connectivity . Finally, a meta-analytic inquiry into the right MFG on the Neurosynth database revealed terms strongly associated with working memory, attentional load, task demands and other higher executive functions  of the chosen PCC/PCUN seed, a main hub of the DMN, with a cluster of brain regions centred on the right MFG extending into the IFG that is commonly associated with cognitive control. Moreover, across the whole cohort of participants, the identified functional connection was linked to ADHD symptom severity with performance in the sustained attention task significantly mediating this relationship. Finally, in an external validation step, we illustrated that the same brain connection identified in our main study also explained dimensional variability in ADHD symptoms in an independent cohort of unrelated healthy young adults, providing evidence for the generalisability of our findings. Taken together, such results collectively highlight the central importance of the identified default mode functional interaction as a familial neurocognitive trait in adults with ADHD, hence meeting the criteria for an endophenotype of this disorder.Previous studies investigating resting-state brain functional connectivity alterations have highlighted DMN connectivity, specifically that of the PCC/PCUN, as a potential new locus of dysfunction in ADHD (Castellanos et al., Notably, the PCC/PCUN to MFG connectivity at rest showed a positive correlation with the current total symptoms and a negative correlation with sustained attention task performance, suggesting a close link between the DMN's functional interactions at rest and both inattention and overall symptomatology, which we replicated in an independent cohort of healthy young adults. Collectively, these functional and behavioural links emphasise the significance of the identified disconnection between the PCC/PCUN and right MFG as a neurocognitive trait, and thus a potential endophenotype of ADHD. Moreover, further mediation analysis revealed a significant indirect effect of DMN connectivity on ADHD symptom severity through the participants' performance in the sustained attention task. In a watershed model of biological pathways that link upstream genes to downstream observable phenotypes, Cannon and Keller have argued for the potential utility of identifying intermediary endophenotypes such as neural circuits and more broader cognitive processes in order to improve our understanding of the mechanisms behind complex mental health disorders (Cannon & Keller, Since the initial report by Fox et al., , anti-coOriginally described as a network of brain regions that showed task-induced deactivations during goal-oriented, attention-demanding paradigms (Mazoyer et al., On the other hand, our group has recently shown DMN engagement during the stable application of learned rules in an externally-oriented, attention-demanding cognitive flexibility task (Vatansever, Menon, & Stamatakis, In relation to the findings of this study, a few limitations should be highlighted. First of all, the results of our study are based on a single brain region within a larger default mode system. Although we used an objective approach to define this seed and focused on a DMN hub that was previously implicated in ADHD, in light of recent evidence indicating functional sub-specialisation within this network (Kernbach et al., Overall, this study suggests that the abnormal DMN connectivity is a significant aspect of the adult ADHD neuropathophysiology that could be employed as a latent neurocognitive trait to enhance statistical power of genetic studies in adult ADHD and as a surrogate efficacy endpoint in the research and development of novel pharmaceuticals for treatment. In addition to previous research showing volumetric differences in cortical grey matter and aberrant white matter connections in ADHD (Pironti et al.,


Whether changes in microbial composition affect brain structure and function is largely elusive, however. This is important as altered brain structure and function have been implicated in various neurodevelopmental disorders, like attention-deficit/hyperactivity disorder (ADHD). We hypothesized that gut microbiota of persons with and without ADHD, when transplanted into mice, would differentially modify brain function and/or structure. We investigated this by colonizing young, male, germ-free C57BL/6JOlaHsd mice with microbiota from individuals with and without ADHD. We generated and analyzed microbiome data, assessed brain structure and function by Principal coordinate analysis showed a clear separation of fecal microbiota of mice colonized with ADHD and control microbiota. With diffusion tensor imaging, we observed a decreased structural integrity of both white and gray matter regions  in mice that were colonized with ADHD microbiota. We also found significant correlations between white matter integrity and the differentially expressed microbiota. Mice colonized with ADHD microbiota additionally showed decreased resting-state functional MRI-based connectivity between right motor and right visual cortices. These regions, as well as the hippocampus and internal capsule, have previously been reported to be altered in several neurodevelopmental disorders. Furthermore, we also show that mice colonized with ADHD microbiota were more anxious in the open-field test.Taken together, we demonstrate that altered microbial composition could be a driver of altered brain structure and function and concomitant changes in the animals’ behavior. These findings may help to understand the mechanisms through which the gut microbiota contributes to the pathobiology of neurodevelopmental disorders.Video abstract. The gut-brain axis (GBA) is a well-recognized bidirectional communication route between gut and brain . One of The role of gut microbiota in host physiology and health has been well established . An incrFirmicutes/Bacteroidetes ratio and a higher relative abundance of Clostridia species [Bifidobacterium was observed in individuals with ADHD, associated with a significantly enhanced predicted biosynthesis potential of the dopamine precursor phenylalanine [Clostridia, resulted in significant short-term improvement in neurobehavioral symptoms in children with autism [An increasing number of studies report possible roles for the microbiota in anxiety and social behavior , 21–23,  species , 32, whe species . For ADH species , 35. An lalanine . Additiolalanine –39. Similalanine , and treh autism . FollowiADHD”) or microbiota from age-matched healthy controls (“micecontrol”). We subsequently performed structural and resting-state functional magnetic resonance imaging (fMRI) and analyzed different aspects of behavior in the microbiota-colonized mice. Extending insights on the impact of ADHD microbiota on brain phenotypes and behavior might increase our understanding of disorder etiology and ultimately open a window of opportunity for the development of novel treatment approaches for ADHD by targeting the microbiota.In the current study, we aimed to investigate the impact of human microbiota from individuals with ADHD on brain function and/or structure and behavior in mice. We hypothesized that the gut microbiota of individuals with ADHD, when transplanted to mice, would modify brain structure and/or function as well as influence behavior, biological domains that are also impaired to various degrees in neurodevelopmental disorders in humans. To test this hypothesis, we colonized young, germ-free mice with microbiota collected from male individuals with ADHD  showed no significant differences between the miceADHD and micecontrol . MiceADHD clustered separately from micecontrol in the PCoA plot, as illustrated in Fig. First, we assessed the microbiota composition of feces from miceADHD vs. micecontrol ; LDA score > 2.0, punadjusted < .05; Table ADHD, while 17 other genera were more abundant in micecontrol. From the genera enriched in miceADHD, 10 belonged to the family of Lachnospiraceae within the phylum Firmicutes. The overall phyla of Proteobacteria and Cyanobacteria were decreased in relative abundance in miceADHD, and also the family of Porphyromonadaceae was present in lower relative abundance. Additionally, from the phylum of Firmicutes, the Eubacteriaceae, Christensenellaceae, and Ruminococcaceae families were less abundant in miceADHD  analysis showed that 31 genera differed in relative abundance between the two experimental groups (miceDiffusion tensor imaging (DTI) was used to assess gray and white matter integrity in several brain regions. Fractional anisotropy (FA) is a summary measure of microstructural integrity, and a higher FA might reflect increased white matter integrity. Mean diffusivity (MD) is an inverse measure of membrane density, and a higher MD might indicate decreased gray matter integrit y.ADHD had a decreased FA in the left and right hippocampus  = 6.4, p = .020; right: F = 4.5, p = .047). Lowered FA in miceADHD was also observed in the right internal capsule  = 9.2, p = .007) and right optic tract  = 4.5, p = .047)  = 13.1, p = .002) of miceADHD. An increased MD was also observed in the fornix of miceADHD  = 4.6, p = .044)  in the corpus callosum  = 4.8, p = .041), left and right hippocampus  = 5.3, p = .032; right: F = 17.0, p = .001), and right internal capsule  = 7.3, p = .014). Finally, we observed an increased axial diffusivity (AD) in the right auditory cortex  = 4.6, p = .044) in miceADHD compared to micecontrol.The MRI data showed that miceleft: F1,0 = 6.4, left: F1,0 = 6.4, ADHD and micecontrol  = 0.07; p = .798).Because of the observed differences in FA and MD in the hippocampus, we also assessed hippocampal volumes. However, we did not find differences in hippocampal volumes between miceADHD to have a decreased FC in the partial correlation analysis between the right motor cortex and right visual cortex  = 5.9, p = .030; Fig. Functional connectivity (FC) patterns, derived from resting-state fMRI (rs-fMRI) data, showed miceADHD spent more time in the corners  = 6.6, p = .017) and less time in the center  = 10.4, p = .004) of the open field arena compared to micecontrol  = 1.8, p = .190) or center  = 1.7, p = .211). This suggests that the altered exploratory behavior in miceADHD reflects increased anxiety. Analysis of locomotion activity did not reveal any differences in velocity  = 0.4, p = .527), total distance traveled  = 0.2, p = .687), or any of the manually scored behaviors, like jumping, wall leaning, rearing, and grooming. Additionally, no differences in home cage activity during day or night were observed between the two experimental groups . Micerol Fig. a. Groupsorners  = .956, p = .338; Fig. ADHD demonstrated a trend toward a lower discrimination index compared to micecontrol during the test phase of the NOR with a half hour interval between familiarization and test phase  = 3.8, p = .063; Fig. F = 1.98, p = .171; Additional file During the familiarization phase of the novel object recognition (NOR) test, used to assess memory, all mice explored both identical objects equally  and by micecontrol  = 0.56, p = .453; Additional file It has been proposed that burying of harmless objects, like glass marbles, reflects a form of compulsive or impulsive behavior . Performp < .01).We investigated, whether the genera that differed in relative abundance, described above and depicted in Fig. Eubacterium showed a positive correlation with the FA of the right and left hippocampus  and a negative correlation with the MD in the right hippocampus . Gastranaerophilales unknown showed a positive correlation with the FA in the right and left internal capsule . Finally, we observed a positive correlation between Holdemania and the FA in the left hippocampus .The relative abundance of Anaerostipes showed a positive correlation with anxiety (time spent in corners) in the OFT  and a negative correlation with the time spent in the center of the open field . Additionally, the relative abundance of the uncultured Porphyromonadaceae , Roseburia , and Ruminococcaceae UCG 004  showed a positive correlation with anxiety (time spent in corners) in the OFT. The relative abundance of Eisenbergiella , Eubacterium rectale group , and Ruminococcus 1  showed a positive correlation with time spent in the center in the OFT.The relative abundance of ADHD had an impaired structural integrity of both white and gray matter, and showed decreased functional connectivity between the right visual cortex and right motor cortex.Given the current knowledge on the association between the gut microbiota and neurodevelopmental disorders via the gut-brain axis –31, we hADHD compared to micecontrol using diffusion tensor imaging (DTI). One of the brain regions selected was the hippocampus. The hippocampus is highly sensitive to aging, life experiences such as stress, and environmental factors like malnutrition and altered microbial composition [ADHD. This is indicative of decreased myelination rather than axonal damage or degeneration [In the current study, we investigated if there were any structural brain differences in miceposition –46. The position . It is tposition , 47–50. neration . A decreneration , 52. Howneration , 16.ADHD in the internal capsule, indicative of decreased myelination in this region. This finding may be related to the observed decreased functional connectivity between the right primary motor cortex (M1) and the right primary visual cortex (V1) of miceADHD because the fibers of these regions course through the internal capsule [We also observed white matter alterations in the right internal capsule. The internal capsule is composed of afferent and efferent myelinated fibers of the cerebral cortex . Abnorma capsule . Both th capsule . Reduced capsule , 59.ADHD showed more anxiety than micecontrol, which was measured as time spent in the corners of the open field. Although this behavior is not specific to ADHD, many psychiatric disorders, including ADHD, MDD, and ASD, are highly comorbid with anxiety [We used various behavioral tests to investigate diverse aspects of mouse behavior that might be influenced by microbiota from individuals with ADHD. One of the tests that the mice performed was the OFT. In this test, we observed that mice anxiety –62. Alte anxiety . This sucontrol and miceADHD , and Ruminococcaceae UGC-004 (Family Ruminococcaceae), positively correlated with anxiety levels, while Eubacterium appeared to be negatively correlated to the severity of anxiety and positively with anxiety reduction. Importantly, these genera were also found to be more abundant in persons with ADHD, compared to controls, as part of our ongoing study of ADHD patients from the NeuroIMAGE study [n = 56) and healthy participants (n = 49) [Changes in the relative abundance of the family  and MDD , 72. OurGE study  (n = 56)(n = 49) .This work should be considered in the light of its strengths and limitations. First, we only used six donor samples  in our study. While this low number of samples may possibly reduce the general distribution of taxonomic groups observable in adult individuals with ADHD, the samples used were carefully selected based only on the disease status . To reduce individual differences, samples were pooled before colonization (ADHD patients or controls). Additionally, the choice of collecting the fecal samples from adult male donors was based on the fact that the adult form of ADHD is thought to be the most severe outcome of this disorder  and to aSecond, we used germ-free mice, housed in isolators, as opposed to antibiotic-treated mice. While this approach limited our behavioral testing options (in terms of equipment), the sterile isolators used in this study provided a contamination-free environment at the time of behavioral testing. Additionally, this allowed us to make sure that the animals were only exposed to the microorganisms from the donor samples, and competition between the native and newly transplanted micro-organisms in germ-free animals was absent, which allows for the study of a defined microbial composition . HoweverBifidobacteria and Faecalibacterium that were observed to differ between ADHD patients and controls [Third, we analyzed the relative microbial composition using 16S rRNA sequencing data. This approach is preferred for comparison across different samples , treatments, or timepoints. Additionally, 16S rRNA sequencing techniques are more cost-efficient than metagenomics sequencing; it shows lower signal distortion due to host contamination  and has controls , 35, couIn conclusion, bacterial components of the gut microbiota of individuals with ADHD are associated with changes in brain structure and function, as well as behavior in mice. Although we do not suggest that ADHD is caused by changes in microbial composition, the observed changes at the brain level, albeit not specific to ADHD, highlight the relevance of the gut microbiota, potentially through decreased myelination. While further research is needed, our findings might increase our understanding of the etiology of psychiatric disorders and ultimately may open a window of opportunity for the development of potentially novel treatment strategies targeting the microbiota in neurodevelopmental diseases.n = 3) and age-matched healthy male participants (n = 3), with an average age of 22.7 ± 1.1 years. The samples were pooled to prevent transplanting individual microbial compositions [Fecal samples were collected from male participants with ADHD  and were approved by the Veterinary Authority of the Radboud University Medical Center  containing a statistical power analysis to minimize group sizes. All applicable (inter)national and institutional guidelines for the care and use of animals were followed and reported in accordance with the ARRIVE guidelines .ADHD”) or healthy participants (“micecontrol”) via oral gavage. Animals were assigned to one of the two BioFlex™ B30 Flexible Film Isolators , in which the animals were group-housed in standard cages with three or four animals per cage , consisting of 46 randomly mixed samples and 2 positive controls (mock communities). The sequenced data was run through the NG-Tax 16S rRNA pipeline [ADHD and micecontrol. Additionally, due to the microbiome changes after each (re-)colonization, a weighted average was calculated for each animal microbial composition, and quality control was performed in several steps . This kit was adapted by the company in order to use it for bacterial DNA extraction (XAS 1220 kit). Amplification of the specific V1-2 region of the 16S rRNA gene was performed. The PCR product was purified using the HighPrep™ PCR kit , and libraries with final loading concentrations of 200 ng/μl were prepared for Illumia HiSeqpipeline . Then asBetween-sample diversity was measured via the phylogenetic-based assessment of difference in overall bacterial community composition (weighted UniFrac). Linear discriminant analysis effect size (LEfSe) was used to evaluate differences in taxa between the two experimental groups. Genera that differed significantly were selected for correlation analysis to link bacterial changes with behavior and brain parameters.All MRI measurements were performed using an 11.7-T BioSpec Avance III small animal MR system . Isoflurane  was used for anesthesia (induction with 3.5% and maintenance with ~ 1.7% isoflurane in a 1:2 oxygen-air mixture). Imaging parameters can be found in table S1  is a marker of the degree of myelination and fiber density of white matter (WM), while mean diffusivity (MD) characterizes an inverse measure of the membrane density. Axial diffusivity (AD) and radial diffusivity (RD) describe the parallel and perpendicular directions of diffusivity, respectively . These sTo assess cerebral blood flow (CBF), we acquired MR perfusion data under resting conditions using standardized protocols as described  with white Plexiglas walls for 15 min. We followed a protocol that has been previously described .The MBT was conducted in a standard-sized cage (37 × 19 × 13 cm) preloaded with 3-cm unused sterile bedding and 15 evenly spaced sterilized black glass marbles with a 14-mm diameter. A protocol previously described was used .This test was performed on dpc 20  and dpc 21  as previously described . Preferehttps://huttenhower.sph.harvard.edu/galaxy). The alpha value for the factorial Kruskal-Wallis test among the colonization groups was set to 0.05, and the threshold logarithmic linear discriminant analysis (LDA) score for discriminant features was 2.0. Pearson’s correlation coefficients between microbial composition and key neurobiological findings were calculated (p < .01) and plotted using the corrplot package in R. Statistical outliers were removed from the dataset. The number of mice that was considered an outlier per test is described  or R version 3.2.4). Datasets were analyzed using one-way univariate analysis of variance  or Kruskal-Wallis test  with Bonferroni post hoc tests, where applicable. Beta-diversity was analyzed using Permanova-S with 10,000 permutations . LEfSe w.2.4. DatAdditional file 1: Title of data: Supplementary figures. Figure S1. Home-cage activity during night and day. Figure S2. Novel object recognition test with 1 h interval between the familiarization and test phase. Figure S3. Number of marbles buried during the marble burying test. Figure S4. Representative photo images of the gnotobiotic isolators that were used in this study. Figure S5. Beta-diversity analyses including the human donors.Additional file 2: Title of data: Supplemental methods and results. Description: Additional methods and results are available in the supplementary text, including methods of (1) experimental design, (2) preparation of human fecal samples, (3) microbiota analysis, (4) behavioral tests, (5) MRI protocols, (6) immunohistochemistry, and (6) statistical analysis. Additional results are also available including cerebral blood flow, total correlation analyses, and immunohistochemical stainings.Additional file 3: Title of data: Supplementary tables. Table S1. Imaging parameters for the anatomical references, rsfMRI, ASL and DTI. Table S2. Statistical outliers which were removed from the dataset per test.


In this study, we critically review the literature concerning the relation of Mg, Fe, Zn, Cu and Se and attention-deficit/hyperactivity disorder (ADHD). Elemental status is estimated using peripheral blood parameters, hair, urine, daily intake and response to supplementation. The observed associations between concentration levels of the elements Mg, Fe, Zn, Cu and Se and ADHD symptoms are contradictory. This is partly due to the heterogeneity and complexity of the disorder. As a trend, lower ferritin and zinc levels can be observed. However, this correlation is not causative, as illustrated by placebo-controlled trials reporting conflicting evidence on the efficacy of supplementation. Well-defined studies on changes in concentration levels of the elements in relation to ADHD symptoms before and after treatment with therapeutics it will be possible to shed more light on the significance of these elements in this behavioral disorder. The discussion on whether a change in concentration of an element is cause or consequence of ADHD is not within the scope of this article. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inappropriate levels of impulsive behavior, hyperactivity and/or inattention . AlthougIn this paper, a literature search was performed on the status of Fe, Cu, Zn, Mg and Se and ADHD in children and adolescents, then reported observations were critically reviewed. Attention deficit disorder (ADD) also forms part of the ADHD spectrum; however, this search was limited to publications in which ADHD is explicitly mentioned. The review is limited to ADHD in children and adolescents. Hence, papers on the adult form of this disease were excluded ,23 becauLiterature was screened for publications between January 2010 and March 2020. Important older references mentioned in some papers are also included. We consulted PubMed, Science Direct, Web of Science and Google Scholar for the search. The terms ADHD, the various elements, elemental status, blood level and daily intake of Fe, Cu, Zn, Se and Mg were used as keywords in various combinations. Publications in other languages with an English summary were included. Plasma, or serum, was the principal monitored medium, but also red blood cells, hair, urine and daily intake were included to evaluate the elemental status. Although normal values for the various elements could be geographically different, the ADHD group was always compared with a control group for the same region. Concluding that there were deficiencies or excessive levels can only be done by using biological activity measurements of related proteins. Most of the time, the elements act as cofactors in various enzymes and proteins, and therefore could interfere in the etiology of ADHD. However, these proteins  were not included in this search strategy. Publications could mention only one element, or a combination with other elements could be presented.The overall aim was to discuss found relations between the various parameters used for evaluating the elemental status and ADHD in children and adolescents and observed response on supplementation.2+) is the second most abundant cation in the human intracellular compartment and is of great physiological importance [Magnesium . In one of these studies Cortese et al.  found thMost publications mention lower intake of Fe in ADHD children ,56,105. Due to the low iron status found in most studies on ADHD patients, supplementation has been carried out with varying success. An iron preparation was mentioned as 80 mg/day  or 5 mg/Zinc is an essential trace element, required for various cellular functions through proteins, enzymes and zinc-fingers . DisturbR by measuring various parameters, including serum zinc concentrations [Case-control trials in several geographical areas have demonstrated lower zinc levels in children diagnosed with ADHD compared to healthy controls ,126,127.trations .Generally, lower zinc levels in hair were published in ADHD ,48,102. Nearly all studies found lower daily intake of zinc in ADHD children ,56,125. Although above mentioned observations suggest an association between low Zn status and ADHD, it has not been demonstrated that zinc deficiency is a causative factor for ADHD nor that zinc treatment is recommended, Zn supplementation studies have been carried out. Randomized, placebo-controlled trials of zinc supplementation either as an adjuvant to psychostimulant treatment or as monotherapy have provided conflicting evidence of efficacy ,141,142.p  >  0.05) copper blood level, compared to this in the with ADHD was associated with a decreased serum Cu/Zn SOD (superoxide dismutase)-level [Copper is involved in catecholamine metabolism via beta-hydroxylase and monoaminoxidase (MAO). Higher copper concentrations lead to lower dopamine levels in rats . In chile)-level . Lower le)-level , were coe)-level ,141. Howe)-level . The Cu/e)-level . They cle)-level . Analysie)-level . No relae)-level . As a coSelenium (Se) is an essential element in a number of selenoproteins, including glutathione peroxidase (GPx), a family of enzymes that protects against oxidative injury by catalyzing the breakdown of hydrogen peroxides . There aThe possible associations of the elemental status of Mg, Fe, Zn, Cu and Se with ADHD occurrence were reviewed. For some elements , there is a trend of reporting lower values in the blood of ADHD patients. However, for both these elements, there were contradictory findings. This was also observed for other elements. These conflicting results mentioned in literature may depend largely on the amount of diagnostic parameters , treatmeThe discussion remains whether low status of various elements results from a decreased appetite as a consequence of ADHD medication. Another explanation of lower daily intake may be that patients with ADHD have an impaired ability to sit still during the meals, leave table earlier and have decreased nutritional intake for various nutrients. Therefore, daily intake should not be taken into account in assessing elemental status of the various elements. The discussion on whether a change in concentration of an element is cause or consequence of ADHD is not within the scope of this article. Evidence and arguments for supplementation of the various elements  is insufficient, unless in a severe deficient status. Maybe, measurement of the nutrients or using peripheral parameters will evolve in the future into other directions. Research on biometals , metalloproteins, metalloenzymes, chaperones , as well as the application of genomics, proteomics and metabolomics, can contribute to an acceleration and improvement of identification of biomarkers in diagnosis and treatment of ADHD . In this


Evidence from studies in adults documents that fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) may be triggers of symptoms in individuals with functional abdominal pain disorders (FAPDs). However, in children, the evidence is very limited. We aim to assess the effects of a low-FODMAP diet compared with a regular diet for the management of children with FAPDs.We will perform a randomized, quadruple-blinded, controlled trial. Seventy-four children aged 8 to 18 years with a FAPD , diagnosed according to the Rome IV criteria, will be randomly allocated to receive either a low-FODMAP diet or a regular diet for 4 weeks. The primary outcome will be the percentage of the responders, defined as the participants who have at least 30% improvement in abdominal pain intensity on a Visual Analogue Scale (VAS) during the last week of the trial compared with baseline, that is at least equal to the Reliable Change Index (≥ 25 mm change on VAS). Other outcomes will include changes in stool consistency, abdominal pain frequency, total scores on the Gastrointestinal Symptom Rating Scale, KIDSCREEN-10 Index and World Health Organization Five Well-Being Index, child’s school attendance and parents’ work absenteeism, and BMI-for-age z-score. Compliance, tolerability of the low-FODMAP diet, and adverse events also will be evaluated. Each FAPD subtype will be assessed separately.There is a need for high-quality evidence regarding the dietary management of children with FAPDs. This randomized controlled trial (RCT) of rigorous methodological design will help to establish the effectiveness, if any, of a low-FODMAP diet for the management of FAPDs in the pediatric population. The findings of this RCT will assist with the development of guidelines and influence the direction of further research.NCT04528914Data and protocol version identifier: 24/08/2020The online version contains supplementary material available at 10.1186/s12937-020-00656-3. Functional abdominal pain disorders (FAPDs) affect 13.5% of children worldwide . The maiThe mechanisms underlying FAPDs are complex. Among them is a brain-gut axis disorder, resulting from the interaction of genetic, environmental, and psychological factors –4. As thWhereas evidence from studies in adults suggests that fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) may be triggers of symptoms in individuals with FAPDs, evidence in children is very limited. However, a low-FODMAP diet is commonly prescribed for IBS in both adults and children, despite the limited evidence of its effectiveness . The ratn = 33)  [f IBS-D] . The chaChange in abdominal pain frequency at weeks 1, 2, 3, and 4 from baseline ) does not include 1.0, or when 95% CI for MD does not include 0. All statistical tests will be two-tailed and performed at the 5% level of significance.Both the primary and secondary outcomes will be analyzed by logistic regression, controlling for three, prespecified, potential confounding variables, i.e., age, sex, and subtype of IBS. The difference between study groups will be considered significant when the Every effort will be made to minimize missing baseline and outcome data. However, the amount of missing data will be reported for each study arm. The reasons for withdrawals, if feasible, will be compared qualitatively between groups. Multiple imputation methods for missing data will be included to address any missing data, as recommended , 30, 31.The study will be performed following the protocol registered before the commencement of screening. However, in case of unexpected and valid circumstances, the protocol registry will be immediately updated and, if required, the bioethical committee will be informed.The independent DSMB will review data after recruitment of 25, 50, and 75% of participants to review the study progress (including rate of recruitment and completeness of data) and all reported adverse events. The number of recruited children will be monitored and kept up to date. In case of an insufficient recruitment rate, defined as associated with the risk of not finishing the study within the established time (which is 2 years), we will advertise our trial among other pediatric wards and primary care physicians, however, only if adequately trained personnel is present.Good Clinical Practice guidelines), and evaluated for duration, intensity, and causal relationship with the intervention. Each serious adverse events will be reported to the project leader within 24 h of the notification. The project leader will notify, within 7 days, the bioethical committee, all investigators involved in the study, and the catering company responsible for the diet provision. Due to the risk of potential nutritional imbalance and growth impairment, the intervention will be guided by professionals with expertise in dietary management.Although the intervention is expected to be safe, all adverse events will be reported in the CRF, classified . An external independent statistician will receive an access to de-identified electronic dataset.The compensation of harms will be covered by study insurance policies. For post-trial care, see The findings of this RCT will be submitted to a peer-reviewed journal, no later than a year after the post-intervention data collection. The abstract will be submitted to relevant national and international conferences. No later than 3 years after data analysis, a full, anonymized, participant-level data set will be archived for sharing purposes. Data analysis will be completed over 3 months after RCT completion.Currently, there are no evidence-based guidelines on the diagnosis and management of FAPDs in children. According to evidence in adults, use of a low-FODMAP diet is promising, however, in children the evidence is scarce . RCTs inThis study will assess the effects of a low-FODMAP diet compared with a regular diet for the management of children with FAPDs . Although high-quality evidence is needed, no study is perfect, as every RCT investigating dietary modifications of FODMAPs has had some limitations. The risk of bias associated with the possibility of the participant or caregiver guessing the nature of the provided diet using other sources cannot be excluded. To minimize this bias, a post-intervention assessment of blinding success, of both participants and researchers, has been planned see  section.This RCT of rigorous methodological design will help to establish the effectiveness, if any exists, of the use of a low-FODMAP diet in the management of FAPDs in the pediatric population. This study will be conducted by a research center with experience in conducting both independent RCTs and trials in children with FAPDs, and in cooperation with a person who is experienced in research involving FODMAPs. The findings of this RCT will fill a gap in current evidence and inform guideline development groups about the actual efficacy of a low-FODMAP diet in the management of FAPDs in children. This RCT may also guide the direction of further research in this area.Additional file 1. SPIRIT 2013 Checklist.


Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity. It is associated with injuries2, traffic accidents3, increased health care utilization5, substance abuse7, criminality8, unemployment1, divorce9, suicide11, AIDS risk behaviors12, and premature mortality13. The cost of adult ADHD to society is between $77.5 and $115.9 billion each year14.Attention-deficit/hyperactivity disorder (ADHD) is a common disorder affecting 5% of children and 3% of adults15. A role for brain dysfunction in the etiology of ADHD was suspected for some time by the mechanism of action of the medications that treat ADHD16, as well as supported by findings from genome-wide association studies (GWAS)18. Although many prior magnetic resonance imaging (MRI) studies had suggested structural and functional differences between the brains of children with ADHD and those without25, machine learning (ML) MRI diagnostic classifiers for ADHD have reported inconsistent results. We and others have examined this body of literature and reported large variations in choices of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. Notably, many prior studies risked data leakage and accuracy inflation by using cross-validation methods without an independent test set26. In addition, the largest dataset that ML classifiers have used thus far was the ADHD-200 Global Competition dataset consisting of 776 children, adolescents, and young adults (7–21 years old27). Only a few studies examined classifiers for adults with ADHD and they all used extremely small datasets (<100 subjects30).ADHD is highly heritable (76% heritability)31, cortical thinning, and reduced surface area32 to be associated with ADHD in children but not adults. Two-thirds of youth with ADHD will continue to have impairing symptoms of the disorder into young adulthood and that persistence continues to decline with age33. The term adult ADHD refers to childhood onset ADHD that has persisted into adulthood, which is how it is defined in DSM 5 and in the ENIGMA-ADHD studies. The ENIGMA-ADHD study found small but significant ADHD vs. control differences in regional volumes and cortical thicknesses for children but not adolescents or adults35. Other studies show that ADHD participants whose brains become more neurotypical were more likely than others to show remission of symptoms37. But, although these longitudinal studies show reductions in case vs. control differences, they also suggest that those differences should be evident to some degree in cases that persist into adulthood.The Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) ADHD Working Group created a large collaborative dataset with sufficient power to detect small effects. The ENIGMA-ADHD working group found small, statistically significant sub-cortical volumetric reductions39 and recently confirmed by a large GWAS40, this idea has been challenged41. Thus, given these prior data and the controversy about the continuity of ADHD into adulthood, we sought to test the idea that the ADHD-associated volumetric reductions seen in children with ADHD would be detected in adults with ADHD by applying ML algorithms. Given that symptoms and impairments persist into adulthood for most children with ADHD43, we hypothesized that ADHD-related brain structure differences in adults would be consistent with those observed in children.Although the expectation of finding substantial continuity between childhood and adult ADHD has been widely accepted31. A total of 151 variables were used including 34 cortical surface areas, 34 cortical thickness measurements, and 7 subcortical regions from each hemisphere, and intracranial volume (ICV). Subjects missing more than 50% of variables were removed. Remaining missing values and outliers (outside of 1.5 times the interquartile range (iqr 1.5)) were replaced with imputed values using multiple imputation with chained equations in STATA15. The final ML dataset consisted 4042 subjects from 35 sites, among which 45.8% were non-ADHD controls  = 1.42) and 54.2% ADHD participants . Ages ranged from four to 63 years old; 60.7% were children  and 39.3% were adults . ADHD diagnosis was significantly biased by sex (X2(1) = 66.9, p < 0.0001), sites (X2(1) = 146.73, p < 0.0001), and age (X2(1) = 4.28, p = 0.04).The current study was approved by all contributing members of the ENIGMA-ADHD Working Group, which provided T1-weighted structural MRI (sMRI) data from 4183 subjects from 35 participating sites (by Aug. 2019). Each participating site had approval from its local ethics committee to perform the study and to share de-identified, anonymized individual data. Images were processed using the consortium’s standard segmentation algorithms in FreeSurfer (V5.1 and V5.3)To balance the confounding factors, we took the following steps. First, we randomly assigned samples to training (~70%), validation ~15%), and test (~15%) subsets within each diagnosis, sex, age subgroup (child vs. adult), and site to ensure that the train/validation/test subsets have the same composition of these variables. Twelve sites that provided only cases or only controls  were excluded during the initial train/validation/test split because their samples cannot provide an unbiased learning during the training and validation steps. These samples were added to the test set for final test evaluation. Supplementary Table 5%, and tThe high correlation among the 151 MRI features suggested the need for feature dimension reduction. Many prior studies have opted for feature selection in which the most important features were retained rather than using all MRI features. Although this approach reduces the numbers of input features, it does not remove the highly correlated relationships among the selected features. As prior MRI studies also suggested small but widespread differences between children with and without ADHD, we chose to use principal factors factor analysis (PFFA) for dimension reduction. With varimax rotation, PFFA on sMRI features of the training set identified 46 factors that explained >90% of the variance. This means that the reduced numbers of 46 non-correlated factors were able to represent majority (>90%) of the variance within the training dataset. We then computed factor scores for subjects in the validation and test sets based on the training set PFFA. We compared the original MRI and PFFA features in a screening pipeline for nine different ML models (see below) to determine which set of features were better for the classifiers.44 to search a large hyperparameter space for each of the models , numbers of units in each layer (4–500) and dropout rates in each layer (0.1–0.9), learning rate (0.00001–0.01) and batch normalization size (4–256). These hyperparameters were chosen for the HyperOpt tuning because of their important role in effective learning, avoiding local minimum and overfitting. The numbers of layers and units determines the complexity of the model. The ideal complexity of the neural network ensures a converging model that was able to learn the predictive features but not overfit the training examples. Early stopping was also implemented to avoid overfitting. We tested different activation functions , and optimizers . We used binary cross entropy as the loss function. Best model architecture and hyperparameters were chosen based on the lowest total validation loss. Final test scores were obtained on the test set with ensemble learning approach46. All ML algorithms were written in Python 3.5.More detailed hyperparameter tuning for MLP was carried out using the Keras API (version 2.3.1), the TensorFlow library (version 1.14.0), and HyperOptOur main analysis pipeline starts with two base models that used data from the corresponding age groups during the model training and validation phase and tested also on data from their corresponding age groups. The child model used only child samples during model training, validation, and hyperparameter optimization, and tested on child test set. The adult model, similarly, was trained and validated on the adult samples and tested on the adult test set. We examined models using MRI features only, as well as those included age and sex information. We also trained a combined model that uses all the training data from both child and adult groups and compared the performance with the age-specific models.Next, we sought to determine if the model trained and validated on the adult samples, the adult model, could be used to predict child ADHD, and vice versa. We hypothesized that if the ADHD vs. control sMRI differences seen in children are also present in adult ADHD brains, then the base models for each age group should be able to predict ADHD in the other age group. To create the largest test sets possible, we tested the child model on all the adult samples, and the adult model on all the child samples.d effect sizes for child and adult test sets. We computed ROC curves and used the area under the ROC curve (AUC) as our primary measure of accuracy. The AUC and its confidence intervals were calculated in Stata 15 using the empirical method and compared with nonparametric approach by DeLong et al.47. We also computed precision-recall (PR) curves and reported the area under the PR curves, as well as the Brier loss for the final models as measures of accuracy and goodness of fit.The sigmoid function in the output layer of the neural network generates a continuous score that assesses the probability for each individual to be classified as ADHD. We name this continuous output the brain risk score. Using the brain risk scores, we calculated Cohen’s The screening results . ROC curves for the two base models are in Supplementary Fig. 2(1) = 3.4, p = 0.065). The areas under the precision-recall curve (AUPRC) were higher for the adult model (AUPRC = 0.74) than the child model (AUPRC = 0.68). Using the model predicted brain risk scores, we calculated the Cohen’s d effect sizes in the test set to be 0.47 for child samples (95%CI: 0.27–0.68) and 0.15 (−0.08–0.39) for the adult samples.Figure p = 0.002). Although prediction AUC was now significant, the increase from the base model without age and sex was not statistically significant (X2(1) = 2.01, p = 0.15). The AUPRC for the adult model also slightly increased to 0.79. Adding age and sex as predictors to the child model did not affect either the AUC, nor the AUPRC. ROC curves of two models are plotted in Supplementary Fig. d effect sizes in the test set were 0.48 for children (95%CI: 0.27–0.69) and 0.39 (0.15–0.63) for adults. All above models had similarly small Brier scores (0.25).After adding age and sex as predictors, the adult model Fig. , top incThe combined model with MRI features produced an overall test AUC of 0.60 (95%CI 0.55–0.64). The test AUC was 0.64 (95%CI 0.58–0.69) on the child subset and 0.54 (95%CI 0.47–0.60) on the adult subset, comparable to those from the age-specific models. Similarly, the combined model with MRI, age, and sex features produced an overall AUC of 0.63 (95%CI 0.59–0.67). The subset test AUC was 0.65 (95%CI 0.60–0.71) on the child subset and 0.56 (95%CI 0.49–0.63) on the adult subset, also statistically comparable to those of the age-specific models.Because the training samples had been balanced for age and sex, these variables are not predictive of ADHD for either the child or adult test sets. To verify this, linear regression using only age and sex and their interactions to predict ADHD in the child and adult samples resulted in non-significant AUCs .d effect size for children, based on the adult model predictions, was 0.17 (95%CI: 0.10–0.24), smaller than those predicted by their age-corresponding models. The child model that used both MRI features and age and sex did not significantly predict ADHD when applied to the adult samples  and a high area under the PR curve (AUPRC = 0.79). Consistent with the ENIGMA findings, our model-predicted brain risk scores had a larger effect size for the children than adults in both the models using MRI features and those with age and sex added. Notably, our effect sizes were two times greater than the largest of those individual regions reported in prior ENIGMA ADHD studies for both children (Cohen’s d = −0.21) and adults (Cohen’s d = −0.16)32.Consistent with previous ENIGMA ADHD findings51. Among those that reported independent test results, classification accuracies varied from 37 to 93%, with an average of 68% (ref. 26). Notably, it is difficult to directly compare the accuracy scores with our AUC scores since many of these studies used imbalanced datasets. Nevertheless, one study, among those, reported classifiers built with only sMRI features. In that study, Yoo and colleagues examined various combinations of fMRI, sMRI features, and genetic data from a balanced cohort of 94 children and adolescents. The unimodal sMRI classifier, using the cortical thickness and volumes, achieved an accuracy of 69.4% and AUC 0.65 in a small independent test set 52. Although the AUC is comparable to our child model, it is not clear how well this model would generalize to other samples given the extremely small sample sizes in both training and test sets. Nevertheless, the authors reported a better AUC (0.70) with a multimodal classifier built with features from both diffusion tensor imaging and sMRI data52.Only a handful of prior ML studies attempted to classify ADHD from controls using only sMRI data. Most used resting-state functional MRI (rs-fMRI), or rs-fMRI in combination with another MRI modality, sometimes including cognitive measurements such as IQ. Many prior studies reported model performance on a cross-validation dataset without using an independent test set. We and many others have warned about the risk of data leakage and model overfitting when using only cross-validation without an independent test set30)). Although these studies reported higher accuracies (74%–80%), all were based on cross-validation results and none reported prediction performance on independent test sets. The improvements we found by adding age and sex to the adult model indicate that these demographic variables must moderate the predictive ability of sMRI features. These demographics moderate the sMRI effects because our regression models show that the demographic variables on their own have no predictive utility . It is possible that there are different age subgroups within the adult dataset that demonstrate different patterns of MRI features. For example, many regions of the brain, including prefrontal cortex, do not fully mature until early adulthood, around age 25 (ref. 53). Perhaps the age group “adults” should not include developing brains prior to age 25. However, we cannot assess for such age effects due to the sample sizes of more refined age groups. Future work should recruit more MRI data particularly for under-represented adolescent and older adult age groups. We have also shown that ML methods dramatically increase the ADHD vs. Control effect size compared with the prior univariate ENIGMA analyses.Although our results from the child and adult base models show that sMRI data are not sufficiently predictive to be useful in clinical practice, they provide crucial pieces of evidence that will be useful in future attempts at predictive modeling. We are the first to confirm in the largest possible adult ADHD MRI sample available, that adults with ADHD differ significantly from adults without ADHD on sMRI features. Only a few prior studies attempted to classify adult ADHD from controls, but all used extremely small dataset . This conclusion must, however, be considered equivocal because the child model did not successfully predict ADHD in the adult samples. To resolve this issue, future studies will need to find a way to better discriminate sMRI features associated with the onset of ADHD and those associated with the persistence of ADHD.Our work should be interpreted in the context of several limitations. First, because we combined data across many sites, we inherit all the limitations of the original studies. Heterogeneity of methods across studies may have added noise to the combined dataset that made it difficult to discriminate the data from people with and without ADHD. Second, we only used structural imaging data. Incorporating other imaging modalities might provide clearer results and conclusions. Third, we used pre-defined structures from ENIGMA standard image processing pipeline as features. It is possible that other methods such as one using 3D images as input features, in a convolutional neural network, would uncover useful features leading to increased classification accuracy. However, the 3D images are not available. Finally, our use of neural networks makes it difficult to clarify the importance of each brain region in the model’s algorithm.Despite these limitations, we have shown that a neural network approach is able to detect case-control sMIR differences in adults with ADHD that could not be detected with standard analyses. We have also provided some evidence for the continuity of sMRI findings from childhood into adulthood.Supplemental Materials