Attention deficit and hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are child-onset neurodevelopmental disorders frequently accompanied by cognitive difficulties. In the current study, we aim to examine the genetic overlap between ADHD and ASD and cognitive measures of working memory (WM) and attention performance among schoolchildren using a polygenic risk approach.N = 55 374 and N = 46 351, respectively). The cognitive outcomes included verbal and numerical WM and the standard error of hit reaction time (HRTSE) as a measure of attention performance. These outcomes were repeatedly assessed over 1-year period using computerized version of the Attention Network Test and n-back task. Associations were estimated using linear mixed-effects models.A total of 1667 children from a population-based cohort aged 7–11 years with data available on genetics and cognition were included in the analyses. Polygenic risk scores (PRS) were calculated for ADHD and ASD using results from the largest GWAS to date (Higher polygenic risk for ADHD was associated with lower WM performance at baseline time but not over time. These findings remained significant after adjusting by multiple testing and excluding individuals with an ADHD diagnosis but were limited to boys. PRS for ASD was only nominally associated with an increased improvement on verbal WM over time, although this association did not survive multiple testing correction. No associations were observed for HRTSE.Common genetic variants related to ADHD may contribute to worse WM performance among schoolchildren from the general population but not to the subsequent cognitive-developmental trajectory assessed over 1-year period. Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are complex neurodevelopmental disorders that emerge during childhood  or variability in reaction time (RT), which reflects attentional lapses  but these findings were not replicated in an independent sample (N = 3681) , a population-based cohort of primary school-aged children designed to analyze associations between air pollution and behavior, cognitive abilities and brain structure and function. A full description of the project is available elsewhere , Barcelona, Spain; and the FP7-ERC-2010-AdG Ethics Review Committee .n-back task  as an indicator of WM accuracy. These measures, 3-back numbers d′ and 3-back words d′ were treated in the analysis as indicators of verbal and numerical WM performance when using words and numbers, respectively, as stimuli. These measures were analyzed as continuous variables. Higher d′ scores indicated more accuracy and thus, better performance.The outcomes of the study were WM and attention performance. WM was assessed using the Attention performance was assessed using the computerized version of the Attention Network Test [ANT,  over 1 year. We used these four repeated measures to model the 1-year developmental trajectories of verbal and numerical WM and HRTSE. The modeled 1-year trajectory may include 1–4 repeated measures of the cognitive measures based on available data.To validate the PRS in our cohort, behavioral data were used to test the association between the PRS and the phenotype. Behavioral measures were obtained at the beginning of the cognitive data collection . ADHD symptoms were assessed using a questionnaire based on the ADHD diagnostic criteria described in Diagnostic and Statistical Manual of Mental Disorders 4th edition [DSM-IV . A full description of the genotyping and quality control procedures is available elsewhere  for SNP inclusion TP = {0.01, 0.05, 0.1, 0.5, 1} were calculated for each disorder. Polygenic scoring was performed in clumped variants (representative genetic variants per linkage disequilibrium block) using r2 > 0.1 as a cut-off for LD independent markers in a 250-kb window. This means that SNPs with the smallest p value in each 250 kb window is retained and all those in LD (r2 > 0.1) with these SNPs are removed. All PRS were standardized to a mean of 0 and a standard deviation of 1. The number of variants included in the PRS for each TP can be found in online Supplementary Table S1.Genotyped data that passed quality control were used to compute PRS using PRSice  of European ancestry and 9 112 387 SNPs , numerical WM  and HRTSE   . Of thesWe tested the cross-sectional association between polygenic risk for ADHD (PRS-ADHD) and ASD (PRS-ASD) and cognitive measures using linear mixed-effects models with the school as a nested random effect. These associations were tested using the cognitive outcomes assessed at baseline. The baseline measures correspond to those obtained in visit 1 when participants completed the cognitive tasks for the first time.In the validation and cross-sectional analysis, we included schools as nested random effects to account for the multilevel nature of the data (i.e. children within schools).sitY is the test score of the cognitive measure for the individual i in school s at visit t, t = {1,2,3,4}; su is random effects at the school level, ivs) were included in the analyses of 1-year trajectories. Thus, the modeled 1-year trajectory may include two–four repeated measures of the cognitive outcomes per individual. Of note, around 73% of the participants have data for the four visits. Previous research in the BREATHE entire sample (n = 2897) have shown age-related growth patterns on both WM and ANT measures over the 1-year period  using linear mixed-effects models with individual children nested within schools as random effects. An interaction between age (centered at visit 1) at each visit and the PRS was included to capture changes in 1-year trajectory associated with polygenic risk for ADHD or ASD as shown below.Because ADHD and ASD present higher prevalence among boys  method .n = 1230), children who were included were significantly more likely to be male and presented better verbal and numerical WM performance as well as less HRTSE. Regarding behavioral measures, children included had lower inattention and total ADHD symptoms. There were no significant differences in terms of age or hyperactivity symptoms (online Supplementary Table S2). Boys performed better for numerical WM and HRTSE measures and presented higher total ADHD, inattention and hyperactivity symptoms (online Supplementary Table S3).Descriptive characteristics of the subjects included in the analyses are presented in d′) is related to better performance in the second (low HRT-SE).Online Supplementary Fig. S1 shows the correlations between cognitive measures in each visit and online Supplementary Fig. S2 summarizes the correlations between the PRS for ADHD and ASD. Measures within each domain (i.e. WM and HRTSE) were positively correlated across the four visits. WM and HRTSE measures were negatively correlated, which indicates that better performance in the first domain (high T <P0.01 (online Supplementary Table S4).Polygenic risk for ADHD was positively associated with ADHD symptoms total score and hyperactivity at all significance thresholds. Positive associations between polygenic risk for ADHD and inattention were observed at all significance thresholds but were not significant at TP = 1  .PRS for ADHD were negatively associated with verbal WM across all significance thresholds with the strongest association observed at = 0.026) . All thePolygenic risk for ASD did not show significant associations with WM measures. Although not significant, a consistent pattern of results was observed suggesting positive associations between PRS for ASD and verbal and numerical WM performance .No significant associations were found between PRS for ADHD or ASD and HRTSE .p value for interaction for T <P0.5 and T <P1 were 0.028 and 0.039, respectively) and numerical WM  indicating that negative associations between PRS for ADHD and WM measures were stronger among boys (online Supplementary Table S5). Stratified results revealed that significant associations were limited to boys (online Supplementary Table S5).Significant interactions between PRS for ADHD and sex were observed on verbal  suggesting that higher polygenic load for ASD is related to increased cognitive growth over the 1-year period . Individuals with higher polygenic risk for ASD would improve faster than individuals with lower loads.A nominally significant association was observed between PRS for ASD at No significant interactions by sex were detected (online Supplementary Table S6).d′) assessed at baseline across all significance thresholds. At all significance thresholds, these associations remained significant following FDR-correction except at TP  < 0.05. The strongest association was observed at TP = 0.05 . Nominally significant associations were also observed with numerical WM performance (d′) for all significance thresholds except TP < 0.01. Of these, one association survived FDR-correction  (online Supplementary Table S7).Similar results were observed after excluding 174 children with an ADHD diagnosis (online Supplementary Table S6). Specifically, PRS for ADHD were negatively nominally associated with verbal WM performance .In the current study, we examined the genetic overlap between ADHD and ASD and cognitive abilities among school-aged children from the general population. We found that polygenic risk for ADHD was associated with worse WM performance. These associations were observed at baseline assessments but not over time. In contrast, PRS for ASD did not show significant associations with any of the cognitive measures analyzed at baseline. Only a nominal association was observed between PRS for ASD and increased cognitive growth in verbal WM over time, although this association did not survive FDR-correction. The exclusion of individuals diagnosed with ADHD from the analyses did not change meaningfully these findings. No associations were observed for HRTSE.As expected, the polygenic risk for ADHD was positively associated with ADHD symptoms total score, inattention and hyperactivity. The association between polygenic risk for ADHD and hyperactivity and inattention was very similar in strength and magnitude. These findings provide additional support for a dimensional conceptualization of ADHD with genetic factors operating through the distribution of the trait   and low intelligence quotient (IQ) scores among adolescents (N = 4958) from the general population , the Lothian Birth Cohorts 1936 and 1921 (N = 1522), and the Brisbane Adolescent Twin Sample (BATS) (N = 921), did not show consistent results regarding the associations between polygenic risk for ADHD and cognitive outcomes, but a negative association was observed with IQ at age 11 years in the Lothian Birth Cohorts  found that verbal-numerical reasoning scores, a cognitive measurement related to IQ, decreased with increasing polygenic load for ADHD , reporting worse WM performance at baseline but similar cognitive growth trajectories for individuals with and without ADHD diagnosis for tasks with numbers as stimuli  and magnitude (difference in the score from first to the last visit) of this improvement . It has been pointed out that a target sample of around 2000 subjects provides sufficient power to detect a significant proportion of variance explained, which is slightly larger than our sample (Marees, Stringer, Claire, & Derks, Our results should be interpreted in the context of several strengths and limitations. One of the strengths of the study regards the obtention of cognitive measures using computerized tests which reduces the examiner bias. Also, we used well-powered GWAS to compute PRS, a key aspect to increase the accuracy of the PRS (Choi et al. To conclude, common genetic variants related to ADHD may be, at least partially, underlying cognitive deficits among school-aged children regarding WM performance. These associations were limited to baseline assessment suggesting that polygenic risk for ADHD is not associated with 1-year trajectories of cognitive development. Our findings suggest that genetic risk variants for ADHD may have pleiotropic effects in WM, a closely related neurodevelopmental domain in the context of ADHD. Further research is needed to characterize the neurobiological mechanisms underlying the genetic overlap between ADHD and WM.


P < 0.05) as the inclusion of hybrid rye increased, but no other differences in ADG were observed. Average daily feed intake linearly increased in phase 1, phase 3, and overall (P < 0.05) as hybrid rye inclusion increased in the diets, and gain:feed was negatively impacted by the inclusion of hybrid rye in the diet . No differences in average fecal scores or diarrhea incidence were observed. On days 21 and 35, blood urea N increased  as hybrid rye increased in the diets; and on day 21, serum total protein also increased  with increasing hybrid rye inclusion in the diet. Mean blood hemoglobin concentration on day 35 increased and then decreased as hybrid rye inclusion increased . On day 21, interleukin (IL) 2 and IL 10 decreased and then increased  as hybrid rye inclusion increased. On day 35, IL 8 and IL 12 increased and then decreased  and interferon-gamma decreased and then increased  as hybrid rye inclusion increased. In conclusion, the ADG of pigs was not different among treatments, but at the highest hybrid rye inclusion level, pigs consumed more feed than if corn was fed and gain:feed was reduced with increasing hybrid rye in diets. Differences in blood serum cytokines indicate the immune system was affected differently when hybrid rye instead of corn was fed.An experiment was conducted to test the hypothesis that growth performance and health status of pigs will not be reduced if hybrid rye is included in diets at the expense of corn during the initial 5 wk post-weaning. A total of 128 weanling pigs (5.6 ± 0.5 kg) were randomly allotted to 32 pens and 4 dietary treatments. Pigs were fed experimental diets for 35 d in three phases with days 1 to 7 being phase 1, days 8 to 21 being phase 2, and days 22 to 35 being phase 3. Within each phase, a control diet primarily based on corn and soybean meal was formulated, and three additional diets were formulated by including 8.0, 16.0, or 24.0% (phase 1), 16.0, 32.0, or 48.0% (phase 2), and 20.0, 40.0, or 60.3% (phase 3) hybrid rye in the diet at the expense of corn. Pig weights were recorded at the start and conclusion of each phase, fecal scores were visually assessed every other day on a pen basis, and blood samples were obtained from 1 pig per pen on days 21 and 35. Results indicated that average daily gain (ADG) in phase 1 increased (linear,  In a previous experiment, no differences in final body weights were observed when hybrid rye was fed to nursery pigs at inclusion rates of up to 12, 21, and 60% in phases 1 (days 1 to 7 post-weaning), 2 (days 8 to 21 post-weaning), and 3 (days 22 to 35 post-weaning), respectively . AverageThe experiment was conducted at the Swine Research Center at the University of Illinois following a protocol that was approved by the Institutional Animal Care and Use Committee at the University of Illinois Urbana-Champaign .ad libitum basis.A total of 128 pigs that were the offspring of Line 359 boars and Camborough sows  with an initial body weight of 5.6 ± 0.5 kg were weaned at 19 to 21 d of age and allotted to four treatment groups. Thirty-two pens were used with four pigs per pen and eight replicate pens per treatment. Weaning weight was used as the blocking factor, sex was balanced among pens, and treatments were randomly allotted to the pens. Pigs were housed in an environmentally controlled nursery barn with fully slatted floors, a nipple waterer, and a stainless-steel feeder. Water and feed were available on an From d 14 post-farrowing and until weaning, pigs were offered a commercial creep feed according to normal farm practices. This diet was based on corn and soybean meal as well as animal proteins and did not contain hybrid rye. Pigs were fed experimental diets for 35 d after weaning and a three-phase feeding program was used. Phase 1 diets were fed for 7 d post-weaning, phase 2 diets were fed from days 8 to 21, and phase 3 diets were fed from days 22 to 35 post-weaning. A total of 12 diets were formulated , and allIndividual pig weights were recorded at the beginning of the experiment and at the conclusion of each phase. Feed additions were recorded daily, and feed left in the feeders at the conclusion of each phase was weighed to calculate feed disappearance. Fecal scores were assessed visually for each pen as a whole every other day by two independent observers using a score from 1 to 5 .g for 13 min at room temperature. Serum was removed from centrifuged tubes and stored at −20°C until analysis. Total protein, blood urea N, and albumin were analyzed using a Beckman Coulter Clinical Chemistry AU analyzer . The concentration of immunoglobulin G (IgG) in serum samples was determined by enzyme-linked immunosorbent assay following the manufacturer’s instructions . Concentrations of interleukin (IL)-1α, IL-1β, IL-1 receptor antagonist (IL-1RA), IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, interferon-gamma (IFN-γ), and tumor necrosis factor-α in serum samples were measured via a porcine-specific multiplex immunoassay kit  and read with a Luminex MagPix instrument .Two 3-mL blood samples were collected from the jugular vein from one pig per pen on the last day of phase 2, as well as on the last day of phase 3. An equal number of barrows and gilts were sampled within treatment, and the same pigs were sampled at each time point. Of the two samples of blood obtained per pig, one was collected in a vacutainer containing ethylenediamine-tetraacetic acid as an anticoagulant, immediately placed on ice, and analyzed for complete blood cell count on a multiparameter automated hematology analyzer . The second sample of blood was collected in a serum vacutainer containing spray-dried silica as a clot activator. Samples were allowed to clot and then centrifuged at 4,000 × XT15, Ankom Technology, Macedon, NY, USA) following hydrolysis using 3N HCl . Amino acids were analyzed at the University of Missouri Agricultural Experiment Station . Diets were analyzed for amino acids on a Hitachi Amino Acid Analyzer, Model No. L8800  using ninhydrin for post-column derivatization and norleucine as the internal standard. Prior to analysis, samples were hydrolyzed with 6N HCl for 24 h at 110°C [method 982.30 E(a); Diet samples were analyzed for dry matter by oven drying at 135°C for 2 h , ADFI, gain:feed (G:F), average fecal scores, and diarrhea frequency were calculated for each of the three phases as well as for the entire experiment. Data were analyzed using SAS 9.4 . The normality of the residuals was confirmed, and outliers were tested for using the UNIVARIATE procedure of SAS. Outliers were defined as observations with internally studentized residuals less than −3 or greater than 3, but no outliers were detected. With the exception of diarrhea frequency, data were analyzed by the MIXED procedure, and the statistical model included diet as the fixed effect and replicate as the random effect. The least-square means were calculated for each treatment group using the LSMEANS statement in PROC MIXED. Contrast statements were used to test for linear and quadratic effects of using increasing concentrations of hybrid rye in the diets. Diarrhea frequency was analyzed by the GLIMMIX procedure with a binomial distribution and was expressed as the proportion of the number of observations of fecal scores 3 or more divided by the total number of observations. Results were considered significant at The chemical compositions of diets were within the expected ranges . No mycoP < 0.05) as the inclusion of hybrid rye increased, but ADG in phase 2 and phase 3 was not impacted by treatment. ADFI linearly increased (P < 0.05) during phase 1, and overall as hybrid rye was added to the diets. The G:F also increased  in phase 1 as the concentration of hybrid rye in the diet increased, but in phase 2, phase 3, and for the overall period, G:F decreased at the greatest inclusion level of hybrid rye in the diets . No differences in average fecal scores or in diarrhea incidence were observed  as hybrid rye was added to the diets; and on day 21, serum total protein also linearly increased (P < 0.05) with hybrid rye inclusion (P < 0.05). Neutrophils on d 21 tended to increase  with increased hybrid rye inclusion in the diet and a tendency  for decreased neutrophils at the intermediate levels and then increased neutrophils at the highest level of hybrid rye on d 35 was also observed. Eosinophils on day 21 tended to increase and then decrease as hybrid rye increased in the diets (P < 0.10). Lymphocytes on day 35 also tended to increase and then decrease as hybrid rye in the diet increased .On days 21 and 35, blood urea N linearly increased (nclusion  and 6. TP < 0.05) and IL-1RA tended to increase  with greater inclusion of hybrid rye in the diet (P < 0.05). On day 35, the concentration of IL-1RA tended to increase and then decrease with higher inclusion levels of hybrid rye in the diet , and IL-8, and IL-12 increased and then decreased  as hybrid rye in diets increased (P < 0.05) as hybrid rye in diets increased.In serum from pigs on day 21, the concentration of IL-1β linearly increased  were fed at the expense of corn in diets for weanling pigs . In phasThe increase in blood urea N on both sampling days as the inclusion of hybrid rye in the diet increased indicates that protein accretion may have been limited due to the reduced energy intake of the pigs as hybrid rye in the diets increased. As a consequence, amino acid catabolism may have increased and utilization of dietary protein may have been less efficient in pigs fed diets containing hybrid rye . The tenMicrobial fermentation of rye fiber increases the quantity and proportion of butyrate in the hindgut digesta, and butyrate exerts anti-inflammatory effects in the host ; howeverIt was hypothesized that hybrid rye may replace a majority of corn in diets for pigs without jeopardizing growth performance during the initial 5 wk post-weaning. Therefore, hybrid rye was included in nursery diets at inclusion rates up to 24, 48, and 60% in phase 1 (week 1), phase 2 (weeks 2 and 3), and phase 3 (weeks 4 and 5). Average body weights and ADG were not impacted by replacing up to 60, 90, and 100% of corn in diets fed to pigs in phases 1, 2, and 3, respectively. However, as hybrid rye inclusion in the diet increased, ADFI increased and G:F was reduced, which was likely a result of the reduced concentration of metabolizable energy in hybrid rye compared with corn. Differences in pro- and anti-inflammatory cytokine concentrations indicate pigs fed hybrid rye had changed immune responses compared with pigs fed corn, but these differences did not influence growth or incidence of diarrhea under the conditions of this experiment.


Previous studies have argued that attention deficit hyperactivity disorder (ADHD) is associated with asthma. However, reliable evidence to verify this association has not yet been reported.To investigate the bidirectional association between asthma and ADHD through a 12-year big data cohort study.The independent variable group was extracted from 3.5 million individuals randomly sampled by the National Health Insurance Service (NHIS). In Study 1, the incidence of ADHD according to asthma was evaluated, while in Study 2, the incidence of asthma according to ADHD was analyzed. Propensity score (PS) matching with several variables was used to obtain a control group.In Study 1, the asthma group included 131,937 individuals and the non-asthma group included 131,937 individuals. The adjusted hazard ratio (aHR) for ADHD in the asthma group was 1.17 [95% confidence interval (CI): 1.11–1.23]. In subgroup analysis, the aHRs for ADHD of individuals in the subgroups male sex, 0–5 years old, 6–10 years old, atopic dermatitis, allergic rhinitis, Charlson comorbidity index (CCI) 1, and CCI > 2 were significant . In Study 2, ADHD was found to significantly affect asthma in all age groups . In the 0∼17 years old subgroup, the association of ADHD with asthma was greater with younger age .From long-term follow-up, the incidence of ADHD was 1.17 times higher in the asthma group than in the control group. The incidence of asthma was 1.10 times higher in the ADHD group than in the control group. Asthma and ADHD have a bidirectional relationship, and childhood asthma and ADHD should be rigorously managed. Attention deficit hyperactivity disorder (ADHD) is a disease characterized by continuous inattention or hyperactivity and impulsivity that hinder functioning and development . The prePrevious studies on the association between ADHD and allergic diseases have primarily been conducted in children. However, for both ADHD and allergic diseases, an increasing trend is observed in all age groups, including children . In a suAsthma patients have a high prevalence of psychological disorders , 9. The On the other hand, an observational study reported that asthma and ADHD were inherited independently . AnotherAs previous studies on the association between ADHD and asthma have not provided consistent results –16, thisAs the NHIS is a single-payer government-run healthcare system operated by the Ministry of Health and Welfare, data from the entire population of South Korea can be collected for insurance claims. In addition, private hospitals are not allowed to withdraw from National Health Insurance, so data from all citizens receiving medical treatment in South Korea are recorded by the NHIS .The National Health Insurance Service—National Sample Cohort consists of an approximately 12-year medical history of 3.5 million individuals, 7% of the total population of South Korea, whose data on sex, age, health insurance premium, and residential area were randomly sampled from the NHIS database. This retrospective cohort study included fundamental demographic variables, such as patient’s sex and age, and medical information, such as the patient’s diagnosis code, treatment history, medication, insurance claims, and hospital visit dates. Asthma and ADHD were classified according to the presence or absence of diagnosis.We conducted a bidirectional study on asthma and ADHD.Based on the International Classification of Diseases, 10th revision diagnosis code (ICD-10 Code), the asthma group was those diagnosed with J45 or J46 twice within 90 days between 2009 and 2010. The ADHD group was those diagnosed with either F90 or F988 twice within 90 days during the same period.In Study 1, we investigated the relationship between asthma as an independent variable and ADHD as a dependent variable. Since the NHIS database provided medical history between 2008 and 2019, we included a 1-year (2008) wash-out period to avoid misinterpreting the order of occurrence between the two diseases. The study was performed in three age ranges: (1) 0–17 years old, (2) 18–60 years old, and (3) 0–60 years old, and our main study group included individuals aged 0–17 years. There were three exclusion criteria: (1) patients with asthma and ADHD with an initial diagnosis before January 1, 2009 during the wash-out period; (2) patients diagnosed with asthma for the first time during the period 2011–2019; and (3) patients with at least one record of ADHD diagnosed earlier or at the same time as the asthma diagnosis. From the asthma cohort (defined earlier), the control group was matched by 1:1 propensity score (PS) matching considering seven covariates, including the Charlson comorbidity index (CCI). We conducted PS matching using a “greedy nearest neighbor” algorithm with a 1:1 ratio . The sucTo enhance the robustness of the results in our main study, we also performed four sensitivity analyses, which showed that the results did not change based on the following: (1) operational definitions for asthma  and ADHD ; (2) matching variables , demographic matching , full matching ; (3) recruitment periods for the asthma group ; and (4) matching ratios .Study 2 investigated the reciprocal of the relationship in Study 1, that is, the relationship between ADHD as an independent variable and asthma as a dependent variable. The criteria for Study 2 were the reverse of those of Study 1. In addition, Study 2 was performed in three age ranges: (1) 0–17 years old, (2) 18–60 years old, and (3) 0–60 years old. The experimental cohort was defined as patients diagnosed with ADHD between January 1, 2009 and December 31, 2010. This time, target disease was defined as a diagnosis of asthma between 2009 and 2019. The time order between the occurrence of asthma and ADHD was the same as that in Study 1, and a 1-year wash-out period was included. There were also three exclusion criteria: (1) patients with asthma and ADHD with an initial diagnosis before January 1, 2009 during the wash-out period; (2) patients diagnosed with ADHD for the first time during the period 2011–2019; and (3) patients with at least one record of asthma diagnosis earlier or at the same time as ADHD diagnosis.The covariates included were as follows: (1) age , (2) residential area , (3) economic status divided by health insurance premium (the insurance premium based on income was divided into 20 deciles) , and baseline comorbidities with CCI. The individual comorbidities included atopic dermatitis  and allergic rhinitis . For the CCI, comorbidities considered with weighting were as follows: (1) Acute myocardial infarction , (2) Diabetes mellitus , (3) Complications from diabetes mellitus , (4) Congestive heart failure (ICD-10 code I50), (5) Peripheral vascular disease (ICD-10 codes I70-I79), (6) Cerebrovascular disease (ICD-10 codes I60-I69), (7) Dementia , (8) Chronic obstructive pulmonary disease , (9) Connective tissue disease , (10) Peptic ulcer (ICD-10 codes K25-K26), (11) Liver disease , (12) Hemiplegia (ICD-10 codes G80-G82), (13) Renal kidney failure (ICD-10 codes N17-N19), (14) Cancer , (15) Cancer without specification of site (ICD-10 codes C76-C80), (16) Acquired immune deficiency syndrome (ICD-10 codes B20-B24).The major outcome was the occurrence of ADHD and asthma in Studies 1 and 2, respectively. The hazard ratio (HR) in the Cox proportional hazards model was calculated considering the time variable (between the end and start points of the study period). When considering the relationship between one independent variable and the dependent variable, the adjusted HR (aHR) was obtained by considering all other variables, whereas the unadjusted HR was calculated without considering other variables. The cumulative HR was obtained through Kaplan–Meier survival analysis, and the R 3.5.3 statistical program  was used to analyze the results.Study 1 investigated the relationship between asthma as an independent variable and ADHD as a dependent variable. There were 131,937 participants in the asthma group and the same number in the PS-matched non-asthma control group. These groups were well-distributed with regard to sex, age, economic status, residential area, atopic dermatitis, allergic rhinitis, and CCI . All SMDThe incidence of ADHD per 10,000 person-years (PY) was 2.90 in the asthma group, and 2.44 in the non-asthma control group. The adjusted HR (aHR) for the asthma experimental group and the control group for ADHD was 1.17 (95% CI: 1.11–1.23), indicating that the incidence of ADHD in the asthma group was 1.17 times higher than that in the control group. The univariate crude HR of the two cohorts for ADHD was 1.20 (95% CI: 1.14–1.25) .In the asthma and matched non-asthma groups, the characteristics associated with an increased incidence of ADHD were as follows: atopic dermatitis, allergic rhinitis, CCI = 1, and CCI > 2. The aHRs of the above characteristics were 1.09 (95% CI: 1.03–1.16), 1.15 (95% CI: 1.03–1.29), 1.06 (95% CI: 1.01–1.12), and 1.49 (95% CI: 1.29–1.73), respectively .For the demographic factors, the aHR values were as follows: (1) male sex: 2.83 (95% CI: 2.68–2.99); (2) lower 20% of insurance premium decile: 1.26 (95% CI: 1.17–1.35), and upper 20% of insurance premium decile: 1.05 (95% CI: 0.99–1.10); (3) 0–5 years old: 1.70 (95% CI: 1.58–1.84), and 6–10 years old: 1.79 (95% CI: 1.66–1.94); (4) residence in metropolitan areas: 1.31 (95% CI: 1.24–1.38) .p-values < 0.01)  .In the case of the 18–60-year-old group, following the same study design, the aHR for ADHD in the asthma group and the non-asthma control group was 1.76 (95% CI: 1.20–2.58). When the experiment was conducted for the entire age group (0–60 years old), the aHR for ADHD in the two groups was 1.21 (95% CI: 1.15–1.27) .Study 2 investigated the reciprocal of the relationship in Study 1, that is, the relationship between ADHD as an independent variable and asthma as a dependent variable. There were 4,132 participants in the ADHD group and the same number in the PS-matched non-ADHD control group. The overall prevalence of ADHD was 41,129/3,474,755 (1.18%) . These gThe incidence of asthma per 10,000 PY was 68.40 in the ADHD group, and 60.44 in the non-ADHD control group. The aHR of the ADHD experimental group and the control group for asthma was 1.09 (95% CI: 1.02–1.16), indicating that the incidence of asthma in the ADHD group was 1.09 times higher than that in the control group. The univariate crude HR of the two groups for asthma was 1.12 (95% CI: 1.05–1.20) .In the ADHD and matched non-ADHD groups, the characteristics associated with an increase in the incidence of asthma were as follows: patients with allergic rhinitis, CCI = 1, and CCI > 2. The aHRs of the above characteristics were 1.19 (95% CI: 1.04–1.35), 3.47 (95% CI: 3.16–3.80), and 1.95 (95% CI: 1.32–2.87), respectively . The aHRFor the demographic factors, aHR values were as follows: (1) male sex: 0.95 (95% CI: 0.88–1.03); (2) lower 20% of insurance premium decile: 1.10 (95% CI: 1.00–1.21), and upper 20% of insurance premium decile: 1.00 (95% CI: 0.93–1.08); (3) 0–5 years old: 2.53 (95% CI: 2.17–2.95), and 6–10 years old: 1.54 (95% CI: 1.44–1.65); (4) residence in metropolitan areas: 1.06 (95% CI: 0.98–1.15) .In the case of 18–60-year-old group, following the same study design, the aHR for asthma in the ADHD group and the non-ADHD control group was 1.16 (95% CI: 0.64–2.09). When the experiment was carried out for the entire age group (0–60 years old), the aHR for asthma in the two groups was 1.10 (95% CI: 1.03–1.17) .ADHD is a common childhood behavioral disorder that is often diagnosed in children under 10 years of age , 26. In Other factors that increased the incidence of ADHD included male sex [aHR: 2.83 (2.68–2.99)], living in metropolitan areas [aHR: 1.31 (1.24–1.38)], and the lowest 20% of economic status [aHR: 1.26 (1.17–1.35)]. ADHD is more common in males. Recent research has shown that female patients with ADHD may easily be missed in the diagnostic process if they do not have significant externalizing symptoms  27). AD. AD27). Similarly, the greater prevalence of ADHD in metropolitan areas can be explained. In Korea, the difference in population density between metropolitan and rural areas is large, so there is a difference in the density of medical facilities. Therefore, it is considered that the number of times each individual visits a clinic dealing with attention deficit and behavioral disorders to receive medical treatment, especially for diseases such as ADHD, decreases in rural areas, which has resulted in a difference in the incidence of ADHD between these regions. In addition, ADHD has a high incidence in infants and children. Therefore, the larger number of children in cities can affect the difference in prevalence. There is also a higher probability of accessing digital media in metropolitan areas and this increases ADHD-related behaviors as reported in several studies .Study 2 investigated the reciprocal of the relationship in Study 1. The incidence of asthma in the ADHD group was 1.10 times higher than in the non-ADHD control group , 5. ThisPS matching can be used to analyze data from randomized controlled trials with larger populations, which has the advantage that bias can be reduced by more effectively treating areas that are more clinically unconstrained. Moreover, studies using PS matching of real registry data often have the benefit of managing datasets and dealing with a broader and less selective population of patients. Our study showed that the distribution of the asthma and non-asthma groups was very similar and that PS matching was well conducted, so the research results are credible , 3. Our Our study has limitations in that it used data from our Health Insurance Service. Our study could not properly reflect other environmental factors because we only used recorded diagnoses. Additionally, there may be confounders in the environmental factors that might affect ADHD or asthma, such as neglect, abuse, family stress, and air or water pollution. However, because of the limitations of the insurance claim-based data provided by the NHIS, we could not access this information. In addition, even though this study used a large dataset, it only reflects findings in the Korean population and these may not be applicable in other countries. Finally, some inaccurate diagnoses may have been included because of the nature of this retrospective insurance claim-based study.Despite these limitations, this study has the following intrinsic advantages: (1) it was a large population-based study using national health insurance data, including detailed health records; (2) PS matching was conducted to compensate for the shortcomings of its retrospective nature and regularize comparisons as much as possible; and (3) sensitivity analyses showed the robust nature of the results despite changes in operational definitions, matching variables, recruitment periods, and matching ratios.In conclusion, our study with long-term follow-up has shown that the incidence of ADHD in patients with asthma was 1.17 times higher than that in patients without asthma, and the incidence of asthma in patients with ADHD was 1.10 times higher than that in patients without ADHD. Clinicians should pay attention to whether patients with asthma, especially male patients and children, exhibit symptoms of ADHD. We suggest follow-up studies to compare the prevalence of ADHD according to asthma severity to verify specific associations. In addition, we showed that younger patients with ADHD are at a higher risk of asthma and need to be monitored carefully to observe and manage any symptoms of asthma. Taken together, asthma and ADHD have a bidirectional relationship; therefore, childhood asthma and ADHD should be rigorously managed.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.This study protocol was reviewed and approved by the Institutional Review Board of JBNUH (IRB No. CUH 2021-10-031). As information used for analyses from the NHIS-National Sample Cohort database was encrypted and anonymized, informed consent was not required.JK: conceptualization, supervision, validation, visualization, and writing—review and editing. SY, MK, and ML: data curation and formal analysis. HP and JK: investigation, methodology, and project administration. JK, SY, MK, and ML: resources and software. HP, YK, DN, SJ, JK, J-HL, and YY: writing—original draft preparation. All authors contributed to the article and approved the submitted version.


Attention–deficit/hyperactivity disorder (ADHD) symptoms have been linked to suicidal behavior. However, little is known about the mechanisms involved in this association. This study examined ADHD symptoms and suicidal ideation during the COVID-19 pandemic and the role of self–perceived stress in this association.Data were analyzed from an online sample of 1,452 Japanese individuals aged 18 to 89 obtained in February 2021. Information was collected on past–year suicidal ideation. ADHD symptoms were assessed with the Adult ADHD Self–Report Scale (ASRS) Screener while stress was measured with the Perceived Stress Scale (PSS−14). Depressive symptoms were assessed with the Patient Health Questionnaire (PHQ−9), while the Generalized Anxiety Disorder scale (GAD−7) was used to assess anxiety symptoms. Logistic regression was used to examine associations.Fifty–one (3.5%) individuals had ADHD symptoms, while more than one in ten (11.7%) of the respondents reported past–year suicidal ideation. In an analysis adjusted for sociodemographic factors, ADHD symptoms were associated with eight times higher odds for past–year suicidal ideation. However, adjusting the analysis for mental health variables (anxiety and depressive symptoms) attenuated the association which became non–significant . In contrast, in an analysis adjusted for mental health, individuals with ADHD symptoms and stress had significantly higher odds for suicidal ideation  whereas, ADHD symptoms without stress were not linked to suicidal ideation .Self–perceived stress is important in the association between ADHD symptoms and suicidal ideation among individuals in Japan during the COVID-19 pandemic. Detecting and managing stress and its effects in individuals with ADHD/ADHD symptoms should be a therapeutic focus for improving health–related outcomes in this population both during the COVID-19 pandemic and beyond. Attention–deficit/hyperactivity disorder (ADHD) is a childhood–onset neurodevelopmental disorder characterized by high and impairing levels of inattention and/or hyperactivity–impulsivity . It has There is also some evidence that adult ADHD may be linked to an increased risk for suicidal behavior. An early review study reported that ADHD appeared to increase the risk of suicide but that it was largely through its detrimental effects on comorbid conditions such as depression . SubsequBuilding on earlier research, which has shown that ADHD symptoms are associated with suicidal behavior in both general and clinical populations , 19, thiThe two previous studies that have examined the association between ADHD symptoms, stress and suicidal ideation both found that perceived stress partially accounted for the association between ADHD symptoms and suicidal ideation. However, both studies focused on subpopulations—students  and emerData were analyzed from an online survey of the Japanese general population that was undertaken over a 2–day period in late February, 2021. At the time there was a state of emergency in place in 10 of Japan's 47 prefectures, with companies being encouraged to allow working from home, restaurants and bars being asked to shut by 8 pm and people told to stay at home whenever possible , 35. TheSuicidal ideation was assessed with a two–part question. Respondents were initially asked, “Have you ever thought of taking your life, even if you would not really do it?” with yes and no answer options. Those who responded in the affirmative were then asked a follow–up question, “Was this… (i) in the last year (scored 1); (ii) or at some other time (scored 2)”. Those who scored 1 on this follow–up question were categorized as having past–year suicidal ideation. This question is modeled on an earlier question that was used when examining the association between ADHD symptoms and suicidal behavior in the general population .ADHD symptoms were assessed with the Adult ADHD Self–Report Scale (ASRS) Screener , 37. ThiThe level of respondents' stress was assessed with the Perceived Stress Scale (PSS−14) . This 14Information was also obtained on several other variables. Besides sex  respondents were categorized into three age groups, 18–34, 35–59 and 60 and above. Educational level was dichotomized into higher education  and less than higher education . Marital status was also classified using two categories: married or not married. Household financial income was measured in millions of yen and assessed using three categories, (i) <4 million, (ii) 4 < 10 million, and (iii) ≥10 million (106.55 JPY = U.S. $1 at the time of the survey). Given the large number of respondents that refused to answer this question (22.7%), and our desire to keep as many people in the analysis as possible, a fourth (iv) “missing” category was also created. Depressive symptoms in the past 2 weeks were assessed with the self–report Patient Health Questionnaire (PHQ−9) . The scop < 0.05 (two–tailed).Descriptive statistics of the study sample stratified by ADHD symptoms status were first calculated. Next, logistic regression analysis was used to examine the association between ADHD symptoms and self–perceived stress. Three models were used in the analysis. In Model 1 the bivariate association between ADHD symptoms and stress was examined. Model 2 included sociodemographic  variables. The fully adjusted Model 3 included the same variables as Model 2 and mental health variables (depressive and anxiety symptoms). Logistic regression analysis was then used to examine the association between ADHD symptoms and suicidal ideation in the past year using the same three–model building process previously described. Finally, logistic regression analysis was again used to examine the associations between ADHD symptoms, suicidal ideation and stress in the context of mental health. To do this a four–category ADHD symptoms–stress variable was created. The reference category included individuals without ADHD symptoms or stress. Category two included individuals experiencing stress only, while category three included individuals with ADHD symptoms only. The fourth category comprised those individuals with both ADHD symptoms and stress. The same analytic model building process was used as in the previous analyses. All analyses were performed with the Statistical Package for the Social Sciences (SPSS) version 24 and were adjusted for location (prefecture). The results are presented as odds ratios (OR) with 95% confidence intervals (CI). The level of statistical significance was set as N = 51) of the sample was categorized as having ADHD symptoms. Mental health conditions were prevalent in the sample with 15.0% of the respondents categorized as having depressive symptoms, while the corresponding figure for anxiety symptoms was 10.8%. Just over one in ten participants (11.7%) reported past–year suicidal ideation. Chi-square tests showed that ADHD symptoms were associated with younger age, not being married, anxiety and depressive symptoms, stress symptoms and reporting past–year suicidal ideation.The analytic sample comprised 1,452 respondents with an average age of 51.6 years (range 18 to 89), with slightly more than half of them being female (51.5%) . A majorIn a bivariate analysis individuals with ADHD symptoms had over twenty times higher odds for experiencing stress  Model 1, . IncludiIndividuals with ADHD symptoms had over 10 times higher odds for past–year suicidal ideation in a bivariate analysis  Model 1, . The incCompared to individuals with no ADHD symptoms and no stress, individuals with stress only and ADHD symptoms only both had over five times higher odds for suicidal ideation in a bivariate analysis Model 1, , while tThis study used data from an online survey of 1,452 Japanese individuals aged 18 and above to examine the link between ADHD symptoms and suicidal ideation and the role of perceived stress in this association during the COVID-19 pandemic. Results from logistic regression analyses showed that ADHD symptoms were strongly associated with both perceived stress and suicidal ideation although mental health (depressive symptoms) mediated the association between ADHD symptoms and suicidal ideation. Further analyses also showed that when controlling for mental health problems, individuals with comorbid ADHD symptoms and stress had significantly higher odds for suicidal ideation compared to those with no ADHD symptoms or stress, while ADHD symptoms with no stress were not associated with suicidal ideation in the fully adjusted analysis.The finding that ADHD symptoms are associated with higher levels of perceived stress accords with the findings of previous research, which has shown that individuals with ADHD/ADHD symptoms may be especially vulnerable to experiencing stress , 21, 44.In an analysis adjusted for sociodemographic factors individuals with ADHD symptoms had over eight times higher odds for reporting suicidal ideation. However, when the analysis was adjusted for mental health problems the association became non–significant with depressive symptoms having an especially strong relationship with suicidal ideation. Earlier studies have produced conflicting results on the role of comorbid mental disorders in the association between ADHD symptoms and suicidality , 47. HowIndividuals with ADHD symptoms and stress had over 3.5 times higher odds for suicidal ideation in a fully adjusted analysis. However, the inclusion of mental health problems in the analysis attenuated the association between ADHD symptoms only and suicidal ideation which became non–significant. This result concurs with earlier research which has shown that stress is associated with suicidal ideation  and thatThis study has several limitations that should be mentioned. The study data came from an online sample which may be susceptible to selection bias due to processes such as self–selection and non–coverage  . Having In conclusion, this study has shown that ADHD symptoms are associated with higher odds for stress and that in turn, stress is an important factor in the association between ADHD symptoms and suicidal ideation. As there is some indication that individuals with ADHD/ADHD symptoms may use maladaptive coping strategies in response to stressful situations , some ofThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee at the National Center of Neurology and Psychiatry, Tokyo, Japan. The patients/participants provided their written informed consent to participate in this study.ASt had the study idea, analyzed the data, and wrote the main text. ASh discussed the analysis and commented on the main text for intellectual content. TI, VR, RK, JI, and YI critically reviewed, commented on, and revised the manuscript. TS supervised the project and critically reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


N = 137) and separately in healthy controls (HC) (N = 45), after that, we compared an equal group of HC and ADHD patients to compare sex-related differences in ADHD patients and HC. In a second step, we studied whether the neural basis of comorbidity patterns is different between male and female patients. We observed that male ADHD patients demonstrated a decrease in functional connectivity (FC) from the GPe to the left middle temporal gyrus compared to female ADHD patients. Moreover, within the full ADHD group (N = 137), there was a lower FC in male patients from GPe to the right frontal pole/middle frontal gyrus compared to female patients. Male ADHD patients with depression demonstrated decreased FC from the GPe to parts of the occipital cortex compared to female ADHD patients with depression. No such effect was demonstrated for overweight or SUD. The current study reveals different FC profiles in males and females with ADHD, which are centered around altered connectivity with the GPe. An improved understanding of sex-differences in ADHD, and the role of comorbid disorders, therein can result in improved diagnostic and therapeutic opportunities for ADHD patients.In the last two decades, there has been a growing body of research that identified sex-related differences in attention-deficit hyperactivity disorder (ADHD). Our objective was to quantify whether these sex differences are based on altered functional brain connectivity profiles. In addition, we investigated whether the presence of comorbid disorders, including depression, substance use disorder (SUD) and overweight, influenced these sex differences. A seed-based connectivity analysis of the external globus pallidus (GPe), an important inhibitory relay hub of the fronto-thalamo-striatal-loop, was performed. In a first step, we searched for sex-related differences in ADHD patients ( Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with cardinal symptoms of inattentiveness, hyperactivity, and impulsiveness, which leads to impairments in everyday life. ADHD manifests in childhood, but persists in a substantial number of the cases into adulthood . HoweverPreviously, in Biederman et al. reported a sex-by-ADHD interaction in the association between ADHD and SUD in referred children, with the strongest association being observed in girls . Girls wThe available data purports that the pathogenesis of ADHD is linked to a dysregulation of impulse control. One possible neuroanatomical pathway underlying this, is that of the indirect inhibitory fronto-basal pathways involving the dorsolateral prefrontal cortex (DLPFC), caudate, putamen, globus pallidus (GP), thalamus, and motor areas , 11. In Thus, the literature to date suggests that there are sex differences in ADHD, and especially in cases with comorbid disorders, but these differences are not yet understood. We postulate that the functional neuroanatomy of impulse control plays a critical role, and that the GP is an attractive candidate to investigate such dysregulated functional connectivity (FC). This might enable us to test whether there are sex differences in basic neuronal strategies in resting-state fMRI neuroimaging. Our objectives were therefore twofold. First, we were interested in studying sex-specific differences between ADHD and healthy controls (HC) for GP-based seed connectivity. Secondly, we wanted to verify these sex-specific neural patterns in ADHD-related comorbidities. First, we searched for sex-specific differences in FC in a large ADHD sample of 137 subjects and separately in 45 HC. In a second step, we selected a similar group of healthy subjects and ADHD patients  to be tested for sex-dependent effects, because the group of ADHD patients were significantly older than the group of HC. Finally, in the sample of 137 ADHD patients we tested whether we could verify sex-specific neural patterns in ADHD-related comorbidities.via local advertising measures and examined by a registered psychiatrist. A total of 45 healthy volunteers  were included. The average body weight of the subjects included was 72.86 kg (SD: 12.91 kg) with an average height of 1.75 m (SD: 0.11 m), which corresponds to an average BMI of 23.59 (SD: 3.08). The participants received an expense allowance of approx. A total of 50 € for participation in the study.Healthy volunteers of both sexes with an age between 18 and 50 years from Germany were included in this study  and is subject to the Declaration of Helsinki of the “World Medical Association: Ethical Principles for Medical Research Involving Human Subjects” and the “Guidelines for Good Clinical Practices (GCP).” In addition, the study was registered as a clinical trial in the German study registry under the ID: DRKS00011209. Written informed consent was obtained from each volunteer before the start of the study.2). Exclusion criteria were other mental illnesses , serious acute or chronic physical diseases, pregnancy, as well as exclusion criteria of the MRI examination. Only patients with at least 4 weeks of stable medication regimen were included. Stimulants, alcohol, and nicotine were stopped on the day of the scan. Patients with antipsychotic medication were excluded. Participants were examined by a registered psychiatrist in Frankfurt in a specialized ADHD-outpatient clinic. In Nijmegen, selection and diagnostic procedures were conducted by trained psychiatrists or psychologists. The project was carried out in accordance with the provisions of the Declaration of Helsinki World Medical Association,   at the Brain Imaging Center in Frankfurt am Main and a 3 T MR scanner (Siemens PRISMA) in Nijmegen to obtain high resolution structural images and resting state functional MRI (rs-fMRI) images.Images of 182 participants underwent a preprocessing algorithm done with the SPM-based CONN-toolbox V 18.b. to minimize the effects of unwanted variability in the blood oxygenation level-dependent (BOLD) signal . The imaN = 137). Age and site (Nijmegen/Frankfurt) were included as covariates in the model to account for age and site-related variability between sub-groups of interest. Separately we performed a multiple regression test to analyze the influence of sex in a group of N = 45 HC with age as covariate. In a second step we chose a group of 72 probands: 36 HC  and 36 ADHD patients  out of study sample to compare sex-related differences in ADHD patients and HC. We matched the ADHD patients and HC by sex, age, and location, since the fMRIs of the HC were performed only in Frankfurt and they were significantly younger than the ADHD patients. We performed matching manually, with the rater blinded to the results of the first- and second-level connectivity analysis of N = 137 ADHD patients and N = 45 HC. In the end, we were able to form a more homogeneous group consisting of 36 HC  and 36 ADHD patients  with a lower SD in ADHD patients compared to the group of N = 137 ADHD subjects .First-level correlation maps were calculated by extracting the residual BOLD-time course from the ROI seeds and correlating these with the other voxels within the brain. These correlation coefficient maps were then converted into a normally distributed z score (Fisher transformation). Transformed correlation maps were used for multiple regression tests and 2 × 2 between-subjects ANOVA interaction. In a first step we performed a multiple regression test to analyze the influence of sex in the full ADHD group . To explore whether the neural basis of comorbidity patterns is different between male and female patients, we used three between-subjects ANOVA tests to study separately the interaction of sex and comorbid depression , sex/SUD and sex/overweight. For correction of multiple testing during second-level statistics we used cluster-wise whole-brain analysis which uses a combination of an uncorrected To validate our seed-based connectivity approach we calculated seed-maps to illustrate the connectivity’s main effect in HC and ADHD patients. The GPe in both, HC and ADHD, showed connectivity with other parts of the basal ganglia and parts of the frontal-  temporal , parietal  and occipital cortex  among others. A detailed overview is given in the In the whole ADHD group of 137 patients we found differences in FC (details are given in N = 36) with ADHD patients (N = 36) (details are given in N = 45) and found a decrease in FC from the GPe to the middle temporal gyrus left in female HC compared to male HC.When comparing sex-related connectivity differences of HC  (details are given in N = 72) and performed independent samples tests. Patients and controls differed in motion. HC showed significantly higher mean-motion and max-motion compared to ADHD patients (p < 0.001). The mean across the mean-motion FD was 0.05 in ADHD patients with a standard deviation of 0.03, in HC the mean of mean-motion FD was 0.1 with a standard deviation of 0.04. The mean of max-motion in HC was 1.35 with a SD of 0.22 and the mean of max-motion in ADHD was 0.38 with a SD of 0.15. We did not detect any sex-related differences in the group of HC in mean-motion (p = 0.17) or max-motion (p = 0.53). The average of mean-motion in males was 0.11 with a SD of 0.05 and 0.09 with a SD of 0.03 in females. The mean of max-motion in males was 1.37 with a SD of 0.27 and 1.32 with a SD of 0.16 in female HC. We did not detect any sex-differences of mean-motion (p = 0.73) or max-motion (P = 0.62) in ADHD patients. The average of mean-motion in males was 0.06 with a SD of 0.03 and 0.05 with a SD of 0.04 in females with ADHD. The mean of max-motion in males was 0.37 with a SD of 0.16 and 0.39 with a SD of 0.15 in females with ADHD. No sex-differences in mean-motion (p = 0.28) or max-motion (p = 0.94) were demonstrated regardless of group either. From the large ADHD sample (N = 137) we calculated additional mean framewise displacements (FD) according to Power et al. (p = 0.17). The average mean FD in males was 0.23 with a SD of 0.12 and 0.26 in females with a SD of 0.14.We extracted mean-motion and max-motion values from the ADHD vs. HC analysis  ADHD, we investigated a sex*comorbidity  interaction. No such effect was demonstrated for overweight or SUD. However, male ADHD patients with depression demonstrated decreased FC between the GPe and the occipital cortex compared to female ADHD patients with depression, with the opposite effects observed in ADHD patients without depression. Taken together, these findings suggest an ADHD-specific attentional defect mediated by the GPe-occipital cortex FC: The occipital cortex forms part of the dorsal attentional network, which maintains attention and suppress stimuli that are irrelevant . Indeed,Interestingly, this is not specific for ADHD as involvement of the occipital cortex has also been described in depression. In a study of resting state fMRI, major depression was linked to a decrease in FC between the ventral attention network and regions of precuneus extending to occipital and posterior cingulate cortex, regions which are involved in visual attention . In a taIn summary, both disorders, ADHD and depression, compromise attention processing, including visual attention and working memory. There have been very few studies exploring the effect of sex and comorbidities in ADHD with resting state fMRI analysis. One study is a rsfMRI study of Park et al. where several regions linked with depression and anxiety were identified by using graph-theoretic network measures: middle frontal gyrus, superior parietal lobule, middle temporal gyrus, and middle occipital gyrus were identified as regions that displayed sex differences and a strong correlation with DSM scores only in the female ADHD group . Like meOur results support the hypothesis that ADHD affects males and females differently as we found less sex-specific differentiation in ADHD patients. We cannot say to what extent our results have a direct influence on differences in the clinical manifestation of ADHD. However, future investigations to correlate the connectivity differences with clinical questionnaires may be a solution.In our motion analysis we did not detect any sex-differences. Surprisingly, the healthy subjects moved significantly more than the ADHD patients, detected in both mean-motion and max-motion. Nevertheless, all our participants showed low and acceptable mean motion. The small but significant difference of mean- and max-motion between HC and patients might come from the fact that almost all HC participants had no prior MRI experience, while the patient group had more MRI experience.N = 45) and third (N = 72) analysis are smaller comparing to our first analysis of N = 137 ADHD patients. The reproducibility of studies with small samples has recently been criticized by Marek et al. . The patients/participants provided their written informed consent to participate in this study.OG, JB, DR, and AR were involved in the conception and design of the study. GD conducted the data collection with the help of DR. GD analyzed the data including the pre-processing steps and wrote the manuscript. GD interpreted the results with the help of OG. All authors contributed to the revision of the manuscript, read and approved the submitted version.


The present work is a replication article based on the paper “Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies” by McGrath and Stoodley (2019). In the original research, the authors used activation likelihood estimation (ALE), a technique to perform coordinate-based meta-analysis (CBMA), to investigate the existence of brain regions undergoing gray matter alteration in association with both attention-deficit/hyper-activity disorder (ADHD) and dyslexia. Here, the same voxel-based morphometry dataset was analyzed, while using the permutation-subject images version of signed differential mapping (PSI-SDM) in place of ALE. Overall, the replication converged with the original paper in showing a limited overlap between the two conditions. In particular, no significant effect was found for dyslexia, therefore precluding any form of comparison between the two disorders. The possible influences of biological sex, age, and medication status were also ruled out. Our findings are in line with literature about gray matter alteration associated with ADHD and dyslexia, often showing conflicting results. Therefore, although neuropsychological and clinical evidence suggest some convergence between ADHD and dyslexia, more future research is sorely needed to reach a consensus on the neuroimaging domain in terms of patterns of gray matter alteration. In their original research, McGrath and Stoodley  aimed toThe advent of magnetic resonance imaging (MRI) technologies has provided an unprecedented opportunity to assess the neurophysiological underpinnings of these two NDDs in vivo and noninvasively. Numerous studies about pediatrics and adults with ADHD suggest functional abnormalities in fronto-striatal and fronto-limbic systems ,12,13,14To fill this gap, McGrath and Stoodley  realizedAmong the various CBMA algorithms, McGrath and Stoodley  resortedpuncorrected < 0.001; minimum cluster size k = 50 mm3) and a less conservative one . In the second step, a conjunction analysis was implemented between the previously obtained dyslexia < TDCs and ADHD < TDCs maps (separately for the conservative and lenient thresholding). This allowed us to identify voxels with a statistically significant overlap between the two disorders [p < 0.05 (based on 5000 permutations) and k = 50 mm3 as above. No conjunction analysis was run for the increase condition due to lack of any overlap already at visual inspection. McGrath and Stoodley [puncorrected < 0.001; k = 50 mm3 . A sole cluster in the right caudate was instead observed for puncorrected < 0.005; k = 50 mm3. In addition to the described main analyses, the authors aimed to assess the possible effect of brain volume and age on the results. Since ALE technique does not allow us to model confounding variables during the estimation of the spatial convergence, different subsets of the original dataset were extracted and separately analyzed. In the first case, only those VBM experiments that originally controlled for total brain volume or total gray matter volume were retained. In the second case, VBM experiments were divided into two groups based on the reported mean age of subjects . Experiments were discarded if the necessary information was missing. The previously observed cluster in the right caudate was still significant in the brain volume-controlled subset. On the contrary, no overlap was found between dyslexia and ADHD in the adult subgroup. In children, a cluster of overlap was observed in the left middle frontal gyrus/supplementary motor area, for ALE map thresholded at puncorrected < 0.005; k = 50 mm3.In the original paper, a two-step procedure was followed. First, the ALE analysis was separately applied to estimate the spatial convergence associated with each of the four possible conditions: dyslexia < TDCs; ADHD < TDCs; dyslexia > TDCs; ADHD > TDCs. The first two contrasts investigated the so-called decrease effect , meaningisorders . The resIn the present paper, we first aimed to test the original dataset analyzed by McGrath and Stoodley  using a As mentioned above, the core element of this replication attempt is the change of technique used to compute the CBMA. This implied several methodological differences that are detailed below.p-values, as implemented in the dedicated conversion utility of SDM (https://www.sdmproject.com/utilities/?show = Statistics).The present replication used exactly the same set of VBM experiments analyzed by McGrath and Stoodley . The lisTo note, McGrath and Stoodley  designedp ≤ 0.05; minimum cluster size = 10 voxels) [As mentioned above, this replication used the PSI-SDM method in place of the ALE originally applied by McGrath and Stoodley . While A voxels) .These steps were repeated twice, for dyslexia vs. TDCs, and ADHD vs. TDCs contrasts. The PSI-SDM algorithm was set to the default parameters .p-values of each meta-analytic map [Finally, we aimed to formally test whole-brain communalities in gray matter variation between dyslexia and ADHD by calculating the overlap between both conditions in each brain voxel. To do so, the two TFCE-corrected maps  have to be added on top of each other and compared via the multimodal function of PSI-SDM software that calculate the most probable gray matter overlap taking into account the presence of noise in the estimation of the ytic map .While ALE does not permit the modelling of additional covariates, these can be included in PSI-SDM to perform meta-regression analyses . First, puncorrected < 0.0005 and minimum cluster size = 10 voxels, as suggested by the SDM team to reach the optimal balance between specificity and sensitivity [The results of the meta-regressions were thresholded at sitivity .McGrath and Stoodley  also tesKnowing that some attempts to find a given effect of interest have yielded null results is of great relevance when running a CBMA ,82. QuanWe aimed to replicate each of the analyses described in McGrath and Stoodley . Moreovepuncorrected < 0.001. For the sake of clarity, it should be mentioned that the use of the uncorrected thresholding is no longer recommended in the ALE field [When looking at the gray matter decrease effect associated with ADHD  McGrath and Stoodley  found 11LE field . Therefop ≤ 0.05; minimum cluster size = 10 voxels thresholding. Five clusters of decrease effect were instead observed at the intermediate step of the analyses when the puncorrected < 0.005; minimum cluster size = 10 voxels threshold was used. Although it is not infrequent in literature to describe results surviving this lenient thresholding, the current recommended statistical standard is TFCE [Since PSI-SDM, as mentioned in the Methods section, can analyze decrease and increase effects together, the replication of this step consisted of a unique ADHD vs. TDCs contrast. Our results showed no effect applying a TFCE  is TFCE . Therefopuncorrected < 0.001 was used.When looking at the gray matter decrease effect associated with dyslexia  McGrath and Stoodley  found 12p ≤ 0.05 nor at puncorrected < 0.005  correction. No conjunction analysis for the increase effect was carried out instead.Although McGrath and Stoodley  found wip ≤ 0.05 nor at puncorrected < 0.005 thresholding.Concerning our results, since no effect was found in the main PSI-SDM about dyslexia, it was not possible to compute the conjunction analysis, neither at TFCE p ≤ 0.05, in line with what observed for the original database. Coherently, four clusters of decrease effect were observed at puncorrected < 0.005 threshold . This was an additional analysis, not implemented in the original research due to methodological constrain. Concerning ADHD, still no effect was found at TFCE hreshold . The inchreshold . As in tpuncorrected < 0.001. When using the more lenient puncorrected < 0.005, a cluster of convergent decrease was observed in the left middle frontal gyrus and supplementary motor area. The authors did not consider the increase effect for this analysis due to the paucity of data. As explained in the Methods section, we decided to leverage on the features of PSI-SDM and perform a meta-regression, rather than separately analyzing the subset. This was in fact the most direct way to test the potential effect of age, as originally hypothesized by McGrath and Stoodley [puncorrected ≤ 0.0005 either in ADHD or dyslexia.In order to evaluate the potential effect of age, McGrath and Stoodley  created Additionally, meta-regression analyses about biological sex and medication indicated no significant effect in both ADHD and dyslexia VBM findings. FSIQ meta-regression was not performed instead due to a large amount of unavailable data about the pertaining variable .puncorrected < 0.005).In order to evaluate the possible effect of total brain volume, McGrath and Stoodley  reduced p ≤ 0.05 since no significant effect was found for dyslexia at that threshold. The only two clusters that survived at this corrected thresholding, based on the subset for ADHD, were localized in the left crus I and crus II of the cerebellum  and no obvious publication bias  [In our replication, as in the case of using the whole dataset, it was not possible to perform the conjunction analysis at TFCE rebellum . It is i peak 2)  were foup < 0.005), we found three clusters of gray matter decrease in dyslexia . The authors did not test their results with more conservative correction thresholds, such as the false discovery rate [Nevertheless, this outcome was not completely surprising. As correctly stated by McGrath and Stoodley  throughoery rate , voxel- ery rate . Therefoery rate . As a fuery rate ,94,95. Iery rate ,93,94. Ip ≤ 0.05 thresholding were found in the left cerebellar crus II and crus I, based on the subset of ADHD experiments that accounted for total brain volume differences. Although the involvement of the cerebellum in this disorder was not reported by McGrath and Stoodley [Although for some of the additional analyses we performed the null experiments for were included, the particular nature of these studies did not really contribute to expand the dataset. On the contrary, the effect of considering null results is rather to further increase the threshold to be reached by the remaining experiments. In line with this, one cluster of gray matter decrease in ADHD was lost after the inclusion of the seven null experiments. In our analyses, the only two clusters surviving the TFCE corrected Stoodley , this isStoodley ,97,98,99Stoodley .A very strict interpretation of the paucity of significant results in our replication would be that neither ADHD nor dyslexia are consistently associated with a pattern of gray matter alteration in the brain. This stance is coherent with a recent ALE cluster-level FWE corrected study by Samea et al.  on pediaA further aspect to be mentioned is the role of the gray matter increase. While some clusters of decrease were found at the uncorrected level of thresholding, no increase was detected in our replication. On the contrary, McGrath and Stoodley  found seDisorder-specific issues and clinical heterogeneity aside, we should note that the CBMA approach in general, and PSI-SDM technique in particular, have some limitations. By definition, coordinate-based techniques have a limited accuracy because they only consider significant foci  instead of the entire voxel-wise statistic parametric maps . HoweverHere, we aimed to replicate the important findings pertaining the existence of brain regions undergoing gray matter alteration in association with both ADHD and dyslexia reported in the McGrath and Stoodley study . Using a


High rate of streptococcus-like infections and related titers has been found in adult ADHD patients. No studies have expressively investigated innate immune system in ADHD patients.To evaluate the relationship between streptococcal infections, autoimmunity and innate immune system in adult ADHD patients.The study sample consisted of adult DSM-5 ADHD outpatients referring to the adult ADHD center of “San Luigi Gonzaga” University Hospital and non-clinical adult controls recruited among general population . All titers were determined in patients’ plasma by specific microwell ELISA kits, whereas genetic polymorphisms were determined by PCR methodology. We compared anti-streptolysin O (ASO), anti-deoxyribonuclease B (anti-DNase B), and anti-basal ganglia antibodies (ABGA) titers of patients with those of controls. Data about history of previous streptococcus/ streptococcus-like infections were collected by ad-hoc form. Furthermore, to investigate the susceptibility to Gram+-borne infections of adult ADHD patients, due to innate immune system impairment, we also evaluated the polymorphism of Toll-like receptors 2, 4, and 9.Although ADHD patients did not show higher rate of both previous infections  and ASO titers , they had really higher levels of anti-DNase B  and ABGA titers . Genetic analysis did not underline differences in polymorphism compared to general population (GENOME browser).The high association between previous streptococcal infections, basal ganglia autoimmunity among ADHD patients was confirmed. TLR polymorphism does not seem to be involved in this type of vulnerability.No significant relationships.


Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder, with onset in childhood and a considerable likelihood to persist into adulthood. Our previous work has identified that across adults and adolescents with ADHD, gray matter volume (GMV) alteration in the frontal cortex was consistently associated with working memory underperformance, and GMV alteration in the cerebellum was associated with inattention. Recent knowledge regarding ADHD genetic risk loci makes it feasible to investigate genomic factors underlying these persistent GMV alterations, potentially illuminating the pathology of ADHD persistence. Based on this, we applied a sparsity-constrained multivariate data fusion approach, sparse parallel independent component analysis, to GMV variations in the frontal and cerebellum regions and candidate risk single nucleotide polymorphisms (SNPs) data from 341 unrelated adult participants, including 167 individuals with ADHD, 47 unaffected siblings, and 127 healthy controls. We identified one SNP component significantly associated with one GMV component in superior/middle frontal regions and replicated this association in 317 adolescents from ADHD families. The association was stronger in individuals with ADHD than in controls, and stronger in adults and older adolescents than in younger ones. The SNP component highlights 93 SNPs in long non-coding RNAs mainly in chromosome 5 and 21 protein-coding genes that are significantly enriched in human neuron cells. Eighteen identified SNPs have regulation effects on gene expression, transcript expression, isoform percentage, or methylation level in frontal regions. Identified genes highlight MEF2C, CADM2, and CADPS2, which are relevant for modulating neuronal substrates underlying high-level cognition in ADHD, and their causality effects on ADHD persistence await further investigations. Overall, through a multivariate analysis, we have revealed a genomic pattern underpinning the frontal gray matter variation related to working memory deficit in ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by pervasive symptoms of inattention and/or hyperactivity/impulsivity that affect normal functioning and development . About 9Converging evidence from anatomical studies suggest that children/adolescents with ADHD have global gray matter volume (GMV) reductions –15 and rDespite the persistent symptom and cognitive impairments in ADHD patients, the associated brain substrates remain understudied and inconsistent. Smaller GMV in the cerebellum was related to severe ADHD symptom  in children, adolescents , 36 and Since ADHD is highly heritable (estimated heritability: 77–88% ) and 12 Multivariate data-driven imaging-genetic fusion approaches may pose a promise for delineating the genetic factors underpinning GMV alterations. These approaches study a comprehensive set of SNPs and brain voxels, aiming to strike a balance between a few candidate variables and all-in-all lumpsum scores. They extract factors in a data-driven manner and assess associations at the multivariate factor level, which reduces the number of tests and boosts statistical power. Sparse parallel independent component analysis (spICA) , as one Using ADHD adult samples for discovery exploration enables us to probe the genetic underpinning of gray matter variations in the frontal and cerebellar regions that are associated with persist ADHD symptom and cognitive impairment. Replicating the results in ADHD adolescent samples will potentially facilitate the identification of the timeline when the genetic factors exert the effects on altering gray matter, which is relevant to ADHD persistence.The discovery cohort was composed of 341 non-related European Caucasian adults (age: 18–63 years) aggregated from two projects: 198 samples from the NeuroIMAGE project  and 143 All participants had an IQ >70, and did not have a psychotic disorder, addictions in the past 6 months, a current major depression, a diagnosis of autism spectrum disorder, epilepsy, neurological disorders, sensorimotor disabilities, or any medical or genetic disorders which might be confounded with ADHD –57. The The replication dataset included 461 European Caucasian adolescents  from 309 families (age: 7–17 years) recruited in the NeuroIMAGE project . All parhttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/) with the default tissue probability map (for adults) or a customized tissue probability map (for adolescents) generated by TOM 8 ). Six genes, i.e., MEF2C, DLGAP2, CADPS2, CADM2, ADGRL3, and UST, played a role in the nervous system development function . In addition, MEF2C, DLGAP2, and CADPS2 exerted effects on excitatory postsynaptic potential (p = 2.24 × 10−4).The identified SNP component highlighted 93 top SNPs . Take MEF2C and CADPS2 as examples; their expression level in DLPFC from early fetal to middle adulthood is presented in supplement Out of the 93 top SNPs, 4 acted as cis-eQTLs of three protein-coding genes and one lncRNA Table , 6 were In this study, we applied spICA to GMV and SNP data from an ADHD adult cohort, and we identified one SNP component significantly and positively associated with two GMV components  , indicatIn summary, spICA identified that one SNP component related to GMV in superior and middle frontal gyri underlying working memory performance in adults and adolescents with ADHD. This association was more significant in ADHD families and in older participants than in controls and younger participants. The top contributing SNPs resided in lncRNAs in chromosome 5 and a set of genes that were enriched in human brain neuron cells. Altogether, we present a new application of our novel multivariate method, which allows new ways to link genome-neuroanatomical variation to uncover new features relevant to ADHD neurobiology.Supplemental materialAnnotation of the identified 93 top SNPs