Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms “ADHD,” and “biomarker” combined with one of “protein,” “blood/serum,” “gene,” and “neuro” were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies. It has th Edition (DSM-5), classified by the American Psychiatric Association . DSM. DSMth EBiomarker is a generic term for signs or characteristics that allows for accurate and objective diagnosis and monitoring of disease progression . They ha2.A search on PubMed, Ovid Medline, and Web of Science was performed using “ADHD,” “biomarker,” “protein,” “blood/serum,” “gene,” and “neuro.” The search was performed by using “ADHD” and “biomarker” combined with one of “protein,” “blood/serum,” “gene,” and “neuro.” We included peer-reviewed original research papers published in English between Jan 2000 and Aug 2022, using either human subjects (diagnosed using DSM-5 or DSM-4) or animal models of ADHD  that reported positive results of investigated biomarkers. We excluded all the review articles, systematic reviews, meta-analyses, books or single-case studies which do not provide population-based information for the purpose of the development of future diagnostic methods. We included 92 original research studies, which are summarized in The majority of the studies were on human participants and included age-matched controls for the ADHD participants to allow for non-biased comparison. Studies using existing databases , 43, 45 Selected articles were categorized into four types of biomarkers: radiographic, molecular, physiological, and histological markers.2.1.There were 51 papers reported using the radiographic techniques alone or with other approaches to identify the biomarkers of ADHD, including EEG, MRI, and near-infrared spectroscopy (NIRS) . These m2.1.1.EEG is typically used to diagnose sleep disorders and seizure activities during epilepsy by attaching electrodes to the scalp to record electrical activity in the brain. We found 20 studies in the database using EEG methods to identify radiographic biomarkers that can be used to diagnose ADHD , right inferior frontal gyrus and right posterior cingulate (p = 0.01 vs. control), right anterior cingulate to posterior cingulate (p = 0.08 vs. control), and between left middle temporal gyrus (BA 39) and left posterior cingulate (p = 0.02 vs. control)  limits the power to detect statistical significance in some comparisons, the results suggest the potential of using these tracts in the white matter to diagnose deficiency in inhibitory control among patients with ADHD. In addition, there are disintegrated activations of the auditory complex in people with ADHD and dyslexia that can be normalized by regularly playing a musical instrument; however, currently, this parameter is not distinguishable between ADHD and dyslexia, therefore not suitable for diagnosis but initial screening (n = 776) from the ADHD-200 database to create a more comprehensive network called “brain mask”. They identified ADHD-specific changes in the neuronal connectivity in the cingulate gyrus and paracingulate gyrus in images of ADHD brains, which may be potentially developed into a diagnostic tool with high accuracy  can identify abnormal activities and connectivity between brain regions in ADHD brains, which were used in eight papers . Increascontrol)  Althoughcreening . Anothercreening . While icreening  applied accuracy . Using taccuracy . It can Multimodal MRI using machine learning  can int2.1.3.NIRS is used to measure regional oxygenation in brain regions, which was used in 10 papers . ADHD pa2.1.4.A study using transcranial magnetic stimulation (TMS) showed reduced short interval cortical inhibition in the dominant motor cortex . It also2.2.SNAP-25 MnlI 2 that are regulated by single nucleotide polymorphisms (SNPs) (LIME1 (cg00446123 and cg20513976) were found to be significantly higher, and those in SPTBN2 (cg02506324) were significantly lower in children with worse attention performance  5’UTR epigenetic changes and serum antibodies can be further used to confirm ADHD diagnosis and/or to predict the efficacy of treatment  has gained attention in the pathogenesis of ADHD. MicroRNAs are small non-coding RNAs that typically suppress gene expression. Using the next-generation sequencing (NGS) technique (Illumina) and support vector machine classification model, several microRNAs were identified by different groups with the potential to diagnose ADHD , which rs (SNPs) . Global s (SNPs) . One stuformance . Using Dformance  found thith ADHD . Spontanith ADHD . Howeverith ADHD . They foith ADHD . Althoug2.3.Physiological biomarkers can also be potentially added to the suite of diagnostic criteria in addition to DSM-5. There were 20 papers that used physiological markers alone or in combination with imagining analysis . Eye phyBiomarkers in the brain and body fluids may also be used to screen for ADHD in high-risk populations. Using MRI imaging, several neurotransmitters, glutamate, N-Acetyl Aspartate and choline, were found to be reduced in the right prefrontal cortex of ADHD brains . A studyCertain chemicals in the patient’s blood or in only the mother’s blood have been suggested to predict the risk of ADHD in their children. A prospective birth cohort study observed a positive association between maternal blood levels of an acetaminophen metabolite measured within 1–3 days postpartum and ADHD diagnosis in offspring . Seleniuvia the gut-brain axis. A study finds Bacteroides ovatus and Sutterella stercoricanisusing populations positively correlated to ADHD symptoms , there was lower activation in bilateral inferior PFC and the left inferior parietal lobe, as well as lower connectivity between right inferior PFC and surrounding brain regions such as the parietal lobes and the basal ganglia . White mThe inferior frontal gyrus has been suggested to be linked to motor response inhibition in the go/no-go tasks . Using fn = 175 both ADHD and age-matched controls) was more than 10 times that by Weigand et al. (n = 16 in each group), suggesting the need for even larger cohort and multiple centre studies to verify the findings. In DSM-5, the last criteria, or criterion ‘E’, states that for one to be diagnosed with ADHD, the symptoms cannot be better explained by another psychiatric disorder. Therefore, when an individual with ADHD also shows any comorbidity , the accuracy of diagnosis may be reduced. A reduced theta/beta ratio in EEG has also been shown to enhance the accuracy of diagnosing ADHD with the presence of comorbidities from 61% to 88% . We did not want to overinterpret studies which have measured clinical or biological factors but have not assessed these as being biomarkers. Therefore, we selected the information that would be potentially useful clinically. We did not list the papers according to ADHD subtypes or age groups. However, we did include the age of the participants in 4.There are several promising biomarkers that can be further researched in larger cohort studies for ADHD diagnosis. Future studies can include participants with diverse ethnic and cultural backgrounds in different countries, as well as compare sex differences in the reliability of these biomarkers for ADHD.HC and BO designed the study and prepared the first draft. YY, DO, SW, and CY performed the literature search. All authros contributed to the writing of the manuscript, and reviewed the final manuscript. All authors have agreed to the published version of the manuscript.This work was supported by grants from Australian National Health and Medical Research Council (APP1158186), the National Natural Science Foundation of China , Guangdong Basic and Applied Basic Research Foundation (2022B1515020012), China Postdoctoral Science Foundation (2022M723649), and Shenzhen Fundamental Research Program .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.


Studies using self-reports indicate that individuals with ADHD are at increased risk for functional impairments in social and occupational settings, but evidence around real-life instability remains limited. It is furthermore unclear if these functional impairments in ADHD differ across sex and across the adult lifespan.A longitudinal observational cohort design of 3,448,440 individuals was used to study the associations between ADHD and residential moves, relational instability and job shifting using data from Swedish national registers. Data were stratified on sex and age .31,081 individuals  in the total cohort had an ADHD-diagnosis. Individuals with ADHD had an increased incidence rate ratio (IRR) of residential moves , relational instability  and job shifting . These associations tended to increase with increasing age. The strongest associations were found in the oldest group (40–52 years at start of follow). Women with ADHD in all three age groups had a higher rate of relational instability compared to men with ADHD.Both men and women with a diagnosis of ADHD present with an increased risk of real-life instability in different domains and this behavioral pattern was not limited to young adulthood but also existed well into older adulthood. It is therefore important to have a lifespan perspective on ADHD for individuals, relatives, and the health care sector.The online version contains supplementary material available at 10.1186/s12888-023-04713-z. Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that persists into adulthood for many individuals , 2. The The few available observational studies suggest that adults with ADHD present with difficulties in different domains of real-life instability. In a recent narrative review of prospective studies on adult outcomes of children with ADHD followed into adulthood, it was concluded that individuals with ADHD have higher rates of quitting jobs and being fired . FurtherPrevious research on ADHD and real-life instability largely consists of studies focused on young adults/individuals < 30 years , thus, vUsing large national registers from Sweden, the current study examined associations between ADHD and real-life instability over a 14-year period, with a focus on job shifting, relationship instability, and residential moves. The aim was to examine these associations in young and middle adulthood, and also across both males and females. Analyses were adjusted for parent education as previous research has established strong associations between ADHD and parental educational level , and becUsing the Swedish Total Population Register , we idenIndividuals with ADHD were identified by at least one diagnosis of ADHD in the NPR by using ICD-diagnoses  or by at least four dispensations of ADHD medications  and non-stimulant medications (Atomoxetine [N06BA09]) in the PDR. Previous research has indicated high specificity for this register-based ADHD definition in Sweden , 38. FurThe LISA-register provides information about the extent to which an individual has changed jobs between two consecutive years. Using this information, we calculated a sum score for each individual, reflecting the total number of job changes during the follow-up across 14 years (between January 2000 and December 2013).Relationship instability was approximated by using the MGR to measure how many children individuals have with different partners during the follow up between January 2000 and December 2013.The LISA-register provides information about residential moves, for each individual, on an annual basis. Using this information, we calculated, for each individual, a sum score reflecting the total number of residential moves during the follow-up across 14 years.The NPR were used to identify psychiatric diagnoses for BPD (F60.3) and SUD using ICD-diagnoses . The validity of the diagnoses of BPD in the NPR has been found to be good in an earlier Swedish register-based study . In lineTo account for overdispersion, a negative binomial regression using the log link function was used to ascertain the relationship between ADHD and (1) job shifting, (2) relational instability, and (3) residential moves throughout follow-up. The analyses were stratified by age groups  and sex . We first examined crude associations controlling for birth year and parent educational level. We then adjusted for SUD, BPD and criminal convictions. Effects of age and sex were tested in separate models. First, an interaction between ADHD and age group was included to determine whether the associations between ADHD and our outcomes were different among different age groups. Second, a separate test of interactions between ADHD and sex were included to assess if there were differences between males and females within each outcome. Finally, we tested a 3-way interaction between age x sex x ADHD to assess whether the sex-ADHD interaction varied in different age groups. Estimates are presented as incidence rate ratio (IRR) with 95% confidence intervals (CI).To address the issue that some individuals are potentially not able to work and move around as much as other people, we did a sensitivity analysis where we excluded individuals with (1) severe intellectual disability, (2) an incarceration in prison longer than 2 years, (3) an annual/and or work-related income at baseline < 41,800 SEK/4,461 EUR, (4) disability pension at baseline, and (5) long term sick leave at baseline (> 183 days). Data management and statistical analyses were performed using SAS software version 9.4  and Stata 15 .The demographic characteristics of the study population are presented in Table Adults with ADHD had an increased rate of residential moves , relational instability  and job shifting  compared to individuals without ADHD. As can be seen from the age-stratified results in Table Males and females with ADHD in the youngest group did not differ significantly from controls without ADHD on job shifting and relational instability Table . They diAs can be seen in Table All associations remained similar after excluding individuals; with severe intellectual disability, who were convicted for any crime more than two years, and/or were not in paid work, had disability pension, had long-term sick-leave at baseline, see Table S2.To our knowledge, this is the first study examining associations between ADHD and register-based indicators of real-life instability, including job shifting, relational instability, and residential moves. Our primary finding was that both men and women with a diagnosis of ADHD presents with an increased risk of real-life instability in different domains, in particular beyond young adulthood. These findings shed important light on the real-life functioning of individuals with ADHD across the lifespan. Such information is critical given that recent research indicate that a large number of adults show elevated levels of ADHD symptoms, , but a dOur findings of an increased risk of job shifting, relationship instability, and residential moves in adults with ADHD are broadly consistent with previous studies on social and occupational outcomes in adults with ADHD , 13, 16,First, the large sample size and long timescale allowed us to study associations between ADHD and real-life instability over 14 years in people aged 18 to 52 years at the start of follow-up. This allowed us to study real-life instability across a large part of the adult lifespan covering younger to middle aged adults. Our findings indicate that associations with real-life instability were more pronounced in middle aged adults with ADHD compared to younger adults with ADHD. The young adults with ADHD (18–29 years at start of follow up) had an 0–25% increased risk of real-life instability compared to individuals without ADHD. In contrast, the older groups with ADHD (30–39 and 40–52 years at start of follow up) had an 4–62% and 5–69% increased risk of real-life instability respectively compared to individuals without ADHD. One potential explanation for this pattern is that real-life instability is more normative in young adults, even in those without a diagnosis of ADHD. However, individuals with ADHD in the youngest age group (18–29 years) tended to make more residential moves compared to individuals without ADHD in the same age.Second, our large sample size allowed us to present results separately for males and females. Overall, a similar pattern emerged across sex, suggesting that both males and females with ADHD show an increased risk of real-life instability. Our findings may, however, suggest that women with ADHD have a more pronounced risk for relationship instability compared to men with ADHD. Higher rates of relationship instability in females with ADHD is in line with findings from a recent review of females with a diagnosis of ADHD .Third, we found that psychiatric comorbidity and severe behavioral problems  influence the risk of real-life instability in ADHD. These findings are consistent with a large body of research indicating that co-occurring psychiatric and behavioral problems have an important role for functional outcomes in ADHD, such as mortality, suicidal behavior, and educational level –46. EvenRegister-based studies capture only treatment seeking individuals. The ADHD-prevalence of 0.91% in our population-based sample is in line with other register-based studies on adult ADHD, but lower when compared with the 2.5% found in studies were all people with ADHD are identified - both treatment seeking individuals with earlier diagnoses and also not previously diagnosed individuals , 48. We Using the number of children with different partners as a measure of relational instability is a crude measure. Also, there is a possibility that outcome misclassification is more pronounced in fathers than in mothers for our indicator of relational instability. This could potentially explain why we only see an association in females but not in males. However, the frequency of misattributed paternity in Sweden is low . Using jOverall, our indicators of instability have the advantage of being concrete reflections of real-life functioning, which could be useful in clinical settings to guide discussions around the treatment plan. On the other hand, our crude real-life indicators may miss important nuances of instability.Our covariates SUD and criminal convictions are robust real-life measures but are indicators of the most severe end of externalizing problem and is probably lacking sensitivity as indicators of more moderate forms of externalizing problems. Since BPD is underdiagnosed in clinical practice and therefore also in the registers, this covariate probably lacks sensitivity as well . Our selBoth men and women with a diagnosis of ADHD present with an increased risk of real-life instability in different domains; a behavioral pattern that is not limited to childhood or adolescence but also exist well into adulthood. Clearly, it is important to have a lifespan perspective on ADHD for individuals, relatives and the health care sector.Below is the link to the electronic supplementary material.Supplementary Material 1 Table S1 and Table S2


Attention deficit hyperactivity disorder (ADHD), a childhood-onset neurobehavioral disorder, often perturbs scholastic achievement and peer-relationship. The pivotal role of glutamate (Glu) in learning and memory indicated an influence of Glu in ADHD, leading to the exploration of Glu in different brain regions of ADHD subjects. We for the first time analyzed GluR genetic variations, Glu levels, as well as expression of Glu receptors (GluR) in the peripheral blood of eastern Indian ADHD probands to find out the relevance of Glu in ADHD prognosis. After obtaining informed written consent for participation, peripheral blood was collected for analyzing the genetic variants, Glu level, and expression of target genes. Since ADHD probands are often treated with methylphenidate or atomoxetine for providing symptomatic remediation, we have also tested post-therapeutic improvement in the ADHD trait scores in the presence of different GluR genotypes. Two variants, GRM7 rs3749380 “T” and GRIA1 rs2195450 “C”, exhibited associations with ADHD (P ≤ 0.05). A few GluR genetic variants showed significant association with higher trait severity, low IQ, lower plasma Glu level, down-regulated GluR mRNA expression, and poor response to medications. This indicates that down-regulated glutamatergic system may have an effect on ADHD etiology and treatment efficacy warranting further in-depth investigation. The worldwide prevalence of ADHD in children is 7.2%3. A lack of adequate improvement in the core symptoms often results in the persistence of ADHD during adulthood4. In children and adolescents, the male: female ratio ranges from 2:1 to 10:1 respectively5 and in adults, the ratio considerably narrows with a range between 1:1 and 2:16. Studies on families with ADHD probands, twins, adopted children, candidate genes, linkage analyses, and high heritability (60–90%)7 indicates a strong genetic component in the pathophysiology of ADHD8. Medications and or behavioral therapy aid in reducing the severity of the major symptoms9. For pharmacotherapy, stimulant medications like methylphenidate (MPH) and amphetamines are prescribed mainly for reducing HA/Imp, while non-stimulant medications like atomoxetine (ATX) are given primarily to alleviate attention deficit3.The primary symptoms of Attention-deficit/hyperactivity disorder (ADHD), a childhood-onset neurobehavioral disorder, are age-inappropriate inattention (IA), hyperactivity (HA), and impulsivity (Imp)12. A few investigators have also explored the involvement of the glutamatergic system16. Magnetic resonance spectroscopy of the brain revealed increased glutamate (Glu) levels in the frontal and striatal areas of ADHD subjects17. Magnetic resonance spectroscopy also revealed positive correlation between anterior cingulate cortex Glu concentration and Imp score18. Additionally, the prefrontal cortex (PFC) of the experimental animal model of ADHD showed an increased Glu uptake20. Investigators have also reported primary symptoms of ADHD, including HA, Imp, and IA, in glutamate receptor (GluR) knock-out mice24.Genome-wide association studies (GWAS), candidate gene analysis, and meta-analytic investigations on ADHD are primarily focused on the dopaminergic, noradrenergic, and serotonergic systems due to the crucial role of these neurotransmitters in the regulation of brain function26. Candidate gene studies indicated that variants of GRIN2B and GRIN2A genes, encoding for the iGluR N-methyl-D aspartate (NMDA) receptors, may increase attention impairment in ADHD patients27. Further, GWAS conducted on the European population has implied that genes encoding for the Glu receptors, GRM5, GRIK1, GRIK4, and GRID2, might be involved in the susceptibility to ADHD30 as well as the severity of HA/Imp18. In the Indian ADHD probands, Glu ionotropic receptor kainite type subtype 1 (GRIK1) genetic variants were reported to affect the symptom severity31. However, to date, the status of mGluR and iGluR genetic variants, as well as expression of different receptors have never been explored in the Indian ADHD probands. We have, for the first time, analyzed a few GluR genetic variations and expression of few Glu receptors (GluR) in a group of Indo-Caucasoid subjects including ADHD probands. Peripheral blood and brain Glu levels were reported to have significant positive correlations32. Hence, for the ease of analysis, we have studied the level of circulating Glu level in these subjects to find out the relevance of peripheral Glu in the disease etiology. Additionally, keeping in mind the relevance of Glu in the intellectual functioning, we have attempted to identify the relevance of Glu on the severity of different traits including executive functioning and efficacy of pharmaceutical intervention.Glutamate (Glu), the primary excitatory neurotransmitter, is derived during the Krebs cycle via the transamination of glucose. Alternatively, Glu can be synthesized from glutamine. Synaptic transmissions of Glu are facilitated through the metabotropic (mGluRs) and ionotropic (iGluRs) receptorsGRM5 (rs905646 & rs11020772), GRM6 (rs762724 & rs2067011), GRM7 (rs3792452 & rsrs3749380), GRIN2A (rs2229193), GRIN2B (rs2284411), GRIA1 (rs1422884 & rs2195450)] were analyzed in a group of ADHD probands, their parents, and ethnically matched control subjects; details on the genetic variants are provided in Supplementary Table Ten genetic variants from 6 GluR genes [Frequencies of rs3749380 ‘TT’ {P = 0.04, Power = 54%, Odds Ratio (OR) = 1.06} and rs2195450 ‘C’ /‘CC’  were higher in the ADHD probands as compared to the controls  = 6.17} was detected in the female probands (Supplementary Table 2 = 0.68) in the ADHD probands and the male probands  as well as in all controls  and male controls  respectively. Gender-based stratified analysis failed to show any LD between these two variants in the female probands  in comparison to the female controls . LD was also strong between GRM6 rs762724-rs2067011 in the ADHD probands  and the male probands . GRIA1 rs2195450 & rs1422884  program revealed the strong independent effect of rs3749380 {Information Gain (IG) 0.9%}, rs11020772 (IG 0.73%), and rs2195450 (IG 0.63%) in the ADHD probands  , was negatively affected by the rs762724 ‘T’ allele and ‘TT’ genotype, rs2067011 ‘GG’ genotype, rs2284411 ‘T’ allele/‘TT’ genotype, and rs1422884 ‘T’ allele/‘TT’ genotype , ADHD probands  exhibited a significant deficit in plasma Glu level  and rs11020772 ‘TT’ (P = 0.003) genotypes  trait scores of ADHD probands carrying the rs3749380 ‘CC’ genotype  receptors, acting as a cation channel, are fundamental for synapse maturation and plasticity34.Adequate glutamatergic signalling is necessary for maintaining the synaptic plasticity crucial for proper learning and memory35. Association with symptom severity was also reported in the Caucasoid ADHD probands36. An earlier investigation on a limited number of Caucasoid children/adolescents with ADHD (N = 9) showed increased Glu levels in the striatum and frontal regions17. On the other hand, proton magnetic resonance spectroscopy of European Caucasoid adults with ADHD (N = 40) revealed lower Glu levels in the basal ganglia37. A lower Glu level was also reported in the left mid-frontal region of Norwegian adults with ADHD (N = 29) as compared to the controls (N = 38)38. A vital role of GRIN2B and GRM7 in responses to MPH treatment was observed by clinical pharmacological studies of ADHD subjects40. The increased glutamatergic tone in the frontal and striatal regions of ADHD subjects was found to be normalized after treatment with stimulants as well as non-stimulants like ATX41. On the contrary, MPH-induced increase in the mesocortical glutamatergic pathways was also evidenced by proton magnetic resonance spectroscopy, possibly through the inhibition of DA reuptake or through a direct effect on the NMDA receptors. This investigation for the first time documented lower circulating Glu levels in the ADHD probands.Association studies and GWAS performed on the Caucasoid subjects revealed a link between the GluR genetic variants and ADHD42. In Australian autistic subjects, gene pathway analysis identified GRM5 rs905646 as a protective SNP, while rs11020772 was recognized as a risk variant43. The present pilot study for the first time documented down-regulated GRM5 expression and lower circulating Glu levels in the peripheral blood of eastern Indian ADHD probands carrying GRM5 rs905646 “A” and rs11020772 “T” variants. Frequencies of rs905646 “A” and rs11020772 “T” were higher in the Indian population as compared to the other Asian populations. The two sites showed strong LD indicating higher chances of being present together. Though no statistically significant associations of the studied variants were detected with individual traits, MDR analysis exhibited a significant independent effect of rs11020772 in ADHD. ATX treatment improved IA rs11020772 ‘GG’ genotypes. Based on these observations we conclude that further analysis involving functional genetic variants may help in elucidating the actual role of GRM5 in ADHD.Amongst the metabotropic receptors, Glu receptor 5 (GRM5), triggering a variety of signaling pathways in the neurons and glial cells, was reported to have an association with intellectual disability and Autism44. The present study revealed strong LD between these two markers in the ADHD probands. In the presence of rs762724 ‘T’ and rs2067011 ‘G’ alleles, the ADHD probands exhibited reduced IQ score, less EF deficit, and reduced GRM6 expression. The differential impact observed for rs762724 ‘T’ and rs2067011 ‘G’ alleles on IQ and EF could be due to the procedural differences used for testing the two parameters; while IQ assessment depends on the momentary performance requiring sustained attention of the participant, EF is assessed from the input provided by the parents or caregivers and may not be influenced by the inattention problem of the proband. As a result, while IA of the probands may reduce the IQ score, it may not be reflected on the EF score. GRM6 variants did not show any significant association with other traits. MPH treatment was beneficial for the probands with GRM6 rs762724 ‘CC’, rs2067011 ‘AA’, genotype. The observed impact of the studied GRM6 variants on GRM6 expression warrants further investigation on the role of GRM6 in the etiology of ADHD using more functional variants.Two other mGluR variants, Glu receptor 6 (GRM6) rs762724 and rs2067011, were reported to have an association with higher myopia in the Han Chinese population45. In the Korean ADHD probands, the biased parental transmission of GRM7 rs3792452 ‘C’ allele was reported40. Poor performance during the Continuous Performance Test was also observed in the presence of this variant35. The present study on Indian ADHD probands revealed marginally higher scores for IA and AI in the presence of the rs3792452 ‘T’ allele. In the presence of GRM7 rs3792452 ‘CC’ genotype, both medicines effectively reduced the HA score. Another GRM7 variant, rs3749380 is a C to T transition at codon 74 (C222T), resulting in a missense substitution (Leu74Ser), and the ‘T’ allele was reported to increase the risk of alcohol consumption46. The ‘T’ allele and ‘TT’ genotype also showed an association with schizophrenia47. Our pilot study revealed a higher occurrence of the rs3749380 ‘TT’ in the ADHD probands, mild negative impact of the “T” variant on all the trait scores, circulating Glu levels, and GluR expression. On both medications, all the traits were improved in the presence of rs3749380 ‘CC’ genotype. The data obtained indicates an influence of GRM7 variants on the down-regulation of glutamatergic transmission which requires further confirmation.The mGluR 7 (GRM7), widely expressed in the cerebral cortex, hippocampus, and cerebellum, was speculated to affect anxiety, fear responses, and working memory27; the probands exhibited fewer errors in the presence of the rs2229193 ‘CC’ genotype27. However, no significant differences in the allele/genotype frequencies for rs2284411 were detected in the Korean ADHD subjects by earlier investigators35. In silico analysis revealed that rs2229193 is a C to T transition at codon 425 (C1275T), resulting in a synonymous change (Leu425), while rs2284411 is a T to C transition at the intron 7. The present genetic analysis showed biased paternal transmission of the rs2229193 ‘C’ allele to the female ADHD probands. Trait scores and executive deficit of the ADHD probands were higher in the presence of the rs2284411 ‘CC’ genotype. Probands having rs2229193 “C” and rs2284411 “C” variants exhibited significantly reduced circulating Glu level as well as GRIN2A and GRIN2B expression. In the presence of rs2284411 ‘TT’, scores for BPr and HA were improved after MPH and ATX treatment. On the other hand, Korean ADHD patients with rs2284411 ‘CC’ genotype were reported to show significantly better treatment response39. This difference in response to treatment in the presence of a particular genotype could be attributed to differences in food habits or other environmental factors in different ethnic groups, which merits further exploration.NMDA receptor variants, GRIN2A rs2229193, and GRIN2B rs2284411, were identified to confer an increased risk of attention impairment in Korean ADHD patients48. However, a meta-analysis later confirmed a significant association of GRIA1 rs2195450 C > T with the risk of migraine in the Asian population only49. The present investigation on the Indo-Caucasoid ADHD subjects revealed an increase in the trait scores in the presence of GRIA1 rs1422884 ‘CC’ genotype, while in the presence of the ‘’CC’ genotype the probands showed reduced Glu level, less executive deficit, and remarkable improvement in HA score after ATX treatment. We have also observed a marginally higher frequency of GRIA1 rs2195450 ‘C/CC’ in the ADHD probands, the biased paternal transmission of the ‘C’ allele, and lower trait scores in the presence of the ‘CC’ genotype. Expression of GRIA1 was significantly down-regulated in the probands group. We conclude from the data obtained that GRIA1 may influence ADHD severity by affecting different traits which needs further exploration in the field.We have also analyzed two regulatory AMPA variants, GRIA1 rs1422884 (feature type-enhancer) and rs2195450 (feature type-promoter), for the first time in the ADHD probands. An earlier study in the Italian population revealed an association of GRIA1 rs1422884 with schizophrenia susceptibilityThe studied genetic variants exhibited various levels of independent as well as interactive effects in the ADHD probands as compared to the ethnically matched control group, as is evident from the MDR analysis. Further analyses, considering individual phenotypes (data not presented for brevity), revealed that BPr score was affected by the synergistic interactions between GRIA1-GRM6 (rs1422884-rs762724), and GRM5-GRM7 (rs905646-rs3749380) variants. The trait IA was also found to be affected by synergistic interactions between GRM5-GRM6 (rs11020772-rs20670110) and GRM5-GRM7 (rs762724-rs3749380). The behavioral traits, ODD and PACS, were synergistically influenced by GRM5 variants (rs905646-rs11020772). The trait scores for EF were found to be affected by the synergistic interaction between GRM6-GRM7 (rs2067011-rs3749380). Based on these observations we concluded that GRM5, GRM6, GRM7, and GRIA1 may affect ADHD etiology in an interactive manner.15. Both these medications affect the dopaminergic transmission and are widely prescribed for symptomatic remediation, even in India52 Besides the direct action on the dopaminergic system, MPH also influences other neurotransmitters including the glutamate receptors53. Pharmacological studies on ADHD revealed that GRIN2B and GRM7 play a vital role in MPH treatment40. Proton magnetic resonance spectroscopic studies indicated an MPH-induced increase in the mesocortical glutamatergic activity through the inhibition of DA reuptake or a direct effect on the NMDA receptors54. Experimental rat models showed influence of MPH on NMDA-receptor-mediated excitatory synaptic transmission55. However, some patients were found to develop acute side effects following intervention with MPH and ATX leading to treatment discontinuation58. Pharmaceutical intervention, through targeted genotyping, was thus thought of as an useful method for subjects requiring long-term intervention60. It is evident from our study that in the presence of the ancestral alleles, treatment outcome was better. Our investigation also revealed that the post-treatment improvements were dependent on the GluR genetic variants as well as the medicine used; while MPH therapy improved the BPr of the probands having the GRIN2B rs2284411 ‘TT’, ATX treatment improved BPr in the presence of GRM7 rs3749380 ‘CC’. We infer from this data that pharmacogenomic investigations may aid in predicting the therapeutic success, thereby minimizing the number of individuals discontinuing pharmacotherapy.During the past decade, pharmacogenomic research on the efficacy of medication in improving ADHD related traits has largely focused on the response to MPH and ATXThe major limitations of the present investigation are (1) analysis of different glutamatergic components only in the peripheral system, (2) analysis of limited number of genetic variants, (3) limitation in the number of female probands, and (4) analysis of only prime ADHD traits. However, the association of GluR genotypes with reduced circulating Glu level, down-regulated GluR mRNA expression, higher trait scores, IQ deficit, as well as treatment outcome of the Indian ADHD probands indicates that an inadequate glutamatergic transmission may be, at least partially, responsible for the severity of the behavioral traits and inappropriate treatment response. Furthermore, this investigation on the glutamatergic system was performed entirely on the peripheral system, and hence, further in-depth analysis in the central nervous system is warranted to understand the actual role of Glu in the etiology of ADHD.61 and DSM-5 criteria1. Probands exhibiting hyperactivity (HA), impulsivity (Imp), or inattention (IA) due to other neuropsychiatric disorders like Pervasive developmental disorders, Intellectual disability, Fragile X syndrome, Down’s syndrome, Prader-Willi syndrome, etc., were excluded from this study. The majority of the recruited ADHD probands belonged to the combined subtype (72.0%), while the HA/Imp (11%) and IA (17%) subtypes were only a few. Parents  of the probands were included in the study for family-based genetic association analysis. An ethnically-matched control group  was recruited for population-based genetic association analysis. The participants were asked to fill up a questionnaire to collect the demographic details and medical histories, symptoms of IA, HA, and Imp, etc. For participation in the study, informed written consent was obtained from the parents or caregivers of the probands and control subjects. The study protocol (No. PR-003–17) was approved by the Manovikas Ethics Committee on Human Subjects, with Scientists, Psychiatrists, Psychologists, Advocates, and Social workers as members.Nuclear families with ADHD probands  were recruited based on the Diagnostic and Statistical Manual of Mental Disorders IV-text revised (DSM-IV-TR)62. The Wechsler Intelligence Scale was used to evaluate IQ63. The BDEF-CA, a practical tool for evaluating executive function, was used to assess the executive deficit64. PACS was used to determine the behavioral problems65. Co-morbid ODD was evaluated by DSM61.The ADHD-associated traits BPr, IA, HA, and AI were assessed by Conner’s Parent and Teacher Rating Scale-revised (CPRS-R)GRM5, GRM6, GRM7, GRIN2A, GRIN2B, GRIA1) were selected based on their functional relevance, minor allele frequency in other ethnic groups, and earlier reports of association with neuropsychiatric disorders, including ADHD , GRM7 , GRIN2A (rs2229193), and GRIN2B (rs2284411), were analyzed using the TaqMan SNP genotyping assays . Genotyping of GRM6 , and GRIA1 was performed by polymerase chain reaction (PCR) amplification in the Applied Biosystems ProFlex™ PCR system, followed by Restriction fragment length polymorphism analysis using HhaI, HpyCH4III, and TaqI-v2 enzymes, respectively.Peripheral blood was collected from treatment naïve ADHD probands, their families, and ethnically matched controls at the time of recruitment. Genomic DNA was isolated from the peripheral blood by the phenol/chloroform methodPeripheral blood samples were collected in a pre-cooled vacutainer from the ADHD probands and age-matched control subjects fasting for 12 h. Plasma Glu levels were measured by competitive enzyme-linked immunosorbent assay (ELISA) following the manufacturer's protocols  and the optical density of the end products was measured at 450 nm in an ELISA plate reader .Expressions of GRM5, GRM6, GRM7, GRIN2A, GRIN2B, and GRIA1 in the peripheral blood were examined in the ADHD probands (n = 23) and age-matched control subjects (n = 19). Briefly, RNA was isolated from the peripheral blood by the TRIzol method  and reverse transcribed into complementary DNA (cDNA), which was later used as the real-time PCR or quantitative PCR (qPCR) template to perform gene expression analysis. The data was normalized against Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression, serving as an internal control.ADHD probands (n = 84), who had adhered to the pharmaceutical intervention and were available for post-treatment follow-up, were included in this analysis. The probands with age-inappropriate HA, residing in the urban areas, and < 10 years of age (n = 52) were prescribed MPH at a dose of 0.3 mg/kg body weight/day for two months, followed by 0.6 mg/kg body weight/day for another 4 months. Probands (n = 32) with significant IA, > 10 years of age, and residing in rural areas where access to MPH is limited, were prescribed ATX at a dose of 0.8 mg/kg body weight/ day for two months, followed by 1.2 mg/kg body weight/day for another four months. All the probands were re-assessed by the CPRS-R after treatment completion.www.oege.org/software/hwe-mr-calc.shtml/) to determine the pattern of the studied variants genotypic frequencies. Population-based comparative analysis and family-based transmission analyses were performed using the UNPHASED version 3.1.767, after 1000 permutations, which takes care of the multiple corrections. Quantitative trait (QT) analysis was performed to identify the association between the genetic variants and ADHD-associated trait scores using the UNPHASED version 3.1.767. Odds ratio (OR) and confidence intervals (CI) were calculated using the Odds Ratio calculator (http://www.hutchon.net/ConfidORnulhypo.htm). The relative risk (RR) of the studied variants was calculated using the relative risk calculator. The Power of the significant observations was calculated using Piface software68. Pairwise LD between the variants was measured using the Haploview program v4.269. MDR v3.0.2 program70 was used to identify the effect of studied variants on ADHD using the case–control data. Confounded effect of other covariates like age, sex, and IQ were not considered for this pilot study. Case–control comparative analysis on the GluRs mRNA expression and plasma Glu levels was performed by the Unpaired T-test using Prism 9.0 . Genotype-based stratified analysis on the Glu levels of the controls and probands were analyzed using the Mann–Whitney statistics. Expression of mRNA in the presence of different GluR genotypes was evaluated using the Kruskal–Wallis test, followed by multiple comparisons using the Dunn’s test. Improvement in the trait scores after pharmaceutical interventions was calculated by 1-Tn/To  as detailed in a previous article50 and is presented as the improvement index. The association between GluR gene variants and treatment-induced changes in the trait scores, measured based on the improvement index, was analyzed by the Mann–Whitney (M–W) U test using the Prism 9.0 software. The data from the test are presented as Mean ± Standard error of the mean (SEM).Hardy Weinberg Equilibrium (HWE) was calculated using the online software (http:// Supplementary Figure 1.Supplementary Tables.


Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals . The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses  were performed to determine relationships between psychological measures with within-network FC  as well as with between-network FC . ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD. In clinical settings, it is often difficult to determine whether the deficits originate from ADHD or ASD; nevertheless, evidence suggests a difference in attentional processing between the two disorders. Several behavioural studies using neuropsychological tests have suggested that patients with ADHD experience problems with sustained attention, whereas those with ASD experience problems with selective attention5. Clarifying the mechanisms underlying these problems would lead to a better understanding of the disorders. However, it is unclear how the neural correlates of attentional processing differ between ADHD and ASD.Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental disorders; ADHD is characterized by inattention and/or hyperactivity–impulsivity, whereas ASD is characterized by deficits in social communication and interaction, repetitive behaviours, and restricted interests. Both ASD and ADHD can cause attention deficits, and inattention and its associated problems are often reported in both disorders7. Regarding LSBNs, the triple network model hypothesis concerns the interactions among three networks in particular—the salience network (SN), default mode network (DMN), and frontoparietal network (FPN)11 are often coactivated or deactivated during attentionally demanding tasks13. The SN detects the importance of internal or external stimuli and switches between networks15. The DMN is active during the resting state, such as passively viewing a stimulus; however, it is also active during social cognition and self-related processing19. It involves cognitive processes unrelated to the current situation (i.e. mind-wandering). In contrast to the DMN, the FPN is active during information processing and is important for maintaining and manipulating working memory, rule-based problem solving, and decision-making in goal-directed behaviour23.Recent neuroimaging studies using functional MRI (fMRI) have suggested that, in cognitive deficits and neuropsychiatric disorders, distributed circuit abnormalities can be characterized by changes in synchronized brain activities . More specifically, resting-state fMRI (rs-fMRI) studies have revealed several networks that are consistently found in healthy people and represent specific patterns of synchronous activity; these networks are typically termed large-scale brain networks (LSBNs)26. In one study in ADHD, changes in within-SN FCs were not demonstrated during the resting state27. Within-DMN FC dysfunction has been consistently identified in ADHD, with no observed changes in within-DMN FCs in ASD29. Within-FPN FC hypoconnectivity during the resting state has been observed in children diagnosed with ADHD31, whereas no alterations were found in within-FPN FCs in adolescents with ASD32. Concerning inter-network connectivity, SN-FPN FC hyperconnectivity during the resting state has been associated with ADHD traits34, while hypoconnectivity has been noted in children with ASD36. SN-DMN FC hypoconnectivity during the resting state has been documented in adults with ADHD37, and hyperconnectivity has been reported in adults with ASD38. DMN-FPN FC hyperconnectivity during the resting state has been identified in both children and adults with ADHD40 as well as in adolescents with ASD41.Regarding these three LSBNs, previous studies have reported within-network as well as between-network FC abnormalities in psychiatric disorders, including ADHD and ASD. As for within-network connectivity, mixed findings were reported in within-SN FCs in ASD, with some showing hypoconnectivity and others showing hyperconnectivity42. Investigating the differences in the neural correlates of attentional processing between the subclinical traits of ADHD and ASD would provide deeper insights into these disorders and help establish interventional strategies in people with potential ADHD/ASD traits. This study aimed to investigate the neural correlates of ADHD and ASD traits in attentional processing among healthy individuals by evaluating the FCs of LSBNs using fMRI. We hypothesized that the subclinical traits of ADHD and ASD would have differential neural correlates in relation to the FCs within and between the SN, FPN, and DMN.Subclinical traits of ADHD and ASD are continuously distributed throughout the general population43, and all participants fell within the normal range. The experimental procedures were fully explained, and all participants provided written informed consent before participating in the study. The study was approved by the ethics committee of the Kyoto University Graduate School and Faculty of Medicine and adhered to the guidelines of the Declaration of Helsinki.The participants were 61 healthy individuals . Two well-trained psychiatrists (KK and MS) confirmed that no participant had any psychiatric disorder or severe medical or neurological illness. The intelligence quotient of each participant was estimated using the Japanese version of the Adult Reading Testr ≤ 0.77) and the Beck Depression Inventory-II (r = 0.38), and its internal consistency and reliability has also been confirmed (Cronbach’s α approximately 0.80)46. The ASRS consists of two questionnaires—Part A (6 questions) and Part B (12 questions)—and the answers are recorded in terms of frequency on a 5-point scale with the following options: never, rarely, sometimes, frequently, and very frequently. Higher ASRS scores indicate higher levels of ADHD traits. The original Part A and Part B questionnaires are formatted with dark-shaded boxes such that endorsements in the darkly shaded boxes signify more severe symptoms. If a respondent marks ≥ 4 responses in the dark-shaded boxes in Part A, they are screened as positive for ADHD 48.The Japanese version of the Adult ADHD Self-Report Scale (ASRS) and the Autism Spectrum Quotient (AQ) were administered, and the scores were recorded as indicators of ADHD and ASD, respectively. The validity of the Japanese version of the ASRS has been confirmed previously based on the Japanese version of Conners’ Adult ADHD Rating Scales–Self Report subscales (0.59 ≤ p < 0.0001) on the AQ than the two control groups. The adults with AS/HFA had a mean AQ of 37.9, which was significantly higher than those of the two control groups (mean AQ score = 18.5 and 20.7). Moreover, 88% of the adults with AS/HFA scored > 33 points, whereas only 3% of participants in the two control groups scored > 33 points. The same study also confirmed the internal consistency and reliability of the AQ scale (Cronbach’s a = 0.81)49. In addition, the reliability and validity of this questionnaire have been tested and confirmed in several studies from various countries52. The AQ consists of 50 items that are rated by participants on a 4-point scale with the following options: definitely agree, slightly agree, slightly disagree, and definitely disagree. The 50 items are divided into five subscales  consisting of 10 items each. The AQ score ranges from 0 to 50, and higher AQ scores indicate higher levels of autistic traits.The Japanese version of the AQ has good validity, as confirmed by a comparative study between a group with Asperger’s syndrome (AS) or high-functioning autism (HFA) and two control groups . Tukey's multiple comparison test revealed that the AS/HFA group scored higher  to estimate focused attention at the behavioural level. The CPT was completed on a laptop computer. We adopted the A–X version of the CPT, in which a series of random one-digit numbers is displayed , and the participant is instructed to press the spacebar as quickly as possible when the number “7” is immediately followed by the number “3.” The task lasted for 16 min 40 s, and the target stimuli occurred at a frequency of 10%. CPT performance was estimated based on reaction time (ms) and the coefficient of variation57. MRI data were acquired using a 3-T MRI system  with a 40 mT/m gradient and a receiver-only 32-channel phased-array head coil. An rs-fMRI scan was performed for 360 s, and the images were acquired using a single-shot gradient echo-planar imaging (EPI) pulse sequence. The participants were instructed to visually concentrate on a fixation cross in the centre of the screen and to avoid thinking about anything specific during resting-state (Rest) data acquisition. Next, they received a 25-s explanation of how to complete the auditory oddball task (Odd) and then performed the task for 390 s. The task consisted of 30 pink-noise sounds (target stimuli) and 150 pure 400-Hz tones (non-target stimuli)58 in a randomized order. All stimuli were presented for 200 ms with a jittered and randomized inter-stimulus interval of 1–3 s in 100-ms units. During the task, participants were instructed to differentiate between target and non-target stimuli by pressing a button with the right thumb as fast and accurately as possible after the target stimulus presentation. We measured the response time for all responses to the target stimuli. All participants practiced before entering the scanner, and we confirmed that they understood the procedure and were able to perform the 16-s practice sessions with 100% accuracy. The total fMRI acquisition time was 775 s. Head movement was minimized using foam rubber pads.As shown in Fig. 3; and total axial sections without intersection gaps, 208. Parameters for the fMRI were as follows: echo time, 30 ms; repetition time, 2500 ms; flip angle, 80°; field of view, 212  ×  212 mm2; matrix size, 64 × 64; in-plane spatial resolution, 3.3125  ×  3.3125 mm2; total axial slices, 40; and slice thickness, 3.2 mm with 0.8 mm gaps in ascending order. A dual-echo gradient-echo dataset for B0-field mapping was also acquired for distortion correction.Structural MRI data were also acquired using three-dimensional magnetization-prepared rapid gradient-echo (3D-MPRAGE) sequences. The parameters for the 3D-MPRAGE images were as follows: echo time, 3.4 ms; repetition time, 2000 ms; inversion time, 990 ms; field of view, 225 × 240 mm; matrix size, 240 × 256; resolution, 0.9375  ×  0.9375  ×  1.0 mmhttps://www.fmrib.ox.ac.uk/fsl), together with FMRIB's Linear Image Registration Tool (FLIRT) for realignment. We removed artifacts and motion-related fluctuations from the images using FMRIB’s ICA-based X-noisier (FIX). We then processed the preprocessed fMRI and structural MRI data using the CONN-fMRI Functional Connectivity toolbox  with the statistical parametric mapping software package SPM12 .We corrected the fMRI dataset for EPI distortion using field map data and FMRIB’s Utility for Geometrically Unwarping EPIs, which is part of the FSL package  when the number of volumes in which the head position was 0.5 mm different from those in adjacent volumes was more than 25% of the total volumes; and (2) when the maximum head motion was > 3.0 mm and > 3.0°. No participants were excluded under criterion 1 and three were excluded under criterion 2. Finally, 48 of 51 participants were included in the FC analysis.59.All functional images were spatially normalised into the standard MNI space , outliers were detected (ART-based scrubbing), and the images were smoothed using a Gaussian kernel with a full-width-at-half maximum of 8 mm using the customized CONN toolbox preprocessing pipeline. All preprocessing steps were conducted exclusively using a default preprocessing pipeline for volume-based analysis (to MNI space). Structural data were segmented into grey matter, white matter, and cerebrospinal fluid and normalised in the same default preprocessing pipeline. Principal components of signals from white matter and cerebrospinal fluid, as well as translational and rotational movement parameters  derived by realignment with FLIRT, were removed with covariate regression analysis using CONN. Using its implemented CompCor strategy32, the effects of nuisance covariates, including fluctuations in fMRI signals from white matter, cerebrospinal fluid, and their derivatives, as well as realignment parameter noise, were reduced. As recommended, band-pass filtering was performed with a frequency window of 0.008–0.09 Hz. This preprocessing step was found to increase retest reliability. We did not remove mean evoked responses prior to task-state FC analysis2. Finally, 48 of 61 participants were included in the FC analysis. We performed a region of interest (ROI)-to-ROI analysis using the CONN toolbox to examine the relationship between FCs and psychological indicators. The networks of interest were the DMN, FPN, and SN. We used ROIs from the widely used Stanford FIND atlas . We performed a one-way analysis of covariance with ASRS or AQ score as an independent variable, FC as a dependent variable, and age and sex as covariates of no interest. Significant connections were identified based on false discovery rate-corrected two-sided The ASRS and AQ scores may have interactive effects and influence overall FC. Therefore, we conducted separate analyses in which either the AQ or ASRS scores were independent variables and the others were considered covariates.66 were preprocessed and analysed using the CONN toolbox. Left-handed individuals were excluded, and the sample was restricted to those aged 12 years and older, resulting in a sample size of 47 participants . In the ABIDE, rs-fMRI data from healthy individuals who completed the Social Responsiveness Scale (SRS)67 were preprocessed and analysed using CONN. Left-handers were excluded, and the sample consisted of those aged 12 years and older. We used the ABIDE data acquired in a single site considering that the way of performing SRS  as well as MRI data acquisition varied among each site, resulting in a sample size of 38 individuals .To evaluate the reliability of the results of FC analyses, the 48 participants of the current study were randomly classified into two groups of 24 individuals, and each of the two groups was subjected to a ROI-to-ROI analysis using the CONN toolbox. Furthermore, the open-access datasets ADHD-200 (https://fcon_1000.projects.nitrc.org/indi/adhd200/) and ABIDE (https://fcon_1000.projects.nitrc.org/indi/abide/) were used to evaluate the reliability of our findings. In the ADHD-200 dataset, rs-fMRI data from healthy participants who completed the ADHD Rating Scale IV (ADHD-RS)https://neuro.debian.net/pkgs/fsl-harvard-oxford-atlases.html) incorporated into the CONN Toolbox. The SN included 7 ROIs , the DMN 4 ROIs , and the FPN 4 ROIs .Moreover, to evaluate the reproducibility of our findings, which were based on the FIND atlas, we additionally analysed our data using 15 ROIs from the default Harvard-Oxford Atlas . Among the ASRS subitems, both Inattention and Hyperactivity scores were positively correlated with AQ Total scores . Many of the other subscales were also strongly correlated with each other.As shown in Table ρ =  − 0.298, p = 0.019), whereas the scores for Hyperactivity were negatively correlated with mean reaction time . In contrast, AQ Total scores and all five subscale scores were not correlated with CPT performance.The ASRS scores were negatively correlated with commission error, mean reaction time, and standard deviation on the CPT Table . Among tDuring the Rest and Odd periods, the ASRS/AQ scores were differentially correlated with FC values between any two ROIs of the SN, FPN, and DMN  and right cerebellar lobule VI (SN) as well as those between the right cingulate gyrus (FPN) and right cerebellar lobule VI (SN). During Rest, there were no correlations between total ASRS scores and intra- or inter-network FC values. During Odd, ASRS Inattention scores were positively correlated with multiple inter-network FC values between the FPN and SN and negatively correlated with multiple inter-network FC values between the DMN and SN. During Rest, ASRS Inattention scores were positively correlated with multiple intra-network FC values within the FPN and negatively correlated with multiple intra-network FC values between the FPN and DMN. During Odd, ASRS Hyperactivity scores were negatively correlated with one intra-network FC value within the FPN and positively correlated with one inter-network FC value within the DMN. During Rest, ASRS Hyperactivity scores were not correlated with intra- or inter-network FC values.During Odd, the AQ scores were positively correlated with FC values between the left and right parietal lobes and those between the left frontal gyrus and right parietal lobe (FPN). We also found a negative correlation between AQ scores and two FC values: those between the right inferior parietal lobule (FPN) and right posterior insula (SN), and those between the left middle occipital gyrus (FPN) and left posterior insula/putamen (SN). During Rest, AQ scores were negatively correlated with intra-network FC values such as those between the left and right inferior parietal lobules (SN). However, the AQ scores were positively correlated with inter-network FC values such as those between the left occipital gyrus (DMN) and right midcingulate cortex (SN) and between the right retro-splenial cortex/posterior cingulate cortex (DMN) and right midcingulate cortex (SN). Regarding the correlation analysis with AQ subscales, during Odd, AQ Social Skill scores were positively correlated with multiple intra-network FC values within the FPN and negatively correlated with multiple inter-network FC values between the FPN and SN. During Rest, AQ Social Skill scores were positively correlated with multiple inter-network FC values between the SN and DMN and negatively correlated with multiple intra-network FC values within the FPN and SN. During Odd, AQ Attention Switching scores were positively or negatively correlated with multiple inter-network FC values between the DMN and SN. During Rest, AQ Attention Switching scores were positively correlated with multiple intra-network FC values within the FPN, as well as with multiple inter-network FC values between the FPN and SN and between the SN and DMN.During Odd, AQ Attention to Detail scores were positively correlated with multiple inter-network FC values between the FPN and DMN and negatively correlated with one intra-network FC value within the FPN. During Rest, AQ Attention to Detail scores were positively correlated with one inter-network FC value between the SN and DMN. During Odd, AQ Communication scores were positively correlated with multiple intra-network FC values within the FPN. During Rest, AQ Communication scores were positively correlated with multiple inter-network FC values between the FPN and DMN and between the SN and DMN, and they were also negatively correlated with multiple intra-network FC values within the SN. During Odd, AQ Imagination scores were positively correlated with multiple intra-network FC values within the FPN and negatively correlated with multiple inter-network FC values between the DMN and FPN and between the SN and DMN. During Rest, AQ Imagination scores were positively correlated with multiple intra-network FC values within the SN and DMN and multiple inter-network FC values between the FPN and SN and between the SN and DMN.Fig. The results of the relationships of ASRS and AQ Total scores with FC values were similar to those obtained when either AQ or ASRS was used as an independent variable and the other was used as a covariate  and left middle temporal gyrus (FPN) and between the left middle occipital gyrus (DMN) and right midcingulate cortex  were positively correlated with performance in the CPT, whereas AQ Total and all subitem scores (i.e. ASD traits) were not. In the FC analyses, the ASRS and AQ scores were differentially correlated with intra- and inter-network connectivity among various ROIs in the DMN, SN, and FPN. Using either the FIND or Harvard-Oxford atlas, during Odd, ASRS scores were positively correlated with FC values within the DMN, whereas AQ scores were positively correlated with those within the FPN. The AQ scores were negatively correlated with FC values between the FPN and SN. Using the FIND atlas, during Odd, the ASRS scores were positively correlated with FC values between the FPN and SN. During Rest, only AQ scores were negatively correlated with one FC value within the SN, and AQ scores were positively correlated with multiple FC values between the DMN and SN. Other than the negative correlation of AQ with one FC value within the SN during Rest, these findings of the ROI-to-ROI analysis were not replicated in the split-half replication analysis or in the replication analysis using open-access datasets. Fig. 70. However, there are no consistent reports regarding the association between ASD and CPT performance71.Adults and children with ADHD generally show lower performance on the CPT72.Our results indicate that the higher the ASRS score, the more favourable the CPT performance and the shorter the reaction time. This suggests that ADHD traits beneath the diagnostic threshold may promote attentional retention functions. A previous questionnaire study reported that CPT performance was intact in healthy children with low-to-moderate ADHD symptoms, indicating that CPT performance can vary widely among different levels of ADHD severity74. Adults with ADHD are known to choose multitasking tasks quite often75. Considering the potential preference for multitasking in individuals with subclinical ADHD, intermediate-level ADHD traits are also expected to demonstrate an inverted U-shaped relationship with attentional processing. If the inverted U-shaped model is accepted, the participants in the present study would be distributed on the left side of the curve. In particular, those with higher  ADHD traits would be located near the apex.To the best of our knowledge, this study is the first to indicate an association between favourable CPT performance and susceptibility to subclinical-level ADHD. Moderate levels of media multitasking are expected to be associated with enhanced cognitive control, and several reports suggest an inverted U-shaped relationship between attentional performance and media multitasking73. In contrast, the trained attention hypothesis argues that frequent multitasking enhances cognitive control and positively affects attention76. The results of the current study suggest that individuals with subclinical ADHD perform better in the CPT, which may be consistent with the trained attention hypothesis.Two hypotheses have been proposed regarding the relationship between media multitasking and attentional functioning: the scattered attention hypothesis and the trained attention hypothesis. The scattered attention hypothesis argues that long-term media multitasking weakens attentional control, meaning that individuals exposed to a multitasking lifestyle are less capable of maintaining focus on relevant tasks in the presence of distractions82. The DMN is commonly activated when the brain is free from external cognitive demands or when it is performing internal tasks84. However, individuals with ADHD exhibit reduced FCs in the DMN during the resting state and reduced inhibitory changes in FCs during task performance86. Our results also showed that ASRS Total and Hyperactivity scores were positively correlated with the intra-network integrity of the DMN during tasks and that ASRS Inattention scores were negatively correlated with the intra-network integrity of the DMN during Rest. These results indicated that, in terms of DMN activities, people with subclinical ADHD traits and those with clinical-level ADHD share some common neural bases.Previous studies reported functional deficits in the DMN among individuals with ADHD88. Previous studies in the general population have suggested that the FC between the SN and FPN—including the FC between the cerebellum (involved in the SN) and prefrontal cortex (involved in the FPN)—increases in response to cognitive demands, especially during auditory cognitive tasks92. In the current study, the SN ROI related to increased FC was limited to cerebellar lobule VI. Previous studies also have shown that the posterior cerebellar lobes, including lobule VI, are activated during cognitive tasks requiring working memory and executive function93. Thus, the positive associations between ADHD traits and the SN (cerebellum)–FPN  FCs during the oddball task (as observed in the current study) may indicate improved sustained attentional functions (insofar as the level of ADHD traits is lower than intermediate). The increased SN–FPN FCs may also explain the results of the behavioural part of this study—that is, improved performance in the CPT in individuals with a higher  level of ADHD traits.Our analysis of inter-network relationships among the SN, FPN, and DMN showed that ASRS Total and Inattention scores of subclinical individuals were positively associated with the integrity of SN–FPN FCs. To the best of our knowledge, no other study has reported increased FC between the SN and FPN during tasks in individuals with ADHD. Some earlier studies in children with clinical-level ADHD showed reduced FC between parietal and cerebellar brain regions during cognitive tasks involving attention and response inhibition97. In particular, we noted decreased FCs between the SN and FPN during tasks associated with AQ Total and Social Skill scores, decreased FCs between the SN and DMN during tasks associated with AQ Imagination scores, decreased FCs within the SN during Rest associated with AQ Total and Communication scores, increased FCs between the SN and DMN during Rest associated with AQ Total and all five subscale scores, and decreased FCs between the SN and FPN during Rest associated with AQ Social Skill scores.Our analysis of intra- and inter-network FCs revealed differential changes in FCs in association with ASD traits. Our results are partially consistent with those of previous rs- and task-based fMRI studies36. Therefore, the decrease in FCs within the SN and their associations with symptom severity98 suggest that the SN may contribute to both overactive and underactive brain functions in individuals with ASD. For example, emotion dysregulation—a common clinical symptom of ASD100—may be explained by the failure of the cognitive control of emotion (i.e. the FPN function) caused by problems in attention when “selecting” stimulus importance (i.e. the “switching” function of the SN). Previous studies have hypothesized that increased SN–DMN FC is potentially associated with repetitive negative self-thoughts in ASD38. Previous fMRI studies have also reported that, in depression, dysfunction within the SN contributes to abnormal engagement and disengagement of the DMN and FPN. This may cause difficulties in disengaging the processing of negative information, thus contributing to the worsening of negative thoughts102. The mechanism behind negative thoughts in ASD may be similar to that in depression, which is often comorbid with ASD104.The SN detects salient internal and external stimuli and regulates other brain regions by switching the brain networks106.ASD traits were negatively associated with FCs within the SN (during Rest) and those between the SN and FPN (during Odd). This indicated that, in terms of selective attentional processing, even people with subclinical ASD traits share common neural bases with those with clinical-level ASD. This may cause problems in attention when “selecting” stimulus importance. ASD traits were positively associated with the FCs of the FPN during Odd; this is consistent with the hypothesis that, in ASD adults, sustained attention is not significantly different from that in people with typical development107. This indicates that people with comorbid ADHD and ASD at the subclinical level and those at the clinical level share some common neural bases.When both AQ and ASRS were used as independent variables, during Odd, AQ and ASRS total scores were positively correlated with FC values within the FPN. During Rest, those scores were positively correlated with FC values between the DMN and FPN and between the DMN and SN. A prior study showed that hyperconnectivity between the DMN and FPN during Rest was found in a group with comorbid clinical-level ASD and ADHD109. This suggests that these traits share similar neural bases. However, different meanings of ASRS Inattention and AQ Switching subscales have been suggested previously111, Therefore, the commonality in neural correlates of these subscales should be cautiously interpreted.Regarding ASRS and AQ subscales, both AQ Attention Switching and ASRS Inattention scores were negatively correlated with FC values between the right thalamus (SN) and left angular gyrus (DMN) during Odd and were positively correlated with FC values of the right middle frontal gyrus and right inferior parietal lobule within the FPN during Rest. Both AQ Attention to Detail and ASRS Hyperactivity were negatively correlated with FC values within the FPN during Odd. Both AQ Imagination and ASRS Inattention scores were negatively correlated with FC values between the DMN and SN during Odd. Among the several similar network interactions shared among the subscales, the similarity in the network interactions between AQ Attention Switching and ASRS Inattention is remarkable. The correlation between these two subitems was also found in previous studiesWhen the 48 participants were randomly divided into two groups for a split-half analysis, the results were partially consistent with the initial findings, although they did not fully replicate those of the entire study population, possibly because the two subgroups were, with 24 participants each, quite small.The analysis of the ADHD-200 dataset did not replicate the results of the current study. However, the results of the ABIDE data analyses were consistent with our results, especially regarding AQ Total and AQ Social Skills, Attention Switching, Attention to Detail, Imagination, and Communication scores, but only partially. The following factors may have contributed to the inability to reproduce our study results: the participants in the ADHD-200 and ABIDE data were younger than those in our study, they were not Japanese and (as in our study), the participants were predominantly male, the rating scales were different , and the evaluation was limited to Rest.115. Previous studies have also demonstrated that the cerebellum participates in many intrinsic connective networks, including the SN and DMN117. Moreover, cerebellar abnormalities in ASD and ADHD have been confirmed by functional and structural MRI; disruptions in cerebellar function may lead to social and cognitive deficits in ASD120, while cerebellar dysfunction in ADHD may lead to time perception deficits, emotional deficits, and childhood motor deficits125. Another possible limitation of the replications may be that the Harvard-Oxford Atlas is a structural atlas, which does not properly represent a functional analysis when compared with the FIND atlas126.When we additionally analysed our data using ROIs from the Harvard-Oxford Atlas, the results of the analysis were not completely replicated but were partially consistent. Whether using the Stanford FIND atlas or the Harvard-Oxford Atlas, during Odd, the AQ scores were negatively correlated with inter-network FC values between the FPN and SN and positively correlated with intra-network FC values within the FPN. The network interactions that could not be reproduced may be related to the fact that the Harvard-Oxford atlas did not include the cerebellum and other regions that are included in the FIND atlas. Specifically, the positive associations between ADHD traits and the SN–FPN FCs during Odd are expected to not be replicated, as they involved the cerebellum. In this study, the inclusion of the cerebellum in the atlas is worthwhile in light of the following facts. Anatomical tracing, human clinical, and stimulation functional imaging studies have firmly established the major role of the cerebellum in cognition and emotion processing127, six participants were screened as positive for ASD. Finally, four participants who had been screened as positive for ADHD exceeded the cut-off score for the AQ. Second, another major limitation is the lack of reproducibility of our findings both within our study population  and in out-of-sample replication (ADHD-200 and ABIDE datasets). Third, we did not examine continuity in attentional function and neural substrates among participants with clinical-level traits. Because we recruited only participants with subclinical traits, we could only evaluate continuity based on comparisons with the results of prior studies. Further studies are needed to perform direct comparisons between patients with subclinical traits versus those with clinical traits. Finally, the present study used the CPT to assess attentional function. However, this test is known to be less sensitive in adolescents with ADHD than in children with ADHD. Moreover, there was no significant difference in performance between high-functioning individuals with ADHD and people with typical development128. Reports suggested that the task difficulty of CPT may be too low to measure attentional function. Some studies have suggested that adding complex distractor stimuli to the CPT can increase the differences in performance between people with ADHD and typically developing individuals. In the present study, we considered the possibility that ADHD traits may facilitate the maintenance of attention. However, future studies should compare performances between individuals with ADHD and typically developing controls using more complex attentional tasks.Our study has some limitations. First, the sample size was relatively small, and the number of individuals with clinical ASD or ADHD traits was quite small. A total of nine participants were screened as positive for ADHD according to Part A of the ASRS. Using a cut-off score of 33 for the AQOur fMRI findings revealed that ASRS and AQ scores were differentially associated with the FCs of different LSBNs, indicating differential mechanisms of attentional processing in individuals with subclinical ADHD or ASD traits. This study indicates that brain imaging may assist in differentiating ADHD and ASD traits when assessing inattentive symptoms associated with these disorders.Supplementary Tables.


The children’s academic performance and behavioural challenges at home and school were positively correlated with the parental ADHD scores for ‘inattention’ and ‘problems with self-care’ subscales. Conclusions: A total of 33% of ADHD children had parents with ADHD. ADHD children with ADHD parents were more likely to have behavioural problems at home and more severe ADHD. However, no statistical significance was noted with medication adherence.Background: Attention deficit hyperactivity disorder (ADHD) is characterised by inattentiveness, hyperactivity, and impulsivity. Up to half of the affected children have a parent with ADHD. In this study, the risk of ADHD among parents of ADHD children was estimated. The associations between parental ADHD and child ADHD severity and medication adherence were determined. Methodology: Parents of children and adolescents diagnosed with ADHD attending the University Kebangsaan Malaysia Medical Centre (UKMMC) were recruited between June to August 2022 and the administered Conners’ Adult ADHD Rating Scale (CAARS) self-report short form, Vanderbilt ADHD Parent Rating Scale (performance section), and Medication Adherence Report Scale (MARS). Results: Forty-five children with ADHD were recruited and 15 out of 45 (33%) parents were detected to have ADHD. ADHD severity was worse in children with ADHD parents for total severity (mean of 34.67 vs. 29.13,  Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by inattentiveness, hyperactivity, and impulsivity with an estimated prevalence of 5.3–7.1% in children and adolescents ,3. In MaIt has been estimated that approximately 4% of adults in the general population meet the diagnostic criteria for ADHD . In apprADHD has high inheritance, which has been reported to range between 71 and 90% for heritability . PreviouMany adults with ADHD also remain undiagnosed due to a lack of public and self-awareness, resulting in a delay in seeking medical attention. As a result, they may struggle to cope in managing their home, family, and work life.Parenting may be affected tremendously in adults with ADHD. It can add to the daily struggle as they grapple with the high needs of parenting as well as attending to their ADHD children’s high-level of needs, even as they try to cope with their own challenges in the work environment and at home. Deficiency in organisational skills, struggling with punctuality, and difficulty in maintaining focus as well keeping emotions in check are well-recognised characteristics of ADHD. These parents may face even more difficulties when living with high expectations in a demanding environment whilst having to deal with their own ADHD characteristics. They may struggle to keep up with their children’s schedules, in maintaining adherence to treatment modalities, and managing their behaviours as parents. Johnstone et al. 2012)  found th12  foundA multi-disciplinary outlook is advocated for the management of ADHD, with drug therapy playing a major role . It has The aim of our study was primarily to estimate the risk of ADHD among parents of children and adolescents with ADHD as well as determine the association between the parents’ ADHD diagnosis and the correlation of parental ADHD rating scores with severity in ADHD children. In addition, we sought to determine the association between ADHD diagnosis and the correlation of parental ADHD rating scores with their children’s adherence to ADHD medications.We hypothesize that parents and children both with ADHD and higher adult ADHD symptom scores are associated with more severe symptoms and poorer compliance to medication in children with ADHD.n = [Np(1 − p)]/[(d [21−α/2 × (N − 1) + p × (1 − p)]. The number of ADHD patients followed-up over a 3-month period under the Child and Adolescent Psychiatry Clinic and Child Development Clinic was 120 children. With a 95% confidence interval level and expected prevalence of parents with ADHD among children with ADHD based on previous studies was 0.4 as well as precision was 5%. Thus, the calculated sample size was 90.The sample size was calculated based on the formula  p)]/[ who were under the follow-up care of the Child and Adolescent Psychiatry Clinic and the Child Development Centre (CDC) at University Kebangsaan Malaysia Medical Centre (UKMMC) . The stuQuestionnaires were distributed to parents and the attending physicians of children and adolescents with ADHD that were enrolled in this study. The questionnaire package included demographic information and validated questionnaires. The demographic data for the parent and child included basic information such as the highest parental educational level, family income status, and any existing medical or mental health conditions in the parent(s). Next, the parents were asked to answer the validated questionnaires including the Conners’ Adult ADHD Rating Scale (CAARS) self-report short form for themselves, and the performance section of the Vanderbilt ADHD Parent Rating Scale and parent-reported Medication Adherence Report Scale for their children. The child’s attending physician were asked to complete the Clinical Global Impression-Severity (CGI-S) and clinician-standardised questions to assess the severity and medication adherence from the physicians’ point of view.The Conners’ Adult ADHD Rating Scale (CAARS) self-report short form ,33 questhttps://www.nichq.org/resource/nichq-vanderbilt-assessment-scales (accessed on 1 July 2022). Items were scored on a Likert scale 1–5  with a total score of 60.In the Vanderbilt ADHD Parent Rating Scale (performance section) ,35, the The medication adherence report scale (MARS) ,37,38 isThe Clinical Global Impression-Severity (CGI-S) ,43,44 isThe clinician standardised questions to elicit medication adherence ,45 form t-tests were utilised to determine the association between parental ADHD diagnosis and disease compliance to medication in children with ADHD. The Pearson correlation test was used to study the correlation between the parents’ ADHD rating scale scores and their children’s ADHD severity scores and between the parents’ ADHD rating scale scores and their children’s adherence to ADHD medication. Pearson correlation coefficients of 0.5 and above were taken as strong correlation whereas from 0.3 to 0.49 was taken as moderate, below 0.29 was deemed as weak correlation, and a p value of <0.05 was deemed statistically significant.All analyses were performed using Statistical Package for the Social Sciences (SPSS) version 26. Categorical data were presented as frequency with corresponding percentages, and continuous data as the mean with standard deviation. Chi-squared tests and Student A total of 54 subjects were approached during the 3-month study period, of whom one refused and a further eight were excluded based on the exclusion criteria . The final total sample size comprised 45 participants.p = 0.047) as well as behaviour at home  to a statistically significant level compared to those whose parents did not have ADHD. For the subscales of ‘academic’ and ‘school behaviour’, children whose parents had ADHD had higher mean scores, reflecting a greater disease severity than those whose parents did not have ADHD, but not to a statistically significant level.In p = 0.67) or a statistically significance level compared to those whose parents did not have ADHD based on the clinician’s rating.In p = 0.207). For the clinician-rated medication adherence, both groups had similarly good adherence reported at 61.5% and 63.2%, and there was no statistically significant difference between the two groups either (p = 0.930).p = 0.036), school behavioural issues , home behavioural issues , and total severity scores . The ‘problem with the self-concept’ subscale was also moderately correlated to the statistical significance of p < 0.05 with the school, home behavioural, and total severity: school behavioural issues , home behavioural issues , and total severity scores . Finally, the ADHD index subscale similarly had moderate correlation with academic performance, school behaviour, and total severity with statistical significance: the children’s academic performance , school behavioural issues , and total severity scores . There was no correlation observed between the CAARS subscales or total score with the clinician rated CGI-S score. The worldwide prevalence of ADHD in the general population has been reported at 2.8% based on a more recent study by Fayyad et al. in 2016 . Recent Previously published research has demonstrated the impact of parental ADHD on their children’s ADHD symptoms and their progress ,48. DawsAgha et al., in 2013, found that parental ADHD scores were correlated with clinician-rated severity . In our Medication adherence is linked to parental support for the child to be able to have a structured environment and thus able to organise themselves well enough to remember to take their medications regularly ,50. A stWe recognise that our study had its limitations. First, the small sample at only 50% might not be powered to ascertain differences for various aspects of the study, especially associations with small effect sizes. There may have also been reporting bias from the parents as well as clinicians due to the subjective perception of performance. Although CAARS is a diagnostic tool used in our clinical setting, it is often used in conjunction with clinical assessment. In this study, a full diagnosis of the at-risk parents was not performed and only the CAARS score was utilised. Thus, the measured value may not truly reflect the true prevalence of ADHD in this population. Furthermore, parents who scored a high CAARS score would require full formal assessment by a trained clinician in order to have a recognised diagnosis of ADHD. Finally, we did not examine both of the parents’ ADHD rating scores, and thus the results were limited to the one available parent that was recruited into the study. The other parent that was not evaluated may have had significant scores that could have the potential significance on the severity of ADHD and adherence to the behavioural, pharmacotherapy, and other modalities of management in ADHD children.In the future, to improve this study, a larger sample size from a more representative sample including other centres should be obtained. Both parents should be assessed for ADHD risk and potentially compared. A more comprehensive assessment of the child’s severity with measures from not only parents, but teachers would make the severity assessment of the children more robust.The total prevalence of parental ADHD in this study was estimated at 33%. There is a significant risk of ADHD in parents of children with ADHD and it may adversely impact the child’s condition and management. Clinicians should strongly consider screening parents for ADHD when a child is diagnosed with ADHD. ADHD in parents is associated with worse outcomes in children with ADHD at home, at school, and for overall functioning. High scores for inattention, problems with self-concept, and ADHD index in the parents’ ADHD subscales correlate with a greater severity of behaviour problem at both school and home as well as overall difficulties for children with ADHD.