Bartonella are obligate parasites of vertebrates. Their distribution range covers almost the entire world, from the Americas to Europe and Asia. Many Bartonella species use rodents as reservoirs, and while much is known about Bartonella infection of rodents in central Europe, its extent is poorly understood in Eastern Europe.Bacteria of the genus Apodemus flavicollis, Myodes glareolus, Microtus arvalis, Apodemus agrarius, Apodemus sylvaticus) in the Chernobyl Exclusion Zone in Ukraine. Total of 36 small mammals were captured in September 2017.The present study examines five rodent species (Bartonella spp. was 38.9% (14/36) in rodents. Obtained four sequences from Apodemus flavicollis, were identical to Bartonella grahamii and B. taylorii.The overall prevalence of Bartonella spp. in rodents in the Chernobyl Exclusion Zone, Ukraine by molecular methods. The sequences show similarity to Bartonella strains occurring in Europe.This is the first report to confirm the presence of  Bartonella comprises small, Gram-negative bacteria which act as obligate intracellular parasites of vertebrates. More than 30 species, as well as three subspecies, have been described [Bartonella species have been associated with emerging diseases, the symptoms of which have a broad spectrum of clinical syndromes ranging from cat-scratch diseases to potentially fatal conditions, such as endocarditis [Bartonella species, including Bartonella elizabethae, Bartonella tribocorum, Bartonella grahamii, Bartonella vinsonii subsp. arupensis and Bartonella washoensis, are causative agents of human infections [B. elizabethae and B. tribocorum are specific to Rattus spp. [Bartonella species are transmitted by blood-sucking arthropods, such as sandflies, lice, fleas [Bartonella spp. have also been found in keds: Lipoptena cervi, Melophagus ovinus [Bartonella species have also been common in selected rodents, ruminants and carnivores; the prevalence of infection in these animals group may be very high [Bartonella species have been described and can be a threat to people. A high prevalence of Bartonella spp. has been observed in rodents in Asia 51%, in America 42% and in Africa 29% [Bartonella grahamii, B. taylori, B. birtlesii and B. rochalimae. In Central Europe, the prevalence of Bartonella infection in small rodents is quite well known. Studies report a 65.8% prevalence in Germany, 9.0% in Slovenia, 3.3% in Croatia, 23.7% in Lithuania and 28% in Poland [Bartonella infection in rodents in Eastern Europe, our understanding of their distribution remains incomplete. Therefore, the aim of this study was to analyse the occurrence of Bartonella species in rodents living near the abandoned city in the Chernobyl Exclusion Zone, Ukraine.The Chernobyl Exclusion Zone (CEZ) was established around the site of the 1986 disaster at the Chernobyl nuclear power plant. The area around the nuclear power plant was irradiated. Since this time, the inhabitants were resettled to another part of Ukraine and wild animals were allowed to return to the abandoned by people areas. The direct consequences of radioactive pollution, as well as the evacuation of the human population and cessation of agriculture and forest management resulted in significant ecological changes, such as the spontaneous restitution of the original natural habitats, the plant and animal populations, and their interdependences. The wild mammal population is now numerous and strong. Many aspects of the consequences of radionuclide contamination of wild animals have been studied during the short time since the catastrophe. For instance, Frantsevich  examinesescribed , 15. Mancarditis , 43, 44.fections , 22, 43,e, fleas , 31, 39;s ovinus , 12, 40.ery high  and stroery high . Many anrica 29% , 28, 42.rica 29% , 37, 41.n Poland , 37, 41.2 and falls entirely within the Polesie Lowland, Russian Plain. The climate in the CEZ is humid, with relatively mild winters and warm summers. The mean annual temperature is 5–7 °C, with a mean temperature in July of 18 °C (max. 32 °C), and in January of − 6 °C (min. − 25 °C). The annual precipitation ranges from 550 to 750 mm. The snow cover lasts on average c. 50 days per year. The mean depth of the snow cover is 12–13 cm [The CEZ is located c. 200 km N of Kiev, Ukraine , 123 m above sea level. This zone cover an area of 2600 kmThe CEZ is surrounded by metal fencing 2 m high. Approximately c. 50 peasants also are still living in the CEZ; only few other persons have constant access to this area. Before the nuclear disaster, the CEZ consisted of farmland with patchy areas of forest. Currently, ca. 60% of the area is covered with forests, 50% of which is pine forest and remaining consist of abandoned arable grounds, meadows, pastures and human settlements.Myodes glareolus)—13 animals, 4 m/9f , yellow-necked mice (Apodemus flavicollis)—12 animals, 4 m/8f (33.3%), and striped field mice (Apodemus agrarius)—seven animals, 4 m/3f (19.44%). Rarer species included the common vole —three animals, 2 m/1f (8.3%), and one male wood mouse (Apodemus sylvaticus).Rodents were collected to the 3.2 km west of Chernobyl city  in September 2017, using Sherman live-traps. The traps were checked two times per day. The captured mammals were transported in their traps to the laboratory for examination. The autopsies were carried out under terminal isoflurane anesthesia. Blood was collected for smear samples and spleen samples were taken. These were stored at − 20 °C and transported to the laboratory. The following animals were captured, in order of quantity: bank vole (Bartonella spp. These primers (1400F and 2300R) amplify an 850 bp fragment of the rpoB gene [2). In the negative control, nuclease-free water was added to the PCR mix instead of the tested DNA. Both reactions were performed using the DNA Engine PTC-200 Thermal Cycler  according to the following program: initial denaturation was performed at 94 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 10 s, annealing at 60 °C for 10 s and extension at 72 °C for 60 s. The final extension was performed at 72 °C for 7 min and then kept at 10 °C. The PCR products were visualized on a 1.0% agarose gel stained with ethidium bromide. Visualization was performed using ChemiDoc, MP Lab software . The resulting product was compared using the Nova 100 bp DNA Ladder Novazym . The PCR amplicons were purified using a QIAEX II Gel Extraction Kit , sequenced in both directions by Genomed (Poland), and contiguous sequences assembled using ContigExpress, Vector NTI Advance 11.0 . The derived sequences were submitted to the GenBank database under the accession numbers MH669401-MH669404. To the phylogenetic studies, there was used Bayesian inference (BI) analysis with MrBayes version 3.2 [The DNA was isolated from spleen using the AX Tissue Mini Kit  according to the manufacturer’s protocol. For the PCR test, the primers rpoR and rpoF were used to detect poB gene . The PCRpoB gene . The PCRsion 3.2 . AnalysiBartonella spp. infection, with the total infection being 38.89% (14/36). In Apodemus flavicollis, the prevalence was 75% (9/12). The prevalence in Microtus arvalis  and Apodemus agrarius  was similar while the prevalence in Myodes glareolus was 15.38% (2/13). No infected individuals were found in the case of Apodemus sylvaticus. Two sequences (Bartonella sp. 1: MH669401-MH669402) obtained in this study share 100% similarity with B. taylorii isolated from Apodemus flavicollis from Lithuania (GenBank: MH547315) and B. taylorii strain M6 from France (GenBank: AF165995). In turn, two sequences (Bartonella sp. 2: MH669403-MH669404) showed 100% similarity with B. grahamii isolated from Myodes glareolus from Lithuania (MH547328) and 98.1% similarity with Bartonella sp. isolated from Clethrionomys rufocanus from Japan (AB290276)  [Apodemus flavicollis was high (75%), and was similar in another countries: Poland (66.6%) [Myodes glareolus was 33.3%. This result was identical in Lithuania [Microtus arvalis, the prevalence of Bartonella spp. infection was 33.3%. This value is comparable with that obtained previously from Poland (27.7%) by Pawełczyk et al. [Apodemus agrarius has been poorly studied. Our studies show prevalence higher (28.57%) than in Slovakia [Apodemus sylvaticus, but we obtained only one animal. Two sequences (Bartonella sp. 1) obtained in this study are identical with B. taylorii isolated from Apodemus flavicollis from Lithuania (GenBank: MH547315) and B. taylorii strain M6 from France (GenBank: AF165995). This suggests that this Bartonella species occurs throughout Europe. Two sequences (Bartonella sp. 2) are identical with B. grahamii isolated from Myodes glareolus from Lithuania (MH547328) and 98.1% similarity with Bartonella sp. isolated from Clethrionomys rufocanus from Japan (AB290276). There is the first evidence of the infection of rodents in Chernobyl Exclusion Zone with Bartonella spp. The similarity of obtained rpoB gene sequences to isolates from Lithuania, as well from France, published in GenBank, indicates that rodents in CEZ are infected with common species of Bartonella. The follow-up studies are necessary to obtain the number of samples enough to statistical analysis, as well study conducted whole year, to describe the seasonal dynamic.Our findings indicate the presence of  22.4%) , and low (66.6%) , Slovaki (66.6%) . The obtithuania  and simi% , and lithuania  or from ithuania . In the k et al. , and in k et al.  identifiSlovakia  and Lithm Poland .7% by PaBartonella common in Europe. The study presents the first record of bacteria genus Bartonella in rodents in the Chernobyl Exclusion Zone.Our findings indicate that rodents in the Chernobyl Exclusion Zone are infected by strains of


SLC6A3) was proposed as a candidate gene for Attention-Deficit/Hyperactivity Disorder (ADHD). We investigated the effect of the SLC6A3 variants on cognitive performance in ADHD and healthy children and teenagers. Participants completed cognitive tasks measuring attentional switching, selective and sustained attention, and effectiveness of alerting, orienting and executive attention. We estimated the effects of 40 bp variable number of tandem repeat (VNTR) polymorphism located in the 3′ untranslated region (3′ UTR)  of the SLC6A3 gene, ADHD diagnosis, age, and their interactions as predictors of cognitive performance. ADHD children demonstrated deficits in most of the examined attention processes, persistent within the examined age range (9–16 years). No significant effects were observed for the interaction of ADHD and the SLC6A3 polymorphism, but the results revealed a significant main effect of SLC6A3 genotype in the entire research sample. Subjects carrying 9R allele performed the switching task significantly worse in comparison to children with 10R/10R or 10R/11R genotype. SLC6A3 polymorphism moderated age-related improvements in orienting and attentional switching. Results suggest that SLC6A3 genotype influence these attentional/cognitive functions which deficits are not the key symptoms in ADHD.Neuropharmacological and human clinical studies have suggested that the brain dopaminergic system is substantively involved in normal and pathological phenotypes of attention. Dopamine transporter gene ( The bottom-up mode is driven by salient stimuli, which evoke automatic allocation of attention. In contrast, top-down processes rely on goals, cognitive strategies and effortful control, aiming to prioritize some stimuli over others. This distinction is clearly reflected in the Posner and Petersen (1990) model of attention, where three functionally and anatomically independent networks are proposed, each of them modulated by a different neurotransmitter2. According to this model, alerting is controlled mainly by acetylcholine, orienting by norepinephrine, and executive attention is dependent on dopamine (DA)3. It has been suggested that prefrontal DA and dopaminergic system-related genes play a dominant role in modulating top-down, but not bottom-up attention4.Attention is usually conceptualized as a set of cognitive processes responsible for filtering and selecting behaviorally relevant information. Hence, it constitutes a precursor of other higher-level cognitive abilities such as learning and memory. In various cognitive models of attention, information selection operates in two modes: automatic or bottom-up versus controlled or top-down5. They might also occur in children with other conditions and even in a healthy population. It has also been shown that children with ADHD may not exhibit impairments of executive functions6. These facts highlight the relevance of searching for the sources of variation in attentional functioning.The quality of attentional functioning varies widely amongst individuals and might also be significantly impaired in some psychiatric disorders, sometimes to the extent suggesting treating it as an endophenotype of the disease, like the Attention-Deficit/Hyperactivity Disorder (ADHD). Even though a variety of executive functions is impaired in children with ADHD, these deficits are not ADHD-specific8. Investigations of the genetic basis of neuropsychiatric disorders focused on variants in genes regulating neurotransmitter efficiency, particularly DA neurotransmission9. The most important DA regulator is the DA transporter (DAT)10 as it terminates DA signaling at the synapse through reuptake of DA into presynaptic terminals11. Thereby DAT regulates the concentrations of both extracellular DA at the synapse and intracellular DA within the presynaptic neuron, and modulates spatial and temporal dynamics of the DA signal14. Therefore, variations in the activity, density, levels of expression and function of DAT provide critical determinants of the synaptic concentration, the availability and also function of DA15. Through DA homeostasis, DAT preserves normal neurological function within the dopaminergic pathways of the central nervous system16. In prefrontal areas norepinephrine transporter (NET), is also capable of mediating the reuptake of DA, which indicates its importance in regulating prefrontal brain activity17. Both DAT and NET are established targets of therapeutics. Atomoxetine is a selective NET blocker, whereas psychostimulants exert their action via interference with both transporters function, resulting in an increase of extracellular DA and norepinephrine levels21.While experience during development, education and other environmental factors might explain some variation in cognitive functions, we know that it is partly determined by genetic factorsSLC6A3 gene; also known as DAT1), located in the 3′ untranslated region (3′ UTR), with repeat numbers between 3 and 13. The 9- and 10-repeat (9R and 10R) alleles are the most frequent in the population22. Imaging studies in non-human primates have documented differences in DAT expression that can correlate with VNTR genotype23. In humans it has been linked to striatal DAT availability, however the findings are inconsistent. Meta-analyses suggest that the 9-repeat allele is associated with higher DAT activity in the striatal brain regions when compared against the 10-repeat allele25.A functional 40 bp variable number of tandem repeat (VNTR) polymorphism was identified in the human gene coding DAT 33. A recent study questioned the association of SLC6A3 gene polymorphism with ADHD while indicating at the same time that the 10R/10R genotype in patients with ADHD affects processing speed and cognitive flexibility34. Similarly, an earlier study on ADHD reported that children homozygous for the SLC6A3 10R allele performed more poorly on a sustained attention task than subjects with other genotypes35.Many studies have investigated the association of this VNTR with ADHD, but with highly variable results. The 10R/10R genotype of 36, with the decline of the DAT availability with age reported most frequently37. Worth noticing here, longitudinal studies on ADHD patients have shown that symptoms of hyperactivity significantly decrease with age38. As the contribution of risk genes to ADHD may not be constant across the life course, the age should be considered an important factor in the analysis of genetic underpinnings of ADHD.The effect of age has been rarely considered when studying the relationship between ADHD phenotype, neurocognitive functioning and the genetic factors. Expression levels of genes can differ across different stages of developmentSLC6A3 and individual differences in attentional processes of healthy and ADHD children and teenagers. We assessed the broad spectrum of these processes with a battery of attentional tests and computerized procedures including measures of sustained and selective attention, attentional switching and efficiency of three attentional networks responsible for alerting, orienting and executive attention. Second, we investigated the moderating role of age and SLC6A3 3′ UTR VNTR variants in diagnosis-related effects.The aim of the study was twofold. First, we focused on the relationship between age= 13.11 ± 2.04; 12 females). The healthy control group was age- and sex-matched to ADHD group . ADHD subjects were recruited among outpatients of the psychiatry clinic at Public Pediatric Teaching Hospital in Warsaw, Poland. The diagnosis was conducted at the clinic by an experienced team of psychiatrists and psychologists according to the diagnostic criteria of the DSM-IV TR  as previously described39 and included: an interview with patients’ parents, Diagnostic Structured Interview for ADHD and Hyperkinetic Disorder according to ICD-10 and DSM-IV TR40, the Behavioral Disorders Supplement of Diagnostic Interview Kiddie-SADS-Present and Lifetime Version, as well as observation of patients’ behavior. The intensity of the ADHD symptoms was rated by both parents and teachers using the ADHD Rating Scale40. The comorbidity diagnosis was based on the diagnostic criteria for ICD-10 , and the diagnosis was verified during no less than three appointments. If the results of all diagnostic methods were consistent, the diagnosis was confirmed, and such patients were invited to take part in our study. The ADHD group included children diagnosed with combined (n = 53) or predominantly inattentive ADHD subtypes (n = 21). In 66% of ADHD participants at least one comorbidity was diagnosed. The most common was oppositional defiant disorder (ODD) present in 47% of ADHD participants. 28% of patients were diagnosed with specific developmental disorders of scholastic skills. The inclusion criteria for our ADHD group were: a confirmed diagnosis, no previous head injuries with a loss of consciousness, and no neurological disease (e.g. epilepsy) or psychiatric disorders, except for ODD or specific school disabilities (e.g. dyslexia), which are highly comorbid in ADHD cohorts. All ADHD participants were asked to abstain from taking stimulant medication at least 24 h before testing.A total of 150 children and teenagers (Caucasian), aged 9–16 participated in the study. The clinical group consisted of 74 participants with an ADHD diagnosis  with a final reaction volume of 20 µL  and 0.2 µM of each primer: F 5′-TGTGGTGTAGGGAACGGCCTGAG‐3′ and R 5′-CTTCCTGGAGGTCACGGCTCAAGG‐3′41. Thermal cycling consisted of a 2 min initial denaturation phase at 94 °C followed by 35 cycles of 30 s at 94 °C, 60 s at the annealing temperature of 60 °C, 45 s at 68 °C, and a final extension step of 5 min at 68 °C. Amplified DNA was separated on a 2% agarose gel and visualized using the Gel Doc EZ system . Five alleles of the SLC6A3 were observed: the 7-repeat (360 bp), 8-repeat (400 bp), 9-repeat (440 bp), 10-repeat (480 bp) and 11-repeat (520 bp). Genotypes for all participants were collapsed into categories based on the presence or absence of the 9R allele .Subjects provided a saliva sample into an Oragene collection and preservation kit . Genomic DNA was isolated using extraction kit  according to the manufacturer’s instructions. The For further analyses subjects were divided into two Genotype groups: the 9R group included 9R/9R (n = 6), 9R/10R (n = 68), 8R/9R (n = 1) and 7R/9R (n = 2) genotypes; the 10R group included 10R/10R (n = 72) and 10R/11R (n = 1) genotypes.The cognitive diagnosis included two computerized procedures and one clinical battery of tests. The choice of measures allowed us to cover all essential facets of attentional functioning using well-validated measures.42. In this task, randomly selected single-digit numbers are displayed on a computer screen. The subject is asked to respond as fast as possible to every digit  except the digit ‘3’ . Responses are given by pressing a computer mouse button with the index finger of the dominant hand. When digit ‘3’ is presented, the subject is asked to withhold the motor reaction. A total number of 200 go, and 25 target trials were presented preceded by 25 training trials. The procedure is presented in Fig. To measure sustained attention, we used SARTAttention effectiveness measures included accuracy and reaction time (RT) indices such as: commission (failure to withhold response to the target) and omission (failure to respond to go digits) errors, mean RT for correct go-trial responses (GoRT), and RT variability (defined as the SDgoRT/MgoRT).43 and flanker tasks44. We used a classic variant of the task as proposed by Fan et al.45  attention (Sky Search - SSG and Map Mission - MapMu), sustained attention , and attentional control/switching (Creature Counting - CL and Opposite Worlds - OA). We did not use the Code Transmission and Score DT, which involve the processing of verbal material and Polish language versions are unavailable. The scores obtained in the Sky Search Dual Task were dropped from the analysis, due to serious problems with following the test instructions by the majority of participants.The TEA-Ch was designed to assess various components of attention in children47. R environment ver. 3.4.248 was used in all analyses.A series of linear regression models were estimated with genotype variant (9 vs 10), diagnosis (ADHD vs control), age (centered), and all their interactions as predictors of cognitive performance. A separate model was estimated for each of the attentional indices described above. In the encoding of nominal variables, sum contrasts were used to facilitate the interpretation of lower-order effects. Due to the presence of heteroscedasticity, all p-values were estimated using heteroscedasticity-consistent standard errors2 = 0.06 . Power calculations were performed in G*POWER (ver. 3.1)49. Calculations were based on the assumption that one extra effect is added to a model containing 5 predictors and R2 for the full model equals 0.20. .Our design offered satisfactory power (80%) in detecting effects larger than fDue to a large number of dependent variables, outlier removal criteria were applied ona task-by-task basis leading to small fluctuations in the final sample size across analyses.In the case of ANT prior to analysis for every subject we removed RTs for correct trials exceeding three standard deviations (SD) above or below the mean. Afterwards, we excluded from the analysis subjects whose performance indices exceeded 3 SD above or below the mean of overall accuracy in the task or mean networks score .In SART before RTs calculation we removed RTs faster than 100 ms. Then outliers were defined based on their mean accuracy and RT indices - if the mean values were higher or lower than 3 SD. In the TEA-Ch no filtering criteria were applied, however small differences in sample size are related to the missing scores in some of the subtests . The number of participants included in the analysis for each dependent variable is reported in Tables p-values for the interaction > 0.1), we decided to report detailed results of more parsimonious models including main effects, and all two-way interactions. Detailed results are reported in Table As none of the regression models revealed a significant third level interaction effects of Age, ADHD diagnosis and Genotype , sustained attention (WdW) and selective attention (MapMu) measured by TEA-Ch Table , Fig. 4.The main effect of group was also significant for total accuracy in ANT, and effectiveness of executive attention and alerting indicating deficits of these processes in ADHD group in comparison to Controls but not in orienting processes . The association between SLC6A3 10R/10R or 10R/11R genotypes and better performance in cognitive tasks50 appears counterintuitive, as the 10R allele has been reported as the risk allele for ADHD51, and related to poorer performance in cognitive tasks28. However, the debate on the role of 10R is far from being settled with studies reporting both opposite51 and null30 effects. In our study, we did not observe effects of genotype on sustained attention neither in the SART nor for the executive attention assessed with the ANT. However, SLC6A3 genotype showed significant associations with the specific orienting measure in the ANT. These results are in contrast to the reports showing associations of SLC6A3 with executive aspects of attention, and not with orienting26 or those suggesting no association between dopaminergic genes and orienting52. However, more recent data provide evidence linking DA signalling53 and the SLC6A3 gene54 with involuntary orienting.Significant effect of the 55. Secondly, there is evidence that set-shifting and other executive functions, such as cognitive flexibility and attention, are mediated by dopaminergic activity56, and that DAT plays an important role in these functions, as was shown in the animal model of ADHD58. In contrast to the study by Garcia-Garcia et al. (2010), who reported that individuals genotyped 10R/10R show more rigid behavior in a task-switching protocol when compared to participants with the 9R allele, we have shown that 10R/10R and 10R/11R children outperform children with 9R allele on TEA-Ch subtests assessing switching abilities (CL and OA)59. It has been shown previously that lower levels of DAT availability are associated with neurocognitive deficits60. The 10R allele, compared to the 9R allele, has been associated with increased transporter protein expression both in vitro61, and in imaging research in humans in vivo25; also the DAT mRNA expression in postmortem midbrain tissue was shown higher for homozygous 10R carriers62. However, results showing the opposite allelic associations have been reported63 and meta-analysis of neuroimaging studies shown increased DAT activity for 9R carriers in striatal brain regions25. Genotype variant moderated the effect of age in our study, such that 9R allele carriers started from a significantly worse level than the 10R group, but finally reached the same level at a higher age.It is important to underline, that intentionally controlled attention shifting/switching shares some of the neural underpinnings with orienting. Firstly, similarities among neuroimaging brain activations for shifting and orienting have been reported64 and no significant age changes in children 7–11 y.o. have been reported65. Our results revealed changes with age in orienting moderated by SLC6A3 genotype. At the youngest age, SLC6A3 9R allele carriers performed worse in comparison to 10R group subjects but improved their performance with increasing age; whereas the performance of 10R group did not significantly change with age. Both alerting and executive attention assessed with ANT improved with age. The alerting network starts developing early, but the network undergoes further development during late childhood in relation to maturation of frontal cortex66. The executive attention network develops later, during childhood and in early adolescence67, which is related to the progressive maturation of the anterior cingulate cortex and lateral prefrontal cortex66.Almost all indices of attentional processes revealed a typical age-related improvements; the only exceptions were orienting and average RT measures in SART. The orienting network starts to develop very early in newborns68. These changes reflect higher effectiveness of voluntary attentional control in older subjects, as it is well established that throughout development endogenous factors come to play an increasing role in determining the locus of attention67.The effect of age was significant for all TEA-Ch subtests. Improvements in  results with increasing age were expected, as the TEA-Ch has been found to be an age-sensitive tool69. Our results, showing age effect in the omission and commission errors number and RT variability, are in agreement with these reports. The lack of effect of age on mean RT might be related to the speed-accuracy tradeoff and certain ambiguity of this particular performance index in SART. Faster average performance in SART might reflect more effective control of the motor reactions, but also poorer inhibitory control. These two factors cannot be easily disentangled and - therefore - mean RT is usually not treated as key performance measure in SART.Research on age-related changes in performance on the SART task is scarce. Measures of sustained attention improve rapidly during the early school-age years and modestly after the age of 1270. They are also congruent with Posner and Raichle’s71 statement accentuating lack of empirical support for the involvement of the orienting network in the psychopathology of ADHD.The results of ANT showing poorer alerting and executive attention in ADHD children in comparison with controls and no differences in orienting are in agreement with previous reports72 and is fully consistent with other research on sustained attention in children with ADHD73. High within-subject intertrial variability, fluctuations in attention, is the most marked symptom of ADHD74. Contrarily to the Bellgrove et al. 35, we did not observe significant genotype-diagnosis interaction. However, it is important to note that our study involved much larger sample and controlled the effects of SLC6A3 in both studied groups (not just in the ADHD subjects) which was not the case in the cited study.The poorer performance of ADHD children in comparison with controls in SART, as observed in our study, has been reported beforee.g.76. ADHD is neuropsychologically and cognitively heterogeneous condition77, however, impairments in specific attentional abilities, such as sustained attention and executive attentional functions, seem to be particularly pronounced5. The results of our study, suggesting the presence of deficits in almost all attentional functions measured with three tests used in the study, are in line with hypotheses suggesting the existence of multiple deficits in ADHD78. Our analyses did not reveal significant interactions of Age and ADHD Diagnosis for any measure of attention. Instead, we observed two additive effects of Age and Diagnosis. The developmental pattern of attentional functions was similar in ADHD and healthy controls, and the lack of interaction implies the presence of persistent attentional deficits in ADHD subjects within the examined age range.Differences between ADHD patients and controls on certain subtests of TEA-Ch were found before. However, the pattern of attention deficits in ADHD across studies was inconsistentSLC6A3 gene polymorphism influence these attentional/cognitive functions which are not involved in the emergence of ADHD symptomatology. For switching, both Genotype and Diagnosis effects have been revealed. However, explanations for poor performance of ADHD subjects in CL and OA tasks might include motivational factors leading to delay aversion79 and non-optimal regulation of activation during task performance80 - thus deficits in functions distinct from the ones influenced by SLC6A3 gene polymorphism.The lack of interaction of Genotype and Diagnosis and the effect of Genotype but not Diagnosis for orienting suggest that the 81. Secondly, the cross-sectional character of the design did not allow us to study the dynamics of developmental changes in attentional functioning. Future longitudinal research investigating the relationship between genotype, changes in ADHD symptomatology and the development of attentional processes would be necessary to establish more clearly the causal contribution of all these factors.Some limitations of our study should be mentioned. Firstly, our sample size might offer statistical power too low to investigate subtle genetic influences. Distribution of sex and ADHD subtypes did not allow us to analyse their effects. However, we run supplementary analysis on male participants only and its results closely resembled the ones reported in the paper. In particular, there were no changes in any of the effects involving the genotype. Additional analysis within the ADHD group did not reveal any effect involving subtypes, which might be explained by either the limited sample size and power of between-subtype comparisons, or non-existence of such effectSLC6A3 polymorphism, age and ADHD diagnosis as well as the exploratory nature of our study. We are aware that some of the reported results might be false-positives. However, this approach increased the chances of revealing significant effects of SLC6A3. In this context, the fact that not a single interaction involving both SLC6A3 and ADHD was detected, speaks strongly for the main thesis of our paper, that is the independence of the effects of dopamine transporter gene and ADHD diagnosis in shaping attentional functioning across the studied age-range.Nonetheless, we still find regression-based approach more appropriate as the possibility of studying all the higher-level interactions allows us to address, at least partially, the limitations of simple between-group comparisons used in the majority of existing literature. It is also important to justify our decision not to report familywise error-corrected p-values, even though we ran multiple regression models. It was motivated by the scarcity of data on the interplay of 83. It has been shown previously that ADHD children following MPH withdrawal display deficits in comparison with both healthy controls and with ADHD children on MPH84. One could argue that the withdrawal might cause attentional impairments as a pharmacological effect. To our knowledge, there are no studies examining differences between the cognitive performance of children with ADHD prior to the commencement of MPH treatment and following a discontinuation of the medication. Nonetheless it has been shown that MPH treatment improves performance of ADHD children, when compared with healthy controls, however, children with ADHD does not reach an undisturbed level of attention80. The main goal of our study was to establish the role of SLC6A3 gene polymorphism in attentional functioning. Meta-analysis85 showed that there is no significant association between SLC6A3 polymorphism and response to MPH treatment. The meta-analysis also found no effects on dimensions of hyperactivity/impulsivity and inattention. Therefore we can assume that the medication effects could not influence the main results of the study.Patients were not drug-naive, hence they were asked to abstain from taking medication at least 24 h before testing, since methylphenidate (MPH) plasma concentration 24 h after drug administration is close to zeroSLC6A3 polymorphism moderated age-related improvements in orienting and attentional switching in the whole research sample, independently from ADHD diagnosis.This study is a significant contribution to the literature on the relationship between attentional functioning, its deficits in ADHD and the SLC6A3 3′ UTR 40-bp VNTR variants by examining additionally the effect of age. The developmental pattern of attentional functions was similar in ADHD and healthy controls. However, deficits observed in ADHD subjects were persistent within the examined age range. No significant effects were observed for the interaction of ADHD and the SLC6A3 polymorphism. The Supplementary table.


Many studies have questioned the stability in diagnosing of ADHD from childhood to adulthood, and the shared and distinct aberrant functional connectivities (FCs) between ADHDchild and ADHDadult remain unidentified. We aim to explore shared and distinct FC patterns in ADHDchild and ADHDadult, and further investigated the cross-cohort predictability using the identified FCs. After investigating the ADHD-discriminative FCs from healthy controls (HCs) in both child  and adult  cohorts, we identified both shared and distinct aberrant FC patterns between cohorts and their association with clinical symptoms. Moreover, the cross-cohort predictability using the identified FCs were tested. The ADHD-HC classification accuracies were 84.4% and 81.0% for children and male adults, respectively. The ADHD-discriminative FCs shared in children and adults lie in the intra-network within default mode network (DMN) and the inter-network between DMN and ventral attention network, positively correlated with total scores of ADHD symptoms. Particularly, inter-network FC between somatomotor network and dorsal attention network was uniquely impaired in ADHDchild, positively correlated with hyperactivity index; whereas the aberrant inter-network FC between DMN and limbic network exhibited more adult-specific ADHD dysfunction. And their cross-cohort predictions were 70.4% and 75.6% between each other. This work provided imaging evidence for symptomatic changes and pathophysiological continuity in ADHD from childhood to adulthood, suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.Attention-deficit/hyperactivity disorder (ADHD) often persists into adulthood, with a shift of symptoms including less hyperactivity/impulsivity and more co-morbidity of affective disorders in ADHD ADHD affects ~5% of children and adolescents, and two-thirds of children with ADHD continue to have persistent, impairing symptoms in adulthood2. Extant imaging studies have reported brain structural and functional differences between patients with ADHD and HCs, both in childhood and adulthood. Many functional magnetic resonance imaging (fMRI) findings heavily support that ADHD involves a distributed pattern of brain alterations4. According to Rubia et al., functional abnormalities in fronto-cortical and fronto-subcortical networks are core deficits in both children and adults with ADHD5.Attention-deficit/hyperactivity disorder (ADHD), one of the most common neurodevelopmental disorders, is characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Relative to healthy controls (HCs), ADHD patients were significantly impaired in psychosocial, educational, and neuropsychological functioning, and the dysfunction could not be well explained by other comorbid psychopathology6. Therefore, it can be inferred that except common neuro-mechanism, there may be some differences in the neural substrates of ADHD between children and adults. However, so far studies in ADHD have mostly focused on either ADHDchild alone or ADHDadult alone. Despite extensive fMRI studies, neither common nor the age-specific underlying imaging substrates of children and adults with ADHD have been identified. What’s more, the continuity of ADHD has been challenged by recent studies7. Identification of shared and distinct patterns of imaging substrates in ADHD with different ages are important to further insights into the neurological mechanism of ADHD.In adults with ADHD, there seems to be a shift of symptoms including persistence of inattention, less hyperactivity/impulsivity and more co-morbidity of affective disorders8. Recent studies have also confirmed this view. The findings of magnetic resonance diffusion tensor imaging studies indicate that white matter tract deficits in ADHD are not just confined to specific brain regions but affect the structural interconnections between regions and hence entire neural networks9. Similar results are shown in the largest neuroimaging meta-analysis10 of structural and functional experiments in children/adolescents with ADHD, including ninety-six eligible studies. The study analyzed pooled structural and functional, sub-analyses restricted to modality, and in-/decreased contrasts. No significant findings were yield in this analysis and no significant convergence was found in structural and functional regional alterations of ADHD. The overall findings highlight regional convergence is lacked in children/adolescents with ADHD, and network interactions rather than just regional abnormalities may be the underlying pathophysiology of ADHD. Although the main concept in imaging neuroscience have been assessing functional segregation in the human brain for many years, namely the localization of regionally specific functions, the pathophysiology of neuropsychiatric disorders is now being increasingly treated in a systematic perspective, i.e., function emerges from the interaction of segregated regions. Therefore, the analysis of functional connectivity (FC) is becoming more and more critical.Lots of researches have been done over the last three decades to explore potential imaging alterations and biomarkers of ADHD. However, their findings are often inconsistent or even conflicting. In recent years, a change in perspective in etiological models of ADHD has occurred, consistent with emerging concepts in other psychiatric disorders such as schizophrenia and autism. These models shift the assumed pathological focus from regional brain abnormalities to dysfunctions in distributed network interconnectivityt-test involved binary comparisons, which may be underpowered in identifying high-dimensional features11. Machine learning is known as automatically decoding regularities hidden in brain imaging data to predict disorders, which shows greater advantages in classifying psychiatric disorder characterized with deficits in heterogeneous distributed systems12. At present, ADHD discrimination with machine learning is mainly concerned with children14. Specially, more attention should be payed in adults with ADHD and the mutual prediction between children and adults with ADHD. Besides, high-dimensional FC matrixes with small samples as input of traditional machine learning algorithm directly would degrade the sorting performance on distinguishing ADHD from HCs. To overcome this drawback, a new feature selection method based on relative importance and ensemble learning (FS_RIEL) we proposed was used to identify both shared and age-specific FC patterns impaired in ADHD in this study.FC features often contains high dimensionality. Early methods such as Thus, the aim of the current study were to explore the shared and distinct FC patterns that could discriminate ADHD from HCs in both children and adults, their association with clinical symptoms, and further investigated the cross-cohort predictability using the identified FCs.15, a clinical and semi-structured interview based on Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV)16.A total of 35 children with ADHD and 28 HCs (7–14 years old) were recruited in the child dataset. The diagnosis was made by a senior psychiatrist based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version (K-SADS-PL)17 by a senior psychiatrist for potential comorbidity.A total of 112 adults with ADHD and 77 age- and sex-matched HCs (18–40 years old) were recruited in the adult dataset. The Conner’s Adult ADHD Diagnostic Interview based on DSM-IV was completed for the diagnosis of adults with ADHD. All adult participants also underwent the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I)All participants met the following criteria: (1) right-handed, (2) no history of head trauma with a loss of consciousness, (3) no history of neurological disorders or other severe disease, (4) no current diagnosis of major depressive disorder (MDD), schizophrenia, clinically significant panic disorder, bipolar disorder, pervasive developmental disorders, or mental retardation, (5) no excessive head movements (>3.0 mm of translation or degrees of rotation in any direction), and (6) a full-scale intelligence quotient (IQ) above 80. Furthermore, participants with any history of psychiatric disorders were also excluded as HCs.ADHD patients were recruited from outpatient clinics of Peking University Sixth Hospital and HCs were recruited by advertisement. Adult participants were scanned in Peking University Sixth Hospital  and Beijing Normal University . All child participants were scanned in Beijing Normal University. Verbal IQ, performance IQ, and full-scale IQ were measured by the Wechsler Child/Adult Intelligence Scale, Third Edition.18, rating one-four . This scale contains nine inattentive and nine hyperactive/impulsive symptoms of ADHD described in the DSM-IV. The higher the scores were, the more serious the ADHD symptoms were.The severity of inattentive symptoms, hyperactive/impulsive symptoms and total ADHD symptoms of all subjects were evaluated by the ADHD Rating Scale-IV (ADHD RS-IV)19 were used to assess the hyperactive/impulsive symptoms in child participants with ADHD. The CPRS is a widely used instrument for screening and evaluating ADHD-related symptoms as well as other behavioral problems frequently associated with ADHD in children. It contains 48 items and can be divided into six factors: conduct problems, learning problems, psychosomatic problems, impulsivity–hyperactivity, anxiety, and hyperactivity index. The parents rate each item using a 4-point Likert-type scale . The higher the score, the more severe the corresponding problem is.Besides, impulsivity–hyperactivity and hyperactivity index in the Conners’ Parent Rating Scale (CPRS)This study was approved by the Research Ethics Review Board of Peking University Sixth Hospital and Beijing Normal University. All subjects provided written informed consent and were fully informed about the research.20 . The first 10 volumes were discarded to allow for magnetization equilibrium. Subsequent data preprocessing included slice timing correction, head motion correction, spatial normalization to the MNI template, resampling to 3 × 3 × 3 mm3, smoothing using a 4 mm Gaussian kernel, temporal band-pass filtering (0.01 Hz to 0.1 Hz), nuisance signal regression , and head motion scrubbing . The registered fMRI volumes with the MNI template were divided into 273 regions according to the Brainnetome Atlas22 incorporating 210 cortical, 36 subcortical, and 27 cerebellar regions.Specific parameters for scanning were shown in the supplement. Two datasets were both preprocessed using the Data Processing Assistant for Resting-State fMRIz score using Fisher transformation, resulting in a 273 × 273 symmetric connectivity matrix for each subject. Removing 273 diagonal elements, we extracted the upper triangle elements of the functional connectivity matrix as prediction features, i.e., the feature space for prediction was spanned by the (273 × 272)/2 = 37,128 dimensional feature vectors. In this study, FC features were described as inter-network FCs and intra-network FCs. According to the study of Yeo and his colleagues23, brain regions can be grouped into seven functional networks for visualization: visual network (VN), somatomotor network (SMN), dorsal attention network (dATN), ventral attention network (vATN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN).Regional mean time series were obtained for each by averaging the fMRI time series over all voxels in each of the 273 regions. Pearson correlation coefficients between pairs of node time courses were calculated and normalized to 24, randomized decision trees (a.k.a. ExtraTrees25), Random Forest26, AdaBoost27, and Gradient Boosting28 were employed to select features with the top 2% relative importance from different models respectively, which were assembled into a feature pool without any repetition. Then, all pooled features were fed into a linear support vector machine with a forward-backward searching strategy (SVM-FoBa)29, obtaining a more refined feature subspace. After that, the label of each subject was calculated by majority voting of the 5 base classifiers used in training. We adopted nested 3-fold cross-validation on the whole training and validation set. Please see more details in Supplementary Figs. 30, ElasticNet31, Fisher-Score32, and Trace_Ratio33 (Kolmogorov–Smimov test) on the classification accuracy, sensitivity, specificity, and feature dimension.To investigate diagnostic features between ADHD and HCs, FS_RIEL was proposed to extract most-discriminative features from high-dimensional FCs and improve the result interpretability by estimating the relative importance of features, which refers to the degree of features  contribute to classification. Five different algorithms including extreme gradient boostingchild and ADHDadult. Next, the correlation between ADHD symptoms and the selected shared and distinct FC patterns were further calculated, deriving out some interesting observations.We first performed the within-cohort classification as mentioned above to extract FCs that can discriminate ADHD from age-matched controls within child dataset and adult dataset, respectively. Then the identified ADHD-discriminative FCs were compared between child and adult cohorts, resulting in the shared and age-specific FCs between ADHDchild from HCchild using features extracted from the ADHDadult cohort classification, and vice versa. Since the participants in child dataset were all boys, hence only male participants  were selected for the cross-cohort prediction. The flowchart of the whole study design was shown in Fig. Moreover, to investigate the stability/continuity in diagnosing of ADHD from childhood to adulthood, a cross-cohort prediction using the identified FCs in discovery cohort were performed. Namely, classifying ADHD34. Specific, every looping was repeated 10 times in this study to ensure the stability of the results. K-fold cross validation is a resample procedure usually used to evaluate the classification performance of the model on a limited dataset, which can reduce the over fitting to a certain extent. After mean value across all k trials is computed, this scheme matters less which part of subjects are used as training set and which are used as testing set. Further, it can also improve the stability of the results and obtain as much effective information as possible from the limited data. Here, 5-fold cross-validation was used in child dataset . The steps of k-fold cross-validation are as follows: the data set is divided into k parts and taken turns using k-1 parts as a training set and the remaining one part as a test set. After looping k times, all subjects were guaranteed to be used in test set independently, and finally, the averaged classifying accuracy of k times is regarded as the whole classification accuracyset Fig.  and 10-fset Fig. , as the child than HCs. Total symptoms, inattentive, and hyperactive/impulsive symptoms rated with the ADHD RS-IV were significantly higher in participants with ADHD than HCs. Details about ADHD subtypes, comorbidities, and medication history were shown in Supplementary Tables Demographics and clinical characteristics of the ADHD patients and HCs were shown in supplementary Tables child from HCchild were 84.42%, 94.29%, and 73.00%, respectively. The classification accuracy of the child data with the proposed model was significantly higher than those with Lasso (p = 0.004), ElasticNet (p = 0.031), Fisher-score (p = 0.036), and Trace_Ratio (p = 0.036). Except the dimensionality of mean related feature space with ElasticNet in child dataset, the dimensionality of FS_RIEL was the lowest  with FS_RIEL were 79.86%, 90.83%, and 64.90%, respectively. The discriminating accuracy obtained by FS_RIEL was significantly higher than those obtained by Lasso (p = 0.003), ElasticNet (p = 0.001), Fisher-Score (p = 0.015), and Trace_Ratio . The top three discriminating FCs to classify ADHDadult-HCadult  were as follows: superior temporal gyrus-fusiform gyrus, ventromedial prefrontal cortex-precuneus, and cerebellum-fusiform gyrus.We compared the selected FC features to explore the shared and distinct FCs of adults and children with ADHD in males because all participants were boys in the child dataset. Figure The distribution of the most important FC nodes distributed within or among well-known brain networks, i.e., intra-network and inter-network was shown in Fig. As to the age-specific ADHD-discriminative FCs, the aberrant inter-network FC between SMN and dATN only exist in child dataset. While the aberrant inter-network FC between DMN and LN was less prominent in child than in adult dataset. Further analysis showed that all of the above FCs did not significantly differ between ADHD patients and HCs in both child and adult datasets.r = 0.400, p = 0.048) in HCchild group, but not in ADHDchild group. Besides, the above variables have a positive correlation trend in adults . Moreover, inter-network FC between SMN and dATN was positively associated with hyperactivity index scores of the CPRS in ADHDchild group  , results further indicated that there are shared ADHD-discriminative FCs between children and adults, which lie in the intra-network within DMN and the inter-network between DMN and vATN, and is also positively correlated with total ADHD symptoms score. On the other side, the inter-network FC between SMN and dATN was uniquely impaired in ADHDchild, positively correlated with hyperactivity index; whereas the aberrant inter-network FC between DMN and LN exhibited more adult-specific ADHD dysfunction. These results showed that shared and distinct patterns of FC were significantly related to symptomatic persistence and changes in children and adults with ADHD and further provided an additional evidence for the continuity of pathophysiology in ADHD. We identified both shared, and age-specific FC patterns impaired in ADHD and further investigated the cross-age predictability using the identified FCs. To the best of our knowledge, this is the first attempt to make a mutual prediction between children and adults with ADHD.In this work, we designed a dedicated imaging study by using both within-cohort classification and cross-cohort prediction based on two valuable ADHD cohorts in order to investigate shared and distinct FC patterns in ADHD36. One published study classified children with ADHD from HCs in different datasets. The classification accuracies in four datasets were 81.8%, 44.0%, 60.9%, and 64.7% respectively14. Though the classifying accuracy was relatively high in one dataset, the accuracies of the other three datasets were unsatisfactory with low reproducibility. The most critical deficiency in the above studies is the potential lack of generalization of the prediction models. This lack of testing of the models on independent validation sets, especially on different age ADHD patients whose neural mechanisms were not exactly the same as ADHD is a neurodevelopmental disorder is a major limitation. In this work, it included both children and adults with ADHD cohorts and reached a relatively high classifying accuracy. Secondly, the identified FCs are consistent not only with previous findings but also with symptom changes of ADHD observed in the clinics (details see the following discussion). The above results suggest that the findings are reliable and clinically meaningful in this work.One of the most key issue in ADHD machine learning studies is reliability. Here, we will state the reliability of the results from the accuracies of the discrimination and the clinical significance of the identified FCs. Firstly, the classifying accuracy of the two cohorts are nearly or over 80%, both significantly higher than four popular machine learning method. Many previous studies only include one dataset, lack of independent datasets to verify the classifying performance37, just consistent with our results that aberrant intra-network FC within DMN and inter-network FC between DMN and vATN were shared in ADHDchild and ADHDadult. In the past decade, aberrant FCs of the DMN have been the most frequently reported brain network in ADHD38. The default-mode interference hypothesis points out that attention deficit, at least in part, is due to the failure to fully and effectively transform from a baseline “default mode” to an active processing mode during cognitive tasks39. Consistent with this hypothesis, recurrent results indicated atypical brain FCs within the DMN in children and adults with ADHD38 and suggested potentially important roles of this network to the pathophysiology of ADHD.The DMN, mainly including medial prefrontal cortex, superior frontal gyrus, posterior cingulate cortex, precuneus cortex, involved in ADHD-linked symptoms including mind-wandering and attentional fluctuations, has been shown to performance abnormal inter-network and intra-network FCs in ADHD40, inattention in ADHD involves altered competitive balance between DMN and several task-positive networks, especially vATN. The DMN was involved in internally directed mentation, and task-positive networks were related to externally directed cognitive tasks41. The vATN  was a key task-positive network and thought to be responsible for regulating switching between externally focused and internally directed mentation42. Lapses of attention arose when inappropriate intrusion of DMN during externally focused tasks due to ineffective regulation by vATN39. Previous studies have also confirmed this view. A study showed altered resting state connectivity between DMN and vATN in children with ADHD43. Similarly, adults with ADHD showed aberrant resting FC within vATN and DMN44. Our results showed a significant positive correlation between vATN-DMN interconnections and total scores of ADHD symptoms in HCchild group. Such correlation of these variables was disrupted in ADHDchild group. It suggested that within a certain range, with the increase of ADHD symptom scores, FCs of vATN and DMN increased to maintain a normal level of subjects’ functions. However, when the score of symptoms exceeded a certain threshold, FCs between vATN and DMN lost this compensatory regulation, that is, with the increase of the score of symptoms, FCs did not make corresponding compensatory changes, leading to the occurrence of ADHD and dysfunctions.Independent lines of studies suggested that attention deficits in ADHD were associated with altered intrinsic connectivity networks interrelationships. According to current theoretical models45, aberrant FCs in SMN would be expected in children with ADHD given the salience of motoric hyperactivity. In line with this clinical observations, a meta-analysis across 55 fMRI studies in ADHD showed that hypoactivation in the SMN was less prominent in adults than in children6. The dATN whose core regions include intraparietal sulcus and the frontal eye field generates and maintains endogenous signals based on current targets and pre-existing information of possible emergencies, and sends out top-down signals to direct the processing of appropriate stimulus features46. The dATN is also involved in associating relevant stimuli with appropriate motor responses46. Thus aberrant inter-network FC between dATN and SMN might underpin impulsivity/hyperactivity and occur only in children with ADHD. Our results showed that dATN-SMN interconnections were significantly associated with hyperactivity index, not with impulsive/hyperactive scores assessed by both the ADHD RS-IV and the CPRS. It suggested that dATN-SMN interconnections might correlated with hyperactive behaviors, not with impulsive behaviors. This requires further study.We found that aberrant inter-network FC between SMN and dATN was unique for children with ADHD. Long-held clinical observations reported that hyperactive and impulsive symptoms decreased with agechild than in ADHDadult. Abnormal FCs of limbic system with the DMN might underlie the susceptibility to affective disorders in ADHD47. Limbic/affective network  is involved in emotional regulation and monitoring of the salience of motivational stimuli48. In addition, emotion regulation relies on the ability to direct attention towards or deflect attention away from emotional stimuli so as to maintain emotional homeostasis or maintain focus on a goal49. The DMN network  may support the abnormal allocation of attention to emotional stimuli and could thus be regarded as the major ‘top-down’ contributor to emotion dysregulation within ADHD50. Together with already evident attention deficits in ADHD, the deficits in the allocation of attentional resources in emotional stimuli would exacerbate emotion dysregulation. Emotion dysregulation is found in around 25–45% of children and between 30 and 70% of adults with ADHD50. Since affective disorders and emotion dysregulation were more prominent in adults with ADHD, this would be expected that aberrant inter-network FC between DMN and LN was more prominent in ADHDadult than in ADHDchild. Unfortunately, we did not collect scores on anxiety or depression symptoms.We also observed that aberrant inter-network FC between DMN and LN was less prominent in ADHD12. Whereas machine learning showed great advantages in exploring the core features of psychiatric disorder characterized with deficits in heterogeneous distributed systems51. Compared with four popular feature selection algorithms, our proposed method not only can classify ADHD from HCs more accurately but also can reduce original feature space into a much more refined subspace, suggesting the FC features extracted with our method are probably the more core deficits of ADHD. Besides, the accuracy was 75.6% for classification of adults with ADHD using features trained from the child dataset, and 70.4% for classification of children with ADHD using features trained from the adult dataset. This added an objective imaging evidence for the continuity of ADHD from childhood to adulthood.Further analysis showed that all of the shared and distinct patterns of FC did not significantly differ between ADHD patients and HCs in both child and adult datasets. There were two possible explanations. The first possible reason was that the features were selected based on relative importance, namely the degree of these features to discriminating ADHD from HCs, rather than on significant difference between groups. The clinical significance of these features did not always mean that these features were significantly different between patients and healthy controls. More likely, the statistical power of simple linear comparisons was insufficient for high-dimensional and nonlinear FC features52. It would be an interesting topic to focus on the common and distinct functional connectivity with different frequency bands in children and adults with ADHD in future.There are some limitations to this study. We studied the shared and distinct patterns of FC in children and adults with ADHD. But all participants were males, and the results might not generalize to females with ADHD. Yet, a small proportion of patients had a history of medicine. ADHD patients were required to undergo a washout period of at least 5 days before the MRI scan to decrease the possible effects. In addition, specific frequency bands  may contribute differentially to functional connectivity. Only conventional frequency band of 0.01–0.1 Hz was used in this study considering this frequency band was thought to be mainly linked to neuronal fluctuationsIn conclusion, we demonstrated that aberrant intra-network FC within DMN and inter-network FC between DMN and vATN were shared in children and adults with ADHD, consistent with default-mode interference hypothesis. Moreover, the results provided evidence that aberrant inter-network FC between SMN and dATN was unique for children with ADHD, in line with that hyperactive and impulsive symptoms decreased with age in ADHD. Whereas aberrant inter-network FC between DMN and LN was indicated more adult-specific ADHD dysfunction. Our findings highlight both shared, and distinct patterns of FC were correlated to symptoms in children and adults with ADHD. These features could be further used to partly make a mutual prediction between children and adults with ADHD, suggesting that FCs may serve as a potential biomarker for ADHD diagnosis. This work may shed new light on the underlying mechanisms of ADHD and provide objective imaging evidence for symptomatic changes and pathophysiological continuity in ADHD from childhood to adulthood.supplementary material


A recent study has applied a novel statistical framework  to the study of circadian activity rhythm (CAR) in adult attention-deficit hyperactivity disorder (ADHD), pointing out the absence of the physiological post-lunch dip. The aim of the present study was to apply FLM to explore the features of CAR in pediatric ADHD. To this end, a secondary analysis of previously collected data was carried out. Twenty-four ADHD children  and 107 controls  were examined. The actigraph model Actiwatch AW64 was used to objectively monitor sleep/wake behavior and CAR. In the original study each participant wore the actigraph on the non-dominant wrist for one week. FLM was applied to examine the differences between groups in CAR. Compared with C, the CAR of ADHD children was distinguished by a higher motor activity during the whole of the daytime and within a reduced time window during the nighttime.  Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of hyperactivity, impulsivity, and inattention. The prevalence of ADHD in childhood and adolescence is high, around 10% . ApproxiWhile excessive motor behavior is an important feature of ADHD, the assessment of this component during the diagnostic process is carried out through standardized scales. One technique that has the potential to provide an objective assessment of motor activity is actigraphy. Actiwatches are wrist-worn devices equipped with an accelerometer that measures the limb activity related to movements occurring during both wake and sleep, i.e., the whole 24 h . TherefoThe topic of CAR has been previously investigated in both ADHD children and adults (for a review see ). GeneraTo the best of our knowledge, no studies have yet applied FLM to describe the minute-by-minute CAR of ADHD children. For this reason, we chose to fill this gap of knowledge by carrying out a secondary analysis of previous data, collected separately in ADHD and C children, with the aim of assessing whether the potential ADHD trait marker detected in adults may already be present in children.With reference to actigraphic sleep parameters, we found two significant differences between the groups , with loThe results of the FLM are reported in The aim of this secondary analysis of previously collected data was to explore, for the first time, the features of CAR in pediatric ADHD through FLM. In this case CAR refers to the 24-h time course of motor activity.Data on actigraphic sleep parameters, reported in The results of the FLM applied to a group comparison between ADHD children and C  point toWithin the ADHD group, the comparison between children belonging to the three ADHD subtypes  is potenSome limitations of the present study should be acknowledged. In particular, the size of the sample of ADHD children is slightly lower compared to previous studies on a similar topic . MoreovIn the present work, we examined 24 ADHD children  who were being treated at the Child Neuropsychiatry Unit of the Fondazione Mondino ), and who participated in a study on actigraphic sleep quality and raw hourly CAR between 2004 and 2006, the results of which have previously been reported as abstracts ,15. PatiIn this secondary analysis of previous data, we also examined 107 C  who had been enrolled in a study on the relationship between body mass index and sleep/wake cycle, assessed through actigraphy , conduct21 = 12.18; p < 0.001), with a higher prevalence of males among the ADHD group. The groups also significantly differed by age, which was lower in ADHD (8.67 ± 1.74) than C (10.25 ± 0.48) .The distribution of gender across the groups of ADHD and C was significantly different  was used to objectively assess sleep and CAR. Using the software Actiwatch Activity and Sleep Analysis 5 (version 5.32), each epoch was scored as sleep or wake according to the algorithm proposed by Oakley , settingThe same software was used to extract minute-by-minute raw motor activity counts across the 24 h, in order to draw the raw CAR.The following actigraphic parameters were calculated: bedtime (BT), the clock time at which participants went to bed and tried to fall asleep; get-up time (GUT), the clock time at which children got out of bed after nocturnal sleep; time in bed (TIB), the interval in minutes between BT and GUT; midpoint of sleep (MS), the clock time that splits TIB in half; sleep onset latency (SOL), the interval in minutes between BT and sleep start (SS); total sleep time (TST), the sum, in minutes, of all sleep epochs between SS and GUT; wake after sleep onset (WASO), the sum, in minutes, of all wake epochs between SS and GUT; sleep efficiency (SE), the ratio between TST and TIB, multiplied by 100; mean activity score (MAS), the average value of the activity counts per epoch over the assumed sleep period; and wake bouts (WB), the number of episodes of sleep interruptions with wake periods of at least 1 min.In the original study, ADHD children and C had been requested to wear the actigraph around the non-dominant wrist for one week . While aIn the present study, the actigraphic sleep parameters of school nights only were computed. Similarly, in order to depict the raw circadian motor activity profile, only school days were examined. To this aim, we visually inspected the actigraphy files to identify and exclude from the analysis the time periods in which children removed the actigraph, as reported in the simplified sleep diary.The study was approved by the Bioethics Committee of the University of Bologna .With reference to each actigraphic sleep parameter, treated as a dependent variable, we performed a univariate analysis of covariance (ANCOVA) with gender  and group (two levels: ADHD children and C) as fixed factors and age as a covariate. Since we performed multiple comparisons, we corrected the significance level through Bonferroni, considering p values less than 0.005 as significant.With the aim of assessing if and when the CAR of groups significantly differed across the 24 h, we applied FLM through the routine “Actigraphy” implemented within the statistical software R [This work was a pilot study on CAR in pediatric ADHD. In this instance CAR pertained to the 24-h motor activity time course. The main findings were the motor hyperactivity observed just in one specific time window during the nighttime  and a less marked post-lunch dip  in ADHD children than C, that seemed to point out a lower homeostatic sleep pressure in pediatric ADHD. Furthermore, such study showed preliminary but promising findings about the usefulness of actigraphy in the discrimination between ADHD subtypes. It would be extremely interesting if future studies could thoroughly examine the CAR of children at the time of the first referral to child neuropsychiatric units for behavioral or attentional problems, before a formal diagnosis of ADHD is made and prior to any type of treatment. This kind of study would allow researchers to explore the possible role of a smoothed/absent post-lunch dip, as a marker of this disorder, more thoroughly, and in a more concrete manner.


The study aimed to investigate if the behaviours suggestive of ADHD were more frequent in a population of children attending the Emergency Department (ED) for injuries, rather than for other causes.A cross-sectional study was carried out. Patients, aged 6 to 17 years, attending the ED for acute injuries and other causes were considered cases and controls, respectively. We used a questionnaire, which investigates the presence in the child of inattention, hyperactivity, and impulsivity. The primary outcome was the number of children with behaviours suggestive of ADHD in cases and controls.p = 0.661). Among these cases, children with evocative ADHD scores had a higher probability  of having had more than five previous ED accesses due to injury, compared to the others.Five hundred forty-five children were enrolled, 251 with injuries and 294 with other complains. Twenty two out of two hundred fifty one (9%) children visited for injuries, and 30 out of 294 (10%) visited for other causes had behaviours suggestive of ADHD (This study did non shown a difference in behaviours suggestive of ADHD between cases and controls, but identified a population of children with behaviours suggestive of ADHD who more frequently access the ED for injuries. During their life, patients with ADHD are at risk of repeated injuries.The number of injuries could be decreased by an early diagnosis and appropriate treatment, but there is no agreement about a screening for ADHD in children accessing the ED for injuriesThere is a population of children with behaviours suggestive of ADHD with a history of repeated ED accesses for injuries.Addressing this population of children, the development of a specific screening tool for behaviours suggestive of ADHD could be considered.Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children and adolescents, with a prevalence that varies between 3 and 5% . It is cAccidents in children and adolescents are the most common cause of visits to the emergency department (ED) and among the leading causes of morbidity and mortality in Europe . PreviouPrevious studies performed in a paediatric ED setting did not reach a decisive verdict regarding any increased risk of injuries in children affected by ADHD , 17. ThiA cross-sectional study was carried out from May to September 2017 at the paediatric ED of the tertiary care children’s hospital Institute for Maternal and Child Health – IRCCS “Burlo Garofolo” of Trieste, Italy. The study protocol received approval from the Bioethics Committee of Friuli Venezia Giulia (CEUR-2017-Os-124-BURLO).Patients eligible for the study were children attending the ED aged from 6 to 17 years old. The exclusion criteria were non-Italian speakers, patients with reported developmental delay, irreparable hearing, visual or intellectual delay, musculoskeletal or neurological diseases and non-self-caused-injury. Enrolment was carried out for approximately 6 hours per day, in the presence of a specially trained research assistant.Children attending the ED for acute injuries, defined as trauma, wounds or burns, were included in the study group. Children attending the ED for causes other than injuries were included in the control group.After the usual ED care and before the hospital discharge, the research assistant approached the parents of the injured and non-injured children for the enrolment. All children’s parents signed informed consent to participate in this study. We collected data on age, sex and nursing triage category of each patient using the Italian national triage category system consisting of four priority levels with increasing severity . Parents were asked to complete the SCOD-Parent Rating Scales-Revised questionnaire for ADHD symptoms . ParentsThe SCOD-Parent Rating Scale is a revision of the Disruptive Behaviour Disorder Rating Scale translated and adapted into Italian, and extensively tested and implemented in the Italian population. The SCOD-Parent Rating Scale questionnaire consists of 42 items following the criteria of the Attention Deficit Hyperactivity Disorder of the Diagnostic and Statistical Manual of Mental Disorders. The answers to each question are based on a rating scale from “0 = never”, “1 = occasionally”, “2 = often”, or “3 = very often” , 18. As p < 0.05) was calculated according to the two-tailed exact Fisher test for contingency tables and using a Mann-Whitney rank sum test for the comparison between cases and controls in the case of continuous variables. To study the association between having had or not more than 5 previous ED visits for injuries and having or not having symptoms suggestive of ADHD, we conducted a bivariate (thus unadjusted) logistic regression analysis. All analyses were done using Stata/IC 14.2 .Data were described as frequencies and percentages, and as medians and interquartile ranges (IQR). Statistical significance   obtained a score higher than or equal to 14 in the questions related to inattention, and 28 children (5%) obtained a score higher than or equal to 12 in the hyperactivity/impulsivity questions.p = 0.661)  showed a probability four and a half times higher  of having had more than 5 previous ED visits for injuries, compared to the children of the same group without ADHD behaviours (n = 229). On the other hand, there was no difference in the frequency of past fractures, the need for previous suturing or antecedent injury-related hospitalisations in children with scores suggestive of ADHD (Table Children with scores evocative of ADHD (HD Table .Table 3This study did not show any differences in behaviours suggestive of ADHD in children and adolescents visiting the ED for injuries compared to other causes. Nevertheless, we detected a population of patients with behaviours suggestive of ADHD whose history was remarkable for numerous injury-related ED visits.Previously published evidence is discordant about the effects of ADHD on children attending the ED because of trauma: one study showed a 3-fold higher risk of being affected by ADHD than controls. Another similarly found a double risk of having higher scores on the Conners’ scale , 19. HowIt is not possible to diagnose ADHD within the ED setting, and nor was this the aim of this study. Nevertheless, this report shows that the core behaviours of ADHD, such as inattention and hyperactivity/impulsivity, could be explored with a brief and focused questionnaire. Surprisingly, we found a population of children with behaviours suggestive of ADHD who had numerous previous injury-related ED visits. This finding supports previous studies that show that a history of repeated traumas, head injuries, or burns are potential indicators of ADHD , 21.Injuries are the most common complication due to the hyperactivity and inattention, and children with ADHD have double the risk of dying compared to peers without ADHD. This increased risk is related to accidents, and it rises to five times by adulthood . SpecifiThis study presents some limitations. First of all, the limited sample size and the lack of red codes may have conditioned the results of the primary outcome. We decided to exclude children with developmental delay, as a category of patients at higher risk for symptoms of ADHD. Moreover, we were not able to enrol patients 24 h a day, so it can be a possible bias. We also took into consideration only one part of the SCOD questionnaire, intending to focus only on behaviours considered highly suggestive of ADHD. Moreover, our cut-off scores suggestive of ADHD were based on symptoms in males, thus only including females with high scores. We did not perform a separate analysis for already diagnosed ADHD patients in which the treatment may have influenced the symptoms’ scores. Finally, we used rating scales based exclusively on subjective observations of parents and we therefore cannot exclude some recall bias regarding the secondary outcomes.In this series, the behaviours suggestive of ADHD were not presented more frequently in patients who visited the ED for lesions compared to other causes, so our results do not support a screening for ADHD symptoms in the ED. Nevertheless, we found a population of patients with behaviours suggestive of ADHD and an unusual history of injury-related ED visits. This specific population of children may benefit from a tool to identify ADHD suggestive symptoms in the ED. Future studies could be aimed at developing and validating a specific questionnaire and better identifying children who could benefit from this screening.


The Reinforcement Sensitivity Theory of Personality has as its main foundation a Behavioural Inhibition System (BIS), defined by anxiolytic drugs, in which high trait sensitivity should lead to internalising, anxiety, disorders. Conversely, it has been suggested that low BIS sensitivity would be a characteristic of externalising disorders. BIS output should lead to increased arousal and attention as well as behavioural inhibition. Here, therefore, we tested whether an externalising disorder, Attention Deficit Hyperactivity Disorder (ADHD), involves low BIS sensitivity. Goal-Conflict-Specific Rhythmicity (GCSR) in an auditory Stop Signal Task is a right frontal EEG biomarker of BIS function. We assessed children diagnosed with ADHD-I (inattentive) or ADHD-C (combined) and healthy control groups for GCSR in: a) an initial smaller study in Dunedin, New Zealand ; and b) a main larger one in Tehran, Iran . GCSR was clear in controls (particularly at 6–7 Hz) and in ADHD-C (particularly at 8–9 Hz) but was reduced in ADHD-I. Reduced attention and arousal in ADHD-I could be due, in part, to BIS dysfunction. However, hyperactivity and impulsivity in ADHD-C are unlikely to reflect reduced BIS activity. Increased GCSR frequency in ADHD-C may be due to increased input to the BIS. BIS dysfunction may contribute to some aspects of ADHD  and to some differences between the ADHD subtypes but other prefrontal systems  are also important. How far is extreme (high or low) sensitivity of the Behavioural Inhibition System  is primarily defined by anxiolytic drugs Gray, . AlthougThe BIS is the most distinctive system  neurobiological vulnerabilities that interact in complex ways with “environmental adversities”  and one in Iran (main study). All procedures were in accordance with the ethical standards of the University of Otago Human Ethics Committee  and with the Helsinki Declaration of 1975, as revised in 2008.1.1TM . A psychiatrist used all three to provide a DSM-IVR diagnosis.The initial study had 31 participants in the final analysis . The main study had 66 participants . For both, age ranged from 7 to 12 years with an average of 9. In the initial study, participants (including controls) were mostly selected by a research assistant from the Early Learning Database at the University of Otago, Department of Psychology. This database contains people invited at birth to go on a research database to be invited to participate in studies in the Psychology Department of the University of Otago. A diagnostic assessment using the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) semi-structured interview with parents  and temporal separation of the studies prevent matching of diagnostic criteria and so tests the generalisability of the results obtained. In both studies, we contacted parents by phone and then emailed them an information package. Parents received an NZ$10 dollar  petrol voucher in recognition of the time and costs of attending. Both parents and children received information sheets and then signed informed consent forms before starting the test. We asked children to be off medication for 24 h before testing. We did not check for other conditions, like anxiety, in the control or experimental groups (see section 4.2).1.2Children arrived at the laboratory with a parent for a 40-min session. After signing the consent forms, they were moved into an electrically body-protected room . They sat on a chair and the researcher accompanied them to ensure that they followed the test instructions. The stimuli were presented on a 14-inch monitor at eye level at a distance of 90–120 cm from the face and the stop signal tone was presented by headphones worn over the ears. All aspects of the experiment were controlled by a program written in Visual Basic 6. In the initial study, all participants were fitted with a Wave Guard cap with Ag/AgCl electrodes and mastoid reference electrodes . A1 and A2 were averaged to create a single reference for processing to which all channels were re-referenced. EEG data were recorded from 19 International 10–20 electrode channels: F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2, Fp1 and Fp2. Statistical analysis for the current report was focussed, a priori, on the right frontal channel F8 as previous studies in adults  or a right arrow (>), to which participants were instructed to make a left or right click, respectively, as quickly as possible. If a response was made, or after 1500 ms, the circle and arrow disappeared. Stop trials were like Go trials except for a 1000 Hz tone, presented for 500 ms, requiring the participants to withhold responding. The SSDs varied systematically between trials. Participants were told that stopping and going were equally important.The SSD on any particular stop trial was controlled by one of the four “staircases”, varied from trial to trial. Correct stopping to a particular staircase moved its SSD value up (harder), incorrect going moved its SSD value down (easier) in 50 ms steps. Except that, when the staircase delivered the mean SSD then the shift was by 100 ms rather than the usual 50 ms; this increased the spread of the SSD distribution and so the discrimination between the three different types of trial analysed.Stevenson’s  modifica1.4Pinhibit) of the 4 staircases in the last 12 moves was calculated to verify that Pinhibit had converged at 50%. The Pinhibit was then calculated for each group of SSDs.For each trial, we recorded trial and block number, trial type (Go or Stop), SSD value, reaction time, staircase index (1–4), staircase moves for each stair case, physical response (left/right/null) and inter-trial intervals. Based on Aron and Poldark’s  study, t1.5EEG data were processed using a purpose-built program in Visual Basic 6, as used in our previous adult studies that developed the biomarker to allow direct comparison. Data collected in the two studies were processed in New Zealand by the same experimenter. The raw data (128 Hz) were first low pass filtered  to remove residual high frequency noise. Eye blink artefacts were removed by first fitting a ballistic template to Fp1 . Each analysed epoch consisted of 32 samples before presentation of the 500 ms tone, 64 samples during presentation, and 32 samples after the end of the tone. We applied a Hanning window (a cosine wave which extracts most power from the middle 0.5 s and little from the two outlying 0.25 ms), which improves transform quality and doubles frequency resolution (to 1 Hz) compared to an equivalent (0.5 s) square window. A log transform then normalised error variance before averages was calculated.Correlation of average SSDs from the first and second staircases with those of the third and fourth staircases tested for stability of SSD values. The 50% probability of withholding or responding had stabilised by the last 12 changes of each staircase. Trials were groups by SSD values that varied the level of conflict. For each participant, 48 Stop trials (4 staircases × last 12 changes) were arranged in the order of ascending SSD and then divided into early, intermediate and late groups. Trials with the same SSD were always put in the same group with unequal numbers of trials in the different groups for some participants. The number of trials in each band differed from 5 to 24 before averaging.Pinhibit converged at 50%) were taken and the spectra for early, intermediate and late SSDs were averaged separately. Any missing data in the window resulted in the entire spectrum for that period of EEG being marked missing. When more than 30% of the trials contributing to an average of spectral power were missing, the average was replaced with missing data markers. Participants were excluded from the analysis when more than 10% of their overall data were missing.The last 12 trials of each staircase  and diagnosis , and repeated measures were SSD , frequency (4–12 Hz), and trial type (Stop and Go).The GCSR component was calculated as follows. The intermediate SSD is where maximum conflict occurs between stopping and going. Late and early SSDs represent lower levels of conflict but higher or lower levels of other aspects of the task such as stopping. The linear and quadratic contrasts of the three levels of SSD allow separation of these two types of effect. For GCSR, the conflict-specific component is extracted as the orthogonal quadratic contrast of SSD. This component is identical to the difference between the intermediate SSD and the average of the short and long SSDs and so the term “quadratic” is descriptive and does not imply the presence of an underlying quadratic function. We also ensure that the difference is specific to the occurrence of the stop signal by testing the difference between the two types of trial (treated by SPSS as a linear trend). So effects common to go and stop trials are eliminated. The EEG statistics reported below are limited to analysis of GCSR . The resultant F ratios are the same as would have been obtained if we had first calculated GCSR and then undertaken ANOVA and so have been reported in this form for simplicity. Note also that effects of frequency were determined via orthogonal polynomial coefficients (with 1 frequency df) eliminating the sphericity problem normally encountered with repeated measures.2.2.1Pinhibit) in short, intermediate and long SSD testing for differences between the three diagnosed groups  and the two genders. The data are presented in Table More than half of the participants did the task only up to the end of block 2 i.e. 512 trials) and did not wish to proceed to block 3. In addition, all participants showed stabilised SSDs in block 2, even those who completed block 3. SSDs were not stable in block 1 as this is a learning period. Behavioural and EEG analysis, therefore, focussed on block 2 to maximise the number of participants included. ANOVAs were carried out on each of MRT, SSRT, Go correct%, and Probability of Inhibition  = 8.208, p < .01; diagnosis × gender: F = 4.227, p < .05). Nevertheless, the two genders did not differ when pooled across diagnosis  = 0.140, p = .712).In the initial study Table A, there Pinhibit measures. However, ADHDs showed reliably lower Go correct% than controls  = 4.364, p < .05).In the main study Table B, we obsN with subdivision by gender, the EEG data were analysed pooled across gender.Given the weak, site-specific, gender effects and the smaller 2.2Overall Figure A, controp < .05, ηp2 = 7%; country × group × frequency[order 5], F = 2.083, p = .13, ηp2 = 4%). These were explored with post hoc ANOVAs.While these results were qualitatively similar across the two studies Figure B, C, thep < .05, ηp2 = 21%). As can be seen in Figure p < .05, ηp2 = 22%; [ADHD-C vs. Control] × frequency[order 4], F = 7.222, p < .05, ηp2 = 26%). In contrast, ADHD-I had negative GCSR across the 5–9 Hz frequency range but this group was not reliably different from either ADHD-C or Control on post hoc ANOVA  = 1.215, p = .282; post hoc [ADHD-I vs. ADHD-C] × frequency[order 6], F = 3.778, p = .071).For post hoc analysis restricted to the initial NZ study, the three groups differed on the order 6 component distinguishing the two studies  = 3.849, p < .05, ηp2 = 12%). As seen in Figure p = .255). In contrast, ADHD-I appeared to have a distinct pattern from the other groups with a noticeable decrease in power at 8–10 Hz but a tendency to positive values at lower frequencies (5–8 Hz). Post hoc ANOVA found a clear difference between ADHD-C and ADHD-I  = 8.167, p = .007, ηp2 = 18%) and a less reliable difference between ADHD-I and controls  = 4.163, p < .05, ηp2 = 9%).For post hoc analysis, restricted to the main Iran study, the three groups differed on the order 5 component that approached a significant difference between the two studies  =4.432,  =4.432, Overall, then, there is only moderate evidence that GCSR of ADHD-C is different from controls. Critically, in both studies, ADHD-C do not have lower values than controls and, unlike ADHD-I, generally do not have negative values in the 5–10 Hz range. The differences between the two sets of results, and complex shapes observed, suggest that the specific shapes of the ADHD curves in Figure 3.3.1We found solid evidence in children of positive GCSR, of the type previously observed in adults, for the control and ADHD-C groups. There were signs of lower GCSR activity for the ADHD-I subtype. Importantly, GCSR at F8 for the control and ADHD-C subtype occurred in both studies and so detection in children of GCSR as a qualitative result is robust in the face of differences in geography, race, culture, size of urban area, recruitment method and diagnostician. Qualitatively there were signs of reduction in GCSR for the ADHD-I group in both experiments. Hence, our BIS biomarker may differentiate, at least on a group basis, between the EEG power of the subtypes  but provides no evidence of a reduction of GCSR in ADHD-C relative to control.We found the predicted reduction in our BIS biomarker for ADHD-I, supporting Quay’s  view thaSSRT has been widely seen as the essential measure characterising behavioural inhibition performance in ADHD. However, a recent meta-analysis by Alderson, Rapport and Kofler  argued tNeo et al.  suggesteADHD subtypes were not distinguishable from the typically developing groups in terms of the Go response speed in either the initial study or the main study. Previous studies have found contradictory MRT results for children with ADHD. According to Alderson et al. , ADHD grIn sum, some ADHD-I symptoms, which involve attention and arousal problems, may be explained by low BIS sensitivity. However, ADHD-C do not appear to differ from controls in terms of BIS sensitivity, contrary to our predictions and despite apparently similar abnormalities to ADHD-I in BIS-related structures . Sample size is particularly important for the interpretation of the variation in GCSR with frequency between studies as this function appears variable  and in the main study (WinEEG) for EEG recording. Two different kinds of EEG caps were used in each centre . All these uncontrolled variables could impact the final results. However, given the overall qualitative consistency of the results obtained, these uncontrolled differences make our overall conclusions stronger by demonstrating generality across the varying conditions.Finally, it should be noted that our BIS biomarker should, theoretically, represent overall sensitivity of the whole BIS – a hierarchical system spanning from the periaqueductal grey to the prefrontal cortex. While changes in it confirm overall BIS involvement in a process, it does not exclude the possibility that sensitivity of discrete parts of the BIS  could underlie some facets of externalising in general and ADHD in particular.3.3core of all ADHD symptoms as previously hypothesised  a low BIS sensitivity in ADHD-I, which causes goal conflict resolution problems; and (2) a high BAS sensitivity in ADHD-C, which causes action stopping problems.BIS dysfunction may then contribute to some aspects of ADHD and to some differences between the subtypes but other prefrontal systems and contributions from, e.g., dopamine, are likely to provide significant additional contributions. The same is likely to be true for the whole externalising spectrum (Beauchaine et al.,