NRXN1), 15q11.2, 15q13.3 (BP4 & BP4.5–BP5) and 22q11.21, and duplications at 1q21.1 distal, 16p11.2 proximal, 16p13.11 and 22q11.21. Six of the CNVs have not been associated with ADHD before. As a group, the 19 CNVs associate with ADHD , even when comorbid ASD and schizophrenia are excluded from the sample. These results highlight the pleiotropic effect of the neuropsychiatric CNVs and add evidence for ADHD, ASD and schizophrenia being related neurodevelopmental disorders rather than distinct entities.Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable common childhood-onset neurodevelopmental disorder. Some rare copy number variations (CNVs) affect multiple neurodevelopmental disorders such as intellectual disability, autism spectrum disorders (ASD), schizophrenia and ADHD. The aim of this study is to determine to what extent ADHD shares high risk CNV alleles with schizophrenia and ASD. We compiled 19 neuropsychiatric CNVs and test 14, with sufficient power, for association with ADHD in Icelandic and Norwegian samples. Eight associate with ADHD; deletions at 2p16.3 ( ADHD is estimated to affect 3.4% of the population worldwide2. Follow-up studies of children have documented the persistence of ADHD symptoms into adulthood in approximately two-thirds of patients3.Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset neurodevelopmental disorder characterized by a triad of signs—age-inappropriate levels of inattentive, hyperactive and impulsive behavior—that lead to severe impairments4 in light of considerable overlap in clinical presentation and because ADHD is diagnosed in a high proportion of children at genetic risk of schizophrenia5. Also, a schizophrenia polygenic score, estimated from an adult population, was found to confer a small but significant risk of childhood ADHD6. A history of ADHD signs is common in individuals who develop schizophrenia, with attentional impairment as a central cognitive feature of both disorders4. Deficits in working memory, cognitive flexibility and attention seen in ADHD are similar to those observed in schizophrenia7. Likewise family-based and twin studies in clinical ADHD samples have shown that signs of autism spectrum disorders (ASD) are common within ADHD families and that more than half of the phenotypic variance in either disorder may be attributable to shared genetic factors8. This is further supported by analyses showing that some sequence variants confer risk of both ADHD and ASD10 and the two disorders share considerable common variant genetic overlap11.Studies have focused on the relationship between ADHD and schizophrenia12. Genome-wide association studies (GWAS) have only recently reached sample sizes with adequate power to yield variants significantly associated with ADHD13. The genome-wide single-nucleotide polymorphism (SNP) heritability of ADHD has been estimated at 22%, corresponding to the contribution of common SNPs to ADHD susceptibility13. This is lower than the heritability estimated from twin studies and suggests that rare variants may also contribute to the risk of ADHD14.Heritability estimates suggest that ~74% of the phenotypic variability in ADHD is due to variants in the sequence of the germline genome20. Evidence for increased burden of large, rare CNVs in ADHD have been reported, suggesting CNVs contribute to the disorder25. Two CNVs have previously been associated with ADHD, 16p13.11 duplications23 and 22q11.21 deletions26. Furthermore, ADHD has been reported in individuals with 1q21.1 distal deletions23 or duplications27, 15q11.2 deletions28, 15q13.3 deletions30 and 16p11.2 proximal deletions31 or duplications32, but have not been statistically tested for association.Rare copy number variations (CNVs) have been associated with cognitive deficits that can result in disadvantages in educational attainment and various adult life outcomes but more directly with increased risk of psychiatric and developmental disorders, including schizophrenia, ADHD, ASD and developmental delayWhile much of the current knowledge on the effects of CNVs and associated risk comes from schizophrenia and ASD samples, the aim of this study was to determine to what extent rare CNVs, previously associated with schizophrenia and/or ASD, associate with ADHD in a combined Icelandic and Norwegian population sample.33.This study was approved by the National Bioethics Committee of Iceland. All individuals signed an informed consent prior to giving a blood or buccal sample. Social security numbers of participants were encrypted through a process overseen by the Data Protection Authority before being analyzedN = 5650) were included on the basis of meeting criteria for ADHD diagnosis, any Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and International Classification of Diseases (ICD-9 or −10)-based subtype  or on confirmed information on treatment with ADHD medication according to the Directorate of Health centralized medication database   class of drugs (amphetamine (01), methylphenidate (04) or atomoxetine (09)), based on the Anatomical Therapeutic Chemical (ATC) Classification System. However, as diagnoses are not registered in the medication database, information on the ADHD status was not available for these individuals. Although, amphetamine can be prescribed for signs and symptoms other than ADHD, individuals prescribed amphetamine make up only 1% of the entire ADHD medication sample and none of them are neuropsychiatric CNV carriers. As methylphenydate and atomoxetine are exclusively prescribed for ADHD in Iceland, we have assumed that the individuals that make up this group have all sought treatment for the signs and symptoms of ADHD.N = 155,122, male N = 71,492) was recruited through various projects at deCODE genetics.While diagnosed male subjects are about twice as many as diagnosed female subjects, the gender ratio is close to 1:1 within the group prescribed medication, and so overall the male-to-female ratio among ADHD subjects is about 3:2. The control sample (N = 3233) were obtained from the Norwegian Mother and Child cohort study 35, which includes children (N = 1858 born between 1999 and 2009) and adults , and from the Bergen adult ADHD study (N = 434)36. Norwegian controls were children (N = 8245) and adults  from the MoBa study, controls from the Bergen adult ADHD study (N = 355) and adult blood donors (N = 5071)  as well as the Norwegian Data Inspectorate approved the MoBa study. For this specific study, a separate REC approval has been obtained. The MoBa data has been linked to the Patient registry to identify ADHD cases. All data have been deidentified prior to analyses.The MoBa study is a nationwide prospective population-based pregnancy cohort that includes 114,500 children, born between 1999 to 2009, and their mothers . Blood samples were collected from both parents during pregnancy, and from the umbilical cord for the children after birth. For a more detailed description of the sample see Magnus et al.36. Random controls were recruited through the Norwegian Medical Birth registry. All participants provided either blood or saliva samples for DNA extraction. All participants provided signed informed consent. The study was approved by the Norwegian regional medical research ethics committee West .The Bergen adult ADHD sample consists of participants recruited through a Norwegian national medical registry and by psychologists and psychiatrists at out-patient clinics. ADHD diagnosis was defined according to DSM-IV criteria as described elsewhereThe Norwegian Blood donors  were included in the control sample. They were all thoroughly screened for diseases, and provided blood for DNA analysis, in line with approval from the Regional Committee for Medical and Health Research Ethics.39 . The samples were genotyped using the Illumina HumanHap  and IlluminaOmni  SNP arrays. Norwegian neuropsychiatric CNV carriers were identified from 36,220 samples genotyped on Illumina SNP arrays .Nineteen CNVs conferring risk of schizophrenia or ASD (‘neuropsychiatric CNVs’) were selected based on recent publications40 was then used to predict CNVs from the SNP array data. Samples with LRR standard deviation over 0.3 or BAF drift over 0.01 were discarded from the analyses. The putative neuropsychiatric CNVs of all samples were confirmed by visual inspection of LRR and BAF plots over each predicted CNV region.Genomestudio  was used to call genotypes, normalize signal intensity data and establish the log R ratio (LRR) and B allele frequency (BAF) for every SNP. PennCNVN = 163,409 (correction factor (see below) adjusted, Icelandic and Norwegian samples), degrees of freedom = 1, significance level = 0.05.The CNVs under investigation are rare, and in order to estimate the minimum population frequency required to have 80% power to detect an association with an OR of above 3.9, we used the effect size calculation in the chi-squared test for association function (ES.w2) in the basic functions for power analysis (pwr) package (version 1.2–2) in R; pwr.chisq.test, P value were estimated using a Fisher exact test (fisher.test) in R. To account for relatedness within the Icelandic and Norwegian samples, the P values were adjusted with a correction factor  estimated using the intercept from LD score regression41. Prior to meta-analysis, the Icelandic and Norwegian ADHD affected and control, carrier and non-carrier counts, were adjusted with the above correction factors, rounded to the nearest integer, and then combined using the Cochran–Mantel–Haenszel χ2 test for count data (mantelhaen.test) in R. We used RStudio  integrating R  and employing the stats base package for the association tests and ggplot2 (version 2.2.1) to generate the power figure.To evaluate whether the neuropsychiatric CNVs were significantly enriched in our Icelandic and Norwegian ADHD sample, the number of case-carriers, case-noncarriers, control-carriers and control-noncarriers was determined per CNV, and CNVs combined, for each sample and an odds ratio (OR) and N = 8883 ADHD affected) , 15q11.2, 15q13.3 (break point (BP)4 & BP4.5–BP5), 16p11.2 distal, 16p11.2 proximal, 16p12.1, 17p12 and 22q11.21 and duplications at 1q21.1 distal, 16p11.2 proximal, 16p13.11, 17q12 and 22q11.21.We compiled a list of 19 neuropsychiatric CNVs that have been shown to confer risk of schizophrenia and/or ASD26, 16p13.11 duplication and 22q11.21 deletion were replicated in the combined Icelandic and Norwegian sample (OR (95% CI) = 2.12 , P = 0.0035 and OR (95% CI) = 10.73 , P = 1.8 × 10−6, respectively; Cochran–Mantel–Haenszel χ2 test for count data and false discovery rate (FDR) adjusted P value); it should be noted that a part of the Icelandic sample was included in the original 16p13.11 duplication study23  and duplications at 1q21.1 distal and 16p11.2 proximal. The remaining six CNVs have not been associated with a diagnosis of ADHD before; the deletions at 2p16.3 (NRXN1), 16p11.2 distal, 16p12.1 and 17p12 and duplications at 17q12 and 22q11.21. Of those, the 2p16.3 (NRXN1) deletion and 22q11.21 duplication were significant in the combined sample (OR (95% CI) = 4.68 , P = 0.0020 and OR (95% CI) = 2.24 , P = 0.0042, respectively; Cochran–Mantel–Haenszel χ2 test for count data and FDR adjusted P value)  and 16p11.2 proximal and duplications at 1q21.1 distal and 16p11.2 proximal) of the remaining 12 CNVs, although not statistically testedχ2 test for count data on the Icelandic and Norwegian ADHD samples to estimate the overall CNV burden. This revealed a significant association with ADHD (OR (95% CI) = 2.43 , P = 1.6 × 10−21; counts adjusted for correction factor, see Methods). The combined neuropsychiatric CNVs have a carrier frequency of 2.15% in the Icelandic and Norwegian ADHD sample compared with 0.86% in the combined controls. The associations with ADHD in the Icelandic and Norwegian samples, separately, were similar (OR (95% CI) = 2.30 , P = 1.4 × 10−11 and OR (95% CI) = 2.66 , P = 7.7 × 10−12, respectively; Fisher’s exact test and corrected P values) . Although, this appears to be mostly accounted for by the six, relatively, more common CNVs tested, as when they are removed the OR is no longer significant for the five remaining CNVs (OR (95% CI) = 1.38 , P = 0.49; Cochran–Mantel–Haenszel χ2 test for count data in the Icelandic and Norwegian sample counts adjusted with correction factors and combined)  still conferred a significant risk of ADHD (OR (95% CI) = 1.94 , P = 1.2 × 10−11; Fisher’s exact test)  = 2.26 , % CI = 2.42. Within the Bergen sample, none of the CNV carriers has a diagnosis of schizophrenia and only one is comorbid ADHD and ASD. It is, therefore, unlikely that the comorbid ASD or schizophrenia are responsible for the risk conferred by the neuropsychiatric CNVs present in the Norwegian sample.For the Norwegian sample, the issue of ADHD comorbid with axis I to III disorders was addressed in a recent study where the authors found children with ADHD in MoBa were registered with fewer abnormal psychosocial situations (axis I disorders) compared with children in the general populationP = 1.6 × 10−17 and OR (95% CI) = 1.25 , P = 0.25, respectively; Fisher’s exact test and corrected P values. P value for difference = 2.4 × 10−5) (Supplementary Tables P = 0.10), trend where the OR is higher in children with ADHD than in adults with ADHD (OR (95% CI) = 2.69 , P = 5.9 × 10−8 and OR (95% CI) = 1.56 , P = 0.11, respectively; Fisher’s exact test and corrected P value) (Supplementary Tables We note that the OR for the neuropsychiatric CNVs appears to be higher in the sample of Icelandic subjects with an ADHD diagnosis than in the medication sample (OR 95% CI) = 3.49 2.72, 4.42), 2, 4.42, = 1.25 0.4, 1.79, 5% CI = 323. The neuropsychiatric CNVs confer a substantial risk of ADHD in the Icelandic and Norwegian samples. As the CNVs are individually rare, we estimated that 14 were powered to detect a significant association. When we looked at carrier status of the neuropsychiatric CNVs separately, eight were significantly associated with ADHD risk after adjusting for FDR: deletions at 2p16.3 (NRXN1), 15q11.2, 15q13.3 (BP4 & BP4.5–BP5) and 22q11.21, and duplications at 1q21.1 distal, 16p11.2 proximal, 16p13.11 and 22q11.21. Two of these CNVs have previously been associated with ADHD, the 16p13.11 duplication and 22q11.21 deletion. Of the remaining six, four have been previously reported with higher frequency of ADHD in carriers but not statistically tested and the deletion at 2p16.3 spanning exons of NRXN1 and the 22q11.21 duplication have not been associated with ADHD diagnosis before.The results of the current study, combining large ADHD samples from relatively homogenous populations, in terms of genetics and health care systems, support previous findings of increased burden of rare CNVs among ADHD patients26. Of other psychiatric conditions associated with the deletion, ADHD has been shown to be the most frequent disorder in children (37%) with the inattentive presentation persisting into adulthood26.The CNV conferring the highest risk of ADHD in our study is the deletion at 22q11.21. The 22q11.2 deletion syndrome (DiGeorge Syndrome) is associated with high rates of schizophrenia spectrum disorders and has been exploited as a genetic model for understanding the development of schizophrenia19 and ADHD44, also reached significance threshold in this study, although it should be noted that a part of the Icelandic sample was included in the original Williams et al. study23.The 16p13.11 duplication, which has been associated with schizophrenia27, including neurodevelopmental and psychiatric disorders, deletions more strongly with schizophrenia19 and duplications with ASD17. An increase in the frequency of ADHD among both 1q21.1 distal deletion (5%) and duplication (29%) carriers has also been reported27. While we confirm this observation with an association of the 1q21.1 distal duplication with ADHD, the 1q21.1 distal deletion is not significant.The 1q21.1 distal CNV has been associated with multiple phenotypes19 but also with learning difficulties and brain structural changes45, as well as an increased frequency of ADHD in carriers28. We see a modest association with ADHD in the combined sample.The 15q11.2 deletion has been associated with schizophrenia46. A review of 15q13.3 deletions, involving 246 cases with deletions overlapping the 15q13.3 (BP4–BP5) region, found an increased frequency of neuropsychiatric conditions, including ADHD (6.5%)29. We see a highly significant association with ADHD, marginally stronger in the Norwegian sample.The 15q13.3 (BP4 & BP4.5–BP5) deletion has previously been associated with mental retardation, seizures, dysmorphic features and schizophrenia19 and ASD17 but also reported a higher frequency (39–60%) of ADHD in carriers47. We report a strong and highly significant association with ADHD in our combined sample. 16p11.2 proximal deletion, although not significantly associated with ADHD, showed a definite reversal of OR after excluding individuals with a diagnosis of ASD or schizophrenia from the ADHD and controls samples.The proximal duplication at 16p11.2 has been associated with schizophreniaNRXN1 have been identified in individuals diagnosed with a range of neurodevelopmental disorders, including intellectual disability, speech and language delay, ASD and schizophrenia48 but to our knowledge not ADHD, apart from two clinical referrals for diagnostic cytogenetic analysis49. We observe a modest association between deletions removing NRXN1 exons at 2p16.3 and ADHD in our study.Deletions spanning exons of 17, severe mental retardation, dysmorphic facial features and heart malformations38 to no signs at all. Notably, while all of the neuropsychiatric CNVs have been associated with increased risk of either schizophrenia or ASD, it has been postulated that the 22q11.21 duplication may confer protection against schizophrenia50. The 22q11.21 duplication has not been previously associated with ADHD in a population sample, although a Danish nationwide CNV registry study did find a modest increase in “any psychiatric disorder” (including ADHD) diagnosis in 22q11.2 duplication carriers51. Furthermore, a prospective study of a cohort of children, found ADHD symptom scores to be significantly higher in 22q11.2 duplication carriers in comparison to typically developing children38. Only the 22q11.21 CNV has been shown to have a mirror effect on a psychiatric disorder: The deletion confers risk of schizophrenia26 whereas the reciprocal duplication has been postulated to be protective against the same disorder37. Interestingly, here we demonstrate that both alleles of the 22q11.2 CNV confer risk of ADHD.Current understanding of the 22q11.21 duplication clinical phenotype is quite diverse, but range from ASDAlthough only a subset of the neuropsychiatric CNVs were predicted to provide well-powered estimates of ADHD risk, all 19 combined were, unsurprisingly, highly significantly associated with ADHD. To explore whether any of the individually non-significant or untested CNVs conferred some latent risk of ADHD, we combined them in two seperate sets. While the slightly more common, but individually non-significant, CNVs combined did reveal an association with ADHD, the smaller set of very rare CNVs combined were not significant. A larger sample of carriers is required to ascertain whether those CNVs associate with ADHD.P values. However, the overall conclusion is that these CNVs, individually or combined, confer risk of ADHD with or without ASD or schizophrenia.A potential confounder of analyses such as the one presented here is the presence of ASD or schizophrenia comorbid with ADHD and the risk conferred by the neuropsychiatric CNVs being attributable to those conditions. We reanalyzed the individual and combined neuropsychiatric CNVs, after excluding individuals with a diagnosis of ASD or schizophrenia in both the ADHD and control samples and found only modest changes in the risk estimates and as expected some change in the As noted, neuropsychiatric CNVs appear to confer greater risk of ADHD in individuals with a diagnosis compared with those prescribed medication for ADHD. The sample with a diagnosis is younger than those on medication and so rather than an actual difference in risk between the two groups this, more likely, reflects a more complete recruitment for the younger sample and so a broader spectrum of ADHD risk variants are represented whereas high risk CNVs are likely to be underrepresented in the older medication sample. With the proviso that diagnostic criteria and clinical practice are stable over time, it is likely that if individuals born prior to the commencement of systematic ADHD screening and diagnosis would be better represented in ADHD study samples, an increase in CNV frequency would also be observed.In this study, we show that neuropsychiatric CNVs, previously associated with schizophrenia and ASD, also confer risk of ADHD. Hence, further emphasizing the pleiotropic effects of CNVs. This adds to the evidence that these disorders are related rather than etiologically distinct entities and supports previous findings of both common and rare variant sharing. While, a unifying factor of the neuropsychiatric CNVs is their negative impact on cognitive abilities and disadvantages in educational attainment that potentially explain part of their association with psychiatric disorders, environmental interactions and other sequence variants in CNV carriers may affect the disorder expressed.Supplementary Information


Individuals with Attention Deficit Hyperactivity Disorder (ADHD) tend to perform cognitive tasks with greater Response Time Variability (RTV). Greater RTV in ADHD may be due to inefficient functional connectivity of the brain during information processing. This study aimed to investigate the relationship between brain connectivity, RTV, and levels of ADHD symptoms. Twenty-eight children aged 9–12 years and 49 adolescents aged 15–18 years performed the Sustained Attention to Response Task (SART) while EEG was recorded. The participants’ levels of ADHD symptoms were measured using self- and parent-rated questionnaires. The ex-Gaussian analysis and The Fast Fourier Transform were used to measure multiple aspects of RTV. Functional connectivity between 64 electrodes was computed during task performance, and global efficiency and modularity were calculated, reflecting integration and segregation of the brain, respectively. There was a positive association between multiple RTV measures and the level of ADHD symptoms, where participants with higher levels of ADHD symptoms showed greater RTV, except for sigma from the ex-Gaussian analysis. More efficient brain network activity, measured by global efficiency, was associated with reduced RTV. Children showed greater RTV and less efficient brain network activity compared with the adolescents. These findings support the view that stable responses are achieved with more integrated (and efficient) brain connectivity. ADHD is a neurodevelopmental disorder characterized by very high levels of inattention, hyperactivity, and impulsivity . Higher One widely-used RTV model is the ex-Gaussian distribution model, which provides an excellent fit to RT data . It exprAnother approach used to analyze RTV is the fast Fourier transform (FFT), allowing for the capture of periodic fluctuations in RT. The FFT measures the power of periodic changes in RT at different temporal frequencies. This analysis of periodic fluctuation has been applied to RT data from individuals diagnosed with ADHD  as they An alternative way to define frequency bands of RTV is by dividing frequency based on the experimental paradigm. This was the approach taken by The neural correlates of RTV have not been widely examined. One explanation of RTV is that increased RTV may reflect inefficient or inconsistent information flow through the brain during performance of a cognitive task . Stable One method with which to characterize patterns of brain connectivity is to apply graph theoretical analysis. In graph theoretical analysis, the brain is considered as a single network, and this analysis describes the patterns of connections between brain areas . The chaAbnormal brain connectivity in ADHD has been demonstrated using both functional and structural connectivity analyses . Using gWhen examining the relationship between RTV and ADHD symptoms it is critical to examine how the relationship changes from childhood to adolescence. During typical development RTV decreases, on average, from childhood to young adulthood, and young adulthood is associated with the most stable responses during the lifespan . With thThe symptoms of ADHD are distributed continuously within the population from extremely low to high levels of inattention, hyperactivity, and impulsivity. Individuals with ADHD populate the extreme high end of its continuum , 2009. HIndividuals with ADHD often, but not always, show difficulty in sustaining their attention. Sustained attention is the conscious processing of non-arousing stimuli over a period of time . BehavioThis study aims to investigate the relationship between brain connectivity, RTV, and levels of ADHD symptoms in children and adolescents on two measures of sustained attention. It is hypothesized that (1) Higher levels of ADHD symptoms will be associated with increased RTV; (2) Children will perform the sustained attention tasks with greater RTV than adolescents; (3) Greater functional integration and decreased strength of segregation of the brain will be associated with reduced RTV and lower levels of ADHD symptoms; and (4) Adolescents will show more integrated and more strongly segregated patterns of brain connectivity than children.M = 10.95 years, SD = 1.06; 9 females) and 49 adolescents aged 15–18 years  participated in this study. One participant from the adolescent group was removed from the sample before analysing the data due to excessive movement during the EEG recording, resulting in a total of 49 adolescents from the initial number of 50 adolescents. Children were recruited from local primary schools. Six adolescents were recruited from local secondary schools and forty-three were recruited from the university’s first year Psychology course. Participants were not pre-selected based on symptoms of ADHD.Twenty-eight children aged 9–12 years  and Psychtoolbox . The expThe Conners 3 questionnaire was used to measure levels of ADHD symptoms. Two forms were used in this study: The Conners 3 parent form was completed by parents of all children and the 6 adolescents recruited through secondary schools, while the self-report form was completed by the university-based adolescents. There was no significant difference between the adolescent  and child  groups on the ADHD Index, t(49.11) = –1.65, p = 0.10. The correlation between the parent and self-report forms is 0.57 for the ADHD Index . Eight cAn estimate of intelligence quotient (IQ) was obtained using the WASI  for partParticipants were asked to complete the SARTs while the EEG was recorded using a 64 electrode Biosemi system. For participants recruited from schools, the WASI was completed before the SART. Parents were asked to complete the Conners 3 questionnaires while the participants were performing the task. The undergraduate participants were asked to complete the Conners 3 questionnaires form while the EEG was being set up.For each participant, for the Fixed and Random SARTs separately, each dependent variable was computed following these steps. A count of the two types of errors was computed. Omission errors are the missed responses to the Go trials. Commission errors are the responses to the No-Go trials. Participants with more than 30 omission errors in either Fixed or Random SART were to be removed from the analysis: no participants were excluded. Trials containing commission errors, response times shorter than 100 ms, and No-Go trials were removed, and subsequently the Mean of RT and Standard Deviation of RT (SDRT) were computed. Three parameters, mu, sigma, tau were extracted by applying the ex-Gaussian model based on the approach by The analysis to compute fast and slow RTV followed the procedure of The frequency range was divided into fast and slow frequency bands using the peak at 0.0772 Hz. The frequency 0.0772 Hz was chosen as it is the reciprocal of one cycle of the 1–9 digit presentation in the Fixed SART (a SART cycle). The Fast Frequency Area Under the Spectra (FFAUS) encompassed all sources of variability faster than once per SART cycle, the area under the curve to the right of the peak at 0.0722 Hz. The Slow Frequency Area Under the Spectra (SFAUS) encompassed all sources of variability slower than once per SART cycle: the area under the curve to the left of the peak at 0.0772 Hz.The EEG was recorded using the 64 electrode BioSemi system with a sampling rate of 512 Hz. In the BioSemi system, a Common Mode Sense (CMS) active electrode placed on the surface of the head was used as a reference electrode during the recording. The recorded EEG with 64 electrodes was imported and pre-processed in Matlab and Fieldtrip .In the software, the EEG data were first referenced to an average of the two ear electrodes. The data were band pass filtered from 0.5 to 40 Hz, and then segmented into trials from -200 to 1200 ms, where 0 was set at the presentation of the target digit of the SART. The data were visually inspected and trials with artifacts such as extraordinary large amplitudes, jumps, and flat signals were removed. Noisy electrodes were manually identified and temporally separated from the data. Independent Component Analysis (ICA) was then applied to remove artifacts such as eye blinks, eye movements, and heartbeats. After ICA, noisy electrodes temporally separated in the previous step were interpolated using the spherical spline method and noisy trials, with amplitudes above 100 and below 100 microvolts, were manually removed from further analysis. The data were then referenced to a common average of the electrodes and detrended. Trials removed for the behavioral analysis were also removed in the EEG analysis, which included incorrect responses, No-Go trials, and trials with less than 100 ms responses.a priori to measure global efficiency and modularity during the baseline and task. The baseline period was from -200 to 0 ms and global efficiency and modularity were averaged over this period to produce baseline values separately for the three frequency bands. These two measures were baseline corrected by subtracting the baseline values, and the maximum absolute change from baseline was identified during the period between 30 and 600 ms after the digit presentation. This peak value reflects how large the two network measures change from the baseline: this could be either positive or negative depending on the direction of change. The period from 300 to 600 ms was selected for the task period. The brain is thought to be involved in higher cognitive processes during this period  with random intercepts. LMEM allows data to have missing values. The behavioral measures of this study were the number of omission and commission errors, mean RT, SDRT, mu, sigma, tau, FFAUS, and SFAUS. The network measures were global efficiency and modularity in three frequency bands  during the task. Additionally, there were three other variables; SART Task (fixed and random), Age (adolescent and child groups) and the ADHD Index scores.To examine the relationships between behavioral measures, ADHD Index, and network measures, three different types of models were examined. Two categorical variables (Age and SART Task) were inserted into all three types of models as predictors. The first type of model aimed to examine the association between the behavioral measures and the ADHD Index. Each behavioral measure was treated as an outcome variable, and the ADHD Index and the two categorical variables were the predictors. A single model was fitted for each behavioral measure. In addition to the association between the ADHD Index and the behavioral measures, the effects and interactions of the two categorical variables on behavioral performance were also examined. In the second type of model, the association between the behavioral measures and the network measures was examined. Each behavioral measure was treated as an outcome variable, and each single network measure was inserted as a predictor, along with the two categorical variables. A single model was fitted for each behavioral measure and network measure. In addition to the association between the behavioral measures and the network measures, interactions between the network measures and the two categorical variables were also examined. In the third type of model, the association between the network measures and the ADHD Index was examined. Each network measure was treated as an outcome variable, and the ADHD Index was inserted as a predictor, along with the two categorical variables. A single model was fitted for each network measure. In addition to the association between the network measures and the ADHD Index, the effects and interactions of the two categorical variables on network measures were also examined.P-values were adjusted using the False Discovery Rate (FDR) method  with 2000 resamples. If the confidence interval did not contain 0, it was considered to be a significant predictor. Pairwise comparisons were performed for the significant interaction terms in the models using the lsmeans package in R . P-value) method . As thisIn this first type of model, Generalized linear mixed effect models (GLMEM) were used for omission and commission errors with the Poisson distribution, given they are count data. LMEMs were used for the other behavioral measures. Behavioral measures were treated as outcome variables and three predictors were inserted in the models – ADHD Index, Age, and SART Task. ADHD Index was centered to reduce collinearity. Categorical variables, Age, and SART Task were coded using the sum contrasts, where children and the Random SART were coded as -1 and adolescents and the Fixed SART were coded as 1. Statistical results were summarized and reported in There were significant differences in task performance between the children and adolescents. The adolescent group performed the SARTs with faster mean RT smaller SDRT, faster mu, β = −35.0, 95% CI = , smaller sigma, smaller tau, smaller FFAUS, smaller SFAUS, and less omission, and commission errors.Participants made significantly more commission errors during the Random than the Fixed SART. There were no other significant differences in performance on the fixed and random SARTs.ADHD Index was positively associated with SDRT, tau, FFAUS, SFAUS, and negatively associated with mu. Other measures were not significantly associated with ADHD Index.There were no significant interactions found.In this second type of model, LMEMs were used to assess the relationship between the RT and network measures. The behavioral measures of mean RT, SDRT, mu, sigma, tau, FFAUS, and SFAUS, were treated as outcome variables. As trials with errors were excluded in order to be able to compute the network measures, associations between omission and commission errors with network measures were not examinable. Network measures, Age, and SART Task were treated as predictors in the models. Network measures were centered to reduce collinearity. Age and SART Task were coded in the same manner as described above. The network measures during the baseline were not examined as they were assumed to be task irrelevant. The network measures during the task and their interactions with Age and SART Task were specifically examined, where Age and SART Task were considered as control variables. Statistical results were summarized and reported in Global efficiency in the theta band was negatively associated with SDRT, tau, FFAUS, and SFAUS. Reduced RTV was associated with a greater increase in global efficiency in the theta band from the baseline to the task. No other significant associations or interactions were found between global efficiency in the three frequency bands and the behavioral measures.There was a significant three-way interaction between modularity in the theta band, Age, and SART Task on SDRT see , and on There were no other significant interactions or main effects.There was a significant two-way interaction between modularity in the alpha band and Age on SFAUS see . There wNo other significant effects or interactions were found.There was a significant two-way interaction between modularity in the beta band and Age for SDRT see . There wThere was a significant three-way interaction between modularity in the beta band, Age, and SART Task for SFAUS see . There wThere were no other significant associations or interactions between modularity in the beta band and the behavioral measures.In the third type of model, network measures were inserted as outcome variables for the three frequency bands  and two time periods . Three predictors were inserted in the models: ADHD Index, Age, and SART Task. ADHD Index was centered to reduce collinearity. Age and SART Task were coded in the same manner as described above. Statistical results were summarized and reported in Relative to children, adolescents performed the tasks with greater increases in global efficiency from the baseline to the task period in the theta and the alpha bands. Compared with children, adolescents showed a greater decline in modularity from the baseline to the task period in the theta, and alpha bands.There were no other significant effects of Age.ADHD Index was negatively associated with modularity in the beta band during the task period.There were no other significant effects of ADHD Index.There was a significant two-way interaction between ADHD Index and Age on global efficiency in the theta band during the task see . There wThere was a significant two-way interaction between Age, and SART Task on global efficiency in the alpha band during the task see . CompareThere were no other significant interactions.Children aged from 9 to 12 years and adolescents aged from 15 to 18 years completed the Fixed and Random SARTs while EEG was recorded. Higher ADHD Index was associated with greater RTV in all the RTV measures except for sigma. The same associations were found in both children and adolescents. As predicted, individuals with higher levels of ADHD symptoms tended to produce more variable responses. Adolescents showed a greater increase in global efficiency and decrease in modularity in the theta and alpha bands from the baseline to task periods, than the children. This suggests that adolescents may be better able to adjust their brain activity to perform the task smoothly relative to children, modifying brain activity be more integrated and less strongly segregated. A greater increase in global efficiency in the theta band was associated with all the RTV measures except for sigma. This indicates that having highly integrated brain activity is beneficial in producing more stable responses, possibly by combining information across the brain more efficiently. In adolescents only, a higher ADHD Index score was associated with a smaller increase from the baseline to the task period in global efficiency in the theta band. This may suggest that there might be a delay in maturation for adolescents with higher levels of ADHD symptoms. Their brains might be less efficient in shifting from the baseline to the task, which might lead to more variable responses.For stable performance, it may be important for the brain to be highly integrated. Global efficiency in the theta band during the task was significantly associated with SDRT, tau, FFAUS, and SFAUS, but it was not associated with mean RT, mu, or sigma. A greater increase of global efficiency in the theta band may be a key characteristic of the brain associated with RTV. The results suggest that greater integration of the brain during the task was associated with more stable performance. Greater integration of the brain, as measured by graph theoretical analysis, is thought to reflect efficiency of information processing . The curThe theta band may play a crucial role in the brain’s ability to perform the task with stable responses, as global efficiency only in the theta band was associated with RTV measures. The importance of this theta band integration in cognitive task performance has been reported in studies applying the event-related networks approach to performance on the oddball task  and the From late childhood to late adolescence, the brain may be developing the ability to efficiently adjust functional connectivity for maximal task performance. Compared with children, adolescents showed a greater increase in global efficiency and decrease in modularity in the theta and alpha bands from baseline to performing the fixed and random SARTs. The results indicate that adolescents were adjusting their brains to be more integrated and strength of segregation to be decreased compared with children when performing the task. Adolescents with higher levels of ADHD symptoms might have difficulty with the brain becoming more integrated when performing the task, which may possibly indicate a delay in maturation of the brain. Adolescents with lower levels of ADHD symptoms showed a greater increase of global efficiency in the theta band from the baseline to the task compared with adolescents with higher levels of ADHD symptoms. Previous studies have suggested that individuals with ADHD showed less brain integration, as measured by the characteristic path length and global efficiency . The assAs hypothesized, individuals with higher levels of ADHD symptoms showed more variable responses. The ADHD Index was positively associated with SDRT, tau, FFAUS, and SFAUS. The results were consistent with previous research and our prediction that higher levels of ADHD symptoms would be associated with greater RTV. In the Ex-Gaussian analysis, any extremely long responses, most likely representing lapses in attention during the task are captured by tau . The strThe level of segregation might be related to RTV differently depending on the age of the participant. Only in the child group was modularity in the beta band negatively associated with SDRT. Additionally, a negative association between modularity in the beta band and SFAUS was found in children only in the fixed version of the SART. Children who showed less reduction of modularity during the task relative to baseline produced more stable responses. Previous research in adults has shown that less segregated brain activity is associated with better cognitive performance . Our resAn expectation for upcoming stimuli helped participants to inhibit their responses to No/Go stimuli, but it might not influence RTV. Participants made more commission errors in the random version of the SART compared to the fixed version, but no other behavioral measures differed between the two SART tasks. A previous study by Even though the participants showed similar levels of RTV between the fixed and random version of the SART, the brain responded differently to the two conditions. Participants in this study were not clinically diagnosed with ADHD, and the analysis was performed using the levels of ADHD symptoms as measured by the Conners 3 ADHD Index. The results could be different with a clinically diagnosed group. Another limitation of the study is the mixed use of the self-report and parent forms of Conners 3 across individuals. The study treated the ADHD Index from parents and self-report as the same variable, as they are highly correlated , howeverOur analysis was performed using surface level EEG recording with 64 electrodes. This allowed us to infer overall connectivity patterns of the surface of the whole brain, but with limited resolution in space. Further studies could consider using source localization methods, fMRI, or simultaneous EEG/fMRI recording to form more complex networks with a greater number of nodes. This would allow for investigations into small local networks existing within the large network and how the small networks communicate each other, such as in the study of hubs of networks . This soAnother limitation of this study was the choice in defining the task period, which was 300–600 ms. It was assumed that this task period is associated with cognitive processes of humans, but this selection of the time window was set by choice rather than determined by a data driven approach. This process of deciding the time window might introduce subjective bias. Future studies could consider adopting different time windows or use a mass univariate approach  to avoidThis study found that a greater increase in global efficiency in the theta band from the baseline to task active status was negatively associated with RTV, where the more integrated the brain, the more stable the performance. We suggest that greater integration of the brain allows for smoother communication between different parts of the brain and subsequently more efficient information flow. As the level of integration was not associated with speed of responses, stability and speed of responses were influenced by different mechanisms in the brain. Adolescents showed greater increase in global efficiency and greater decrease in modularity in the theta and alpha bands during the task compared with children. As children develop into adults, their brains are more efficiently able to shift state as needed for the task at hand, possibly reflecting cognitive maturation. As predicted, individuals with higher levels of ADHD symptoms performed the SARTs with greater RTV. Through the measurement of different aspects of RTV, this research reveals that the general stability of responses is not simply linked to ADHD symptoms. Higher levels of ADHD symptoms might be associated with more lapses in attention (tau), greater difficulty in sustaining attention (FFAUS) and maintaining arousal levels (SFAUS), which may contribute to increased RTV. SDRT, tau, FFAUS, and SFAUS were associated with both ADHD symptoms and global efficiency in the theta band but not with sigma. With these similar patterns of associations, ADHD symptoms and global efficiency in the theta band might explain similar aspects of RTV. From the examination of associations between ADHD symptoms and network measures, only in the adolescent group was global efficiency in the theta band during the task associated with the ADHD Index. This suggests that adolescents with greater levels of ADHD might have a delay in maturation, thus exhibiting lower levels of integration activity and increased RTV.The datasets generated for this study are available on request to the corresponding author.The University of Melbourne Human Research Ethics Committee, the Victorian Department of Education and Training, and the Catholic Education Office in the Archdiocese of Melbourne approved the study, in accordance with the 1964 Declaration of Helsinki. Parents and children provided written informed consent prior to each child’s participation in the study. Adult participants provided written informed consent prior to the study.KM and KJ contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript. MM helped with the analysis and the writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bartonella bacteria are prevalent in mammals and cause numerous human infections. Understanding how this genus has evolved and switched hosts in the past can reveal how current patterns were established and identify potential mechanisms for future cross-species transmission. We analyzed patterns of Bartonella transmission and likely sources of spillover using the largest collection of Bartonella gltA genotypes assembled, including 67 new genotypes. This pathogenic genus likely originated as an environmental bacterium and insect commensal before infecting mammals. Rodents and domestic animals serve as the reservoirs or at least key proximate host for most Bartonella genotypes in humans. We also find evidence of exchange of Bartonella between phylogenetically distant domestic animals and wildlife, likely due to increased contact. Care should be taken to avoid contact between humans, domestic animals and wildlife to protect the health of all.As humans move and alter habitats, they change the disease risk for themselves, their commensal animals and wildlife.  Bartonella, a common and well-studied bacterial genus in wildlife and humans that can cause cat scratch disease, trench fever and other diseases. We showed that humans are likely changing disease risk for themselves and the animals in their environment by moving themselves and domestic animals, as evidenced by large geographic movements of infections or shared infections in distantly related species. Not only does this increase our knowledge about Bartonella, an important emerging pathogen, but our investigation can serve as a model for elucidating the driving role of humans in changing disease landscapes.As humans move around the globe they contact new environments, potentially introducing novel diseases to wildlife, domestic animals and humans. Understanding how current infection patterns were established and how humans have likely altered them can help protect human, animal and environmental health. We traced the evolution of and distribution of globally distributed, pathogenic  Bartonella bacteria are an exception to this pattern. This genus of bacteria has been found in numerous taxa and is usually at high prevalence [Bartonella is a blood-borne pathogen, found in many animals. It is the cause of cat scratch fever, Carrion’s disease and trench fever as well as a number of incidents of endocarditis in humans and has been hypothesized to be the cause of unexplained febrile illness in a number of cases [Bartonella from both 16s rRNA genes and citrate synthase (gltA) to determine the evolutionary history of Bartonella, patterns of host switching and geographic constraint and its spillover into humans or from human commensals into wild species. The citrate synthase gene is known to give high power to discriminate between Bartonella strains and is one of the most commonly sequenced Bartonella genes [Bartonella species [Human movements and actions have numerous impacts for wildlife disease ,2. Theseevalence . Bartoneof cases ,6. Therela genes ,8. We alResearch was approved by the Stanford University Administrative Panel on Laboratory Animal Care (protocol 26920) and conducted under the appropriate Costa Rican permits .Bartonella transmission between species, sequences were downloaded from Genbank on 30 November 2016 using the search term “Bartonella gltA” and again on 21 May 2018 using the search terms “Bartonella” AND (“gltA” OR “GltA” OR “GLTA” OR “glta” OR “citrate synthase”) and limiting the search to sequences uploaded in the previous 900 days in order to update our dataset with the most recently published sequences. A separate search was conducted using the search term “Bartonella 16s” on 1 February 2017. Insect microbiome studies that detected Bartonella were also used [Bartonella from Costa Rican bats in a mosaic agricultural landscape, including previously published [Bartonella strains were published independently from their hosts; in this case we searched other literature to find the source of the strain. Sequences that were not in fact Bartonella gltA were removed manually and sequences were aligned using the Geneious alignment algorithm and refined using MUSCLE in Geneious (version 8.1.9 [Bartonella) were excluded to reduce the size of the resultant phylogenies. This was especially the case for the published genomes of named Bartonella species; many of these sequences lacked data on when and where they were isolated and were therefore excluded from our analysis. We also excluded some fragments that were too short or had substantial missing data within the alignments, as well as fragments which misaligned significantly at the ends, causing us to doubt the quality of these end base calls. Sequences that contained one or two base pair deletions not found in other sequences were also eliminated as we doubted the quality of the sequences. Sequences with deletions in multiples of three base pairs were retained as these likely represent actual deletions of amino acids. Alignments were manually inspected and corrected. Two alignments were produced, one of 540 bp and one of 277 bp. The first contained 677 sequences in total and the second included 1,060 unique sequences.In order to ascertain broader patterns of spillover and lso used . BartoneIn order to test for patterns in host specificity and biogeography we also constructed Bayesian phylogenies using BEAST 2  for the Bartonella was constrained to be monophyletic with Brucella melitensis as an outgroup. The first model tested was a strict clock model with a constant population size coalescent model with vague priors as has been used for previous phylogenetic analyses of Bartonella [Artibeus lituratus and Artibeus watsoni-associated Bartonella was estimated to have evolved at the divergence of the two bat species  and the prior distribution was estimated with a log-normal distribution with a mean of 8.5 mya (SD = 2.73) based on previous estimates [We tested two different models for the phylogenetic hypothesis based on the 540 bp fragment. Both analyses were run with a gamma site model with empirical base frequencies, an estimated proportion of invariant sites and all nucleotide transition/transversion frequencies except the CT transition rate estimated. The gamma shape prior was set to an exponential distribution with a mean of 1; the proportion of invariant sites was set to a uniform distribution between 0 and 1; all nucleotide substitution rates were set to a gamma distribution with an alpha of 2 and a beta of 0.5 or 0.25 for transitions and transversions respectively. In all cases rtonella ,21 with stimates –27 collastimates . We usedBrucella melitensis to be an outgroup but no other calibrations were included. All gltA model were run for 2.5 x 107 generations and sampled every 50,000 generations.For the 277 bp tree we ran our simulations with a GTR distribution, an estimated proportion of invariant sites and a gamma distribution of rates. We tested two models, a strict clock, constant population size coalescent model as described in the first model for the 540 bp alignment and a birth death model with a relaxed log normal clock as described in the second model for the 540 bp alignment. In both models we constrained gltA models converged with all parameters showing an effective sample size (ESS) over 100  and most showing an ESS over 200. The two models for the 540bp alignment were compared using AICM of the likelihood [All kelihood  with 1,0Bartonella we constructed a phylogeny using sequences from the 16s rRNA gene. All 450 sequences were aligned and trimmed to the same length (259 bp) in Geneious. We constructed a phylogenetic hypothesis in BEAST 2 using a strict clock and a birth death model with vague priors as described in the birth death models for the gltA genes with Rhizobium leguminosarum as an outgroup. The model was run for 107 generations; most ESS were above 300, though the birth rate and death rate ESS were roughly 100. As we were not concerned with speciation dynamics but rather broad topology, we considered this hypothesis to be sufficiently sampled.In order to understand the evolutionary origin of gltA MCC tree was used in an analysis of host specificity and geographic conservation between related Bartonella species. Many nodes did not have good support so we conducted all analyses using only nodes with a Bayesian posterior probability of the likelihood of 0.7 or above. Using the fitDiscrete function in geiger [The 277 bp n geiger , four mogltA, by examining the location of Genbank records with identical genotypes and by searching the literature for the distribution of named Bartonella species. A host switch or geographic shift was inferred so as to capture the minimum number of shifts with Bayesian posterior probabilities of at least 0.7. We also assessed shifts at posterior probabilities of the likelihood of at least 0.9 and 0.95 to ensure our results were robust regardless of our cut-off.Host switches and sharing of clades between geographic regions was assessed by manually examining the MCC phylogenetic hypothesis based on a 277bp fragment of Bartonella, we categorized genotypes or monophyletic clades as being found in one or more of the following categories: rats, other rodents (excluding rats), humans, domestic carnivores, wild carnivores, domestic artiodactyls, wild artiodactyls, shrews, and bats. We also noted other wild animals that were rarer in our dataset . Genotypes found in rats, domestic animals and humans were categorized as “human-associated” and the rest as “non-human-associated”. Human-associated Bartonella in this study does not necessarily mean the genotype has been found in a human but rather in a human or a commensal animal or an ectoparasite on a human or commensal animal. Sometimes the metadata contained within Genbank and the publications were insufficient to allow us to distinguish the exact species from which the Bartonella was isolated. All genotypes isolated from Rattus sp., their ectoparasites or an organism denoted “rat” were counted as human-associated rats. Although many species of rodents are commensal with humans (e.g. [In inferring the influence of humans on ns (e.g. ), all otBartonella transferring between host orders represented in our dated, 540bp phylogenetic hypothesis to determine the ages of such transfers and test whether transfers to humans were more recent than other transfers. For these analyses we only used clades with at least 0.7 posterior probability support, which meant that a few (3) clades were assessed as older than they may actually be. In each of these cases the transfer involved a genotype found in a human, meaning our hypothesis testing is conservative. Difference between zoonotic transfers and other transfers was assessed using one-tailed t-tests assuming unequal variance on the inferred node age and lower bound of the 95% highest probability density to account for uncertainty in dating of the nodes.Additionally, we determined all instances of All alignments, metadata and R code are available in the supporting information.gltA sequences, we analyzed 2,515 277 bp sequences, 1,060 of which were unique and used to construct a phylogenetic hypothesis. Information on identical sequences used to infer host and geography transfers is included in gltA fragment for 677 genotypes, a subset of the larger dataset.Starting with 2,564 gltA and a 259 bp fragment of the 16s rRNA gene both support an origin for Bartonella in the environment and in the guts of insects  was a lambda model in which lambda was 0.96, indicating near Brownian motion evolution along the phylogeny , one from roe deer (HG977196) and four from cats and dogs . Four garasites ,36; B. b peruana ; B. anca peruana  and B. q gerbils  and groum humans ; B. quinBartonella has infected domestic carnivores seven times  and artiodactyls  once; in five instances rodents and domestic carnivores shared Bartonella but the direction of transmission could not be inferred  and in two instances Bartonella from domestic animals has infected rodents . Bats and rodents have exchanged Bartonella at least five times . Bats and domestic animals have exchanged Bartonella six times; in four cases bat-associated Bartonella has infected dogs ; in one case B. bovis infected a bat ectoparasite (JN172054) and in another case Bartonella from a dog may have infected a bat (FJ946847/ HM545137).Rodent-hosted Bartonella between domestic and wild animals (Bartonella associated with domestic carnivores infected wild carnivores. Domestic cat-associated Bartonella has been found in lions (KX499327), mongooses (MF959421) and a cheetah (KX499331); domestic dog-associated Bartonella (B. vinsonii berkhoffii) has infected a fox (KU292568). Additionally, B. rochalimae has been found in a number of wild canids such as coyotes and foxes and has infected domestic dogs. And Bartonella has been shared between (1) a skunk, raccoon, cats and dogs (e.g. CP019782/ CP019786) and (2) domestic cats, lynx, mountain lions, cheetahs and lions (e.g. KX499327/ KX024503). Additionally, in at least two cases Bartonella transferred between domestic artiodactyls and wild artiodactyls–in one case a clade included roe deer, elk, cattle and sheep; in the other B. bovis was found in elk (KB915625).In eight cases, there was a transfer of  animals . In threBartonella between rodents and shrews, which are phylogenetically quite distant but presumably share the same terrestrial habitats and some of the same ectoparasite vectors ; a Bartonella strain found in a Japanese marten (AB611852) and one in a raccoon (KU292571) in clades of rodent-associated Bartonella; the evolutionary divergence of Bartonella in Antechinus and kangaroos; the divergence of human and non-human primate B. quintana and the grouping of artiodactyl-associated Bartonella with strains found in bats.We also inferred nine transfers of  vectors . SimilarBartonella was transferred between orders or within orders between wild and domestic animals (B. quintana) and 1 instance involved domestic animals, rats and humans as well as other rodents and shrews. Nearly twice as many instances of host shifts were associated with human influence than natural transitions.We inferred 64 instances where  animals ; 42 of tAdditionally, we noted 118 instances in which monophyletic clades or single genotypes contained genotypes isolated from more than one continent/ geographic region, 56 of which spanned both the Old World and New World, denoted in parentheses . Of the Bartonella into humans were also more recent than other divergences we inferred based on both the estimated age of the node  and the lower bound of the 95% highest posterior density  and rodents (subsp. arupensis). We inferred at least three separate transfers of B. vinsonii (two instances of B. v. arupensis and one instance of B. v. vinsonii) from rodents into humans based on phylogenetic relationships , confirming previous conclusions [Bartonella detected in a febrile patient in Thailand (GQ200856) as having over 95% identity with B. queenslandensis, a genotype first found in Australian rodents and also found in numerous Asian rodents, suggesting a previously unappreciated possible rodent-human transmission. Interestingly, one Bartonella genotype that was recovered from a Polish forest worker (HM116785) most closely resembled genotypes found in European Myotis, a genus of bat, and their ectoparasites . That most strains isolated from humans are related to domestic or peridomestic animals strongly indicates that spillover of Bartonella requires close contact between humans and the natural reservoirs of these infectious strains.Using the literature  and isolB. henselae, B. koehlerae and B. quintana were closely related to Bartonella detected in African rodent ectoparasites and an Asian bat. Similarly, B. mayotimonensis was closely associated with genotypes of Central American bats detected in this study. This same isolate has also been found in bats in Europe [Bartonella strains but that infrequent contact between bats and people prevents transmission. Rather most of the transmission we infer requires the transmission of Bartonella into a domestic or peridomestic animal, which can then transmit it to humans. Despite the noted host specificity of Bartonella . TheBartonella into humans than any other group, followed by domestic carnivores. Rodents also transmitted the most Bartonella to domestic animals and bats, though infections likely originating from wildlife such as bats in domestic animals are also relatively common. One potential explanation for the prominence of rodents in host switching may be the generalist tendencies of their ectoparasites. Bartonella is vectored by arthropods but some ectoparasites, such as blood sucking hippoboscid flies, are very highly host specific [Bartonella that have originated in rodents and infected other species such as humans . Con. ConBartns , with representatives on nearly every continent. This clade also contains at least five zoonotic genotypes of Bartonella, as well as genotypes found in dogs and ectoparasites on dogs, bats and a cow, underscoring the important role of human commensals in spreading disease to humans, domestic animals and wildlife and across the globe.Another interesting pattern that emerged when examining the tree as a whole was the impact of humans in spreading globally ,47,57–61There was a lot of uncertainty in the dating of our divergence times  perhaps due to the small fragment we were able to analyze and the depth of evolutionary history we were exploring. Additionally, there are many genotypes that may have died out or have not been sampled that mean even our minimum divergence date estimates are likely conservative. We cannot therefore state with certainty that humans are responsible for moving other species around, changing disease risk for themselves and wild animals. However, the finding that strains associated with humans or their domestic animals were more likely to be found globally, the finding that transfers between humans and other groups were the most recent ones, and the diverse placement of human infections across the phylogeny strongly support a role for humans changing their disease risk as they insert themselves and their associated animals into new habitats and ecosystems.Bartonella diversification  and a white-footed mouse, a North American native (AY064534). If the introduced bacteria have adverse fitness consequences, this could be another human-mediated conservation concern. Domestic animals also present a health risk to wild animals and other domestic animals; B. bovis was found in a cat, an elk and a bat ectoparasite and Bartonella has been transferred between Old World bats and dogs. The pet trade exacerbates this by shipping exotic animals all over the world, changing the pool of available infections for both the introduced and native species [Such movements and increased contact between humans, domestic animals and wildlife not only disguise geographic patterns of ustralia , in RattF075162; )) but ha species . IntroduBartonella is a rich system for examining the impacts of humans on patterns of infectious disease spread within species and between species, across landscapes and across the globe. Phylogenetic inferences about the origin of infections should be interpreted with caution as they are heavily influenced by available data and the taxa that have been sampled. There may be many missing links between those we inferred but the hosts simply have not been sampled. At least some part of the noted host specificity of Bartonella seems to be due to ecological factors regulating exposure rather than immunological incompatibility. Given the diversity of sources of zoonotic strains, including divergent strains with similar clinical presentations, physicians and researchers should consider a broad range of potential animal hosts and screen for a wide range of Bartonella genotypes when investigating the source of a suspected Bartonella infection.Overall our findings show that New sequences generated as part of this study have been uploaded to Genbank (accession numbers MH234314 –MH234380).S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TablegltA metadata, Breitschwerdt [Reservoir and geographic data are derived from schwerdt  and cite(DOCX)Click here for additional data file.S1 FigBartonella transferring between hosts that are used in inferring the timing of host transfers as detailed in The numbers in brackets indicate the lower and upper bounds of the 95% highest posterior density of the height of that branch, an estimate of the age of divergence of the node that the branch leads to. The width of the circle at each node is proportional to the Bayesian posterior probability support at that node. The blue tick mark numbers indicate the well-supported instances of (PDF)Click here for additional data file.S2 FigRattus sp. rodents, non-Rattus rodents, wild animals, domestic animals. “SM” indicates a shrew or rodent  as sometimes the actual host was not given and “Unk” indicates the continent from which the genotype was isolated is unknown. If a Bartonella species name is listed at a tip but not a Genbank accession number, the accession number is that of the sequence used in Judson et al. [The color of the tip label indicates the order of the host from which the genotype was derived (ectoparasites are colored according to their host). Highlighted tips indicate that multiple samples had the same sequence and the duplicate sequences can be found in n et al. .(PDF)Click here for additional data file.S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 FileOnly one representative per group is included in the phylogeny and (XLSX)Click here for additional data file.S4 FileThis file lists every inferred host transfer or finding of a genotype or monophyletic clade on more than one continent. A legend for interpreting each category appears on the second sheet of the file.(XLSX)Click here for additional data file.S5 FileThis file lists every well-supported host switching event captured in our 540bp phylogenetic hypothesis. A legend for interpreting each column appears on the second sheet of the file.(XLSX)Click here for additional data file.S6 File(NEX)Click here for additional data file.S7 File(NEX)Click here for additional data file.S8 File(NEX)Click here for additional data file.S9 File(TREES)Click here for additional data file.S10 File(TREES)Click here for additional data file.S11 File(TREES)Click here for additional data file.S12 File(R)Click here for additional data file.


In the present study parenting stress and the broader phenotype are investigated in two highly common developmental disorders, namely Attention Deficit Hyperactivity Disorder (ADHD) and specific reading impairment (dyslexia). Within a total sample of 130 parents, 27 were parents of children with ADHD (P-ADHD), 38 were parents of children with a diagnosis of dyslexia (P-DYS) and the other 65 participants were parents of children with typical development (P-TD). A battery of cognitive tasks was administered which included verbal and non-verbal Intellectual Quotient (IQ), reading speed (passage and nonwords), verbal fluency and the Attention Network Task (ANT). Reading history, symptoms of ADHD in adults and parenting stress were measured through questionnaires. Group differences evidenced that the P-DYS group had lower scores in the reading tasks, in the verbal fluency task and in the reading history questionnaire. Conversely, the P-ADHD group had more transversal cognitive weaknesses  and the highest scores in parenting stress and ADHD symptoms, together with poor reading history. The groups did not differ in the ANT task. Parenting stress was predicted, on the whole sample, by lower socioeconomic status (SES) and number of family members and higher ADHD symptoms. Implications for research and clinical settings are discussed. In the past few decades a great amount of research led to an outstanding knowledge about the genetic, biological and neural basis of developmental trajectories in both typical and atypical populations. After Morton and Frith’s causal model , many stThe concept of broader phenotype derives from the definition of endophenotypes (or intermediate phenotypes) , describThus, parents themselves might have similar cognitive weaknesses as their offspring, although at the behavioral and clinical level they might not meet criteria for diagnostic classification, or they did not undergo clinical evaluation in their life course. For example, parents of children with developmental dyslexia might themselves show weaknesses in phonological awareness skills, which is considered a clinical marker of reading impairment . In the Dyslexia and ADHD are two of the most frequently occurring developmental patterns within the broader classification of neurodevelopmental disorders defined by DSM-5 . DyslexiADHD is a neurodevelopmental disorder whose main behavioral manifestations regard symptoms of inattention, impulsivity and hyperactivity, which occur in more than one social context  and significantly interfere with everyday life and social adaptability. These symptoms may resolve in adolescence or persist into adulthood, although the characteristics that mediate persistence or remission of ADHD during adulthood are still largely unexplored . From a In sum, a neurobiological origin has been postulated both for ADHD and dyslexia, due to the interaction of genetic, epigenetic and environmental factors. Although different theoretical models have been proposed for each of these neurodevelopmental disorders, it is recognized that their aetiology is best explained within a multiple deficit model in which genetic factors interact with other risk factors connected with the pre- or perinatal period ,16. FurtVarious studies have ascertained that both ADHD and dyslexia run in families, with an increased probability for parents who have family risk for the disorder to have a child with clinical symptoms for that disorder: up to 66% for reading impairment ,19 and uBased on the intergenerational multiple deficit model  both parSigns of intergenerational transmission of executive functions (EF) have been examined in a number of studies, although with mixed results. Cuevas et al.  found moThe analysis of parent-child correlations in reading skills has been the focus of many family risk studies in the area of reading impairments. Black et al.  reported“the stress reaction to the demands of being a parent” parent”  the child characteristics, which, due to the cognitive and behavioural impairments, might increase the demands and therefore the cost of parenthood; (2) parents’ cognitive and psychological resources. Based on the literature previously discussed, parents of children with neurodevelopmental disorders might themselves have weaknesses at the cognitive and behavioural levels, often in the same areas of those manifested by their offspring. This, in turn, might amplify their perception of being inadequate in their parental role, particularly if their own weaknesses had never been appropriately recognized and treated in their life course. In the literature, higher levels of parenting stress have been documented in parents of children with a wide range of developmental disorders, included ADHD and dyslexia.As far as ADHD was concerned, a meta-analysis reviewed research studies on parenting stress in families of children with ADHD (age 12 or younger) and showed that parents experienced much higher levels of parenting stress compared to parents of typically developing children . Similardyslexia, [With reference to SLD, there is still a paucity of research on parenting stress. Some studies were conducted on parents of children with mild or moderate intellectual dysfunctions and higher levels of parenting stress were found , togetheyslexia,  observedyslexia, , for simyslexia,  found thyslexia, , involviyslexia, . In bothTo conclude, this introduction evidenced two main points. On the one hand, there is increasing evidence about the broader phenotype of developmental disorders, which suggests that parents of children with neurodevelopmental disorders might share cognitive and behavioural weaknesses with their offspring. On the other hand, a vast literature evidenced that parents of children with neurodevelopmental disorders such as ADHD and dyslexia, have higher levels of parenting stress compared to parents of children with typical development. However, scarce evidence has been collected on the interplay between parents’ cognitive profiles and parenting stress in populations of children with ADHD and dyslexia.(1)To investigate group differences in cognitive and behavioral indexes of reading and attention impairments comparing three groups of parents: parents of children with ADHD (P-ADHD), parents of children with developmental dyslexia (P-DYS) and parents of children with typical development (P-TD). This first objective was intended to verify if the three groups of parents did indeed show endophenotypic patterns specific for their offspring developmental profile. Based on the literature review, P-DYS are expected to fail in reading related measures, whereas P-ADHD are expected to differ compared to the other groups in ANT task and in behavioural scales assessing ADHD symptoms in adulthood. Differences between mothers and fathers will be considered.(2)To investigate group differences in parenting stress. Based on previous studies higher levels of parenting stress have been reported for parents of children with dyslexia and parents of children with ADHD compared to parents of typically developing children. We therefore expect replying these findings and evaluating, for the first time, differences in parenting stress between parents of children with dyslexia or ADHD.(3)To analyse the relationships between cognitive indexes and parenting stress in the whole sample. Specifically, through a step-wise regression model the study aimed at evaluating which factors better predicted parental stress, an important index assessing parent-child interactions and known to correlate with parental styles. Cognitive measures are expected to significantly predict parenting stress, based on the assumption that parenting stress depends on parents’ ability to cope cognitively with offspring’s requests.The aims of the present study were threefold:χ2(2) = 1.78, p = 0.4). Participants volunteered for the study and signed the informed consent and data treatment documents before starting. This study was carried out in accordance with the recommendations of American Psychological Association’s Ethical Principles (1982), and the research ethics committee of the AUSL of Bologna approved the project .The study was conducted on a total sample of 130 parents (68 mothers and 62 fathers), with a mean age of 43.8 years (5.08 SD). Within the total sample, 38 were parents of children with a diagnosis of dyslexia (P-DYS) (mean age: 43.65 ± 5.08 years); 27 were parents of children with ADHD (P-ADHD) (mean age: 43.33 ± 5.7 years). The other 65 participants (mean age: 44.12 ± 4.98) were parents of children with typical development (P-TD). Parents of children with ADHD were recruited at the Maggiore Hospital, Department of Developmental Psychiatry and Psychology, in the city of Bologna. Children underwent a full clinical evaluation by a multidisciplinary team and, according to ICD-10 criteria, they received a diagnosis of ADHD (F 90.0), in the absence of comorbidity with dyslexia. The P-DYS group was recruited at LADA lab (Laboratory for the Assessment of Learning Disorders), Department of Psychology, University of Bologna. To be included in the study, they had to be parents of a child who received a diagnosis of specific reading disorder (ICD-10 code: F 81) in the last 2 years. None of the children with dyslexia had comorbidity with ADHD. The P-TD group was recruited through leisure centers and through word of mouth. None of the children of the control group had been previously referred for showing risk factors for dyslexia or ADHD. All parents fulfilled inclusion criteria as follows: being biological parents; free of psychiatric disorder; Italian monolinguals, family not in care of social services; intellectual functioning within the normal range . Independently of their civil state (married or not), we asked both parents to take part in the study. Depending on their willingness to volunteer, for some children only one parent was included in the study. In the final sample there were 60 couples participating and 10 single parents  , civil state, and evaluation of previous scholastic achievement. Parents were also asked to evaluate their children scholastic achievement  and to indicate the number of family members.(1)Kaufman Brief Intelligence Test-2. Passage reading, taken from the “Reading tasks for the secondary schools” . Partici(3)Non-word reading task, taken from the “Battery for the assessment of developmental dyslexia and dysorthographia” . Partici(4)Semantic fluency: The participants were asked to produce as many words within the same semantic category (professions) in a test interval of 1 min.(5)+) appeared on the center of the screen for 400 ms; (b) a cue (*) was presented for 100 ms; (c) a fixation period was provided for 400 ms after the cue; (d) the target arrow and the flankers were presented simultaneously until the participant’s response or up to 1700 ms, (f) the target and flankers disappeared after response and the next trial began. Participants were instructed to focus on the fixation point and to respond by pressing a key on the computer keyboard, as quickly and accurately as possible, with their left hand when the arrow pointed to the left and with the right hand when it pointed to the right. A training phase consisting of 24 trials was administered.Attentional Network Task (ANT) . This ta(1)Adult Reading History Questionnaire-Revised (ARHQ-R) : The ARH(2)Adult ADHD Self-Report Scale (ASRS-v1.1) Symptom Checklist. The Symptom Checklist is an instrument based on DSM-IV-TR criteria for ADHD, developed by World Health Organization (WHO). It includes 18 questions , based on a five points Likert-scale.(3)Parenting Stress Index (PSI) . The PSIIn order to test group differences in background information, we performed a set of ANOVAs applying Bonferroni post-hoc tests. For categorical variables, we tested differences in groups’ distribution through Chi-Square (χ) analysis. Then a set of ANOVAs and MANOVAs were run with Group  and Role  as independent factors and scores at cognitive tasks and questionnaires as dependent variables. Finally, in order to investigate which factors predicted Parenting Stress a regression model was run. In the first step SES, parents’ age and number of family members were included, in the second step cognitive variables were included , and the third step included behavioral measures of reading history and ADHD symptoms in parents.A summary of mean values related to background information is reported in p = 0.06, η2 = 0.04), parents’ age  = 0.26; p = 0.7, η2 = 0.0) and civil state (χ2(4) = 4.6, p = 0.33); more than 85% of parents in each group were married or living together. There was instead a difference in mean SES  = 8.08; p < 0.001, η2 = 0.12), with the P-TD group showing higher values compared to the other two groups (p < 0.01), which did not differ from each other. The P-TD group also reported to have reached brilliant scholastic results in a higher percentage of cases (58.5%) than the P-DYS (41.1%) and the P-ADHD group (χ2(4) = 17.48, p < 0.01). Families in the ADHD group reported to have a lower number of children compared to families in the TD-group, who, in turn, had less children than families in the P-DYS group  = 13.02; p < 0.001, η2 = 0.17).The three groups did not differ for children’s age  = 2.87; p < 0.001, η2 = 0.23). It emerged that children in the P-DYS group were considered to have lower performance compared to the TD group in all disciplines considered. The ADHD group differed from the control group only in Writing skills (p < 0.01) and children with ADHD were reported having better scores in reading (p < 0.001) and grammar (p < 0.05) compared to the group of children with dyslexia.Considering parents evaluation of their offspring scholastic achievement, the MANOVA showed a main effect of group  = 5,74; p = 0.001, η2 = 0.07). There was no effect for Role  = 2.11; p = 0.12, η2 = 0.03), nor for the interaction Group*Role =2.03; p = 0.09, η2 = 0.03). Univariate analyses and Bonferroni post-hoc comparisons showed that the P-ADHD group had significantly lower scores compared to the P-TD group in both Verbal (p = 0.001) and Non Verbal IQ (p = 0.001).In p < 0.001, η2 = 0.16) and a marginal effect of Role  = 3.9; p = 0.05, η2 = 0.03), but the interaction Group*Role was not significant  = 0.4; p = 0.67, η2 = 0.006). Parents of children with TD outperformed compared to both P-ADHD (p < 0.001) and P-DYS (p < 0.01). Mothers tended to produce more words than fathers (p = 0.5).Considering Verbal Fluency, the ANOVA showed a main effect of Group  = 12.04; p < 0.001, η2 = 0.10). Both univariate analyses were significant  = 17.41; p < 0.001, η2 = 0.2; Nonwords:  = 8.93; p < 0.001, η2 = 0.13) and Parents in the DYS group were slower compared to the P-TD group in both tasks (p < 0.001), whereas P-ADHD differed from the P-TD group only in passage reading speed (p < 0.05). Finally, to analyze results from the ANT task we performed two MANOVAs, one on accuracy and the other on RT parameters, see As far as reading measures were concerned, the MANOVA on passage and nonword reading speed showed a main effect of Group  = 7.06; p = 0.78, η2 = 0.01; RT:  = 1.82; p = 0.096, η2 = 0.047), nor it was for Role  = 1.11; p = 0.35, η2 = 0.03; RT:  = 0.3; p = 0.8, η2 = 0.01) or for the interaction Group*Role  = 0.65; p = 0.68, η2 = 0.017; RT:  = 0.3; p = 0.8, η2 = 0.01).In both analyses the main factor Group did not result to reach statistical significance  = 0.54; p < 0.001, η2 = 0.16), with Bonferroni post-hocs showing that both P-DYS (p < 0.001) and P-ADHD (p < 0.01) reported more problems in reading history and attitude compared to the P-TD group. There was no effect of Role  = 0.0; p = 0.9, η2 = 0.0), nor any significant interaction Group*Role  = 1.57; p = 0.21, η2 = 0.02).In p < 0.001, η2 = 0.08) also in the ASRS Questionnaire addressing ADHD symptoms in adulthood. Bonferroni post-hoc comparisons revealed the P-DYS group did not differ from either the P-TD (p = 0.27) group or the P-ADHD group (p = 0.33). Instead, the P-ADHD group showed higher scores (p < 0.01) compared to the P-TD group. There was no effect of Role  = 0.52; p = 0.47, η2 = 0.004), nor of Group*Role interaction  = 1.2; p = 0.3, η2 = 0.02).There was a main effect of Group  = 5.19; p < 0.001, η2 = 0.25), but there was no effect of Role  = 0.06; p = 0.8, η2 = 0.001), or of the interaction of Group*Role  = 0.11; p = 0.89, η2 = 0.002). Bonferroni post-hoc comparisons showed that the P-ADHD group had higher scores both compared to the P-TD (p < 0.001) and P-DYS (p < 0.001) groups, which did not differ each other (p = 0.3). The same pattern was observed when considering the single subscales , where P-ADHD participants always obtained higher scores compared to the other two groups.Finally, considering the Parenting Stress Index, an ANOVA was run on the Total score and a main effect of Group arose  = 20.23; β = −0.24, p < 0.05), minor number of family members  and higher ASRS score (symptoms of ADHD in parents)  were significant predictors of higher parenting stress.In order to investigate which factors predicted Parenting Stress a hierarchical regression model was run. Results are presented in The present study investigated the cognitive and behavioral profiles of parents of children with neurodevelopmental disorders, specifically ADHD and dyslexia, and with typical development. The aims were threefold. First, we wanted to evaluate the broader phenotype of ADHD and dyslexia by assessing putative endophenotypes of these disorders within family members (parents). Secondly, we wanted to test differences in levels of parenting stress amongst the three groups. Finally, the study aimed to test which cognitive and behavioural indexes better predicted parenting stress. Considering the first aim, group differences on demographic, cognitive and behavioural variables depicted a complex picture of overlapping and distinct features amongst the three groups considered. Both P-ADHD and P-DYS parents had lower SES compared to parents of typically developing children, but the three groups did not differ in civil state, parents’ and children’s age. The difference in SES between P-ADHD and P-TD groups is quite consistent with data reported in the literature , and inMoving to the cognitive profile, it emerged that the P-ADHD differed from the P-TD group in both verbal and non verbal IQ whereas the P-DYS did not differ from the other two groups. Furthermore, both the P-DYS and P-ADHD group significantly underperformed compared to the P-TD group in verbal fluency and passage reading speed, whereas in nonword reading speed it was the P-DYS group that underperformed compared to the P-TD group. There were no differences between the three groups in the ANT task, either considering accuracy or speed parameters.When considering the questionnaires assessing behavioural symptoms of ADHD and troubles in reading history, it emerged that both P-DYS and P-ADHD groups reported a significant poor reading history compared to P-TD group. Instead, in the ASRS questionnaire, only the P-ADHD group showed consistent signs of ADHD symptoms.Finally, when analyzing parenting stress, the P-ADHD group resulted to report the highest level in all dimensions considered: parental distress, defensive response, difficult child, difficult child-interaction. In contrast, the P-DYS group did not differ from the P-TD group, although mean values tended to be slightly higher in DC and DCI scalesTaken as a whole, these results evidence some aspects of specificity with regards to the two distinct profiles analyzed, i.e., ADHD and dyslexia. In particular, the group of parents of children with dyslexia show a profile quite similar to what is reported in the literature for children with dyslexia: they show a fully adequate intellectual functioning, similar to that of parents of children with typical development. Nevertheless, they present specific weaknesses in reading speed, notably they underperform compared to the other two groups in reading nonwords, which is a task assessing the phonological basis of decoding skills. These weaknesses are also associated with poorer reading history, remarkably with mean scores in the clinical range. They do not differ from the other groups in behavioral symptoms of ADHD, suggesting that for these parents ADHD symptomatology is not crucially associated to their profile and its occurrence might be explained as an epiphenomenon of their cognitive profile . This isThe profile of parents of children with ADHD is instead much less clear-cut. They seem to have more transversal cognitive weaknesses involving intellectual functioning, word reading speed and verbal fluency. However, they do not show specific impairments in attention networks, and do not differ from P-TD group on a phonological based measure such as non-word reading. At the behavioural levels, they report both reading history troubles and adult ADHD symptoms, the latter being significantly higher compared to those of parents of typically developing children. They also reported the highest level of parenting stress compared to the other groups. The higher incidence of ADHD symptomatology in parents of children with ADHD confirms previous data  and reinThe present study also considered the parental role (fathers vs mothers) and it was found that, except for a tendency for mothers to have better verbal fluency skills compared to parents, no other significant differences emerged in demographic, cognitive and behavioral indexes considered, and none of the group*role interactions resulted to be significant. This trend might be considered as indirect support of the non-random mating hypothesis ,48, withFinally, considering the predictors of parenting stress at the cognitive and behavioural level it emerged that behavioral (ADHD symptoms in parents) and demographic  significantly predicted parenting stress, within a model that explained around 27% of variance. Thus, parents with higher levels of ADHD symptoms, lower SES and smaller family sizes are more susceptible to higher levels of parenting stress. Instead, markers of parents’ cognitive profile did not seem to be crucially associated to how parents cope with the demands of their role. This aspect would deserve further investigation in future studies, since, to our knowledge, this is an under-investigated area.This study has a number of limitations that might limit the generalizability of results. First of all, the sample size, which, particularly for the ADHD group, is relatively modest. Furthermore, the selection of markers of the broader phenotypes for the two clinical groups considered might have included other measures such as paradigms of delay aversion and time perception, which have been advocated for being important endophenotypic measures of ADHD. Referred to the broader phenotype of dyslexia further studies might include more specific measures of phonological awareness that are considered as putative markers of the broader phenotype of dyslexia ,31. FinaConsidering the main aims of the study the paper offered a rich pattern of findings. As far as parent’s group differences in cognitive profile were concerned the study highlighted that parents of children with ADHD had transversal cognitive weaknesses , whereas the P-DYS group showed more specific falls in the reading related tasks. Turning to the second aim, it emerged that the P-ADHD had the highest levels of parenting stress and ADHD symptoms. Finally, the main predictors of parenting stress resulted to be a lower SES together with a minor number of family members and higher levels of ADHD symptoms in parents.Lastly, some clinical implications might be proposed. First of all, the fact that parents of children with dyslexia or ADHD might themselves show some cognitive weaknesses that are similar to those of their offspring needs to be taken seriously into account when clinicians suggest intervention programs and, especially, when they recommend parental best practices for dealing with children impairments. Actually, many parents might encounter significant difficulties in fulfilling these requirements because they have problems similar to those of their children and might not be endowed with the necessary cognitive and psychological resources to accomplish clinician requirements. On the other hand, to see the glass half full, clinicians might valorize parents’ experience trying to understand how they have afforded difficulties in their life course, what themselves would have or have found helpful, and treasure their knowledge of their children habits and demands. Within a systemic perspective, it would be important to accurately consider strengths and weaknesses within the family system, avoiding transmitting feeling of guil in parents who actually already perceive themselves as non-sufficiently adequate in managing parenthood demands.