Compared to the general population, adult Attention-Deficit / Hyperactivity Disorder (ADHD) is more prevalent in patients with Alcohol Use Disorder (AUD). Impaired behavioral inhibition is a common characteristic in both ADHD and AUD. Relapse risk is increased in patients with AUD and comorbid, untreated ADHD and in AUD patients with increased neural cue-reactivity.In this study, we examined the interaction between neural correlates of behavioral inhibition and alcohol cue-reactivity with a hybrid imaging task.n = 49 in our final analyses: Individuals had a diagnosis of either AUD (n = 13), ADHD (n = 14) or both (n = 5), or were healthy controls . The functional magnetic resonance imaging paradigm aimed to examine the combined effects of both an interference-inhibition task (“Simon-task”) and an alcohol cue-reactivity task. Instead of segregating by diagnostic group, we pursued a dimensional approach in which we compared measures of AUD and ADHD severity, as well as the interaction of both, using multiple regression analyses.Out of 69 adult study participants, we included The four groups did not differ on the behavioral level on either the inhibition task or the alcohol cue-reactivity task. However, brain activation in frontal control and reward-related regions during completion of the combined tasks were related to ADHD and AUD severity (symptom load). During presentation of both alcohol cues and the inhibition task, participants with higher AUD and ADHD symptom load exhibited greater BOLD (blood oxygen level dependent) responses in subcortical reward-related regions.Our findings support the hypothesis that ADHD additionally diminishes inhibition ability in individuals with AUD. This may increase relapse risk when confronted with alcohol cues. Further, it is crucial for patients with comorbid AUD and ADHD to take into account not only reduced cognitive control over behavioral inhibition but also simultaneously heightened alcohol cue-reactivity.The online version of this article (10.1007/s00213-020-05492-1) contains supplementary material, which is available to authorized users. Attention-Deficit/ Hyperactivity Disorder (ADHD) is a common childhood onset mental health disorder persisting in many cases until adulthood , the prefrontal cortex (PFC), motor regions, the angular gyrus (AG) and subcortical regions like the insula and the striatum all seem to be involved  AUD-only individuals, 2) ADHD-only individuals, 3) AUD + ADHD individuals and 4) healthy controls, we expected measures of response inhibition to correspond significantly with ADHD severity and cue reactivity to correspond significantly with AUD severity. Furthermore, we hypothesized that cue-type  would have an effect on measures of response inhibition and differentially affect the 4 groups: we expected individuals with both AUD and ADHD diagnoses to show the most pronounced inhibitory deficits upon alcohol cue presentation. In other words, we hypothesized the existence of an additive interaction effect between the axes of ADHD and AUD severity. Individuals with high symptom load in both domains should show more significant impairments than individuals with high symptom load in only one domain on both the behavioral and neural level. We expected a positive relationship between subcortical cue-reactivity and AUD severity that becomes more pronounced the higher the concurrent ADHD symptom load. By contrast, we expected to observe a negative relationship between prefrontal brain activity and ADHD severity that becomes more pronounced the higher the concurrent AUD symptom load.n = 13), ADHD (n = 14) or both (n = 5), or were healthy controls . For the AUD group, individuals with a diagnosis of an at least moderate AUD  pointed to the right or to the left side of the screen. The target appeared in one of four possible locations on the screen . A congruent trial is one in which the arrow appears on the same side of the screen in which it is pointing (e.g. arrow appearing in the top/bottom left corner and pointing to the left side). An incongruent trial is one in which the arrow appears on the opposite side of the screen in which it is pointing (e.g. arrow appearing in the top/bottom left corner and pointing to the right side). The target was displayed on a background that showed either an alcohol image, a neutral image, or a scrambled image. For each picture category  30 images were chosen and presented four times throughout the task, once in each of the task conditions . Every image was presented for 1500 ms. A total of 360 trials was conducted in an event-related design with 90 trials for each of the 2 × 2 conditions [congruency (congruent / incongruent) x side (left /right)]. Participants were asked to press the left button of the arrow keyboard when the target was pointing to the left side of the screen and to press the right button when the target was pointing to the right side. Examples for the task images are displayed in Fig. Images for the alcohol category were chosen from our own alcohol picture series  covering the entire brain. A field map acquisition was conducted to correct the fMRI data for geometric distortion caused by magnetic field inhomogeneities . Additionally, a 5:21 min anatomical scan was performed to acquire a T1-weighted 3D MPRAGE (Magnetization Prepared- Rapid Gradient Echo) dataset . Images were presented to patients using MRI Audio/Video Systems goggles . Tasks were presented using Presentation® software .Scanning was performed using a 3 T whole-body tomograph , which we used to acquire 233 T2*-weighted transversal echo-planar images . The first five scans were excluded to avoid artifacts caused by magnetic saturation effects. The remaining 228 scans were corrected for residual geometric distortion based on the acquired magnetic field map, spatially realigned to correct for head motion, temporally realigned to minimize temporal differences in slice acquisition and normalized to a template provided by MNI . Subsequent smoothing was performed using an isotropic Gaussian kernel (8 mm FWHM).Statistical analysis of the pre-processed fMRI data on the first-level was performed by modeling the six different conditions of interest  as explanatory variables within the context of the general linear model on a voxel by voxel basis and convoluted with the canonical hemodynamic response function. Also, motion regressors from the preprocessing were included. Incorrect and missing responses were included as regressors of no interest. Furthermore, a quality check was performed. Subjects with excessive head movement (> 3 mm/ 3°) or other artifacts were excluded from the subsequent analysis.Resulting contrast images of interest from the first-level analysis are the main effects “cue type  and congruency (“incongruent vs. congruent”) and their interaction effects . We used a dimensional approach for each contrast of interest separately instead of segregating groups by diagnosis. For this purpose, the individual contrast images were included in second-level multiple regression analysis to identify brain regions with effects of ADHD symptom load, AUD symptom load and their corresponding interaction. Additionally, we conducted group comparison analysis segregating groups by diagnosis using a full factorial model see . These smeanADS)*ADHD-SR - meanADHD-SR)) for the overall and impulsivity factor scores of the ADHD-SR separately. Age was included as covariate of no interest in all analyses. To control for multiple statistical testing, the probability for a family-wise error (FWE) was set to 0.05. Using 10,000 Monte Carlo Simulations in AFNI’s 3dClustSim  a voxel-wise-threshold of P < 0.001 in combination with a cluster-extend-threshold of k ≥ 151 was determined for the imaging analysis with an automatically conducted estimation of smoothness. For further analyses, we created functional masks in the following manner. We identified regions surviving in all second-level analyses and created spheres of 10 mm diameter around the peak activation of each cluster using WFU_PickAtlas in SPM8. After checking the overlay of mask and clusters, for stretched clusters we created a second sphere around the peak voxel of the largest sub cluster. This approach prevented us from losing relevant regions. From these functional and sphere masks, we created the final functional regions of interest (ROI) as intersection masks around the peak voxels using the image calculator  in SPM8. The resulting masks were used to aggregate contrast values  for each subject. We will refer to them as “functional masks”. The anatomical localization of the results was determined using xjView  and visualized using MRIcro , interference effects as well as sample characteristics, questionnaires and aggregated contrast values from the fMRI analyses were analyzed using the Statistical Package for the Social Sciences (SPSS) version 24.0 . The general interference effect was calculated by subtracting the mean RT of the correct congruent trials from the mean RT of the correct incongruent trials. By comparing this interference effect between neutral/scramble and alcohol trials, the interaction between interference effects and stimulus type can be examined, which corresponds to the distraction by alcohol cues influencing the interference effect. Further, relevant questionnaire scores were calculated and a descriptive analysis of the sample was performed. Analyses of variance (ANOVA) were used to demonstrate possible group differences with respect to both questionnaire scores and behavioral measures. Regarding the main effects of AUD and ADHD symptom load, and the interaction effect of both on RT and interference effects, multiple regression analyses were performed. In order to assess and visualize the behavioral measures (RT and interference effects) and aggregated contrast values from the fMRI analyses against AUD and ADHD symptom load, we performed a median split of the cohort into high and low AUD and high and low ADHD load, respectively. This allows analyzing neural activity and behavioral outcome in high/ low AUD with regard to ADHD symptom load, and vice versa. Further, possible interaction effects of AUD and ADHD severity can be assessed.From the overall sample of 69 participants, 49 subjects were included in the fMRI analysis: 13 AUD, 14 ADHD, 17 healthy controls and five AUD + ADHD. Reasons for excluding participants from the final fMRI analysis were incomplete data, heavy movement in the scanner and not meeting inclusion criteria . Further details are displayed in Table p > 0.1). Regarding the difference between low and high levels of AUD severity, higher levels of ADHD severity (“impulsivity”) lead to a smaller general interference effect in individuals with low levels of AUD  were neither dependent on AUD or ADHD severity, nor did they show an interaction effect between AUD and ADHD symptom load. Regarding the interaction of congruency and cue type , the analyses resulted in significant clusters for associations between brain activation and AUD severity and also for the interaction of AUD and ADHD severity. No significant results were found for the main effect of ADHD severity. ADS sum score correlated negatively with activation in the left and right precuneus, the inferior parietal lobule , the left and right cuneus and the left pre- and postcentral gyri see . SignifiFor detailed results following extraction of contrast values from several ROIs defined by the aforementioned peak coordinates see Table ADHD prevalence rates are higher in the AUD population than in the general population , and insula as ADHD severity increased. These regions have all been previously implicated in mediating various aspects of the development and maintenance of addictive behaviors. The thalamus is, in general, seen as a hub serving multiple purposes. In addiction research, it plays a role in salience attribution and has been observed to show hyperactivation in the presence of drug cues but hypoactivation during response inhibition . An exhaustion of cognitive capacities through the interference effect of the task may have reduced the ability to inhibit alcohol-related neural reactivity. It has been previously shown that in adult ADHD, a decrease of activation occurs in inhibitory control regions during inhibition tasks . This is in line with previous findings on ADHD and the reward system. Stark et al.  observedIn individuals with high ADHD symptom load (‘impulsivity’), a positive correlation between AUD severity and brain activity in reward-related regions has also been observed (insula). Here, the same explanation may be applied: The cognitive capacity of these individuals has already been used up for the demanding interaction task. Therefore, weaker cognitive control remains for suppressing cue-reactivity in reward-related regions, which leads to hyperactivation, representing a potentiation of impaired inhibitory control in individuals suffering from both AUD and ADHD. However, this mechanistic explanation needs further examination, since the current study does not provide conclusive evidence for this claim. In fact, we found a positive correlation for high ADHD individuals between brain activity in the right thalamus, right inferior frontal gyrus, and right lingual gyrus and AUD symptom load. This does not necessarily contradict our suggestion that the previously identified increased cue reactivity for high AUD/high ADHD individuals might be a result of depleted cognitive resources. Instead, hyperactivation of these inhibitory and attention-related regions in high ADHD/high AUD individuals could reflect precisely the ‘taxing’ cognitive effect exerted on high ADHD individuals by a comorbid AUD diagnosis. The lingual gyrus is part of the primary visual area and is related to perception and recognition of familiar scenes or encoding of complex pictures . Prefrontal cognitive control may be exercised in these individuals in order to succeed in our combined task. The interference effect can be ‘handled’ by individuals only suffering from AUD, but not those with a concurrent ADHD diagnosis. In reward-related regions, a weaker reactivity to alcoholic stimuli can be explained as a result of higher cognitive control over this neural response. Further, previous studies observed a decrease of neural reactivity to substance-related cues in subcortical reward regions that was, amongst others, associated with SUD severity . The primary analysis was conducted using a dimensional analysis approach. To further explore the interaction effects of the multilayered data (stimuli vs. congruency and AUD vs. ADHD severity) we used median-splits for AUD and ADHD severity, respectively.Functional alterations in reward and cognitive control related regions were observed to correlate with AUD symptom severity and were also affected by the interaction of AUD and ADHD severity - despite the absence of differences on the behavioral level of inhibition. This discrepancy may indicate a compensation of inhibitory deficits by increased neural activation, but could also be explained by increased sensitivity to detection of neural alterations versus behavioral responses. Increased neural activation in the combined task was observed in reward related, subcortical regions with increased symptom severity of AUD and ADHD in high ADHD and high AUD individuals, respectively. This suggests that AUD + ADHD participants are more severely affected than participants with only one of the disorders. The strongest interference effect for alcohol vs. neutral cues in this group might be a result of deficits in ignoring alcohol distractors due to reduced inhibition ability, which suggests that treatment of ADHD might be highly relevant in these patients. Furthermore, this patient group might be a candidate for interventions that aim to reduce cue-reactivity. Taken together, the results of our study suggest that AUD patients should be screened for ADHD, as identification of ADHD is highly relevant for individually adapted treatment and may result in improved relapse prevention.ESM 1(DOCX 1378 kb)


N-body models of planetary accretion and tracking tungsten isotopic evolution, and find that this probability is <1.6–4.7%. Mixing in up to 30% terrestrial materials increases this probability, but it remains <10%. Achieving similarity in stable isotopes is also a low-probability outcome, and is controlled by different mechanisms than tungsten. The Moon’s stable isotopes and tungsten isotopic composition are anticorrelated due to redox effects, lowering the joint probability to significantly less than 0.08–0.4%. We therefore conclude that alternate explanations for the Moon’s isotopic composition are likely more plausible.The Earth and Moon have identical or very similar isotopic compositions for many elements, including tungsten. However, canonical models of the Moon-forming impact predict that the Moon should be made mostly of material from the impactor, Theia. Here we evaluate the probability of the Moon inheriting its Earth-like tungsten isotopes from Theia in the canonical giant impact scenario, using 242  Tungsten isotopes between the Earth and Moon are compared in this new study. The authors find that traditional models of Moon formation are very unlikely to reproduce the Moon's Earth-like isotopic composition. They have slightly different W isotopic compositions, but this difference is approximately equal to the effects of disproportionate chondritic late accretion, so that their pre-late veneer W isotopic compositions were likely the same6. These observations have inspired many theories of lunar formation, including different dynamical regimes for the Moon-forming impact that create more mixing10, post-impact Earth–Moon isotopic equilibration via vaporized silicate12, and Earth and Theia coincidentally having the same isotopic composition .The Earth and Moon have similar or identical isotopic compositions for many elements, including O  to 5–8% (see ref. 14) to 20–40% (see ref. 15). It has also been shown that this Theia-inheritance mechanism can reproduce the Moon’s Earth-like Ca, Ti, and Cr isotopic compositions, but the probabilities of doing so were not evaluated16.Several previous studies have evaluated the likelihood of the Moon’s Earth-like oxygen isotopic composition being inherited from Theia, finding probabilities ranging from <2% (see ref. 17). In the early Solar System, the now-extinct 182Hf decayed to 182W with a half-life of 9 Ma. Hf is lithophile, whereas W is moderately siderophile, so they are fractionated by core formation. A body’s mantle W isotopic composition depends on the timescales of accretion and differentiation and on the physical mechanisms of core formation  , not on source material.The Moon’s tungsten isotopic composition provides a fundamentally different and more restrictive constraint than these stable isotopes  and Eccentric Jupiter and Saturn (EJS) simulations used here24, with the main differences being a higher resolution  in the simulations used here and a much larger number of simulations (100 CJS/EJS in this study versus 8 in ref. 19) to allow for more quantitative assessment of probabilities. A Monte Carlo model showed that producing an Earth and Moon with the same W isotopic composition by this mechanism has a probability of <1%, if most (>80%) lunar material derived from Theia17.Previous studies of the Moon’s W isotopic composition have used inverseHere, we take a different approach, using detailed forward models of accretion and core formation based on a large number of dynamical simulations to better constrain the probability of an Earth-like lunar W isotopic composition inherited from Theia. We also consider the formation of the Moon from an Earth–Theia mixture, but do not directly quantify the probabilities of large extents of mixing or post-impact equilibration since these hypotheses are sensitive to different processes . We find that the probability of the Moon inheriting an Earth-like tungsten isotopic composition from Theia is <1.6–4.7%; forming the Moon from up to 30% terrestrial materials increases this probability, but it remains <10%. Reproducing the Moon’s Earth-like stable isotopic composition is anticorrelated with tungsten due to redox effects, decreasing the joint probability to <0.08–0.4% and suggesting that other explanations for the Moon’s isotopic composition may be more likely.N-body simulations of terrestrial planet accretion in the inner Solar System: 50 CJS (see ref. 24), 50 EJS (see ref. 24), and 142 Grand Tack (see ref. 25) simulations. Each simulation began with ~80–100 larger planetary embryos and ~2000 smaller planetesimals, whose orbital evolutions were tracked for 150–200 Ma until they accreted into planets (see refs. 25) (see “Methods” section). The simulations also included the Sun, Jupiter, and Saturn. The three accretion scenarios considered here differ primarily in the orbits and migration histories/timescales of the gas giant planets, and their resulting effects on the extent and timescales of radial mixing in the disk .Our models are based on a large suite of 182W excess in a body’s mantle is expressed as a 182W anomaly, X182W and X184W are mole fractions of 182W and 184W, respectively, and CHUR is the chondritic uniform reservoir. The Earth and Moon both have inferred pre-late veneer ε182W = 2.2 (see ref. 5), with a measurement uncertainty of approximately ±0.05 ε units on lunar samples (see refs. 6) and an uncertainty of ±0.15 ε units based on the calculated pre-late veneer composition of the Earth (see ref. 5). ε182W depends on the abundance of radiogenic 182Hf , as well as the abundance of a stable isotope of W. Therefore it is sensitive to the relative concentrations of Hf and W, quantified as We tracked the isotopic evolution of Earth and Theia analogs formed in these simulations (see “Methods” section) by coupling the simulations to models of protracted core formation, calculating their Hf–W isotopic signatures as tungsten partitions between metal and silicate with each impact. We included the effects of partial metal–silicate equilibration and an initial oxygen fugacity gradient in the disk (see “Methods” section). The radiogenic 3 (the portion of a body’s W budget hosted in silicates), and ε182W in two example Earth–Theia pairs. The Earth’s WO3 generally increases as it grows, because W becomes less siderophile at greater depths. If Theia originates in the inner circumstellar disk with a reduced composition, its WO3 starts low and remains low due to the low pressures and temperatures of its interior. If it starts in the outer disk with an oxidized composition, its WO3 is initially higher. Seventy-three percent of Theia analogs in CJS/EJS simulations and 23% in Grand Tack simulations have mass-weighted semimajor axes (MWSMA) of 1.5 AU or greater, giving them more oxidized compositions. ε182W in Earth and sometimes Theia is very high at early times . This is because reduced conditions and low pressures and temperatures cause W to be more strongly siderophile , compared to 48% of Earth analogs (median 2.1). For k = 0.8, Grand Tack Earth analogs match the observed ε182W on average27, but again many Moon analogs have high anomalies. For a lunar DW = 30, 85% of Grand Tack Moon analogs have ε182W that are higher than the observed value (median 6.7), compared to 54% of Earth analogs (median 2.3).The Earth and Theia were tracked until their collision, then a Moon was formed from Theia material with single-stage core formation and no post-impact mixing or equilibration (see “Methods” section). As an endmember case, we begin by considering the formation of the Moon from entirely Theia materials, then later discuss the effects of mixing Earth and Theia materials to form the Moon. Figure ε182W are higher than the measured value when the Moon analog is formed from Theia material , would result in an even lower probability of forming a Moon with an Earth-like ε182W. This probability applies to CJS, EJS, and Grand Tack scenarios, and to different values of k and lunar DW . Note that at larger Earth–Moon tungsten anomaly differences, the Grand Tack scenario generally produces lower cumulative probabilities, since its planets form faster, resulting in larger ε182W27. A probability of <1.6–4.7% is qualitatively consistent with earlier work showing that it is possible to produce an Earth-like ε182W in the Moon in some instances28. Our findings agree well with an earlier study suggesting that this is a low probability event, with a likelihood on the order of 1% (see ref. 17), but here we have quantified this probability using dynamically-consistent accretion and core formation models, with provenance and mass evolution constrained by N-body simulations.Figure als Fig. , there iDW = 30, Moons formed entirely of Theia materials have a 1.9% chance of matching the Earth’s ε182W within ±0.15 ε units. Forming the Moon out of 90% Theia + 10% Earth increases this probability to 3.8%, while using 70–80% Theia + 20–30% Earth increases the probability to 9.4%, and using 50% Theia + 50% Earth results in a 17.0% probability of a match  would have also been a low-probability event . If the Moon’s isotopic composition was indeed inherited from Theia, both of these low probability events would have to have occurred. With a probability of 5–8% for oxygen (see ref. 14) and <1.6–4.7% for tungsten, the likelihood of the Moon inheriting Earth-like O and W isotopic signatures simultaneously is only <0.08–0.4%. This probability may be lower by a factor of >3–4 (see ref. 11) or higher by a factor of ~4–5 (see ref. 15) if different estimates for the probability of an Earth-like lunar O isotope signature are used.It is possible to form a Moon with an Earth-like εfHf/W requires the accretion of a lot of oxidized material from the outer disk, otherwise tungsten is too siderophile at the low pressures and temperatures of the interiors of Theia and the Moon. Moon analogs with a Moon-like fHf/W and ε182W have MWSMA much more similar to Mars than to Earth . If Theia originated from a Mars-like MWSMA and the Moon derived primarily from Theia, the Moon would be expected to have a stable isotopic composition more similar to Mars than to Earth, contrary to observations, though there is considerable uncertainty in the initial stable isotope distribution in the protoplanetary disk . Moon analogs with an Earth-like MWSMA have a very high fHf/W of ~100 and ε182W values that are significantly higher than the Moon’s on average . Of these Theia analogs, 0/20 have fHf/W = 8–30 (median 1100) and 3/20 have ε182W = 3.5–7 (median 34). Similarly, our Grand Tack simulations produce 186 Theia analogs with MWSMA <1.5 AU, of which 2/186 have fHf/W = 8–30 (median 1700) and 14/186 have ε182W = 3.5–7 (median 25), due to the low pressures and temperatures of their interiors, reduced conditions in the inner disk, and rapid accretion. While in principle these Theia fHf/W and ε182W could produce a Moon having an Earth-like isotopic composition, our dynamical models show that such a parameter combination in the inner disk is very unlikely.A previous study concluded that Theia had to have ε182W alone, or the <0.08–0.4% if tungsten and oxygen isotopes were independent. As a result, we consider the Theia inheritance model to be difficult to reconcile with isotopic observations. Alternative models such as enhanced Earth–Theia mixing  or post-impact equilibration  are likely to have a higher probability of reproducing the observed isotopic compositions. For example, high energy giant impacts that can potentially allow post-impact isotopic equilibration were common in the end-stages of accretion, with ~85% of bodies experiencing at least one late giant impact with a modified specific impact energy of >2 × 106 J/kg (see ref. 12). Achieving a high degree of Earth–Theia mixing when the two bodies are similar sizes requires impact velocities that are common in accretion simulations, with ~20% of planets experiencing an impact with a mass ratio of ≥0.4 (see ref. 7). Future studies should focus on further quantifying the probabilities of these scenarios using dynamical and geochemical constraints.Because of the anti-correlated constraints, the probability of producing a Moon with an Earth-like isotopic composition inherited from Theia is actually significantly lower than the <1.6–4.7% chance of matching 24 were run using the MERCURY code31. The CJS case had Jupiter and Saturn on non-eccentric orbits as predicted by the Nice Model . In the EJS scenario, Jupiter and Saturn were placed on their modern-day orbits. The CJS/EJS simulations started with ~80 planetary embryos with masses of 0.01–0.06 Earth masses (M⊕) and ~2000 planetesimals with masses of ~0.001 M⊕. The 142 Grand Tack simulations25 were run using the Symba code33. The Grand Tack scenario involves an inward then outward migration of Jupiter and Saturn to truncate the inner disk34. These simulations started with ~100 planetary embryos and ~2000 planetesimals. Different simulations featured different embryo:planetesimal mass ratios  and different embryo masses . In all CJS, EJS, and Grand Tack simulations used here, all collisions were treated as inelastic mergers, neglecting the possibility of hit-and-run or erosive impacts . This is a reasonable approximation, as incomplete accretion has been shown to have only a small effect on the resulting ε182W36. These simulations have been previously published, and further details may be found in those studies25.The 50 CJS and 50 EJS simulations37 tracking 13 elements, including Hf and W. This model included a spatial gradient in initial oxygen fugacity in the disk, constrained to reproduce the FeO contents of the terrestrial and Martian mantles on average: 4 log units below the iron–wüstite buffer (IW–4) inside of 1.5 AU, and IW–1.5 outside of 1.5 AU . As planets accreted and differentiated, their oxygen fugacities were evolved self-consistently39. The metal–silicate partition coefficient of tungsten DW  varied with changing pressure, temperature, composition, and oxygen fugacity40 in a planet’s interior as it grew. The full model was primarily used in calculations based on CJS/EJS N-body accretion simulations24. The fast model was simpler, designed for more rapid exploration of parameter space27. This model tracked only Hf and W, and used a constant value for DW, with a spatial gradient in initial fHf/W in the disk19 to reproduce the observed variation in fHf/W between Earth and Mars. The fast model was primarily used in calculations based on Grand Tack N-body simulations25.Geochemical calculations were performed using two different models of Hf–W isotopic evolution during core formation, a “full model” and a “fast model”. The full model consisted of a detailed model of core formation⊕ and a semimajor axis of 0.75–1.25 AU, and the fast model identified Earth analogs as any surviving planet with a mass of 0.5–2 M⊕ and a semimajor axis of 0.387–1.524 AU. In both models, Theia analogs were defined as the last body to collide with the Earth that contained at least one planetary embryo. Both models included variable degrees of metal equilibration k, used here with whole mantle equilibration. Results from CJS and EJS simulations were combined, due to their resulting isotopic signatures being statistically indistinguishable. Results from different values of k were combined to obtain probabilities. The two models were benchmarked against each other using Grand Tack simulations25, showing agreement in Earth’s ε182W to within typically <0.4 ε units . The Earth analog was assumed to accrete the Theia analog . In Grand Tack simulations, the Earth analog was also assumed to accrete the late veneer , since the late veneer in these simulations usually contributes a substantial fraction of the Earth analog’s mass. In CJS/EJS simulations, the small late veneer was not accreted by the Earth analog. Finally, the Earth and Moon analogs were isotopically evolved to the present for comparison with observations.The Hf–W isotopic evolution of Earth and Theia analogs was tracked until the time of the Moon-forming impact. A Moon analog was formed from a portion of Theia, having the mass of the Moon and a core that was 2% of its total mass. For most of the results shown here, no terrestrial material was incorporated into the Moon analog, as an end-member case. We also considered cases where the lunar mantle was derived from a mixture of Theia and Earth materials, with up to 50% Earth materials by mass. The Moon analog underwent an episode of single-stage core formation, using a constant Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1


With increased consensus regarding the validity and reliability of diagnosing Borderline Personality Disorder (BPD) in adolescents, clinicians express concern over the distinction between BPD and Attention-Deficit/Hyperactivity Disorder (ADHD), and its co-morbidity in clinical settings. The goal of this study was to evaluate differences between BPD, ADHD and BPD + ADHD in terms of co-morbid psychiatric disorders and a range of self-reported behavioral problems in adolescents.N = 550 inpatient adolescents with behavioral and emotional disorders that have not responded to prior intervention. We took a person-centered approach  and compared adolescents with ADHD, BPD and ADHD+BPD in terms of co-occurring psychiatric disorders and behavioral problems. We performed a regression analysis to test whether BPD symptoms make an incremental contribution to the prediction of psychiatric symptoms over ADHD symptoms.Our sample consisted of The severity of almost all co-occurring disorders, aggression, self-harm, suicidal thoughts, and substance use, were higher in the ADHD+BPD group. Borderline symptoms made an incremental contribution to the prediction of psychiatric symptoms beyond the contribution of ADHD.Severity and co-morbidity may be helpful factors in distinguishing between ADHD and BPD in clinical practice and the co-morbidity of these two disorders may indicate a worse clinical outcome. Borderline Personality Disorder (BPD) is a complex and severe mental disorder which is characterized by impulsivity, affect dysregulation and dysfunctional interpersonal relationships. Individuals with BPD demonstrate pervasive patterns of unstable interpersonal relationships, pronounced impulsive and self-damaging- behavior, unstable identity and difficulties in emotion regulation  which suWith increased consensus regarding the validity and reliability of diagnosing Borderline Personality Disorder (BPD) in adolescents, clinicians express concern over the distinction between BPD and Attention-Deficit/Hyperactivity Disorder (ADHD) in clinical settings. Attention Deficit and Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity in addition to problems in organization and staying focused . These aThere are multiple ways in which the high rates of co-morbidity between ADHD and BPD can be conceptualized and studied. One approach may be to use variable-centered factorial analyses to examine whether ADHD and BPD represent different clinical presentations of the same underlying construct or constellation of symptoms. Another approach may be to use variable-centered approaches in longitudinal designs to examine whether ADHD and BPD are sequentially related portions of the same construct or disorder. A third approach, which is consistent with a more traditional DSM-based medical model approach is to take a person-centered view by asking if ADHD and BPD have differential correlates when considered separately and combined . This apHowever, these findings are based mostly on adult studies and the outcome of the co-occurrence of ADHD and BPD in adolescence (the time period in which BPD symptoms are likely to emerge) is not well studied. In addition, previous studies of adults with ADHD and BPD have largely conducted purely categorical comparisons of the disorders and none of these studies were designed to investigate the relationship between ADHD and BPD symptoms from a symptom-based viewpoint. To our knowledge, only one study has been conducted on this topic in adolescents. With a relatively small clinical sample, Speranza et al. compared BPD and ADHD+BPD adolescents in terms of their traditional Axis I and II diagnoses. Nine 11%) of the 85 BPD adolescents received an additional ADHD diagnosis, and this ADHD+BPD group evidence higher incidence of Disruptive Behavior Disorder diagnosis compared to the BPD only group . Apart f1% of theTaking into account the gap in the literature on this subject in adolescents, the aims of the current study were twofold. First, we aimed to compare inpatient adolescents who received a diagnosis of ADHD, BPD or both (ADHD + BPD) diagnoses in terms of their psychiatric disorder severities determined with a semi-structured interview, and self-reported behavioral problems. We hypothesized that adolescents with both disorders would demonstrate a greater number of symptoms of DSM based traditional Axis I diagnoses and other behavioral problems  compared to subjects with only ADHD or BPD. In addition, based on previously discussed literature reporting that severe BPD symptoms in adulthood are related to a diagnosis of ADHD in childhood , we hypoParticipants in the current sample were recruited between October of 2008 and June of 2016 from an inpatient psychiatric hospital located in the United States which serves adolescents with behavioral and emotional disorders that have not responded to prior intervention. All procedures in the current study were approved by the appropriate human subjects review committee. Parents of admitted adolescents were approached and invited to participate in the study. If parents consented, adolescents were approached to provide assent. Adolescents had to demonstrate sufficient proficiency in English to provide assent and to complete study assessments, and they were excluded from participation if they had a diagnosis of schizophrenia or other psychotic disorders, bipolar disorder, autism spectrum disorder or an IQ < 70. Study assessments were administered by trained research coordinators and/or doctoral-level clinical psychology students. Assessments were conducted individually and in private within the first 2 weeks following admission.N = 805 consecutively admitted adolescents and their parents, n = 52 declined participation, n = 117 were excluded from participation based on study criteria and n = 86 were excluded because of absent data on measures utilized in the current study. The final sample consisted of n = 550 adolescents , with the following racial/ethnic breakdown: 77.6% White (n = 427), 5.8% multiracial or other (n = 32), 3.3% Asian (n = 18), 1.6% Black or African American (n = 9), 0.2% American Indian or Alaskan Native (n = 1) and 11.5% unspecified (n = 63). Based on the Diagnostic Interview Schedule for Children – Computerized Version (DISC-IV) [Of ite n = 4, 5.8% mup = 0.19). However, the gender distribution of the groups were different   received a diagnosis of ADHD without BPD, 116 (21.1%) received a diagnosis of BPD without ADHD and 57 (10.3%) received both ADHD and BPD diagnosis  [. Results of the DISC-IV are categorically coded to indicate the presence or absence of each disorder: 0 = no diagnosis, 1 = intermediate diagnosis, 2 = positive diagnosis. In the current study, the DISC-IV was utilized to determine ADHD diagnostic status of the participants and the overall number of diagnoses each adolescent received. In addition, the DISC-IV also reports the total number of symptoms endorsed by adolescents for each disorder. In the current study, total number of reported symptoms  were conceptualized as the severity criterion for each disorder and used as a continuous outcome variable in analyses. Also, total number of all reported symptoms were used in the current study as a severity variable to evaluate overall symptomatology. To avoid confusion, the name of the disorders analyzed in this study  will indicate their symptom severity in the remaining part of the article .DISC-IV)  is a strDISC-IV) . ResultsThe Childhood Interview for DSM-IV Borderline Personality Disorder (CI-BPD [p < .0005) between raters on item 10 of the CI-BPD  and two-way agreement  between raters  was good. In the current study, the interview’s dichotomous rating  was utilized to determine BPD diagnostic status and a continuous score (each item scored 0–2 and summed) was utilized for group comparisons and regression analyses.  [ (PAI-A)  is a sel (PAI-A) . In the Statistical analyses were performed using SPSS 20.0 statistical software . All variables were inspected for normality with the Kolmogorov-Smirnov test. First, the sample was divided into three groups based on the diagnostic results of the DISC-IV and the CI-BPD. The first group consisted of adolescents who met diagnostic criteria for ADHD and not for BPD. The second group consisted of adolescents who met diagnostic criteria for BPD and not for ADHD. The third group consisted of adolescents who met diagnostic criteria for both ADHD and BPD. To test our first hypothesis, Kruskal Wallis analysis was conducted to determine whether significant group differences existed among the aforementioned groups in terms of the number of diagnoses they received on the DISC-IV, the total number of symptoms they endorsed for each diagnosis on the DISC-IV (except for ADHD and BPD), their total scores on the CI-BPD and their T-scores on the PAI-A ALC, DRG, SUI, AGG and BOR-S subscales. The between-group analyses were conducted using Mann Whitney U test performing a post-hoc Bonferroni correction. Finally, a hierarchical regression model was conducted on the full inpatient sample (regardless of their diagnostic group) to determine whether number of BPD symptoms had incremental predictive validity over total number of ADHD symptoms in predicting overall psychopathology –our second hypothesis-, operationalized as total number of symptoms endorsed on the DISC-IV. At the first step, we have tested the predictive value of ADHD, age and gender on total psychiatric symptoms determined with DISC-IV. Then, we have added the total number of BPD symptoms of the participants to the model at the second step.We tested our first hypothesis with comparison analyses and found that the groups were significantly different in terms of all psychiatric symptoms and behavioral problems except for Substance Abuse, Conduct Disorder and Obsessive Compulsive Disorder symptoms Tables  and 3. Pp < 0.001). When we added BPD symptoms -at the second step-, we found that adding BPD symptoms to the model resulted in statistically significant incremental validity over ADHD symptoms on total psychiatric symptoms    and self-reported behavioral problems. For example, alcohol  and drug problems , aggression , suicidal ideation  and self-harm scores  were higher in the combined group than in the ADHD or BPD groups. Results of the hierarchical regression analysis furthermore suggested that, despite the overlap between BPD and ADHD, BPD symptoms have an incremental contribution to total psychiatric symptoms beyond ADHD symptoms, suggesting some unique features of each disorders in addition to overlap. Our sample showed that ADHD and BPD are highly associated with each other, as reported in several previous studies. However, very most studies conducted in adolescents on this topic reported lower rates of co-occurrence of ADHD and BPD, compared to our findings. Korsgaard et al. investigated personality disorders in adolescents and found that only 4 out of 21 ADHD patients (19%) also received a BPD diagnosis [Against the background of clinician concern over the distinction between BPD and ADHD, and its co-morbidity in clinical settings, the goal of the current study was to evaluate differences between BPD, ADHD and BPD + ADHD in terms of co-morbid psychiatric disorders and a range of self-reported behavioral problems in inpatient adolescents. Consistent with findings from adult samples, results of the current study suggest that there is high co-morbidity between ADHD and BPD  and that patients who meet the diagnostic criteria of both disorders demonstrate greater symptoms of numerous interview-based psychiatric disorders  compared to BPD only subjects . HoweverThese comparison analyses demonstrated that the patients in the ADHD+BPD group had higher levels of psychiatric symptoms, behavioral problems and aggression than the subjects with BPD only. Therefore, our findings support the previous adult studies claiming that a combination of BPD and ADHD is a more complicated clinical presentation which could contribute to difficulties in the treatment process and conclude in worse outcomes, and may be a different sub group of BPD .Our finding that BPD symptoms demonstrated an incremental contribution to the prediction of total psychiatric symptomatology suggests that BPD symptoms are related to several psychiatric symptoms regardless of ADHD symptoms supporting the hypotheses that, although they might share several similar developmental pathways and clinical presentations, ADHD and BPD may contribute unique features in the outcomes of patients’ lives , 45. It The findings of this study should be considered in light of several limitations. We conducted our study in a psychiatric inpatient sample. However, as we have mentioned before, many patients with ADHD and BPD do not need to be treated by hospitalization unless their clinical appearance is complicated and severe. Also, although adolescents were evaluated at admission we did not consider their previous treatments including previously prescribed medications. Furthermore, although most of our data were obtained via semi-structured interviews, some was obtained via self-report (i.e. PAI-A), although we consider the multiple method approach in our study a strength. Also, our study did not include a healthy control group to compare the variables among the clinical and healthy groups. Finally, given a general move towards dimensional assessment of latent constructs in psychiatry and clinical psychology, our person-centered approach may be viewed as out of step with current trends. However, we aimed to provide information that can readily used by clinicians when they assess for ADHD and BPD using traditional psychiatric nosology which is still very much in place in clinical settings.The findings of this study demonstrated that ADHD and BPD have different psychiatric symptomatology. In addition, subjects who meet criteria for both the BPD and ADHD diagnoses may have more severe psychiatric and behavioral problems compared to individuals with only ADHD or BPD. We believe that these findings could help clinicians to better understand adolescents with ADHD and BPD in the diagnosis and treatment processes.


Attention deficit hyperactivity disorder (ADHD) has the highest prevalence among psychiatric disorders in children. The present study investigated the effect of adenotonsillectomy on the symptoms of ADHD in a 6-month follow-up.This cross-sectional study was performed on 100 patients referred for respiratory problems during sleep due to adenotonsillar hypertrophy (ATH). The patients’ parents were asked to complete the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition checklist as a standard benchmark for ADHD before, 2 weeks, and 6 months after the surgery. The data were analyzed by SPSS software (version 20) through paired t-tests and McNemar’s test.The age averages of male and female children were 7.15 and 8.4 years, respectively. The frequency of ADHD in the studied population was 30%, which is much higher than the prevalence of this disorder in the normal population. In the second week after the surgery, the mean score of ADHD decreased from 4.97±2.97 (attention deficit [AD]) and 6.77±1.61 (hyperactivity disorder [HD]) before the surgery to 3.86±2.25 (AD) and 4.28±2.02 (HD) 2 weeks after the surgery (P=0.001). After a 6-month follow-up, these figures further decreased .Adenotonsillectomy had a significant effect on the improvement of ADHD symptoms. There is a necessity for checking patients with ADHD for ATH, especially in case of sleep disorders, sleep apnea, snoring, or mouth breathing. Attention deficit hyperactivity disorder (ADHD) has the highest prevalence in psychiatric disorders among children and adolescents , as it cThis disorder is a long-term illness manifesting itself throughout life, from preschool to puberty, and its manifestations may change from preschool to adulthood. The prevalence of this disorder is reported to be 3-7% in the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) . The preA limited number of studies have been conducted on the relation between adenotonsillar hypertrophy (ATH) and psychiatric and behavioral disorders. A study performed by Soylu et al. showed that in preschool children, in addition to mouth breathing and breathing disorders during sleep, ATH may also be associated with psychiatric symptoms and abnormalities in children within the age range of 5-12 years, and adenotonsillectomy improved the psychiatric symptoms and severity of these disorders in most cases . PassaliSeveral studies have been carried out on the prevalence of ADHD in Iran. Ali Moradi et al. reported that the prevalence rate of ADHD in Neyshabur, Iran, was 12.5% (95% CI: 10-14.8%). The prevalence rates of ADHD were 10.9% (95% CI: 7.8-14%) and 14.3% (95% CI: 10.5-18.2%) in male and female adolescents, respectively. The prevalence rate of ADHD was higher in female subjects; however, the difference was not statistically significant .In a study conducted by Ahmadi et al. in Besat Hospital in Hamedan, Iran, most children who were diagnosed with ADHD and underwent adenotonsillectomy improved within a month . With thThis cross-sectional study was carried out on a total of 100 children. The sample size was estimated based on a study by Ahmadi et al. . In thisThe sample size was also estimated based on the difference of the hyperactivity score; however, it resulted in a smaller sample size. The study subjects were selected from the patients referred to the ENT clinic of Shafa Hospital in Kerman within the age range of 3-15 years and undergoing adenotonsillectomy in 2014-2015. Convenience sampling was continued until 100 patients entered the study. The study objective was explained to the parents of the patients. In addition, written consent was obtained from the parents. The inclusion criteria were children with ATH who had sleep disorders, including snoring, apnea, restless sleep, and frequent awakening. These patients had a history of symptoms related to sleep breathing disorder and had frequently undergone antibiotic treatments; therefore, they had to go through surgical treatment as the final option. The exclusion criteria were other diseases causing upper airway obstruction , obesity, craniofacial anomalies, chronic inflammatory diseases, recurrent tonsillitis, acute tonsillitis, serious psychiatric illnesses , and major neurological disease, including seizure. All the demographic information, including age and gender, was recorded in a checklist. The presence of ATH was determined by physical examination and recorded in the checklist. The size of the tonsil and/or adenoid was graded from +1 to +4, as +1 and 4+ indicated 25% and 100% blockage of the airway, respectively. The magnitude of the adenoid blockage was based on the lateral radiography of the neck and amount of blockage in the choanae .The chilA total of 100 children, including 53 male and 47 female subjects, entered this study, and all the children were followed up to the end of the study. The age averages of male and female children were 7.15 (range: 4-11 years) and 8.4 (range: 4-13 years) years, respectively. The frequency of ADHD in the studied population was 30%, which is much higher than the prevalence of this disorder in the normal population. Among the subjects with ADHD, 66% n=20) and 33% (n=10) of the patients were male and female, respectively. The patients were assessed 2 weeks after the surgery, in which improvement was observed. After 6 months, more male and female children showed improvement in ADHD symptoms.  and 20 and 33%The ADHD is the most prevalent psychiatric disorder among children and adolescents  accountiIn another study conducted in Hamadan, Iran, 59 children within the age range of 6-12 years with ADHD underwent adenotonsillectomy, and ADHD symptoms improved in 41 and 51 patients 1 month and 3 months after the surgery, respectively. However, eight children were reported with no improvement. The AD and HD scores significantly decreased from 1.76 and 2.10 to 1.52 and 1.83 after 1 month and later to 1.24 and 1.52 after 3 months, respectively. The results of the aforementioned study showed that adenotonsillectomy is effective in the improvement of ADHD symptoms in children with ATH .Dadgarnia et al. evaluated the effect of adenotonsillectomy on ADHD symptoms among 35 children within the age range of 5-12 years with ATH in Yazd, Iran. In the aforementioned study, 6 months after adenotonsillectomy, the frequency of the combined-type ADHD significantly decreased . The ADHD inattention score, HD score, and ADHD combined score significantly improved 6 months after the surgery. Dadgarnia et al. also concluded that upper airway obstruction occurring due to ATH is probably causing the symptoms of ADHD (The results of a study conducted on 27 patients in Brazil showed that after tonsillectomy, daytime sleepiness, reaction time, and errors of omission and commission decreased. In addition, their performance in tests of visual attention improved . SimilarMoreover, in the aforementioned study, it was suggested that physicians should check the symptoms of ATH among children with sleep disturbance and ADHD symptoms because adenotonsillectomy can improve the quality of life among these children .Sleep-disordered breathing is common among 1% of children, and one of the possible causes is ATH ,16. In aMany authors have suggested the assessment of children with ADHD symptoms for obstructive sleep-disordered breathing which can be caused by ATH ,26. SleeOther studies have reported that ATH causes increased arousal during sleep, shorter total sleep time, and decreased sleep efficiency in children . ScientiAccording to the results of the present study, adenotonsillectomy had a significant effect on the reduction of the symptoms of ADHD among patients with ATH. Since ADHD is the most prevalent cause of the referral of children to psychiatric clinics, these patients should also be examined for ATH, especially if they have a positive history of sleep disorders, sleep apnea, night-time snoring, or mouth breathing.


Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder. Effective long-term treatment options are limited, which warrants increased focus on potential modifiable risk factors. The aim of this study was to investigate associations between maternal diet quality during pregnancy and child diet quality and child ADHD symptoms and ADHD diagnosis.This study is based on the Norwegian Mother, Father and Child Cohort Study (MoBa). We assessed maternal diet quality with the Prenatal Diet Quality Index (PDQI) and Ultra-Processed Food Index (UPFI) around mid-gestation, and child diet quality using the Diet Quality Index (CDQI) at 3 years. ADHD symptoms were assessed at child age 8 years using the Parent Rating Scale for Disruptive Behaviour Disorders. ADHD diagnoses were retrieved from the Norwegian Patient Registry.In total, 77,768 mother-child pairs were eligible for studying ADHD diagnoses and 37,787 for ADHD symptoms. Means (SD) for the PDQI, UPFI and CDQI were 83.1 (9.3), 31.8 (9.7) and 60.3 (10.6), respectively. Mean (SD) ADHD symptom score was 8.4 (7.1) and ADHD diagnosis prevalence was 2.9% . For one SD increase in maternal diet index scores, we saw a change in mean (percent) ADHD symptom score of − 0.28 (− 3.3%) ) for PDQI scores and 0.25 (+ 3.0%) ) for UPFI scores. A one SD increase in PDQI score was associated with a relative risk of ADHD diagnosis of 0.87 . We found no reliable associations with either outcomes for the CDQI, and no reliable change in risk of ADHD diagnosis for the UPFI.We provide evidence that overall maternal diet quality during pregnancy is associated with a small decrease in ADHD symptom score at 8 years and lower risk for ADHD diagnosis, with more robust findings for the latter outcome. Consumption of ultra-processed foods was only associated with increased ADHD symptom score of similar magnitude as for overall maternal diet quality, and we found no associations between child diet quality and either outcome. No causal inferences should be made based on these results, due to potential unmeasured confounding.The online version contains supplementary material available at 10.1186/s12888-021-03130-4. The World Health Organization (WHO) defines neurodevelopmental disorders as one of today’s greatest public health challenges , with AtADHD aetiology is multifactorial and complex , involviFindings from investigations into associations between child diet and ADHD have been equivocal. Some research on ADHD patients have found suboptimal levels of several nutrients, including zinc , iron 1, magnesiIt has been suggested that an optimal maternal diet during pregnancy might be even more crucial to child ADHD outcomes than early childhood diet . SurprisWe have not identified any research investigating overall maternal diet quality during pregnancy in relation to child ADHD diagnosis. Moreover, the limited amount of literature investigating prenatal and child diet in relation to neurodevelopmental problems associated with ADHD has indicated that there exists a possible association , 28. HowThe aim of this paper is to investigate the associations between both maternal diet quality during pregnancy and child diet quality, using diet quality indices, and maternally reported child ADHD symptoms at 8 years and child ADHD diagnosis retrieved from the Norwegian Patient Registry.We followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) statement as the reporting guideline  see Add.n = 102,152 mother-child dyads. This data was linked with data from the Norwegian Medical Birth Registry (MBRN) and with the Norwegian Patient Registry (NPR), a national administrative health registry with patient information reported from hospitals and outpatient clinics in Norway. We utilised two different study designs in this paper; a prospective cohort design and a registry-based case cohort design. Figure The Norwegian Mother, Father and Child Cohort Study (MoBa) is an ongoing prospective population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health, aiming to investigate how genetic- and environmental factors affect health outcomes . PregnanThe MoBa food frequency questionnaire (FFQ) was administered around gestational week 22 and provides comprehensive information about maternal dietary habits and intake of foods, beverages and dietary supplements since beginning of pregnancy . The MoBWe evaluated maternal diet quality during pregnancy by applying two dietary indices to the FFQ-derived data. The first index, called prenatal diet quality index (PDQI) (possible range of scores = 0–110), captures adherence to the Norwegian food-based dietary guidelines. We have previously used this index to investigate associations with child developmental outcomes . The secThe PDQI is largely based on the well-known Healthy Eating Index . We calcThe second maternal dietary index, the UPFI, was created by calculating the relative contribution of energy from ultra-processed foods to total energy intake. Following the NOVA classification , foods aChild intake of 36 food and beverage items was assessed by parent-reported food intake questions included in the MoBa questionnaire at 3 years. We evaluated overall child diet quality by defining the Diet Quality Index (DQI), as developed by Huybrechts et al. for use in pre-school children , which a. The original DQI also includes a meal pattern component, but there were no questions on child meal patterns in the MoBa questionnaires. Below is a brief description of each component. For more detailed information on the calculation method, see Additional file The CDQI consists of the following components: diet diversity, diet quality and diet equilibrium, where the total CDQI score is the average score across the three components, with higher scores equalling better diet qualityThe diet diversity component (possible range of 0–100%), reflects the intake variation in the foods by giving one point if the child had consumed at least one daily serving from each main FG.The diet quality component (possible range of − 100 - 100%) is used to categorize food intake, within each FG, according to their quality, into preferred foods  factored by 1, moderation foods  factored by 0 and the “low nutrient, energy dense” foods ; factored by − 1. After categorization based on intake in grams per day, the foods were summed and divided by the total food intake.The diet equilibrium consists of adequacy and moderation (possible range of 0–100%). The diet equilibrium component introduces the concept of dietary balance to the CDQI, taking into account both “moderation” and “adequacy”: intake moderation (percentage of intake exceeding the upper level of the recommendation), with particular focus on limiting the intake of low nutrient and energy dense foods, subtracted from intake adequacy of foods in the preferred group , divided by total number of FGs.Child ADHD symptoms were assessed in the MoBa eight-year questionnaire, using items from the Parent Rating Scale for Disruptive Behaviour Disorders . MothersWe obtained information about children’s ADHD diagnoses from the Norwegian Patient Registry (NPR). The NPR originated in 1997, and person-identifiable records are available from 2008 onwards . The NPR2; maternal education , Bachelor (15 years), Master + (17+ years)); smoking during pregnancy (yes/no); alcohol intake during pregnancy (yes/no); reported symptom score of maternal prenatal depression (0–3) and ADHD (0–4); maternal age (in years); child sex (boy/girl); parity (0–3); child diet quality score at 3 years (CDQI) and child season of birth (in quarters)  . For matachelor 1 years, M2; maternal education , Bachelor (15 years), Master + (17+ years)); maternal symptoms of ADHD (0–4); maternal age (in years); maternal diet quality score during pregnancy (PDQI); child sex (boy/girl); parity (0–3); reported child sleep problems at 3 years (yes/no); and child season of birth (in quarters) . The maternal variables included, except for ADHD symptoms, were assessed during pregnancy. Lastly, we included birth quarter as a covariate for all exposure variables to increase the precision of our estimates, as children born in October–December are 45% more likely to have an ADHD diagnosis compared to children born in January–March [For child diet quality, the final covariate set comprised: maternal pre-pregnancy BMI in kg/mry–March .Reliability and validity of the PDQI in the MoBa sample have been reported previously . We evalWe evaluated the strength of associations between the diet quality indices and outcomes by estimating Bayesian regression models, using a beta-binomial likelihood for ADHD symptoms and logistic regression for ADHD diagnosis. For the analyses we utilised the R statistical software, version 3.4.3  and the For all exposure variables, we used child sex and maternal ADHD symptoms as stratification variables to explore possible differences in associations between strata. MoBa assesses maternal ADHD symptoms in the 3 year questionnaire with the Adult ADHD Self-Report Scale (ASRS), which consists of six questions relating to symptoms of adult ADHD based on the DSM-IV diagnosis criteria . StratifFor child ADHD symptoms as outcome, we report the results as average marginal effect (AME), i.e. the average change in the outcome score for a one standard deviation (1SD) change in the exposure. We report both change in mean score (absolute AME) and corresponding percent change (relative AME) with corresponding credible intervals (CI’s) . For ADHD diagnosis, we report the results as relative risk (RR) with corresponding CI’s. For each exposure, we fit two separate models; one crude and one adjusted including all covariates.Based on prior predictive simulations, we set the prior for population level (fixed) effects to a normal distribution with a mean of 0 and a standard deviation of 2, and the prior for the standard deviation of random effects to a half-normal distribution with a mean of 0 and a standard deviation of 2 . Note thParticipation and loss to follow-up in MoBa is not random but dependent on maternal education, age and child parity, which can lead to biased estimates . TherefoFor all models we used complete cases for the variables included in each respective model, see Table We investigated whether the UPFI was able to differentiate between groups known to have different diet quality: smokers vs. non-smokers, lower (< 12 years education) vs higher (master degree) education, younger mothers (<=21 years) vs older mothers (> = 30 years) and people presenting with and without depressive symptoms. We calculated standardized mean differences between groups, reported as Hedges’ g, with a medium effect size seen for low vs. high education (g = 0.59), low vs. high age (g = 0.48) and smoking vs. no smoking (g = 0.53) and small effect size for with vs. without depressive symptoms (g = 0.24). In addition, the UPFI score correlated with energy adjusted intakes of key nutrients in the FFQ in the expected direction (fibre (r = − 0.50), sugar (r = 0.59), protein (r = − 0.55) saturated fat (r = 0.30)), as well as with the PDQI (r = − 0.53). In a recent paper , we descReliability analysis revealed an Omega total of 0.69, which is satisfactory. We also explored how well the CDQI was able to differentiate between groups related to the mother  and the child  known to have different diet quality. We calculated standardized mean difference between groups, reported as Hedges g. We saw a large effect size for maternal diet quality during pregnancy (g = − 0.93 (<=20th percentile vs. > = 80th percentile)) and screen time at 3 years (g = − 0.79 (> = 3 h/day vs. < 1 h/day)), medium effect size for maternal education (g = − 0.64 (< 12 years education vs. master degree)) and child outdoor time at 3 years (g = − 0.55 (< 1 h/day vs. > 3 h/day) and small effect size for maternal age (g = − 0.35 (<=21 years vs. > = 30 years)). Also, the CDQI showed correlations with the maternal dietary indices in the expected direction .We saw a strong relationship between ADHD symptoms in the MoBa 8-year questionnaire and ADHD diagnosis, as the median ADHD symptom score of children with a registered diagnosis was + 2.8 SD higher than the score of those without a registered diagnosis.The mean (max) PDQI, UPFI and CDQI scores were 83.1 (107.1), 31.8 (89.5) and 60.3 (86.5), respectively. The mothers were on average 30.2 years of age at delivery, with 67% having at least a bachelor’s degree. The mean (range) age when the children were first registered with an ADHD diagnosis in NPR was 8 (2.6–13.8) years, with 80% being 6 years old or older. For additional details on descriptive statistics, see Table For a one standard deviation (1SD) increase in mean diet quality score, we obtained the following results for ADHD symptom score in the adjusted models: a change in mean ADHD symptom score of − 0.28  for the PDQI and + 0.25  for the UPFI, corresponding to a relative change in mean ADHD symptom score of −3.3%  and + 3% , respectively Fig. . These cFor ADHD diagnosis, we found a relative risk in the adjusted models of 0.87 CI: 0.79, 0.97) for the PDQI. To compare, for a 1SD increase in ADHD symptom score, the relative risk of ADHD diagnosis is 3.3 . We found no reliable change in risk for the UPFI ) or CDQI   and both child ADHD symptom scores and child ADHD diagnosis, and a positive association between the contribution of ultra-processed food to total maternal energy intake in pregnancy (measured by the UPFI) and child ADHD symptom scores. We found no reliable association between UPFI and child ADHD diagnosis, or between child diet quality (measured by the CDQI at 3 years) and either outcome.The difference in results for maternal diet quality during pregnancy and child diet quality at 3 years might be due to difference in measurement detail, since the maternal FFQ is more extensive than the questions about food intake in the child questionnaire (255 vs. 36 food items), which is likely to influence the specificity and accuracy of the instrument and subsequent dietary index scores calculated from those instruments. Nevertheless, we acknowledge that the child dietary questions include the main foods and beverages consumed at three years of age.If the associations between maternal diet quality during pregnancy and child diet quality and ADHD symptoms and diagnosis are due to causal effects from maternal diet quality on child development, these results would support the idea of prenatal programming. During critical stages of the prenatal period, the foetus may be particularly sensitive to environmental influences like maternal nutrition, and the subsequent insults based on these influences may sustain long after birth and into adulthood . A propoWe found no associations between child diet quality at 3 years and either outcome, which adds to the so far undecided evidence base investigating child diet and ADHD. A recent meta-analysis found healthy dietary patterns to be associated with low ADHD symptoms in children (3–11 years old) and adolescents (12–16 years old) and unhealthy dietary patterns a risk factor for ADHD, but the authors emphasize that due to limitations in study design across most included studies, these associations do not constitute evidence for causal effects . Also, tWe found associations of similar magnitude for both maternal diet quality indices in relation to child ADHD symptom score, however the associations were small. This is in line with the small association estimates found in a previous review and meta-analysis investigating maternal diet quality and child neurodevelopmental outcomes. More recently, Mesirow and colleagues  studied Isaac and Oates  propose For child ADHD diagnosis, we only found associations with the PDQI (indicates adherence to the Norwegian food-based dietary guidelines) and not for the UPFI (indicates consumption of ultra-processed foods). It is likely that the ADHD diagnosis outcome (retrieved from patient registry) is less afflicted by bias than the ADHD symptom score outcome (based on parent reported symptoms), which might be one reason for the difference in results for the two outcomes. If we assume that the results for ADHD diagnosis are more accurate due to less presence of bias, compared to the associations seen for the ADHD symptom score outcome, it can be an indication of overall maternal high diet quality during pregnancy being more important for ADHD development than the proportion of intake of ultra-processed foods, which are mostly of low nutritional value. This is in line with recent findings investigating maternal dietary patterns and birth outcomes, which found stronger associations with preterm birth for the healthy pattern compared to the unhealthy, and the opposite trend for birth weight , but onlIn sum, the evidence so far relating to the comparison of association strengths between healthy and unhealthy diets is equivocal. Perhaps the most plausible reason for this is due to heterogeneous methodological approaches related to the dietary definitions and statistical analyses chosen, which should serve as an encouragement to develop standardized methodological approaches within the nutritional epidemiological field.The strengths of this study include a large sample size, validation of dietary indices used and use of IPW to account for selection bias. We use robust statistical methods that to our knowledge have not been previously used within the nutritional epidemiology field. Also, investigations of associations between prenatal diet quality and child ADHD diagnosis has not previously been investigated.One limitation of this study is that self-reported information, particularly in relation to aspects of health, introduces many challenges –77, and Another limitation is that the MoBa is a selected group of participants and not representative of the whole population of Norwegian mothers and children. On average, MoBa mothers are older and more educated than the general pregnant population in Norway , and botThere are some limitations related to the information on ADHD diagnosis from the NPR. The youngest children in MoBa were born in 2009 and we have information on ADHD diagnosis in MoBa up to 2017 in our data file, hence the youngest children are 8 years old in the NPR sample. As children, especially girls, might receive an ADHD diagnosis at an age older than 8, there might be some false negatives in the sample, which has been found in the comparative pregnancy cohort in Denmark . In addiIn this paper we found that higher overall maternal diet quality and lower consumption of ultra-processed foods in pregnancy were associated with lower child ADHD symptom scores, but the association estimates were small. For child ADHD diagnosis, lower risk was found only for higher overall maternal diet quality measure. We found no associations of child diet quality at 3 years with either outcome.We emphasize that no inferences regarding causation should be made based on these results, as unmeasured confounding could contribute to the observed associations. Also, as this is the first study investigating maternal and child diet quality in relation to both child ADHD symptom score and ADHD diagnosis, it is too early to draw firm conclusions about the associations we found. Instead we encourage more research conducted on this topic, with robust methodological approaches related to study design, variable definitions and statistical analyses, which will allow for better identification of possible causal relationships.Additional file 1. The Impact of a Community-Oriented Problem-Based Learning Curriculum Reform on the Quality of Primary Care Delivered by GraduaAdditional file 2. PDQI score calculation. Detailed description of the PDQI score components and calculationsAdditional file 3. Supplementary Table. Foods and beverages from group 4 in the NOVA classification included in the Ultra-Processed Food Index (UPFI)Additional file 4. CDQI score calculation. Detailed description of the CDQI score components and calculationsAdditional file 5. Supplementary figure. Visualization of covariate selection for maternal diet quality during pregnancy as exposure via a Directed Acyclic GraphAdditional file 6. Supplementary figure. Visualization of covariate selection for child diet quality at 3 years as exposure via a Directed Acyclic Graph.Additional file 7. Supplementary table. Absolute AME and corresponding relative AME change in ADHD symptoms for one SD increase in PDQI, UPFI and CDQI scoreAdditional file 8. Supplementary table. Relative risk for ADHD diagnosis for one SD increase in PDQI, UPFI and CDQI scoreAdditional file 9. Supplementary figure. Relative AME (%) change in ADHD symptom score for one SD increase in PDQI, UPFI and CDQI score, stratified by child sex and maternal ADHD symptoms Additional file 10. Supplementary figure. Relative risk for ADHD diagnosis for one SD increase in PDQI, UPFI and CDQI score, stratified by child sex and maternal ADHD symptoms


ADHD is defined by behavioral symptoms that are not well characterized in relation to ADHD’s neurobiological mechanisms. This approach has limited our ability to define ADHD nosology and predict outcomes because it does not systematically examine facets of the disorder such as the inability to maintain cognitively effortful activities, as promoted in the NIMH RDoC approach. Existing data indicate ADHD is associated with differences in reward valuation and processing, but we do not know whether ADHD is also associated with higher levels of aversion to exerting cognitive effort and/or altered reward x effort interactions. Our ongoing study addresses this knowledge gap by examining individuals’ preferences between rewards associated with minimal effort and reward alternatives with a higher payoff but higher effort costs (“effort discounting”); thereby permitting us to characterize differences in biases and tradeoffs during effort-related decision-making in ADHD. The study takes advantage of a well-defined sample of ADHD-diagnosed and healthy control individuals to address three aims. First, we determine whether ADHD is associated with steeper discounting of larger, more effortful rewards. Second, we examine the subjective perception of effort in youth diagnosed with ADHD and healthy controls using tasks requiring varying levels of cognitive effort. Third, we explore relationships amongst indices of effort discounting, theoretically-related traits , biomarkers of effort-related decision-making (eye movements and pupil size), and various cognitive measures. Successful completion of the aims will permit us to better characterize ADHD-healthy control differences and lay a foundation for more computational approaches to ADHD diagnostic criteria. The inability or reluctance to engage in cognitive  effort is a cross-cutting feature of numerous psychopathologies and related to the Research Domain Criteria (RDoC) construct: Reward Valuation; subconstruct: Effort . One sucIn the last few years, interest in the neurobiological mechanisms underlying effort-related decision-making has been growing, but most of these studies have examined physical effort, rather than cognitive effort, e.g., squeezing handgrips –5, typinIn terms of component cognitive mechanisms, previous research in ADHD has focused on reward valuation and reward processing ,11, on cN-back memory task, then made decisions between small and large rewards associated with a low or high N-back requirement. Data from these studies, and research using hypothetical tasks  effort has been previously discussed in connection with psychopathology and is recognized in the RDoC, very little research has attempted to move this concept from the qualitative to the quantitative realm, as proposed in this study. The methodology under-development in this project is highly innovative with the intention of elucidating the mechanisms underlying the concerns faced by these clinical populations in a novel way. By systematically examining the effect of changes in the ISI and Stimulus Duration parameters on performance on the cognitive effort tasks, as well as the subjective ratings of effort, we hope to provide a methodology to permit responses to sustained attention and working memory loads to be quantified under Third, successful completion of The study design has a number of potential limitations. First, as with any quasi-experimental study, the ADHD-diagnosed and healthy control individuals may differ in ways that are related to their group membership that affect cognitive effort discounting, for example, differences in the types and severity of co-morbid conditions like depression and co-occurring use of substances/medications. The Health Questionnaire will provide some data about these sources of group differences and permit some exploratory analyses of the robustness of effects when participants are omitted. However, such analyses would only provide a foundation to justify future research, rather than provide a definite answer to the role of these comorbidities in differences in effort discounting. A second study limitation is that if group differences in effort discounting are primarily due to differences in perceived difficulty, our plan to control for perceived difficulty level by matching groups on these ratings may eliminate group differences in cognitive effort discounting. However, such a lack of differences is still informative and suggest that the neuropsychological processes involved in devaluation of effortful rewards are not different between groups; rather differences reported outside the laboratory are driven by ADHD-diagnosed individuals perceiving the cognitive effort costs as higher.A “dislike of mental effort” is a diagnostic criteria for ADHD, a trait closely associated to attention and working memory . Howeve


Background: Iran has serious problems with traffic-related injuries and death. A major reason for traffic accidents is cognitive failure due to deficits in attention. In this study, we investigated the associations between traffic violations, traffic accidents, symptoms of attention-deficit/hyperactivity disorder (ADHD), age, and on an attentional network task in a sample of Iranian adults. Methods: A total of 274 participants  completed questionnaires covering demographic information, driving violations, traffic accidents, and symptoms of ADHD. In addition, they underwent an objective attentional network task (ANT), based on Posner’s concept of attentional networks. Results: More frequent traffic violations, correlated with lower age and poorer performance on the attentional network tasks. Higher symptoms of ADHD were associated with more accidents and more traffic violations, but not with the performance of the attentional tasks. Higher ADHD scores, a poorer performance on attentional network tasks, and younger age predicted traffic violations. Only higher symptoms of ADHD predicted more traffic accidents. Conclusions: In a sample of Iranian drivers, self-rated symptoms of ADHD appeared to be associated with traffic violations and accidents, while symptoms of ADHD were unrelated to objectively assessed performance on an attentional network task. Poor attentional network performance was a significant predictor of traffic violations but not of accidents. To increase traffic safety, both symptoms of ADHD and attentional network performance appear to merit particular attention. Compared to Western European countries and the USA, in Iran, the prevalence rate of traffic-related mortality is high ,2,3. SpeSafe driving is the product of multiple cognitive functions, to process diverse multi-sensory inputs and coordinate them with motor-controlled movements. Plausibly, driver distraction and inattention lead to errors, and can cause failures in driving performance; inattention has been identified as one of the main causes of vehicle crashes . With reDistractors interfering with attention and perception may also disrupt driving behavior ,30, and ®) to assess different cognitive processes  deserves particular attention. By definition, more severe symptoms of ADHD correlate with poorer decision making , lower ieviously : visual Previous research ,55,56 haVaa  showed iWith respect to the heterogeneous pattern of results on the association between symptoms of ADHD and driving, methodological issues, the fact that not all individuals with ADHD show more risky driving behavior, and diagnostic issues are all possible contributors. Nonetheless, there are reasons to expect that adults with ADHD are at greater risk of causing motor vehicle accidents than adults without ADHD .As a further contribution to this complex pattern, in a previous study , we founThree key observations can be made at this point: 1. the associations between symptoms of ADHD and driving behavior appear to be mixed; 2. the moderating effect of symptoms of ADHD on the relationship between attentional network tasks and driving behavior remain unclear; 3. there remains very little research on these issues.Next, higher age and slower visual processing speed are associated ,71. HereWith regard to driving experience, there is evidence that, compared to novices, experienced drivers commit fewer attention-related driving errors ,73. LikeLast, it might be expected that, compared to traffic non-offenders, traffic offenders will report more traffic accidents, though the association between traffic-offender status and performance on attentional network tasks and self-reported symptoms of ADHD remains unclear. Given this uncertainty, another aim of the present study was to compare traffic offenders with traffic non-offenders with regard to the symptoms of ADHD, attentional network task, and other aspects of driving behavior.Based on the evidence summarized above, we formulated three hypotheses and four research questions. Following others ,23,25,74We believe that the present study has the potential to clarify the complex pattern of associations between driving behavior, age, self-reported symptoms of ADHD, and objective cognitive executive processes. The findings may help inform stakeholders concerned with reducing traffic accidents in Iran.A total of 274 adults  took part in the study. As in the previous study [As in the previous study , adults Overall, participants needed about 60 min to completing the entire assessment (with 10 min for the cognitive testing). The ethical committee of the Tabriz University of Medical Sciences  approved the study, which was performed in accordance with the rules laid down in the Declaration of Helsinki and its later amendments.Participants completed a questionnaire on demographic information .As in the previous study , particiWe also asked participants to indicate their driving experience (in years) and their frequency of driving , with higher scores reflecting higher driving frequency.Next, based on information taken from the official penalty register, participants were labeled as traffic offenders or traffic non-offenders.As in the previous study  participhttps://github.com/docksteaderluke/CRSD-ANT). All participants performed the cognitive assessment individually at the Motor Behavior Laboratory of the University of Tabriz. The laboratory had an average temperature of 21 °C and sufficient artificial light. Participants were comfortably seated in front of a 17-inch monitor , controlled by an Apple Mac® computer with a 2.3 GHz Intel® Core i5. The screen was placed on a desk of conventional size and height. Participants were asked to sit upright so as to maintain a distance of 60 cm from the screen. The tasks involved participants pressing keyboard buttons. The attentional network test (ANT) was developed to measure the efficiency of each of the attention networks, namely: 1. alertness/vigilance; 2. orientation/selection; and 3. executive function/conflict [To assess attentional network tasks objectively , the following software was employed: “Attentional Network”; retrievable from: conflict . For eacTo assess alerting and orienting the procedure was as follows. Before the target appears, four cue conditions are introduced; in addition, the target can be either above or below the fixation point. The no cue condition is the baseline. The center cue appears at the fixation point with alerting involved only. The double cue condition has two cues at the two possible target locations with alerting involved but not orienting. The spatial cue appears at the target location with both alerting and orienting involved. The difference between no cue condition and double cue condition provides an index of the efficiency of the alerting network. The difference between spatial orienting cue and center cue provides an index of the efficiency of the orienting network.To assess executive control/conflict, two flanker arrows appear on both left and right side of the central target. The flankers are either congruent (pointing to the same direction as the target) or incongruent (pointing in opposite directions). Therefore, the executive control of attention can be measured by subtracting the mean reaction time (RT) of the congruent condition from the mean RT of the incongruent condition. We use the subtraction of reactions times in the ANT as a measure of the efficiency of the networks .Outcome measures were the number of correct answers, and the percentage of correct and incorrect answers.The duration of the first fixation was 1600 ms; the maximum allowable response time was 1700 ms, and the duration of the final fixation was set to 3500 ms.The procedure in the present study consisted of 155 trials  and took about 10 min to complete. All participants provided written informed consent. This study was approved by the Tabriz University of Medicine Sciences (IR.TBZMED.REC.1395.1138).® 25.0  for Apple Mac®.Pearson correlations were computed for associations between age, number of accidents, number of traffic violations, symptoms of ADHD, and processing speed of attentional network tasks, along with driving experience and driving frequency. Next, to predict the number of accidents and traffic violations, age, ADHD symptoms, driving experience and frequency, and processing speed of attentional network tasks were introduced as independent dimensions in two multiple regression analyses. Finally, a series of independent t-tests was performed to investigate differences between traffic offenders and non-offenders. The nominal level of significance was set at alpha < 0.05. All statistical calculations were performed with SPSSOf the 274 participants  who took part in the study, 89.7% were car drivers and 10.3% motorcycle drivers.Higher numbers of traffic violations were associated with more accidents and with poorer performance on attentional network tasks. A higher number of accidents was unrelated to performance on attentional network tasks.Greater driving frequency was associated with longer driving experience but was unrelated to performance on attentional network tasks, age, accidents, at fault accidents, traffic violations, or ADHD scores.Longer driving experience was associated with greater age, more accidents, fewer traffic violations, more frequent driving, better performance on attentional network tasks. Longer driving experience was unrelated to ADHD scores.As shown in As shown in Self-reported symptoms of ADHD were uncorrelated with assessed performance on attentional network tasks  the dependent variables.Self-reported traffic violations were predicted by younger age, higher self-reported symptoms of ADHD, greater driving frequency, and poorer performance with respect to attentional network task , while attentional network sub-scores and driving experience were excluded from the equation as these variables did not reach statistical significance.The number of self-reported accidents were predicted by higher self-reported symptoms of ADHD, lower age, greater driving experience and driving frequency, while performance on attentional network tasks  were excluded from the equation, as these variables did not reach statistical significance.n = 135) and traffic non-offenders (n = 128).Traffic non-offenders did not differ from offenders with regard to age, driving experience, driving frequency, attentional network performance or symptoms of ADHD .We found that, among 274 drivers with an average age around 32, higher numbers of self-reported traffic accidents and traffic violations were associated with objectively assessed poorer performance on attentional network tasks, and with higher self-reported symptoms of ADHD. Age was unrelated to attentional network functioning. Against expectations, a higher number of accidents was unrelated to attentional network performance. Next, more frequent self-reported traffic violations and accidents were predicted by a combination of younger age, self-reported symptoms of ADHD, higher driving frequency and longer driving experience. This latter finding supports the notion of driving as a complex integration of multiple cognitive functions that involve attention and personality traits. Last, officially defined traffic offenders and traffic non-offenders did not differ from each other . We believe that the present findings add to and expand upon the current literature in an important way in showing that (self-reported) symptoms of ADHD impacted neither positively nor negatively on objectively measured attentional network tasks. In contrast, the symptoms of ADHD did appear to increase the risks of self-reported traffic violations and accidents.Here, we consider the hypotheses and exploratory questions.Our first hypothesis was that self-reported traffic violations and accidents as reflections of poor driving would be associated with poorer functioning of attentional network tasks, as objectively assessed. The hypothesis was only partially supported. Though higher self-reported traffic violations were associated with poorer functioning of attentional network tasks , self-reported accidents were entirely unrelated to this area of cognitive performance. Thus, the present results only partly confirm previous findings ,23,25. IOur second hypothesis was that self-reported symptoms of ADHD would be associated with poor driving behavior, as reflected in reported traffic violations and accidents, and this was supported. Thus, the present results do replicate what has been observed in previous studies both in other countries  and in IOur third hypothesis was that older age would be related to poorer functioning of attentional network tasks as objectively assessed, and this was supported. Accordingly, the present results are consistent with findings reported from previous studies ,71.Our first research question concerned associations between self-reported symptoms of ADHD and objectively assessed functioning of attentional network tasks. As shown in Our second research question concerned which of the dimensions of age, symptoms of ADHD, attentional network functioning, driving frequency and driving experience would best predict traffic accidents and traffic violations. As reported in The third research question concerned the extent of association of driving frequency and driving experience with dimensions of attentional network tasks and symptoms of ADHD. As shown in The fourth and last research question concerned the differences between officially defined traffic offenders and non-offenders with respect to other traffic-related dimensions, the functioning of attentional network tasks, and the symptoms of ADHD. We found no such differences. As shown in The novelty of the study should be balanced against the following limitations. First, the voluntary character of the study might have biased the sampling of participants and their adherence to the study conditions. Second, by definition, cross-sectional designs do not allow for causal inferences. Third, data gathered under laboratory conditions such as performance on the attentional network tasks might not reflect real life driving behavior, where cognitive and environmental complexity is both higher and more unpredictable. Fourth, self-rated symptoms of ADHD should have been verified against a thorough psychiatric interview performed by experienced psychiatrists or clinical psychologists. For this reason, we have emphasized throughout that participants were assessed on the basis of self-rated symptoms with ADHD, and we do not claim to have assessed adults on the basis of a thorough diagnosis of ADHD. Given this, future studies in this specific field should also consider a full clinical assessment of ADHD. Fifth, unassessed psychological and physiological traits, such as arousal, alertness, daytime sleepiness, motivation, test anxiety, and increased cortisol concentrations as a proxy for increased psychophysiological arousal, might have distorted two or more dimensions in the same or opposite direction. This holds particularly true, as health-related issues, such as depression, anxiety, and poor sleep, might negatively impact testing driving behavior under laboratory conditions ,10,79. SAmong a sample of Iranian drivers, higher self-rated ADHD traits were associated with more frequent accidents and traffic violations, while these traits were entirely unrelated to objectively assessed functioning of attentional network task. The pattern of results suggests more intertwined links among cognitive processes, age, symptoms of ADHD, and traffic violations. The findings are clinically relevant, because they point to a differentiated view of the performance and behavior of drivers with ADHD traits. Policymakers and stakeholders could usefully consider the present results in their efforts to increase traffic safety in Iran.