Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study’s primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information.Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation.The data-driven method–ReHo provides four brain regions  with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection  in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling  validation (using a simple linear classifier), surpassing the use of traditional seeds.This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model. It has high potential for providing auxiliary diagnosis information through machine learning.-The proposed data-driven strategy uses the concept of regional homogeneity to reduce the difficulty of seed selection in rs-fMRI.-The established model was applied to a database including 73 patients with and 76 individuals without ADHD; 83.24% accuracy was achieved, and the model was unbiased.Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in childhood and adolescence. The reported incidence of ADHD differs between both studies and countries. The estimated worldwide prevalence of ADHD is up to 7.1% in children and adolescents . The DiaCurrently, the standard diagnostic procedure for ADHD is based on clinical interviews and symptom questionnaires. Although these questionnaires serve as objective and quantitative measures in conjunction with the subjective criteria listed in DSM fourth edition (DSM-4) and DSM-5, diagnosis is still time-consuming. Clinically, diagnosis requires more retrospective reports from parents and teachers than it does patient self-reports . IntegraAlthough physiological characteristics are not included in the current diagnostic criteria, the existing body of neuroscientific evidence from structural and functional neuroimaging studies indicates a correspondence between the abnormal behaviors used for ADHD diagnosis and brain features; for example, some brain areas may have a smaller volume and be prone toward hyperactivity or hypoactivity  in patieResting state is defined as spontaneous brain activity in the absence of any specific cognitive task during the acquisition of a brain signal or image; this definition reduces the difficulty of clinical diagnosis of ADHD. Rs-fMRI has been widely applied in the diagnosis of neurological or psychiatric diseases such as ADHD, autism spectrum disorder, Alzheimer’s disease, depression, etc . A varieSeed-based correlation analysis (SCA) is the oldest and most popular method for investigating abnormal brain activity in rs-fMRI research on ADHD and other psychiatric disorders . Due to The aim of this study is to evaluate the feasibility of diagnosing ADHD by analyzing rs-fMRI data. To achieve this goal, we propose a smart seed selection method for diagnosing ADHD in children with rs-fMRI data. We refer regional homogeneity (ReHo) analysis results. This data-driven method can determine specific seeds with within-group homogeneity and between-group differences. Furthermore, principal component analysis was used to reduce the SCA feature dimensions, and linear discriminant analysis (LDA) was used to distinguish healthy children and those with ADHD. The classification accuracies of different feature extraction approaches were also compared.1 The original data sets are available on the websites of eight institutions: New York University Child Study Center (NYU), Peking University, Bradley Hospital/Brown University, Kennedy Krieger Institute, NeuroIMAGE Sample, Oregon Health & Science University, University of Pittsburgh, and Washington University in St. Louis. Except for the NYU data set, all these data sets had unbalanced data samples. For example, some centers only provided fMRI data for patients with specific ADHD subtypes. To avoid interference due to the heterogeneity of different medical centers and acquisition machines, we used only the NYU data set. The NYU data set covered 123 children with ADHD ; among these patients, 77 were diagnosed with ADHD-combined, 44 with ADHD-inattentive (AD), and two with ADHD-hyperactive (HD). The data set also included 99 controls (48 boys and 51 girls) with mean age 12.2 ± 3.1 years. To consider the reliability of classification, this study considered ADHD-combined and ADHD-inattentive patients as the ADHD group. Moreover, the following exclusion criteria were used: the image contains excessive head movement, the control group has non-ADHD psychiatric symptoms, the ADHD group has neurologic or psychiatric comorbidity  between the two groups. The gender factor showed significant difference between the ADHD group (F/M = 16/57) and normal group . This study used gender as a covariate in the random sampling validation of the feature extraction.In this study, rs-fMRI data sets were downloaded from the ADHD-200 Global Competition.Stepwise structural and functional data preprocessing was previously conducted by the Neurobureau community using the Athena pipeline . This piThe detailed architecture of the proposed approach is displayed in x = 8, y = 7, z = 38), ventro-medial prefrontal cortex , and the precuneus/posterior cingulate cortex  as the seeds with a 6-mm radius. FC between each seed and the whole-brain voxels were estimated with Eq. (1). Voxel-wise correlation coefficients that indicate significant differences between groups were reserved as features for classifier training (p < 0.01 and cluster size ≥ 10).Functional connectivity (FC)-SCA is the oldest and most popular FC analysis method in rs-fMRI research on ADHD and other psychiatric disorders. FC denote long-range correlations between seed voxels or ROIs and other individual voxels in the brain area. The selection of seeds typically has a strong theoretical basis; a seed could be a reported lesion or a hub or node in the brain network. Three seeds are often used in studies of the resting state and ADHD. In this study, we selected the dorsal anterior cingulate cortex   was used to measure brain intrasimilarity. If a given voxel has a high intensity, its cluster has high regional temporal synchronization. For a given voxel ce (KCC) . The homW is KCC among the given voxels from 0 to 1 and indicates the similarity of the activity of voxels in a cluster. Ri is the sum of the rank of the ith time points, n + 1) × K]/2 is the mean of all Ri, K is the number of neighboring voxels plus 1 , n is the rank .in this equation, z transformation was performed on the whole-brain FC of ReHo seeds (ReHo-FC) of each individual.Although relevant literature and map templates provide some guidance for seed selection, the representativeness of the selected regions for abnormalities in the ADHD brain is still controversial. If the seed is too small, the average signal is not representative; if the seed is too large, excessive useless signals are captured. In this study, we propose a novel data-driven seed selection method based on a ReHo map. After generating the ReHo map, we select the four largest clusters as seeds for the whole-brain FC calculation. The clusters (named ReHo seeds) marked as having significant differences on the ReHo map have significantly greater regional similarity among the ADHD and control groups, respectively. If the average signals extracted from two ReHo seed differ in regional representation between groups, voxel-wise FC calculations are expected to enable the generation of distinguishable features FC. To eliminate individual differences, the Fisher’s t-test was used to determine candidate features. As suggested in AlphaSim  of classifier training and testing, the number of the principal components (PCs) was determined as that with the best classifying performance for the training set; the search range included PCs that explained 50–90% of total variability.After feature estimation, two steps were performed to extract features for classifier training. First, the whole-brain voxel-wise two-sample AlphaSim , a clustFisher’s LDA was used to distinguish normal and ADHD children. LDA is a well-known scheme for pattern recognition . Zhu et The performance of distinguishing subjects with and without ADHD using rs-fMRI functional connection features, specifically LFC, ReHo , and ReHt-test  were mostly located in the mPFC (130 voxels), temporal pole (90 voxels), putamen (89 voxels), and motor area (53 voxels). Abnormal regions were also identified in the occipital lobe and cerebellum  was used to estimate regional activation patterns through indices of localized concordance. On the basis of regional intrasimilarity, the ReHo method indicated that several localized areas were highly discriminative regions between the ADHD and control groups. As presented in rebellum . Most ofHowever, we observed that the ReHo seed  did not substantially overlap with either the anatomical or functional atlases. p < 0.01, got over 33% voting and with at least 10 voxels connected in the cluster) in children with ADHD compared with healthy controls. The detailed regional information is presented in At present, no ideal biological test for ADHD diagnosis exists. Currently, ADHD is primarily evaluated by observing the symptoms listed in the diagnostic criteria. The availability of more physiological information  is key to improving the diagnosis or treatment of ADHD by medical teams. Such physiological information can provide objective information about cognitive deficits and increase the confidence of the medical team in diagnosis. Rs-fMRI is an excellent acquisition method for physiological brain information for people with psychiatric disorders. However, analyzing rs-fMRI data and confirming observations is challenging due to the lack of a preexisting model; in this study, we propose a procedure for automatically determining effective seeds for investigating brain abnormalities in ADHD (ReHo-FC). The procedure reduces the difficulty of seed selection  in estimations of brain interconnections, improving the search for an effective seed; Our proposed feature combination of ReHo-FC and LFC can achieve a success rate of 84.87% (LDA) for identifying patients with ADHD by 10 times random sampling of the data from 149 individuals in the NYU data set. The identified ReHo seeds have been noted in studies on ADHD. The present results are sufficient for a medical center to construct an auxiliary diagnostic information system in accordance with its needs and resources .As noted in the previous sections, the challenge of SCA is the difficulty of selecting or defining an effective ROI as a seed. The use of anatomical and functional atlases to select seeds and then estimating brain networks is a straightforward method of analyzing abnormal FC and resting state networks (RSNs). In this study, we compared the overlap between the identified ReHo seeds and the atlas maps. The overlapping area was only approximately 10–40% of the atlas map size. Asynchronous voxels may affect the representativeness of the extracted signal. Moreover, the difficulty of using multisite data is an obstacle to identifying effective auxiliary FC patterns for diagnosis. The scanning device, parameters, and protocol may differ between sites participating in the ADHD-200. For example, individuals in the NYU data set were required to close their eyes, but this is optional for individuals in the Peking data set. Moreover, individuals are insufficiently categorized into subgroups or uncategorized in some data sets. The aggregation of data sets recorded from different institutions generates a clustered structure within the data that may cause a batch effect . The preAnother approach, deep learning, is usually presented as an end-to-end learning process. Deep learning can alleviate feature engineering requirements and reduce domain knowledge requirements. Its multilayer, nonlinear learning strategy provide more flexibility in features extraction and classification. Hence, deep learning is a promising approach for informaticists in abnormal brain investigation. In the practical application part of the ADHD-200, most studies have used general atlases to generate whole-brain FC features for the deep learning model. The approximate classification accuracies of the general deep learning architecture were approximately 60–75% . Mao et Although the reasons for abnormal RSNs in ADHD are unclear, recent studies in neuroscience have identified a number of abnormal RSNs, including the DMN, ventral attention, dorsal attention, and the frontal and motor networks in patients with ADHD . We usedThis study may have a limitation in the generalization for classification of ADHD because we excluded subjects of a small sample size  to enhance evaluator confidence in diagnosis. Second, although our proposed features can achieve good classification rates with the NYU (83%) and Peking (94%) data sets, the deleterious effects of using multisite data sets cannot be ignored. At present, each data set is used independently, which enables the extraction of numerous meaningful features and results in an improved the accuracy rate. We expect the system to undergo more substantial development at various hospitals and pediatric centers. Finally, by integrating the models of multiple independent centers with methods such as reinforcement learning, transfer learning, bagging, and bootstrapping, developers can increase the clinical confidence in the auxiliary medical information provided by the method.Publicly available datasets were analyzed in this study. This data can be found here: ADHD-200.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.T-HH completed the writing of the manuscript and the summary of all the data. C-CK and F-ZS provided rs-fMRI analysis. S-FL provided the machine learning analysis. All authors contributed to the article and approved the submitted version.


Dissolution experiments using Cr-doped UO2, performed as a function of Cr content in a simplified groundwater solution and under oxic conditions, established that the addition of Cr to the UO2 matrix systematically reduced the normalised dissolution rate of U at 25 and 40 °C. This effect was most notable under dilute solution conditions, and is the result of galvanic coupling between Cr and U, resulting from the presence of Cr2+ in the UO2 matrix, as corroborated by activation energy determination. Under conditions of solution saturation, where schoepite ((UO2)8O2(OH)12·(H2O)12) and Na2U2O7·6H2O were identified as secondary phases, the rate of U dissolution was invariant with Cr content. Moreover, at 60 °C, the trend was reversed and the rate of U dissolution increased with increasing Cr content. Under these conditions, other factors, including U solubility or bicarbonate-surface interactions, exert a stronger influence on the U dissolution kinetics than Cr. Increased grain size, a feature of Cr-doped UO2 fuel, was also found to reduce the normalised dissolution rate of U. In establishing the mechanisms by which Cr dopants influence UO2 fuel dissolution, it can be concluded that, overall, Cr-doped UO2 nuclear fuel possesses similar dissolution kinetics to undoped UO2 fuel, giving confidence for its eventual disposal in a geological facility.Alternative UO This will consist of a multi-barrier containment system, designed for the retention of radionuclides and mitigation against radioactive elements reaching the biosphere. While engineered to promote minimal degradation, there is expected to be a point in time at which groundwater will reach spent fuel and facilitate dissolution processes.The current option for the final disposal of spent UO2 fuel has been well established through many years of investigation of dissolution under anoxic, reducing and oxic conditions, e.g. refs. 3. For the latter, dissolution rates are quoted to range from between 1 and 7 mg m−2 d−1, with the highest rates obtained in the presence of carbonate ions due to the propensity of U6+ to complex with CO32− to form soluble species4. However, the development and use of alternative UO2 fuels, adapted by doping with elements such as Cr2O3 to enhance in-reactor performance7, necessitates further investigation to understand whether dissolution rates are influenced by doping.The dissolution of spent UO4, dissolution of the UO2 matrix is understood to be the rate-determining step of spent fuel degradation, which proceeds via the oxidation of U4+O2 to soluble U6+O22+ species. This mechanism is governed by the availability of oxygen to diffuse through the lattice, enhanced by the presence of oxygen vacancies (Ov)8. In the new alernative nuclear fuel, Cr-UO2, it has been shown that Cr doping of UO2 results in the substitution of Cr2+ onto U4+ sites in the lattice, with the concurrent formation of Ov and U5+ to form a compound with a stoichiometry of 9. As such, the presence of Ov and U5+ defect species should tend to increase the oxidative dissolution of doped UO2, which may reduce durability in a geological disposal facility when compared with conventional, undoped, UO2 fuel types.Although complex in final composition and microstructure post-fission2 dissolution behaviour indicated the opposite behaviour, that the presence of Cr decreased the dissolution rate of U relative to pure UO210. The authors postulated that the high pH conditions of their study  caused the surface of Cr-UO2 to be more resistant to oxidation than UO2, or it otherwise prevented the release of U6+ species10. A slower rate of U dissolution compared with undoped UO2 was also reported for the dissolution of commercially fabricated Cr/Al-doped UO2 under oxidative conditions (H2O2), although it was stated that the behaviour may be due to differences in the surface area between samples, rather than the presence of Cr11. These authors were unable to establish a mechanism to satisfactorily explain why the addition of Cr influenced the UO2 behaviour.However, a recent study focused on Cr-doped UO2 was also observed for trivalent Gd3+-doped UO215. At room temperature and in bicarbonate solution (pH 7– 8.5), the dissolution rate of U was an order of magnitude lower for Gd-doped UO2, while a drop of almost two orders of magnitude was observed at 50 and 75 °C, thought to confirm a 'matrix stabilisation effect' of Gd in UO215. It was postulated that stabilisation was conferred by a decrease in the degree of UO2 oxidation (U4+O2 to U4+/5+4O9) due to the substitution of U4+ cations for Gd3+ and potential formation of MO8-type defect clusters14. The same behaviour was observed for Dy3+-doped UO216. MO8-type defect clusters have not been reported for Cr-doped UO2, therefore, another mechanism must exist.It may be possible to partly explain these observations by reference to other doped systems, for example, a reduction in U dissolution rate compared to pure UO2 fuel is known to result in an enlarged grain size when compared to standard UO27. Grain boundaries have been shown to influence the dissolution behaviour of UO2 and spent fuel analogue materials including CeO2 and ThO2 and Ln-doped CeO224. Assessment of the transformation of grain boundaries during dissolution found that these features contribute significantly to the release of U . It was hypothesised that the concentration of Ov defects at grain boundaries creates high-energy reactive surface sites for dissolution to initiate18. This suggests that a decrease in the quantity of grain boundaries, which is associated with a larger grain size, as observed for Cr-doped UO2, could reduce the number of energetically reactive sites and, thus, reduce dissolution rates.The addition of Cr to UO2, and to underpin the mechanisms that govern U release from Cr-doped UO2, semi-static oxic dissolution experiments in a simplified bicarbonate groundwater solution were performed. Two possible mechanisms were evaluated: (1) Cr-induced changes to the crystal chemistry and (2) microstructure. For (1), materials were prepared using a range of Cr-dopant concentrations, above and below the solubility limit of Cr in UO2, and the dissolution kinetics of U were determined as a function of Cr content and dissolution temperature. These materials were prepared such that there was a fixed grain size, to enable isolation of the chemical effects of Cr on the dissolution rate from those of the microstructure. For (2), materials were prepared at different sintering temperatures, producing pellets with varying grain size, to quantify the extent to which microstructure influences the dissolution of Cr-doped UO2.To establish the influence of Cr as a dopant on the dissolution kinetics of UOPellets sintered at 1700 °C were used to study the effect of Cr content and dissolution temperature on the dissolution rate of U, independently from microstructure effects.2 materials utilised in the dissolution experiments was detailed fully by Smith et al. 9. Briefly, for all concentrations of Cr-doped UO2, the sintered pellets presented a homogenous distribution of Cr2+ within the UO2 matrix, charge compensated by Ov defects. Above the solubility limit of Cr within UO2 (700–1200 ppm)28, precipitates of Cr were observed to reside within grain boundaries, hypothesised to be Cr3+2O329. When sintered at 1700 °C, the presence of these precipitates in Cr-doped UO2 resulted in a reduction in grain size when compared to undoped UO2 , as observed in industrially synthesised Cr-doped UO °C Fig. . Complet °C Fig. , which iL(U) (g m−2), at 25, 40 and 60 °C as a function of time  (g m−2 d−1), as shown in Figs. −1, of U leached from undoped UO2, which is reported to reach a solubility limit with respect to schoepite, of around 10−5 mol L−1, in saline, carbonate-containing solutions under oxic conditions33. The presence (or absence) of secondary phases was also used to support the identification of the regimes, as discussed later. The common regimes for all temperatures were interpreted as being: (1) RL,i, reflecting an initial dissolution rate where solution conditions were dilute and saturation effects were not observed, i.e. where U concentrations were ~10−7 mol L−1 , i.e. where U concentrations were ~10−5 mol L−1 and a plateau, or drop, in NL(U) was observed  RL,L−1 Fig. ; and (3)ved Fig. .Fig. 2NoL,gb, where the normalised dissolution rate of U increased at later time points  × 10−3 g m−2 d−1 for undoped UO2 and (1.31 ± 0.13) × 10−3 g m−2 d–1 for 1490 ppm Cr-doped UO2. The corresponding dissolution rates in the RL,t regime were (1.10 ± 0.09) × 10−3 g m−2 d−1 and (0.39 ± 0.04) × 10−3 g m−2 d−1 for undoped and Cr-doped UO2, respectively  × 10−3 g m−2 d−1 for undoped UO2 and the highest Cr-doped UO2 material, respectively. Similar behaviour was observed in the RL,t regime  × 10−3 g m−2 d−1 for the highest concentration of Cr-dopant. It follows that the galvanic coupling effect of Cr2+ in UO2, as described for the 25 °C experiment, also occurs at 40 °C.The normalised dissolution rate of U, at 40 °C, in the RL,i Fig.  regime aime Fig. , with raL,ss was (0.03 ± 0.01) × 10−3 g m−2 d−1 for UO2 and (0.74 ± 0.10) × 10−3 g m−2 d−1 for 147 ppm Cr. The molarity values of U  were close to the solubility limit of U in saline, bicarbonate solution under oxic conditions, meaning that the solution was close to saturation with respect to U-containing secondary phases. The absolute normalised dissolution rate of U for UO2 doped with higher concentrations of Cr were negative, concurrent with the precipitation of U-bearing secondary phases, identified via SEM imaging of the material surfaces post-dissolution  than the Cr-doped UO2  (see method section for details), determined for undoped and Cr-doped UO2 at 25, 40 and 60 °C using the RL,i dissolution rates are shown in Fig. 2 dissolution in bicarbonate solution, the undoped UO2 gave an Ea value of 49.40 ± 0.04 kJ mol−1, consistent with a dissolution mechanism that proceeds via surface-controlled reactions38. As the Cr content is increased, the activation energy significantly increased, reaching (78.90 ± 0.01) kJ mol−1 for the highest concentration of Cr-dopant. This suggests that, in the initial stage of dissolution (i.e. without the effects of solution saturation), Cr influences surface-controlled dissolution reactions, consistent with the hypothesised galvanic coupling effect, even in bicarbonate solutions.Calculations of the apparent activation energy (E2 and after 79–86 days for Cr-doped UO2 (Table The steady-state regime was reached after 100 days of dissolution at 60°C for undoped UO2); and an angular chip-shaped phase 8O2(OH)12·(H2O)12) were also present in UO2 doped with the highest concentration of Cr ; however, there was no clear trend between Ea and Cr content. This supports the hypothesis that at later stages of dissolution, when the solubility limit of U in bicarbonate solution has been reached, the effect of Cr is negligible. Interestingly, the Ea remains unchanged for UO2 doped with the highest concentration of Cr, which gave an Ea of RL,i = 74.63 ± 0.01 kJ mol−1 and RL,ss = 79.68 ± 0.02 kJ mol−1. To fully understand the mechanistic behaviour, a wider range of Cr-doped UO2 concentrations should be investigated in future.The apparent activation energy in the Rime Fig. , where s2, prepared in a reducing atmosphere, were subject to heat treatment at different sintering temperatures of 1400, 1500, 1600, and 1700 °C. A clear increase in grain size with increasing sintering temperature was observed, from ~2–14 μm  at 25 °C for 100 days  within each set of materials sintered at the different temperatures, it is clear as the sintering temperature is increased, the change in grain size has less of an effect on the normalised dissolution rate, i.e., the gradient becomes more shallow with increasing temperatures, most probably due to the level of densification attained.These materials were subject to dissolution in simulant groundwater solution 29. Uranium (VI) nitrate hexahydrate in solution (UO2(NO3)2·6H2O, The British Drug House (BDH). B.D.H Laboratory Chemicals Division, >98%, 0.3 mol L−1) was mixed with various amounts of chromium (III) nitrate nonahydrate in solution (Cr(NO3)3·9H2O, Sigma Aldrich, 99.99%, 1.6 mol L−1) and precipitated at room temperature using concentrated ammonium hydroxide solution . The pH was monitored to reach between pH 8–10 for the successful co-precipitation of U and Cr, confirmed by Inductively Coupled Plasma Optical Emission Spectroscopy  of the supernatants, where 99.9% precipitation for each element was achieved. The yellow precipitate was washed in deionised water, vacuum filtered and dried overnight at 90 °C to eliminate any remaining hydroxide. The resultant precursor powder was converted to oxide via thermal treatment at 750 ˚C for 4 h under a reducing (95% N2(g)–5% H2(g)) atmosphere, followed by dry milling at 35 Hz for 15 min to increase homogeneity and powder reactivity. Pellets of 6 mm were uniaxially pressed at 2.5 tonnes using a stainless steel die, and sintered for 8 h in a reducing (95% N2(g)–5% H2(g)) atmosphere. A range of sintering temperatures, of 1400, 1500, 1600 and 1700 °C, were used to control the microstructure (grain size). All surfaces were ground using SiC paper, polished to 1 μm using diamond suspension and thermally etched at ~85–90% sintering temperature to ensure an equal surface finish for all materials and to reveal the grain structure for characterisation.A wet chemical, nitrate precipitation method was used to synthesise both Cr-doped and undoped UO2 single phase was carried out via X-ray diffraction (XRD) using a Bruker D2 Phaser diffractometer utilising a Cu Kα source from 10° to 100° 2θ with a step size of 0.02° and a step time of 2 s. Geometric densities of sintered pellets were calculated using the average geometry, measured using calibrated digital callipers, and average mass, measured using a calibrated five-point balance, to determine the geometric specific surface area of each pellet. Archimedes density was also determined for each individual pellet prior to dissolution, presented as an average of 10 measurements. The microstructures of the Cr-doped and undoped UO2 were characterised by scanning electron microscopy (SEM) using a Hitachi TM3030 SEM operating with an accelerating voltage of 15 kV. Images were taken at 500x magnification across five points of each pellet and the average grain area of ~500 grains was measured using ImageJ.Confirmation of the UO3). Pellets were crushed using a pestle and mortar and ~20 mg of powder was completely dissolved, under temperature and stirring, in 5 mL HNO3. The solutions were then measured for Cr concentration by Inductively Coupled Plasma Mass Spectroscopy . Grain size and density were quantified as a function of both Cr content and sintering temperature.The total Cr content within the sintered materials was assessed by a complete digest in concentrated nitric acid (2 M HNO3 bicarbonate solution) in a PTFE container. No atmospheric control was applied throughout the experiment, which is important to note since the dissolution medium was in equilibrium with CO2 in the air. The pH was measured to be in the range of 7.8 to 8.2 (±0.2 pH units) for all experiments at all times.Long-term durability experiments were conducted in the PLEIADES National Nuclear User Facility, using thermally etched, sintered pellets in duplicate, which were submerged in 50 mL of simulant groundwater solution  on UO3 for analysis by ICP-MS. A fresh solution of the same volume was replaced to maintain the surface area to volume (SA/V) ratio of approximately 1.3 m−1. Aliquots of solution were taken on days 1, 3, 7, 14, 21, then weekly up to day 98 and then bi-weekly for the duration of each experiment, which varied depending on dissolution temperature. It should be noted that Covid-19-induced laboratory closures severely restricted the sampling points for several experiments, over a period of ~6 months. Each long-term experiment was terminated after a constant concentration of U was measured, within error, for four consecutive time points.At specific time points, an aliquot of 2 mL of the dissolution medium was removed, filtered (0.22 µm) and diluted by a factor of 10 in 1% ultra-pure conc. HNO−1) was converted to the mass of the element in solution for each time point (m(U)(t), mg) using the volume of each aliquot  via Eq. −1 for KED and 28 ng L−1 for STD analysis modes) for the duration of all experiments and, therefore, could not be measured.The concentration of U (C(U), ng Lloss(U)(t), mg) of each pellet, and the initial pellet mass  was used to determine the percentage of mass remaining for each pellet via Eq. L(U) (g m−2) was calculated from Eq. SA, m2 g−1) of each pellet, while the normalised rate of U dissolution (RL(U), g m−2 d−1) was determined via the gradient of NL(U) as a function of time.The cumulative mass loss over time (ma) according to the Arrhenius law, Eq. L(U) was taken in the initial regime of dissolution ), for the intended regime, over the reciprocal absolute temperature was taken to determine the Ea (kJ mol−1) for undoped and Cr-doped UO2, as a function of Cr content.The dependence of the dissolution rate on temperature was assessed by determination of the activation energy (EThe presence of secondary phases, formed during dissolution, was determined via SEM/EDX using a Hitachi TM3030 SEM coupled with Bruker Quantax EDX system. XRD of the pellet surface, post-dissolution, was performed as described above, between 5° and 100° 2θ with a step size of 0.02° and a step time of 2 s. To improve diffraction pattern analysis, the precipitates were gently removed from the surfaces of pellets and analysed. XRD patterns were indexed using PDF SIEV + software. Geochemical modelling using PHREEQC-3 and the LLNL database was used to identify potential species that reached saturation limits in the dissolution.Supplemental material


Autism and attention-deficit/hyperactivity disorder (ADHD) often co-occur. This survey of 288 New Zealand parents of children diagnosed with autism (n = 111), ADHD (n = 93), or both conditions (n = 84), examined the relations between age of diagnosis and early atypical development, the age specialist consultation was needed and types of specialists seen. Co-occurring autism and ADHD was associated with an earlier ADHD diagnosis and a later autism diagnosis. Parents of children with both diagnoses reported less atypical development in language and social behaviours compared to parents of children of autism, and this co-occurring group also experienced longer wait times to diagnosis, and saw more types of specialists prior to a diagnosis, than those with autism. Autism spectrum disorder (hereafter referred to as autism) and attention-deficit/hyperactivity disorder (ADHD) are both neurodevelopmental conditions. Autism is characterised by impairments in social communication and interaction and rigidity of behaviour . This may be because autism is developmentally apparent earlier and thus co-occurring ADHD could be diagnosed sooner due to earlier health seeking  or ADHD (+ autism)  the number and type of parent-reported areas of atypical development in the first year of life; (b) the age at which parents felt specialist consultation was needed; (c) the behaviour that led to specialist consultation and whether this aligned with the child's subsequent diagnosis; (d) the wait time from when parents felt specialist consultation was needed to when a diagnosis was given; (e) the types of specialist seen before a diagnosis; (f) any additional diagnoses; and (g) parental agreement that their child’s behaviour matched their diagnosis.Ethical approval for this study was provided by the Human Ethics Committee at Victoria University of Wellington, New Zealand [Approval number 28993]. Participation in the study was both voluntary and anonymous. Participants were asked not to include any information which would make them or their child identifiable. Participants were also informed that they did not have to answer any question and were given "prefer not to say" options.Parents of children with autism, ADHD, or autism + ADHD were invited to participate in this survey. Participants were eligible for inclusion if they were: (a) parents, legal guardians, or caregivers , (b) the child had a clinical diagnosis of autism, ADHD, or autism + ADHD, and specified the age of diagnosis (as reported by parents), (c) the child was aged 18 years or younger, and (d) the parents and child were living in New Zealand. Convenience sampling was used, as participants were recruited by an email sent by Autism New Zealand and ADHD New Zealand, as well as information about the study shared on various autism and ADHD social media groups. Participants were sent a link to the questionnaire within the advertisement.The survey was hosted on Qualtrics from the 15th of March until the 1st of June, 2021. Many of the questions used in this survey were based on those in The Australian Autism Biobank's Family History Questionnaire  and "Now think of the period between 7 and 12 months, can you recall anything unusual about your child's behaviour during that time?" . The parents then selected which of 30 areas of atypical development were retrospectively relevant to their child at each age. These were based on the areas identified by Waddington et al. (in press) and Guinchat et al.  that led them to seek specialist help. Parents were asked how old the child was when they first had concerns that warranted the need to see a specialist and to describe what particular concerns led to seeking specialist help in an open-text box. The concerns were then coded by WS  according to whether the concerns fit with a DSM 5 description of the particular diagnosis(es) the child obtained. The coding was based on ADHD concerns, autism concerns or behaviour/other category. A further 30% were coded by KC  with an 88% interrater agreement.The next section related to specialists seen, additional diagnoses and age of diagnosis. Parents were asked to select the types of specialists/professionals seen before receiving a diagnosis from a list and an "Other" option with text provided. The next question asked whether their child was diagnosed with autism, ADHD, or autism + ADHD. Depending on parents’ responses, they were directed to the child’s age of diagnosis for either autism only, ADHD only, or an age for both diagnoses. The last three questions of this section asked, "What diagnosis best described the behaviours of your child?" with the diagnostic options of autism and ADHD with main symptoms in brackets, a "both" and a "neither" option. A further two questions about any additional diagnoses with options including sensory processing disorder, epilepsy/seizures, oppositional defiant disorder, intellectual disability, global delay, learning disability and "other" and finally whether a misdiagnosis had occurred. A final section asked four open-ended qualitative questions, which are not included in the analyses.Data analysis was performed using IBM SPSS Statistics software (version 26). A chi-squared test of independence with Bonferroni correction determined whether there were significant differences across diagnostic groups for gender, ethnicity, household income, highest education and absence or presence of siblings. A Kruskall Wallis test was used to analyse the age of participants across diagnostic groups.Household income data was collapsed from 13 $NZ 10,000 increments to four $NZ 50,000-increments. In the highest level of education question many of the "Other" responses related to diplomas or trade qualifications, thus, a new category was added to encompass these responses. It was not possible to do an analysis for parent relationship to child due to having too few participants in any other category except mothers. Categories with fewer than 10 participants in each group were considered for exclusion or merging into a wider category to increase the reliability of group measures in statistical analysis. Excluded from statistical analysis of gender were 'non-binary' (n = 6) and 'prefer not to say' (n = 2) and excluded from analysis of siblings was the category of "twins" (n = 9). None of the "Other" ethnicities reached a threshold for analysis and were thus grouped together. Each participant’s ethnicity was categorised as ‘Māori’  if one of their stated ethnicities was Māori.p values by 3.Non-parametric statistical tests were also used to answer the research questions as the data was not normally distributed. Mann Whitney-U tests were used to compare the age of autism and ADHD diagnosis, time to diagnosis, and the alignment of parents’ description of behaviour with symptoms of the child's subsequent diagnosis. The autism + ADHD group was not included in the latter analysis due to the presenting behaviour description requiring symptom agreement of both an autism and ADHD diagnosis and most parents only described one presenting behaviour. Kruskall-Wallis tests were used to compare differences across the three diagnostic groups in terms of: (a) age of children at the time of the survey; (b) total number of parent-reported atypical development; (c) age of from when specialist was sought; (d) age when specialist was sought; (e) the number of different types of specialists seen, (f) the total number of additional diagnoses across the three groups. The Kruskal–Wallis test presented the post-hoc p values in relation to each group combination and are reported. Chi-squared tests were used to compare differences across the three diagnostic groups in (a) atypical development in each domain at ≤ 6 and 7–12 months; (b) alignment between parents’ perceptions of the child's behaviour presented to a specialist and the child's subsequent diagnosis; (c) alignment between diagnosis and parent perceptions of child's ongoing behaviour; and (d) additional diagnoses. To control for multiple comparisons, a Bonferonni correction was used for post-hoc tests by multiplying all The parents of 355 children participated in the survey. Of these, 52 parents did not report their child's diagnosis, four parents reported that their children were over 18, and 11 only completed the demographic questions, and were thus excluded. This resulted in a final sample of 288 parents of children diagnosed with either (1) autism; (2) ADHD; or (3) autism + ADHD. Table 2  = 4.40, p = 0.623], ethnicity [X2  = 9.11 p = 0.058] or gender . A significant difference was found for the association between the diagnostic group and having a younger sibling  with post-hoc testing indicating a lower proportion of younger siblings in the ADHD group compared to the autism or co-occurring group. There were no other significant differences in the likelihood of having no siblings  or older siblings  across diagnostic groups. There were significant differences in family income across diagnostic groups . Post-hoc testing indicated that the autism + ADHD group had a smaller proportion of participants in the $NZ50,000–99,999 group compared to both the autism and ADHD only groups.Chi-squared tests indicated that there were no significant differences across diagnostic groups in terms of highest level of education . Post-hoc testing determined that the age of the autism group was significantly lower compared to those with both autism + ADHD and those diagnosed with ADHD, p < 0.001 and p = 0.045, respectively. The age of ADHD children was also significantly younger than the autism + ADHD group (p = 0.040).A Kruskal–Wallis test determined that there were significant differences in child age across diagnostic groups . Chi-square test for the presence or absence of any area of atypical development did not significantly differ between the three groups, .A Kruskal–Wallis test was used to compare total number of domains of atypical development indicated by parents and there was no statistically significant difference between groups in atypical development ; social ; stereotyped behaviour ; motor , behaviour ; medical issues X2  = 2,88, p = 0.24); or abnormal function .Chi-square tests revealed no significant difference across diagnostic groups in domains of language . Bonferroni-adjusted pairwise comparisons determined that ADHD had significantly fewer parent-reported areas of atypical development than autism (p < 0.001) and autism + ADHD (p = 0.014) with no significant difference between autism and autism + ADHD (p = 1.000). Chi-square test for the presence or absence of any area of atypical development did not significantly differ between the three groups,  = 5.25, p = 0.073).A Kruskal–Wallis test was used to compare total number of domains of atypical development indicated by parents and there were significant differences between groups , medical issues  and abnormal function [X2  = 2.50 p = 0.287]. There were significant differences in the language domain . There were a higher proportion of parents reporting atypical development in the language domain if their child had autism than autism + ADHD, and a significantly lower proportion for the ADHD group compared to both autism and autism + ADHD groups. There were also significant differences across diagnostic groups in the social domain , with the a higher proportion in the autism group reporting atypical social development than the ADHD and autism + ADHD groups and a lower proportion in the ADHD group than all other groups.There were no significant differences in domains of behaviour , with a greater proportion of parents in the autism + ADHD group reporting atypical stereotyped behaviour than the ADHD group, and the ADHD group had a lower proportion than the autism and the autism + ADHD groups. There were also significant differences in the motor domain  with a higher proportion of parents reporting atypical motor development in the autism group and a lower proportion of parents reporting atypical development in the ADHD group.There were significant differences in the stereotyped behaviour domain  and  respectively. The age a specialist was needed did not differ between the autism + ADHD and ADHD groups .A Kruskal–Wallis H test indicated that the age (months) at which parents felt specialist help was needed differed between the three diagnostic groups  = 96.45, p < 0.001. Parents of children with autism most frequently presented with autistic behaviour(s). The ADHD and the autism + ADHD group showed far greater inconsistency with a greater reporting of behaviours that do not fit with an autism or an ADHD diagnosis.Further analysis of the specific behaviours which parents report led them to seek specialist help are shown in Table p = 0.445. The wait time from specialist contact to diagnosis for the autism only and autism + ADHD group were significant with the autism only group (M = 82.83) waiting significantly less time for an autism diagnosis than an autism + ADHD group (M = 109.53), U(n = 188) = 5582.50, z = 3.345, p = 0.001.Mann–Whitney U tests were used to examine the time (months) between when a specialist was needed to diagnosis for ADHD and autism groups compared to the autism + ADHD group. Analysis indicated that the wait time from age parents felt a specialist was needed to diagnosis for ADHD only group and the co-occurring group was not significant U (n = 162) = 3 506.00, z = 0.763 p = 0.024. The same was true within the ADHD only group. Parents who presented with concerns that indicated a symptom of ADHD waited significantly less time than parents who presented with concerns that did not indicate a symptom of ADHD, U  = 444.00, z = − 4.572 p < 0.001.A further Mann–Whitney U analysis examined whether the type of behaviour described by parents as motivating specialist help aligned with a symptom of the child's subsequent diagnosis and whether this affected time to diagnosis. In the autism only group, parents who described a symptom of autism as motivating them to seek specialist help waited significantly less time for a diagnosis than parents of autism only children who presented with concerns that did not indicate a symptom of autism, U  = 616.50, z = − 2.254, H(2) = 14.776, p = 0.001]. Bonferroni-adjusted pairwise comparisons determined that the number of types of specialists seen was significantly higher in those with autism + ADHD compared to those diagnosed with ADHD alone and autism alone . There was no difference in the number of types of specialists seen between the ADHD and autism only groups (p = 0.340).A Kruskal–Wallis test was used to compare the number of different types of specialists seen between the three diagnostic groups, determining that at least one group differed [2  = 26.55 p =  < 0.001] and sensory processing disorder was more likely to be diagnosed with autism than ADHD and autism + ADHD [X2  = 13.21 p < 0.001]. Intellectual disability (ID) was not significant across the groups ; [X2  = 2.72 p = 0.257].A Kruskal–Wallis test was used to compare the number of additional diagnoses between the three diagnostic groups, determining that no group significantly differed; H(2) = 4.86, p = 0.089. However, certain diagnoses had a significant correlation with particular diagnostic groups. Oppositional defiance disorder was more likely to be diagnosed in addition to ADHD than autism and autism + ADHD [X2  = 1.75 p = 0.417]. Within the ADHD group, 25% of parents disagreed that ADHD was the best diagnostic description for their child's behaviour and selected both autism + ADHD as a better explanation. Within the autism group, 20.7% disagreed that autism was the best diagnostic description for their child's behaviour and selected both except for 3 parents, who opted for neither. Within the co-occurring group, 16.9% disagreed that autism + ADHD was the best diagnostic description for their child's behaviour and selected either ADHD (n = 8) or autism (n = 7).There were no significant differences between groups about whether parents believed that the diagnosis explained their child's behaviour [XThis study sought to understand similarities and differences in early childhood diagnostic experiences for children with diagnoses of autism, ADHD or both. In line with previous research, ADHD diagnoses were significantly earlier for the autism + ADHD group compared to the ADHD only group, and autism diagnoses were significantly later for the autism + ADHD group compared to the autism only group . The results provide further data that children with autism + ADHD may present with a different and more complex diagnostic picture. This presentation of retrospective parent-reported atypical development appeared as a "half-way" point with higher reported atypical development than an ADHD group, but lower than an autism group. Along the diagnostic pathway the co-occurring group resembles more closely the characteristics of an ADHD group, such as with timing of when a specialist was needed. In other measures, an autism + ADHD group presented with neither autism nor ADHD patterns such as parents' descriptions of the concern that led to specialist help being more ambiguous, possibly contributing to the co-occurring group having longer wait times and seeing more types of specialists between seeking help to diagnosis than the autism only or ADHD only groups.The parent reported observation of atypical development demonstrated that under 6 months there were no significant differences across diagnostic groups, but by 12 months statistically significant differences were occurring. The significant differences between increased atypical development reported in the autism only group compared to the ADHD group, was to be expected. Core autism behavioural features of language, stereotyped behaviour and social atypical development were significantly more reported in the autism group than the ADHD group which fits with diagnostic models that suggest ADHD symptoms are more apparent in later childhood and autism symptoms are observable at 12 months (Ozonoff et al., The autism + ADHD diagnostic group had fewer areas of reported atypical development than the autism group but more than the ADHD group. The prevalence of atypical language and social development in the first year of life appeared to be markedly less for autism + ADHD group. Atypical language development is a known trigger for early health seeking and less atypical development in this area is likely to contribute to a delay in an autism diagnosis (Zablotsky et al., The average age when parents first sought a specialist differed significantly. The delay in diagnosis may start, in part, with a delay in parents of children with autism + ADHD seeking specialist help compared to an autism only group (Stevens et al., Parents presenting with a specific symptom-related behaviour which corresponds directly to a particular diagnosis are likely to trigger a particular diagnostic pathway (Becerra-Culqui et al., Parents of the co-occurring group described presenting behaviours to specialists which did not fit with key symptoms that might trigger a diagnosis of either disorder, but rather the most common concerns parents described appear to be "other", including behaviour, then autism and then ADHD symptoms. This might result in a less clear referral to a specialist or a referral to a specialist who does not have a specialty in autism or ADHD diagnosis. Evidence of this was seen in the increased number of types of specialists that the co-occurring group saw before receiving a diagnosis. Eggleston et al. ’s study This study elucidated further where along the diagnostic pathway delays in diagnosis might be occurring. Stakeholders in the diagnosis and treatment of children might consider that parents of children with autism + ADHD often present with later behavioural and other types of concerns, and with less parent-reported atypical development of language and communication than an autism only group. A further consideration might also be that parents presenting with concerns around behaviour and unclear referrals may warrant a more direct referral to a multidisciplinary team rather than referrals to a number of types of individual practitioners. Referrals that trigger specific developmental symptoms of a particular diagnosis are likely to encounter a more straightforward diagnosis pathway. In addition, specialists might consider that a first diagnosis of ADHD should not preclude further assessment of autism, or that autism should at least not be ruled out. Specialist might also want to consider discussing with parents, whose children are first diagnosed with ADHD, the key symptoms of autism so that parents feel empowered to return for the possibility of an autism diagnosis sooner. These suggestions have the potential to reduce the delay in diagnosis for autism + ADHD children and improve the diagnosis and treatment for children and their families.One limitation of this study is that it did not use an epidemiological sample, and may not be representative of the broader population of these groups. A more educated and homogenous sample of society often answer online surveys (Eggleston et al., The categories of diagnosed children representing autism, ADHD and autism + ADHD are not static, and it is possible that children in the single diagnosis categories will, at some point in the future, move into the co-occurring group. However, this possible movement does not detract from the snapshot in time of these diagnoses and how they might relate to atypical development, timing and type of parental concerns, diagnosis experience and current diagnosis of the child. To enhance the level of evidence further studies might also look at the risks of mis-diagnosing co-occurring ASD + ADHD. A greater understanding of any undesirable consequences in giving a younger child a co-occurring diagnosis should be addressed.There are limitations for parents self-reporting. The first concerns that led to specialist help also has a recall bias, particularly once diagnosis is known, and parents might retrospectively recall concerns that fit with the diagnosis. The survey uses parental report as the basis for diagnostic criteria, which had not been clinically confirmed. Miodovnik et al., , howeverThe study found that the diagnosis of autism in an autism + ADHD population is delayed. A delayed diagnosis is significant for this high need population as it reduces the opportunities for early intervention. The delay in diagnosis may be due to parents noticing less atypical development in language and social behaviours, the presentation of less autism-specific concerns to a specialist and at a significantly later time than an autism only group. A lack of flagging for a diagnosis potentially might result in multiple referrals to different specialists and possibly a longer diagnostic pathway. These factors may contribute to a delay in an autism diagnosis for an autism + ADHD child.


ADHD (attention‐deficit/hyperactivity disorder) affects 5% of children on average. Despite the high need to access services for ADHD treatment, not all children with ADHD utilise healthcare services equally. This study aims to systematically synthesise evidence of equity and equality in health service use/costs and health‐related quality of life (HRQoL)/wellbeing of children with ADHD across socioeconomic (SES) classes.rd January 2023 in English and focused on children. Study quality was assessed using the Critical Appraisal Skills Program (CASP), Joanna Briggs Institute (JBI) and Mixed Methods Appraisal Tool (MMAT) checklists.The literature search was conducted across seven databases . The search was limited to peer‐reviewed articles published to 2325 out of 1207 articles were eligible for inclusion. The results showed that SES was associated with different types of healthcare utilisation. Only three studies were found on HRQoL/well‐being. Children with ADHD from low SES families had lower HRQoL than children from high SES families.This study found that a social gradient exists in both healthcare service use and children's HRQoL among those with ADHD. Despite the high need to access services for ADHD treatment, not all children with ADHD utilise healthcare services equally. SES was associated with different types of healthcare utilisation. Only three studies were found on HRQoL/well‐being. Children with ADHD from low SES families had lower HRQoL than children from high SES families. This study found that a social gradient exists in both healthcare service use and children's HRQoL among those with ADHD. ADHD is one of the most common neurodevelopmental disorders, but healthcare is not utilised equally among children with ADHD.SES may be a key factor when accessing treatments, use of services, and children's HRQoL for children with ADHD.No previous systematic reviews have examined the associations between SES, service utilisation and HRQoL in children with ADHD.Children with ADHD from low SES families had lower health service use and HRQoL than children from high SES families.This review highlights a need to address unmet treatment needs resulting from healthcare service access barriers for children with ADHD from low SES families.Attention‐deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, with a prevalence globally estimated at 2%–7%  increases in an almost linear way for children with ADHD  use of services/cost and (2) children's HRQoL/wellbeing among those with ADHD. A synthesis of the existing literature on this topic will aid policy makers in efficiently delivering healthcare services across social groups and improving the wellbeing of children with ADHD.rd January 2023 using seven electronic databases: Academic Search Complete, MEDLINE Complete, PsycINFO, ERIC, Global Health, CINAHL, and EconLit. Primary and secondary research studies published in English (due to limited resources available for translation) were included if they examined inequality across SES in using health services for childhood ADHD or in children's HRQoL in any country. ADHD status could include clinical diagnosis, parents' reporting, or via a research diagnosis. Studies were excluded if they did not address the research questions ; not related to HRQoL, service utilisation, or costs), included people older than 18 years of age (without separately reporting results for children or adolescents), or were not published in English. The search terms included broad terms relevant to equity/equality, children, ADHD, service use/costs, and HRQoL . Extracted articles were then screened through a two‐stage process considering the inclusion criteria. Articles were first screened with titles and abstracts by the primary reviewer (AS) and verified by the senior reviewer (HL). Both eligible and inconclusive articles from the first stage were then compiled for full‐text screening. Two reviewers (AS and SWAD) independently screened full texts, and any discrepancies were discussed with the senior reviewer (HL).Data extraction was completed by one reviewer (AS) using MS Excel and cross‐checked by another reviewer (SWAD). The information extracted included the author name(s), year of publication, study aims, ADHD diagnosis, study design and duration, location (country), population (age and sample size), outcomes measures, the instrument or tools used, results of outcomes of interest (association of SES with service utilisation/cost/HRQoL), and other factors found to be associated with service utilisation/cost/HRQoL.The quality of cohort studies, randomised controlled trials (RCT) and systematic reviews was appraised using Critical Appraisal Skills Program (CASP) checklists  articles from database search and four articles from hand‐search of references were identified. After removing duplicates, of 705 articles from the database and hand‐search of references, 25 met the inclusion criteria for the final synthesis  ‐ the UK, Sweden, Denmark, Canada, Germany, and TaiwanCountries with a universal healthcare system (with a combination of public and private insurance): Australia, the Netherlands, and South KoreaThe number of participants in these studies ranged from 79  found that higher SES was related to a higher uptake of health services, , Sayal et al.  reportedNo studies reported on SES and service utilisation from countries with a universal healthcare system with a combination of public and private insurance.For countries with a non‐universal healthcare system, a US study found that the cost of ADHD treatment was higher for high SES or degree‐holder parents than parents with low SES or lower education qualifications , similar to the US, studies found a negative association between SES and medication use. Jablonska et al.  reportedIn addition to SES, studies demonstrated a significant association of ADHD service utilisation and costs with other factors. As expected, service use and costs were higher for children with higher severity symptoms and co‐occurring conditions , two studies explored the association between SES and children's HRQoL/wellbeing Owens, . van derWhile there are systematic reviews on how ADHD has impacted on either or both service utilisation/costs and HRQoL of children with ADHD  where there are universal public health insurance systems reported that low SES was associated with increased service utilisation whereas most studies from the US where there is no universal public healthcare system indicated that high SES families, with their private health insurance, have more access to services. Finding that higher SES families access a broader range of services for children with ADHD, including interventions with add‐ons such as CAM and alternative treatments, despite the same level of ADHD treatment needs as low SES families (Kemper et al., Findings on the association between low SES and increased medication use is consistent with a previous literature review exploring the association between ADHD and service use in youth published 15 years ago (Leslie & Wolraich, Although we did not specifically explore the association between SES and educational services, and thus, did not find any study that examined the association between SES and educational services, the negative impact of ADHD on functioning at school including academic attainment is well‐documented in the literature (Lee et al., In addition to SES, there are a number of other factors that were associated with service utilisation. Specifically, needs factors including the presence of co‐occurring conditions and severity of ADHD contributed to the uptake level of services among children with ADHD (deJong et al., While there is literature on the negative impacts of ADHD on children's HRQoL (Lloyd et al., The strengths of this study include the extensive literature search, and rigorous assessment of the available current literature. However, as limitations, non‐English literature was not included. Secondly, the search may have missed some literature as there is a possibility that some search terms may not have been included in the search strategy. Thirdly, this review is focused on children with ADHD, and the findings here may not apply more generally to children with other disorders.Given ADHD treatment can improve outcomes for children with ADHD (Shaw et al., This systematic review found a positive association between SES and health service utilisation and HRQoL in children with ADHD. The financial barrier is one of the main factors affecting health service utilisation for children with ADHD. This review highlights a need to address unmet treatment needs resulting from healthcare service access barriers, especially for children with ADHD from low SES families. Policy‐makers should also consider reducing the disparities in the provision and accessibility to healthcare services to children with ADHD from all social gradients to improve HRQoL for children with ADHD. Given the scarcity of research exploring the association between SES and children's well‐being/educational services, future research exploring these topics is warranted.Abraham Sevastidis: Conceptualisation; Data curation; Formal analysis; Methodology; Writing – original draft. Sithara Wanni Arachchige Dona: Data curation; Formal analysis; Methodology; Writing – original draft; Writing – review & editing. Lisa Gold: Supervision; Writing – review & editing. Emma Sciberras: Writing – review & editing. David Coghill: Writing – review & editing. Ha Nguyet Dao Le: Conceptualisation; Funding acquisition; Methodology; Supervision; Validation; Writing – review & editing.The authors have declared that they have no competing or conflicts of interest.Ethical approval was not required for this research review.Supporting Information S1Click here for additional data file.


N = 434), and replicate the results in an independent sample (N = 219) of children  aged 7–14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy—suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (The online version contains supplementary material available at 10.1007/s00787-022-01986-9. The prevalent co-occurrence of Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) reflects a pressing problem for diagnosis and treatment in child psychiatry , 69, 75.A growing body of literature is concerned with the diagnostic validity of classification based on expert consensus , such asRecent work hypothesizes that ADHD and ASD should not be conceptualized as distinct neurodevelopmental conditions but rather as manifestations of one overarching condition with a similar etiology , 42, 80.A parallel line of reasoning suggests equifinality in relation to atypical attention which may stem from inhibitory atypicalities when it comes to ADHD but social atypicalities in the case of ASD phenotypes , 82. ThiDifficulties in executive function have been identified in both ADHD and ASD in childhood and in adult samples , 33, 62.Regarding brain-level overlap, studies that investigated the neural basis of ADHD and ASD similarly suggest similarities in the brain systems involved. Both conditions are increasingly characterized as conditions related to atypical brain connectivity involving differences in the interplay between large-scale brain systems rather than arising from focal differences in specific brain areas. The most prominent differences in ADHD have been observed in the integration of the default mode network (DMN), the salience network (SN), the ventral attention network (VAN), and the frontoparietal attention network FAN, , 76). Fu. Fu76]).In the current study, we tested different explanations for the overlap between ASD and ADHD by exploring (1) whether the overlap of the diagnostic groups stems from undistinguishable behavioral profiles, (2) whether their overlap arises from overlapping subgroups , or (3) whether their overlap arises from an underlying behavioral continuum. In a first step, using random forest classification, we investigated whether participant scores on the behavioral measures  have sufficient discriminatory power to assign participants to diagnostic groups. Based on these results, we then employed a community detection approach to identify potential subtypes across the ADHD-ASD spectrum. By employing taxometric analyses, we then tested whether the classification performance may arise because of an underlying continuum.https://childmind.org, date of access: February 21st, 2019). The initial sample consisted of 475 children  between 7 and 13 years of age. This sample is part of a larger cohort of the Healthy Brain Network Biobank based on a community-referred recruitment model of children with developmental psychopathology (see [n = 72 [29.38%]), Autism Spectrum Disorder (n = 39 [15.92%]), Specific Learning Disorder with Impairment in Reading (n = 34 [13.88%]), Language Disorder (n = 33 [13.47%]), Generalized Anxiety Disorder (n = 23 [9.39%]), and other less frequent diagnoses , ADHD-Inattentive Type (n = 13 [5.31%]), and other less frequent diagnoses .The current analysis was based on the existing data obtained from the Child Mind Institute Biobank database (ogy (see ), and reogy (see . Accordiogy (see . A thirdhttps://aspredicted.org/ya7wr.pdf).The analysis steps .The code for the analyses is available via the Open Science Framework  is a questionnaire with 18 items that assesses potential strengths and weaknesses related to ADHD symptoms on a single parent-rated scale. It uses items from the Swanson Nolan And Pelham IV .The Social Responsiveness Scale  is a 65After careful consideration of how to best match the different behavioral instruments used in the different samples, we decided not to use the Disruptive Behavior Disorders Rating Scale from the CMI data. We therefore not fully followed our pre-registered analyses plan, and focused solely on the SRS and SWAN data for the purpose of replication with the independent replication sample.Replication sample The independent replication sample consisted of 219 children  between 8 and 12 years . For the purpose of replication, we focus solely on the SWAN and SRS data. The replication sample was from the Oregon ADHD and Autism project and included community recruited volunteers with ADHD and/or ASD diagnosis confirmed by multi-method, multi-measure, best estimate procedure based on the DSM-IV. For detailed description of this sample, see Karalunas et al. ). The community detection algorithm converged at a stable solution that showed a good separation between the identified groups (Q = 0.92). The four large groups showed different profiles of questionnaire scores  = 738.01, p < 0.001; group x scale: F  = 63.49, p < 0.001, see Fig. χ2 = 288.10, p < 0.001). A second group (C5: high symptoms) had scores around 1 SD above the mean and consisted of two-thirds of children with ADHD and one-third of children with ASD . The other groups had contrasting symptom profiles. One group (C1: SWAN↑) showed low symptoms on the SRS scales, but high symptoms on the SWAN scales and consisted mostly of children with ADHD . Another group (C4: SRS↑) showed elevated symptoms on the SRS scales with lower ratings on the SWAN scales and consisted to equal proportion of children from all diagnostic categories .The community solution consisted of five groups with four large groups  were slightly older compared to the rest of the sample, and there were more females in the cluster with low symptoms (C2) and more males in the cluster with high symptoms C5). The other clusters did not deviate in sex ratio or age from the rest of the sample  contained one girl from the TD group, and two boys with both an ADHD diagnosis and an ASD diagnosis (ages between 7 and 9 years). This cluster was characterized by low social awareness ratings (AWR) and a high score for inattention (IN).Q = 0.93, see Figure S3). The first cluster mostly contained participants with a diagnosis of ADHD . The second cluster mostly contained participants from the TD group . The third cluster mostly contained participants with ASD diagnosis . The behavioral profiles of these groups were similar to the C1, C2, and C5 in the main sample. Similar to C1, CR1 showed was characterized by low scores on SRS scales and high scores on the SWAN scales. Like C2, CR2 showed low scores for all scales and CR3 showed high scores on all SRS scales and lower scores on the SRS scales like CR4.Community clustering in the replication sample produced clusters that were highly similar to the clusters identified in the main sample. Community clustering converged on a stable solution with three groups , and when including the ADHD and TD groups (mean = 0.35). The ASD-TD comparison was consistent with a categorical account (mean = 0.76). There was no strong support for either a categorical or a dimensional account for the comparison between ADHD and ASD . To test if the results were influenced by edge cases, we conducted a further taxometric analysis that only included cases that were assigned to one of the major clusters in the consensus community clustering analysis. Taxometric analysis indicated that all groups identified through community clustering were more compatible with a dimensional than a categorical account . The comparative curve fit index (CCFI) can be used to investigate if a latent construct is dimensional (CCFI < 0.4) or categorical  to define ADHD and ASD is more sensible than the other. In other words, the autistic and ADHD related behavioral traits as assessed in the current sample cannot, unambiguously, be characterized as either two separate clinical entities or as two opposite ends of a spectrum using taxometric analysis or community detection based on symptom scales. In contrast, we show that the difference between ADHD children and typical children in the current sample is dimensional, while the difference between ASD children and typical children can best be characterized as categorical. Whereas our results do not support the recent literature arguing for an underlying dimension that explains the frequent overlap of the two conditions, they do highlight the importance of studying ASD and ADHD in tandem, as has been suggested by many developmental researchers before  represent a taxometric difference between ADHD and ASD or (b) indicate an underlying condition with ADHD and ASD as opposite ends of a dimension. Our results suggest that neither a e e.g., , 69, 80), 80catege  is in accordance with the overarching continuum hypothesis, under this hypothesis, we would also assume that high ADHD symptoms would be present in those with moderate ASD traits. While we did detect a cluster (C4) with somewhat lower ASD traits (C4), contrary to this hypothesis, these individuals did not show high ADHD symptoms.This is also reflected in the performance of the classification algorithm that confused around a third of ADHD and ASD cases, while classifying 80% of TD cases correctly. Moreover, as all identified clusters contain some combination of ADHD and ASD diagnoses, clinically, our results imply that screening for ADHD in individuals with an ASD diagnosis is imperative. Theoretically, our results underline taking a dimensional approach regarding the behavioral symptoms of ADHD and ASD that could advance knowledge about genetic, brain, and cognitive underpinnings of symptomatology. Dimensional analyses are useful when the association of clinical predictors with dimensional scores is constant for a relevant dimensional severity range . To drawSecond, this study is the first to combine multiple analysis approaches with replication of cutting-edge subtyping and taxometric procedures to shed new light on one of the oldest psychometric issues in the field of atypical development research, i.e., the question of whether mental disorders should be thought of as discrete categories or as continua. In their review of psychometric modelling approaches, Borsboom and colleagues (2016)  note thaThird, previous autism research has suggested a taxon higher up the proposed gradient scale than DSM classification suggests, i.e., a ‘highly severe’ ASD subgroup . We findDespite several strong points of this study, including pre-registration, cutting-edge statistical techniques, a large sample size, and replication in an independent sample, several limitations should be considered when interpreting our results. First, it is important to note that the questionnaires used in this study are not specifically designed to distinguish diagnostic groups. Their purpose is to assess diagnostic severity and create symptom profiles—the latter allows us to investigate profiles that are more common in the ASD vs. the ADHD group. Although the validity of these assessments may vary with the clinical utility of the behavioral instruments used for this purpose, our results consolidate the findings of a recent grouping study based on core-domain symptomatology  and cortical thickness, which suggested that ASD, ADHD, and OCD lie on a continuum with typical developmental profiles . Second,Additionally, it should be noted that a taxometric approach to unveiling the latent structure of psychological conditions is not uncontroversial in psychometrics , 56. We Third, the current research on ADHD and ASD is highly skewed toward childhood, including this study. There are strong indications that the co-occurrence between ADHD and ASD is dependent on age (for review see ). For exIn conclusion, this study supports those voices in the literature that are doubting the categorical differences between the consensus-based sets of ADHD symptoms and ASD symptoms; however, we also cannot state unambiguously that ADHD and ASD should be characterized as two opposite ends of a spectrum or as two separate clinical entities. In the long run, the statistical developments might result in a non-binary answer to the kind vs. continua question in psychiatry based on a novel way of conceptualizing non-linear transitions between different psychiatric conditions that follow from the complex interplay of their symptoms and the individual environment. For now, it is unambiguous that ADHD and ASD traits need to be studied in tandem.Supplementary file1 (DOCX 19256 KB)Below is the link to the electronic supplementary material.