Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD.Utilising a large sample, we aimed to assess reward processing in response to reward type  and reward phase  in ASD.Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age).post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD.Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD. The social motivation hypothesis postulates that individuals with ASD from early in development do not perceive social stimuli to be as rewarding as typically developing individuals, which may have an impact on the development of social learning and social skills.5 To assess atypical motivation, reward processing is commonly investigated during the anticipation of a potential reward (‘wanting’), the delivery of the reward (‘liking’) or during both phases. Further, different types of rewards can be assessed, with non-social  rewards being most commonly investigated across psychiatric conditions, and it has been postulated that there is a specific impact on social rewards, as detailed in the social motivation hypothesis. Supporting the concept of atypical social reward processing in ASD, one study showed reduced activation in the ventral striatum,6 a key region for reward processing comprising the nucleus accumbens and caudate head, compared with control participants when receiving social rewards. A similar effect was observed in another study in more dorsal parts of the striatum.7 However, other studies did not find functional striatal differences between ASD and typically developing individuals for social rewards during delivery9 or anticipation.8 Similarly mixed results exist for non-social rewards: although some previous studies report ventral striatum hypoactivation in individuals with ASD when receiving monetary rewards,12 this has not been found8 or only at uncorrected thresholds13 elsewhere. Results for the anticipation of monetary rewards are also inconsistent with some studies suggesting ventral striatum hypoactivation in ASD individuals see for example references 12, whereas another showed no difference between participants with ASD and typically developing participants.7Neurobiological evidence in favour of a social motivation atypicality is, however, mixed.5 Comparing individuals with ASD to typically developing individuals, the authors reported striatal hypoactivation during social as well as non-social rewards in ASD. Only Clements et al4 investigated anticipation and delivery phases separately. They report hypoactivation of the left caudate during anticipation of social rewards, and hyperactivation during the anticipation of non-social rewards. In contrast, during reward delivery, striatal (left nucleus accumbens and caudate) hyperactivation to social rewards and right caudate hypoactivation to non-social rewards were observed in ASD. These findings suggest opposing atypicality patterns for social and monetary reward types between reward phases and do not imply typical non-social reward processing in ASD.Some of the inconsistency of previous findings is likely because of the heterogeneity of ASD itself, but also the relatively small sample sizes examined . Two recent meta-analyses have partly addressed the latter issue by summarising the current literature.Across the seven studies assessing social reward processing, caudate hypoactivation was linked  to the severity of autistic traits as measured with the Social Responsiveness Scale (SRS). This meta-analysis was an important first step to provide a more comprehensive insight into atypical reward processing in ASD. However, the number of included studies is still small  and should be regarded as exploratory. Further, task designs were heterogeneous, which might have increased variability in brain responses and influenced task-specific effects. Finally, although some studies included in the meta-analysis administered social and non-social reward conditions in the same sample, some only assessed one type of reward, limiting direct comparability.15 However, co-occurring ADHD or symptoms have not been examined in the majority of studies exploring reward processing in ASD .On top of these, another challenge is the fact that ASD and attention-deficit hyperactivity disorder (ADHD) frequently co-occur and atypical reward processing with ventral striatum hyporesponsiveness for monetary rewards is often reported in individuals with ADHD.17) provides a deeply phenotyped data-set of children, adolescents and adults with and without ASD who performed a social and a monetary reward task. The task was chosen based on its ability to reliably elicit ventral striatum reward signalling18 and allows for the analysis of both reward anticipation and reward delivery phases. We comprehensively assessed differences in reward signalling based on clinical diagnosis as well as dimensional autistic traits. Based on the recent meta-analysis,4 compared with typically developing individuals, we hypothesised that neurofunctional responses in the ventral striatum would show a pattern of increased activity in ASD during monetary reward anticipation and reduced activity during social reward anticipation – and the opposing pattern during reward delivery. We expected to observe this pattern in categorical case–control comparisons as well as in dimensional analyses based on autism traits. Further, based on previous findings in the, to our knowledge, only study exploring reward processing in individuals with autism, ADHD, and co-occurring autism and ADHD,16 we hypothesised an additive effect of ADHD co-occurrence, with reward processing being most severely altered in autistic individuals with elevated ADHD symptoms.Hence, the brain functional mechanisms underpinning reward processing in ASD remain unclear. We therefore investigated reward-related brain responses in a large, well-powered sample of individuals with ASD. The Longitudinal European Autism Project  assessment at one of six study sites: Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK (KCL); Autism Research Centre, University of Cambridge, UK (UCAM); Radboud University Nijmegen Medical Centre, the Netherlands (RUNMC); University Medical Centre Utrecht, the Netherlands (UMCU); Central Institute of Mental Health, Mannheim, Germany (CIMH); University Campus Bio-Medico of Rome, Italy (UCBM).17 and for a comprehensive clinical characterisation of the LEAP cohort we refer the reader to Charman et al.19 For this study, the final sample consisted of n = 212 participants with ASD and n = 181 typically developing participants (see https://doi.org/10.1192/bjp.2022.157). Standard operating and quality control procedures leading to the final sample are detailed in the Supplementary material.The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving patients were approved by the local ethical committees of participating centres and written informed consent was obtained from all participants or their legal guardians (for participants <18 years). For further details about the study design we refer the reader to Loth et al,20) and Autism Diagnostic Observation Schedule 2nd edition (ADOS-221) within the ASD group. We used the total raw score on the Social Responsiveness Scale 2nd edition (SRS-222) to assess continuous autism traits, which was available across the study sample. Parent-rated scores were collected for ASD and typically developing individuals, except for typically developing adults where only the self-report was assessed. We used self-rated scores wherever parent-rated scores were not available. Parent- or self-report of a psychiatric disorder was an exclusion criterion for the typically developing group. Information on the presence of a confirmed diagnosis of ADHD was not available in our sample. As a proxy, we estimated diagnostic status by applying DSM-5 criteria based on symptom scores collected with the parent- and self-rated ADHD DSM-5 rating scales.23Participants in the ASD group had an existing clinical diagnosis of ASD according to the DSM-IV/ICD-10 or DSM-5 criteria. ASD symptoms were comprehensively assessed using the Autism Diagnostic Interview-Revised  and a monetary incentive delay (MID) task2*-weighted echo-planar imaging sequence (repetition time (TR) = 2 s, echo time (TE) = 30 ms, flip angle = 80°, matrix: 64 × 64, FOV: 192 × 192 mm, in-plane resolution: 3 × 3 mm, slice thickness: 4 mm, gap: 1 mm, 28 axial slices). A total of 151 volumes were obtained for each task, oriented approximately 20° steeper than the AC-PC plane.fMRI data were acquired on 3T scanners from different manufacturers  and harmonised as much as possible across sites . Functional images were acquired using a blood-oxygen-level-dependent (BOLD)-sensitive Thttp://www.fil.ion.ucl.ac.uk/spm/), including a two-pass realignment procedure, slice time correction, registration of the functional mean image to the Montreal Neurological Institute template and spatial normalisation into standard stereotactic space, application of resulting normalisation parameters to the functional time series, resampling to 3 mm isotropic voxels and smoothing with an 8 mm full-width at half-maximum Gaussian Kernel.Image preprocessing followed standard processing routines in SPM12  and task  was calculated.SID and MID tasks were combined as two sessions in a general linear model (GLM) at the single-subject level . Within-subject effects were addressed at the subject-level by quantifying within-subject effects of condition as differential response to win cues as compared with neutral cues for reward anticipation and differential response to successful win compared with neutral feedback for reward delivery. Note that Based on within-subject contrasts we assessed reward-specific brain activation (within-subject effect of condition) and differential reward-specific responses between tasks (within-subject interaction condition × task) across the entire sample and tested for between-group differences. Contrast images were subjected to second-level GLMs with between-subject factor group  and covariates age, gender assigned at birth and site.n = 69) and without (n = 118) co-occurring ADHD based on estimated diagnostic status  and compared with typically developing participants while controlling for age, gender assigned at birth and site  implemented in SPM12 is based on random field theory.The impact of ADHD co-occurrence was explored in a separate model, where the ASD group was split into subgroups with  for each participant and both tasks were extracted and analysed using SPSS Software package for Windows. Separate repeated measures ANOVAs with the within-subject factor task  and between-subject factor diagnosis  and covariates age (mean centred), gender and study sites (dummy coded), were used to assess group differences for both reward processing phases  in the left and right ventral striatum. To correct for investigating left and right ventral striatum activity separately, the critical alpha threshold was adjusted to P < 0.025 based on the Bonferroni procedure. Additionally, Bonferroni-correction was applied to all post hoc pairwise comparisons. To assess the effect of autism and ADHD traits, SRS-2 raw scores and ADHD rating scale scores were added as an additional covariate of interest in separate models. Interaction terms between diagnosis and SRS-2 or ADHD rating scale were added as well. The impact of ADHD co-occurrence was explored in another separate model, where the between-subject factor diagnosis comprised three levels .To increase sensitivity for putative case–control differences in the ventral striatum, we performed ROI analysis within a well-established F = 22.84, PFWE = 0.017, k = 8) during reward anticipation. A post hoc T-test showed that activation was reduced in participants with ASD compared with typically developing individuals. See Reward-specific brain activation differed between diagnostic groups at the whole-brain level in the right ventral striatum  = 14.163, P < 0.001, partial 2η = 0.036) and right  = 18.693, P < 0.001, partial 2η = 0.046) ventral striatum ROI (Table 3) with reduced activation in the ASD group  compared with the typically developing group . See F = 2.754, P = 0.098, partial 2η = 0.007; right ventral striatum: F = 2.999, P = 0.084, partial 2η = 0.008).Individuals with ASD differed from typically developing individuals on average regarding reward-specific brain activation within the left  = 4.829, P = 0.029, partial 2η = 0.013) and right ventral striatum  = 4.719, P = 0.030, partial 2η = 0.013) yielded increased activation in the ASD group (Table 3)  compared with the typically developing group  but this did not survive correction for multiple comparisons  = 1.057, P = 0.304, partial 2η = 0.003; right ventral striatum: F = 1.684, P = 0.195, partial 2η = 0.005).The difference regarding reward-specific brain activation between ASD and typically developing individuals within the left (sons See ). There –ADHD and ASD+ADHD subgroups yielded a significant effect of group in the left  = 5.172, P = 0.006, partial 2η = 0.032) and right (F(1307) = 6.761, P = 0.001, partial 2η = 0.042) ventral striatum , whereas there was no significant difference between the typically developing group and the ASD+ADHD subgroup  or between the two ASD subgroups .During reward anticipation, ROI analysis comparing the typically developing group, and the ASDatum see . Post hoF = 3.715, P = 0.026, partial 2η = 0.024, see –ADHD subgroup compared with the typically developing group  and no difference between the typically developing group and the ASD+ADHD subgroup  or between the two ASD subgroups . Across both reward processing stages, there was no significant effect of group on the whole-brain level and no significant interaction with the type of reward .For reward delivery, a borderline significant effect of group emerged in the right ventral striatum  were observed during reward delivery in the right superior medial frontal gyrus and the left amygdala, pallidum and (at trend-level) the ventral striatum, respectively. These effects did not differ between the ASD and typically developing groups. For reward delivery, we were not able to replicate the effect of diagnosis when investigating only female participants, only right-handed participants or when excluding participants from RUNMC or KCL. Although this likely reflects decreased statistical power because of reduced subsample sizes, it also warrants further exploration of potential sources of heterogeneity in future studies. Details on the control analyses are provided in the Supplementary material.Post hoc power sensitivity was determined using G*Power.26 A repeated measures ANOVA with the within-subject factor task  and between-subject factor diagnosis  and n = 393 participants at a critical alpha of 0.025 would be sensitive to effects of partial 2η = 0.0109 with 80% power. This means the study would not be able to reliably detect effects smaller than partial 2η = 0.0109. The slightly reduced sample size for reward delivery with n = 379 participants yields 80% power at partial 2η = 0.0113 and would thus not be able to reliably detect effects smaller than this.+ADHD and ASD–ADHD) and n = 317 participants at a critical alpha of 0.025 would be sensitive to effects of partial 2η = .012 with 80% power. This means, the study would not be able to reliably detect effects smaller than partial 2η = 0.012. The slightly reduced sample size for reward delivery with n = 307 participants yields 80% power at partial 2η = 0.0124 and would thus not be able to reliably detect effects smaller than this. These partial 2η = values all represent small effects sizes.A repeated measures ANOVA with the within-subject factor task  and between-subject factor diagnosis , we report the same ventral striatum pattern for monetary and social rewards. We conclude that, in ASD, general hypoactivation during the anticipation of rewards reflects attenuated ‘wanting’ of rewards independent of social content. Our finding is in line with a previous study investigating negative social and monetary reinforcement27 and extends beyond ASD and ADHD to other conditions such as schizophrenia and bipolar disorder,28 pointing towards a potential shared motivational shift in these conditions that need further investigation.These results do not support opposing effects of social and monetary reward types, but rather point towards a general hypoactivity of ventral striatum in ASD during anticipation of rewards. This is in contrast to the hypothesis of a predominantly social motivation deficit.4 and previous studies showing striatal hypoactivity during monetary reward delivery12 it is in line with studies showing no significant group differences during social reward,9 monetary reward8 or showing differences only at an uncorrected threshold.13 In summary, our results suggest that both monetary and social rewards are eliciting reward-related brain activity upon delivery that is not strikingly different in individuals with ASD and typically developing individuals. Behaviourally, individuals with ASD did not differ from typically developing individuals regarding reaction times and accuracy , which is in line with previous findings, see for example references 29.Our results on reward delivery do not show substantial differences between ASD and typically developing individuals. Although this is in contrast to meta-analytic findings1 we would expect both reduced ‘liking’ as well as reduced ‘wanting’ of social rewards in ASD, but no alterations for non-social rewards. The generally reduced activation of the reward system in the ASD group compared with the typically developing group during anticipation of rewards strongly suggest reward processing alterations in autism go beyond social stimuli. The lack of a striking difference between the typically developing and ASD groups for reward delivery indicates that alterations in wanting a reward seem to be more substantial than alterations in liking a reward. This might potentially reflect an altered link between ‘liking’ a reward and ‘wanting’ a subsequent reward, irrespective of reward type, and could suggest generally atypical reinforcement-dependent learning30 and/or salience processing in ASD, see for example reference.31 A hypothesis of generally atypical reward processing in ASD is, however, challenged by studies reporting elevated reward system responsivity in ASD to stimuli of high interest for autistic individuals or to food images.12 These findings indicate intact, possibly even hyperactive reinforcement-dependent learning when stimuli with high individual interest are involved. Recent models have tried to address this by proposing an imbalanced response for different reward stimuli.3 Future work is therefore needed, exploring potential changes in feedback loops underlying altered reinforcement-dependent learning in ASD using connectivity metrics and different reward types, as well as exploring links to atypical salience processing in ASD.Incentive stimuli, such as the arrows used in the present study, have gained reward value (wanting) through learning processes by linking them to pleasant outcomes (liking). If individuals with ASD perceived social stimuli as less rewarding,29 our results are in line with previous studies also finding no association with dimensional autism measures.12 Previous studies argued that their null findings might be because of insufficient power and insufficient range of scores in the ASD group,12 which is less of a concern in the present findings. Recent electrophysiological findings might point towards a diverging trajectory of building reward anticipation for high versus low autistic traits, which might mask associations with autistic traits based on the time point when reward anticipation is measured.32Although significant differences between diagnostic groups were found, we did not observe significant associations between autism trait scores (SRS-2) and functional brain activation across the whole sample or within the ASD and typically developing groups separately. In Supplementary analyses (see Supplementary Table 4) we assessed the effects of autism trait scores for MID and SID separately, but observed no significant effect in this separate analysis. Although others have found associations between dimensional autism measures and reward-related brain activity,post hoc pairwise comparisons revealed that ventral striatum activity was reduced only in those individuals with ASD that had subthreshold levels of ADHD (ASD–ADHD) compared with typically developing participants, whereas those individuals with ASD that had elevated ADHD levels  did not differ significantly from the typically developing group or the ASD–ADHD subgroup. During reward delivery, differences between the three groups were not strong enough to reach statistical significance when correcting for multiple comparisons. However, the direction of the effect also suggested the largest deviation for the ASD–ADHD group. These results support an alternative hypothesis of ADHD symptoms partly countering ASD-related motivational atypicalities. This would be in line with a previous finding, where individuals with ASD showed generally low ventral striatum reactivity, and individuals with ADHD showed high ventral striatum reactivity to both social and monetary reward types.13 However, this study did not differentiate between reward anticipation and delivery.Elevated ADHD symptoms did not have an additive effect on reward system atypicality in ASD, in contrast to our hypothesis. Dimensional analysis of ADHD symptoms yielded no significant results. During reward anticipation, 14, but see also reference 33), findings are more inconsistent in children (for example see reference 34). For monetary reward delivery, increased ventral striatum activity in ADHD has been reported . Importantly, information on social reward processing in ADHD is scarce. To our knowledge, the present study is the first to address co-occurring ADHD symptoms during reward anticipation and delivery separately. Instead of an additive effect of previously described atypicalities in these two conditions, our findings suggest ASD with co-occurring ADHD might represent a subgroup of individuals that, on average, shows no statistically distinct neuronal deviation from typically developing individuals nor those with ASD. This is especially important in light of the ongoing search for biomarkers underlying these neurodevelopmental conditions. It further refutes a potential concern that reward system alterations in ASD could, in truth, be driven by co-occurring ADHD. However, information on the presence of a confirmed diagnosis of ADHD was not available in our sample, and a questionnaire-derived proxy was used instead. This might have a significant impact on our findings, as ADHD-like behaviours might have been misclassified. Consequently, our finding requires further investigation using clinically confirmed information on ASD–ADHD co-occurrence.Although during monetary reward anticipation, ventral striatum hypoactivation is discussed as a fairly consistent finding in adults and adolescents with ADHD . Thus, we cannot rule out that findings in the delivery phase were influenced by motor activity. Third, we used static images for social reward, which likely reduced ecological validity. Fourth, task order was fixed across all participants. Thus, task order might have had an impact on the effect of stronger differential brain activation in the MID compared with the SID task. Finally, although incentive delay tasks are widely used to study reward processing, they do not allow us to pinpoint brain activity in relation to just one reward-processing aspect, but comprise multiple additional subconstructs such as reinforcement-based learning, reward valuation and reward prediction.5 given the similar sample size while both meta-analyses did not address ADHD symptoms. Although further exploration of the underlying mechanisms is needed, the present study advances our understanding of the neural underpinnings of ASD by suggesting attenuated reward seeking independent of social content that is partly countered by co-occurring ADHD.In summary, the present study demonstrates a significant reduction of ventral striatum activity during the anticipation of rewards in individuals with ASD irrespective of the type of reward, and subthreshold hyperactivity of ventral striatum during the delivery of these rewards. In contrast to our hypothesis, altered reward processing was not exacerbated by elevated ADHD symptoms. This might suggest generally atypical reward processing in ASD that is partly countered by co-occurring ADHD. This provides important insights, specifically as the impact of co-occurring ADHD has not been consistently assessed in previous studies on reward processing alterations in ASD and might contribute towards the heterogeneity of findings. It might further explain discrepancies between our findings and the two meta-analyses,Future studies on reward processing, especially on reward anticipation, in ASD should thus collect and report data on ADHD symptoms and diagnosis effects. The current study presents just one step towards a broader and deeper understanding of reward processing alterations in autism, which should ultimately improve services for autistic individuals, for example by establishing alternatives to reinforcement-based interventions where needed or by including personalised rewards as alternatives to generic rewards when employing established reinforcement-based interventions.


MnlI variant (rs3746544) of the synaptosomal‐associated protein 25 (SNAP‐25) gene is associated with ADHD. In this study, we investigated the role and underlying mechanism of SNAP‐25 MnlI variant in cognitive impairment and brain functions in boys with ADHD.Attention deficit/hyperactivity disorder (ADHD) is a hereditary neurodevelopmental disorder characterized by working memory (WM) deficits. The We performed WM capacity tests using the fourth version of the Wechsler Intelligence Scale for Children (WISC‐IV) and regional homogeneity (ReHo) analysis for the resting‐state functional magnetic resonance imaging data of 56 boys with ADHD divided into two genotypic groups . Next, Spearman's rank correlation analysis between the obtained ReHo values and the WM index (WMI) calculated for each participant.Compared with G‐allele carrier group, there were higher ReHo values for the left medial prefrontal cortex (mPFC) and higher WM capacity in TT homozygote group. Contrary to TT homozygote group, the WM capacity was negatively correlated with the peak ReHo value for the left mPFC in G‐allele carrier group.MnlI variant may underlie cognitive and brain function impairments in boys with ADHD, thus suggesting its potential as a new target for ADHD treatment.These findings suggest that SNAP‐25  To explore influences of MnlI variant (rs3746544) on WM and its potential approach, brain network, we conduct a study, measuring intelligence, using ReHo method to analyze the rs‐fMRI data and do correlation analysis. We also speculate that the WM capacity and ReHo value may be correlated. Our findings provide novel preliminary insights into the underlying mechanisms of the SNAP‐25 MnlI variant and suggest its potential as a target in ADHD treatment.According to the previous study, boys with ADHD have WM capacities deficit, also SNAP‐25 gene affects WM. Therefore, the 22.1A total of 66 boys diagnosed with ADHD at the Health Department of Shenzhen Children's Hospital from July 2013 to July 2015 were enrolled in this study. On the one hand, children were eligible for enrolment if they were (1) between the ages of 8 and 10 years; (2) had an intelligence quotient of >70, as measured by the Wechsler Intelligence Scale for Children version IV (WISC‐IV); (3) of normal hearing and eyesight; (4) of Han Chinese ethnicity; and (5) right‐handed. Each study participant was assessed based on the Schedule for Affective Disorder and Schizophrenia for School‐Aged Children Present and Lifetime Version (K‐SADS‐PL)  amplification was performed with EX‐Taq polymerase and GC buffer . The PCR protocol started with a denaturing cycle at 94°C for 2 min, followed by 30 cycles of 94°C for 30 s, 52°C for 30 s, 72°C for 45 s and, finally, an extension step at 72°C for 8 min.The SNAP‐25 rs3746544 genotyping of peripheral venous blood samples from the participants was performed using the FlexiGene DNA Kit  according to the manufacturer's instructions. The following primer pairs were used: forward, 2.32, acquisition matrix = 256 × 200, GRAPPA = 2, slice thickness = 1 mm without gap, slice number = 176, and acquisition time (TA) = 3 min 48 s. The settings for the collection of rs‐fMRI data based on an echo‐planar imaging sequence were as follows: TE = 30 ms, TR = 2,000 ms, phase‐encoding direction = A > > P, FA = 90°, acquisition matrix = 94 × 94, GRAPPA = 2, slice thickness = 3 mm, 32 transversal slices, 130 volumes, and TA = 4 min 28 s. Prior to the rs‐fMRI examination, participants were asked to relax, lie still, and close their eyes whilst staying awake.We used a 3.0T MRI scanner (Siemens Skyra) to acquire data on structural images and resting‐state fMRI (rs‐fMRI). The parameters for the three‐dimensional (3D) T1‐weighted isotropy volumetric sequence were as follows: repetition time (TR) = 2300 ms, echo time (TE) = 2.26 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, field of view (FOV) = 256 × 200 mm2.4http://www.restfmri.net/forum/DPARSF), RS‐fMRI Data Analysis Toolkit , and MATLAB‐based Statistical Parameter Mapping . We discarded the first 10 time points of each functional time series for the resting state data corresponding to scanner calibration and magnetization equilibration. Next, the data were subjected to slice timing, head motion correction, spatial normalization to the Montreal Neurological Institute template, resampling to 3 × 3 ×3 mm3, temporal band‐pass filtering and regressing of nuisance signals, such as six‐head motion parameters, white matter, and cerebrospinal fluid. Another group of 10 subjects was excluded because their data had a maximum displacement of >1.5 mm in any of the cardinal directions  or a maximum spin  of >1.5°.A radiologist with 10 years’ experience of brain function study and blinded to genotypic information performed the preprocessing of the raw fMRI data using the Data Processing Assistant for Resting‐State fMRI . The ReHo brain maps for each participant were obtained, and individual ReHo maps were generated for each data set.We calculated the ReHo values for each voxel of the whole brain using the REST 1.2 software  was calculated from the Digit Span and Letter‐Number Sequencing scores.2.7t‐test) results, defining the Peak MNI coordinate, with 3 mm radius. The ReHo peaks of each ROI in the two MnlI variant genotypic groups  were then calculated. In addition, we performed Spearman's rank correlation analysis between the calculated WMI and the ReHo peak values of each ROI in the TT homozygote and G‐allele carrier groups.The medial prefrontal cortex (mPFC) of the bilateral cerebral hemispheres was defined as the region of interest (ROIs). ROIs were set based on the second level group analysis  were performed using SPSS version 18.0 for Windows. The two‐sample t‐test was used to compare the differences between the two genotypic groups. Data were presented as the mean ± standard deviation (SD). If p value < .05 for the WMI between two genotypic groups, then it will be considered having statistically significant meaning.Statistical analyses of the two 2.8.2t‐test for conducting the independent assessment of each group. The two‐sample t‐test was used to compare differences between the two genotypic groups. Here, age was considered a covariant. A cluster of more than 10 voxels with p < .05 after AlphaSim correction was deemed statistically significant.We used the one‐sample 2.8.3Spearman's rank correlation analysis was performed to determine the association between the ReHo values and WMI for the two groups.33.1MnlI variant genotypes: 36 boys in the TT homozygote group and 20 boys in the G‐allele carrier group . TT homozygotes had higher WM quotients than G‐allele carriers . In addition, the former had a higher median for the WM intelligence quotient than the latter.The 56 study participants were divided into two groups based on their SNAP‐25 up Table . There w3.2t‐test (AlphaSim corrected), TT homozygotes showed higher ReHo values for the bilateral mPFC than G‐allele carriers amongst boys with ADHD  in G‐allele carriers, but not in TT homozygotes. As a cranial capability of temporarily storing information and controlling cognitive function, WM is often impaired in ADHD patients  the relatively modest sample size for the MRI study due to the challenge in asking children with ADHD to listen to instructions, (2) the relatively modest sample size of the genetic study due to high cost, and (3) the narrow age range of 8−11 years, unlike other studies that included other age groups. Thus, in the future, we hope to conduct follow‐up studies with larger age groups.MnlI variant in WM capacity in ADHD children revealed insights into the role of SNAP‐25 MnlI variant in the left mPFC, suggesting its potential as a novel target in ADHD treatment. Future longitudinal studies should be conducted to elucidate the changes in WM capacity and mPFC functions in response to early‐stage interventions.Our investigation of the underlying mechanism of the SNAP‐25 The authors declare no competing or conflicts of interest.https://publons.com/publon/10.1002/brb3.2758.The peer review history for this article is available


In this contribution, we present some of the first data on the elemental signature of deep crustal fluids in a basalt-hosted, low-chloride magmatic-hydrothermal system. Down-hole fluid samples (850–1600 m) from wells in the Theistareykir and Krafla geothermal fields in the Northern Volcanic Zone of Iceland were combined with well-head samples of condensed vapor, cuttings of altered rock, and fresh basalt  for this area of Iceland). Results show that the deep fluids are relatively enriched in base metals and (semi)-volatile metals  compared to local basalt. We interpret this enrichment in volatile metals to reflect a significant element input from magma degassing. Boiling of this deep fluid results in a well-head fluid composition that is significantly depleted in most elements. This well-head fluid has a distinct elemental signature, including a depletion in Sb that is mirrored in the altered rocks, and a depletion in the base metals that shows their selective sequestration in scale minerals, likely sulphides. As expected, the element content and patterns in surface fluids can thus not be interpreted to directly reflect that of the deep reservoir fluid. The behaviour of elements in Theistareykir and Krafla fluids is consistent, and largely agrees with similar data obtained for the Reykjanes geothermal system in SW Iceland. We therefore posit that our results are representative for this geological setting and indicate a significant magmatic degassing cation input to deep fluids, variably modified by water–rock interaction. Iceland is at the forefront of this development, and currently obtains 62% of its energy needs from geothermal plants2. Sustainable exploitation does require an in-depth understanding of the fluid sources and evolution in these systems and investigating geothermal fluid composition is therefore essential5.Hydrothermal fluids are increasingly of interest as a sustainable and clean source of heat and energy and are regarded as one of the cornerstones of transitioning to a CO12 and can therefore elucidate key questions in economic geology including the sources and fluxes of metals and fluids, and the aqueous concentrations and speciation of ore elements15. In particular, the elemental and isotopic composition of geothermal fluids can put constraints on the respective contributions of magma degassing and rock leaching to the metal content of active magmatic-hydrothermal systems16.Geothermal resources also provide a unique opportunity to access deep fluids in production wells and study the mobility of elements in the crust. Active hydrothermal systems related to magma intrusions  are historically regarded as analogues to ore-forming systems for many decades17, once recalculated to the reservoir conditions. However, surface fluids have generally experienced boiling, which results in fractionation of the elements between fluid and vapor, and precipitation of scales that selectively sequester elements depending on the scale mineralogy9. Thus, the composition of reservoir fluids can differ drastically from the fluids sampled at the surface. In addition to fluids, actively exploited geothermal fields can also provide access to fresh and altered reservoir rock cores, cuttings and scale, and thus allow to fully explore the behaviour of elements in the deep system, as well as physical–chemical changes en route to the surface.Fluids in geothermal fields are normally sampled at the wellhead, given the technical challenges of sampling downhole fluids. The compositions of these surface fluids are commonly regarded as representative of deep fluid element signatures20. In the Taupo Volcanic Zone, New Zealand, geothermal reservoir fluids in an alkalic basalt hosted hydrothermal system have been shown to be metal-laden with up to 23 ppb Au, 2400 ppb Ag, and 4850 ppb As9. Basalt-hosted deep fluids in the Reykjanes peninsula in the plume-MOR setting of Iceland have lower metal concentrations despite their equivalent Cl content, with concentrations of up to 6 ppb Au, 34 ppb Ag, < 1 ppb As, and concentrations for Cu, Zn, and Pb at 16, 26, and < 1 ppb, respectively21. Nonetheless, the Reykjanes fluids result in enrichments in scale precipitates of up to 950 ppm Au and 2.5% Ag22.Down-hole devices have been developed and used to sample reservoir fluids directly and thereby enable the metal concentrations of pre-boiled geothermal fluids to be determined23, and the adjacent Krafla field which is hosted in basalt and rhyolite24. Fluids have been characterized for a full suite of elements, O and H isotopes, and noble gases, allowing us to determine the mobility of elements in the deep reservoir, their fate during transport toward the surface, the sources of fluids and metals, and the impact of host rock geology. Direct compositional information on deep fluids is still extremely limited and this new data enables a deeper understanding of fluid-mediated element mobility in magmatic-hydrothermal systems with direct implications for global element cycling.In this contribution, we present compositions of surface and deep-sampled fluids from the Northern Volcanic Zone (NVZ) of Iceland: The newly developed Theistareykir geothermal field hosted in tholeiitic basalts erupted from a Holocene volcanic system5. The NVZ has been the main zone of spreading in northern Iceland for the past 6–7 Myr25. The NVZ is composed of five NNE striking left-stepping en échelon volcanic systems23, amongst which the Theistareykir shield volcano and the Krafla caldera, both presently exploited for geothermal energy.The Northern Volcanic Zone (NVZ) is a unique geological observatory providing a window into a sub-aerial mid-ocean ridge–plume setting and its related magmatic-hydrothermal systems. In the NVZ, the interaction of the Icelandic mantle plume is small but present26. A total of 18 deep wells (ranging from 1500 to 3000 m depth) have been drilled to date and the geochemistry of extracted fluids is still under evaluation27. The Theistareykir reservoir is hosted by basalts  of Icelandic mantle plume31. Natural surface geothermal manifestations include mud pots, fumaroles and steam vents. The recent start of exploitation implies that fluids have been minimally disturbed by exploitation and re-injection. Fluids sampled at the well-head are dilute, have chlorine as the dominant anion (from < 10 to ca. 600 ppm) and are near-neutral pH27. Downhole temperatures are among the highest recorded in potential production wells in Iceland at up to 380 °C in well ÞG-0332 -chloride type. This type of fluid composition is unusual in Icelandic geothermal systems and reflects fluid that is more mature40 .Most Icelandic high-temperature geothermal fields are composed of near-neutral pH and Ca-HCO17. Temperature and pressure profiles were collected in the well immediately before sampling and indicate ca. 265 °C and 55 bar at 1420 m and 290 °C and 75 bar at 1600 m in well ÞG-01 and 235 °C and 50 bar at 850 m in K-21  to recover any precipitate, and this fluid was also collected. All fluids were stored in pre-cleaned PFA containers. Samples from the deep fluids were analysed for the first time for O and H isotopes according to the methodology described in ref.5. For anions by Ion Chromatography at ETS , and for major and trace elements by HR-ICP-MS at ActLabs . Four units of fresh basalt were sampled across the Theistareykir area. Each unit was sampled at 3 different locations for a total of 12 samples. For Krafla, only the most recent lava flow (from the 1984 Krafla Fires) from the Leirhnjúkur area was collected. Samples of altered rock cuttings recovered from well ÞG-01 at 1600 m were provided by ÍSOR. These combined samples are taken here to represent the fresh basalt host rock of the geothermal reservoir and its altered equivalent. However, we acknowledge that basalt taken at the surface is likely to have experienced additional degassing compared to the intrusive rock below the surface. The fresh and altered samples were crushed and milled to below ~ 1 µm. This powder was then pressed into a pellet without additives and analysed by LA-ICP-MS at McGill University following the approach of ref.41. Full method details are given in the supplementary file.Fluid samples were collected in August 2019 from wells ÞG-01 (Theistareykir) and K-21 (Krafla) at the well-head and at depths of 850 m for K-21, and for ÞG-01 at 1420 and 1600 m from the surface. Surface fluid samples were collected using a fluid separator while the well was producing, whereas the deep samples were collected when the well was shut-in to insert the deep sampler. Deep fluid samples were collected using the in-situ Ti-sampler of Brown and Simmons (2003)2H, δ18O) from ÞG-01 and K-21, re-analyzed during the same campaign as the deep fluids sampling are consistent with literature data for these two fields42, reflecting contributions from a magmatic source, meteoric water, and at least one source of glacial water42, as also confirmed by noble gas isotopic signatures5. Deep fluids sampled at 1420 m (ÞG-01) and at 850 m (K-21) show δ2H values (− 108.4 ‰ and − 90.1 ‰ respectively) that are similar to their respective counterparts sampled at the well-head , near-neutral solutions dominated by Na–K–Cl. The deep fluids have a higher TDS . The Theistareykir deep fluids have an acidic pH between 2 and 2.6. The pH of the K-21 deep fluid was not directly measured, but the charge balance shows a cation deficit that also points towards an acidic fluid. This would be consistent with the acidic character of these waters in some portions of the field1. Aluminium is distinctly higher, whereas As is lower than these examples. For Na, K, and Cl, the Theistareykir deep fluid concentrations are between those found in conventional and supercritical reservoir fluids.Calcium, Mg, Fe, Sb, Te, Zn, Cu concentrations in the deep fluids at Theistareykir are the same order of magnitude as deep fluids from other geothermal fields including Iceland (Reykjanes), New-Zealand (Wairakei), Japan (Kakkonda), USA (Geyser), and Mexico (Los Humeros)48.Figure Figure ca. 2. We interpret this to reflect residual enrichment of these elements during alteration as a result of their immobile nature, and we therefore normalise the altered rock composition to the content of these elements in the fresh basalt50. The normalized altered over fresh composition is shown in Fig. 51, despite the Reykjanes fluids having much higher Cl-contents.The altered rock cuttings from the Theistareykir well at 1600 m show an overall enrichment in all the elements except Cu and Pb compared to the fresh Theistareykir basalt, especially for the most refractory elements including Zr, Nb, Hf, Th, U and the REE. These refractory elements have a common enrichment factor of The temperature and pressure profile in the wells Fig.  indicate52, this indicates that the deep fluids must also have a near-neutral to slightly alkaline pH. This is consistent with the secondary mineral assemblage  observed for Krafla and in the nearby Namafjall geothermal field53. However, the fluids sampled at depth—when the wells were shut-in—were acidic (pH 2–2.6). We interpret this discrepancy to reflect influx of a low pH fluid into the well, likely a condensate of acidic gases  into groundwater. Such hypogene acid fluids have also been reported in the Tiwi field in the Philippines54 and the Miravalles field in Costa Rica55. These acidic gases may have three origins: (1) they could be derived directly from the magmatic intrusions that are associated with the Krafla and Theistareykir geothermal fields but this would result in significant δ2H and δ18O shifts of the hydrothermal fluids that are not observed, (2) they could be generated by the condensation of vapor on the way to the surface, but this process would unlikely be able to lower the pH to 2.5 (rather closer to 5–6), or (3) represent shallow condensation of the acid vapor formed in secondary boiling and which is emitted in fumaroles in the Theistareykir field close to well ÞG-01. Given that these geothermal waters have a low capacity of buffering the pH due to their low element concentrations and content of dissolved CO2, even a small amount of acidic water mixing with deep fluids can result in a significant drop of the pH (Fig. The fluids collected at the surface exhibit a near-neutral pH, and given that boiling generally produces a vapor with a lower pH than the 1-phase source fluidIn this interpretation, the deep fluids do not represent a pristine deep fluid composition. It is unclear to what extent the fluid has been modified by addition of elements from the acidic fluid influx, or by interaction between the acidified fluid and its now-in-disequilibrium host rock mineralogy. The wells were only shut-in for few hours prior to the deep sampling so fluid-rock interaction may not yet have significantly affected the element concentrations. Indeed, carbonates, present in cuttings as a secondary phase, would be expected to dissolve in the acidified fluid, but Ca and Sr concentrations follow the overall trends of the earth-alkali elements in Fig. The fluid compositions reflect multiple processes at work in the geothermal system, including a primary magmatic input, dilution by modern and ancient groundwater, water–rock interaction with associated element mobilization and sequestration, boiling and its associated element partitioning, and scale formation and its associated element sequestration. The elements and the media analyzed here respond variably to these processes, thereby potentially allowing for their impact to be identified and quantified.58. We therefore interpret this additional source to be magma degassing and for the behaviour of these elements in the Theistareykir and Krafla deep fluids to reflect a strong contribution from the magmatic system. This is supported by noble gases, which indicate a direct transfer of material from the magma to the geothermal fluid, as evident in the 3He/4He ratio5.The basalt-normalized fluid compositions show higher values for the semi-volatile metals, and particularly low values for the refractory metals and the REE. This can be interpreted as preferential mobilization of the semi-volatile metals during water–rock interaction, addition of the semi-volatile metals from a different source, and/or selective precipitation of the refractory metals and REE. The altered-over-fresh rock composition shows more or less conservative behaviour for the REE Fig. c, and wa20, and therefore a stronger mobilization of the LREE from the magma.The fluids are enriched in LREE over HREE. This is not the result of water–rock interaction, because the LREE act conservatively in the altered rocks whereas the HREE, Y and Sc are leached Fig. c. Rathervs. deep fluid follows, to some extent, vapor–liquid partition coefficients49 (Fig. 59.The differences between surface and deep fluids can be expected to mainly reflect boiling and the associated element partitioning between vapor and residual liquid, with the surface fluid dominantly, or even exclusively, reflecting vapor. The volatile metals are less depleted than, for example, the base metals in the surface fluids, which is consistent with their preferential fractionation into the vapor, and the element pattern in surface s49 Fig. a. Concen60. The sulphides, in particular, can be an important host for base metals, as observed in sulphides from Reykjanes well fluid precipitates19.The base metals are among the most depleted in the surface fluids compared to the deep fluids Fig. a, yet thGeothermal production fields in magmatic settings provide a unique opportunity to study the behaviour of elements in magmatic-hydrothermal fluids, in particular by providing direct access to deep fluids. The Theistareykir and Krafla fluids provide a consistent story of metal enrichment and depletion patterns. The two fields share common background conditions for the mobilisation of metals in deep geothermal fluids with similar reservoir host rocks and secondary mineralogy, fluid temperatures, and the same local meteoric water and regional groundwater. These conditions are typical for basaltic magmatic settings, and we therefore posit that the results obtained here are representative for such systems, showing that element concentrations reflect a magmatic degassing input variably modified by water–rock interaction. There are significant changes between the deep fluid composition and that at the surface, in absolute concentrations, but especially in the element signature as a result of boiling and scale precipitation. This means that element content and pattern from surface fluids cannot be interpreted as directly reflecting the deep reservoir fluid composition, and the recalculation commonly applied cannot correct for this.Supplementary Information.


Impaired emotion recognition is common in individuals with attention-deficit/hyperactivity disorder (ADHD) and may, via deficient emotion self-regulation, relate to the frequently co-occurring affective and social problems. The present study used an emotional face-matching task and functional magnetic resonance imaging (fMRI) to investigate neural responses during the processing of angry and fearful faces and visuo-spatial control stimuli. Additionally, measures for emotion dysregulation, ADHD type, and age were investigated in relation to the behavioral and neural fMRI data. We utilized a sample of 61 adolescents/young adults with ADHD and 51 age-matched healthy controls (age range: 12–28 years). Participants with ADHD had higher emotion dysregulation scores than controls. They also reacted slower and less accurate in response to emotional but not visuo-spatial control stimuli. Neural response differences between emotional and visuo-spatial trials were significantly smaller in cases, particularly in the left amygdala. While coupling between the right amygdala and bilateral ventromedial prefrontal cortex was stronger for emotional than visuo-spatial stimuli in control subjects, levels of positive coupling between the trial types did not significantly differ in participants with ADHD. Neither emotion dysregulation scores, nor ADHD type or age were related to the behavioral and neural processing alterations during the emotional face-matching task. Results indicate that emotion recognition deficits in ADHD are particularly associated with lower amygdala activation to emotional stimuli and alterations in the functional connections of the amygdala to medial prefrontal areas. Emotion recognition deficits and associated neural alterations were unrelated to emotion dysregulation, ADHD type, or age. With an estimated prevalence rate of 5.3% in children and adolescents  and 2.5%An important aspect of adequate emotion regulation is the accurate recognition and interpretation of emotional stimuli. Individuals with ADHD were found to be less accurate in identifying emotions. Besides other factors, such as subconscious and experience based reward estimations or cognitive reappraisal , 10, emoWhile most neuroimaging research in ADHD has focused on frontostriatal, frontocerebellar, and frontoparietal circuits, few studies investigated the functional connections between the amygdala and the prefrontal cortex during tasks requiring emotion recognition in ADHD. The limited number of studies regarding emotion perception and recognition/matching during functional magnetic resonance imaging (fMRI) revealed evidence for case–control differences in affective arousal structures, including the ventral striatum, cingulate cortex, anterior insula, and, most consistently, (left) amygdala. Significant results have been repeatedly shown for the amygdala, although with inconsistency regarding the laterality or direction of the effects –20. RestThe main objective of the present study was to investigate amygdala reactivity and functional connections of the amygdala and prefrontal cortex in adolescents and young adults with ADHD as compared to healthy controls during the processing of fearful and angry facial stimuli. We chose matching of fear and anger given the previously reported conduct problems and impairments to recognize these emotions in children with ADHD . A seconIndividuals with ADHD and healthy controls participated in NeuroIMAGE II, the third wave of an integrated genetics-cognition-MRI-phenotype project focusing on ADHD . InitialThe diagnostic procedure was based on DSM-IV-TR criteria  and is dn = 21, only right handed participants were studied to reduce variability due to lateralization differences), low quality of behavioral , imaging data , and incomplete diagnostic data (n = 3). This resulted in a final sample of 112 individuals  and sex). Among the 61 participants with ADHD, 36 had predominantly inattentive ADHD, 6 predominantly hyperactive/impulsive ADHD, and 19 combined ADHD. Due to the small number of participants with hyperactive/impulsive ADHD, they were merged with participants with a combined diagnosis in all analyses.The group examined here is a subsample of the 302 participants included in the third wave. The total group is composed of individuals who took part in the previous waves and new recruits added on account of dropouts (particularly within the control group). For the present study, however, only those individuals who could unambiguously be assigned to either the control or ADHD group were considered. These ambiguous or subthreshold participants had a symptom count that was neither indicative of an ADHD diagnosis  nor classified them as unaffected . Also, individuals who did not meet the criteria for daily living impairments or onset-age but had multiple symptoms fell into this category . From tStimulant medication was discontinued 48 hours prior to testing. Data acquisition took place at the Donders Institute for Cognitive Neuroimaging, Radboud University Nijmegen, Netherlands. Participants (and their parents when < 18 years old) gave written informed consent for participation. Ethical approval was granted by the Regional Ethics Board .http://www.macbrain.org). Half the trials depicted women and the other half men. Ellipses of the visuo-spatial trials consisted of scrambled face-stimuli pixels. Responses were given with left or right button presses. The task is well-established and served to investigate drug effects on amygdala reactivity [In two emotion matching and three visuo-spatial control blocks, each with six trials of 5 s in length, participants were asked to match the facial emotion (fear or anger) or spatial orientation  of an upper stimulus with one of two lower stimuli, in line with a previous study 31). The three simultaneously presented facial stimuli always depicted faces of different individuals of the same sex . Echo-time (TE) weighted summation was used to combine all five echoes into a single data set. T1-weighted high-resolution structural volumes were acquired with an MPRAGE sequence .Functional MRI data acquisition was performed with a 1.5 T Magnetom Avanto . To acquire T2*-weighted blood oxygen level dependent (BOLD) images, accelerated multi-echo EPI sequences, which additionally reduce image distortion and increase BOLD sensitivity, were used . For each volume, 37 axial slices were generated in an interleaved and ascending order . The statistical analysis of the behavioral data was conducted with R software [Reaction time and hit rate differences between the control and ADHD group during the emotion matching task trials were analyzed using mixed linear models with diagnostic status as the grouping variable and task condition as the repeated measures variable (one model for reaction time and one for hit rate). Using partial sums of squares software .FMRIB software library (FSL 5.0.11 ) was useAt subject level, GLM were generated with FSL FEAT  to estimz value images for different contrasts and group-level models. Using the previously mentioned grouping algorithm, a two-group GLM with binary regressors for participants with ADHD and healthy controls was created . Age and sex were demeaned across participants and included as covariates of no interest. Since a somewhat lower IQ and use of medication is an intrinsic feature of ADHD [With the individual beta contrast maps and associated maps of variance estimates, FSL FLAME 1 was used to calculate  of ADHD , IQ and Statistical maps were calculated for the groups’ mean effects and the between group contrasts (ADHD > HC and ADHD < HC). A similar model, but with binary regressors for the groups with predominantly inattentive ADHD and combined or hyperactive/impulsive ADHD, was created to evaluate differences between ADHD participants with and without hyperactive/impulsive symptoms. To investigate possible age- and emotion dysregulation-dependent effects, models for the two main participant groups in interaction with these continuous covariates were used.F tests were conducted and the same variables that had been used for the behavioral analysis were considered as covariates to investigate additional main or interaction effects. Subsequent t tests were performed to investigate the hemisphere-specific group mean differences. The statistical analysis of the mean beta values was conducted with R software [Region of interest (ROI) analyses were conducted with ROI masks of the left and right amygdala  correction with cluster forming thresholds of 2.3 [Further analyses used psychophysiological interaction (PPI) maps to investigate functional connectivity between BOLD responses in the left and right amygdala and other parts of the brain  . PPI anas of 2.3 .Demographic details are provided in Table F tests were used to investigate the ADHD- and task condition-specific influences on the behavioral results of the emotional face-matching task. Neither for reaction times nor for hit rates did the considered covariates show significant effects. Thus, no additional covariates were entered into the final models. Diagnostic status and task condition had a significant interaction effect on the average reaction times  = 20.814, p < 0.001, partial-η2 = 0.159). Post hoc comparisons between the two participant groups indicated significant mean difference in emotion and visuo-spatial trials  = 4.946, p < 0.001, d = 0.9; visuo-spatial trials: ADHD mean ± SD = 0.913 s ± 0.166 s, healthy control mean ± SD = 0.838 s ± 0.223 s, t(df = 109) = 2.053, p < 0.001, d = 0.0.38). For mean hit rates, no significant interaction effect of the diagnostic status and task condition could be found. Solely a significant main effect of the diagnostic status could be detected  = 7.919, p = 0.006, partial-η2 = 0.159). Although not significant, the average mean difference in hit rates between ADHD and healthy control subjects was more evident in emotion trials than in visuo-spatial trials .Two mixed linear models were constructed and partial To detect brain activity differences in group-specific BOLD activation during the emotion matching task (emotion > shape contrasts), the ADHD > HC and ADHD < HC contrasts of the ADHD-HC GLM were used. The ADHD > HC contrast did not reveal any significant clusters after FWE cluster-level correction. For the ADHD < HC contrast, significant clusters were present in the left amygdala, hippocampus, and subcallosal gyrus, cuneus and lingual gyrus, right superior and middle temporal, and left lateral occipital cortex and fusiform gyrus. Figure F tests, a trend towards statistical significance for the interaction effect between diagnostic status and laterality was revealed  = 3.120, p = 0.080, partial-η2 = 0.028). Further, a significant main effect of diagnostic status was found  = 4.401, p = 0.038, partial- η2 = 0.038).To study activation differences between emotion and shape stimuli within the amygdala in more detail, an ROI analysis was performed. None of the considered covariates showed significant main or interaction effects. Accordingly, they were not entered into the final model. Using partial t tests, only the mean difference of the left amygdala proved to be significant (t(df = 90) = 2.676, p value = 0.009, d = 0.522). Analyses of ADHD type, emotion dysregulation scores, reaction times, accuracy, and age did not reveal any significant main or interaction effects. However, we found a trend level significant interactive influence of group membership and left amygdala activity on reaction times and accuracy during the emotional trials.Participants with ADHD had smaller activity differences in both the right and left amygdala when compared to healthy controls . In controls, the right amygdala showed significantly stronger positive coupling with the bilateral vmPFC and frontal pole in emotional as compared to visuo-spatial trials. In contrast, levels of coupling were not significantly different between the two stimuli conditions in participants with ADHD. Here, opposing tendencies were observed. Low positive coupling between the amygdala and vmPFC and frontal pole was seen during the visuo-spatial but not the emotion condition. Statistical analysis confirmed that healthy controls showed a significantly larger vmPFC and frontal pole PPI effect than participants with ADHD Fig. . The aveWe investigated neural processing patterns of 112 adolescents/young adults with ADHD and healthy controls during the performance of an emotional face-matching task, and considered the influence of ADHD type, emotion dysregulation, and age. Participants with ADHD were overall slower and made more errors on emotional but not visuo-spatial control trials. During the processing of emotional faces as compared with the visuo-spatial stimuli, they showed less activity in the left amygdala and hippocampus, occipital regions, fusiform gyrus, and posterior fraction of the temporal cortex. The left amygdala finding was supported with a subsequent ROI analysis. Furthermore, healthy controls showed positive coupling between the right amygdala and vmPFC during emotional but not visuo-spatial trials. On the contrary, participants with ADHD showed negative coupling between the two structures during emotional trials but positive coupling during the visuo-spatial trials. For the ADHD participants, however, these task condition-specific differences were not significant.Behavioral findings are in line with studies in pediatric and adult ADHD samples in which evidence for a reduced accuracy and delayed responding to emotional content, such as emotional faces or social feedback, was found –14, 51. The notion of deficient emotion recognition capabilities in ADHD was, to some extent, supported by the whole-brain and ROI group comparison of the task fMRI data. While the activity differences between participants with ADHD and healthy controls in structures commonly associated with general attentional performance were primarily not significant, decreased amygdala activation was found in participants with ADHD. In addition to its central importance for general affective arousal, the amygdala is indispensable for the recognition of emotions . In connResults of the PPI analysis further suggest that not only altered amygdala activation, but also the functional connections with medial prefrontal structures may be associated with emotion recognition in ADHD. In contrast to the healthy controls, the coupling of the right amygdala and vmPFC in participants with ADHD did not significantly depend on the emotional magnitude of the stimuli. Reciprocal connections between amygdala and vmPFC and further information relay to dorsal prefrontal structures are seen as being crucial for emotion recognition and categorization , 54, 55.Neither behavioral results nor neural activity during task processing were related to the emotion dysregulation scores. While this may imply that processes that cause deficient emotion regulation in ADHD are not properly covered by the applied emotion matching task, it is also possible that the validity of the emotion dysregulation symptom scores, which were derived from a parent questionnaire (CPRS-R:L), is insufficient. It was recently shown that cognitive tasks and questionnaires, which are both commonly used to measure self-regulation, frequently lack an empirical relationship, while cognitive tasks only show limited ecological validity . AdditioFurther, the applied task only required matching of a restricted range of emotional facial expressions without having to explicitly recognize them. Future investigations might utilize alternative experimental tasks that better capture whether a certain emotion has actually been recognized , 63. It In conclusion, the current study shows a possible link between emotion recognition deficits and ADHD in adolescents and young adults. Results showed smaller BOLD-response differences between emotion and visuo-spatial trials, particularly in the area of the left amygdala, and dysfunctional connectivity between the right amygdala and vmPFC. The results may indicate that emotion recognition deficits in ADHD are associated with abnormalities in affective arousal structures and their functional connections to medial prefrontal areas. Participants with ADHD also had more emotion regulation problems than healthy controls, however, neither this, nor ADHD type, nor age were related to emotion recognition and associated neural processing alterations.


We examined children in Da Nang, a dioxin contamination hotspot in Vietnam, twice at 5 and 8 years of age, and investigated sex- and age-dependent differences in the effects of dioxin exposure on attention deficit hyperactivity disorder (ADHD) symptoms. We also studied autistic traits in children with ADHD symptoms. A total of 163 children participated in follow-up surveys at 5 and 8 years of age and were included in the present analysis. ADHD symptoms were assessed using an ADHD rating scale with inattention and hyperactivity-and-impulsivity (hyperactivity) subscales. Autistic behaviors were evaluated using the Autism Spectrum Rating Scale (ASRS). Perinatal dioxin exposure was indicated by dioxin levels in maternal breast milk. In boys, hyperactivity scores were significantly higher in the high 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) group only at 5 years of age. In girls, hyperactivity scores at 8 years of age were significantly higher in the high TCDD group, which was significantly associated with those at 5 years of age. In girls, ASRS unusual behavior scores were significantly higher with higher TCDD exposure and hyperactivity scores at 8 years of age. These results suggest that high perinatal TCDD exposure may increase ADHD likelihood and autistic traits, particularly in girls of 7–8 years of age. Endocrine disrupter chemicals (EDCs), such as polychlorinated biphenyls (PCBs), are suggested to have important roles in increasing the prevalence of neurodevelopmental disorders and behavior problems in children exposed to EDCs during the perinatal period . AttentiFrom 2008 to 2009, we followed a birth cohort living in a dioxin-contaminated area in Da Nang, Vietnam, near a former U.S. military airbase, and investigated the effects of perinatal dioxin exposure on growth and neurodevelopment of children at various ages. Previously, we reported that lower planning ability associated with increasing toxic equivalency values of polychlorinated dibenzodioxins and polychlorinated dibenzofurans (TEQ-PCDD/Fs) in boys at 5 years of age . In 8-yeRegarding emotional and behavioral disorders, we reported increased children with autism spectrum disorder (ASD) behaviors associated with perinatal 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure at 3 years of age for both sexes (boys are more likely than girls) . In the Furthermore, children with ASD often show inattention and impulsivity-hyperactivity and are diagnosed with ADHD as a comorbidity of ASD  because Thanh Khe and Son Tra districts in Da Nang city were chosen as the study areas. These districts are located within 10 km of the former Da Nang airbase and are dioxin contamination hotspots originating from herbicide spraying in Vietnam. The subjects were children from the Da Nang birth cohort, including 238 mother-and-infant pairs (158 pairs in Thanh Khe and 80 pairs in Son Tra), recruited in 2008–2009 and followed up from 1 month to 8 years old ,25.A total of 168 mother–infant pairs  participated in the follow-up surveys at 5 and 8 years of age. Because of missing behavioral examination data, 163 children (94 boys and 69 girls) were enrolled in the present analysis.The children’s perinatal dioxin exposure was estimated using the dioxin levels in their mothers’ breast milk collected a month after the birth of these children. Seventeen 2,3,7,8-substituted congeners of polychloro-dibenzodioxins (PCDDs) and polychloro-dibenzofurans (PCDFs) were measured using a high-resolution mass spectrometer , and the total toxic equivalents (TEQ) of PCDDs and PCDFs (TEQ-PCDD/Fs) were calculated by summing all of the values obtained by multiplying each congener concentration with reference to the WHO 2005 toxic equivalent factor . The estThe attention deficit hyperactivity disorder rating scale (ADHD-RS): parent ratings (2–5 years) was used to evaluate the children’s behavioral disorders by parents or caretakers when children reached 5 and 8 years of age. The ADHD-RS includes two subscales, an inattention score (inattention) and impulsivity and hyperactivity (hyperactivity) score and a total scale score (ADHD). The values of each scale were standardized by age and sex with a reference to percentiles in the score sheets with the range of 1 to 99 percentiles . Since t®); Multi Health Systems Inc.,  with three subscales, social communication (SC), unusual behavior (UB), and Diagnostic and Statistical Manual for Mental Disorders (DSM), and total scale (TOT) after percentile rank conversion from raw values to T-scores with the range of 25 to 85 using the technical manual for ASRS™ [Autistic behavior was evaluated using autism spectrum rating scales (ASRS) Autism Spectrum Rating Scales™  for Windows  was used to perform statistical analyses. TCDD concentrations and TEQs in breast milk were base-10 logarithmically transformed to normalize data distribution. Basing on the previous publications regarding the possible effective benchmarks of dioxin levels in this cohort, the cutoff values for high and low TCDD and TEQ-PCDD/Fs groups were set as 3.0 pg/g lipid and 17.6 pg-TEQ/g lipid, respectively. These cutoff values were derived from the 88th percentile concentration of TCDD and the 75th percentile level of TEQ-PCDD/Fs in breast milk .General linear models were used to compare the mean ADHD-RS and ASRS scores between high and low TCDD or TEQ-PCDD/Fs groups after adjusting for covariates, including age, parity, maternal education, family income, gestational weeks, birth weight, and age in months at examination of children. Spearman’s rho was used to analyze correlations between ADHD-RS scores at 5 and 8 years of age and between ADHD-RS scores and ASRS scores.p < 0.05 for t-test).The characteristics of mothers and gestational weeks and weight at birth and age in months and body size at the time of the survey at 5 and 8 years old are shown in The geometric means with geometrical standard deviations of 17 PCDD/Fs congeners and TEQ-PCDD/Fs in maternal breast milk are shown according to child sex in For each sex, ADHD-RS scores for inattention, hyperactivity, and ADHD were examined at 5 years of age and compared between high and low TCDD and TEQ-PCDD/Fs exposure groups . In boysThree ADHD-RS scores, inattention, hyperactivity, and ADHD scores, examined in the 8-year-old survey were also compared between high and low TCDD and TEQ-PCDD/Fs groups, and the results for each sex are shown in Higher ADHD-RS scores associated with increasing dioxin exposure were found in boys at 5 years of age and girls at 8 years of age, and therefore, Spearman’s correlations between the ADHD-RS scores at 5 years and 8 years of age were analyzed in high and low TCDD groups and all children for each sex . In the In the survey at 5 years old, we also examined autistic traits in children using the ASRS scale and compared the mean SC, UB, DSM, and TOT scores between high and low dioxin exposure groups for each sex . At thisTo clarify the relationship between the ADHD-RS and ASRS scores, the Spearman’s correlation between ADHD scale and ASRS scale scores at 5 and 8 years of age was analyzed . SignifiIn the present study, increased ADHD symptoms were observed in children perinatally exposed to dioxins at higher levels than 3 (pg/g lipid) for TCDD and 17.6 (pg-TEQ/g lipid) for TEQ-PCDD/Fs compared with less exposed children. These exposure levels were similar to those which showed neurodevelopmental deficits in our previous studies for children from the Da Nang cohort ,14,15. HIn boys, hyperactivity and ADHD scores at 5 years of age were significantly higher in the high TCDD group than in the low TCDD group. However, we could not confirm the presence of increased children with ADHD traits based on these findings because 5 years old is not the eligible age for ADHD diagnosis (7–8 years old) with reference to the ADHD criteria in the DSM fourth edition .In contrast, hyperactivity scores in girls at 8 years of age were significantly higher in the high TCDD and TEQ-PCDD/Fs groups than in the low exposure groups. In addition, in girls, hyperactivity scores at 8 years of age were significantly associated with those at 5 years of age, suggesting that ADHD symptoms, particularly impulsivity-hyperactivity, are increased at preschool age and reached levels sufficiently high to diagnose hyperactive-impulsive-type ADHD (ADHD-PH) in early school age (DSM fourth edition criteria). However, these results are inconsistent with two epidemiological facts in unexposed children with ADHD: (1) ADHD is more frequently found in boys and (2) a predominantly inattention type (ADHD-PI) is more common than ADHD-PH in girls . These dIn the present study, we analyzed child behavior based on a parent rating scale and did not examine their performance using neuropsychological function tests, such as continuous performance tasks (CPTs), which are commonly used to examine children with ADHD. Children with ADHD often have impaired neuropsychological functions, including vigilance, working memory, and response inhibition, which are related to their characteristic ADHD symptoms . TherefoThe WHO has carried out a series of exposure studies on the levels of PCDDs, PCDFs and PCBs in breast milk. The first WHO-coordinated exposure study took place in 1987–1988, the second round in 1992–1993, and the third round in 2000 to 2003 . TemporaAssociations between cord blood PCB and impulsivity examined using CPTs were reported at 4, 8, and 9 years of age in children in a birth cohort study in Oswego, USA ,3. A sigIn the Korean general population aged 12–15 years in the National Health and Nutrition Survey, a significantly increased prevalence of attention deficit disorder associated with increasing levels of serum dioxins, including HpCDD, OCDD, and HpCDF, was reported ; howeverIn children aged 7–17 years included in the Seveso second generation study in Italy from 2014 to 2016 whose mothers were exposed to extremely high levels of TCDD due to an industrial explosion in 1976, neurophysiological functions associated with attention and hyperactivity in children were examined using CPTs . HoweverIn summary, these results from previous studies in countries other than Vietnam suggest perinatal EDCs exposure; for example, PCBs may increase children with ADHD symptoms at 8–9 years old ,7,8,9,10We previously reported increased autistic traits as reflected by ASRS scores in children with perinatal exposure of TCDD ≥ 3.5 (pg/g lipid) in both sexes (boys > girls) when children from the present birth cohort reached 3 years of age . In the In contrast, in girls, UB scores, which were significantly higher in the high TCDD group, significantly correlated with hyperactivity scores at 5 and 8 years of age. These results suggest that high TCDD exposure may increase behavioral disorders in the form of combined ASD and ADHD, which became clear at an early school age. For these children at 9 years of age, we also reported that TCDD exposure may influence the EEG power of the mirror neuron system of the brain, particularly in girls, which contributes to social-emotional behavior and is often found in children with ASD . These nIn boys aged 8 years, the Colorado Learning Difficulties Questionnaire (CLDQ) reading scores were significantly higher and reading errors of some passages in the high TCDD exposure group suggested learning difficulties at school . HoweverIn contrast, in girls, an increased covert aggression score was significantly associated with higher TCDD levels and ADHD-RS scores . HoweverAnxiety and depression are common comorbidities in children with ADHD . In futuOur study reported increased children with ADHD associated with dioxin, particularly TCDD, exposure during the perinatal period. To our knowledge, this is the first study to report a significant association between perinatal TCDD exposure indicated by dioxin levels in maternal breast milk and ADHD. Similarly, previous studies in the USA reported the impact of prenatal PCB exposure on the increased prevalence of ADHD in children ,3,4; howIn addition, the sample size of high TCDD group, particularly in boys, is a limitation of the present study that decreases the statistical power in comparisons of ADHD-RS and ASRS scores between high and low exposure groups. In addition, we have no control group, which should be enrolled from unexposed populations in unsprayed north Vietnam. However, they have very different maternal education levels and economic statuses from those in Da Nang located in south Vietnam, which are too strong confounding factors for adjusting associations between dioxin exposure and increased children with ADHD.Another limitation is the ADHD-RS, which is based on the ADHD criteria in the DSM fourth edition, which is an older version than the DSM fifth edition  used at Perinatal TCDD exposure temporally increased hyperactivity-and-impulsivity in boys only at a pre-school age (5 years). In contrast, in girls, TCDD exposure increased hyperactivity-and-impulsivity at an early school age (8 years) and unusual ASD behavior at a younger age. These results suggest that school-aged girls perinatally exposed to high TCDD levels are more likely to show ADHD symptoms combined with autistic traits than boys of the same age.


Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood disorder, but often goes unrecognised and untreated. To improve access to services, accurate predictions of populations at high risk of ADHD are needed for effective resource allocation. Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Retrospective population cohort study.South London (2007–2013).n=56 258 pupils with linked education and health data.Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. Ethnic group and language biases were weighted using a fair pre-processing algorithm.Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples  and clinical samples (AUC 0.72 and 0.70). Precision-recall curve analyses were less favourable. Sociodemographic biases were effectively reduced by a fair pre-processing algorithm without loss of accuracy.ML approaches using linked routinely collected education and health data offer accurate, low-cost and scalable prediction models of ADHD. These approaches could help identify areas of need and inform resource allocation. Introducing ‘fairness weighting’ attenuates some sociodemographic biases which would otherwise underestimate ADHD risk within minority groups. We leveraged a large and innovative data linkage between routinely collected health and school data.Machine learning approaches were applied to predict attention deficit hyperactivity disorder in a population and clinical cohort.Models were weighted to account for ethnic group and language biases.Further validation is needed to establish the stability and generalisability of these models.Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by a persistent pattern of inattention, hyperactivity and impulsive behaviour.Pooled estimates from population surveys suggest that 5.3% of the global population meet criteria for ADHD.Overall, there remains a need for accurate indicators of level of need which can inform commissioning and resource distribution. At the school level, mental health screening questionnaires administered by teachers can be used to identify pupils at risk of ADHD, which in could turn provide an estimate of ADHD prevalence for that school.15 16Therefore, there remains a need for accurate and unbiased ADHD prediction methods which are scalable to large populations or geographical regions. Administrative pupil-level data are already routinely collected and curated by most schools, and are available for whole populations.This was a retrospective cohort study, using an existing data linkage between the National Pupil Database (NPD) and South London and Maudsley National Health Service Foundation Trust Child and Adolescent Mental Health Services (SLaM CAMHS) .18 1918 1910.1136/bmjopen-2021-058058.supp1Supplementary dataSLaM is one of Europe’s largest providers of mental healthcare, serving the London boroughs of Croydon, Lambeth, Lewisham and Southwark. SLaM is the monopoly provider of local CAMHS services, including ADHD diagnostic services.CRIS data for SLaM CAMHS services have been linked at the individual level to the NPD. The NPD is a longitudinal database of pupil-level and school-level data.The CRIS-NPD linkage process has been described in detail elsewhere.https://www.maudsleybrc.nihr.ac.uk/facilities/clinical-record-interactive-search-cris/cris-data-linkages/.The health and education data linkage used in this study was developed in consultation with several clinical, patient and caregiver groups via the SLaM Biomedical Research Centre patient engagement programme.The analysed sample comprised children aged between 6 and 7 years, who were enrolled in mainstream state educational services , and who had education and attainment characteristics as captured at the Early Years Foundation Stage Profile  and at Key Stage 1 . Pupils were included if they were resident of the SLaM catchment area from 1 September 2007 to 31 August 2013. A resulting n=57 149 pupils were eligible for inclusion.Individual-level diagnosis of ADHD was measured from both structured and unstructured diagnosis fields in CRIS between September 2007 and August 2013. In structured fields, ADHD diagnosis was defined as International Classification of Diseases 10th Revision codes F90.0, F90.1, F90.8 or F90.9 .21–24School performance indicators were collected from EYFSP and KS1 datasets in the NPD. The EYFSP comprises teacher-assessed social, linguistic, physical and cognitive development at the end of the first year of schooling.10.1136/bmjopen-2021-058058.supp2Supplementary dataTo minimise the potential for reverse causality, we excluded pupils who were diagnosed with ADHD prior to their KS1 assessment . We used a complete case approach, excluding pupils with missing data from our eligible sample . The resulting n=56 258 formed our population cohort.To develop and compare machine learning classifiers, the dataset was randomly divided into a training set , validation set  and test set  with a similar proportion of ADHD cases (~1%) in each set. The training set was used to train the algorithms, while the validation set was used for model refinement  and comparison. Final performance was evaluated on the test set. We compared several machine learning algorithms to classic statistical logistic regression (LR) approaches, and also a supervised approach based on neural networks, using a multilayer perceptron (MLP). The following classifiers were compared: LR, random forest (RF), support vector machines (SVM), Gaussian Naive Bayes (GNB) and the MLP. For GNB, we first tested that continuous features associated with each class were distributed according to a Gaussian distribution. For the neural network, we used a four-layer architecture .27Model performances were measured with the area under the receiver operator characteristics curve (AUC). We also report precision, recall and F1-score with a fixed classification threshold of 0.5. The F1-score was calculated using the ‘weighted’ option, which calculates metrics for each label and finds their average weighted by support. For each given value, a 95% CI of the outcome measure was estimated by bootstrapping 100 times the subjects in the set. We also examined the precision-recall curve (PRC) with precision (y-axis) and recall (x-axis) plotted for different probability thresholds. All the experiments with machine learning algorithms were conducted in Python with functions from the scikit learn library for classification (V.0.20.3).For LR and RF, tuning of the hyperparameters was made using grid search with 10-fold cross-validation on the validation set (full details available by request to the author). The GNB classifier had no parameters to tune. Tuning of SVM was done empirically . To compTwo criteria were defined for the final models’ selection: performance and transparency, that is, the models’ ability to determine feature contribution, which enabled examination of potential biases inherent to the data resources used. High-performing models were considered a priori as having AUC ≥0.85 on the validation set.Models which were both transparent and performed well were tested for diagnostic efficiency, the purpose being to assess whether the predictive performance from the population sample could generalise to clinical populations. To test the specificity of the best models for ADHD from other clinical mental health problems, we repeated our approach on a purely clinical cohort containing only the children who were present in the CRIS dataset, thus containing samples of children who presented with ADHD and non-ADHD diagnoses. Once all models were tested on the full set of features, we then refined the models to improve accuracy, reduce overfitting and increase potential for translation into busy clinical settings—making the model less vulnerable to missing information. To identify the most important features for LR models, we used beta coefficients, while RF feature importance represents the decrease in node impurity on a given branch within a decision tree, weighted by the probability of reaching that node.We investigated bias reduction for the LR and RF models trained on the population cohort using the AI Fairness 360 toolkit.In total, n=652 pupils in our sample were diagnosed with ADHD  . In the Once all models were tested on the full set of 68 features, we refined the models to improve accuracy, reduce overfitting and increase potential for translation into busy clinical settings. Resampling methods used to deal with class imbalance did not improve the performances significantly. Overall, random subsampling performed better than Tomek links. As for ensemble approaches, a slight improvement in performance was noticed for RF only, while LR obtained better performance when using resampling methods. The PRCs for both population and clinical cohorts are displayed in This study aimed to predict pupils with a high likelihood of ADHD diagnosis using data available at the population level. To our knowledge, it is the first study to apply machine learning approaches to large-scale, routinely collected linked health and education data. These innovative methods show that ADHD in general population and clinical cohorts can be predicted with high levels of accuracy. Promisingly, this level of predictive accuracy is comparable with traditional survey-based screening methods in ADHD, and with other studies using routinely collected data to predict health outcomes.35–38These findings suggest that machine learning approaches can accurately predict ADHD in large general population and clinical cohorts using existing and widely available administrative data. These models may therefore be suited to provide health intelligence to local policymakers at low cost, and inform decisions on the allocation of disorder-specific resources at an area, school or service level. Tentatively, these models may also offer a means to predict ADHD at the individual level, although greater precision would be desirable for this purpose—this is an area for future work.We also demonstrate that sociodemographic biases inherent in the data and algorithms can be reduced with a fairness reweighting step to ensure that pupils from certain backgrounds are not systematically overlooked as being at risk of ADHD. In a geographical area with a large proportion of people from ethnic minority and non-English-speaking backgrounds, minimising such biases is crucial for accurately predicting need for ADHD provision in the population.24This therefore highlights the need to build fairness measures which weight for limited access to diagnosis in certain ethnic and social groups. However, it is important to note that these statistical approaches are unlikely to remove all potential biases inherent to service provision data: for example, parents also influence help-seeking for ADHD, but their characteristics are not fully captured in datasets like the NPD, and so could not be used in reweighting processes.Our study benefited from a large and novel data linkage capturing almost all pupils from a defined geographical catchment. This reduces the risk of building models on biased and non-representative samples. These approaches are also scalable, as they use information already routinely collected by schools. We used a set of machine learning algorithms that are state-of-the-art techniques and are representative of the wide spectrum of machine learning methods: an ensemble method (RF), a kernel-based method (SVM), a Bayesian method (Naive Bayes) and a neural network (MLP).However, some limitations should be highlighted. While we sought to address biases in clinical data using reweighting algorithms, biases in the education data (such as inconsistent representation of private or home-schooled pupils) have not been addressed. We also handled missing data using a complete case analysis, which can introduce bias if the missing data are not randomly distributed. An alternative would have been to explore imputation methods; however, given that we compare models of varying complexities (from LR to MLPs), we would have had to adapt the imputation method to the model, resulting in varied datasets. The comparison between the resulting models would therefore have been unfair. Encouragingly, the proportion of missing data was very low, and therefore unlikely to materially bias the findings. Nonetheless, further validation of these models is warranted.41We have assumed that diagnostic and treatment discrepancies between different ethnicities and language groups are due to disparity. It could be argued that such discrepancies are in fact reflective of genuine underlying differences in ADHD prevalence in these populations. However, existing evidence suggests that ADHD is underdiagnosed and undertreated in certain ethnic groups; plausible mechanisms along the care-seeking pathway which may result in these disparities have been discussed elsewhere.15 16For reweighting, we dichotomised ethnicity as white/non-white, and language as English/non-English—conducting a more granular reweighting procedure using more precise groupings would be an important area for future research. We also did not conduct reweighting on the basis of any other sociodemographic characteristics such as gender or deprivation, and make no assumptions as to their role in differential diagnoses—again, these could be areas for future work.To identify ADHD cases, we used diagnostic codes recorded in structured fields. Evidence suggests that ADHD can be classified from diagnostic codes in electronic health records with a high degree of accuracy; however, such methods of ascertainment are imperfect, and the possibility of diagnostic or administrative misclassification does remain.42 43Replication in other regions or populations would also be important to assess the generalisability of these models, although at present, other educational and clinical data linkages on this scale are scarce. Finally, some of the features used in the analysis had very few observations for the ADHD cases. The potential impact of this is understudied and warrants further investigation.44 45Overall, this study demonstrates that machine learning approaches using readily available education and clinical data show promise in predicting ADHD in source populations. It also highlights that biases inherent in routinely collected data can be mitigated using fair weighting processes. With further validation and replication work, these methods have the potential to estimate burden of need for ADHD provision, thereby informing resource allocation and policy decisions.