Methylphenidate is a widely used and effective treatment for attention-deficit/hyperactivity disorder (ADHD), yet the underlying neural mechanisms and their relationship to changes in behavior are not fully understood. Specifically, it remains unclear how methylphenidate affects brain and behavioral dynamics, and the interplay between these dynamics, in individuals with ADHD. To address this gap, we used a novel Bayesian dynamical system model to investigate the effects of methylphenidate on latent brain states in 27 children with ADHD and 49 typically developing children using a double-blind, placebo-controlled crossover design. Methylphenidate remediated greater behavioral variability on a continuous performance task in children with ADHD. Children with ADHD exhibited aberrant latent brain state dynamics compared to typically developing children, with a single latent state showing particularly abnormal dynamics, which was remediated by methylphenidate. Additionally, children with ADHD showed brain state-dependent hyper-connectivity in the default mode network, which was also remediated by methylphenidate. Finally, we found that methylphenidate-induced changes in latent brain state dynamics, as well as brain state-related functional connectivity between salience and default mode networks, were correlated with improvements in behavioral variability. Taken together, our findings reveal a novel latent brain state dynamical process and circuit mechanism underlying the therapeutic effects of methylphenidate in childhood ADHD. We suggest that Bayesian dynamical system models may be particularly useful for capturing complex nonlinear changes in neural activity and behavioral variability associated with ADHD. Our approach may be of value to clinicians and researchers investigating the neural mechanisms underlying pharmacological treatment of psychiatric disorders. Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder that affects approximately 5–10% of children and adolescents worldwide –4. TheseOne of the most robust behavioral phenotypes associated with attentional deficits in childhood ADHD is intra-individual response variability (IIRV), which measures dynamic fluctuations in trial-to-trial performance , 16. IncBayesian dynamical system models are now increasingly used to investigate latent brain states and dynamic brain circuits associated with them , 21, 22.A substantial body of human functional neuroimaging studies has uncovered a core set of distributed regions in the salience network (SN), anchored in the anterior insula and dorsal anterior cingulate cortex, and frontal-parietal network (FPN), anchored in the dorsolateral prefrontal and posterior parietal cortex, that support attention and cognitive control –27, as wPrevious studies have reported that methylphenidate alters regional activation in these brain networks during cognitive performance in individuals with ADHD . A recenIn a previous study, we investigated the impact of methylphenidate on dynamic interactions between the SN, FPN and DMN . We extrTo address methodological limitations of prior studies, we used a novel Bayesian switching linear dynamic systems (BSDS) model  to invesWe investigated the impact of methylphenidate on dynamics of latent brain states and its relation with IIRV changes in children with ADHD. We conducted a randomized placebo-controlled double-blind crossover study Figure  with 27 We first tested the hypothesis that children with ADHD would exhibit abnormal dynamics of brain states compared to their TD peers. We evaluated this by examining occupancy rates and mean lifetimes of latent brain states between the two groups. Our second hypothesis was that methylphenidate would restore normal dynamics of brain states in children with ADHD by reducing the difference in occupancy rates of affected brain states between the two groups.Next, we determined the effect of methylphenidate on the latent brain states in relation to changes in IIRV induced by medication. We hypothesized that medication-induced changes in brain state dynamics would underlie reduced behavioral instability.Finally, we analyzed differences in functional connectivity patterns between children with ADHD and TD children, as well as the impact of methylphenidate on these patterns and their contribution to the medication effect on IIRV in children with ADHD. We hypothesized that aberrant latent brain states would be characterized by abnormal functional connectivity between SN, FPN and DMN, and that medication-induced changes in functional connectivity would contribute to reduced IIRV.This study involves re-analysis of a previous published dataset . The stuThirty-four children with ADHD and 65 TD children were recruited at the University of Fukui Hospital, Japan. Figure During the second visit, within 1 to 6 weeks after the first visit, children with ADHD underwent a resting-state fMRI scan and performed the continuous performance task (CPT) after they took the second medicine: children who took OROS-MPH at the first visit took the placebo at the second visit under double-blind randomly assigned conditions, and vice versa.TD children completed the same resting-state fMRI scan once without either OROS-MPH or placebo. Inclusion criteria for both groups included no contraindications for magnetic resonance imaging (MRI), full-scale intelligence quotient (FSIQ) > 70 , no history of severe head trauma or neurological abnormalities . To minimize the potential impact of sex differences, we included only male participants, consistent with previous ADHD imaging studies , 48. Part-tests.A standardized CPT was administered to children with ADHD outside the MRI scanner under both methylphenidate and placebo conditions. The task consisted of a Go/NoGo paradigm in which children were presented with either a target or non-target stimulus on the screen for 100 msec, once every 2 s for 15 min across three 5-minute blocks. The target stimulus was a triangle, while the non-target stimulus was either a circle or a square. Children were required to press a button when a target stimulus was presented, and withhold response to non-targets. The test performance has been normalized to age-adjusted T-scores. IIRV was quantified using reaction time (RT) and standard deviation (RTSD) from correct Go trials. We examined medication-induced performance differences using paired Functional images were acquired in a 3-T scanner  and a 32-channel head coil with a T2*-weighted gradient-echo echo-planar imaging sequence . In total, 201 volumes were acquired for a total scanning time of 7 min 42 s. The participants were instructed to stay awake with eyes closed. Head movement was minimized by the placement of memory foam pillows around their head.http://www.fil.ion.ucl.ac.uk/spm) and DPARSF [Resting state fMRI data were analyzed using SPM12  was applied to each time series to reduce head-motion-related artifacts; the resulting time series was further band-pass filtered (0.008 Hz>f > 0.1 Hz).Time series of the 1We used a BSDS model  to uncovt-tests were then conducted to determine differences in occupancy rate and mean lifetime between TD children and children with ADHD in placebo condition. A threshold of p < 0.05 with family-wise error (FWE) correction was used to determine statistical significance in multiple comparisons. For brain state features that showed significant between-group differences, we specifically tested whether (i) methylphenidate reduced aberrancies in latent brain states using a paired t-test between methylphenidate and placebo conditions in children with ADHD, and (ii) methylphenidate normalized aberrancies in latent brain state to the level of TD children using two-sample t-tests between TD children and children with ADHD in methylphenidate condition. Statistical significance was determined at p < 0.05.ROI timeseries from typically developing (TD) children, children with ADHD under placebo (Placebo) and methylphenidate (MPH) conditions were grouped together and analyzed in a common BSDS model. This yielded latent states common to all three groups. Key measures extracted from BSDS include occupancy rate and mean lifetime of latent brain state as well as functional connectivity of states. Two sample p < 0.05.To understand the relationship between the latent dynamic brain state and out-of-scanner behavioral performance, we used multiple linear regression models to examine whether occupancy rates and mean lifetime of each brain is related to behavioral performance. Other potential confounds such as age, social-economic status (SES) and IQ were also included in the model. Statistical significance was determined at p < 0.05 with false discovery rate (FDR) correction for multiple comparisons.To determine which dynamic functional connections are important for distinguishing different brain states, we conducted paired t-tests to examine medication effect on the covariance matrix between latent brain states derived from BSDS analysis and two-sample t-tests to examine group differences. The results were thresholded at Pearson’s correlation between the difference of methylphenidate versus placebo in function connectivity of the aberrant latent brain state and its difference in IIRV. The results were thresholded at p < 0.05 with FDRcorrection for multiple comparisons We conducted multiple linear regression analysis to examine the relation between methylphenidate-induced changes in functional connectivity and medication effects on IIRV, while controlling for potential confounds, such as age, IQ and SES. Statistical significance was determined at p < 0.05.To determine whether medication effect on functional connectivity between the SN, FPN and DMN contributes to its effect on sustained attention, we first conducted Ninety-nine children, including 34 boys with a clinical diagnosis of ADHD and 65 TD boys (6–15 years old) completed the study. Children with ADHD were enrolled in a double-blind, placebo-controlled crossover design as described previously (39–41). Children with ADHD were scanned twice using resting-state functional MRI under methylphenidate and placebo conditions, which were counterbalanced across participants. Each child with ADHD took osmotic release oral system methylphenidate (OROS-MPH) (1.0 ± 0.1 mg/kg) in the methylphenidate condition and lactose in the placebo condition. For ethical reasons, TD children were scanned once without administration of methylphenidate or placebo. Participants with excessive head motion during the scanning, psychiatric comorbidity or neurological abnormalities were excluded (see Methods and Supplementary Methods for details), leading to a final sample of 27 children with ADHD and 49 TD children.ps > 0.05, two-sample t-test, Table ps < 0.001, two-sample t-test, Table p = 0.45). Head motion in children with ADHD in the MPH condition (0.058 ± 0.014 mm) was significantly lower than those in placebo condition (p < 0.001) and in TD (p < 0.002).The two groups did not differ in age and handedness .Children with ADHD completed a standardized continuous performance task  outside Latent state variables play a crucial role in characterizing the dynamics of brain activity. We used BSDS to probe latent state dynamics of a cognitive control system comprising key nodes of the SN, FPN, and DMN: bilateral anterior insula, middle frontal gyrus, frontal eye fields, inferior parietal lobe, posterior cingulate cortex (PCC), ventromedial prefrontal cortex (VMPFC), and pre-supplementary motor area  Fig. . These RBSDS uncovered 5 distinct latent brain states labeled S1, S2, S3, S4 and S5 in a combined group of children with ADHD under placebo condition and TD controls Fig. . OccupanF4,365 = 5.89, p = 0.0001, Fig. ps > 0.1). Post-hoc two-sample t-tests further revealed that the occupancy rate of S2 was significantly different between children with ADHD under placebo and TD children  with factors group  and state  Fig.  revealedps > 0.1).We conducted a similar ANOVA analysis to examine group differences on mean lifetimes across all the latent brain states but did not find significant interaction and main effects of group and state .Next, we tested whether methylphenidate could normalize abnormal state dynamics in children with ADHD. A two-way ANOVA with factors medication  and state  revealed a significant main effect of state .Because the occupancy rate of state S2 was significantly different between children with ADHD under placebo and TD children, we further specifically tested whether medication normalizes the occupancy rate of S2 in children with ADHD. A pairwise These results suggest that methylphenidate administration reduces aberrant latent brain states associated with cognitive control systems in children with ADHD, normalizing them to levels observed in TD children.p = 0.047, Supplementary Table ps > 0.05). These results show that methylphenidate-induced reductions in aberrant latent brain states are associated with behavioral improvements in ADHD.We then determined whether methylphenidate-induced changes in brain state dynamics are associated with changes in IIRV in the CPT. We focused on state S2 which we found to be aberrant in children with ADHD, as detailed above. Specifically, we conducted a multiple linear regression analysis to evaluate the relationship between methylphenidate-induced changes in occupancy rate and mean lifetimes of S2 and medication induced changes in IIRV controlling for potential confounding influences of age, IQ and SES. Methylphenidate-induced changes in occupancy rate of S2 is the only significant predictor for medication-induced changes in IIRV .We sought to determine differences in functional connectivity associated with S2, the aberrant latent brain states in children with ADHD. Here we leveraged the ability of BSDS to identify connectivity patterns associated with each brain state in each individual. Compared to TD children, children with ADHD under placebo showed stronger functional connectivity between the PCC and VMPFC nodes of the DMN . These results demonstrate that methylphenidate normalizes abnormal latent brain state functional connectivity of the DMN in children with ADHD.Next, we examined the effect of methylphenidate on functional connectivity in the aberrant brain state, S2, in children with ADHD. We found that the medication reduces hyper-connectivity between PCC and VMPFC in children with ADHD .Finally, we examined whether methylphenidate-induced changes in functional connectivity of the aberrant brain state, S2, are associated with medication-induced changes in IIRV. We found that medication-induced changes in functional connectivity between left AI node of the SN and PCC node of the DMN in state S2 is significantly correlated with medication-induced changes in IIRV in children with ADHD .Methylphenidate is a commonly used stimulant for treating inattention symptoms in children with ADHD. We used a novel state-space approach to investigate aberrancies in latent brain states associated with dynamics of cognitive control networks in childhood ADHD and then determined whether methylphenidate normalizes these aberrancies and improves behavioral performance on a sustained attention task. Our analysis revealed aberrant brain state dynamics, which were remediated by methylphenidate and correlated with improved attention. Methylphenidate also improved brain state-related functional connectivity and remediated hyper-connectivity in the DMN. Additionally, medication-induced changes in cross-network functional connectivity between the DMN and SN predicted medication effects on sustained attention. Our findings demonstrate that methylphenidate remediates attention problems in children with ADHD by normalizing aberrant dynamic brain mechanisms, thus advancing our understanding of the brain mechanisms associated with the effect of methylphenidate on attention.One prominent neurocognitive model proposes that higher levels of attentional fluctuations in individuals with ADHD may arise from abnormal brain dynamics . ConsistThe current study overcomes these limitations by using a novel BSDS algorithm to uncover latent brain states characterized by unique spatiotemporal circuit properties. BSDS identified 5 latent brain states, characterized by distinct patterns of intrinsic functional connectivity between key nodes of the SN, FPN and DMN. Among these states, state S2 was found to have a significantly higher occupancy rate in children with ADHD than TD children, indicating abnormal brain dynamics during resting state. Our previous study found differences in brain connectivity between children with ADHD and their TD peers . HoweverMoreover, changes in the occupancy rate of state S2 induced by methylphenidate were found to predict medication’s effect on sustained attention, as measured by the IIRV in a continuous performance task. While previous studies have shown that methylphenidate can lower IIRV in children with ADHD , the dynTogether, our findings suggest that a single dose of methylphenidate can remediate aberrant latent brain state dynamics of the cognitive control system in children with ADHD, and, furthermore, changes in brain dynamics induced by methylphenidate are associated with improved sustained attention.Our next goal was to uncover latent space features that define the abnormal state S2 in children with ADHD and determine whether methylphenidate can normalize these features. Each latent brain state is characterized by distinct patterns of functional connectivity between the key nodes of the SN, FPN and DMN. These networks play an essential role in a wide range of attention and cognitive control tasks, with a high degree of reproducibility –54. PrevOur findings provide novel evidence on aberrant functional connectivity of cognitive control systems in children with ADHD and the impact of methylphenidate on these systems. We found that, compared to controls, children with ADHD exhibited higher functional connectivity between the two key nodes of the DMN, the posterior cingulate cortex (PCC) and the ventromedial prefrontal cortex (vmPFC). This is in line with previous findings that show ADHD-related dysfunction in the DMN . FurtherOur last goal was to determine whether changes in functional connectivity induced by methylphenidate in the abnormal state S2 were linked to medication’s impact on sustained attention. We used IIRV from a sustained attention task as high response variability is a hallmark of inattention. We found that methylphenidate’s impact on functional connectivity between left AI and PCC in state S2 was associated with its effect on IIRV in children with ADHD. This aligns with the triple-network model of attention and cognitive control .The triple-network model suggests that the SN plays a critical role in facilitating access to attention and cognitive control resources and modulating its interaction with the FPN and DMN, which are involved in externally-oriented attention and internally-oriented mental processes. Studies in neurotypical adults have shown that attention and cognitive control modulates interaction between key nodes in the SN (e.g. AI) and DMN (e.g. PCC) , 65 and We acknowledge several limitations in this study. Our study participants were all male, not all drug naïve, and spanned a wide range from 6 to 15. To control for the effects of psychostimulants, all participants were medication-free for at least 5 half-lives prior to the MRI scans. For ethical reasons, participants were allowed to take medication between study days. Our randomized placebo-controlled double-blind crossover design involved similar washout protocols for the methylphenidate and placebo groups, thus controlling for the effects of use of medication between study days. However, it is still possible that the effect of medication may be influenced by administration procedures, the length of washout and medication dosage. Additionally, it is unknown how the diverse neurodevelopmental stages across participants impact the findings . FurtherOur study sheds new light on the mechanisms by which methylphenidate improves attention and cognitive control in children with ADHD. Using a Bayesian dynamic systems model, we investigated how methylphenidate modulates the dynamics of latent brain states and how this modulation affects behavioral variability. We found that children with ADHD exhibited aberrant latent brain state dynamics compared to typically developing children, with a single latent state showing particularly abnormal dynamics. Notably, methylphenidate was found to normalize the abnormal dynamics of this latent brain state, which was characterized by hyper-connectivity in the default mode network. Furthermore, our analysis revealed that methylphenidate-induced changes in state-specific functional interactions between the salience and default mode networks were associated with improved sustained attention. These findings provide new insights into the impact of methylphenidate on dynamic brain states and its ability to mitigate behavioral instability and inattention in children with ADHD. By revealing the circuit-level and state-specific mechanisms underlying the therapeutic effects of methylphenidate, our findings have the potential to inform the development of more effective treatments for ADHD. More generally, our Bayesian dynamical systems models may be of value to clinicians and researchers investigating the neural mechanisms underlying pharmacological treatments for a wide range of psychiatric disorders.Supplementary Material


Low birth weight (LBW) is a leading cause of neonatal morbidity and mortality, and increases various disease risks across life stages. Prediction models of LBW have been developed before, but have limitations including small sample sizes, absence of genetic factors and no stratification of neonate into preterm and term birth groups. In this study, we challenged the development of early prediction models of LBW based on environmental and genetic factors in preterm and term birth groups, and clarified influential variables for LBW prediction.We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birth groups, we trained AI-based models using genetic and environmental factors of lifestyles. We then clarified influential environmental and genetic factors for predicting LBW in the term and preterm groups.We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively. We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related to toll-like receptor regulations and infection reactions are influential genetic factors for prediction in the preterm LBW model.We developed precise early prediction models of LBW based on lifestyle factors in the term birth group and genetic factors in the preterm birth group. Because of its accuracy and generalisability, our prediction model could contribute to risk assessment of LBW in the early stage of pregnancy and control LBW risk in the term birth group. Our prediction model could also contribute to precise prediction of LBW based on genetic factors in the preterm birth group. We then identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which are major targets for understanding the LBW to address serious burdens on newborns' health throughout life.The online version contains supplementary material available at 10.1186/s12884-023-05919-5. Low birth weight (LBW) is defined as a birth weight of less than 2500 g  which afThe development of early onset and treatment prediction of diseases are the most important targets of precision medicine for the early intervention and prevention of diseases , 10. Of The Birth and Three-Generation (BirThree) Cohort Study  of the TIn this study, we challenged the development of early prediction models of LBW using large-scale environmental and genetic data of 20,000 subjects of the BirThree Cohort study. We stratified research subjects into preterm and term birth groups, and established the preterm LBW model and the term LBW model, respectively. We used health assessment data including self-report questionnaires and laboratory tests collected in the early stage of pregnancy as environmental data, and maternal and paternal SNP array data as genetic data. The developed artificial intelligence (AI) models were interpreted to identify influential features for prediction of LBW and differences between preterm and term birth groups.We developed early prediction models of LBW using both environmental and genetic factors from the BirThree Cohort study. Because of the differences of the developing mechanism, we stratified subjects into preterm and term birth groups and developed early prediction models for each group. From the developed models, we identified influential features for prediction of LBW and differences between preterm and term birth groups.The study population was selected from pregnancies, their partner, and neonates of the BirThree Cohort Study. BirThree cohort study recruited the regional population of the Miyagi Prefecture from 2013 to 2016 through 48 hospitals, including both advanced hospitals and urban/rural clinics. We used the following data collected in the BirThree cohort study to train early prediction models: 1) health assessment data, including maternal laboratory test data and questionnaire data in the early stages of pregnancy, 2) maternal and paternal SNP array data, and 3) fetal ultrasonography data. Among the data, we collected health assessment data and SNP array data based on uniform protocol in Tohoku University. As for fetal ultrasonography data, we collected data from daily medical practice as linkage data from 48 hospitals. The list of variables for health assessment data are provided in Supplemental table n = 263), who did not have neonatal medical records (n = 158), and who had missing birth weight data (n = 11). Using these criteria, we included 22,711 neonates and their 21,581 mothers and 8,593 fathers in the dataset  building datasets of health assessment datasets, 2) imputation of missing values using multiple imputation by chained equations (MICE) , and 3) As for fetal ultrasonography data, we performed preprocessing of 13 fetal ultrasonography items, including estimated fetal body weight (EFBW), as follows: 1) quality control (QC) of fetal ultrasonography data, 2) spline interpolation, 3) imputation of missing values by MICE, and 4) prediction of fetal ultrasonography data at the late stage of pregnancy  using data in the early stage of pregnancy  see .p-values of genome-wide association analysis  sample-based and probe-based QC, 2) dimension reduction of the SNP array data by pruning based on linkage disequilibrium and filtering of SNPs using ysis see .We developed early prediction models for all health assessment data, fetal ultrasonography data and SNP array data as follows: 1) feature selection using the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC-LASSO)  or recurTo avoid overfitting, we performed tenfold internal–external cross validation by 10 times learning using 9/10 of the dataset as training/test data and remaining 1/10 of the dataset as validation data. We evaluate the performance of a developed model using the mean of the F1-score in 10 time learning and area under the curve (AUC). We then performed gene enrichment analysis on SNP array data to obtain summary weights and importance score of gene functions calculated from feature importance of interpretable models among developed models see .We built 7 datasets consisting of health assessment data, SNP array and fetal ultrasonography data. The mean gestational ages at data collection of health assessment data were 20.64 (± 6.54) and 19.79 (± 7.93) for laboratory test and questionnaire data, respectively  LBW neonates and their 2,140 mothers and 827 fathers. The number of preterm births was 1,397, including 1,077 (77.09%) LBW neonates. The number of term births was 21,042, including 1,248 (5.93%) LBW neonates. We identified 927 mothers and 339 fathers as parents of preterm LBW neonates and 1,213 mothers and 488 fathers as parents of term LBW neonates.We developed generalized early prediction models of term and preterm LBW using the BirThree cohort data consisting of an unbiased population. To develop prediction models, we conducted two feature selection methods, HSIC LASSO and RFE, for 7 datasets to build 14 variable lists for term and preterm birth groups, respectively . As a result, we identified the importance for the prediction for each variable . Our prediction model could inform risk of LBW in the early stage of pregnancy and is expected to control LBW risk through early intervention and/or improvements of lifestyles of pregnancies. Second crucial result is that the interpretation of models yielded influential features for both term and preterm birth groups. The influential features were radically different between the two groups: variables related to eating habits and variants related to fetal growth in the term birth group and variants related to inflammatory response and antimicrobial humoral response in the preterm birth group. Our findings contribute to a deeper understanding of the mechanism of LBW and are expected to be candidate biomarkers and new therapeutic targets of LBW.In this study, we developed early prediction models of LBW in the term and preterm birth groups, named the term LBW model and preterm LBW model, respectively. The performance of the term LBW model reached AUC of 0.96 based on questionnaire data completed in the early stage of pregnancy. Because the mean gestational age at completion was 20.64 gestational weeks (6.54 SD), this result showed the possibility of early information of LBW risk to risk control through early intervention and improvements of lifestyles of pregnancies in the term birth group Table . The terThe preterm LBW model showed high performance with only the SNP array data, which reached AUC of 0.95. This result showed the possibility of the early estimation of the risk of LBW before pregnancy in the preterm birth group. While assessment of the LBW risk using genetic factors based on our established model is still not practical in clinical settings because our study is not clinical level but research level. To realize the early prediction of LBW in clinical settings, more research including clinical trials are required. Furthermore, a new era in which all patients have their own individual genomic information is forecasted to arrive in the near future . In thisIn the term LBW models using questionnaire data, we identified eating habits as the most influential health assessment category because of the dominant high importance (29.17–84.16%). This result is reasonable because poor nutritional status and specific dietary patterns such as preconceptional high fat and sugar diet are known risk factors for LBW , 27. AmoIn the preterm LBW models, as for genetic factors, the gene functions in the regulation of Toll-like receptor signaling pathway, showed high importance in the preterm LBW model. The Toll-like receptor signaling pathway is a gatekeeper of inflammatory activity, and relations for preterm birth have been reported . Gene fuWe accomplished to establish the prediction models for single modal data for both preterm and term birth groups. For the preterm birth group, the models based on SNP array data were well-performed. The interpretation of models reveals the important gene functions for LBW including the toll-like receptor signaling pathway and the antimicrobial response. As for the term birth group, we observed high-performance in the models based on health assessment data. The interpretation reveals that eating habits showed dominant high importance for LBW.Based on our crucial findings, we are planning to focus on establishing the multimodal prediction models based on genetic and environmental factors to handle the gene-environmental interactions.This study has several limitations. Firstly, we were not able to evaluate the performance of models based on paternal SNP array data in the preterm birth group because of the overfitting. This overfitting is caused by paternal small sample size in the preterm birth group. Secondly, we did not consider genetic and environmental interactions because of extreme complexity and difficulty to develop adequate AI models. Thirdly, this study cannot perform external validation using other cohorts. This limitation is because of a lack of comparable large-scale cohorts that recruit both pregnancies and their partner and collect both comprehensive environmental and genetic factors.To our best knowledge, this is the first challenge to develop precise and generalized early prediction models of LBW based on both comprehensive environmental and genetic factors collected from over 23,000 pregnancies. Because of the differences in the mechanism of LBW in preterm and term birth groups, we wish to develop both preterm LBW and term LBW models based on the stratification of subjects by gestational age. The performance of the early prediction models reached AUC 0.95 and 0.96 for preterm LBW and term LBW models, respectively, and F1-scores of 0.90 for the preterm LBW and 0.95 for term LBW models based on genetic and environmental factors, respectively. Because of the accuracy and generalisability, our prediction model is expected to assess risk of LBW in the early stage of pregnancy and control LBW risk through improvements of lifestyles of pregnancies in the term birth group. Our prediction model is also expected to contribute precise prediction of LBW based on genetic factors in the preterm birth group if pregnancies and their partners know their own personal genetic data in the upcoming future.Interpretation of the preterm LBW models identifies the influential variants for prediction. The gene functions of these variants are regulations of inflammation and antimicrobial humoral response, which may be involved in LBW through shortening gestational age due to fetal inflammation triggered by bacterial invasion. These variants are expected to be a new research target to clarify the detailed mechanisms of LBW in the preterm birth group. While in the term LBW model, variants about fetal growth among genetic factors and eating habits that can be controlled by patients were influential for prediction among comprehensive genetic and environmental factors. These influential features are expected to be major targets for understanding genetic and environmental contribution for LBW to address serious burdens on newborn’s health throughout life triggered by LBW.Additional file 1. Supplemental Document. Supplemental Figure 1. Scheme of the all analyses performed in our study. Supplemental Figure 2. The proportion of the importance score of the term LBW models. Supplemental Figure 3. The proportion of the importance score of the preterm LBW models. Supplemental Figure 4. The proportion of the importance score of the preterm LBW models. Supplemental Figure 5. F1-scores with randomly selected variables. Supplemental Table 1. The list of variables of the health assesment data. Supplemental Table 2. The datasets for both early and full-term prediction models. Supplemental Table 3. List of health assessment variables included in the models. Supplemental Table 4. The F1-scores for both early- and full-term prediction models. Supplemental Table 5. Details of the performance of early prediction models based on SNP array data. Supplemental Table 6. The feature importances of the early prediction models based on health assesment data. Supplemental Table 7. The feature importances of the early prediction models based on SNP array data. Supplemental Table 8. The gene enrichment analysis for the term LBW model. Supplemental Table 9. The gene enrichment analysis for the preterm LBW model. Supplemental Table 10. The proportion of the datasets. Supplemental Table 11. Number of selected features by feature selection. Supplemental Table 12. The feature importances of the early prediction models based on fetal ultrasonography data. Supplemental Table 13. The performance of the bagging models. Supplemental Table 14. The performance of the positive/negative controls. Supplemental Table 15. The previously reported locus among variants in the early prediction models. Supplemental Table 16. Summary of the population distribution of datasets.


This paper reflects on the use of neurotherapeutics for attention‐deficit/hyperactivity disorder (ADHD). ADHD is the most imaged child psychiatric disorder, with over 3 decades of magnetic resonance imaging (MRI) research. Findings are relatively homogeneous compared to other psychiatric conditions with consistent evidence for differences, albeit small, relative to healthy controls in the structure and function of several frontal, parietotemporal, and striatal brain regions as well as their inter‐regional structural and functional connections. The functional deficits have been targeted with modern neurotherapeutics, including neurofeedback  and non‐invasive brain stimulation . Except for electroencephalography‐neurofeedback, the majority of neurotherapeutic studies have been relatively small, with very heterogenous research protocols and outcome measures and—likely as a consequence—inconsistent findings. Furthermore, most brain stimulation studies have tested effects on cognitive functions rather than clinical symptoms. So far, findings have not been very promising. Future studies require systematic testing of optimal protocols in large samples or homogenous subgroups to understand response prediction that could lead to individualized treatment. Diagnostic and Statistical Manual of Mental Disorders  is defined in the People with ADHD commonly have problems with so‐called “executive functions,” which are higher‐level cognitive functions necessary for mature adult goal‐directed behaviors, such as motor response inhibition, working memory, switching, sustained attention, and intraindividual response variability ‐neurofeedback has been tested for over 46 years, with the latest meta‐analyses showing small and some non‐significant findings  and without (n = 6) fMRI‐neurofeedback of the dorsal anterior cingulate in four weekly scans of 60 min  and electromyography‐neurofeedback (n = 9). Only NIRS‐neurofeedback showed significant improvements in clinical ADHD symptoms and in performance in inhibition and attention functions, which was, however, not superior to EEG‐ or electromyography‐neurofeedback ‐neurofeedback trained upregulation of the left dorsolateral prefrontal cortex (DLPFC) in 11 hour‐long sessions over 4 weeks in nine ADHD children and compared it with EEG‐neurofeedback , transcranial direct current stimulation (tDCS), and trigeminal nerve stimulation (TNS), have been applied to ADHD only very recently, over the past decade. These stimulation techniques are thought to influence cellular and molecular mechanisms involved in use‐dependent local and distant synaptic plasticity, that is, GABA and glutamate‐mediated long‐term potentiation, which may lead to longer‐term brain plasticity  compared to sham  of five daily sessions of 1 Hz‐rTMS over the left DLPFC showed fewer teacher‐rated inattention and parent‐rated hyperactivity/impulsivity symptoms 1 week after treatment compared to baseline  that stimulated both DLPFC and IFC combined with cognitive training, but which needs replication, there is relatively little evidence that several sessions of rTMS over a frontal site improve ADHD symptoms or cognition. However, studies were relatively underpowered and conducted relatively few session numbers of rTMS with only two studies in children without a placebo condition.In tDCS, scalp electrodes apply a weak, relatively painless and persistent direct electric current to underlying brain regions with the current passing between a positively charged anode and a negatively charged cathode. Anodal stimulation leads typically  to an increase, and cathodal stimulation to a decrease  of the excitability of underlying neurons via the generation of subthreshold alterations of neuron membrane potentials that modify spontaneous discharge rates; this can increase or decrease cortical function and synaptic strength. Compared to TMS, tDCS is much easier to apply, cheaper, and less painful and hence more suitable for children. Side‐effects are minimal and typically transient, such as itching and reddening of the scalp site of stimulation in some people  and in n‐back reaction times  and of processing speed (g = 0.14), but not of attention , unlike the rTMS studies, were conducted in children rather than adults with ADHD, presumably due to the high tolerability and low side‐effect profile. Most studies applied one to five sessions of about 20 min of tDCS in children or adults with ADHD, with the exception of our study, which applied 15 sessions. Only four studies tested for clinical symptoms, three studies after five sessions of tDCS of DLPFC and one study after 15 sessions of tDCS of right IFC; two studies in nine and 15 ADHD patients, respectively, found an improvement with real compared to sham tDCS on clinical inattention symptoms, which persisted 1 or 2 weeks later  is another non‐invasive intervention with minimal side‐effects. TNS transmits small electrical currents transcutaneously via a self‐adhesive, supraorbital electrode to excite  the supratrochlear and supraorbital branches of the ophthalmic nerve (V1) located under the skin of the forehead. The supraorbital nerve has widespread connections to the brain, in particular the reticular activation system, locus coeruleus, brain stem, thalamic, frontal and other cortical areas  or sham NF may be most suitable. Yoked NF has the advantage that it matches the experimental condition on all aspects, except gaining control over the experimental ROI signal, which perfectly controls for motivation and visual stimulation , and placebo effects, and excludes global effects . Regional stimulation/NF could also negatively affect homologue regions in the opposite hemisphere, which may be indirectly downregulated via interhemispheric inhibition. There is in fact some evidence that stimulating right prefrontal regions could potentially be related to changes in mood in ADHD , potential costs that may accompany the benefits, and their potential for individualized treatment depending on clinical or cognitive ADHD subtypes. Personalizing treatment based on baseline neurocognitive or brain‐imaging patterns in ADHD, or using machine learning or Bayesian optimization methods (Lipka et al., K.R. has received a grant from Takeda Pharmaceuticals for another project and consultancy fees from Lundbeck and Supernus Pharmaceuticals.


The original article included incorrect author affiliations for some of the authors. Below is the correct list of affiliations for each.Alys Granados, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada.Catherine Sun, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada.Jason T. Fisher, School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada.Andrew Ladle, School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada.Kimberly Dawe, Quest University Canada, Squamish, British Columbia, Canada.Christopher Beirne, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada.Mark S. Boyce, Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.Emily Chow, British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Cranbrook, British Columbia, Canada.Mitchell Fennell, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada.Nicole Heim, Ktunaxa Nation Government, Cranbrook, British Columbia, Canada.Joanna Klees van Bommel, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada.Robin Naidoo, World Wildlife Fund‐US, Washington DC, USA; Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, British Columbia, Canada.Michael Procko, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada.Frances E.C. Stewart, Department of Biology, Wilfrid Laurier University, Waterloo, Ontario, Canada.A. Cole Burton, Department of Forest Resources Management and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada.We apologize for this error.


Rates of diagnosed attention-deficit hyperactivity disorder (ADHD) may be increasing in the UK.Estimate incidence and prevalence of ADHD diagnoses and ADHD prescriptions in UK adults and children in primary care.We conducted a cohort study using IQVIA Medical Research Data, a UK primary care database. Rates of ADHD diagnoses and ADHD prescriptions were calculated between 2000 and 2018 for individuals aged 3–99 years, analysed by age, gender, social deprivation status and calendar year.Of 7 655 931 individuals, 35 877 (0.5%) had ADHD diagnoses; 18 518 (0.2%) received ADHD medication prescriptions. Diagnoses and prescription rates were greater in men versus women, children versus adults, and deprivation status (nearly double in most deprived versus least deprived quintile). By 2018, the proportion of ADHD diagnoses was 255 per 10 000 (95% CI 247–263) in boys and 67.7 per 10 000 (95% CI 63.5–71.9) in girls; for adults, it was 74.3 per 10 000 (95% CI 72.3–76.2) in men and 20 per 10 000 (95%CI 19.0–21.0) in women. Corresponding figures for prescriptions were 156 per 10 000 (95% CI 150–163) in boys, 36.8 per 10 000 (95% CI 33.8–40.0) in girls, 13.3 per 10 000 (95% CI 12.5–14.1) in men and 4.5 per 10 000 (95% CI 4.1–5.0) in women. Except among 3- to 5-year-olds, the incidence and prevalence of ADHD diagnoses and prescriptions have increased from 2000 to 2018 in all age groups. The absolute increase was highest in children, but the relative increase was largest among adults .The incidence and prevalence of both ADHD diagnoses and medication are highest among children. Proportionally, rates increased most among adults during 2000–2018. ADHD diagnoses and prescriptions are associated with socioeconomic deprivation. Measuring ADHD diagnoses, as well as ADHD prescriptions, is important because ADHD is sometimes managed non-pharmacologically. The aim of this study was to provide estimates of rates of ADHD diagnoses and prescriptions of ADHD medication in UK primary care, both new and ongoing, between 2000 and 2018. We also aimed to describe how these rates differ by age, gender and socioeconomic deprivation status, in children, adolescents and adults.This was a population-based cohort study, using data from IQVIA Medical Research Data (IMRD). IMRD incorporates data from The Health Improvement Network (THIN), a Cegedim database. Reference made to THIN is intended to be descriptive of the data asset licensed by IQVIA.Our study examines incidence and prevalence based on diagnoses and prescriptions coded in primary care records, which is likely to be lower than the prevalence of people with ADHD symptoms in the community. We therefore favour the terms ‘new ADHD diagnoses/prescriptions’ in place of incidence, and ‘proportion of people with ADHD diagnoses/prescribed ADHD medication’ rather than prevalence in this paper, although the operational definition is the same. ‘New ADHD diagnoses’ refer to the time the diagnosis is made and not the onset of symptoms, which cannot be estimated from primary care records.IMRD holds ethical approval to collect and supply data for research purposes from the NHS London – South East Research Ethics Committee (reference number 18/LO/0441). This study was granted approval by IMRD's Scientific Review Committee in January 2022 (reference number 21SRC062).10 Almost all of the UK population are registered with a primary care practice.11 The database includes diagnoses coded with the Read Code system,12 and all prescriptions issued by primary care clinicians. IMRD also contains information on year of birth, gender and social deprivation status. Based on individuals’ residential postcode linked to population census data from 2001, deprivation is estimated by using the Townsend score, a combined measure of unemployment, car ownership, home ownership and household overcrowding.13 The scores are defined for areas of around 150 households, and grouped into quintiles.IMRD contains de-identified data drawn from routinely collected primary healthcare records. Data from approximately 12 million people are included in the database. Information collection begins from when each person registers with an IMRD-affiliated general practice. The IMRD sample is generally representative of the whole UK primary care population.14 However, once this is completed, the responsibility for ongoing prescription and physical health monitoring is usually transferred to primary care practices, under a ‘shared-care’ agreement.14 This system, however, is not seamlessly implemented across the UK. There are service gaps in ADHD care, particularly for adults, and shared-care agreements can be precarious.15 Diagnosis and treatment may also be made in the private sector. There is an incentive for patients to seek shared care for ADHD medications between their private psychiatrist and National Health Service (NHS) primary care provider , but many primary care clinicians are reluctant to engage into shared-care agreements with a private provider.15In the UK, ADHD is diagnosed by psychiatrists or paediatricians in secondary care. Initiation and stabilisation of medication for ADHD should additionally be performed by secondary care.16 and acceptable computer usage.17 Both are measures of quality assurance. General practices and individuals missing data on Townsend deprivation quintile were excluded, removing 138 practices. A further seven practices supplied less than a full calendar year of eligible data and were removed, leaving 649 practices from which the study population was drawn.This study used anonymised data from electronic health records; individual consent was therefore not applicable. All data used in this study was taken from primary care practices that fulfilled both criteria of acceptable mortality recording18 The primary outcome of interest was a ‘new’ diagnosis of ADHD, or a first prescription of ADHD medication in primary care. When calculating rates of new diagnoses/prescriptions, we excluded individuals with an ADHD diagnosis or an ADHD prescription before start of follow-up , or within 6 months of registering at a general practice, as these were more likely to reflect coding of pre-existing diagnoses or the continuation of prescriptions initially prescribed at another practice.19For calculations of new diagnoses/prescriptions, we included all individuals aged 3–99 years who were permanently registered at a participating general practice between 1 January 2000 and 31 December 2018. ADHD diagnoses are rarely made in children under 3 years of age.To calculate the proportion of people with ADHD diagnoses or with prescriptions for ADHD medication, all individuals with at least one full calendar year of data in the study period were included.20 the full list is given in Supplementary Table 1, available at https://doi.org/10.1192/bjo.2023.512. Any individual with their first record of an ADHD diagnosis occurring during the follow-up period was defined as a new case; the time of first diagnosis was taken as the earliest recorded date of a qualifying code.A list of Read Codes indicating an ADHD diagnosis was constructed with established methods;A new prescription was defined as the first recorded prescription during the follow-up period of any of the following: methylphenidate, dexamfetamine, dexamfetamine with amfetamine, lisdexamfetamine, atomoxetine and guanfacine. Methylphenidate and dexamfetamine also have a UK product licence for use in narcolepsy. Individuals with a prescription for ADHD medication, a recorded diagnosis of narcolepsy at any time (Read Code F271.00) and no recorded ADHD diagnosis at any time, were treated as medication non-users when reporting prescription rates/proportions.For proportion calculations, individuals’ date of diagnosis was taken as the earliest recorded date of a qualifying code; if this occurred before cohort entry, they were treated as having ADHD diagnoses from the entry date onward. Ongoing prescription of ADHD medication was defined as at least two prescriptions within a year of one another, for any of the medicines listed above.Rates of ADHD diagnoses and medication were analysed separately for children (aged 3–17 years) and adults (aged 18–99 years).Rates of new ADHD diagnoses and first-time ADHD prescriptions were estimated per 100 000 person-years at risk (PYAR) as the total number of new ADHD diagnoses or first-time ADHD medication prescriptions recorded between 2000 and 2018, divided by the total number of person-years of follow-up. Rates of new ADHD diagnosis and ADHD prescriptions were additionally estimated by age, gender, Townsend score and calendar year.The proportion of ADHD diagnoses and prescriptions was determined by dividing the number of individuals with an existing record of a diagnosis or a prescription by the total number of individuals in the eligible cohort.Exploratory analyses demonstrated a significant interaction between age and gender; therefore, all analyses were stratified by gender.Poisson regression was performed to obtain confidence intervals for crude incidence and prevalence rates. Multivariable Poisson regression models, with (log)person-time as an offset, were used to determine incidence rate ratios, adjusting for age group and Townsend deprivation score. Likewise, prevalence risk ratios were estimated by adjusting for age and Townsend deprivation scores. Multilevel random intercept models were used to adjust for the effect of clustering by general practice.Statistical analysis was performed with Stata version 17 for Windows .21 We analysed the subgroup of those who received ADHD medication without an ADHD diagnosis in our data-set, by examining all clinical codes recorded within 2 years of their first ADHD medication prescription, to evaluate other possible indications for the prescriptions.Prior work has reported that ADHD medication is frequently prescribed to people without a recorded ADHD diagnosis.In total, 7 655 931 individuals contributed at least one full calendar year of data. Of these, 35 877 individuals (0.5%) had a diagnosis of ADHD, and 18 518 (0.2%) individuals received at least two prescriptions within a year for ADHD medications from primary care. A total of 3088 people were prescribed ADHD medications, but did not have a recorded diagnosis of ADHD; of these, 251 (8%) had a diagnosis of narcolepsy . Those wIn children, new diagnosis rates were highest between 6 and 9 years of age, and in adults, new diagnosis rates were highest between 18 and 29 years of age . New diaThe overall rate of new ADHD diagnoses increased from 2000 to 2018 in both males (the rate doubled in those aged under 18 years and increased nearly 20-fold in those aged over 18 years) and females (the rate quadrupled in those aged under 18 years and increased 15-fold in those aged over 18 years)   to 36.7 per 100 000 PYAR (95% CI 18.9–64.1); girls: from 17.6 per 100 000 PYAR (95% CI 6.5–38.3) to 6.5 per 100 000 PYAR (95% CI 0.8–23.3)).The overall proportion of ADHD diagnoses in male children aged 3–17 years was 175 per 10 000 (95% CI 174–177), or 1.8%; in female children aged 3–17 years, it was 37.7 per 10 000 (95% CI 37.1–38.3), or 0.4%. For male adults aged 18–99 years, the overall proportion was 28.8 per 10 000 (95% CI 28.6–29.0), or 0.3%; for female adults aged 18–99 years, it was 7.2 per 10 000 (95% CI 7.1–7.3), or 0.07%.The proportion of ADHD medication use for male children was 90.3 per 10 000 (95% CI 89.5–91.2), or 0.9%; and for female children it was 17.1 per 10 000 (95% CI 16.8–17.5), or 0.2%. In male adults, the proportion of ADHD medication use was 4.1 per 10 000 (95% CI 4.0–4.2), or 0.04%; and in female adults, it was 1.4 per 10 000 (95% CI 1.4–1.5), or 0.01%.The proportions of both diagnoses and medication use were highest among young people aged 10–15 years and 16–17 years, and higher in areas of greater deprivation (approximately two-fold higher in the most deprived quintile than in the least deprived quintile)  and 4. TOverall, the proportion of ADHD diagnoses during 2000–2018 increased in children (it approximately doubled for boys and trebled for girls) and, proportionally, more so in adults (it increased over 18-fold in men and over ten-fold in women)  and adults (it increased 30-fold in men and 15-fold in women)  has been estimated as approximately 5% in children.26 If this figure applies to the UK, then even our highest prevalence estimate  suggests that only a subset of people with ADHD symptoms are identified as having ADHD in clinical practice, and that rates of clinical diagnosis of ADHD in the UK may still increase in years to come. ADHD is therefore likely underdiagnosed rather than overdiagnosed in the UK.22Increases in the proportion of people diagnosed with ADHD and receiving ADHD medication may have multiple drivers, which include a genuine increase in the underlying frequency of the condition; increasing awareness of the condition among patients, caregivers and medical professionals, thereby ameliorating underdiagnosis;14 One possibility is that clinicians are better able to correctly diagnose other neurodevelopmental disorders that can mimic ADHD in this age group. Changes in social support may have had an effect: the 1998 ‘Sure Start’ initiative increased investment in support for disadvantaged children under 5 years of age,27 and although many centres have been wound down, the initiative transformed early years services in local authorities and its legacy remains, including in the parenting programmes for early intervention that still run in many local authorities.28 In children under 5 years of age, ADHD expression often takes the form of challenging behaviour; a tendency for services to direct parents toward parenting programmes and social prescribing29 may have resulted in fewer children in this age group being referred to psychiatric services. The National Institute for Health and Care Excellence guidelines, which, from 2018, have recommended group-based parent training programmes as a first-line intervention for children under 5 years of age before referring to specialised ADHD services, are consistent with this approach.14Conversely, we found that new ADHD diagnoses and the proportion of ADHD diagnoses dropped substantially in children aged 3–5 years. Medication use remained rare in that age group throughout the study, in keeping with national guidance recommending that ADHD medication is only used for children aged <5 years in exceptional circumstances.Further possible explanations include that general practices may have erroneously coded young children as having ADHD (perhaps intending the meaning as ‘possible ADHD’) in the past and have become more stringent in coding recently, which might reflect greater recognition of the diagnostic criteria for the condition. In addition, waiting times for specialist ADHD clinics may simply be so long that children referred when under 5 years of age are not seen until they are older.30 other previous UK-based prevalence studies reported only medication, rather than diagnostic prevalence,7 or either excluded or did not report diagnostic prevalence changes over time in this age group.4The pattern of reduced diagnostic prevalence in children under 5 years of age was seen in one other UK primary care database study, where the prevalence of ADHD in children aged 0–5 years dropped by approximately 75% between 1998 and 2009;32 This rate of decline exceeds that predicted by age-related symptom decline. Instead, this is likely to reflect significant issues in accessing transition care between child and adult services for adolescents,33 potentially leading to abrupt, unplanned cessation of medication.The prevalence of ADHD medication usage peaked in the 10–15 year age group, and was lower in older age groups. Notably, ADHD medication usage was lower in the 16–17 year age group than the 10–15 year age group, despite a slightly higher prevalence of ADHD diagnoses. This pattern has been noted in other studies.34We observed very large differences in diagnosis and prescription rates in adult age groups, with diagnostic rates in the 18–29 year age group in 2018 that were approximately three times those in the 30–39 year age group, and about twelve times greater than in the 40–49 year age group. Although changes in the community prevalence of ADHD with time could explain this, it seems more likely that there is a substantial proportion of undiagnosed ADHD in older age groups. Awareness of the diagnosis and treatment may be higher in younger adults, who are increasingly coming forward to seek ADHD care. This poses a significant challenge to health services in the UK, given that waiting times for NHS adult ADHD assessments are already long (up to 5 years).35 possibly because of under-recognition of ADHD and under-referral of girls in childhood.We found new ADHD diagnoses and medication usage, and the proportions of both, to be substantially higher in males compared with females in both adulthood and childhood. The male:female ratio for new diagnoses did, however, decrease in adulthood , supporting prior observations that women tend to be diagnosed later in life,9 are retained into adulthood, for both ADHD diagnoses and prescriptions of ADHD medication. The relationship between ADHD and socioeconomic deprivation is complex. Low socioeconomic status may have a causal effect on ADHD, via increased exposure to risk factors during the pre- and perinatal period, and during childhood. There may also be genetic confounding , labelling and reporting bias , and reverse causality .36We also demonstrated that the previously reported associations between ADHD incidence/prevalence and socioeconomic deprivation in UK children30 medication prevalence6 or medication prevalence among people with ADHD diagnoses.7 We found that people prescribed ADHD medication were predominantly a subset of people with ADHD diagnoses, with only 17% of medication users having no record of a diagnosis. This proportion is considerably lower than that reported in the USA.21 A small proportion of medication users without ADHD diagnoses in our study had narcolepsy (8%); the remainder likely represented either uncoded ADHD or off-label use. Conversely, 42% of people with ADHD diagnoses had not received any ADHD medication from primary care. Few of these individuals may have instead received medications from secondary care only; however, a more plausible explanation is that they are not regular users of ADHD medication.Previous UK-based studies have examined diagnostic prevalence,34 This coincides with a surge in demand for mental healthcare during the COVID-19 pandemic, particularly for child and adolescent mental health services.37 It is likely that the prevalence of ADHD and ADHD medication usage has continued to increase since the end of our study period, and that both primary and secondary care have come under unprecedented pressure.Our most recent data are from 2018. Reports suggest referral numbers for ADHD assessments, and their waiting lists, have continued to increase in the UK.34 and our data suggests the need for adult ADHD care will only continue to increase. It is important that primary care clinicians are given appropriate training and support to prescribe and monitor ADHD medication safely, as ADHD medication use in primary care is increasingly common.These results have clear implications for clinicians and policy makers; thus, we should be aware that ADHD may previously have been under-recognised in both children and adults. We may expect an increasing number of people needing ADHD care from both primary and specialist care, which is likely to be particularly large in areas of deprivation. Children aged 6–9 years are still the group in which ADHD is most likely to be first diagnosed, and resources should be allocated accordingly to child and adolescent psychiatry. Adult ADHD services are also in dire need of appropriate resourcing, with very long wait times in the UK,38 or partially39 in primary care. This would be a substantial shift away from the UK's current national guidelines, which state that a diagnosis of ADHD ‘should only be made by a specialist psychiatrist, paediatrician or other appropriately qualified healthcare professional with training and expertise in the diagnosis of ADHD’.14 In addition, these guidelines state that ADHD should only be diagnosed based on a full clinical and psychosocial assessment, full developmental and psychiatric history and observer reports.14 Few primary care clinicians currently have the training to do so, or the time . Novel solutions could involve a hybrid approach, developing the role of primary care practitioners with special expertise in ADHD in management and perhaps even diagnosis.39It has been suggested that some people with ADHD could be diagnosed and managed entirelyPrimary care records contain rich, longitudinal information on participants’ health. Further work could use the cohort of people with ADHD identified here to examine physical health outcomes and comorbidities in people with ADHD, which is particularly relevant given the substantial increase of adults with ADHD.This study presents findings from a very large sample, using real-life primary care data, for general practices that are representative of the UK population. The findings are therefore likely generalisable within the UK.14 Given that ADHD medications are generally initiated in secondary care, and transferred to primary care once stabilised, our measure of medication use lags behind the actual inception of medication, and instead captures the point of transfer from secondary to primary care prescribing. This initiation and stabilisation period will vary, but generally takes around 8–12 weeks. This study also cannot determine the numbers of people referred to secondary care with ADHD symptoms, but rather only individuals with a confirmed diagnosis that has been communicated to primary care.This study only captured ADHD medication prescriptions in NHS primary care and not secondary care, which will underestimate the overall incidence and prevalence of medication usage. However, it is standard practice to transfer ongoing prescribing from both public and private secondary care sectors to NHS primary care in most cases.Our study period finished in 2018; since then, various events, including the COVID-19 pandemic, have had a substantial effect on mental health services. It is likely that the incidence and prevalence of ADHD in the UK has continued to change between the end of our study and the present date. Finally, as discussed above, our study captures administrative prevalence  and not the community prevalence of symptoms meeting the ADHD diagnostic criteria in a population-based sample.In conclusion, the incidence and prevalence of both ADHD diagnoses and medication are highest among children, but rates have proportionally increased the most among adults between 2000 and 2018. Both adult and childhood ADHD are associated with socioeconomic deprivation. Further investment and service planning is required to ensure that the rising numbers of people with ADHD (which is likely to increase further) receive prompt, safe and effective care in both primary and secondary care.


Attention-deficit/hyperactivity disorder (ADHD), a neuropsychiatric condition characterized by inattention, hyperactivity, and impulsivity, afflicts 5% of children worldwide. Each ADHD patient presents with individual cognitive and motivational peculiarities. Furthermore, choice of appropriate therapy is still up to clinicians, who express somewhat qualitative advice on whether a child is being successfully cured or not: it would be more appropriate to use an objective biomarker to indicate whether a treatment led to benefits or not. The aim of our work is to search for such clinical biomarkers. We recruited 60 ADHD kids; psychopathological scales were administered at recruitment and after six weeks of therapy. Out of such a cohort of ADHD children, we rigorously extracted two specific subgroups; regardless of the initial severity of their disease, we compared those who obtained the largest improvement (ΔCGAS > 5) vs. those who were still characterized by a severe condition (CGAS < 40). After such a therapy, methylation levels of DNA extracted from buccal swabs were measured in the 5′-UTR of the DAT1 gene. CpGs 3 and 5 displayed, in relation to the other CpGs, a particular symmetrical pattern; for “improving” ADHD children, they were methylated together with CpG 2 and CpG 6; instead, for “severe” ADHD children, they accompanied a methylated CpG 1. These specific patterns of methylation could be used as objective molecular biomarkers of successful cures, establishing if a certain therapy is akin to a given patient . Present data support the use of post-therapy molecular data obtained with non-invasive techniques. The most recent studies by our group focused on biomarkers useful as predictors of therapy efficacy; this study in particular focused on patients affected by attention-deficit/hyperactivity disorder (ADHD), a neuropsychiatric condition characterized by inattention, hyperactivity, and impulsivity . It is eADHD symptoms are complex and heterogeneous . AffecteDAT1). The DAT1 gene encodes a member of the sodium- and chloride-dependent neurotransmitter transporter family; a dopamine (DA) active transporter which is crucial for DA reuptake. Differential expression of DAT1 gene, with consequent alterations of basal DA, has been proposed to explain ADHD pathogenesis, at least in part [Many studies on ADHD take into consideration a well-known human gene such as SLC6A3 also called Dopamine Transporter  with a diagnosis of ADHD formulated (from April 2010 to March 2012) by the Child Psychiatry Unit of Tor Vergata University according to DSM-IV-TR criteria .This study was approved by the Ethical Committee of ISS, Prot. CE-ISS 09/270 of 15 July 2009 ; the parents signed written informed consent for their child well before participating. The Code of Ethics of the World Medical Association (“Declaration of Helsinki”), as printed in the British Medical Journal (18 July 1964), were fully respected.Once the children were recruited as patients, direct observations and clinical questionnaires were administered. The Children’s Global Assessment Scale (CGAS) valued the severity of psychiatric disorder . The CGAAccording to current guidelines and practice in Italy, the more severe ADHD children were assigned to MPH therapy (CGAS < 40). Less severe ADHD children were assigned to CBT (41 < CGAS < 55); in case of unsuccessful CBT, patients were moved to MPH. Both therapies lasted for six weeks at least after reaching optimal dosage and/or compliance, after which all scales were repeated. The Δscores  were calculated to assess the extent by which children’s symptoms either improved or did not respond to therapy.After these six weeks of therapy, during a follow-up of patients, a sample was collected for each patient using a buccal swab , a non-invasive technique for sampling DNA.n = 9; 8 males and 1 female) was composed of patients who were still “severe” . The second subgroup  consisted of children who, regardless of the initial severity of their disease and regardless of which therapy, managed to obtain the largest improvement after therapy based on the calculation of Δscores (ΔCGAS >> 5). This subgroup of children was termed the “tail” of most “improving” subjects.Starting from the initial sample of sixty ADHD patients, two specific subgroups of children were rigorously selected. The first subgroup  according to the manufacturer’s protocol: 0.5 micrograms of DNA from each subject was treated with bisulfite using a DNA methylation kit (Zymo Research), and the levels of methylation were determined as previously described ,11.DAT1 at +713 from TSS) and are shown in the following sequence: 1CGG 2CGG 3CGG CTT GCC GGA GAC T5CG 6CGA GCT C7CG. The first three CpG loci (1CGG 2CGG 3CGG) represent the first motif; CpG loci 5 and 6 (5CG 6CG) were the second motif we studied . Our interest instead was to investigate whether the pattern of methylation can be precise  at the level of specific CpG motifs present in the DAT1 5′-UTR region [The purpose of this new and pioneering calculation was to ascertain whether the 5′-UTR of the R region ,14.DAT1 5′-UTR. The pair is made up by multiplying raw levels of two CpGs together. In fact, since methylation is presented as a percentage, their product represents the probability that such two events occur simultaneously. Therefore, the pairs denote the probability that two CpGs are simultaneously (de)methylated at a given time. In this way, we aimed at looking to the most probable pattern of “getting” (de)methylated CpGs.The very simple, although sophisticated, approach (that we present here) evaluated linear correlations between singles and pairs of CpGs at the p < 0.05) only for r > ± 0.82. For r < 0.33, the correlation was not significant [In particular, we calculated all these possible correlations using Pearson’s coefficient of correlation. A single CpG can be methylated (M) or demethylated (100 − M = D). The same happens in the pair of multiplied CpGs in which each component of the product can be either methylated (M) or demethylated (D). A positive or negative correlation was significant  between M1 and D2M3; a significant positive correlation (r = 0.92) between M1 and D6M3; a significant positive correlation (r = 0.88) between M3 and D2M1; a significant positive correlation (r = 0.90) between M3 and D6M1; a significant positive correlation (r = 0.86) between M1 and D2M5; a significant positive correlation (r = 0.83) between M5 and D2M1; a significant positive correlation (r = 0.95) between M3 and D2M5; a significant positive correlation (r = 0.97) between M3 and D6M5; a significant positive correlation (r = 0.97) between M5 and D2M3; a significant positive correlation (r = 0.96) between M5 and D6M3; a significant positive correlation (r = 0.97) between M3 and M1M5; and a significant positive correlation (r = 0.91) between M5 and M1M3.A significant positive correlation (All the results can be viewed in The improving subjects were characterized by:r = 0.85) between M3 and M2M5; a significant positive correlation (r = 0.85) between M3 and M2M6; a significant positive correlation (r = 0.82) between M3 and D1M5; and a significant positive correlation (r = 0.87) between M3 and M5M6.A significant positive correlation  within the improving subgroup. To uniquely characterize the two subgroups, it is evident that CpGs 3 and 5 cannot be useful; both present the same methylation “dynamics” (not levels).The present work is based on two selected subgroups composed of quite a limited number of subjects. In creating specific subgroups of patients from a larger initial sample, small samples are often obtained that are still useful for the aims of a study if they are extremely specific. However, a further in-depth study should take an even larger initial sample of ADHD children, in order to obtain larger and even more specific subgroups of greatly ‘improved’ and greatly ‘severe’ ADHD children.It would also be interesting to further separate children who improved into two: (a) patients with mild/moderate ADHD and improved after being treated with CBT vs. (b) those who had a severe condition and improved after treatment with MPH. However, the clinical setting is heterogeneous by definition, in that there were  mild/moderate ADHD patients needing to deal with MPH as well as those who had a severe condition yet improving after a careful CBT treatment.Our sample also lacks any children characterized by mild/moderate ADHD and who did not respond to CBT. In fact, among all the children who were initially given this non-pharmacological therapy, the few who did not get any recovery either had to be moved to MPH prescription or abandoned the therapy and, therefore, the study as well. With a larger sample of ADHD children, it may also become possible to study such a subset. However, further expanding the number of individuals to-be-recruited, solely to analyze the therapies , might not bring any valuable benefit in terms of the exploitation of present applications. The declared goal of our study was to obtain a healing-predictor biomarker regardless of the therapy used. Ultimately, an increased recruitment of ADHD children, whose number should at least double, would entail doubling the required time-window (three years in our hands) or doubling the involved Clinical Centers, which may have an ineligible cost/benefit analysis.In addition, unfortunately, methylation levels were measured only once following six weeks of CBT or MPH therapy. It would be advisable to repeat the approach exploiting multiple measures, namely before, in between, and after therapy, just to confirm the dynamic pattern we found as a reliable biomarker of improvement. The clinical experiment should be also replicated by verifying whether other treatments for ADHD (and their variable rates of therapeutic success) do produce the same changes within methylation patterns of the DAT1 5′-UTR.When searching for a biomarker to recognize the success of a therapy, the sampling of DNA should be non-invasive, even more so when patients are children. In many recent studies, buccal swabs or saliva are used to analyze the methylation of genes involved in psychiatric diseases. Recent studies demonstrate that DNA methylation levels in epithelial cells and in the brain are very similar ,25. ThisIn general, the 5′-UTR of DAT1 gene has the characteristic feature of possessing palindromic sequences, which may turn out to provide even further clinical biomarkers. For instance, within 50 bp downward from the sequences we studied presently, there are two other interesting motifs: +747 CACCCGACG +755 and +799 CGTCGGGTG +807 . It is apparent that the second (CpGs 16–17) motif is exactly the same sequence that is complementary to the first (CpGs 8–9) motif if going backward, and identical to the first (CpG 8-COS/9-COS) motif on the inverted-direction noncoding DNA  . As suchThese traits, with alternative possibility, could very well undergo somewhat coordinated actions; when the double-helix opens, the CpG 16/17 may loop back and, antiparallel, bind to the CpG 8/9 trait; then, such loops may be proposed to serve as a checkpoint to keep the two strands separated  when transcription shall occur.The present methylation patterns are exclusive of specific and well-characterized subgroups. Our punctual methylation patterns were found in those individuals either who have greatly recovered or who have not undergone any improvement after any therapy. In very recent years, the personalized medicine is becoming more and more popular ; indeed,A large number of children and young adults with ADHD are taking a psychostimulant drug for a long time without benefiting from it, and this is clearly harmful ,30. The We hypothesized the following objective biomarkers: (a) the methylation of CpG 2 and CpG 6 could objectively demonstrate the successful caring of the ADHD child; (b) the methylation of CpG 1 could be used as biomarker of a severe ADHD diagnosis who will not improve. As a whole, a biomarker for (un)favorable prognosis may be served by (anti)parallel dynamics between CpGs 3/5 and CpGs 2/6. This kind of evidence could indicate, perhaps objectively, if the chosen therapy was or was not the most suitable for that particular ADHD child.


Formerly Consultant Psychiatrist, Tooting Bec, St George's and St Thomas’ Hospitals, London, UKThe Lancet, drawing attention for the first time to a group of young drug users.Shortly after his appointment to a consultant psychiatrist post at Tooting Bec Hospital in 1961, Thomas Bewley, who died recently at the age of 95, began to accept patients with alcohol problems as well as small numbers of heroin addicts. From this latter experience, in 1964 he published a seminal paper in Alongside his work in addiction, Thomas became involved in the newly established Royal College of Psychiatrists and, first as Sub-Dean and then as Dean, he worked to extend and improve the training of psychiatrists working in services that were not attached to established teaching hospitals. He also furthered the promotion of the addiction specialist group to the status of a College Faculty. In 1984 he was elected President of the Royal College of Psychiatrists, the first President not to be an academic. During his presidency, he established the College Research Unit to further the development of evidence-based treatments. He was also appointed an adviser on drug dependence to the World Health Organization. In 1988 he was awarded the CBE.Thomas Bewley was born on 8 July 1926 into a medical family, the son of Geoffrey Bewley and Victoria Bewley (née Wilson). His grandfather and father were prominent Dublin doctors and sometime superintendents of the Quaker Mental Hospital there. His mother had trained in medicine and his sister followed the family tradition and became a psychiatrist. Thomas trained in medicine at Trinity College, Dublin, and in psychiatry at St Patrick's Hospital. A separate branch of the Bewley family were tea and coffee importers who had been the first to challenge the monopoly of the East India Company, and to this day they claim that their Quaker values determine their business practice.Thomas married Beulah (née Knox), later Dame Beulah, a distinguished epidemiologist, in 1955, having come to work in England in 1954. They had five children. He worked first in Claybury Hospital and then, after three unsuccessful applications, was accepted in 1956 at the Maudsley Hospital, London. Here he learned the need for precise formulation of a patient's management under the exacting supervision of Felix Post and developed an interest in alcohol addiction under D.L. Davis.In 1957 the Bewleys moved to the USA, where Thomas studied problems of alcoholism in different ethnic groups at the University of Cincinnati. On returning to the UK, he undertook locum posts as a senior house medical officer at Tooting Bec Hospital and in 1961 was appointed a consultant psychiatrist there.Thomas Bewley developed an approach to addiction that viewed the addict as a patient in need of all round medical care and treatment. He described himself as a ‘Quaker atheist’ and his approach to clinical practice was based on Quaker values of tolerance and care in the tradition of the Retreat at York. He was both a dedicated clinician and one who worked tirelessly to establish his approach to alcoholism and drug dependency across the whole country and to improve and maintain standards in psychiatry. He will be remembered also as a sympathetic and encouraging mentor of trainee psychiatrists and, perhaps more importantly, by a diminishing number of younger colleagues, for his gentle and tactful advice as they embarked on consultant responsibilities.Bulletin as ‘Ezra the Scribe’ and wrote a history of the College entitled Madness to Mental Illness.2Somehow, alongside this busy and distinguished professional career, Thomas (never ‘Tom’!) maintained a vigorous and eclectic intellectual life. He was an avid reader, a possessor of an extensive library, a lifelong theatre and opera goer, a bridge player and a formidable chess competitor. He was the author of witty contributions to the College Thomas died on 26 June 2022. Beulah died in 2018. Their daughter Sarah died in 2003. He is survived by his other four children, Susan, Louisa, Henry and Emma, and his granddaughter Hannah.


Gut-brain cross-talk may play an important role in modulating neurodevelopment. Few studies have examined the association between antimicrobials that influence infant gut microbiota assemblage and attention deficit hyperactivity disorder (ADHD).To examine the association between maternal prenatal antimicrobial use and ADHD in offspring at 10 years of age.Data are from the Wayne County Health, Environment, Allergy and Asthma Longitudinal Study, a racially and socioeconomically diverse birth cohort in metropolitan Detroit, Michigan. Maternal antimicrobial use was extracted from the medical record. ADHD diagnoses were based on parental report at the 10-year study visit. Poisson regression models with robust error variance were used to calculate risk ratios (RR). Cumulative frequency of exposure to antibiotics, and effect modification were also evaluated.Among the 555 children included in the analysis, 108 were diagnosed with ADHD. During pregnancy, 54.1% of mothers used antibiotics while 18.7% used antifungals. Overall, there was no evidence of an association between prenatal antibiotic exposure and ADHD , but there was an increased risk of ADHD among those with mothers using 3+ courses of antibiotics . Prenatal exposure to antifungals was associated with a 1.6 times higher risk of ADHD . In examining effect modification by child sex for antifungal use, there was no evidence of an association among females , but among males, prenatal antifungal use was associated with 1.82 times higher risk of ADHD .Maternal prenatal antifungal use and frequent prenatal antibiotic use are associated with an increased risk of ADHD in offspring at age 10. These findings highlight the importance of the prenatal environment and the need for careful use of antimicrobials. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder distinguished by difficulties paying attention, impulsivity, and hyperactivity . It has Prescription medication use during pregnancy has increased over time and about 70% of women take at least 1 prescription medication during pregnancy . AntimicDespite the known impact of antimicrobials on the infant gut microbiota, the few studies that have examined prenatal exposure to antimicrobials and ADHD have reported conflicting findings . For exaStudies examining the association between maternal prenatal use of antifungal medications and ADHD are also lacking, although there is some evidence that systemic first trimester exposure to oral imidazole derivatives may increase the risk of neurodevelopmental defects . A betteData for this analysis are from the Wayne County Health, Environment, Allergy and Asthma Longitudinal Study (WHEALS) birth cohort. WHEALS recruited pregnant women who were in their second trimester or later and were seeing a Henry Ford Health System obstetrics practitioner at 1 of 5 clinics . All womAll participants provided written informed consent, and at the age 10 visit, children provided written informed assent. The study was approved by the Institutional Review Board at Henry Ford Health System.As described previously, antimicrobial use was abstracted from maternal prenatal and delivery medical records . AntibioAt the age 10 visit, the caregiver (95% the mother) reported if the child had ever been diagnosed with ADHD as well as other neurodevelopmental outcomes. Caregiver report of a “suspect” diagnosis was also classified as positive for the primary analysis. Additionally, a subset of 325 WHEALS children who had received care from Henry Ford Health System providers had their medical records abstracted for additional health information, including ADHD diagnoses and other neurodevelopmental outcomes. Given that we have previously shown near perfect agreement between self-reported and medical record-based diagnosis for ADHD  in this cohort , the staOf the 1258 maternal-child pairs in the WHEALS cohort, 890 (71%) women had information on prenatal antimicrobial use. Of those, 569 of the children had information on ADHD diagnoses. Children with other neurodevelopmental outcomes , but not ADHD, were removed from the analysis (N = 14), leaving a total of 555 maternal-child pairs in the final analytic sample.During the prenatal interview, mothers self-reported race-ethnicity, insurance coverage, household income, education, marital status, previous pregnancies, smoking during pregnancy, household environmental tobacco smoke (ETS), alcohol use, indoor pets, history of asthma and allergies, and home address, which was used to define urban or suburban residence. Prenatal and delivery records were abstracted to obtain mode of delivery, body mass index (BMI) at the first prenatal visit, gestational age at delivery, and birthweight. Sex- and gestational-age adjusted birthweight z-scores were calculated using the United States population in 1999–2000 as a reference . Breast Frequencies and percentages for categorical covariates as well as means and standard deviations for continuous covariates were used to describe group differences . Additionally, standardized differences (D)—defined as the difference in means or proportions divided by standard error—were used to quantify effect size, with large effect sizes considered absolute value of D > 0.2. Because selection bias due to loss to follow-up or non-response can affect the internal validity of estimates, inverse probability weighting (IPW) was used to correct for potential bias. Analytic sample inclusion was used as the outcome in a logistic regression model with the following covariates (baseline covariates hypothesized as affecting loss to follow-up): maternal age at birth, maternal race, insurance coverage, household income, marital status, maternal education, location of residence (urban vs. suburban), maternal prenatal smoking, prenatal ETS, prenatal alcohol use, prenatal indoor pets, maternal allergies and asthma, mode of delivery, parity, child sex, gestational age at birth, and birthweight. The predicted probability of inclusion for each subject was extracted from this model; weights were calculated as the inverse of the “treatment” received. In other words, if p = probability of inclusion, then IPW = 1/p for included children, and IPW = 1/(1-p) for excluded children. Covariate balance was assessed using D, with imbalance defined as absolute value > 0.20.mianalyze procedure was used to pool estimates.To evaluate the association between prenatal antimicrobial use and ADHD, risk ratios (RR) and corresponding 95% confidence intervals (CI) were obtained from Poisson regression models using a robust error variance . In all In addition to examining any antimicrobial exposure, risk of ADHD by number of exposures  were also calculated, but this could only be examined for antibiotic use due to small sample sizes for antifungal use . Systemic versus vaginal route of exposure was also examined for antifungal use ; route of administration could not be examined for antibiotic use, as most exposures were systemic. Further, tests for differences in timing of antimicrobial exposure throughout pregnancy were calculated using multiple informant models, where a score test of the equality of all the trimester-specific parameter estimates was used to evaluate differences across trimesters . Effect Among the 555 children included in the analysis, 108 (19.5%) were diagnosed with ADHD and 447 were considered neurotypical (NT). A total of 300 (54.1%) mothers used antibiotics during pregnancy while 104 (18.7%) mothers used antifungals during pregnancy; 71 women used both antibiotics and antifungals during pregnancy. A breakdown of trimester-specific use, number of exposures, and route of administration is provided in The children who were included in the analysis were different from those members of the cohort study who were excluded due to lack of follow-up or missing data. A comparison of children included and excluded from analyses here are shown in Maternal and child characteristics and their association with ADHD are presented in The association between maternal prenatal antimicrobial use and ADHD is shown in Additionally, we hypothesized that child sex, mode of delivery, and breastfeeding status at 1 month may modify these effects . InteracWhen timing of exposure was examined , no evidWe found that maternal prenatal antifungal use was associated with increased risk of ADHD in offspring. Results also suggested that the effect was restricted to males, where prenatal antifungal use was associated with 1.82 times higher risk of ADHD. Additionally, the effect appeared to be primarily driven by exposure to vaginal antifungals. Though an overall association was not detected for prenatal antibiotic use, an increased risk of offspring ADHD was observed among women who had 3 or more exposures to antibiotic medications during pregnancy. This study presents evidence supporting the importance of the prenatal environment in determining postnatal child health outcomes such as ADHD. Furthermore, the fact that the findings were restricted to males is aligned with observations of a higher prevalence of ADHD among males [Strengths of this study include its prospective design and a diverse unselected study population with detailed prenatal exposures. In addition, data on antimicrobial use and last menstrual period was extracted from the medical record. As such, this data is not subject to recall bias.Our analysis considered several potential confounders and sub-analyses of E-values suggest that our main findings are robust.While we know the antimicrobials were prescribed, it is unknown if the medications were filled or taken as prescribed. In addition, over-the-counter antifungals are not captured, thus exposures may be underestimated. Additionally, inaccuracies in pregnancy dating could have caused inaccuracies in the trimester specific exposure analyses. Finally, as we reported elsewhere , data onCandida overgrowth [Candida was nearly 2 times higher in children with autism spectrum disorder compared to neurotypical children [There is very limited data about prenatal exposure to antifungals and ADHD. Although not focused on the prenatal period, one large Danish study utilizing the Danish National Patient Registry found an increased risk of ADHD in those treated with anti-infectives (including antimycotics)  . We did ergrowth , 30; in children . Prenata, 2.46) .children . Our anaPrevious studies support our results which suggest that infrequent prenatal antibiotic use is not associated with ADHD. Hamad et al. examined the association between antibiotic exposure in the first year of life (defined as 1 or more prescriptions filled) and ADHD using a matched-cohort and sibling cohort design . SimilarCandida overgrowth, worse ADHD symptoms [Candida colonization is associated with offspring colonization, few offspring actually become colonized, so maternal antifungal use is unlikely to be associated with use in neonates [Candida infection in extremely low birthweight neonates is itself associated with neurodevelopment [Candida infection worsened ADHD symptoms [Despite the suggestive findings, it is also possible that the maternal infection that rendered the use of the antimicrobials, and not the effects of the antimicrobial itself, contributed to the increased risk of ADHD. Such alternative hypotheses are feasible as infections can alter the type and distribution of immune and inflammatory cells, which can impact foetal and placental development . For exasymptoms , and othsymptoms . While mneonates . Despiteelopment . The cohsymptoms , which hThis study provides evidence for an association between maternal prenatal antifungal use and ADHD and that this association is restricted to male offspring. In addition, our analyses suggest that there is an association between frequent prenatal antibiotic use and ADHD. Additional studies are needed to confirm the association between prenatal antifungal use and ADHD and to elucidate potential pathways by which prenatal antifungal use might increase risk of ADHD. Future studiesS1 Table(DOCX)Click here for additional data file.