Despite a well-established link between childhood traumatic brain injury (TBI) and elevated secondary attention-deficit/hyperactivity disorder (s-ADHD) symptomology, the neurostructural correlates of these symptoms are largely unknown. Based on the influential ‘triple-network model’ of ADHD, this prospective longitudinal investigation aimed to (i) assess the effect of childhood TBI on brain morphometry of higher-order cognitive networks proposed to play a key role in ADHD pathophysiology, including the default-mode network (DMN), salience network (SN) and central executive network (CEN); and (ii) assess the independent prognostic value of DMN, SN and CEN morphometry in predicting s-ADHD symptom severity after childhood TBI.The study sample comprised 155 participants, including 112 children with medically confirmed mild-severe TBI ascertained from consecutive hospital admissions, and 43 typically developing (TD) children matched for age, sex and socio-economic status. High-resolution structural brain magnetic resonance imaging (MRI) sequences were acquired sub-acutely in a subset of 103 children with TBI and 34 TD children. Parents completed well-validated measures of ADHD symptom severity at 12-months post injury.Relative to TD children and those with milder levels of TBI severity , children with severe TBI showed altered brain morphometry within large-scale, higher-order cognitive networks, including significantly diminished grey matter volumes within the DMN, SN and CEN. When compared with the TD group, the TBI group showed significantly higher ADHD symptomatology and higher rates of clinically elevated symptoms. In multivariable models adjusted for other well-established risk factors, altered DMN morphometry independently predicted higher s-ADHD symptomatology at 12-months post-injury, whilst SN and CEN morphometry were not significant independent predictors.Our prospective study findings suggest that neurostructural alterations within higher-order cognitive circuitry may represent a prospective risk factor for s-ADHD symptomatology at 12-months post-injury in children with TBI. High-resolution structural brain MRI has potential to provide early prognostic biomarkers that may help early identification of high-risk children with TBI who are likely to benefit from early surveillance and preventive measures to optimise long-term neuropsychiatric outcomes. Finally, we expected that, after adjusting for other well-established risk factors, altered morphometry of the DMN, CEN and SN would prospectively predict higher s-ADHD symptomatology at 12-months post-injury.We expected significant group differences in DMN, SN and CEN morphometry, such that the severe TBI group would show diminished DMN, SN and CEN volumes compared to TD controls and children with milder head injuries. Based on prior research : GCS 13–15 on hospital admission, loss of consciousness (LOC) <1 h, negative brain CT/clinical MRI; (ii) complicated-mild TBI (n = 14): GCS 13–15 on admission, LOC <1 h, positive brain CT/clinical MRI finding; (iii) moderate TBI (n = 26): GCS 9–12 on hospital presentation, LOC 1–24 h; (iv) severe TBI (n = 15): GCS 3–8 on admission, LOC >24 h. The TD group (n = 43) were aged 5–16 years at recruitment and were group-matched to the TBI group on sex and SES. SES was assessed using the Australian Socioeconomic Index 2006  p = 0.050), we covaried for SES in all subsequent analyses. With respect to injury-related characteristics  reported in The primary caregiver retrospectively evaluated the child's pre-injury functioning using the Child Behaviour Check List (CBCL-6–18)  were used to assess ADHD symptom severity. The ADHD Problems subscale consists of seven items aligned with DSM criteria for ADHD and shares five items with the Attention Problems scale (10 items), which is designed to more comprehensively assess symptoms of inattention associated with ADHD. T scores of 65 and above were used to denote borderline-clinical range symptomatology.CBCL-6–18  – Global Functioning Scale . As reported previously . Details of the procedures are provided in previous publications  defined in FreeSurfer variables . In these analyses, group membership was entered as the between-subjects factor and network-based morphometry (DMN v. CEN v. SN) was entered as the within-subjects factor.SPSS Version 25.00 (IBM Corporation) was used for screening and analysis of all study data. Statistical assumptions were met for each analysis, unless otherwise stated. The variance inflation factor (VIF) was used to assess for multi-collinearity. One-way analysis of covariance was used to assess for group differences in ADHD symptom severity after adjustment for SES, participant sex and age. Significant omnibus results were followed by Bonferroni-corrected Multivariable regression analyses were used to assess prospective relationships between network-based morphometry  and ADHD symptom severity in children with TBI. In each multivariate model, measures of network-based morphometry were modelled simultaneously with adjustment for other established s-ADHD risk factors, including acute TBI severity indexed by the GCS, SES, post-injury family functioning and retrospective ratings of pre-injury child adaptive skills. Participant age, sex, pre-injury CBCL externalizing symptoms and estimated total intracranial volume (TIV) were also entered as covariates in all multivariate regression models. This approach enabled us to assess if measures of network-based brain morphometry are unique and therefore useful markers of s-ADHD symptom severity in children with TBI.p ⩾ 0.25).In total, 137 participants had useable MRI for FreeSurfer to produce volumetric data. A sensitivity analysis found no significant differences in the pre-injury, demographic or injury-related characteristics of those with and without useable MRI. Groups did not significantly differ on measures of estimated TIV or whole brain volume . The timF = 8.97, p < 0.001, η2 = 0.218]. As shown in Group means and standard deviations for network-based morphometry are presented in p < 0.05), and children with mild, complicated-mild and moderate TBI   see .Table 4p ⩾ 0.17).In total, 129 participants  completed standardised parent-report measures of ADHD symptomatology at 12-months post-injury. A sensitivity analysis found no significant differences in the pre-injury, demographic or injury-related characteristics of those with and without 12-month ADHD symptom rating data  and the CBCL Attention Problems scale . Similarly, we found that compared to the TD group, scores within the borderline-clinical range (T > 65) for the CBCL DSM-oriented ADHD Problems scale were significantly more common in the TBI group . Although the TBI group also displayed higher rates of impairment than TD controls on the CBCL Attention Problems scale, the difference did not reach statistical significance .As expected, the TBI group displayed significantly higher ADHD symptomatology than the TD group on the CBCL DSM-oriented ADHD Problems scale  and the CBCL Attention Problems scale . Post-hoc tests showed that in comparison to TD children, children in the moderate TBI and severe TBI groups had significantly higher levels of inattention and DSM-oriented ADHD symptomology measured by the CBCL Attention Problems and ADHD Problem scales, respectively  = 4.42, p < 0.001]. As shown in p = 0.015), such that diminished DMN volume was independently associated with higher ADHD symptomatology. Greater ADHD symptomatology was also independently associated with poorer family functioning (p = 0.016) and lower pre-injury adaptive functioning (p = 0.005). The remaining covariates were not independently related to outcome .The final adjusted regression model predicting CBCL DSM-oriented ADHD Problems was significant . As shown in p = 0.033), such that diminished DMN volume was associated with higher attention problems. Family functioning was also an independent predictor of outcome (p = 0.025), such that worse family functioning was prospectively associated with more frequent attention problems. The remaining covariates were not independently related to outcome .The final adjusted regression model predicting CBCL Attention Problems was also significant [p = 0.71; CEN: p = 0.47; SN: p = 0.39).Moderation analyses using hierarchical regression models revealed no statistically significant interactions between brain network morphometry and history of surgical intervention on s-ADHD symptom severity in the TBI sample .Despite a well-established association of childhood TBI with elevated s-ADHD symptomatology, the neurostructural brain correlates of these post-injury symptoms remain largely unknown. To address this substantial gap in evidence, we aimed to assess the effect of childhood TBI on DMN, CEN and SN morphometry, and evaluate whether neurostructural alterations within these higher-order cognitive networks have independent prognostic value for predicting s-ADHD symptom severity. As expected, the severe TBI group showed altered network-based brain morphometry, including significantly diminished grey matter volume within the DMN, CEN and SN. In multivariable models adjusted for other well-established s-ADHD risk factors, altered DMN morphometry independently predicted higher s-ADHD symptomatology at 12-months post-injury, whilst SN and CEN morphometry were not significant independent predictors. Findings from our relatively large childhood TBI cohort without preinjury ADHD suggest that neurostructural alterations within higher-order cognitive circuitry may represent a prospective risk factor for elevated s-ADHD symptomatology at 12-months post-injury.Compared to the TD group, rates of clinically elevated ADHD symptomatology were significantly greater in the TBI sample overall. Similarly, when symptoms were evaluated dimensionally, children with TBI had significantly higher ADHD symptomatology than their TD peers 12-months post-injury. Although the association of childhood TBI with elevated ADHD symptomatology is relatively well documented . To address this caveat and strengthen the quality of evidence regarding the prevalence of s-ADHD and its correlates after childhood TBI, future research should incorporate structured clinical interviews and standardised teacher ratings of ADHD symptoms in larger samples of children with more severe TBI. Furthermore, while the current study is strengthened by the inclusion of a well-matched group of TD children, future research should incorporate an additional orthopaedic injury comparison group to establish whether the current pattern of findings is brain injury-specific.Secondly, we cannot entirely rule out the possibility that s-ADHD symptoms in our TBI sample are at least partly explained by pre-injury psychiatric and/or developmental vulnerabilities, including symptoms of ADHD and disruptive behaviour, which may place children at elevated risk for TBI. Nevertheless, there are several factors that do not support this possibility. Firstly, retrospective ratings of pre-injury functioning showed that the TBI groups did not significantly differ from TD controls on measures of pre-injury externalizing symptoms, adaptive behaviour or internalizing symptoms. Viewed collectively with the finding that all results remained statistically significant after adjusting for pre-injury externalizing symptoms, our results suggest that s-ADHD symptoms in our sample are more likely explained by brain injury-related processes than neurodevelopmental vulnerabilities pre-dating the injury.Moreover, because we relied on structural imaging data collected at a single, sub-acute time-point to evaluate prospective associations between network-based brain morphometry and s-ADHD symptom severity, causal relationships between these variables cannot be established, and the stability of the structural brain abnormalities in the long-term post-injury could not be assessed in this study. Additionally, since structural brain MRI was acquired during the acute/sub-acute recovery period when valid and reliable estimates of s-ADHD symptom severity could not be obtained concurrently with the structural MRI, we were unable to examine cross-sectional associations between sub-acute volumetric differences and concurrent s-ADHD symptom severity.Finally, our imaging protocol used only structural imaging techniques and did not include any functional neuroimaging sequences (e.g. resting state connectivity) that may have provided additional information regarding the neural correlates of s-ADHD symptoms in our sample. To address this limitation, future research using functional MRI would help to elucidate whether s-ADHD symptom severity is related to aberrancies in time-varying engagement of the SN with the DMN and CEN in children with TBI.In addressing the paucity of prospective research to identify neurostructural brain correlates of elevated s-ADHD symptomology after childhood TBI, we show that childhood TBI is associated with significantly higher s-ADHD symptomatology and selective neurostructural alterations within higher-order cognitive networks proposed to play a key role in ADHD pathophysiology. After adjusting for other well-established risk factors, we found that altered DMN morphometry was prospectively associated with higher s-ADHD symptomatology at 12-months post-injury. These findings suggest that neurostructural alterations within higher-order cognitive circuitry may represent a potential prognostic marker of s-ADHD symptomatology, which may aid early identification of high-risk children with TBI who could benefit from early surveillance and preventive measures to optimise long-term neuropsychiatric outcomes.


Cognitive control deficits are a hallmark of attention deficit hyperactivity disorder (ADHD) in children. Theoretical models posit that cognitive control involves reactive and proactive control processes but their distinct roles and inter-relations in ADHD are not known, and the contributions of proactive control remain vastly understudied. Here, we investigate the dynamic dual cognitive control mechanisms associated with both proactive and reactive control in 50 children with ADHD (16F/34M) and 30 typically developing (TD) children (14F/16M) aged 9–12 years across two different cognitive controls tasks using a within-subject design. We found that while TD children were capable of proactively adapting their response strategies, children with ADHD demonstrated significant deficits in implementing proactive control strategies associated with error monitoring and trial history. Children with ADHD also showed weaker reactive control than TD children, and this finding was replicated across tasks. Furthermore, while proactive and reactive control functions were correlated in TD children, such coordination between the cognitive control mechanisms was not present in children with ADHD. Finally, both reactive and proactive control functions were associated with behavioral problems in ADHD, and multi-dimensional features derived from the dynamic dual cognitive control framework predicted inattention and hyperactivity/impulsivity clinical symptoms. Our findings demonstrate that ADHD in children is characterized by deficits in both proactive and reactive control, and suggest that multi-componential cognitive control measures can serve as robust predictors of clinical symptoms. Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopment disorder with prevalence rates ranging from 5% to 10% of school-aged children worldwide . StrikinThe DCC model posits that there are two distinct operating modes underlying cognitive control: reactive and proactive control  Fig. 1c1c. ReactProactive control refers to one’s ability to deploy, in advance, strategies to control an automatic, habitual, or prepotent process given the foreknowledge of interference or a countermanding event , 16, 21 n < 20) [Reactive control has been the mainstay of cognitive control studies in ADHD , 28, 29.n < 20) , 32. Becn < 20) , it is iIn contrast to reactive control, proactive control has been largely ignored in studies of ADHD despite its potential to uncover robust components of cognitive control dysfunction associated with the disorder. The limited research on proactive control to date has yielded inconsistent findings. One study reported that incarcerated adolescents have more difficulty using proactive control strategies compared to the control group and this difference was associated with a diagnosis of ADHD . Yet anoTo address these critical gaps in the literature, we used two experiments involving a standard SST and a cue-based stop signal task (CSST) , 37, 38 Proactive control, including context-, performance-, and anticipation-driven proactive control, were examined separately in the CSST and SST using both model-free and model-based approaches Fig. . The CSSNext, performance-driven proactive control, or post-error slowing, was measured by differences in RT between Go trials after unsuccessful stop trials (GoPUS) and Go trials after successful Go trials (GoPSG) in the SST Fig. . To furtstop) and anticipation-driven proactive control capacity was quantified by the correlation between trial-wise Go RT and pstop [Last, anticipation-driven proactive control was determined by the extent to which Go RT was modulated by the trial-wise expectation of stop signals in the SST. The dynamic belief model (DBM)  was usednd pstop , 26, 27 Finally, little is known about the relationship between reactive and proactive control in children and to what extent the dual control mechanisms predict core symptoms of ADHD, such as inattention and impulsivity/hyperactivity. Here we purposely examined whether children who have better reactive control also have greater proactive control function and tested whether component measures of reactive and proactive control can predict core symptoms of ADHD.stop, than TD children. We further predicted that TD children who have better reactive control would also have better proactive control but this relation may be dampened in children with ADHD. We also hypothesized that behavioral measures from the dynamic DCC model would predict ADHD clinical symptoms, such as inattention and hyperactivity/impulsivity.We hypothesized that children with ADHD would have longer SSRT relative to TD children. We also predicted that children with ADHD would have smaller context-driven response slowing and less post-error slowing than TD children. The DDM allowed us to further determine the contribution of latent decision-making components underlying post-error slowing . We hypoOne hundred and seven children (9–12 years old) were recruited from the local community. 50 children with ADHD  and 30 TD children  who completed two runs of SST and two runs of CSST and met task performance criteria (see the “Methods” section for details) were included in the analyses. Table ps > 0.2, two-sample two-tailed t-test). Children with ADHD had severe inattention and hyperactivity and impulsivity symptoms relative to TD children , suggesting no violation of the Race Model [Participants achieved good performance in the SST and with high accuracy and fast RT on Go trials and targeted accuracy (close to 50%) on Stop trials. RT on UnsuccStop trials was significantly shorter than on Go trials . This pattern indicates that performance-based step-wise adjustments of SSD were implemented successfully in both groups. Children with ADHD had significantly longer SSRT than TD children , suggesting no violation of the Race Model [Participants achieved good performance in the CSST with high accuracy and fast RT on Go trials and targeted accuracy (close to 50%) on Stop trials. RT on UnsuccStop trials was significantly shorter than on Uncertain Go trials , suggesting that individuals who made more adjustments on reaction time (slowing more) benefitted in stopping accuracy. We also found that TD children showed a greater post-error effect on stop accuracy than children with ADHD .). The 95% confidence interval of post-error effect on stop accuracy was from 15% to 26% in TD children and from 6% to 16% in children with ADHD.To further examine whether post-error slowing benefits stopping performance, we compared the accuracy of Stop trials after Unsuccessful Stop trials (StopPUS) and Stop trials after Successful Go trials (StopPSG) and found a significant post-error effect on stop accuracy . We then computed the correlation between pstop and RT across Go trials for each child, with higher correlations indicating more response slowing when participants believe that the stop signal is more likely to occur. We found that children with ADHD had significantly lower correlations than TD children . When examining each group separately, however, we found a significant correlation in TD children . A Fisher’s Z test confirmed that the correlation coefficients are significantly different between the two groups .Proactive control induced by task context had no significant correlation with SSRT in the whole cohort . When examining each group separately, however, we found a significant correlation in TD children . A Fisher’s Z test confirmed that the correlation coefficients are significantly different between the two groups .Proactive control induced by performance monitoring had no significant correlation with SSRT in the whole cohort . When examining each group separately, however, we found a significant correlation in TD children . A Fisher’s Z test demonstrated that the correlation coefficients are marginally significantly different between the two groups .Proactive control induced by anticipation of stop signals had a significant correlation with SSRT in the whole cohort (p = 0.02), proactive control induced by performance monitoring is a marginally significant predictor of SSRT (p = 0.05), and anticipation of stop signals is a marginally significant predictor of SSRT (p = 0.06) in TD children . Proactive control induced by performance monitoring was significantly correlated with Hyperactivity/Impulsivity scores  and hyperactivity/scores .We further examined whether dual control mechanisms can predict core symptoms of ADHD. Specifically, we trained multiple linear regression models based on behavioral measures of reactive and proactive control  to predict inattention and hyperactivity, and impulsivity scores. A leave-one-out cross-validation procedure was applied, and model performance was assessed by the correlation between predicted and observed clinical scores. We found that behavioral measures indexing dual control mechanisms can significantly predict inattention  and that the predicted and observed hyperactivity/impulsivity scores were significantly correlated . Furthermore, we determined that prediction models built on both reactive and proactive control measures are marginally more robust than prediction models built on reactive control measures alone in predicting hyperactivity/impulsivity scores .Then, we tested whether the inclusion of proactive control measures is important for clinical symptom prediction. To examine this question, we trained the prediction model using only the reactive control measure, SSRT. We found that the predicted and observed inattention scores were marginally significantly correlated  or did occur (performance monitoring).We conducted a systematic investigation of cognitive control deficits in children with ADHD in a dynamic framework of a dual cognitive control model. Our findings suggest that relative to their TD peers, children with ADHD suffer from weak reactive control functions. They also have a diminished capacity to learn from trial history and performance and adjust their behavioral strategy accordingly, highlighting deficits in reactive control. The dual cognitive control model is a robust cognitive framework for predicting behavioral problems in ADHD. Our findings thus provide novel insights into understanding the dynamic and multi-componential mechanisms underlying cognitive control deficits in children with ADHD.One hundred and seven children (9–12 years old) were recruited from the local community. Informed written consent was obtained from legal guardians of the children and all the study protocols were approved by the Institutional Review Board of Stanford University. Participants who completed two runs of each of SST and CCST and met task performance criteria (Go accuracy is above 50% and Stop accuracy is between 25% and 75%) were included in the final analysis, resulting in 50 children with ADHD  and 30 TD children . See Table Conners 3rd Edition. ADHD with conduct disorder and oppositional defiant disorder were not excluded as they are common comorbidities [Conners 3rd Edition and Strengths and Weaknesses of Attention-Deficit/Hyperactivity-symptoms and Normal-behavior (SWAN) rating scale. We used the SWAN to investigate the relation between cognitive task performance and clinical symptoms because the SWAN can capture variance between average behavior and far above average range, which is well suited for dimensional analyses [Children and their guardians completed a clinical and neuropsychological assessment session. ADHD diagnosis was informed by the children’s guardians and further confirmed using the bidities , 62 , each trial started with a white cross in the center of the screen for 200 ms and was followed by a green arrow. Participants were told to make a left or right button press response if a left- or right-pointing green arrow  was presented, correspondingly. Occasionally (33% chance), the green arrow quickly turned to red  and participants needed to withhold button press responses when the color change was detected. The interval between the onsets of the Go and Stop signals was the stop-signal delay (SSD). The SSD was initiated at 200 ms and its value was adapted based on trial-by-trial performance in a staircase fashion. The SSD increased by 50 ms if a participant successfully withheld a response in the last stop trial; and the SSD decreased by 50 ms if a participant failed withholding a response in the last stop trial. When there was no Stop signal, the Go signal was presented for 500 ms and the response window was 1.5 s. Participants completed two runs of the SST in the scanner and each run included 96 trials  with jittered inter-trial intervals between 1 and 4 s.In the cued stop-signal task (CSST), each trial started with a white or green cross (Cue) in the center of the screen for 200 ms and followed by a green arrow. The white cross represented the Uncertain Go trial, such that the green arrow could change to red (33% chance) and participants would need to withhold their response when the green arrow turned to red. In the trials with the white cross, all the parameters were the same as the SST. The green cross represented the Certain Go trial, wherein the green arrow never changed color so no response withholding was needed. Therefore, the white and green crosses represented two different task rules: one with the possibility to stop and the other with no stopping requirement at all. Participants completed two runs of the CSST in the scanner and each run included 80 trials  with jittered inter-trial intervals between 1 and 4 s.Reactive control is measured by SSRT in the SST and CSST. First, we examined whether the behavioral data violated a main assumption of the Race Model, that the mean RT in UnsuccStop trials should be shorter than the mean RT in Go trials [T−mean SSD, where T is the point when the integral of the observed distribution of Go RT in the SST or Uncertain Go RT in the CSST equals the probability of unsuccessful stopping. For each individual, we computed SSRT in the SST and CSST and evaluated the intra-subject reliability of SSRT estimation using Pearson’s correlation.o trials . Then, So trials : SSRT = Context-driven proactive control is quantified by response slowing modulated by task cues in the CSST, which is Uncertain Go RT relative to Certain Go RT.Feedback-driven proactive control is measured using post-error slowing in the SST, which is the RT difference between GoPUS and GoPSG. Next, DDM was used to further investigate the decision-making processes underlying post-error slow (see below).Anticipation-driven proactive control is measured by the correlation between Go RT and pstop in the SST.ps > 0.05)Shapiro-Wilk test was used to test the normal distribution of data. Levene’s test was used to confirm similar variance in behavioral measures between groups (a) describing the distance between two decision boundaries, drift rate (v) describing the rate at which evidence is accumulated for a given decision, and non-decision time (t) which is representative of those aspects of response time not included in decision making . Here we used the DDM to disentangle latent decision-making processes underlying post-error slowing. Specifically, the DDM was applied on GoPUS and GoPSG trials to estimate condition-specific decision boundary, drift rate, and non-decision time for each subject. Changes in decision boundary, drift rate, and non-decision time induced by errors were calculated by the differences between GoPUS and GoPSG conditions. Then, between-group differences were tested using two-sample t-tests. DDM estimation was conducted using fast-dm [The DDM has been extensively used to estimate two-choice decision-making processes . In this fast-dm .We carried out model diagnosis analyses to evaluate the goodness-of-fit of the DDM with the behavioral data. The model diagnosis analyses indicated a good model fit of the DDM in the post-error slowing effect. See Supplementary Method and Supplementary Results for details.We used a well-validated DBM , 27 to ek, subjects believe that the chance that an inhibitory cue will occur  is rk and the chance that no inhibitory cue will occur is 1−rk. The model assumes that subjects believe that rk has a probability α of being the same as rk−1  and a probability 1−α of being re-sampled from the prior distribution π(rk):sk refers to the true trial type of trial k ; p(rk−1|sk−1) refers to the posterior distribution conditional on the last observed trial k−1; π(rk) is assumed to be a β distribution with prior mean pm and shape parameter scale.The DBM estimates the belief about the chance of an inhibitory cue occurring in the coming trial based on trial history . On an iThe model also assumes that subjects update their prior belief using Bayesian inference, and therefore the posterior distribution is computed based on Bayes’ rule:k being a Stop trial is determined byThe probability of trial stop based on subjects’ trial history . We used the same model parameters {pm and scale}, which define the β distribution, as in the previous study since they have been well validated in the stop-signal task [α, which defines the re-sampling rate from the prior distribution, using an independent dataset (see below).In sum, the model allows us to estimate trial-by-trial anticipation of inhibitory cues pnal task . We optiBecause the parameters in the DBM were tuned based on behavioral data from adult participants , 27, we α from 0.2 to 0.7 with increments of 0.05 and determine the saturation value of α by how well the model fits the data. To test model fitting, we examined the correlation between go RT and pstop on aggregate trial-wise data across participants. The assumption is that participants will adjust their response strategy  when they have high expectations for the occurrence of a stop signal in the coming trial. Specifically, for each α, we binned the data for each small range of pstop, computed averaged pstop and go RT within each bin, and calculated the correlation between binned pstop and go RT. The optimal α for the young participant is 0.3. Details of this analysis can be found in the Supplementary Results and Supplementary Figs. To find out the optimal α for the young participants, we gradually changed α varying from 0.2 to 0.7 and replicated all the main findings , can predict core symptoms of ADHD (inattention and hyperactivity/impulsivity), we conducted multiple linear regression analysis and evaluated the model performance using leave-one-out cross-validation (LOOCV). Each time, one data point was selected as a test set and the rest of the data were used as a training set. The training set was then used to train a multiple linear regression model, which was then applied to the test set for classification. This procedure was repeated To further examine whether proactive control components play an important role in predicting core symptoms of ADHD, we trained a multiple linear regression model based on a reactive control measure alone, i.e., SSRT, and then compared model performance with the model trained on dual control measures.Supplemental Material


The diagnostic criteria for attention deficit hyperactivity disorder (ADHD) reflect the behavioural and functional outcomes of cognitive processes. Historically they have been based on external observations and lack specificity: clinical cohorts of children meeting diagnostic criteria show that around 40% may also meet diagnostic criteria for oppositional defiant disorder (ODD). We have proposed a clinical model to explain this: the Mental Effort Reward Imbalances model of ADHD (MERIM). This model views the lower levels of task completion that underlie several of the diagnostic criteria for ADHD as being due to a summation of deficits in executive functioning and reward processing. The subjective experience of inadequate reward from task completion may explain the reduced motivation, negativity, and oppositional attitude associated with ODD. The hypothesis for this study is that descriptions of affected individuals’ attentional characteristics could be more specific for the executive functioning deficits associated with ADHD than the current symptom-based approaches. To test whether this might be usable in practice, we conducted a workshop that aimed to characterise in depth the patterns of attention experienced by adults with ADHD and how they impact functioning. Three main patterns were described: (1) complete lapses in attention; (2) partial attention to a task; (3) attending to multiple tasks and distractions, either simultaneously or in rapid sequence. All of these resulted in reduced productivity. They also described strategies for managing their attention deficits. Some people used distractions positively, to stimulate the mind to remain active and engaged rather than losing focus. Multi-tasking could also achieve this by providing higher levels of stimulation, however, the stimulation could itself become a distraction. Interest or stress might maintain engagement; extremes could sometimes lead to hyperfocusing, which was typically infrequent but could be highly productive. Focusing on executive functions may improve diagnostic sensitivity, as the current criteria fail to identify people who function adequately due to their use of strategies that mitigate the effects of their attentional deficits. Such people may present with secondary depression or anxiety rather than clear, behavioural symptoms of ADHD. With further development, the approach described in this paper may provide a more simple and fundamental way of recognising ADHD within the community. In the longer term, focusing more specifically on executive functions may provide cohorts with a ‘purer’ form of ADHD for scientific study. The increased awareness of ADHD occurring in adulthood is associated with substantial diagnostic challenges. The present paper considers whether a new approach to diagnosing ADHD based on attentional characteristics might be more specific and sensitive for the executive functioning deficits in adult ADHD than the current symptom-based approaches and the particular emphasis placed on functioning during childhood. The behavioural characteristics associated with attention deficit hyperactivity disorder (ADHD) ,2 or hypThere is substantial overlap in the diagnostic criteria of the DSM and ICD for ADHD and HD respectively. However, they do have differences and, as a result, they identify different cohorts. In a representative sample of German children, the prevalence of ADHD according to DSM-IV was 5% compared to only 1% for ICD-10 HD [DSM and ICD both have in common that they are symptom-based and describe the observable behavioural outcomes of mental processes. The main symptoms are categorized as inattention, hyperactivity/hyperkinesis, and impulsivity. Given that these lack specificity, there is the proviso that the problems cannot be better explained by another condition, which would have to be considered first. For example, a child who does not appear to listen when spoken to might be deaf, or alternatively, might have a problem understanding spoken language. One particular problem in adulthood is the frequent association of ADHD with substance use disorder (SUD), which can itself be associated with attentional problems .A large cohort study of children with ADHD identified the lack of specificity for the DSM-IV diagnostic criteria, finding that 40% of diagnosed children also fulfilled the DSM-IV diagnostic criteria for oppositional defiant disorder (ODD) . No explThe inferential lack of specificity for attentional deficits in the diagnostic criteria is likely to represent a significant problem for investigating the neuroscience of ADHD using cohorts diagnosed according to currently accepted criteria. Hoogman et al. identified differences between 1713 people with ADHD and 1529 controls in subcortical structures of the brain, including the amygdala which has a role in emotional regulation . AlthougWe felt that a 40% overlap of ODD with ADHD was too great to be a chance association. We hypothesized that the DSM-IV diagnostic criteria were not specific for ADHD and were systematically also selecting children with ODD. We proposed a model to explain this association, which we called the Mental Effort Reward Imbalances model of ADHD (MERIM) ,12 Figu. This isSeveral of the DSM diagnostic criteria relate to task completion, for example, reluctance to start or to persist with tasks requiring sustained mental effort. The result of this is less achievement. However, although attentional deficits could result in less achievement due to the greater level of effort or determination required, this is not the only possible mechanism. We suggested that ODD could be considered a reward deficiency syndrome , such thAlthough the MERIM was derived in a clinical rather than a strictly scientific research setting, there is scientific evidence for reward processing deficits  and a nuAlthough ADHD was initially considered to be a condition of childhood, it has become apparent that it may affect up to 5% of adults . The diaCurrent diagnostic processes for diagnosing ADHD in adults often rely on retrospective evidence of attentional deficits during childhood, which may be difficult to substantiate and subject to recall bias ,19. AnotAdults who have grown up with undiagnosed ADHD may have developed comorbidities such as depression or anxiety. These may mask the underlying ADHD, with difficulties in concentration and motivation being attributed to a secondary disorder. Co-existing substance use disorder can also complicate the diagnosis of ADHD ,19.The current symptom-based criteria not only lack specificity but also overlook the likelihood that adults could supply a more detailed and insightful and perhaps a more specific description of their attentional deficits.In this study, we aimed to look beyond a symptom-based approach to ADHD and to examine in greater depth the individuals’ perceptions of the cognitive processes underlying their ADHD-related functional impairment. This may also lead to a greater understanding of the ways that attention has effects on daily functioning.Detailed exploration of the attentional processes of adults with ADHD may increase the sensitivity of the diagnosis. This approach may also provide greater specificity by allowing the attentional deficits to be differentiated from reward deficiencies.However, it is likely that this methodology will identify a slightly different cohort from those diagnosed using the existing diagnostic criteria. In the longer term, if such detailed inquiry turns out to be more specific for the executive functioning deficits associated with ADHD than the current diagnostic criteria, this could provide cohorts with a ‘purer’ form of ADHD for scientific study.To investigate the experiences of adults with ADHD in relation to their attentionTo explore illustrative models or analogies for conceptualising ADHD, with a view to helping other people to have a greater understanding of the condition.To develop a greater understanding of the types of attentional deficits and their impact on functioning.To develop ways of conceptualizing ADHD that may help others within the community gain insight into what it means to have ADHD.This study principally used a qualitative design in the context of a two-hour workshop conducted in 2019 by AP and RS with assistance from DB and LF. Demographic and medical data were also collected using non-identifiable questionnaires. Essentialist and inductive thematic analysis, modelled by Braun and Clarke  and HahnThe 29 participants were adults recruited through an advertisement from an ADHD community support organisation. Twenty-four participants returned anonymous questionnaires: 11 women , 12 men , and one identified as ‘other’. Their mean age at diagnosis of ADHD was 32 years, a maximum of 49 years. Only five were diagnosed before the age of 18 and three did not specify any age of diagnosis. The mean estimated age that ADHD first affected their functioning was 9 years, with only two unable to recall having significant problems in childhood. Fourteen were currently taking a stimulant.The research team came from a range of professional backgrounds and included one doctor , one medical student (LF), one psychology student (DB), and one community representative of adults with ADHD (RS).The thematic analysis of the verbatim transcriptions of the meeting and email responses focused on addressing the exploratory research questions: “what are the characteristic experiences of attention of adults with ADHD”, and “what models best conceptualise these experiences”. These questions were presented in a simplified form to the participants . ThematiA total of ten overarching categories emerged from the thematic analysis, three of which were dropped as they were not directly relevant to the study’s research questions. Data saturation was achieved for the retained categories for the research question: “what are the characteristic experiences of attention of adults with ADHD”. However, the models to conceptualise ADHD were diverse and although they could be sorted into themes, which showed some consistency with the descriptions of attention, it is likely that data saturation was not reached. The thematic analysis is shown in All participants mentioned at least one way in which ADHD impaired their daily functioning or brought them distress. Participants most frequently reported that their ADHD symptoms affected their studies and work; some also reported impairments in their social life. Specific problems included not getting things completed and missing deadlines, mental fatigue, exhaustion, anger or frustration, and underachievement.Participants agreed that completing a task required “disproportionate energy”, with one participant suggesting that he was functioning at only 40% of his intellectual capacity. When giving descriptions of their attention, participants often linked these to strategies that they used to compensate for the attentional difficulties. Some strategies needed to be carefully balanced or they could take over and themselves become distractions.Many of the participants stated that their ADHD was only recognised in adulthood. For some, this was a recent realisation after ADHD was diagnosed in their child. Their awareness of their ADHD gave them new insight into the cause of their many day-to-day difficulties.Participants described three distinct patterns of attentional deficits: complete lapses, doing a task without fully attending, and attending only briefly due to distractions. Hyperfocusing was also described.Some participants described complete lapses in attention, such that they would be unable to recall what they had carried out.My distractibility is so out there it’s not true, erm, to the extent I sometimes don’t even create memories of where I put something down, you know, I find things that I’ve lost.Two described contemporaneous difficulties with memory while waiting their turn to speak.Uh so actually I am going to refer to my phone because during the time that we have had this opportunity, I have forgotten at least twice what I wanted to say.G’day, um I was almost going to sit down because I already forgot about what I was actually going to say um but uh now I have blanked again. What was it I was going to say?One participant described his perception of not attending to anything.I don’t really know what happens to my attention most of the time… I am not really paying attention to anything, like I am doing different things at different times, like I burn my toast all the time and that sort of thing because the attention is going all different places and never really settled on one thing, um, so what happens to it is it’s not there… It would just fly away like every other thought does.Participants described doing a task without focusing on it. This was particularly common for reading tasks and was often described as not processing the information.I can sit there, read the chapters word for word, over and over again, and not process a thing… I don’t remember, I don’t understand what I have just read. And then I get home and be like ‘oh I am so exhausted from a day of studying nothing’.Several participants commented that they were unable to work on a task efficiently because they would get distracted by other tasks and would forget what they had planned to do first.My attention sort of goes left, right, and centre and jumps all over the place and I start one task and then I think “Oh I have got to do, oh look, I’d rather do this one now”. So, I start doing that task and then the next thing pops into my head and I start doing the third task and then I’ll realise “Oh I should have actually stuck with the first task”.With writing an email um I’ve got to look at what the previous email was, make sure that I answer to those points, and then also say what else I want to say but by the time I remember all those things to do, I have already checked the next email and I have forgotten everything from the first one.Your brain’s … giving you too many tasks that when you’re just trying to do one task in hand, whereas most people have that filtration system that can go okay this is task A, sit down and do it, alright now I’ve done task A where’s task B.Several participants described the ability to ‘hyperfocus’. This was typically infrequent and could be positively associated with exhaustion. One man explained that at a stage of extreme tiredness, the constant stream of distracting thoughts might sometimes abate, leaving him able to work without distractions.And that comes once in a month maybe…. the half-sleepy state early morning at 3 a.m. or late nights at 2 a.m. have been really productive.Like it’s due by midnight, 11:59. … then like suddenly two hours before it is due it starts just flowing crap and it’s just like “oh now I’m going to find that space”.Right well once I am in the zone I could keep going for hours, but it takes hours to get there. … I would start seriously writing the essay at about 2 or 3 a.m.Um, I am okay to just go ahead and work for just like 12 h on end. Like I can work for 12 h on end and that’s it, but then some days I won’t do anything at all.Participants described an ongoing need for stimulation. Interesting or stimulating tasks could be completed; less engaging tasks could be made more interesting by adding complexity; multitasking could stimulate the mind and keep it working on a less interesting task.I think having ADHD your mind’s constantly striving for stimulation so trying to do a task if it somewhat becomes boring your brain thinks—like—it’s almost trying to compensate.You’ve got to be doing something. My mind’s got to be intellectually working. I can’t just—it’s like I can’t just switch off. You know—I’ve got to be intellectually working all the time.Concentration could only be sustained if a task was sufficiently interesting.If it’s not a puzzle I won’t do it. And I will just sit there and stare at it. And my mind will just jump off to other things.So when it’s something as interesting, I can focus for up to hours, and the problem is, time just vanishes. So, it happens to me every day now ‘cause I’m enjoying what I do. So if I haven’t enjoyed what I do, then I just struggle.Tasks could be made more engaging by over-complicating them or attending to multiple stimuli simultaneously as a means of maintaining attention. However, such stimulation might itself become a distraction. Making simple tasks more complex and engaging could lead to mental fatigue.My mind … is not focusing on what I really need to get done … my mind’s kind of creating, making it more interesting but overcomplicating it, over analysing it.When we sit down and watch a movie … the movie is fine and I am not bored by the movie but it is just not stimulating enough for me … Whether it’s like just messaging a friend on Facebook, or just walking around the room, or going through my drawers, or… cleaning up the room, … something else needs to be going on at the same time or else I’ll just fall asleep.If I’ve got to go and clean that room or paint that room … the radio has to be on all the time, erm, because I think it occupies and stops me some of the time being diverted or distracted.So, if you’ve got a really dull task like housework or tidying the house I will go from one end, I’ll do it in an interesting way … if I am listening to a podcast I could spend you know a couple of hours. I could spend ages if … there’s not something so exciting that it’s pulling my attention.One participant found that social engagement was helpful for maintaining attention.My attention immediately gets better when I am talking to someone; I am more engaged in that context than when left to my own devices.Stress and frustration were often experienced and could enhance efficiency. A few participants mentioned using stress as a form of stimulation, finding that it increased attention span. Some were able to work more productively because of elevated stress and “panic” associated with an approaching deadline.If that human person had been stimulated, they’d focus on a task much longer than if a person wasn’t. So, as a child, I would use stress as a form of stimulation.If I was stressed … my concentration would last 3 h, for the entire length of the exam. If I wasn’t under that environment it would last 5 min.I find if I’ve got tasks which are really pressure I can concentrate…One participant found that even the increased anxiety associated with an impending deadline might be insufficient to stimulate their brain to be able to focus.So, it is 11:15 p.m., there are 44 min until the deadline and my attention has very quickly been distracted somewhere else. This may be in combination with the anxiety that it’s approaching. This may be in combination with my brain being completely in a zombie state or whatever reason it’s like I am not recognising that that deadline is in 45 min time so I’m often in that sort of numb state. And I would say that my anxiety increases even when my brain is still dead and I am not focused.Participants described difficulty getting started, often due to postponing the task or getting distracted and doing something else. Sometimes seeking out distraction appeared to be a purposeful effort to postpone the task. Participants recognized their aversion to tasks that should have been manageable.And then, er, if it’s a dull task it literally can feel painful to try to do it so, erm, even if I know it’s good for meWhen it’s up to me, I can’t focus, I don’t know where to start. I know that I’ve got to get rid of this stuff, but it’s a struggle to get started.I have been told that if I don’t like something I don’t do it… I mean a herd of wild elephants could be behind me, but I would not move. … And the thing is, erm sometimes, even at home, and I have to do a task, maybe self-care, maybe scheduling, maybe planning, these are tasks I dread, I fear. So, I don’t want to do them. So, I mean what I would rather do is have a tab of Netflix open so I mean, YouTube open on another tab, Reddit on another tab. … So, it’s just 10 different tabs.The brain is going all the time. Over analysing, overthinking, etc. … huge amount of overwhelming procrastination and I found that I am going to do a task I almost open myself up to being distracted. So okay and everything is on the table ready to go … oh got to go to the loo. Something needs to be done along the way and I have completely forgotten what I was doing. Then I think, “Oh no, I am supposed to be in the toilet and then I am supposed to be doing what I am doing.” I can get distracted a dozen times within an hour and still not get to sit down and start that task and it is often when the task is somewhat overwhelming and yet there’s no good reason for it to be a problem. It’s often a thing that I am well and truly able to do, but to sustain the attention to get started…I often feel like there’s an internal self-sabotaging mechanism going on ‘cause even sometimes when I have eliminated everything on the list I got no other excuses no reason to procrastinate, I’ll sit in front of the computer and I’ll switch to ABC news. And so, it’s that there’s—even at that point—there’s still a sabotaging thing happening in my head.The participants described a variety of analogies to illustrate their experience of ADHD and specifically the way that they felt that their experience was different from that of other people.… one of those handheld whizzes—you know—chop up like veggies or whatever and its either on and fully on and its whirring … and going crazy or its totally off.It’s … like a record or a track DVD or something that keeps skipping, just gets sorted and then something grabs the attention, … or somebody got their finger on the on/off button. So, you’re on, you’re off, you’re on, you’re off…To me, it’s basically a multi-core processor trying to do 30 tasks and I keep jumping from task to task.My brain feels like a gigantic flywheel brimming with ideas and spinning at colossal speeds … but by the time I get around to engaging with the smaller wheel (society), the conversation has already shifted.having 15 televisions all with 15 different television shows on them. And it would be deciding which one of those I am going to watch if not all of them all at once.It’s like a million different radio channels not quite locking in.Like those websites … that have all those big bright rainbow flashing things … and as you’re trying to go through the task there are all these other pop-ups that like keep coming up that kind of drag your attention.having a song repeating in my head all the time or having something in my mind all the time in the background.being in a room with a lot of people talking … its like being on the side of a busy highway with all the traffic.lots of little ‘rivulets’ of streams … some grains will fall away and all the water will flow down in one side … it goes off there and then it might come back.a herd of bullock and you want them to plough the field. But … one keeps running off here and one keeps running off there … Or I’ll chase after one of the bullocks and then the others are just standing there.A dog … it’s doing the- a lot of things at the same time, gets easily distracted, jumping at noises. However, if you give the dog a scent, a bone to focus on, or if you throw a stick, it will go and it will work until it reaches it and brings it back to you.A computer without enough RAM, like it has 4 GB of RAM trying to run windows 10, it will boot in and it can run but as soon as … it tries to do tasks it—BSOD (blue screen of death) and restarts itself in recovery mode and deletes the previous task it was trying to run.It’s like my hard drive needs fragmenting … all the information is there but maybe you don’t have the RAM or the consolidation or instructional information to be able to access it quickly so your hard drive really needs defragmenting.Some participants incorporated the normalising effect of ADHD medication into their models.Imagine you have a cloud above your head and imagine there are tasks you need to do like, washing clothes, work out what to eat this week, and so on … in the ADHD mind you triple that by 10 … It all looks a mess and there is little way to sort it without medication.When I take my medication it’s like adding an extra 4 GB of RAM and it operates at normal levels. The problem with this is when the meds wear off I’m back to the original 4 GB, trying to cope with the fallout and energy usage of the 8 GB, so it needs like an hour to recover.The participants gave a wealth of information. Their lived experience mapped onto well-recognized associations of ADHD and in the descriptions of their day-to-day functioning, there was a level of consistency in the types of problems experienced. Participants described three main patterns of attentional dysfunction contributing to these problems. These consisted either of complete lapses in attention which could extend to losing track of time; partial attention—attempting a task but without fully concentrating; or attending to multiple tasks and distractions, either simultaneously or in rapid sequence. These resulted in not getting tasks completed. Such disorganisation could be compounded by memory difficulties: forgetting which tasks had been started and needed finishing. Interest or stress might maintain engagement; extremes could sometimes lead to hyperfocusing. This was typically an infrequent occurrence and might occur late at night, after several hours of attempted task initiation, particularly with the added pressure of an approaching deadline. Those whose attention might wane often used distractions positively, to stimulate the mind to remain active and engaged rather than losing focus. Multi-tasking could also achieve this by providing higher levels of stimulation. Some people required multiple sources of stimulation to defray boredom. One problem experienced was that the stimulation could itself become a distraction. Procrastination was common, particularly if a task was perceived as difficult or arduous. Procrastination might take the form of looking for other tasks or distractions as an excuse for postponement or simply being overwhelmed by the thought of the task.Participants commonly reported difficulty processing and almost immediately forgetting information, which suggests that their comorbid memory difficulties may be a direct consequence of their attentional impairments. Atkinson and Shiffrin propose that in order for information to be stored and later retained as memories, the sensory stimuli must first be interpreted and encoded, which requires sufficient attentional control . This maThe models of ADHD, although diverse in topic, broadly mapped onto the attentional difficulties described. Dominant themes which were prevalent in the models were the overwhelming and exhausting nature of living with ADHD and difficulty coping with attentional deficits. Whilst participants described a variety of different models, all detailing different areas of their experience, the most frequently mentioned and agreed upon model was the computer analogy. A similar model, known as the information processing model , has oftWe observed that when asked to come up with a model describing ADHD, participants responded quite quickly and easily. This suggests a level of awareness and insight into their experience. Some participants incorporated their experience of medication for the treatment of ADHD into their model. The description of the positive benefit enriched the models and may help others to understand what it means to have ADHD treated effectively with medication. The most popular subject matter was to compare the mind with an electronic device, such as a computer. However, as developing an analogy is likely to be a creative process, applying the qualitative research methodological concept of data saturation may not be appropriate.A strength of this study is the number of participants who contributed. They were able to listen to the responses of those who spoke to the assembled group and further develop some of the themes. Those who were unable or reluctant to address the meeting had the opportunity to record their contributions one-to-one with a member of the research team during the break, or to send them in an email. A further strength is the diversity of the research team deriving from two different disciplines: medicine and psychology; and also, the community advocate for ADHD.A limitation is that our sample of adults with ADHD may not be representative. People who choose to attend such a meeting may be biased towards those who are more intellectually able; such a perception of themselves could be inferred in some of their responses. Some of their descriptions of living with ADHD suggested a profound impact on their day-to-day functioning. It is therefore possible that these might represent more extreme manifestations of ADHD. Of those returning questionnaires, a substantial proportion had been diagnosed relatively recently, which may suggest an underrepresentation of people whose ADHD extended from childhood. Alternatively, it could relate to a substantial pool of undiagnosed adults.The findings from this qualitative study confirmed that adults with ADHD may give insightful descriptions that could characterize the experience of living with ADHD. Developing new illustrative analogies for the functional difficulties associated with ADHD may help society to become more supportive towards individuals with this disabling condition.The participants’ perceptions of having a shorter attention span could be objectively investigated, together with the amount of time spent daydreaming. This line of research may demonstrate measurable differences between people with and without ADHD.Detailed investigation of the attentional processes may improve diagnostic sensitivity for ADHD, as the current criteria could fail to identify people who function adequately due to their use of strategies that mitigate the effects of their attentional deficits . Such peThe inferential lack of specificity for attentional deficits in the diagnostic criteria is likely to represent a significant problem for investigating the neuroscience of ADHD using cohorts diagnosed according to currently accepted criteria. However, changing the diagnostic approach is likely to identify a slightly different cohort from the current diagnostic procedures. In the longer term, justification for this new approach may come from neuroscience, when the findings from cohorts of people with traditionally diagnosed ADHD with and without symptoms indicative of reward deficiencies can be compared.


The use of stimulant medications among G6PD-deficient individuals was 17% higher for the methylphenidate class of drugs , and there was a 16% elevated risk for amphetamine use . Conclusions: G6PD deficiency signals an increased risk of ADHD diagnosis, more severe presentations of ADHD and a greater need for psychiatric medications to treat ADHD.Background: Glucose-6-phosphate dehydrogenase (G6PD) deficiency, impacting 4.9% of the population and more prevalent in Mediterranean communities, is a common enzymopathy with potential relevance to Attention Deficit/Hyperactivity Disorder (ADHD). This study investigated this association. Methods: The clinical characteristics of 7473 G6PD-deficient patients and 29,892 matched case–controls (selected at a 1:4 ratio) from a cohort of 1,031,354 within the Leumit Health Services database were analyzed using Fisher’s exact test for categorical variables and the Mann–Whitney U test for continuous variables. Results: In total, 68.7% were male. The mean duration of follow-up was 14.3 ± 6.2 years at a mean age of 29.2 ± 22.3 years. G6PD deficiency was associated with an increased risk of being diagnosed with ADHD (Odds Ratio (OR) = 1.16 ,  Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that affects approximately 5–10% of children and 2–5% of adults worldwide. While ADHD is highly heritable, its etiology is complex, and there is strong evidence for an interplay of inflammatory processes with genetic risk in the development of ADHD  and amplAnother intriguing but under-studied line of research also implicates the immune–inflammatory pathways’ association with oxidative stress in the development of ADHD ,8,9,10. Glucose-6-phosphate dehydrogenase (G6PD) is an enzyme that facilitates the production of nicotinamide adenine dinucleotide phosphate (NADPH) and glutathione (GSH), both of which play essential roles in maintaining the balance of oxidation–reduction in the body ,12. G6PDIn 2010, Ghanizadeh et al. suggest that G6PD deficiency may be a predisposing factor for ADHD . In theiSome studies suggest an association between ADHD and a subset of patients with lower vagal tone and higher oxidative stress . These sNewer investigations have revealed links between ADHD and a range of infectious disorders ,26,27, aThe main objective of the present study was to evaluate the possible association between G6PD deficiency and ADHD, in light of our understanding of neuro-inflammation as a contributing factor, in a large-scale population-based study.We performed a retrospective population-based cohort study using electronic health records (EHRs) from Leumit Health Services (LHS), a large Health Maintenance Organization (HMO) in Israel. This study was approved by the LHS institutional review board .Data were retrieved from the LHS data warehouse. The study population was extracted from a pool of over 1,000,000 individuals who had been members of LHS for at least two years up to the data extraction date. LHS maintains a computerized database continuously updated with information related to subjects’ demographics, medical diagnoses, laboratory tests, hospitalizations, and medication prescriptions.In this cohort study, two groups of individuals were compared. The first group comprised individuals who have a confirmed diagnosis of G6PD deficiency or whose G6PD activity levels were tested at LHS and yielded a measurement below 4 U/g Hg.An algorithm was utilized to randomly choose individuals without G6PD deficiency as a control cohort. The algorithm required precise matching for variables such as gender, age, socioeconomic status (SES), a category based on geographic area, ethnic group , and year of first documented visit in the LHS EHR system compared to individuals with G6PD deficiency. The ratio of controls to individuals with G6PD deficiency was set at 4:1. Individuals with G6PD deficiency for whom the matching algorithm could not identify adequate controls with a precise match in variables were excluded from the analysis.Demographic and clinical data, ICD-9 diagnosis records, the most recent recorded body mass index (BMI), blood pressure (BP) measurements, and smoking habits were obtained from the LHS electronic database. In addition, the last available results for a specific set of laboratory tests conducted by LHS laboratory facilities were also extracted.ADHD was diagnosed according to the Israeli Ministry of Health criteria, following the international evaluation requirements: the diagnosing physician must be a senior physician specializing in the ADHD field , and the diagnosis was established according to the Diagnostic and Statistical Manual  criteria .SES was defined according to the child’s home address, using the Israeli Central Bureau of Statistics classification of 20 subgroups. Classifications 1–7 were defined as low SES, 8–13 as medium SES, and 14–20 as high SES.The likelihood of confounding was reduced by strictly matching for essential variables  between the control group and the cases.Descriptive statistics were used to describe the baseline cohort demographic and clinical characteristics, and the statistical association between categorical variables was assessed using Fisher’s exact test. Continuous variables were compared using the Mann–Whitney U test. Data extraction was performed using programs developed by the Leumit Research Institute in Python, Pandas, and SQL. Statistics were computed on de-identified data using R statistical software, version 4.0.4.From a group of N = 1,031,354 individuals who had been insured for at least two years by LHS with a documented medical history in the EHR, we could identify 7827 subjects with a G6PD deficiency diagnosis. The strict matching procedure resulted in selecting 7473 subjects with G6PD deficiency and 29,892 control subjects (at a ratio of 4:1 controls) with very similar gender, age, SES category, ethnic group, and year of first recorded visit to LHS. In total, 354 G6PD-deficient individuals were excluded due to insufficient matching controls. The socio-demographic characteristics of the study groups are shown in p < 0.001. G6PD-deficient individuals were characterized by higher rates of being non-smokers . Having a diagnosis of diabetes mellitus (DM) was negatively associated with G6PD deficiency . Being obese (BMI ≥ 30) also has a negative association with G6PD deficiency .Clinical characteristics for the two groups are presented in p < 0.001; OR = 1.12 , p = 0.048, respectively).As evidence of the increased diagnoses of ADHD among G6PD-deficient individuals, the rates of physician visits were significantly higher among subjects with G6PD deficiency than the matched controls . A signip < 0.001, and for amphetamine use, OR = 1.16 , p = 0.047)  .The rates of physician visits and stimulant agent use among ADHD patients with and without G6PD deficiency.p < 0.001). We also observed significantly higher rates of the use of stimulants among ADHD patients with G6PD deficiency as compared to non-G6PD-deficient patients , However, we did not observe significant differences in the drug classes prescribed between the two groups.We compared the healthcare resource utilization of the 1040 patients diagnosed with ADHD in the G6PD-deficient group with the 3650 patients diagnosed with ADHD in the matched non-G6PD-deficient group. We observed a higher number of visits to neurologists per ADHD patient in the G6PD-deficient group than in the non-G6PD-deficient group  creation, which maintains the supply of reduced glutathione in the cells used to mop up free radicals that cause oxidative damage. Oxidative stress is also one of the proposed etiological mechanisms of ADHD, although it is not yet clear enough ,10. It iNADPH is required for the synthesis of neurotransmitters such as dopamine and serotonin . These nThe dysregulation of reactive oxygen species (ROS) represents a crucial mechanism that can contribute to a heightened and more severe inflammatory response in individuals, thereby increasing the symptomatic nature of infection diseases ,45,46. SA comparison of healthcare resource utilization shows that patients diagnosed with ADHD and G6PD deficiency sought specialists more often and purchased more stimulants when compared with ADHD patients who are not G6PD-deficient, suggesting a possibly increased severity of ADHD symptoms among these G6PD-deficient patients. Thus, it could be that apart from a common etiological mechanism, G6PD deficiency could be an additive factor that increases the severity of already existing ADHD.We observed a substantially higher rate of visits to adult neurologists and adult psychiatrists by patients with ADHD and G6PD deficiency. Several explanations are plausible. First, it is currently acceptable that although the diagnosis of ADHD is typically made in childhood or adolescence, patients diagnosed with ADHD often require lifelong follow-up and treatment. This continuity of care leads individuals with ADHD to seek psychiatric services from adult specialists, especially during the transition from adolescence to adulthood.Secondly, the substantially higher rate of visits to adult neurologists and adult psychiatrists of our patients with ADHD could be explained by the specific healthcare landscape and access to child and adolescent neurology and psychiatric services in Israel. Most child neurologists and psychiatrists work in hospital settings, and we might have had a limited ability to include them in our analysis. According to the Israel Child and Adolescent Psychiatric Association, the number of child and adolescent psychiatrists is limited, with a notable gap between the psychiatric care needed and the care actually received. Consequently, many individuals under the age of 19 years, including those with ADHD, end up receiving treatment from adult psychiatrists, which can also explain the observed higher rates of adult neurologist and psychiatrist visits.Some studies described a positive association between G6PD deficiency and a risk of diabetes mellitus (DM) ,50, but A population-representative Chinese birth cohort showed that G6PD deficiency was associated with a lower BMI, which line aligns with our data.Our findings showed an association between G6PD deficiency and a lower BMI, consistent with the finding from a population-representative Chinese birth cohort . Future The major strength of the present study is its extensive scope, population-based nature, and utilization of real-world data. Furthermore, the study used advanced digital data to analyze various demographic, clinical, and laboratory factors that may influence the risk of ADHD. As far as the researchers know, this is the first large epidemiological study to thoroughly examine the association between G6PD deficiency and ADHD using a considerably large nationwide database.The main limitation of this study pertains to its observational and retrospective design, which means that some unidentified potential confounders may have impacted the outcomes. Therefore, we report our conclusions using the terminology of association and probability rather than causality.Since our sample primarily consisted of Middle Eastern and Mediterranean populations who identify as Caucasians, and as G6PD deficiency is also of a higher prevalence among East Asian and African populations, these findings may not represent the entire scope of the G6PD community, and the results may not apply to other ethnic groups. However, our study encourages further research to replicate our results in different populations and in a prospective design to identify causality and underlying mechanisms.Both ADHD and G6PD deficiency are genetically conferred developmental disorders that begin in infancy and co-travel within the population. More research is required to determine their interplay across the lifespan. It is possible that the oxidative stress seen in G6PD deficiency results in subtle damage to the developing brain which, when combined with genetic vulnerability or compounded by adverse environmental factors, may contribute to the development of ADHD. Oxidative stress remains a proposed etiological mechanism of ADHD, although there is not enough evidence yet to state with certainty that it is causative. More research into the etiological mechanisms of ADHD through a lens of oxidative stress is warranted.This population-based nationwide study demonstrated a significant association between G6PD deficiency and ADHD. Physicians and allied healthcare providers need to be aware of this potential association for several reasons. First, G6PD deficiency is a sex-linked disorder that manifests with the greatest severity among males, but a large population of females experience symptoms below the thresholds customarily associated with disease among males. Diagnosing G6PD deficiency among women remains a challenge due to sex-based presentation, and different laboratory methods are required for determining females with the disorder, but there may be substantial neurological consequence to undiagnosed and untreated disease processes. Awareness of the cognitive and behavioral consequences of these disorders should prompt practitioners to look for the sex disparities in G6PD and in ADHD presentations among their patients. Second, regardless of the sex of the patient, there are substantial neuropsychiatric consequences of G6PD that may be preventable with early diagnosis and prevention strategies. Finally, if new etiological mechanisms are identified, treatments for individuals with G6PD and ADHD may be improved. Currently, there is evidence that stimulant treatment with methylphenidate alters oxidative stress pathways, though studies conflict as to whether treatment is associated with improved  or more


Attention Deficit–Hyperactivity Disorder (ADHD) is a psychiatric condition that shows developmentally inappropriate levels of inattention, hyperactivity, or impulsivity. Symptoms begin at a young age and usually include a lack of attention, poor concentration, disorganization, difficulty completing tasks, forgetfulness, and losing things. It is important to diagnose and treat the disorder at a young age so that the symptoms do not persist into adulthood and cause other comorbid conditions. Learning difficulties, motor impairment, anxiety, or depressive disorders may occur with this condition. To improve the academic careers of children with ADHD, we focused on a specific innovative educational approach  that could improve basic learning skills  to prevent or manage any learning difficulty that could occur with ADHD. The Universal Design for Learning is an individualized approach that combines current neuroscientific knowledge, creating personalized teaching based on the strengths and weaknesses of the student. The goal of this study is to analyze the impact that this approach has on basic learning abilities. We found that both interventions led to improvements in test performance, indicating that interventions were necessary to enhance reading, writing, and arithmetic skills. Furthermore, the group that received an educational intervention based on Universal Design for Learning demonstrated a more significant improvement in these areas. Additionally, we propose that the set of techniques implemented by teachers in the classroom helped children to read, write, and perform math tasks correctly and more fluently. Attention Deficit–Hyperactivity Disorder (ADHD) is a psychiatric condition that has long been acknowledged as having an impact on children’s functioning. Individuals affected by this disorder exhibit levels of inattention, hyperactivity, or impulsivity that are not developmentally appropriate. Symptoms typically manifest at an early age and commonly include a lack of attention, poor concentration, disorganization, difficulty completing tasks, forgetfulness, and misplacing items. Diagnosing and treating the disorder early in life is crucial to prevent the persistence of symptoms into adulthood, which can lead to the development of other comorbid conditions . ADHD isIn this study, we examined a group of 60 individuals diagnosed with Attention Deficit–Hyperactivity Disorder (ADHD) starting from the third year of primary school. These subjects were divided into two groups, each consisting of 30 individuals. All participants were selected from five primary schools in Caserta, Italy, and came from similar socio-cultural backgrounds in terms of their parents. The family and environmental context did not influence educational attainment in either group. The inclusion criteria for the study were as follows: (a) belonging to the same class level (third grade), (b) having a diagnosis of ADHD without any additional comorbidities as determined by the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) , (c) posAfter the initial assessment, both groups received two different types of interventions, lasting for five months. The data were collected and analyzed at the FINDS Neuropsychiatry Outpatient Clinic by licensed psychologists in collaboration with the Rome University of International Studies (UNINT).As shown in The protocol used consists of the following tests:SES: a self-administered questionnaire was utilized to gather information regarding the educational level and profession of the parents. This questionnaire also indicated the individual or family’s position within the social system . This indicates that there are notable interactions among the subscales, time, and type of treatment. Specifically, at T1, both interventions led to significant improvements in the three subscales of the MT tests. However, in Group 2 (G2), there was a more significant improvement in the correctness parameter compared to Group 1 (G1)  . This indicates that there are notable interactions among the subscales, time, and type of treatment. Specifically, at T1, both interventions led to significant improvements in the two subscales of the AC-MT tests. However, in Group 2 (G2), there was a more significant improvement in the correctness parameter compared to Group 1 (G1)  . This indicates that there are notable interactions between the two variables, time, and the type of intervention. Specifically, both treatments resulted in an improvement in the BVSCO scale. However, this improvement is more significant in Group 2 compared to Group 1  . This indicates that there are notable interactions between the two variables, time, and the type of intervention. Specifically, both treatments resulted in a reduction of externalizing symptomatology in the CBCL scale. However, this reduction is more significant in Group 2 compared to Group 1  [F  = 178.112,  Group 1 .The significance of a multimodal approach in the treatment of ADHD cannot be overstated. While psychostimulant medications are often the first-line treatment for ADHD and can lead to substantial symptom reduction, it is essential to acknowledge that medication alone is not a comprehensive intervention ,62,63. SBy adopting UDL principles, educators can create a more inclusive learning environment that addresses the specific needs of students with ADHD, promoting their academic success and overall well-being. A multimodal approach that incorporates UDL alongside other interventions can optimize the outcomes for children with ADHD and help them reach their full potential. In our study, we focused on the educational intervention and investigated the impact of Universal Design for Learning as an individualized approach that can address the specific needs of children with ADHD in comparison to traditional educational strategies commonly employed in schools.We found that both interventions led to improvements in test performance, indicating that interventions were necessary to enhance reading, writing, and arithmetic skills. Furthermore, the group that received an educational intervention based on Universal Design for Learning demonstrated a more significant improvement in these areas. Additionally, we propose that the set of techniques implemented by teachers in the classroom helped children to read, write, and perform math tasks correctly and more fluently. Regarding engagement, UDL promotes creating a positive and inclusive learning environment where students with ADHD feel motivated and supported. Offering choices and opportunities for self-regulation can empower these learners to actively participate in the learning process and foster a sense of autonomy.We attribute our results to the improved self-regulation stimulated by the individualized approach as supported by previous studies . Over thIn conclusion, this study aimed to compare two commonly used educational interventions for managing ADHD in school settings. The intervention employing traditional personalized teaching strategies demonstrated a positive effect on children’s basic learning skills, underscoring the importance of early intervention. However, Universal Design for Learning proved to be more effective in fostering these abilities. This highlights the value of personalized teaching interventions that consider individual differences beyond mere symptomatology.Complex Implementation: Fully integrating UDL principles into curriculum design and instruction requires significant changes in educational practice. This may necessitate time, resources, and adequate training for educators;Access to Technological Resources: UDL encourages the use of digital technologies to provide multiple modes of content presentation. However, not all schools or districts have the resources to offer a wide range of technologies or reliable internet connections;Measurement and Assessment: Assessing student performance within the UDL model can be more complex compared to traditional assessment methods. Measuring student progress through flexible and personalized assessments can pose challenges;Lack of Experimental Evidence: Despite UDL’s strong theoretical foundations and research basis, there may still be a lack of long-term experimental evidence to demonstrate its effectiveness in every educational context;Financial Resources: Full implementation of UDL may require significant financial investments for acquiring resources, technologies, and educator training.Finally, we acknowledge the limitation of our study and emphasize the importance of conducting follow-up assessments to verify the stability of the observed improvements over time. While UDL offers a flexible and inclusive approach to teaching and learning, it does present certain limitations and challenges that warrant consideration:Despite these limitations, UDL remains a valuable approach to creating an inclusive and flexible learning environment that can benefit many students with diverse needs. However, it is important to continue scientific research using the UDL to provide additional data and develop a more solid scientific foundation for this educational methodology, especially considering the scarcity of studies.


The further southeastward propagation of squall line with broken line type seems reinforced by low-level moist transport from the Java Sea. This study also suggested that this unusual event of a long-lived squall line might occur more frequently in the warming upper ocean in the IMC.The short-lived tropical squall lines could trigger weather-related hazards to the northern part of the Indonesia Maritime Continent (IMC), such as Sumatra and Kalimantan. Herein, we investigated the rare propagation event of the long-lived Sumatra squall line associated with a severe storm surge that induced coastal inundation in Java-Bali with devastating impacts from 22 May–2 June 2020. With a comprehensive approach combining observational, numerical, and analytical studies, for the first time, we proposed the possible mechanism related to the long-lived squall line over the IMC, which represents the largest equatorial tropical region with the most complicated air-sea interaction area in the world. Our findings suggest that the long-lived squall line related to the supercell-like thunderstorm initiated from multicell over central Sumatra on May 20, 2020, continuously propagated southeastward until several days later reached Bali. The near-quasi steady convective line has 6 hours of time travel from central Sumatra to west Java. The supercell-like rapidly develops from multicell with a deep convective updraft under the strong and fast cold pool (∼13.8 m s ConsequFrom 23 May to June 2, 2020, a coastal surge spread along the north and south of Java-Bali Islands, recorded as the worst inundation due to its devastating impact, which occurred simultaneously and sequentially in several coastal areas from western Java to Bali, Indonesia ,6. ThesePrevious studies stated that the Sumatra squall line was a common weather disturbance related to severe weather destruction over the Malacca Strait. This squall line type originated over Sumatra and propagated over the Malacca Strait, affecting significantly damaged infrastructure ,10. HoweIn this study, we considered the long-lived Sumatra squall line during 20–21 May 2020, depicted by the Global Satellite Mapping of Precipitation (GSMaP) , which induced a long-term period of coastal flood inundation indicated by sea level anomalies of coastal along Sumatra, Java, and Bali Islands, from 23 May to June 2, 2020 a–c. In tWe used observational, numerical, and analytical studies to understand how the physical and dynamic processes of squall line propagation triggered the sea level anomalies. In this case study, we used the Weather Research and Forecasting (WRF) model  with iniAs mentioned in the previous study, ∼5 km grid spacing is sufficient to capture the heavy rainfall event related to the mesoscale process over IMC  since we2https://webcritech.jrc.ec.europa.eu/TAD_server/Home?group=IDSL%7C), as well as daily merged satellite altimetry multi-mission data provided by the E.U. Copernicus Marine Environment Monitoring Service . The synoptic conditions related to atmosphere parameters over the near-surface using ERA5 data  and Program House of Artificial Intelligence, Big Data, and Computational Technology for Biodiversity and Satellite Imagery, 10.13039/501100020638BRIN [1/III·6/HK/2023]. Widodo Setiyo Pranowo was also partially supported by 10.13039/501100020638BRIN under the Extreme Marine Weather Disaster Program House of Research Organization of Earth and Maritime [B-197/III.4/PR.07.00/1/2023]. The results of this study also support the development of a numerical prediction of atmosphere-ocean knowledge using deep learning artificial intelligent (NAKULA) and maritime information system in Indonesia (SEMAR) at the Research Center for Climate and Atmosphere, 10.13039/501100020638BRIN. The simulations described in this study were completed on the High Performing Computing from the Computational Laboratory of Atmospheric Numerical Prediction (KRESNA) at the Research Center for Climate and Atmosphere, 10.13039/501100020638BRIN.The Erma Yulihastin: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.Ibnu Fathrio: Conceived and designed the experiments; Performed the experiments. Albertus Sulaiman: Analyzed and interpreted the data; Wrote the paper. Rahaden Bagas Hatmaja; Suaydhi Haries Satyawardhana; Fadli Nauval; Dwiyoga Nugroho; Widodo Setiyo Pranowo; Rikha Bramawanto; Abdul Basit; Subekti Mujiasih; Muhammad Furqon Azis Ismail; Sopia Lestari; Herlina Ika Ratnawati; Jalu Tejo Nugroho; Danang Eko Nuryanto, Thomas Djamaluddin: Contributed reagents, materials, analysis tools or data.The authors declare that there are no conflicts of interest regarding the publication of this paper.