Attention Deficit/Hyperactivity Disorder (ADHD) and autism spectrum disorder (ASD) are common neurodevelopmental conditions. People with these conditions face different challenges, from symptom-related difficulties such as sustaining attention, to finishing their education and holding a job, and dealing with stigma. The social and economic costs of ADHD and ASD are tremendous, with the majority of costs due to productivity loss , special education costs and loss of wellbeing  , 2. ThesLooking at it from a different perspective: positive aspects and strengths associated with neurodevelopmental conditions.” It comprises of seven articles that have adopted a different outlook and therefore have generated novel information about ADHD and ASD.We are proud to present this Research Topic “n = 346) and ASD (n = 131) children, Dupuis et al. found that autistic children had greater attentional strength than children with ADHD did. However, attentional strength was not associated with impairment and cognitive flexibility in ASD. In Stolte et al., different aspects of creativity  were investigated in relation to ADHD and ASD symptoms in the general adult population (n = 470). They found divergent thinking to be positively correlated with the number of ADHD symptoms; this was confirmed in a different case-control sample. Also, creative achievements in the domain of “expression”  were associated with ADHD. For ASD no obvious associations with creativity were found but the ASD subdomains of “imagination” and “social difficulties” generated questions for future research.Two studies aimed to compare ADHD and ASD on the specific cognitive functions of attention and creativity. Based on a community sample of ADHD  and Prepulse inhibition of the startle reflex (n = 37). Creativity was measured by creative thinking-drawing production and subcomponents of divergent creativity . They showed that children with a higher P50 testing amplitude tend to have a higher divergence of fluence and flexibility at a nominal significance level. Prepulse inhibition was not associated with either general creativity or its subcomponents. This study showed that although sensory- and sensorimotor gating and attentional difficulties are unrelated, creativity and quantitative measures of divergent thinking such as fluency and flexibility, are at least indirectly related to decreased sensory, but not sensorimotor gating. Also, lower sensorimotor gating seems to benefit tasks related to general creativity.Previous studies on positive ADHD-related characteristics have largely focused on one particular strength, namely creativity. Creativity has been reported in individuals who had clinical or subclinical symptoms of psychiatric conditions such as ASD, ADHD, bipolar disorder, and schizophrenia, although its association is not very straightforward –7. StoltSchippers et al. aimed to extend the identification of strengths in ADHD to move beyond researching creativity alone. They performed a citizen science project, collaborating with the Dutch organization for people with ADHD, and collected data on self-reported positive characteristics of ADHD in 206 adults with ADHD. This resulted in a list of 116 positive aspects which were categorized into Creativity, Being dynamic, Flexibility, Socio-affective skills, and Higher-order cognitive skills. They concluded that awareness about strengths of ADHD might help individuals with ADHD and their environment to better cope with and accept their diagnosis. Awareness of strengths might also facilitate better choices for education or occupation. However, to incorporate the self-reported positive aspects in the understanding of ADHD, future research should focus on quantifying strengths in ADHD relative to non-ADHD samples, and investigating the link between these aspects and clinical characteristics of ADHD. A goal of this work is to implement such new knowledge in psychoeducation and find its way to society.Colzato et al. aim to move away from a unipolar view of cognitive control, in which optimal cognitive control is characterized by complete persistence on one goal while ignoring goal-irrelevant information. In the meta-cognitive control model, the authors suggest replacing this with a bipolar account of cognitive control. Here, variation in cognitive processing style is characterized by two poles: one of persistence—which corresponds to the original idea of cognitive control as described in the unipolar model—and one of flexibility—which is less selective and exclusive and facilitates switching between tasks, ideas and actions and is beneficial for tasks requiring integration of information with varying relevance. The authors conclude by outlining the relevance of their model to task performance in neurodevelopmental disorders such as autism and tic disorders.In their theoretical paper, per se is a novel way of looking at neurodevelopmental conditions, also investigating positive environments can deliver important new information about neurodevelopmental conditions in the context of prevention and increased quality of life. Einziger et al. wrote a review about sensitivity to aspects of the home environment and parenting in the context of ADHD. These factors might moderate genetic liability and the negative effects of these environmental factors are well-established, but the opposite, the positive effect of high quality environments and positive parenting are under-investigated. The review contributes to increased insights for designing preventive interventions and for identifying those children that are especially sensitive and could benefit from such interventions; also recommendations for future work are given.Not only investigating strengths Furukawa et al. is a timely reminder of the value of using input from children themselves for health-care but also for research. The authors investigated the wishes of 299 school-aged children with ADHD by means of using the Three Wishes task in which they could indicate what they wanted to change in their lives. Many wishes were related to immediate fulfillment but also to positive interpersonal relationships. This work highlighted the diversity and typicality of needs, desires and hopes of children with ADHD.The work of To conclude, the published articles in this collection highlight the diversity of strengths that are reported in the context of neurodevelopmental conditions to span personality, emotional, cognitive variables as well as responsivity to positive environmental stimulation. While almost all these areas are understudied in relation to neurodevelopmental conditions, we think that these aspects have the potential to change the prospects of individuals with such conditions for the better. We anticipate that the identification of these missing aspects will change the ways we use to approach neurodevelopmental conditions. It will have an impact on coping with the condition and will provide new perspectives for healthcare, as well as supporting improved quality of life.MH wrote the manuscript. FZ and CG critically reviewed the manuscript. All authors approved the final version.


The association with ADHD was stronger at higher quartiles than in the lower quartiles of PM10 exposure; however, it was not statistically significant. Our results suggested that long-term PM10 exposure was associated with increased ADHD in children and adolescents. Children diagnosed with ADHD suffer from a variety of social activity and have a significant economic burden. Therefore, it is considered an important role to find out the effects of environmental risk factors, including air pollution, on children and adolescents. This may also help to increase the body of knowledge in this field and to stimulate further research.Many epidemiological studies have suggested that air pollution adversely affects neurodevelopment in children; however, evidence is still lacking. This study aimed to determine the association between particulate matter (PM) exposure and attention-deficit/hyperactivity disorder (ADHD) in children and adolescents. Data were obtained from the Korean National Health and Nutrition Examination Survey 2008–2018. Outcomes were defined from parental reports of ever doctor-diagnosed ADHD, and ADHD cases were matched to non-cases with 1:10 age–sex matching. Individual exposure levels were assigned according to each study participant’s administrative address during the year of diagnosis. Multivariate logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). After age–sex matching at a 1:10 ratio, the final study participants comprised 1,120 children aged 6–19 years old. A unit increase in the PM Attention-deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity, and impulsivity. In the diagnostic criteria of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders 2013), the age of onset was changed from 7 years to 12 years, and the symptom threshold for patients aged ≥17 years was lowered from six to five symptoms [3, the agThe cause of ADHD is unclear. The development of ADHD is known to be related to the dysregulation of neurological processes caused by dopamine and norepinephrine, which are neurotransmitters in the frontal lobe of the brain . In a fuThe worldwide prevalence of ADHD in children and adolescents has been reported to be 3.4%  . The pre2), sulfur dioxide (SO2), and lead (Pb), as risk factors for ADHD. According to systematic literature reviews, some studies have reported air pollution as a significant determinant of neurodevelopmental disorders; however, evidence is still limited owing to heterogeneities in the exposure window period, methods of exposure assessment, and potential confounding factors including noise, parental stress, other air pollutants, and secondhand smoke [ADHD is a complex and multifactorial disorder resulting from the interaction between genetic and environmental risk factors. The risk factors include maternal drinking and smoking, toxin exposure, brain damage, low birth weight, postnatal socioeconomic factors, and high heritability related to dopamine pathways ,13. A rend smoke ,18.In Korea, only a few epidemiological studies on ADHD have been published, and evidence is insufficient on the association between air pollutant exposure in children and the development of neurodevelopmental disorders. This study aimed to investigate the relationship between PM and ADHD by using a nationally representative sample from the Korean National Health and Nutrition Examination Survey (KNHANES).Data were obtained from the 2008–2018 (IV–VII) cycles of the KNHANES for children aged <20 years. KNHANES is a nationally representative, cross-sectional survey of the non-institutionalized civilian Korean population and is conducted with a complex, multistage, stratified, and probability cluster sampling design . In 2008www.airkorea.or.kr accessed on 21 May 2021) from region-specific government sites and were reported to the National Ambient Air Monitoring Information System. These data were confirmed and finalized by the National Institute of Environmental Research. The measurements were considered valid if the hourly data met the 75% data completeness criteria.PM concentration data were collected by Air Korea (The study area included 16 administrative divisions and 333 monitoring stations (December 2018) across South Korea. The mean daily concentration at each monitoring site was first calculated. If >75% of the data were complete, the value was considered valid. Thereafter, we calculated the annual concentration at the monitoring sites with a missing rate of <25%. Finally, we averaged the annual means at the 16 administrative divisions.10 concentration were linked to the individual administrative district codes for the year of diagnosis.To assign exposure measurements, the study population was restricted to children with information on the age at diagnosis of ADHD collected from parental reports. For children with ADHD, the diagnosis year was calculated as the difference between the survey year and the diagnosis age. For the control group, the diagnosis year was calculated by assuming the same diagnosis age for the sex–age matched pairs. The annual average values of PM10 was assessed using the annual mean concentration at diagnosis (lag0) and the average measurements between the year of diagnosis and the previous 4 years (lag0–1 to lag0–4). As KNHANES is a cross-sectional survey, data on the participants’ changes of address were not available. Therefore, we assumed that the participants lived in the same region during the exposure period. As exposure data are available from 2001, we excluded children diagnosed with ADHD before 2005 to account for the exposure time window.Exposure to PM2), absence due to illness, and self-reported health  were used as individual factors. Socioeconomic factors included family income and insurance type . Region was categorized as rural  or urban . Household food security, parental stress, parental heavy drinking, and secondhand smoking were also evaluated as familial risk factors. Household food security was classified as secure or mildly insecure (food security and mild food insecurity without hunger) and moderately/severely insecure (moderate food insecurity with hunger or severe food insecurity with hunger). Parental stress was divided into low (very low or low) and high (high or very high) levels for one or both parents. Parental drinking status was also included as non-/low drinking and heavy drinking. Secondhand smoke exposure at home was defined as having any smoker in the household (yes/no) or having a parent who was a current smoker.We investigated several covariates and potential confounders identified in the literature. Age, sex, body mass index (BMI)  were assessed using the chi-square test for categorical variables and the independent 3 increase in PM10 concentration. The effect of air pollutants on ADHD was estimated using both continuous and categorical variables, and the average exposure levels were categorized into quartiles. We conducted stratified analyses using the 1-year average PM10 concentration to confirm the existence of additional confounding according to age group at diagnosis, sex, region, and secondhand smoke exposure at home. All of the analyses were performed using SAS  and R . The statistical significance level was set at α = 0.05.A logistic regression model was used to estimate the association between air pollution exposure and ADHD. The crude model included only exposure variables based on the age–sex matching set. The final model included age, sex, BMI, absence due to illness, self-reported health status, family income, type of health insurance, food security, parental stress, parental heavy drinking status, secondhand smoke exposure at home, proportion of the elderly, proportion of basic livelihood security recipients, and proportion of people with no high school diploma. The results were reported as estimated odds ratios (ORs) with 95% CIs per 10 µg/m10 for the entire country during the study period (2005–2018) are shown in Among the 13,441 children aged 6–19 years, we identified 134 ADHD cases and 13,307 controls. After age–sex matching of the ADHD cases and controls, 1,120 participants remained for analysis. The annual mean concentrations of PM10 and ADHD (3 increase in the 1-year (lag0) average of the annual mean PM10 concentrations, the OR for ADHD was 1.44 . Compared with the lowest quartile of PM10 (Q1), the OR for ADHD in the highest quartile (Q4) was 1.81 . When the exposure period was longer, the increase in PM10 showed a positive association with ADHD; however, it was not statistically significant. For each exposure period, the association with ADHD was stronger at higher quartiles than in the lower quartiles.Multivariate analysis of the various exposure time windows revealed a significant association between PMand ADHD . For a 110 exposure on ADHD was significantly stronger in girls, in participants living in urban areas, and in those with exposure to secondhand smoke .10 of 10 µg/m3 was significantly associated with ADHD . Children in the highest quartile (Q4) of exposure had a higher OR for ADHD than those in the lowest quartile . This study suggests that PM influences the pathogenesis of neurodevelopmental disorders.This study investigated the association between PM exposure and ADHD in children and adolescents aged 6–19 years. Our results showed that an increase in PMADHD is a complex, highly heritable, and heterogeneous disorder. Individuals with ADHD differ from each other in different ways at many levels, such as genetic risk, environmental exposure, brain structure, and cognitive function . We aime10–2.5) were shown to increase antioxidant markers (heme oxygenase 1 (HO-1) and superoxide dismutase 2 (SOD-2)) that induced physiological changes in the central nervous system and activated inflammation (interleukin 1 beta (IL-1β) and tumor necrosis factor-alpha(TNFα)) and unfolded protein response (X-box binding protein 1 spliced form (XBP-1S) and heat shock 70 kDa protein 5 (BiP)) [The pathological pathway between PM exposure and ADHD remains unclear. There are two main mechanisms how PM affects the central nervous system. One is through the olfactory epithelium, directly to the brain without going through the blood–brain barrier (BBB), and the other is through the systemic circulation through the BBB to the brain ,22. Some5 (BiP)) . Studies5 (BiP)) ,27. An i5 (BiP)) . PAH, a 5 (BiP)) .10 and 1.03  for NO2. Another study [10, NO2, and SO2 were associated with hospital admissions for ADHD. Both studies suggested that long- or short-term exposure to air pollution is associated with ADHD. Although the current study had a different design, the same association was observed.In Korea, only a few studies have investigated the relationship between air pollution and ADHD in children and adolescents. In a study using the National Health Insurance Service-National Sample Cohort (2002–2012) , infantser study  found th2 as a traffic-related air pollutant was associated with attention deficit. Children prenatally exposed to suspended PM (PM with aerodynamic diameter less than 7 µm (PM7)) showed delayed behavioral development such as attention, self-regulation, inhibition, and impulsivity at age 5.5 years, and it was related with ADHD in childhood [2 and PM with the risk of ADHD in children aged 3–10 years [2, and SO2, on various neurodevelopmental disorders (including ADHD) in children [A previous study  reportedhildhood . In cont10 years . A Swedi10 years  also shochildren ,18. AlthThis study had several limitations. The reliance of outcomes on parental reports of doctor-diagnosed ADHD may be less reliable than a confirmed diagnosis using diagnostic tools or administrative records. The prevalence of ADHD in this study was 1.0% (134 children), which was significantly lower than the prevalence of 5.9–9.0% among children and adolescents in a previous study . However10 concentrations between monitoring sites within each district were generally high (r > 0.69), suggesting that the variation in PM10 concentration in the district would not be significant. Although assigning an average exposure to each individual at a fixed monitoring site would introduce a Berkson error, the error is not expected to significantly affect the measurements and estimates [2.5 exposure because the data were only available from 2015. Our study subjects were from 2008 to 2018, and the number of subjects was insufficient to evaluate PM2.5 exposure. Further analysis of the effects of PM2.5 would be needed in the near future.Another important limitation is the method used to assign individual exposure levels. KNHANES provides 16 administrative codes for the residential addresses of survey participants, and variations may exist in air pollution concentrations within the districts. However, the correlations of PMstimates . To miniWe also cannot completely rule out the potential residual confounding, especially due to the large spatial resolution of the data. To reduce this potential bias, the models were adjusted for several ecological variables, including the proportion of basic livelihood security recipients, proportion of the elderly, and people with no high school diploma.Although KNHANES data include blood lead and cadmium levels, the model was difficult to analyze because of the low response rate in this study. The thyroid hormone is an essential factor for normal brain development, maturation, and function. PM and absorbed neurotoxins can potentially disrupt brain development due to dysregulation of thyroid hormone levels ,39. In a10 exposure and ADHD tended to be greater in the model with secondhand smoke exposure. This suggests that environmental factors influence the risk of ADHD; however, an inverse interaction was not observed.The strength of our study is that we considered ecological and familial risk factors as covariates using nationally representative sampling data. Ecological factors, as indicators of the sociophysical environment, were included in the model, along with individual socioeconomic factors. In addition, parental risk factors such as parental stress, secondhand smoke exposure, and heavy parental drinking were analyzed as potential confounding factors. In particular, secondhand smoke exposure has been reported to be associated with neurodevelopmental delay and poor learning ability in children and adolescents . Our stu10 exposure in children and adolescents, the statistical significance was not consistent when comparing various exposure periods. The results of stratification analyses suggest that girls, children living in urban areas, and children who are exposed to secondhand smoke at home are more vulnerable to the effects of PM.We applied a case-control design using age–sex matching. Because the prevalence of ADHD was different between males and females, potential confounding may have been controlled by matching. Although our results showed an association between ADHD and PM10 exposure and ADHD in children and adolescents. Our result supports the findings of previous studies on the cognitive health effects of air pollutants. Children diagnosed with ADHD suffer from problems with educational, occupational, social activity, and other psychiatric disorders, and the economic burden is significant [In conclusion, this study demonstrated the association between PMnificant ,43. In K


A large and growing body of neuroimaging research has concentrated on patients with attention-deficit/hyperactivity disorder (ADHD), but with inconsistent conclusions. This article was intended to investigate the common and certain neural alterations in the structure and function of the brain in patients with ADHD and further explore the differences in brain alterations between adults and children with ADHD.We conducted an extensive literature search of whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies associated with ADHD. Two separate meta-analyses with the seed-based d mapping software package for functional neural activation and gray matter volume (GMV) were carried out, followed by a joint analysis and a subgroup analysis.This analysis included 29 VBM studies and 36 fMRI studies. Structurally, VBM analysis showed that the largest GMV diminutions in patients with ADHD were in several frontal-parietal brain regions, the limbic system, and the corpus callosum. Functionally, fMRI analysis discovered significant hypoactivation in several frontal-temporal brain regions, the right postcentral gyrus, the left insula, and the corpus callosum.This study showed that abnormal alterations in the structure and function of the left superior frontal gyrus and the corpus callosum may be the key brain regions involved in the pathogenesis of ADHD in patients and may be employed as an imaging metric for patients with ADHD pending future research. In addition, this meta-analysis discovered neuroanatomical or functional abnormalities in other brain regions in patients with ADHD as well as findings that can be utilized to guide future research. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition DSM-5) and International Classification of Diseases ICD-10), attention-deficit/hyperactivity disorder (ADHD) is a widespread neurodevelopmental disorder affected by multiple factors, which are often manifested as inattention and/or impulsiveness hyperactivity 0, attent, and the and InteAt present, studies in various fields, such as genetics , 7, etiovia quantitatively calculating the differences in density or volume of the gray matter and white matter of each voxel in the MRI. However, most of the current research results are inconsistent. Some studies reported decreased gray matter volume (GMV) in the right and left anterior cingulate cortex (ACC)  at the voxel level  and reflex (ACC)  and the ex (ACC)  in patieex (ACC) . Sutcubaex (ACC)  reportedex (ACC)  analyzedFunctional MRI (fMRI) describes the activation region of neural excitation by monitoring blood oxygen levels and using deoxyhemoglobin as an endogenous contrast agent, characterized by low time resolution and high spatial resolution . At presRecently, the results of a combined VBM and fMRI analysis  showed tBased on previous theories and empirical studies in ADHD, we hypothesized that both functional and structural alterations in frontostriatal and fronto-cingulate circuits occur in patients with ADHD relative to HCs. In addition, studies suggest that some symptoms of ADHD in children will gradually get better with age but will not completely disappear. Therefore, we expect that more brain areas in the children group will show changed GMV and abnormal activation compared to the adult group. It may also be associated with delayed maturation of certain brain regions. The objective of our study was to explore the common and specific neural alterations in the structure and function of the brain in ADHD and further explore the differences in brain alterations between adults and children with ADHD. Eventually, the significance of these research results and their prospective development paths are examined and discussed.A comprehensive literature search of fMRI and VBM studies of ADHD from January 2010 to November 2021 was conducted primarily using PubMed, as well as additional searches in the Web of Knowledge and Science Direct databases, with a combination of the following keywords: “voxel-based morphometry” or “VBM” or “morphometry” or “gray matter” or “functional magnetic resonance imaging” or “fMRI” and “ADHD” or “Attention Deficit/Hyperactivity Disorder.” In addition, a manual search was conducted in reference lists of previous meta-analyses. Eligible original studies were identified through reference tracking and consultation to retrieve high-quality meta-analyses and review articles.x, y, z) for changes in standard stereotactic space ; (4) the diagnosis of patients with ADHD had to be based on DSM-IV-TR, or DSM-V, or ICD-10; and (5) they were peer-reviewed and published in English as an article. Studies were excluded if (1) the peak coordinates were not reported; (2) VBM was not used and only ROI analyses were used; (3) the patient data were duplicated or no eligible contrasts were found; and (4) neurological or psychiatric comorbidities  were found in the patient group. In the case of incomplete or ambiguous information, we will contact the author for clarification.Studies were eligible if (1) they involved task-related fMRI or VBM study; (2) they compared patients with ADHD and HCs; (3) the whole-brain outcomes were presented in three-dimensional coordinates  was applied extensively in a recent meta-analysis. We plan to use it to analyze the GMV and functional neural activation changes between patients with ADHD and HCs, following related guidelines (MOOSE) for meta-analyses. In this study, we summarized the AES-SDM data processing procedure. First, the peak coordinates extracted from each dataset were manually imported into the software. Second, the maps of the lower and upper bounds of possible effect sizes were reconstructed for each VBM or fMRI study separately using an anisotropic non-normalized Gaussian kernel. Peak coordinates were converted to Montreal Neurological Institute (MNI) space. After that, this mean analysis was processed, which consists of calculating the mean of the voxel values in the different studies. This mean is weighted by the inverse of the variance and accounts for inter-study heterogeneity. In addition, the results of some studies were p-value or z-value, which needed to be converted to t-statistics online; full width at half maximum (FWHM) was set to 20 mm because this setting was optimal to balance sensitivity and specificity, and other parameters included voxel P = 0.005, peak height Z = 1, and cluster extent = 10 voxels (The anisotropic effect size seed-based d mapping (AES-SDM) (version 5.15) meta-analytic software package (0 voxels .Table 2) for the next subgroup analysis, namely, adult and pediatric (including adolescents and children) subjects, using a random effects model under the same threshold as before. To examine the effects of age, gender, and IQ, meta-regression analyses were performed. Finally, to assess whether the results were replicable, a jackknife sensitivity analysis was performed, in which the same analysis was repeated in one study, excluding one data at a time. Moreover, Egger's test was used to examine the possible publication bias. A statistical threshold p-value of <0.005 was used for all meta-analyses  were reviewed, and 144 full-text publications (61 VBM studies and 83 fMRI studies) were evaluated for eligibility. The final sample included 29 VBM research  and 36 fMRI research (850 patients with ADHD and 813 HCs) studies  were screened, with an increase of 21 documents (six VBM studies and 15 fMRI studies) discovered W-tests in the whole-group VBM analysis indicated that patient groups did not differ in age , and the patient groups have a slightly lower IQ than the control groups . The chi-squared test revealed that both groups had a significantly higher proportion of boys/men . In the whole-group fMRI meta-analysis, patient groups did not differ in age , and the patient groups have a slightly lower IQ than the control groups , but a higher percentage of patients with ADHD were boys/men . Details of the regression analysis results of the subgroups are shown in The Wilcoxon Patients with ADHD showed significantly lower GMV in the bilateral anterior cingulate cortex/median cingulate/superior frontal gyrus/olfactory cortex, the left precentral/postcentral gyrus, the left inferior frontal gyrus opercular/orbital part, the left supramarginal gyrus, the left caudate nucleus, the left anterior thalamic projections, the right gyrus rectus, and the corpus callosum compared to HCs. In contrast, the results also showed an increase in the left striatum/lenticular nucleus, the right caudate nucleus, and the right anterior thalamic projections see .Patients with ADHD showed overactivation in the middle occipital gyrus, the right insula, the right precuneus cortex, the left cerebellum hemispheric lobule, and the left fusiform gyrus compared to HCs. In contrast, the results also showed hypoactivation in the bilateral superior temporal gyrus, the left middle/inferior temporal gyrus, the left superior/middle/inferior frontal gyrus, the right precentral /postcentral gyrus, the left insula, and the corpus callosum see .In patients with ADHD, there was an overlap between decreased GMV in the left inferior frontal gyrus and hypoactivation compared to HCs  see .Individuals were divided into two independent subgroups, namely, adult and pediatric groups (including adolescents and children). The findings of the adult group's (11 datasets) analysis were substantially in line with the pooled analysis, except for the additional significant GMV decrease in the right middle frontal gyrus. In addition, relative to the pooled analysis, no significant GMV decrease regions were found in the left supramarginal gyrus and the left inferior frontal gyrus. Similarly, the results also indicated increased GMV in the left striatum and the left lenticular nucleus. The results of the pediatric group's (19 datasets) analysis showed GMV decrease in additional brain regions—the bilateral middle frontal gyrus, the bilateral superior temporal gyrus, and the left insula. Similarly, relative to the pooled analysis, no significant GMV decrease regions were found in the left supramarginal gyrus, the left caudate nucleus, and the left anterior thalamic projections, the left precentral /postcentral gyrus, and the right gyrus rectus. In addition, the results showed increased GMV in the left striatum, the left middle occipital gyrus, the right caudate nucleus, and the right anterior thalamic projections see .Similarl to the previous analysis, we divided individuals into adults and pediatrics (including adolescents and children), two independent subgroups. The findings of the adult group (11 datasets) were partly consistent with the pooled analysis, and significant hypoactivation in the bilateral insula, the left striatum, the left lenticular nucleus, the right superior temporal gyrus, the right precentral/postcentral gyrus, the right caudate nucleus, the right anterior thalamic projections, and the corpus callosum was found. In addition, significant overactivation regions were found in the left middle occipital gyrus and the right middle temporal gyrus. Relative to the pooled analysis, in the pediatric group (22 datasets), no significant hypoactivation was found in the right superior temporal gyrus and the right postcentral gyrus. On the contrary, overactivation of the right fusiform gyrus was found. The rest was largely consistent with the pooled analysis see .P > 0.05 for all comparisons, Bonferroni corrected), indicating that there was no publication bias. Jackknife reliability analyses revealed that the findings were robust in terms of disorder differentiation.Egger's tests were conducted to investigate the potential publication bias. The results of the Egger's tests were non-significant  are smaller in size in patients with ADHD. Simultaneously, we also found significant hypoactivation in the right postcentral gyrus. Previous research  found thIn addition, our analysis results suggest that the temporal lobe may play a crucial role in ADHD. The results of the meta-analysis of fMRI indicate significant hypoactivation in the bilateral superior temporal gyrus and left middle/inferior temporal gyrus. However, decreased GMV was found in the bilateral superior temporal gyrus of the pediatric group alone. Sowell et al.  found reIt is worth mentioning that we simultaneously found reduced corpus callosum GMV in patients with ADHD relative to HCs and showed hypoactivation in the corpus callosum in fMRI. Considering the anterior or genu region of the corpus callosum connects homologous areas of the frontal cortex , theorieThe meta-analysis of Jagger-Rickels et al.  found reIn our study, increased activation was also found in the left middle occipital gyrus, right precuneus, and left cerebellar hemisphere among patients with ADHD compared to HCs. Occipital cortex, as the center of visual cortex, is responsible for the processing of visual information. To maintain attention, occipital cortex interacts with the dorsal attention network , inhibitThe main results of the whole-group analysis were also found in the adult group and the pediatric group, which further supported the whole-group results. Alterations in the left superior frontal gyrus and the corpus callosum persisted in the pediatrics group and adult group, which we believe further illustrates the crux of this brain region. Subgroup analysis found relatively more brain areas with reduced GMV and abnormal activation in children and adolescents compared to adults. Longitudinal MRI studies , 123 havOur study has several limitations that are inherent in all meta-analyses. In the first place, since it was based predominantly on the peak coordinates of published studies, instead of raw statistical brain maps, it may be underpowered to identify some results with small or moderate effect sizes. Second, for the many papers included in this meta-analysis, different statistical thresholds may have been applied. Additionally, the heterogeneity of the methodologies among VBM and fMRI studies could not be prevented, such as the differences in MRI machines, smoothing kernel size, statistical thresholding, and so on, which might have caused conflicting results. Moreover, fMRI studies are mostly based on tasks in different attention, inhibition, time, and motivation domain, and then, the results may not be precise enough. Future studies should minimize these differences and confounding variables to get a more convincing result.ADHD has far-ranging structural and functional abnormalities in the brain. The VBM meta-analysis found reduced GMV in the left superior frontal gyrus and the corpus callosum in patients with ADHD compared to HCs. Studies in task-related fMRI found that patients with ADHD showed low activation in the abovementioned brain regions. We hold the opinion that abnormal alterations in the structure and function of the left superior frontal gyrus and the corpus callosum may be the key brain regions involved in the pathogenesis of ADHD in patients and may be employed as an imaging metric for patients with ADHD pending future research. This meta-analysis also identified neuroanatomical or functional abnormalities in other brain regions in ADHD. Besides, subgroup analysis further explored and supported the above results, and this information can be applied to guide future studies. However, there is a need for more research in this area because of the lack of clarity on the confounding effects. Our results, as solid as it appears, should be read with caution.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.MY and XG designed the experiment. MY, XG, XN, and MZ performed the experiment. ZY, SH, JC, and YZ modified the experiment and drafted the manuscript. All authors contributed to the article and provided approval for the final version of the manuscript.


Attention-Deficit/Hyperactivity Disorder (ADHD) affects approximately 4.4% of adults in the US and impacts multiple domains of daily life including education, workplace performance and interpersonal relationships. Although an increasing number of individuals with ADHD are now entering later life, there is very little research on how ADHD risk may impact cognitive function during aging. As such, there is value in understanding the association between polygenic risk for ADHD and cognition during different stages of later life. This study utilized data from the Health and Retirement Study which surveys 37,000 Americans biennially and aimed to determine if there was an influence of ADHD risk on cognitive performance when individuals were young-old (ages 65-74) or middle-old (ages 75-84). Only participants who responded in 2006, 2016, and to the Venous Blood Study were selected. The resulting sample size of 403 African-ancestry individuals (AA) and 2286 European-ancestry individuals (EA) was compared on executive function-focused measures as well as delayed recall measures. Results showed that there was no significant effect of ADHD risk on memory-related measures at both time-points for AA and EA individuals. However, there was a statistically significant association between ADHD risk and performance on the executive function measure for EA older adults who were middle-old (p = 0.028), but not when they were young-old; no such association was observed for AA adults. This finding suggests that ADHD risk may influence cognition among older adults and has significant implications for treatment and care of individuals with ADHD throughout the life course.


Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility  when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group . Finally, regional neural flexibility was capable of differentiating ADHD from TDC  and of predicting ADHD severity using clinical measures of symptom severity . In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw  Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders in children, affecting 3–5% of children worldwide , 2. ADHDA large number of neuroimaging studies have observed that symptoms of ADHD are driven by atypical brain network organization and impaired functional connectivity (FC) –12. DisrCognitive flexibility is a critical aspect of human cognition  and has In this study, we aimed to determine whether neural flexibility can serve as a biomarker to differentiate children with ADHD from TDC and whether it is associated with ADHD severity. Specifically, we implemented machine learning methods on neural flexibility estimates to: (1) distinguish children with ADHD from TDC; and (2) to assess symptom severity in children with ADHD. We hypothesized that children with ADHD would exhibit lower neural flexibility than that of TDC and that it would successfully differentiate groups and predict symptom severity. Furthermore, although pharmacological treatments can ameliorate the core symptoms of ADHD and improve subjects’ future functional outcomes , only a We used two sites from a publicly available, multi-site ADHD dataset, the ADHD-200 study : Peking RsfMRI data were preprocessed using FSL , which ip value for each correlation coefficient was estimated using the MATLAB function corrcoef, and only connections significantly different from zero were retained .After preprocessing, a 5 mm sphere around the coordinates defined by Power264 atlas , was defN is the total number of possible community changes and ni is the number of times node i changes its community label. To account for pseudo-randomness of the community detection algorithm, the Generalized Louvain method was repeated 100 times and mean values of all community-based measures were taken. Whole brain and network-level neural flexibility were calculated as the mean neural flexibility of all nodes of the entire brain and within a predefined canonical network from the Power264 atlas, respectively.A multilayer network was constructed by connecting each node to itself in adjacent time windows Fig. . Dynamicp < 0.05. False discovery rate (FDR) correction was performed for multiple comparisons. The extreme gradient boosting (XGBoost) algorithm was used to develop two models: a classification model to differentiate subjects with ADHD from TDC, and a regression model to predict ADHD severity of individuals. Optimally predictive combinations of region-wise neural flexibility were identified based on their ranked nodal importance for the classification and regression models, respectively. Specifically, we evaluated the model performance of the top N . Age, sex, mean FD, and imaging site were included as covariates. Statistical significance was considered as Additional details and analyses are provided in After data preprocessing and quality control, a total of 180 ADHD subjects and 180 TDC were included in the statistical analyses. Demographic and clinical information, as well as motion parameter estimates, are summarized in Table p values < 0.05, FDR corrected for 14 networks). Additional region-level analysis revealed similar patterns  was achieved using the PKU dataset Fig. . Using tR2 score from tenfold CV with ten repetitions was 0.079. In contrast, an average R2 of 0.2794 was achieved using the features selected from the brain regions with the highest 28 importance scores  were consistently observed, suggesting that they may play key roles for delineating ADHD from TDC as well as for predicting ADHD severity. Indeed, using these 11 regions, comparable performance to the CR_c and CR_r models was achieved from ten times tenfold cross validation within the PKU dataset (There are several limitations of this study. First, as publicly available datasets, the imaging parameters and the rating systems of IQ and ADHD severity differ between PKU and NYU cohorts. Nevertheless, our prediction models were generalizable across the two cohorts. Second, we only considered males in the machine learning models due to the limited female subjects with ADHD. Third, we opted not to adjust IQ since the ADHD subjects tends to exhibit low IQ and controlling this variable can provide counterintuitive estimates of the effects of interest . FinallyIn conclusion, we investigated the dynamic functional network reconfiguration in ADHD. Significantly decreased neural flexibility was observed in children with ADHD spanning multiple brain functional networks, supporting the multi-network explanations of ADHD. Using the XGBoost approach, core regions critically important for differentiating ADHD from TDC, as well as predicting ADHD severity were reported. Finally, we were able to successfully classify group membership and predict ADHD severity using an independent testing dataset, demonstrating the robustness of these approaches. Our study demonstrated the potential clinical utility of neural flexibility to diagnose children with ADHD and monitor disease severity.SUPPLEMENTAL MATERIAL


Treatment-related changes in ADHD, depressive symptoms and LRTC were investigated in the whole population and within each group. Our results revealed significant LRTCs existed in all investigated frequency bands. There were, however, no significant LRTC-differences between ADHD-patients with and without depressive symptoms at baseline and no LRTC-changes following treatment. However, depressed ADHD patients did seem to benefit more from the therapy with psychostimulant based on self-report.Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder, characterized by core symptoms of inattention, hyperactivity and impulsivity. Comorbid depression is commonly observed in ADHD-patients. Psychostimulants are recommended as first-line treatment for ADHD. Aberrant long-range temporal correlations (LRTCs) of neuronal activities in resting-state are known to be associated with disorganized thinking and concentrating difficulties  and with maladaptive thinking . It has yet to be examined whether (1) LRTC occur in ADHD-patients, and if so, (2) whether LRTC might be a competent biomarker in ADHD comorbid with current depression and (3) how depression affects psychostimulant therapy of ADHD symptoms. The present study registered and compared LRTCs in different EEG frequency bands in 85 adults with ADHD between groups with (The online version contains supplementary material available at 10.1007/s00406-022-01452-2. Adult attention deficit/hyperactivity disorder (aADHD) is a frequent neurodevelopmental disorder with a worldwide prevalence of at least 2.8% . It is cIn a range of studies, aADHD has been associated with co-occurrence of depressive disorder –7. In seNeural oscillations arising from synchronized electrical activity of numerous neurons during resting state are not random but follow complex temporal structures . It was ADHD is associated with several neurocognitive deficits including difficulty in inhibiting non-beneficial behaviors, poor working-memory, concentration abilities and emotional instability. ADHD shares symptoms liking cognitive impairment in Schizophrenia , depressFor the treatment of aADHD, psychostimulants are recommended as first-line treatment as they have been shown to improve attention and enhance central nervous system (CNS) arousal . Given tFurthermore, several previous studies demonstrated significant associations between LRTC and severity of depressive symptoms in clinically depressed patients –43 and aAgainst this background, the main aims of this study were to examine the presence of LRTC in aADHD in resting EEG and to investigate whether LRTC are modulated by means of pharmacological intervention with methylphenidate in these patients. The second aim of this study was to explore whether LRTC is yet a competent biomarker of pathophysiology in aADHD with additional depressive symptoms, and if so, how and to which extend the effect of pharmacological intervention with extended release methylphenidate is modulated by comorbid depressive symptoms.Data of the current study were taken from a previously published multicenter, single-arm, open label clinical trial in ADHD patients  with a rFor the current analyses, patient data were included if they met the following criteria: clinical DSM-IV diagnosis of ADHD confirmed by a psychiatrist and psychologist, no evidence of current suicidality, anxiety or adjustment disorders, no history of substance abuse or dependence and no psychotic disorders. Patients also had to have completed a titration phase with extended release methylphenidate for 4 weeks. Exclusion criteria were demonstration of acute episodes of major depression according to ICD-10 during patient screening, pathological activity or excessive artifacts in resting EEG and remaining insufficient EEG epochs for LRTC analysis  at baseline or at the final visit, respectively.r = 0.90) and internal consistency (r = 0.91), significant correlations were found to impulsivity in Eysenck’s Impulsivity Questionnaire, and excitability, aggression, emotional instability and satisfaction on the Freiburg Personality Inventory in ADHD patients [ADHD symptom severity: At baseline, a set of self-report measurements was used to evaluate ADHD-related symptoms: the short German Wender Utah Rating Scale (WURS-k)  was admipatients . The Gerpatients  was perfpatients . In addiDepressive symptom severity and group distinction: Severity of depression was assessed using the German Beck Depression Inventory-II (BDI-II) . Its sumClinical related symptoms. Moreover, a set of questionnaires was employed to assess other psychological problems and status of each participant: the Severity Scale of the Clinical Global Impression (CGI-S) is a 7-point scale that requires clinician to rate the overall clinical severity of a participants’ illness at the time of assessment. The German Inventory of Interpersonal Problems (IIP)  identifiTreatment. After diagnosis, all participants received 20 mg of extended release methylphenidate as initial dose. The dose was then increased weekly in steps of 20 mg until individual weight-based target doses (i.e. 40 mg/60 mg/80 mg per day) were reached. The dose could be adjusted or stopped due to any side effects at any time.Reassessment. At the final visit, reassessment of all the above-mentioned instruments, except the WURS-k, allowed evaluation of the change from baseline due to the pharmacological intervention.To collect EEGs in eyes-closed resting condition, all participants were placed in a semi-supine position in a sound-attenuated and temperature-controlled room. They were instructed to rest with their eyes closed for a 15 min recording session. EEG was recorded from 31 Ag/AgCl active electrode positions according to the extended international 10–20 system using EasyCap . Each EEG channel was referenced to a common average. Two bipolar electrodes monitored horizontal and vertical eye movements. Electrode impedance was kept below 10 kΩ.The EEG signal was analyzed in the Brain Vision Analyzer Software Version 2.1  and MATLAB . The data were first high pass-filtered at 1 Hz and down-sampled to 200 Hz. The first 10 min of the total 15 min EEG period were then selected in order to reduce the impact of different CNS arousal states on the LRTC in case participants had been falling asleep. Eye movements, muscle and cardiogenic artefacts were removed with an independent component analysis (ICA). After the ICA, the segmented EEG was recombined into continuous signals for subsequent analysis.Afterwards, bandpass filtering was applied to filter signals in delta 1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz) and beta (13–25 Hz) bands for all electrodes . In case of significant results, we conducted exploratory post hoc analyses for each single electrode. Statistical analyses were conducted using SPSS Statistics 25.0 .The relationship between LRTC and clinical symptoms was quantified by calculating Person product-moment correlation coefficient between LRTC in corresponding frequency band, depressive and ADHD symptoms.Before we performed statistical comparisons, we executed Kolmogorov–Smirnov Tests to examine the normality of distribution of relevant variables. The results of Kolmogorov–Smirnov Tests are summarized (Table S1) in supplementary file. According to these results, non-parametric tests were applicable for age, CGI-S, BDI, MADRS, CAARS, WURS-K and LRTC, while parametric tests were for QoL and IIP.U Tests were performed. Baseline vs. Final comparisons of treatment related changes in LRTC were investigated by Wilcoxon Tests.In order to compare the LRTC between ADHD patients with (ADHD +) and without (ADHD-) additional depressive symptoms at baseline, Mann–Whitney-The correlational analyses and comparisons were done separately for LRTC in corresponding frequency band. Bonferroni adjusted alpha levels of 0.0125 per test was applied to avoid type I error inflation.t-Tests for normal distributed metric variables (QoL and IIP), while Mann–Whitney-U Tests were applied for non-normally distributed variables . Person Chi-Square Test were used for nominal variables (sex and dosage). The treatment related changes were investigated by paired-sample t-Tests/Wilcoxon Tests. Statistical significance value for these tests was set at 0.05.The differences of clinical characteristics at baseline between ADHD + and ADHD- was examined by independent sample Additionally, depending on the selected tests their corresponding between-group (ADHD + vs. ADHD-) and within-group  effect sizes (Cohen’s d or |r|) were calculated.The final sample consisted of 85 aADHD patients, who completed a titration phase with extended release methylphenidate for 4 weeks. There were 28 aADHD patients with additional depressive symptoms allocated into ADHD + group, while 57 aADHD patients without depressive symptoms in ADHD- group. This group distinction was based on the cut-off score cut-off 13) in the BDI-II at baseline. Their main clinical characteristics are presented in Table 3 in the Both groups are similar in sex, age and dosage. We obtained higher scores for individuals in the ADHD + group regarding depressive symptoms (MADRS) and clinical global impression (CGI-S) based on external assessment, than for those patients in the ADHD- group. The former also had significantly higher T-scores regarding currently ADHD severity as assessed by subjective rating (CAARS) and higher scores regarding ADHD severity in childhood based on subjective retrospective diagnosis (WURS-K). Furthermore, individuals in the ADHD + group as compared to those in the ADHD- group experienced more interpersonal difficulties (IIP) and lower quality of life (WHOQOL-BREF).Since the scaling exponent provided a quantitative measure for the LRTC of EEG signals, the scaling exponent in the 0.5–1.0 range indicates the presence of LRTC. According to this criterion, the mean LRTC was significantly present in the delta , theta , alpha  and beta  bands. The topographical distributions  and descriptive statistics at each electrode site (Table S2) are summarized in supplementary file.r =  – 0.228, p = 0.036) as well as between mean alpha-LRTC and T-score for subscale HY/RE of the CAARS , but they were not significant after Bonferroni correction (p ≤ 0.0125). No further significant associations were found between mean LRTC measured at resting EEG and T-scores obtained via CAARS and BDI scores , theta , alpha  or beta  bands. It is of note that a small effect size (|r|= 0.191) was found for the difference in mean theta-LRTC between ADHD + and ADHD-. Table Results of Mann–Whitney-U-Tests did not show any significant differences between ADHD + and ADHD- regarding mean LRTC in the delta . There was no clear indication that ADHD- and ADHD + group different regarding the LRTC-difference between baseline and final visit (supplementary file Table S3). The corresponding detailed statistical results are consolidated in Table In the entire sample population, analyses of changes from baseline to final visits revealed a significant improvement in depressive  and ADHD symptoms (CAARS). This improvement was in accordance with improvement in other clinical symptoms (CGI-S) as well as in interpersonal conflicts (IIP) and increased quality of life (WHOQOL-BREF) measured by questionnaires. Refer to Table The similar significant improvement was obtained in the ADHD + and the ADHD- groups: patients in the ADHD + group showed significant improvements in all investigated measurements. Likewise, patients in ADHD- group showed significant improvements in their existing symptoms, i.e. all investigated measurements except BDI. Patients in the ADHD + group had slightly larger effect sizes than patients in the ADHD- group for CGI-S (0.863 vs. 0.781), MADRS (0.465 vs. 0.396), IIP (0.630 vs. 0.392) and WHOQOL-BREF ( – 0.548 vs.  – 0.383). Additionally, effect sizes for differences in scores of CAARS subscale DSM-IA (0.796 vs. 0.793), DSM-HYI (0.785 vs. 0.772), IA/ME (0.852 vs. 0.768), HY/RE (0.852 vs. 0.837) and IMP/EL (0.839 vs. 0.728) between baseline and final visit were slightly larger in ADHD + than in ADHD- group. However, these two groups did no differ significantly regarding these differences between baseline and final visit (supplementary file Table S3).The first aim of this study was to examine the presence of LRTC in adult patients with ADHD and to investigate their treatment-related changes. As a second aim of this study, we compared LRTC, severity of clinical symptoms and status between comorbidly depressed and non-depressed aADHD patients. We also investigated to which extend the effect of pharmacological interventions with psychostimulants was influenced by the additional depressive symptoms.To the best of our knowledge, this is the first study that demonstrated the presence of LRTC in aADHD. Significant LRTCs were observed across all investigated EEG frequency bands. LRTCs are considered to reflect the developmental trajectories of human brains from childhood to adolescence, during adolescence and even up to early adulthood after which the temporal structure stabilizes . PreviouTwenty-eight out of 85 of our aADHD patients reported additional depressive symptoms at baseline, which resulted in a prevalence of comorbid depression of about 33% in this study. Notably, this is not a representative prevalence since the patients with acute episodes of major depression according to ICD-10 criteria were already excluded during patient screening. Nevertheless, our aADHD patients reported depressive symptoms based on BDI-II. These patients did experience more difficulties in diverse interpersonal domains, perceived lower quality of life and showed worse self-concept as well as more emotional impulsivity  and slight attenuated theta-LRTC in depressed aADHD with a small effect size (|r|= 0.191). Our findings, however, are in inconsistence with previous results reporting higher values of theta-LRTC in diagnosed depressed patients [As above-discussed, our depressed aADHD showed further impairments due to the additional depressive symptoms, such further impairment on LRTCs is potentially reflected by the negative, but non-significant correlation between theta-LRTC and severity of depressive symptoms Below is the link to the electronic supplementary material.


The Strengths and Difficulties Questionnaire (SDQ) is a worldwide questionnaire used for the early identification of behavioural/emotional symptoms in children and adolescents with neuropsychiatric disorders. Although its prognostic power has been studied, it has not yet been tested whether SDQ: (i) can identify pathognomonic symptoms across a variety of neurodevelopmental and neuropsychiatric disorders, (ii) can capture emotional and behavioural problems associated with the main diagnosis, as well as shared transdiagnostic dimensions, and (iii) can detect changes in symptomatology with age. The present study evaluated nearly 1000 children and adolescents overall with Global Developmental Delay (GDD), Intellectual Disability (ID), Language Disorder (LD), Specific Learning Disorder (SLD), Autism Spectrum Disorder (ASD), Attention Deficit/Hyperactivity Disorder (ADHD), Mood Disorder (MD), Anxiety Disorder (AD), and Eating Disorders (ED). We found that SDQ: (i) can identify the core symptoms in children with ASD, ADHD, MD, and AD via specific subscales; (ii) can capture the associated emotional and behavioural symptoms in children with LD, GDD, ID, SLD, and ED; and (iii) can detect changes in the symptomatology, especially for GDD, LD, ASD, ADHD, and AD. SDQ is also able to recognise the transdiagnostic dimensions across disorders. Our results underscore the potential of SDQ to specifically differentiate and identify behavioural/emotional profiles associated with clinical diagnosis. Childhood and adolescence are sensitive periods characterised by determinant changes in the physical, emotional, and social domains ,2. DurinGlobally, an estimated 10–20% of youths usually experience mental health conditions that often remain underdiagnosed and undertreated , with deOf importance, most neurodevelopmental disorders, including Autism Spectrum Disorders (ASD) and Intellectual Disability (ID), as well as Specific Learning Disorders (SLD) and Language Disorders (LD), are commonly associated with poorly recognised behavioural and emotional problems ,18,19,20Early screening offers an important opportunity to rapidly detect and treat behavioural and emotional symptoms. Therefore, it is not surprising that, over the past two decades, several studies ,23,24,25One worldwide screening tool used to detect neuropsychiatric symptoms in children and adolescents is the Strengths and Difficulties Questionnaire (SDQ) . With goFurthermore, a recent longitudinal study  demonstrA recent study by Vugteveen et al.  investigThe present study aimed to investigate whether the SDQ is a reliable and valid tool for identifying the relevant symptoms in nearly one thousand children and adolescents diagnosed with a variety of neurodevelopmental and neuropsychiatric disorders. The present study also aimed to explore whether the SDQ could be considered a screening tool for the recognition of shared transdiagnostic dimensions in terms of the behavioural and emotional problems associated with neurodevelopmental and neuropsychiatric disorders. Finally, the present study aimed to test whether the SDQ is a sensitive measure to capture changes in symptomatology with age.If the SDQ were a reliable and valid screening questionnaire, we would expect that it could detect symptoms of neuropsychiatric disorders , as well as associated behavioural and emotional problems .Out of 1000 children and adolescents attending the Child and Adolescent Neuropsychiatry Unit of the Bambino Gesù Children’s Hospital for an initial screening neuropsychiatric visit, a group of 952 participants were further referred for an in-depth neuropsychiatric examination and selected between December 2016 and March 2019 : 8.9 ± 4.8 years).Participants underwent a child neuropsychiatric examination conducted by experienced developmental neuropsychiatrists. The clinical diagnosis derived from the developmental history and the clinical examination based on the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria .Participants were divided into nine groups based on the clinical diagnosis according to the DSM-5 criteria  as folloSDQ  is a brihttps://www.sdqinfo.com) (accessed on 1 December 2016), and therefore, it is practical and free to use within clinical settings.The SDQ has been validated for use in many countries across different languages, and it is commonly adopted in many European and Extra-European countries, including the United Kingdom , FinlandThe percentage of participants who obtained normal, borderline, and clinical scores in each SDQ subscale was calculated.p-value ≤ 0.01 was considered significant . Post hoc comparisons across the diagnostic groups on each SDQ subscale were run by using the mean ranks of all pairs of groups [Diagnostic groups (GDD vs. ID vs. LD vs. SLD vs. ASD vs. ADHD vs. MD vs. AD vs. ED) were compared on the SDQ subscales  using the Kruskal–Wallis ANOVA, because the assumption of normality was not fulfilled. Bonferroni’s correction for multiple comparisons was applied to account for the five comparisons of diagnostic groups on each SDQ subscale, and a f groups .rho) between age and the scores on the SDQ subscales were run separately for each diagnostic group. Bonferroni’s correction for multiple comparisons was applied to account for the nine comparisons of the diagnostic groups within each SDQ subscale, and a p-value ≤ 0.0055 was considered significant .To test whether the scores on the SDQ subscales were age-dependent, Spearman’s correlations (p < 0.001), Conduct Problem Subscale (H(8) = 87.39, p < 0.0001), Hyperactivity/Inattention Subscale (H(8) = 144.11; p < 0.001), Peer Problems Subscale (H(8) = 110.47; p < 0.001), and Prosocial Behaviour Subscale (H(8) = 132.47; p < 0.001).The Diagnostic Groups differed in each SDQ subscale analysed: the Emotional Problems Subscale (H(8) = 166.15, With the exception of the Emotional Problems Subscale, at least half of the participants with GDD showed a prevalence of clinical/borderline scores in the remaining SDQ subscales.p < 0.0001), AD (p < 0.001), and ED (p < 0.0001) but not compared to ID (p = 0.99), ASD (p = 0.99), MD (p = 0.99), SLD (p = 0.03), and ADHD (p = 0.04) groups.Comparisons between groups revealed that participants with GDD showed significantly worse scores in the Hyperactivity/Inattention Subscale than the groups with LD (p = 0.009) and better scores than groups with ASD (p < 0.0001) but did not significantly differ from ID (p = 0.90), LD (p = 0.03), ADHD (p = 0.99), MD (p = 0.99), AD (p = 0.99), and ED (p = 0.99) groups.In the Peer Problems Subscale, they also obtained worse scores than the SLD group (p < 0.0001), SLD (p < 0.0001), MD (p = 0.005), AD (p < 0.0001), and ED (p < 0.001) but not than with ID (p = 0.95), ASD (p = 0.99), and ADHD (p = 0.02).Further, they obtained significantly worse scores in the Prosocial Behaviour Subscale than the groups with LD (p < 0.0001), AD (p < 0.0001), and ED (p < 0.001) but not than ID (p = 0.05), LD (p = 0.40), SLD (p = 0.04), ASD (p = 0.99), and ADHD (p = 0.48) groups.Conversely, they obtained significantly better scores in the Emotional Problems Subscale than the groups with MD (p < 0.0001) but did not differ from the ID, LD, SLD ASD, MD, AD, and ED  groups.In addition, they obtained significantly better scores in the Conduct Problems Subscale only compared to the ADHD group .p < 0.0001) but not compared to GDD (p = 0.05), SLD (p = 0.99), ASD (p = 0.99), ADHD (p = 0.99), AD (p = 0.99), ED (p = 0.99), and MD (p = 0.02) groups.A comparison between the groups revealed that participants with ID exhibited significantly worse scores in the Emotional Problems Subscale than the groups with only LD (p = 0.002) but did not differ from the groups with GDD (p = 0.99), LD (p = 0.12), SLD (p = 0.99), ASD (p = 0.99), ADHD (p = 0.08), MD (p = 0.99), and AD (p = 0.37).Similarly, in the Hyperactivity/Inattention Subscale, they exhibited significantly worse scores than the groups with only ED (p < 0.0001) and SLD (p < 0.0001) but did not differ from the groups with AD (p = 0.01), GDD (p = 0.90), ASD (p = 0.99), ADHD (p = 0.20), MD (p = 0.99), and ED (p = 0.45).In the Peer Problems Subscale, they obtained significantly worse scores than the groups with LD (p = 0.006) but did not differ from the SLD (p = 0.04), GDD (p = 0.95), LD (p = 0.99), ADHD (p = 0.99), MD (p = 0.99), AD (p = 0.80), and ED (p = 0.90) groups, while, in the Conduct Problems Subscale, their scores did not significantly differ from the GDD (p = 0.99), LD (p = 0.21), SLD (p = 0.50), ASD (p = 0.99), ADHD (p = 0.23), MD (p = 0.99), AD (p = 0.99), and ED (p = 0.99) groups.Conversely, they obtained significantly better scores in the Prosocial Behaviour Subscale than the groups with only ASD , SLD (p < 0.0001), ASD (p < 0.01), ADHD (p < 0.0001), MD (p < 0.0001), AD (p < 0.0001), and ED (p < 0.0001) but did not differ from the group with GDD (p = 0.40).A comparison between groups revealed that participants with LD exhibited significantly better scores in the Emotional Problems Subscale than the group with ID (p < 0.0001) but did not differ from the GDD (p = 0.99), ID (p = 0.21), SLD (p = 0.99), ASD (p = 0.99), MD (p = 0.05), AD (p = 0.99), and ED (p = 0.99) groups.In the Conduct Problems Subscale, they obtained significantly better scores than the group with only ADHD (p < 0.0001), ASD (p < 0.0001), and ADHD (p < 0.0001) but did not differ from the ID (p = 0.12), SLD (p = 0.99), MD (p = 0.21), AD (p = 0.99), and ED (p = 0.86) groups.Similarly, they showed significantly better scores in the Hyperactivity/Inattention Subscale than the group with GDD (p < 0.0001), ASD (p < 0.0001), and MD (p < 0.0001) but did not differ from the GDD (p = 0.03), SLD (p = 0.99), ADHD (p = 0.23), AD (p = 0.99), and ED (p = 0.99) groups.Further, they exhibited significantly better scores in the Peer Problems Subscale than the group with ID (p < 0.0001) and ASD (p < 0.0001) but did not differ from the ID (p = 0.99), SLD (p = 0.17), ADHD (p = 0.99), MD (p = 0.99), AD (p = 0.99), and ED (p = 0.99) groups.In the Prosocial Behaviour Subscale, they obtained significantly better scores than the group with GDD , while obtaining significantly worse scores than the LD group (p < 0.0001). However, they did not differ from the GDD (p = 0.04), ID (p = 0.99), ASD (p = 0.99), ADHD (p = 0.99), AD (p = 0.05), and ED (p = 0.50) groups.A comparison between groups revealed that the participants with SLD exhibited significantly better scores in the Emotional Problems Subscale than the group with MD (p < 0.0001) but did not differ from the GDD (p = 0.99), ID (p = 0.50), LD (p = 0.99), ASD (p = 0.99), MD (p = 0.15), AD (p = 0.99), and ED (p = 0.99) groups.In the Conduct Problems Subscale, they displayed significantly better scores than the group with ADHD (p < 0.001) and ADHD (p < 0.0001) but did not differ from the GDD (p = 0.03), ID (p = 0.99), LD (p = 0.99), MD (p = 0.99), AD (p = 0.99), and ED (p = 0.02) groups.Similarly, they obtained significantly better scores in the Hyperactivity/Inattention Subscale than the group with ASD (p = 0.009), ID (p < 0.0001), ASD (p < 0.0001), and MD (p < 0.0001) but did not differ from the LD (p = 0.99), ADHD (p = 0.07), AD (p = 0.99), and ED (p = 0.99) groups.In the Peer Problems Subscale, they also showed significantly better scores than the group with GDD (p < 0.0001), ASD (p < 0.0001), and ADHD (p = 0.001) but did not differ from the ID (p = 0.04), LD (p = 0.17), MD (p = 0.99), AD (p = 0.99), and ED (p = 0.99) groups.Further, in the Prosocial Behaviour Subscale, they obtained significantly better scores than the group with GDD  and better scores than the MD group (p < 0.0001) in the Emotional Problems Subscale, while they did not differ from the GDD (p = 0.99), ID (p = 0.99), SLD (p = 0.99), AD (p = 0.03), and ED (p = 0.16) groups.A comparison between the groups revealed that participants with ASD exhibited significantly worse scores than the group with LD (p < 0.0001), AD (p < 0.0001), ED (p < 0.0001), and SLD (p < 0.001), but they did not differ from the GDD (p = 0.99), ID (p = 0.99), ADHD (p = 0.99), and MD (p = 0.99) groups.In the Hyperactivity/Inattention Subscale, they displayed significantly worse scores than the groups with LD (p < 0.0001), LD (p < 0.0001), SLD (p < 0.0001), ADHD (p < 0.0001), AD (p < 0.0001), and ED (p < 0.001), but they did not differ from the MD group (p = 0.99).In the Peer Problems Subscale, they also obtained significantly worse scores than the groups with GDD (p = 0.006), LD (p < 0.0001), SLD (p < 0.0001), ADHD (p < 0.0001), MD (p < 0.0001), AD (p < 0.0001), and ED (p < 0.0001) but did not differ from the GDD group (p = 0.99).Further, they showed significantly worse scores in the Prosocial Behaviour Subscale than the groups with ID (p < 0.0001) but did not differ from the GDD (p = 0.99), ID (p = 0.99), LD (p = 0.99), SLD (p = 0.99), MD (p = 0.99), AD (p = 0.99), and ED (p = 0.99) groups.Conversely, they obtained significantly better scores in the Conduct Problems Subscale than the group with ADHD  and significantly better scores than the group with MD (p < 0.0001). However, no other differences were found with the GDD (p = 0.48), ID (p = 0.99), SLD (p = 0.99), ASD (p = 0.99), AD (p = 0.02), and ED (p = 0.22) groups.A comparison between the groups revealed that participants with ADHD exhibited significantly worse scores in the Emotional Problems Subscale than the group with LD (p < 0.0001), LD (p < 0.0001), SLD (p < 0.0001), ASD (p < 0.0001), AD (p < 0.0001), and ED (p = 0.003), but they did not differ from the ID (p = 0.23) and MD (p = 0.34) groups.In the Conduct Problems Subscale, they displayed significantly worse scores than the groups with GDD (p < 0.0001), SLD (p < 0.0001), AD (p < 0.0001), and ED (p < 0.0001) but did not differ from the GDD (p = 0.04), ID (p = 0.08), ASD (p = 0.99), and MD (p = 0.01) groups.Similarly, in the Hyperactivity/Inattention Subscale, they obtained significantly worse scores than the groups with LD (p = 0.001) and significantly better scores than the group with ASD (p < 0.0001). However, in the Prosocial Behaviour Subscale, they did not differ from the GDD (p = 0.01), ID (p = 0.99), LD (p = 0.99), MD (p = 0.99), AD (p = 0.22), and ED (p = 0.72) groups.In the Prosocial Behaviour Subscale, the group with ADHD displayed significantly worse scores than the group with SLD (p < 0.0001) but not compared to the GDD (p = 0.99), ID (p = 0.20), LD (p = 0.23), SLD (p = 0.07), MD (p = 0.32), AD (p = 0.99), and ED (p = 0.99) groups.Conversely, they displayed significantly better scores in the Peer Problems Subscale than the group with ASD , LD (p < 0.0001), SLD (p < 0.0001), ASD (p < 0.0001), and ADHD (p < 0.0001) but did not differ from the groups with ID (p = 0.02), AD (p = 0.03), and ED (p = 0.99).A comparison between the groups revealed that the participants with MD exhibited significantly worse scores in the Emotional Problems Subscale than the groups with GDD (p = 0.004) but did not differ from the GDD (p = 0.99), ID (p = 0.99), LD (p = 0.99), SLD (p = 0.99), ASD (p = 0.99), ADHD (p = 0.01), and AD (p = 0.66) groups.In the Hyperactivity/Inattention Subscale, they displayed significantly worse scores than the ED group (p < 0.0001) and SLD (p < 0.0001) but did not differ from the GDD (p = 0.99), ID (p = 0.99), ASD (p = 0.99), ADHD (p = 0.32), AD (p = 0.02), and ED (p = 0.68) groups.Further, in the Peer Problems Subscale, they showed significantly worse scores than the groups with LD (p = 0.005) and ASD (p < 0.0001) but did not differ from the ID (p = 0.99), LD (p = 0.99), SLD (p = 0.99), ADHD (p = 0.99), AD (p = 0.99), and ED (p = 0.99) groups.Conversely, in the Prosocial Behaviour Subscale, they showed significantly better scores than the groups with GDD (p = 0.99), ID (p = 0.99), LD (p = 0.05), SLD (p = 0.15), ASD (p = 0.99), ADHD (p = 0.34), AD (p = 0.99), and ED (p = 0.99) groups.In the Conduct Problems Subscale, no differences were found for the MD group compared to the GDD  and LD (p < 0.0001), but they did not differ from the groups with ID (p = 0.99), SLD (p = 0.05), ASD (p = 0.03), ADHD (p = 0.02), MD (p = 0.03), and ED (p = 0.99).A comparison between the groups revealed that participants with AD exhibited significantly worse scores only in the Emotional Problems Subscale than the groups with GDD (p < 0.0001) but did not differ from the GDD (p = 0.99), ID (p = 0.99), LD (p = 0.99), SLD (p = 0.99), ASD (p = 0.99), MD (p = 0.99), and ED (p = 0.99) groups.Conversely, they showed significantly better scores in the Conduct Problems Subscale than the ADHD group (p < 0.001), ASD (p < 0.0001), and ADHD (p < 0.0001), but no differences emerged compared to the groups with ID (p = 0.36), LD (p = 0.99), SLD (p = 0.99), MD (p = 0.66), and ED (p = 0.41).Similarly, in the Hyperactivity Subscale, they obtained significantly better scores than the groups with GDD (p < 0.0001) but not compared to the GDD (p = 0.99), ID (p = 0.01), LD (p = 0.99), SLD (p = 0.99), ADHD (p = 0.99), MD (p = 0.02), and ED (p = 0.99) groups.In the Peer Problems Subscale, they also displayed significantly better scores than the group with ASD (p < 0.0001) and ASD (p < 0.0001), but they did not differ from the ID (p = 0.80), LD (p = 0.99), SLD (p = 0.99), ADHD (p = 0.22), MD (p = 0.99), and ED (p = 0.99) groups.Further, they obtained significantly better scores in the Prosocial Behaviour Subscale than the groups with GDD  and LD (p < 0.0001) but did not differ from the groups with ID (p = 0.99), SLD (p = 0.49), ASD (p = 0.16), ADHD (p = 0.22), MD (p = 0.99), and AD (p = 0.99).A comparison between the groups revealed that the participants with ED displayed significantly worse scores in the Emotional Problems Subscale than the groups with GDD (p < 0.003), while they were not different from the GDD (p = 0.99), ID (p = 0.99), LD (p = 0.99), SLD (p = 0.99), ASD (p = 0.99), MD (p = 0.99), and AD (p = 0.99) groups.Conversely, they obtained significantly better scores in the Conduct Problems Subscale than the group with only ADHD (p < 0.0001), ID (p = 0.002), ASD (p < 0.0001), ADHD (p < 0.0001), and MD (p < 0.01) but did not differ from the LD (p = 0.86), SLD (p = 0.02), and AD (p = 0.41) groups.In the Hyperactivity/Inattention Subscale, they also showed significantly better scores than the groups with GDD (p < 0.001) but did not differ from the GDD (p = 0.99), ID (p = 0.45), LD (p = 0.99), SLD (p = 0.99), ADHD (p = 0.99), MD (p = 0.68), and AD (p = 0.99) groups.Similarly, they obtained significantly better scores in the Peer Problems Subscale than the group with ASD (p < 0.001) and ASD (p < 0.0001), but they did not differ from the ID (p = 0.90), LD (p = 0.99), SLD (p = 0.99), ADHD (p = 0.72), MD (p = 0.99), and AD (p = 0.99) groups.They also exhibited significantly better scores in the Prosocial Behaviour Subscale than the groups with GDD (p < 0.0001), LD (p < 0.001), ASD (p < 0.001), ADHD (p < 0.001), and AD (p < 0.001), meaning that they displayed worse scores in the Emotional Problems Subscale as they grew older.As shown in p < 0.005), meaning that they showed better scores in the Conduct Problems Subscale as they grew older.Age was significantly and negatively correlated to the scores in the Conduct Problems Subscale in the group with LD , meaning that they displayed worse scores in the Peer Problems Subscale as they grew older.Moreover, age was significantly and positively correlated to the scores in the Peer Problems Subscale in the groups with ASD and AD , meaning that they displayed better scores in the Prosocial Behaviour Subscale as they grew older.Last, age was significantly and negatively correlated to the scores in the Prosocial Behaviour Subscale in the groups with GDD and LD .No further correlations were found in the other diagnostic groups between age and scores on the SDQ subscales, as well as in the Hyperactivity/Inattention Subscale , as well as the core symptoms of ADHD . Indeed, we found that children and adolescents with ASD generally scored significantly worse than other groups on the subscales assessing difficulties in communication and social cognition, such as the Peer Problems and Prosocial Behaviour Subscales, and that, in the same subscales, the majority of them exhibited at-risk or clinically relevant scores. Our results are in line with previous findings  that shoIt has been widely recognised that children with ASD have severe problems with peers, as supported by studies documenting fewer friends at school, less reciprocity of friendship, and narrower social networks than their classmates ,47. The Considering ADHD, we found that children and adolescents with ADHD generally scored significantly worse than other groups on the subscales assessing hyperactive behaviour/inattention and conduct problems and that, in the same subscales, more than two-thirds of them exhibited at-risk or clinically relevant scores. Our results provided evidence that the Hyperactivity/Inattention Subscale of the SDQ showed good agreement with the diagnostic criteria for ADHD, in agreement with previous studies ,31. ConsThe SDQ also detected symptoms that are highly associated with ADHD, such as well-documented dysfunctional social abilities. Accordingly, we found that more than half of children and adolescents with ADHD obtained at-risk or clinically relevant scores on the Peer Problems Subscale. According to previous studies , this suIn addition, we found that the emotional symptoms increase with age, as documented by our results on correlations. Several studies have demonstrated a high likelihood of finding the psychopathological conditions associated with ADHD, including emotional problems such as anxiety and major depression ,57. ThesConsidering neuropsychiatric disorders, we found that the SDQ has the potential to also detect the core symptoms of both MD and AD. Indeed, the majority of our children and adolescents with MD and AD obtained at-risk or clinically relevant scores and scored significantly worse than other groups on the Emotional Problems Subscale of the SDQ. This subscale measures negative physical or cognitive symptoms  and mood state , in accordance with the MD and AD diagnostic DSM-5 criteria . MoreoveOf note, correlations revealed that emotional and peer problems have significant and positive associations with age in participants with AD. This finding is in line with studies documenting that AD symptoms are greater with increasing age  and are Considering children and adolescents with ED, we found that three-quarters of them had clinical scores on the Emotional Problems Subscale in agreement with studies reporting severe depressive symptoms, especially during adolescence in this population ,63.Regarding the other diagnostic groups for which the core symptoms could not be captured directly by the SDQ subscales, the tool was able to identify the transdiagnostic dimensions of neurodevelopmental and neuropsychiatric disorders, as well as changes in symptomatology with age.Considering neurodevelopmental disorders, we found that scores on emotional problems in LD have a significant and positive association with age. Our results were in line with studies indicating that one area of particular vulnerability for children and adolescents with LD is emotional difficulties ,65,66 thIn addition, we found that participants with GDD and ID showed a rather similar profile on the Conduct Problems, Hyperactivity/Inattention, and Peer Problems Subscales. From a developmental perspective, we must consider that children presenting with GDD often result in ID, as is evident from the overlap in their symptomatology. The GDD and ID did not differ from the other neurodevelopmental disorders, such as ADHD and ASD, in the Hyperactivity/Inattention Subscale. Studies documented that children and adolescents with developmental disabilities were at a heightened risk for developing ADHD  and condRegarding SLD, more than half of the participants with SLD showed borderline or clinical scores in the Emotional Problems Subscales. The presence of internalising symptoms  in SLD have been well-documented in the literature ,75,76. EOur study has some limitations.A first limitation was the lack of a control group, which should be included in future studies to compare the prevalence of the symptoms we found with that of the typically developing population.Another limitation was the absence of a complete description of our patients’ characteristics. Future studies should include a detailed description, including the demographic  and clinical characteristics of the patients .An additional limitation was the wide age range of participants. Including participants with a more homogeneous age range or stratifying them by age could better clarify the relationship between age and symptom development.In addition, the number of participants considering each diagnostic group was limited, but the results are encouraging and useful for designing and executing a large-scale study using the SDQ.Finally, future studies should include multi-informant analysis, considering self-reporting and teacher report questionnaires. It should augment the generalisability of the results into the daily professional’s routine.Overall, our results highlighted the clinical utility of using the SDQ as a potential tool to conduct the initial screening, and then further refer to patients for more structured neuropsychiatric evaluation. This is especially true for the early identification of core symptoms of ASD, ADHD, MD, and AD. Clinical assessments, such as the neuropsychiatric assessment, are complex and expensive. The SDQ can be integrated into daily clinical practice as an initial filter and then refer for the structured assessment only patients who score at-risk or clinical on the SDQ and who need further investigation.Our results also showed that SDQ can detect behavioural and emotional problems that may be associated, with GDD, ID, SLD, and ED. Without a doubt, research on comorbid neuropsychiatric disorders has advanced in recent decades. However, because the comorbidity is associated with the cognitive and behavioural deficits of the primary disorder and influences its development; treatment should also focus on the comorbidities. The SDQ can bring precisely more attention to the associated symptoms to develop intervention plans that also address the comorbid problems.In summary, our results indicated that the SDQ is a valid parent report questionnaire for identifying diagnostic and associated symptoms in the children and adolescents assessed in our study.Specifically, the SDQ allowed us to detect specific symptoms for ascertaining the diagnosis in children and adolescents with ADHD, ASD, MD, and AD. In addition, SDQ can detect transdiagnostic emotional and behavioural problems associated with neurodevelopmental and neuropsychiatric disorders to better guide parents in their management.