In a recent meta-analysis, we found that atopic diseases, like asthma and allergic rhinitis, occur more frequently prior to the onset of attention-deficit/hyperactivity disorder (ADHD). Our aim was to determine the temporal order of the association between daily fluctuations in atopic disease symptoms and in ADHD symptoms in individual participants. In this observational study among 21 participants, age 7–16 years, we performed a replicated time-series analysis of symptom fluctuations in asthma and/or allergic rhinitis and ADHD. Data were collected through parents who filled in a daily online questionnaire during up to 50 days. In each individual, we investigated the temporal order of fluctuations in atopic disease symptoms and ADHD symptoms using a vector autoregressive (VAR) model while using sleep problems and medication use as covariates. For 16 out of 21 participants, we constructed a VAR model. For a majority of the participants, significant associations were detected between atopic disease symptoms and ADHD symptoms. The results were heterogeneous; the direction, sign, and timing of the relationship between ADHD, atopy, sleep problems, and medication use varied between individuals. This study provides additional evidence that the symptom expression of atopy and ADHD are related. However, the connection between both diseases in children is found to be heterogeneous within our study population. Attention-deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder with onset of symptoms before the age of 12 years , atopy [Control of Allergic Rhinitis and Asthma Test (CARAT)], and sleep problems [Children’s Sleep Habits Questionnaire (CSHQ)] as rated by one of the parents in the week before the initiation of the study, were determined to describe the sample characteristics.To assess the severity of ADHD symptoms during the diary period, we used the SNAP IV questionnaire, which is a frequently used, validated tool in ADHD studies that is based on the 18 DSM-IV ADHD items , 7. The Co-variables like age, gender, sleep problems, and medication use were determined during inclusion and as part of the daily measurement. To address the possible involvement of sleep problems, as suggested by the previous research into atopic eczema and ADHD , 11, in A vector autoregressive (VAR) model was used to investigate the dynamic associations between changes in symptoms in each individual participant as well as the possible involvement of covariates in this association . An impoA VAR model consists of a set of regression equations, in which each of the variables is regressed on its own lagged values (autocorrelation) as well as the lagged values of the other variables (cross-lagged associations) . We builp value ≤ 0.05 was considered to be statistically significant. Per individual model, multiple diagnostic tests were performed to test for stability and homoscedasticity of the model [p values < 0.05 were considered to denote statistically significant associations and p values < 0.10 were considered to denote the trend of associations.To assess the temporal order of the association between atopy and ADHD symptoms, and the possible involvement of sleep problems therein, we determined the independent cross-lagged associations between each variable. In addition, we assessed the contemporaneous correlations between symptoms of atopy, ADHD, and sleep problems per participant to determine the simultaneous associations between (or co-occurrence of) the expression of symptoms. These can be computed from the residuals of the VAR model . A p valhe model . For sixSimulation studies have shown that, for VAR modeling, a minimum of 30 time points is needed, although larger numbers of observations yield more reliable results . We chosn < 30) were completed during the data collection. A description of the characteristics of our study sample is presented in Table Five of the twenty-one participants  were excluded from analyses, because not enough observations (p < 0.05) cross-lagged association between some of the variables  and sleep problems , while increases in sleep problems were followed by decreases in ADHD symptoms . In participant 10, an increase in ADHD symptoms was followed by an increase in symptoms of atopy . In contrast to the other participants, in participant 11, a negative association was detected in which a decrease in atopic symptoms  was followed by an increase in ADHD symptoms. Participant 15 showed a worsening of sleep, preceded by an increase in ADHD symptoms . In participant 18, sleep problems and atopic symptoms were associated in both directions, i.e., an increase in sleep problems preceded an increase in atopic symptoms , while an increase in atopic symptoms preceded a decrease in sleep problems .In all models, an optimal lag of 1 day was selected to be optimal. Both the temporal direction as well as the sign (positive or negative) and size of the effect varied between the participants. Five out of the 16 participants showed a significant (es Table . In partIn addition to the dynamic (cross-lagged) associations between symptoms of atopy, ADHD, and sleep problems, we also addressed the co-occurrence of these symptoms. Table B = 0.14; p = 0.048) and ADHD medication: an increase in sleep problems was followed by an increase in ADHD medication. In participant 21, significant cross-lagged associations between allergic-related medication on one hand, and ADHD , sleep problems , and atopy  on the other hand were detected.In seven participants, the medication use related to ADHD  and to allergy  varied during the study period. For this reason, these variables were included in the VAR models of these participants as an additional endogenous parameter. A number of cross-lagged associations between medication use and the other variables were present: Participant 10 showed a significant cross-lagged association between sleep problems  in participant 2 and a negative association between ADHD medication and sleep problems  in participant 2 and  participant 10. Furthermore, we observed a positive association between allergic medication and ADHD  and between allergic medication and atopy  in participant 21.When evaluating the contemporaneous associations between medication use and the symptom expression of the different diseases, we detected a negative association between ADHD medication and atopy  and in participant 16 between inattention and atopic symptoms .A separate cross-lagged association appeared in participant 3 for between inattention and sleep problems , and sleep problems and inattention  in participant 4, sleep problems and inattention  in participant 7, and sleep problems and hyperactivity/impulsivity  in participant 9.As regards the contemporaneous associations between the different symptoms, also similar results were observed as in the original analyses. The contemporaneous association between atopy and ADHD, and sleep problems and ADHD in participant 10, atopy and ADHD in participant 16, and sleep problems and ADHD in participant 21 were accounted for by symptoms of inattention. In comparison with Table To our knowledge, this is the first study using time-series analysis to examine the temporal order of an association between atopic disease symptoms and ADHD symptoms. For a majority of the participants, significant associations were detected between atopic disease symptoms and ADHD symptoms. However, the results were heterogeneous, with varying direction, sign, and timing of the relationship between ADHD, atopy, sleep problems, and medication use between participants. In other words, the observed associations were not consistent across the participants. Regarding ADHD symptoms as outcome, our analysis showed a relationship between symptoms of allergy or sleep problems and ADHD symptoms in some of the participants. There was also negative cross-lagged association present between sleep problems and ADHD. The cross-lagged association between atopy and ADHD differed in direction between the participants. Regarding atopic symptoms as the outcome, only one participant showed a significant cross-lagged association between ADHD and allergy symptoms. When assessing the more direct contemporaneous relationships, our analyses showed an association in many of the participants between ADHD symptoms  or ADHD medication use, allergy, and/or sleep problems. The associations differed in both sign and strength.Based on different group-based observational studies, the association between atopy and ADHD is considered weak-to-moderate , 16. AltA recent study showed that treatment of allergic rhinitis decreased ADHD symptom severity significantly compared with healthy controls and children with pure ADHD . In our Taking into account the group-based cross-sectional and prospective association between atopy and ADHD in the literature , it mighOur findings should be interpreted in the light of the strengths and limitations. Examining symptom fluctuations in allergy, ADHD, and sleep problems on a daily basis allowed us to observe the concurrent as well as the temporal relationships between the different variables. A limitation was the small sample size of the study, which limits the generalizability of the results. However, due to the multiple repeated measurement points per participant, it was possible to examine each individual participant in detail and to explore the possible temporal order of the association between atopy and ADHD. Combining both study types, with repeated measurements of symptom expression of both atopy and ADHD in a large cohort, would benefit the study of the association between both diseases.Another limitation has been that the participants were followed daily for a maximum of 50 days, but the effects of the symptom expression of one of the diseases could accumulate over time to have a larger and more significant effect on the long term , 22. ForTime-series analysis is a potentially useful method to assess the temporal order of the association between atopy and ADHD. This study provides additional evidence that the symptom expression of atopy and ADHD is related. However, the connection between symptom expressions of both diseases in children is heterogeneous, and differs in sign, direction, strength, and timing in our study population. To enable improvement in the care of patients, personalized treatment needs of children with both atopic disease and ADHD should be assessed at an individual basis.


Objective: To measure levels of vitamin A (VA) and vitamin D (VD) and the symptomatic association of their co-deficiencies on attention deficit hyperactivity disorder (ADHD) in Chinese children (6–9 years).Methods: Eighty-two children  with ADHD were recruited as ADHD group. A total of 106 healthy children were recruited as the healthy control (HC) group. Serum levels of retinol and 25-hydroxyvitamin D (25(OH)D) of all children were evaluated using high-performance liquid chromatography (HPLC) and HPLC-tandem mass spectrometry. The Swanson, Nolan, and Pelham IV Rating Scale (SNAP-IV) was employed to assess the clinical symptoms of ADHD.Results: Children suffering from ADHD had significantly reduced serum levels of retinol and 25(OH)D compared with those of HCs, and the prevalence of VA deficiency and VD deficiency were higher in children suffering from ADHD. Serum concentrations of 25(OH)D and retinol were linked closely with the presence or absence of ADHD after adjustment for age, body mass index, season of blood sampling, and sun exposure. Serum concentrations of 25(OH)D and retinol showed a negative correlation with the total scores of SNAP-IV. Children with ADHD as well as VA and VD co-deficiency had increased SNAP-IV total scores and ADHD inattention subscale scores.Conclusion: VA deficiency and VD deficiency in children with ADHD were increased in comparison with that in HCs. VA and VD co-deficiency associated with ADHD symptom severity. Attention should be paid to regular testing of VA levels and VD levels. However, the mechanism of VA and VD in ADHD needs to be further studied. Interventional studies on VA and VD supplementation are recommended to further verify the relationship between VA and VD co-deficiency and ADHD. Attention deficit hyperactivity disorder (ADHD) is considered to be the most common childhood-onset neurodevelopmental ailment . In ADHDThe exact etiology of ADHD is not known, but has been postulated to occur as a result of the interplay of multiple psychological and biological factors. The impact of the environment and nutrition might lead to epigenetic changes  and may make a key contribution to ADHD development –6. MicroGenetic and environmental studies have implicated dysfunction of the dopamine system in ADHD pathogenesis . MethylpIn vivo studies have shown that VA deficiency damages the plasticity of hippocampal synapses and impairs spatial learning and working memory, and that these actions are mediated by retinoic acid nuclear receptor-α (Vitamin A (VA) deficiency is widespread in developing countries, especially in pregnant women and children . Studiesceptor-α , 21. Theceptor-α . Furtherceptor-α . In the ceptor-α . TherefoSome authors have reported a correlation between VD deficiency and ADHD –11 but oHere, we measured levels of VA and VD in Chinese children (6–9 years) with ADHD and explored their symptomatic association with ADHD.The Research Ethics Committee of the First Hospital of Jilin University  approved the study protocol (2017-314). This study is registered in the Chinese Clinical Trial Registry (ChiCTR-OPC-17013502).Participation in this case–control study was voluntary. Before recruitment, the parents of children suffering from ADHD provided written informed consent.The exclusion criteria were children: (i) with other neurodevelopmental or neurological disorders ; (ii) with chronic diarrhea, malnutrition, or a history of micronutrient supplementation within the last 3 months; (iii) taking medications that may affect the metabolic process of VA and VD such as glucocorticoids, benzodiazepines and other drugs that may affect liver and kidney function.Diagnostic and Statistical Manual of Mental Disorders (DSM-5) ADHD criteria  fulfilled the ADHD criteria. The mean age of participants was 7.1 ± 0.9 years. Thirty-six participants presented with a predominantly inattentive type, five with a predominantly hyperactive type, and 41 with a combined type. Fourteen participants also had learning disabilities.n = 106; 79 boys and 27 girls; mean age: 7.1 ± 0.7 years) were recruited from Child Developmental and Behavioral Division of the First Hospital of Jilin University in the same period. They did not have the symptoms of neurodevelopmental disorders or other disorders associated with the disturbance of micronutrient homeostasis.The healthy control (HC) group (The parents or caregivers of children in the ADHD and HC group completed a questionnaire created by our research team (on the basis of standardized guidelines) on demographic features . An electronic scale and stadiometer were employed for measurement of the weight and height of participants in lightweight clothes with bare feet. We categorized children according to the age-percent distributions of the body mass index (BMI). Children with a BMI less than the 85th percentile were classified as “normal,” those with a BMI at the 85–95th percentile were considered “overweight,” and children with a BMI above the 95th percentile were categorized as “obese” .The Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) is an unstructured questionnaire on behavior completed by a child's parent and teacher . The cheAfter an overnight fast, venous puncture was undertaken and a sample of peripheral blood (3 mL) was taken from each child, followed by serum isolation via centrifugation (within 6 h). Serum samples were stored at −20°C. Serum concentrations of retinol and 25(OH)D were measured via high-performance liquid chromatography (HPLC) and high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) in the clinical laboratory of Jilin Hehe Medical Center . The criteria used for evaluation of HPLC and HPLC–MS/MS have been discussed extensively in our previous work . A serumWe divided the year into two periods: summer season (June–October), and winter season (November–May). This strategy has been found to be rational according to the results obtained in Boston  . Changcht-test or a non-parametric Mann–Whitney U-test was employed to compare the continuous data of the two groups. A chi-square test was conducted to test the variation in the proportion of categorical variables among the two groups and z test was performed for pairwise comparisons. Spearman's correlation coefficient was employed for evaluation of the significance of correlation factors with the serum concentrations of retinol and 25(OH)D. The correlation between the serum concentrations of retinol and 25(OH)D and the presence or absence of ADHD was determined via multivariate logistic regression. The Kruskal–Wallis H test was used to compare the differences of the total and subscale score of SNAP-IV among three groups. The Bonferroni-adjusted significance test was used for pairwise comparisons. P < 0.05 (two-sided) was considered significant.SPSS v22.0  was employed for analyses. The normality of the data distribution was evaluated using the Kolmogorov–Smirnov test. Continuous data are the mean ± SD or median percentile (P50), P25 or P75. Categorical data are given as frequencies with percentages. According to the distribution of the analyzed variable, an independent-sample The serum concentrations of 25(OH)D and retinol in the two groups are outlined in The correlation coefficient for the serum levels of retinol and 25(OH)D and the SNAP-IV total score and SNAP-IV subscale score are shown in P = 0.023) and serum 25(OH)D level  to be associated significantly with or not associated with ADHD after adjustment for age, BMI, season of blood sampling and sun exposure . The subscale score of ADHD inattention in the VA and VD co-deficiency group were increased significantly in comparison with those in the VD deficiency-only group (Bonferroni-adjusted P = 0.035) and the VA and VD normal group (Bonferroni-adjusted P = 0.014).  were found to have VA deficiency or VD deficiency. Of these children, 31 cases had VA and VD co-deficiency, 45 cases had VD deficiency only, 6 cases had normal VA and VD, and no cases had VA deficiency alone. There were significant differences in the total score of SNAP-IV and the subscale score of ADHD inattention between the VA and VD co-deficiency, the VD deficiency only and the normal VA and VD group. The SNAP-IV total score in the VA and VD co-deficiency group were increased significantly in comparison with those in the VA and VD normal group (Bonferroni-adjusted  0.014). . No signThe current study elicited three main findings. First, children living with ADHD had significantly reduced serum levels of retinol and 25(OH)D than those of HCs, and that the prevalence of VA deficiency and VD deficiency was higher in ADHD children. Second, a negative correlation was found between serum levels of retinol and 25 (OH) D with the adverse symptoms of ADHD. Third, children with ADHD in the VA and VD co-deficiency group had increased SNAP-IV total scores and ADHD inattention subscale scores, which revealed that co-deficiency of VD and VA had a considerable association with ADHD symptoms in children.VD is a neuroactive steroid associated with neuronal differentiation and development of the nervous system. Various studies have indicated that VD plays an important part in promoting normal development of the brain. Also, its role in neurotransmission alterations, neuro-immunomodulation, and antioxidation leading to mood disorders, neuropsychiatric and behavioral disease have been reviewed in various studies –37. AccoStudies on VD and ADHD have been reported in recent years. Kamal et al.  conducteThere is a lack of clarity regarding the key pathologic variations associated with ADHD. The dopaminergic dysfunction of the prefrontal cortex is associated with ADHD pathogenesis , 40, 41.In vivo studies have revealed that VA deficiency damages hippocampal synaptic plasticity and impairs spatial learning and working memory mediated by retinoic acid nuclear receptor-α . Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.H-HL and X-JY designed the work, collected and analyzed the data, and drafted the manuscript. C-XW, J-YF, and BW collected and analyzed the data and revised the manuscript. F-YJ contributed to the concept and design of this study, reviewed, and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Attention-deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and adolescents. People who have this disorder are characterized by presenting difficulties in the processes of sustained attention, being very active, and having poor control of their impulses. Despite the high prevalence of this disorder and the existence of various tests used for its diagnosis, few data are available regarding the usefulness and diagnostic validity of these tools. Given the difficulties that these subjects present in executive functions, the aim of this study was to evaluate whether the Neuropsychological Assessment of Executive Functions battery for Children (ENFEN, for its acronym in Spanish,  The term executive functions  is a relatively recent construct, developing its object of study in areas of knowledge such as neuroscience, neuropsychology, or psychiatry. In recent decades, these disciplines have undergone considerable evolution due to the development of successive theoretical models from cognitive psychology, as well as the advances made in regard to studying brain activity . This faIn 1980, Luria proposed three functional units in the human brain: (a) alertness/motivation, (b) reception/processing/storage of information, and (c) programming/control/verification of brain activity , Stroop, Trail Making Test, phonetic verbal fluency, etc.] show sensitivity in identifying frontal dysfunctions and are unspecific in measuring alterations in the ES. The validity of these batteries in the evaluation and diagnosis process is diminished by the lack of operability in the description of EFs and the structure of the batteries themselves, as well as the artificiality of the tests that compose them .The broad spectrum of ADHD and its high comorbidity, together with the difficulty in conceptualizing EFs, has created the need to establish more precise diagnostic protocols. With the objective of finding cognitive profiles that help diagnose ADHD, On the other hand, Therefore, EFs are mediated by the prefrontal cortex and demand a higher and more complex cognitive-behavioral requirement than is commonly needed to perform goal-orientated behavior. One of the main problems that ADHD subjects present is the difficulty in inhibitory control and the organization of tasks that require a set of consecutive steps , which lThe diagnosis of ADHD is a complex process, with no known biological markers and with the current psychometric tools only providing rough information about the presence of deficiencies in cognitive skills associated with ADHD. Therefore, finding an effective tool to measure these symptoms or cognitive skills is one of the most solid strategies in order to support the diagnosis of the disorder. Traditionally, health professionals have used instruments to measure frequency of appearance of symptoms; however, it is becoming increasingly common to find the use of tests that measure cognitive abilities related to ADHD deficiencies.Studies that have tried to establish possible cognitive patterns to help the diagnosis of ADHD are scarce, most of them being carried out with batteries that evaluate general cognitive aptitudes. For this reason, the aim of the present study was to verify if the ENFEN battery allows to establish specific executive performance profiles for subjects with ADHD. The specific objective consisted of studying the existence of differences between the executive performance profiles measured with the ENFEN battery, between a group of subjects with a normalized development and two other groups of subjects with ADHD, one with a manifest “cognitive step” and another in which the “cognitive step” is nonexistent, understanding the concept “cognitive step” as the unusual fluctuations, in comparison with oneself, in scores on cognitive tests that require competence in skills usually affected by ADHD such as EFs, WM, and speed of processing information .n = 63, 8.78 ± 1.93 years, 37 boys and 26 girls) presented no pathology. Another group  was diagnosed with ADHD, of which 72  had a “cognitive step” in the WISC-IV test and the rest did not .The sample was made up of 197 children, aged between 6 and 12 years (8.53 ± 1.92), from six public schools (four from Alicante and two from Málaga) and two charter schools (Málaga). One group (The inclusion criteria for the selection of the groups were as follows:Normotypic development group (NDG): (a) failure to meet the ADHD diagnostic criteria according to the ADHD Rating Scale DSM-IV and (b) presenting a general capacity index, measured with IV version of Wechsler Intelligence Scale, greater than 80 , 2019.ADHD group with a cognitive step: (a) presenting an ADHD diagnosis taking into account the criteria of the ADHD Rating Scale DSM-IV, (b) presenting a general capacity index, measured with the IV version of Wechsler Intelligence Scale, greater than 80 , 2019, aADHD group without a cognitive step: (a) presenting an ADHD diagnosis taking into account the criteria of the ADHD Rating Scale DSM-IV, (b) presenting a general capacity index, measured with IV version of the Wechsler Intelligence Scale greater than 80 , 2019, aThis instrument evaluates, in family and school settings, the dimensions of inattention and impulsiveness/hyperactivity by analyzing the frequency of appearance of nine symptoms in each domain. The Spanish adaptation of this scale was used , taking This tool explores general intellectual ability. It is a cognitive test for children between 6 and 16 years of age. The Spanish version  was usedM = 5.5, SD = 2).This battery of tests allows a global evaluation of EFs in children between the ages of 6 and 12. Specifically, it analyzes aspects such as vocabulary range, cognitive flexibility, graphomotor and visuomotor coordination, WM, planning and sequencing capacity, inhibition capacity, or resistance to interference. It is applied individually, and its duration is approximately 20 min. It is made up of four tests  divided into six scales. Lastly, a total score for each task was obtained, and raw scores were transformed into sten scores  printed with random color inks , with the color name and color ink never matching. The child had to say the color ink of each word.An associative, comparative, and predictive study method was designed . For thiThe evaluations were carried out in the order of the following sequence. First, the ADHD Rating Scale DSM-IV (RS) was completed by parents/legal guardians and teachers, in a single day, with an approximate duration of 10 min each. This battery was used to determine the presence or absence of ADHD symptoms in both the clinical and control samples. A positive diagnosis was corroborated when subjects scored positively on six or more items out of the nine established for each context (family/school). On the other hand, the children were evaluated with the WISC-IV and the ENFEN on two different days, with a duration of 45–90 min approximately. The protocols defined in each instrument were used, with the evaluation taking place individually and in a place free of noise and endowed with privacy.The groups were formed based on the scores on the ADHD Rating Scale DSM-IV (RS) and the WISC-IV.(forward stepwise) was generated to determine the predictive capacity of the ENFEN on the diagnosis of ADHD. As model fit measurements, the Omnibus and Hosmer-Lemeshow parameters were calculated.The data were subjected to descriptive and inferential analyses. The normality was explored by evaluating the skewness and kurtosis of the data set, as well as by the Kolmogorov-Smirnov test. After this, to analyze the differences in the scores between the groups, Kruskal-Wallis analyses were carried out to assess the existence of differences between all groups and the Mann-Whitney U test to specify the differences in pairs. In addition, Cohen’s d (1988) was used to measure the effect size, taking into account the levels determined by R2 Nagelkerke = 0.55). This model shows a good fit, correctly classifying 82.7% of cases, with appropriate values from the omnibus test  and the Hosmer-Lemeshow test . Beta values show a negative value in the equation in the cases of phonological fluency, color path, rings, and interference, as well as positive values in semantic fluency and gray path.A binary logistic regression analysis was performed to determine the diagnosis of ADHD using the ENFEN battery . The genRegarding the sensitivity and specificity of the different ENFEN tests in their ability to diagnose ADHD, the semantic fluency and gray path tests provide a poor diagnosis with an area under the curve (AUC) of 0.52 and 0.59, respectively, while the phonological test provided a mediocre diagnosis . The remaining tests gave a good diagnosis but presented large differences between sensitivity and specificity, specifically the color , interference , and ring tests .The purpose of this study was to verify if the ENFEN test allows to establish specific profiles of executive performance for subjects with ADHD and thus to demonstrate if there are differences in these profiles between anormotypic group and two other groups of subjects with ADHD, one with a manifest “cognitive step” and another in which the “cognitive step” was nonexistent.Following In the case of semantic fluency, classic studies note the existence of different neural systems that are activated in verbal fluency tasks in the face of phonological or semantic instructions, associating the frontal lobe with the first type  and the Other studies have also found relationships between EFs and ADHD using different psychometric measurement scales. The interference subtest that the ENFEN battery presents is derived from its predecessor, the Stroop test . Using tIn line with the efficacy of the measurement of EFs for the diagnosis of ADHD, other investigations , 2019, wFinally, from the data obtained from the binary logistic regression, it is determined that lower values in variables with a negative sign  and higher values in those with a positive sign (semantic fluency and gray path) can be considered as a characteristic sign through which to configure a specific response profile in relation to EFs in the assessment of ADHD. We understand that this is due to the fact that the subtests, in which subjects diagnosed with ADHD obtain higher scores, are less demanding in EFs, which allows them to achieve scores closer to those expected by subjects of the same age who are not affected by this disorder.Therefore, taking into account all these findings, we can conclude that people diagnosed with ADHD present a characteristic performance profile when executing tasks that demand the proper functioning of the ES and its EFs, as is the case of the ENFEN battery. This unique cognitive profile common to the clinical group in relation to the control group is something that has been under study for more than a decade and presents empirical evidence with various psychometric tools .The practical implication of these results is in line with providing support in the clinical diagnosis that is carried out in children’s mental health units. In addition, the ENFEN tool can be valued as a suitable psychometric instrument in the psychoeducational field, helping professionals in a school environment to be more aware of the areas of cognitive development in which a student diagnosed with ADHD will have more difficulties, and in doing so, providing more adjusted and effective psychopedagogical measures when it comes to supporting students in their adaptation to the school environment.For future research, it would be interesting to apply the ENFEN battery at the same time as another instrument of similar characteristics that also evaluate the development of EFs, in order to carry out comparisons between the scores of both tools and degree of correlation in the detection of deficiencies associated with the diagnosis of ADHD. If positive relationships are found between different tests and the ENFEN battery when measuring EFs, this would support and strengthen the data obtained in this study.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the University of Alicante, assigning it file number UA-2018-03-08. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.IN-S provided the project idea, article concept, and design; planned the timeline; substantially involved in the data, material, and article acquisition; mainly responsible for drafting, writing, and revising the review article; and responsible for selection and final approval of the scholarly publication. RL-C provided substantial help with the concept and design; contributed substantially to the project by drafting and revising the review article; and was responsible for the final approval of the scholarly publication. JG-F provided substantial help with the concept and design; contributed substantially to the project by drafting and revising the review article; and was responsible for the final approval of the scholarly publication. MR-F, CG-G, and RJ-R participated in drafting the work and revising it critically with respect to important intellectual content in all its phases. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by symptoms of inattention, impulsivity, and hyperactivity. Neurophysiological correlates of ADHD include changes in the P3 component of event-related brain potentials (ERPs). Motivated by recent advances towards a more dimensional understanding of ADHD, we investigate whether ADHD-related ERP markers relate to continuous variations in attention and executive functioning also in typically-developing children. ERPs were measured while 31 school children (9–11 years) completed an adapted version of the Continuous Performance Task that additionally to inhibitory processes also isolates effects of physical stimulus salience. Children with higher levels of parent-reported ADHD symptoms did not differ in task performance, but exhibited smaller P3 amplitudes related to stimulus salience. Furthermore, ADHD symptoms were associated with the variability of neural responses over time: Children with higher levels of ADHD symptoms demonstrated lower variability in inhibition- and salience-related P3 amplitudes. No effects were observed for ERP latencies and the salience-related N2. By demonstrating that ADHD-associated neurophysiological mechanisms of inhibition and salience processing covary with attention and executive functioning in a children community sample, our study provides neurophysiological support for dimensional models of ADHD. Also, temporal variability in event-related potentials is highlighted as additional indicator of ADHD requiring further investigation. ADHD is the most commonly diagnosed psychiatric disorder in children2, affecting 3–5% of school-aged children, and is associated with problems in social and emotional functioning, diminished academic success, and a globally increased risk for psychiatric problems3. Children affected by ADHD typically demonstrate poorer performance on tasks requiring response inhibition, working memory, planning, and the flexible adaptation of action and thought, which is often attributed to a core deficit in executive functions4 but alternatively also to more fundamental deficits related e.g., to the allocation of attentional or cognitive resources6. While many earlier studies suggest that such executive deficits differentiate properly between children with and without ADHD7, newer findings reveal a more heterogeneous picture, with some but not all studies reporting clear dissociations between groups of clinically affected patients and groups of typically-developing controls8.Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by developmentally inappropriate symptoms of inattentiveness, hyperactivity, and impulsivity12 and even subclinical variations in ADHD symptoms significantly impact cognitive functioning and psychological wellbeing13. This suggests that the investigation of continuous variations in ADHD-related behavior can provide insights into whether or not neurocognitive models of ADHD  can be generalized to explain behavioral variations in attention and executive functions within the general population16.ADHD has mostly been treated as categorical concept. Accordingly, investigations of behavioral and neural mechanisms underlying ADHD primarily relied on group comparisons between clinically affected patients and healthy controls. However, these approaches ignore that symptoms vary substantially between affected persons17. Multiple variations of Stop-Signal, Go-NoGo, and Continuous Performance Tasks (CPT19) revealed differences between clinically affected patients and groups of healthy controls17. In such tasks, children with ADHD typically demonstrate higher rates of omission and commission errors and increased variability in reaction times20. This goes along with changes in neurophysiological correlates of these cognitive processes. One of the most established markers of ADHD is a reduced amplitude and longer latency of the inhibition-related P3 component of the event-related brain potential (ERP)22. However, these ERP effects have so far not been studied extensively in the context of a dimensional framework of ADHD, and it is therefore not yet clear how these ERP components are related to individual differences in problems in attention and executive functioning.Neurophysiological investigations of ADHD focused primarily on neural mechanisms of selective or sustained attention, response inhibition, effort allocation, or distractor interference23. Most evidence for aberrant salience processing in ADHD comes from functional MRI studies focusing on interactions between large-scale functional brain networks25 and from ERP studies using a wide variety of different experimental paradigms28. However, most of these studies focused on concepts like motivational salience27 or emotional salience29 and do not assess neurophysiological responses to ‘pure’ physical stimulus salience. Initial efforts into the direction of disentangling motivational and perceptual effects of salience were made by a recent investigation that differentiates between salience effects in the P3 linked to novelty, targetness (task-relevance), and deviancy of the stimuli within an adult ADHD sample30. Task-irrelevant stimulus salience also elicits inhibitory processes indexed in the ERP by anterior N2 effects31, but these were not modulated by ADHD in the study of Godefroid and Wiersema30. To the best of our knowledge, such ADHD-related effects of physical stimulus salience have so far not been studied in children.Recent research further suggests that children with ADHD exhibit greater distractibility to salient but task-irrelevant events32. Several studies found increased variability in response times over the course of an experiment and less consistent error rates in clinically affected patients when compared with groups of healthy controls34. Recently, research has begun to investigate whether potential associations between ADHD and different levels of intra-subject variability also manifest neurophysiologically. These studies focused primarily on the investigation of inhibitory processes and found evidence for higher variability in the amplitude36 and latency35 of inhibition-related ERP components  in ADHD. In contrast, ADHD-related variability of ERP components associated with the processing of physical stimulus salience has not yet been investigated. However, the distractibility that is typical for ADHD becomes particularly relevant in the presence of salient but task-irrelevant stimulation and is known to fluctuate over time14. Investigating the variability over time of neurophysiological mechanisms involved in salience processing may thus provide valuable contributions to the understanding of ADHD-related behavioral symptoms.A related but independent line of research suggests a reduction of the stability (or increase in variability) of cognitive processes as a major component of the cognitive deficits in children with ADHD22. Potential associations between ADHD-related behaviors and P3 and N2 effects elicited by the processing of task-irrelevant physical stimulus salience will be explored, as the available literature did not allow us to formulate specific hypotheses. The second focus of our analyses was to investigate the role of intra-individual variability of ERP components over time. Although available empirical evidence is only sparse and provides rather preliminary insights, we hypothesized that more problems in attention and executive functioning should go along with higher variability in inhibition-related P3 amplitudes (Hypothesis 2a) and latencies (Hypothesis 2b)36. Finally, we explore for the first time potential associations between individual differences in ADHD-related behaviors and the temporal variability of ERP effects  elicited during the processing of physical stimulus salience.In the present study, we assessed the association between an integrated index of behavioral ADHD symptoms and its relationship to established neurophysiological markers of ADHD, in a subclinical sample of 9 – 11 year old school children. Based on previous research, we hypothesized that children with more problems in attention and executive functioning should show reduced P3 amplitudes (Hypothesis 1a) and longer P3 latencies (Hypothesis 1b) associated with the inhibition of a dominant task responsehttps://github.com/KirstenHilger/AttentionGo1. The data of the whole project will be deposit on zenodo.org after the project is completed . The data used in the actual study can be assessed from the authors upon direct request.All analysis code used in the current study has been deposited on GitHub at th grade of the German school system, that is, in the first year of secondary school. Recruitment was conducted via local advertisements and special-organized information seminars for parents and children at participating schools. Recruitment was restricted to two out of four different school types in the German school system  as problems in attention and executive functioning were suggested to be more frequently present among respective students. As compensation for their general participation in the project, children and their families received coupons for special events  and additional participation in the electroencephalography (EEG) recordings was reimbursed with book vouchers. The data used in the actual study was acquired during the first out of three measurement bursts. 32 children (aged 9–11 years) completed the EEG measurement. We ensured that all children had normal or corrected to normal vision and hearing abilities and were free of any neurological, psychological, or developmental disorders (beyond ADHD). Four children had a formal diagnosis of ADHD. All of those children were treated with methylphenidate (10–20 mg), which was taken early in the morning (between 7 and 8.30 am) also at the day of EEG measurement; EEG measurements took place after school in the late afternoon (3 – 7 pm). One participant was excluded due to technical problems during data acquisition, so that the final sample comprised 31 right-handed children . Note that the sample size of the present study was limited by the project from which subjects were recruited. A power calculation indicated that given the current sample size, only large effects  can be detected with 80% statistical power. We therefore treat effects smaller than around r =  ~ 0.48 as hypothesis-generating in nature.The current study was conducted as part of a longitudinal project that investigates developmental trajectories of children with varying degrees of attentional and executive function problems and that focusses on temporal fluctuations in cognitive, emotional, and social behavior on various different time scales. All children were in the 539; German adaption: Conners Skalen zu Aufmerksamkeit und Verhalten-3. Version40, from which three subscale scores  as well as the ADHD Index were computed. The ADHD Index was used as measure of interest in all analyses. It was developed for the study of pediatric samples in the age range of 6 – 18 years and is commonly used in psychological work with ADHD children40). Note that for visualization, the ADHD Index t-scores (instead of the ADHD Index sum scores) are shown as only t-scores allow for assignment of children into different verbally-labeled categories (see Results). Potential confounding effects of intelligence41 were controlled for by using individual SPM raw scores (see above) as covariate of no interest in all analyses.Problems in attention and executive functions were assessed with the German version of the Conners’ scales for attention and behavior . The CPT involves the continuous presentation of letter sequences, typically over the course of several experimental blocks. We adapted the ‘not X’ version of the classical CPT in which participants react via button press (space bar) to all presented letters (Go condition) except the letter X (NoGo condition)19. As we aimed to additionally assess neural markers of the processing of physical stimulus salience, a third condition was introduced in which a red square was presented at the center of the screen . The distance between child and screen was 120 cm. Black letters (size was matched to fit into a square of 10 × 10 cm) were presented on a light grey background . Each subject completed a practice session until the examiner was confident that the child fully understood the task. The task consisted of 800 trials , presented in eight consecutive blocks . Between blocks, short breaks were introduced whose exact durations (max. 5 min) were determined by the children. The order of the stimuli within blocks was pseudo-randomized to ensure that each block consisted of 25% NoGo trials (‘X’) and 75% Go trials. Within each block, 10% of all trials were SalientGo trials, so that 13.33% of all Go trials were from the SalientGo condition (similar proportions were used before44). Further, it was ensured that at least two StandardGo trials were presented between two NoGo stimuli, between two SalientGo stimuli, and between NoGo and SalientGo stimuli.The task was presented on a computer in a soundproof cabin using PsychoPy . All participants were instructed by the same experimenter  to press the space bar whenever a letter or a red square appeared on the screen, and to withhold their response when the letter ‘X’ was presented. In addition, children were advised to sit still and to avoid—as much as possible—any movements of their heads. Responses were registered between 200 and 1500 ms after stimulus onset only46. Trials in which no response was registered within this interval were treated as unanswered . In total, the task took between 21:20 and 42:43 min depending on the individual-specific break durations (most participants made very short breaks and finished the task after 23–28 min), and was followed by a resting-state measurement for which separate instructions were provided after finishing the task (data not included in this report).Stimuli appeared on the screen for 150 ms followed by an inter-stimulus interval (ISI) of 1,500 ms, summing up to a total trial duration of 1650 ms  in correctly answered StandardGo trials (Standard HIT), RTs in correctly answered SalientGo trials , the variability of these RT measures over time , and finally, the percentage of commission and omission errors. The commission error ratio was calculated by dividing the number of false alarms (FA) by the total number of inhibition trials (FAs + Correct Inhibitions). Errors of omission occurred when children failed to press the space bar on Go trials, that is, when a letter other than ‘X’ or a red square appeared on the screen (MISS). The omission error ratio was calculated by dividing the number of missed trials by the total number of Go trials .SD above or below the mean of the respective variables of interest). Specifically, we used Spearman’s rank order correlation (rho) when the Shapiro–Wilk test indicates significant (p < 0.05) deviation of the data from normality and Pearson’s correlation (r) otherwise. For comparing different conditions (manipulation check) one-sample student t-tests were used after significant deviations of the data from normality were ruled out via the Shapiro–Wilk test . For extreme group comparisons  student independent samples t-tests were used after significant deviations of the data from normality were ruled out with the Shapiro–Wilk test  and significant deviations from homoskedasticity were ruled out with the Levene’s test .To test for associations between individual differences in ADHD Index and these behavioral parameters we used partial correlations  after outliers were excluded  at https://github.com/KirstenHilger/AttentionGo1.git.All pre-processing and analysis scripts can be found as reproducible scripts (jupyter notebooksEEG data were recorded with 64 active Ag/AgCl electrodes (arranged in an extended 10–20 layout) using an actiChamp amplifier  and Fz as online reference electrode. Sampling rate was 1000 Hz. Impedance levels were kept below 10 kOhm, and an online low-pass filter with a high cutoff frequency was set to 280 Hz (notch filter off). Eye electrodes were put below the right eye (SO2) and lateral to both eyes . Two electrodes  were placed bilaterally on the mastoid bones.48 (https://mne-tools.github.io/). At first, the data were re-referenced to average reference (excluding M1 and M2), down-sampled to 200 Hz, and band-pass filtered between 0.1 and 30 Hz. Eye movements and muscle artefacts were corrected via independent component analysis (ICA) using MNE’s ICA preprocessing tool (https://mne.tools/stable/generated/mne.preprocessing.ICA.html). FastICA was applied on the data which for this procedure were high-pass filtered at 8 Hz to guarantee data stationarity and thus improved convergence of the ICA decomposition. 60 ICA components were extracted for each subject. Eye movement and muscle artefact components were manually identified on six exemplary subjects through visual inspection and matching with typical EOG activity component maps50. For the rest of the participants, these artefact components were then identified via correlation . Components automatically identified by this procedure were removed from the 0.1–30 Hz band-pass filtered data and the cleaned data were then re-referenced to average mastoids . Subsequently, data were segmented into epochs of 1100 ms length time-locked to the onset of the stimulus (− 200 to 900 ms) and baseline-adjusted by subtracting the mean amplitude in the prestimulus period (− 200 to 0 ms) from all further data points. Finally, preprocessed data were manually screened to ensure high data quality. In one subject this revealed one single abnormal channel which was manually interpolated.Preprocessing and further analyses of EEG data were conducted in MNE Python51) were calculated on time windows and electrodes that were determined via visual inspection of grand-average difference waves (as common in child ADHD research54). In brief, the inhibition-P3 was statistically analyzed in a time window between 300 and 600 ms at electrode POz, for the salience-P3 we extracted data from 350 to 600 ms at electrode POz, and the salience-N2 was examined on electrode Fz between 250 and 350 ms. Because we observed a rather posterior localization of the P3 component , we decided to extract also the signed area amplitude and fractional area latency values of the P3 on electrode Pz (in the same time windows) to allow for direct comparison with previous investigations57. Please see the respective parts of the results section for further details. Temporal variability of ERP components was operationalized as standard deviation (SD) of amplitude and latency measures over time.Event-related potentials (ERPs) were calculated for each subject by averaging over all trials within each condition, i.e., StandardGo, NoGo, and SalientGo. Incorrect trials representing omission errors in both Go conditions or commission errors in the NoGo condition were excluded. For statistical analyses of the inhibition-P3 (NoGo condition minus StandardGo condition), the salience-P3, and the salience-N2 , signed area amplitudes and fractional area latencies (50%SD above or below the mean of the respective variables of interest). Pearson’s correlation was used to compute the partial correlations except in cases where the Shapiro–Wilk test indicates significant (p < 0.05) deviation of the data from normality. In those cases Spearman’s rank order correlation (rho) was used. To protect against false positives resulting from multiple statistical tests, we corrected for the number of correlation tests conducted in relation to each ERP component . For each of these components, we tested for possible associations between the ADHD Index and the amplitude, latency, as well as the variability of these two parameters. Statistical significance was thus accepted at p < 0.0125 .Finally, associations between individual differences in ADHD Index and the strength (amplitude) and timing (latency) of the inhibition-P3, the salience-P3, and the salience-N2 as well as their variability were examined by calculating partial correlations while controlling for age, sex, and intelligence after exclusion of outliers , and R .Statistical analyses were conducted in Python , Matlab , JASP  and the Ministry of Education and Cultural Affairs of the state of Baden-Württemberg. Informed written consent according to the Declaration of Helsinki and the guidelines of the German Psychological Society was obtained from all children and their parents, respectively.t-scores are depicted in Fig. t-scores < 60, which is interpreted as ‘average—typical levels of concern’. Nine children had a t-score ≥ 60 but < 65, i.e., ‘high average score—slightly more concerns than typically reported’, and the remaining three children had t-scores ≥ 65 but < 70, i.e., ‘elevated score—more concerns than typically reported’. Specifically, two of these three children had an ADHD Index t-score of 69, and one participant had a t-score of 65  indicating ‘many more concerns than typically reported’ were not observed in our sample . For the sake of completeness, Fig. Descriptive statistics of the ADHD Index Table , Fig. 2 18 and reaction times varied between subjects within a plausible range. Participants needed more time to react to SalientGo compared to StandardGo trials (t(30) = 5.991, p < 0.001, d = 1.076). No significant correlations were observed between the ADHD Index and behavioral markers of response inhibition  or salience processing . Note that we also found no such associations a) when testing for differences between extreme groups, i.e., upper vs. lower third of the ADHD Index distribution (commission errors: t(18) = 1.081, p = 0.294 , 95%-CI: − 0.414 to 1.367, Cohens d = 0.483; mean RT in SalienceGo HIT trials: t(18) =  − 0.714, p = 0.485, d = − 0.319; standard deviation of RT in SalienceGo HIT trials: t(18) =  − 0.632, p = 0.535, d = − 0.283; salience effect in RTs: mean RT in SalienceGo HIT trials—mean RT in StandardGo HIT trails: t(18) =  − 0.876, p = 0.393, d = 0.393), b) when repeating the correlation analyses with the three subscales of the ADHD Index , or c) when testing for logarithmic, quadratic, or cubic relationships . For completeness, we tested also for associations between ADHD Index and the mean RT in StandardGo HIT trials , the standard deviation of RT in StandardGo HIT trials , and the omission error ratio .Descriptive statistics of performance measures in the Salience CPT task are also listed in Table 59, indicating selective attention towards the presented stimuli60. Especially in the NoGo condition, a further positivity became visible at around 200 ms over anterior scalp sites, which might reflect a P2 component. A noticeable negativity over frontal electrodes was observed at around 300 ms, especially in the SalientGo condition, which we interpret as N231. Following this effect, a marked positivity developed between 300 and 700 ms with a localization over central and posterior electrodes as expected for the P3. Note that such a late timing of the P3 component is not unusual and seems to be typical for pediatric samples in comparable paradigms61.Grand-average ERPs for all three conditions  are shown in Fig. 61. Within this time window, the difference between NoGo and StandardGo ERPs was significantly different from zero  = 16.87, p =  < 0.001, The neural signature of inhibitory processes  = 17.30, p =  < 0.001, 61; signed area amplitude; one-sample two-tailed t-test: t(30) = 14.98, p < 0.001, Salience processing  also elicited an enhanced  P3 component Figs. . This P3p < 0.0125 was applied as described in more detail in the Methods section. All correlation results are summarized in Table The main aim of the current study was to examine the association between continuous variations in ADHD symptoms (as indicated by the ADHD Index score) and neuronal markers of inhibition and salience processing. These correlations were tested on the electrodes and time windows identified in the previous section, and a corrected significance threshold of r = − 0.34; p = 0.078), but a trend towards longer latencies of the inhibition-P3 in children with more parent-reported ADHD symptoms . Next, we assessed possible associations with the variability of these neuronal responses over time. Contrary to our Hypothesis 2a, we observed significantly less variability in the amplitude of the inhibition-P3 in children with more ADHD symptoms . Our results furthermore revealed that children with more ADHD symptoms exhibited significantly less variability in the amplitude of the salience-P3 , latencies , or the variability of the salience-related N2 .Finally, we tested for associations between ADHD Index and salience-related N2 effects. No significant correlations were observed between the ADHD Index and mean amplitudes  showed a positive but non-significant relationship with parent-reported ADHD symptoms. The absence of a reliable correlation is, however, not due to a lack of variability in the behavioral outcome measure, as the ADHD Index showed substantial between-person differences.Deficits in response inhibition have been identified as one of the most crucial deficits in ADHD, and the investigation of respective event-related potentials (ERPs) provides insights into spatial and temporal characteristics of neural mechanisms underlying inhibitory control processes62. The absence of this effect in our community sample suggest that such evaluative processes are not fundamentally impaired in typically-developing children with more problems in attention and executive functioning. Delayed P3 latencies in ADHD, on the other hand, are interpreted as indicator of an inappropriately late allocation of attention63, which may further hinder the fast and successful inhibition of the dominant behavioral response. Our trend-level results concerning the P3 latency is not incompatible with this assumption, but provides no robust basis for evaluating it either. However, as our sample of children did neither show the expected association in behavior nor exhibit obvious behavioral problems at school, we speculate that also children with relatively higher ADHD Index scores are able to compensate any existent neurophysiological predispositions for attentional and executive problems64. In contrast, when neurophysiological alterations exceed a critical level, compensation would fail and observable behavioral problems may emerge—making a diagnosis of ADHD more likely. However, our observation that the ten children with the highest ADHD Index (including those four children with ADHD diagnosis) did not differ in their behavior from those ten children with the lowest ADHD Index lends no direct support for this interpretation and indicates a need for further empirical investigation of potential compensatory mechanisms.In contrast to our Hypothesis 1, we observed no attenuation of the amplitude of the inhibition-P3 in children with higher levels of reported ADHD symptoms and only trend-level support for longer latencies of the inhibition-P3. Reduced P3 amplitudes in ADHD have been interpreted as indicator of diminished capabilities to evaluate and process the target stimulus and in general as reflecting a lower degree of process control during response inhibition25 and even though ERP studies revealed associations between ADHD and the processing of motivational or emotional salience29, potential relations between ADHD and electrophysiological indices of ‘pure’ physical stimulus salience have only rarely been investigated in previous EEG research. One previous investigation demonstrated that it is quite hard to disentangle pure effects of stimulus salience from effects of novelty (indexed by the early-developing anterior novelty P365), targetness30, or effects of stimulus deviance . In our ‘Salience Continuous Performance Task’  paradigm, the ‘X’ is rare (a deviant) but not physically more salient than the other stimuli, while the red square is rare  and physically salient. Also, it requires the same response as the StandardGo condition (and thus does not differ in its ‘targetness’), and both deviant stimuli  are identical throughout the entire experiment, so that their neurophysiological correlates do not reflect novelty responses. Therefore, our experimental task allows to isolate cognitive processes related to the processing of a perceptually salient  but task-irrelevant stimulus—and may thus be well-suited for investigations of the mechanisms underlying the increased distractibility in children with ADHD.Although much neuroimaging research on ADHD has focused on the salience network and the identification of neural correlates of increased distractibility in children with ADHD67. Godefroid and Wiersema30 observed ADHD-related effects in the salience-P3 of opposite direction , but only in response to the novelty of the stimuli and not in response to their targetness or deviancy. Thus, our result do not contradict their findings but provide important insights into the importance of further differentiation between different aspects of saliency, i.e., between mechanisms of novelty, targetness, deviancy, and physical salience, and their contributions to the increased distractibility in ADHD. Note, that the later timing of the salience-P3 in contrast to the inhibition-P3 , the salience-related P3 deflections are typically later-developing and characterized by a steeper increase68.We observed no associations with the ADHD Index at the behavioral level and no relation with salience-P3 latencies, but significantly reduced salience-P3 amplitudes in children with higher ADHD Index scores. The posterior salience-associated P3 component is suggested to evolve from cingulate and temporo-parietal brain regions and to reflect the ascribed importance of a stimulus-P3 Fig.  is not s30. Although the N2 has frequently been investigated in response to NoGo stimuli in both adult and child samples with ADHD69, respective findings are hardly comparable with ours, as in most of these studies the N2-elicting stimulus was both salient and task-relevant . Processes related to response inhibition vs. handling task-irrelevant physical salience can thus not be clearly dissociated on these grounds. To sum up, our study introduces a new variant of the CPT paradigm that allows to investigate relatively ‘pure’ effects of physical stimulus salience, different from more motivational-behavioral salience effects of novelty or targetness, and our results suggest that ADHD-related behaviors are associated with the amplitude of P3 effects elicited by stimulus salience. However, as our sample size was small, the reported effects can only be treated as explorative and hypothesis-generating, requiring further empirical investigation.The absence of any relationship between ADHD Index scores and the N2 amplitude or latency converges with results from a previous group-comparison study that found no significant N2 differences between adults with ADHD and healthy controlsIn contrast to the rather selective results for correlative associations between ADHD Index and mean amplitudes and mean latencies, associations between ADHD Index and the temporal variability of ERP components were markedly present—even in our comparably small sample. However, in contrast to Hypothesis 2, we observed a negative association between amplitude variability and ADHD Index (less variability in the inhibition-related P3 amplitude in children with higher ADHD Index), and no association in respect to the variability of inhibition-P3 latencies.36. However, even though these studies provide initial evidence in favor of an association between ADHD symptoms and ERP amplitude variability, these two studies differed markedly from the present investigation in numerous aspects: Both studies a) investigated clinical samples and, in the case of Gonen-Yaacovi et al.35 studied adult ADHD patients, b) applied different experimental paradigms during EEG acquisition  both studies operationalized ERP variability in different ways, i.e., different from each other and different from the current study. Gonen-Yaacovi et al.35 assessed neural variability of a different EEG component, i.e., of the sensory-evoked P100 and N100 components , while Myatchin et al.36 assessed global ERP amplitude variability, i.e., pooled across scalp electrodes and across target and non-target stimuli, and within a broad time window of 0 – 600 ms. We thus do not necessarily consider our results of a negative correlation between inhibition-related P3 amplitudes and ADHD Index as incompatible with these previous data or with the general hypothesis that ADHD symptoms may be related to maladaptive patterns of ERP amplitudes over time. Rather, we conclude that further research is needed to specify the circumstances under which positive or negative associations with ADHD symptoms arise.Our hypothesis concerning inhibition-P3 amplitude variability was based on two previous investigationsFinally, we also explored potential associations between ADHD symptoms and the variability of the P3 and N2 components associated with salience processing. Similar as for the inhibition-P3, we observed significantly less variability in the amplitudes in children with higher ADHD Index, but no significant effects concerning the latency of this component or in respect to the salience-N2. Based on these finding, we propose that the focus of ADHD studies that was previously primarily on tasks requiring some form of response inhibition and on mean ERP components, should be broadened to additionally consider salience-related processes and especially the temporal variability in related neural mechanisms.10. Empirical research supporting this proposal demonstrated continuous covariations between ADHD symptoms in healthy individuals on the one hand and behavioral13 or neural16 characteristics on the other hand. Importantly, all neurophysiological associations reported in the present study were also observed across a continuous and broad range of individual variations. In our subclinical sample, we found trend-level support for one effect that is a relatively established finding in ADHD group-comparison research, i.e., longer inhibition-P3 latencies22, and interesting associations with ERPs evoked by the processing of stimulus salience. We therefore propose that some of the neural characteristics that have previously been identified as biomarkers of ADHD may reflect more general mechanisms linked to individual variations in behavior and cognition that are only in their extremes ‘defined’ as ADHD.A more dimensional conceptualization of ADHD was recently proposedAs already stated in the Methods section, the sample size of 31 children (which was restricted by pragmatic reasons) limits the ability of the study to detect effects of weak or moderate effect size. We therefore stress that such results (correlations < 0.48) can only be treated as hypothesis-generating in the present study, but may serve to inform future studies with larger sample sizes. Also, we assessed rather subtle variations in a dimensional marker within a subclinical sample, which may also call for a replication in larger samples to ensure sufficient statistical power. Obvious candidates for such future research are the robust correlations observed between ADHD Index and two P3 variability measures. We propose that future studies should also consider variability measures rather than exclusively focusing on mean  amplitude and latency effects.Also, we cannot fully rule out potential selection biases when registering for the present study. Due to the a-priori restriction to specific types of secondary schools, our sample was not representative of the entire population of school-aged children, and parents who believed that their child may have problems with attention or executive functions may have been more willing to participate in and support respective research. However, as this circumstance may have broadened the range of the ADHD Index distribution, this might be a strength rather than a weakness of the current study.71), combining multiple ratings from different sources would be preferable in future studies to provide more robust estimates of behavioral variation. Finally, by adopting a cross-sectional approach, our results do not allow to draw any conclusions in respect to the question of whether the observed neurophysiological signatures of ADHD symptoms will persist into adulthood, or whether they may rather reflect a delay in cortical development that can potentially be overcome . Our results suggest that future longitudinal research should also take into account the temporal variability of neurophysiological measures.Further, our study relied on only one integrated questionnaire measure to estimate the level of ADHD-related behaviors, i.e., the ADHD Index rated by the children’s parents. As it has been shown that the estimates of ADHD symptoms can vary substantially between different scales and more or less objective sources of information (children vs. parents vs. teachersOur study investigated established neurophysiological markers of ADHD symptoms in a subclinical community sample of school children. Unlike previous group comparison studies with clinical ADHD samples, we found no evidence for amplitude reductions of the inhibition-P3 in relation to ADHD-related behaviors, but provide trend-level support for a delay of inhibition-related P3 latencies. However, we identified the temporal variability of inhibition-P3 amplitudes as a possible correlate of ADHD. Our study introduced a new version of the classical Continuous Performance Task that allows to simultaneously assess, in addition to effects of response inhibition, also effects of physical stimulus salience, and our results suggests temporal variability of the amplitude of the salience-related P3 as additional marker of ADHD. We extend previous group-comparison research to the continuous range of subclinical variations and show that some neurophysiological indicators of clinical ADHD may represent more general mechanisms of attention and executive functioning. This supports recent proposals of a more dimensional conceptualization of ADHD, according to which the diagnosis of ADHD would represent an extreme end of continuous variations not only in attention and executive functioning but also in the underlying neural processes.


The present paper presents a fundamentally novel approach to model individual differences of persons with the same biologically heterogeneous mental disorder. Unlike prevalent case-control analyses, that assume a clear distinction between patient and control groups and thereby introducing the concept of an ‘average patient’, we describe each patient's biology individually, gaining insights into the different facets that characterize persistent attention-deficit/hyperactivity disorder (ADHD).Using a normative modeling approach, we mapped inter-individual differences in reference to normative structural brain changes across the lifespan to examine the degree to which case-control analyses disguise differences between individuals.At the level of the individual, deviations from the normative model were frequent in persistent ADHD. However, the overlap of more than 2% between participants with ADHD was only observed in few brain loci. On average, participants with ADHD showed significantly reduced gray matter in the cerebellum and hippocampus compared to healthy individuals. While the case-control differences were in line with the literature on ADHD, individuals with ADHD only marginally reflected these group differences.Case-control comparisons, disguise inter-individual differences in brain biology in individuals with persistent ADHD. The present results show that the ‘average ADHD patient’ has limited informative value, providing the first evidence for the necessity to explore different biological facets of ADHD at the level of the individual and practical means to achieve this end. Case-control research paradigms ignore these sources of heterogeneity; they assume that each diagnostic group is a distinct entity. A key goal in many such studies is to identify biological markers that are reliable indicators of disease state. However, markers identified through this approach generally explain only a small part of the variance linked to mental disorders  is a prevalent and impairing neurodevelopmental disorder, which persists into adulthood in a substantial part of the patients  with a standard 8-channel head coil. A high-resolution T1-weighted magnetization-prepared rapid-acquisition gradient echo (MPRAGE) anatomic scan was obtained from each participant, in which the inversion time (TI) was chosen to provide optimal gray matter–white matter T1 contrast [repetition time (TR) 2730 ms, echo time (TE) 2.95 ms, TI 1000 ms, flip angle 7°, field of view (FOV) 256 × 256 × 176 mmhttp://www.fil.ion.ucl.ac.uk/spm; Ashburner and Friston, Prior to gray matter volume estimation, all participants’ T1 images were rigidly aligned using statistical parametric mapping version 12 (SPM-12). Subsequently, images were segmented, normalized, and bias field–corrected using ‘new segment’ from SPM12  at the 5% inference level using threshold-free cluster enhancement.First, we assessed group-level deviations from the normative model. For this, individual gray and white matter deviation maps were fed into PALM .Next, the individual maps of deviation were thresholded at |Z| > 2.6. These maps reflect the deviation from the normative model at the individual level. Note that the use of a fixed statistical threshold across participants allows for a simplified comparison between participants in terms of numbers of extreme deviation from the normative model, even when the overall distribution of deviations of a participant is shifted. We also repeated the analyses correcting for multiple comparisons at the individual participant level using the Benjamini and Hochberg procedure  as well as inattention .et al., et al., et al., et al., Z-score maps thresholded at ±2.6, this pattern was confirmed, and additional regions showing negative mean deviations were observed in the anterior cingulate, insula, and frontal cortex (b). No differences in mean deviations between patients and controls were observed in white matter , although some positive and negative mean deviations exceeded the z-score threshold of ±2.6 in patients: for instance, temporal brain regions showed positive deviations, while frontal and parietal regions showed negative deviations .l cortex b. No dif2(1) = 23.64, pcorr. < 0.001). This effect was driven by a larger percentage of negative deviations in participants with persistent ADHD  than in healthy individuals . In white matter, significant differences in the percentage of extreme negative deviations were observed between groups as well  = 18.02, pcorr. < 0.001); again, a significantly higher proportion of negative deviations was seen in participants with persistent ADHD  than in healthy individuals . No differences between groups were observed in positive deviations on measures in gray and white matter (online Supplementary Table S1). As only the percentage of extreme negative deviations were significant between individuals with persistent ADHD and healthy participants we focus our characterizations of those deviations on extreme negative deviations in gray matter, we report the other extreme deviations in different modalities in the supplement but report the main outcomes in the section below as well.An analysis of the total percentage of extreme negative deviations in gray matter across the groups showed that participants with persistent ADHD differed significantly from healthy individuals . In white matter, we also observed greater overlap in participants with ADHD than in healthy individuals, again involving regions around the hippocampus and the basal ganglia . A scattered pattern of positive deviations was seen in the  overlap maps for participants with ADHD as well as for healthy individuals in both gray and white matter. The overlap maps of the extreme negative deviations partly resembled the pattern observed in the mean deviation analyses of cases and controls (b), also when detecting extreme deviation based on the FDR . Further, nine out of the ten most negatively deviating patients showed extreme values in the cerebellum , but not symptom scores, stimulant medication, or comorbidity, before correction for multiple comparisons (online Supplementary Table S1); for the extreme positive deviations, we did not find any associations that were even nominally significant.Participants with ADHD showed overlap in local gray matter negative deviations in more than 2% of patients primarily in the cerebellum, hippocampus, and basal ganglia; less overlap in negative deviations was observed in healthy individuals b. In whicontrols b, also wrebellum , althougWe mapped the biological heterogeneity of persistent ADHD in reference to normative brain aging across the adult lifespan, based on voxel-based morphometry derived brain measures. In participants with ADHD, we observed robust mean deviations in gray matter from the normative model in the cerebellum, temporal regions, and the hippocampus. However, at the individual level, we found that few brain loci showed extreme negative deviations in more than 2% of the participants with ADHD, providing a measure for the  inter-individual variation between adults with persistent ADHD.et al., et al., et al., et al., et al., et al., Case-control comparisons show small to medium effect sizes of (gray matter) alterations in adult ADHD patients  a single locus contributing to a disorder and (ii) group homogeneity. This approach has been extended by pattern classification studies, which consider multiple voxels at once and show that using structural MRI the predictions of ADHD range from about 60% to up to about 90% accuracy indicating a high variability between studies (Bansal et al., et al., The present results allow for a novel interpretation of earlier large-scale pattern recognition studies in ADHD, which often showed relatively low accuracy in discriminating ADHD cases from controls (Wolfers We are confident that the present results and the main conclusions are replicable in follow-up studies. However, a few limitations require a discussion. First, we had to use 10-fold cross-validation in healthy individuals and out of sample predictions in individuals with persistent ADHD, as our healthy sample was too small to split it into two. This potentially introduces a small bias. Second, we did not find associations of symptom scores with the percentage of deviation from the normative model. However, the measures we used to assess symptoms rely on self-report, which is generally noisier than measures from diagnostic interviews. Finally, our sample did not allow to inspect the effect of comorbidities and other potentially confounding factors on the obtained results as the comorbidities were inconsistent across individuals. In future studies, we envision that normative models are built on the basis of large population samples. These population-based normative models can subsequently be applied to cohorts that sample ADHD using the same inclusion criteria for healthy individuals as for those with a disorder. In this way normative modeling is complementary to classical case-control comparisons as it allows for the investigation of individual differences. Here, we show that an approach relying on case-control differences is not sufficient to understand ADHD and its biological heterogeneity.In conclusion, while our group level effects are largely in line with existing literature on ADHD, our approach also shows that the disorder is a much more biologically heterogeneous on the individual level than previously anticipated. We thus need to move towards descriptions of biology for the individual patient to improve our understanding of ADHD. The present results provide the first quantitative estimate of the degree of biological heterogeneity, in terms of spatial overlap of an individual's extreme gray and white matter deviations, linked to ADHD. In this way, we provide valuable information to improve the nosology and characterization of the different facets of persistent ADHD.