The objective of this population-based register study was (1) to investigate the association between young adults diagnosed with attention-deficit/hyperactivity disorder (ADHD) and subsequent labour market marginalisation (LMM) in two comparison groups, i.e. matched young adults from the general population without ADHD and unaffected siblings to persons with ADHD and (2) to assess the role of comorbid disorders.n = 9718). Crude and multivariate sex-stratified hazard ratios (HRs) with 95% confidence intervals (CIs) were measured 5 years after the diagnosis of ADHD for the risk of disability pension, long-term sickness absence (SA) (>90 days), long-term unemployment (>180 days) and a combined measure of all three in young adults with ADHD compared to their siblings without ADHD and a matched comparison group.This study included all young adults in Sweden, aged 19–29 years, with an incident diagnosis of ADHD 2006–2011 , a nearly three-fold higher risk of long-term SA  and a 70% higher risk of long-term unemployment  compared to the matched comparison group. The risk estimates were lower compared to siblings for disability pension  and long-term SA  but higher in the long-term unemployed . Comorbid disorders explained about one-third of the association between ADHD and disability pension, but less regarding SA and long-term unemployment.Young adults with ADHD have a high risk for different measures of LMM and comorbidities explain only a small proportion of this relationship. During the past decades, there has been an increased awareness that attention-deficit hyperactivity disorder (ADHD) is not only affecting children as previously believed, but that symptoms of inattention might also persist into adulthood. In a study including 10 countries on three continents, about 3.5% of individuals in working-age were found to have symptoms of ADHD   to assess the risk of LMM − measured as long-term unemployment, long-term SA and disability pension − and a combined measure of these three outcome measures, in young adult men and women with incident ADHD and compare it to comparison groups consisting of persons in the general population without ADHD and unaffected siblings to persons diagnosed with ADHD and (2) to investigate the role of comorbid disorders in these relationships.Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA), hosted by Statistics Sweden: all socio-demographic variables, year of emigration and unemployment; (2) Microdata for Analysis of Social Security (MIDAS) hosted by the Swedish Social Insurance Agency: date, duration and grade of SA and disability pension; (3) the Multi-Generation Register (MGR), hosted by Statistics Sweden, with information on parents and siblings to persons with ADHD; (4) the National Patient Register (NPR): primary and secondary diagnoses for ADHD and all comorbid disorders during the year of the cohort entry date ; and (5) Cause of Death Register: date of mortality (2005–2016). Databases 4 and 5 are hosted by the Swedish National Board of Health and Welfare.Data from five registers were merged individually based on the de-identified unique personal identification number given to all permanent residents in Sweden. Information was available for each individual retrospectively from 1 January 2005 and prospectively until 31 December 2016 from the following five Swedish nationwide registers: (1) n = 4727) at the CED or a record of ADHD medication before the CED (n = 2202) were excluded. The final study population comprised of 9718 young adults with ADHD.The study base was made up of 16 647 young male and female adults between 19 and 29 years of age (mean age = 23.4 years) who had the first primary or secondary diagnosis of ADHD from either inpatient or specialised outpatient health care. This information was derived from the NPR between 2006 and 2011. The year of the incident diagnosis served as the CED. Individuals who had an ongoing disability pension (n = 48 590). The second comparison group consisted of siblings not affected by ADHD in the same age range (19–29 years) as their siblings with ADHD and who were not on disability pension (n = 5582). Siblings were matched on both mother and father, i.e. only full siblings were considered in the analysis. In the case of two (or more) siblings with ADHD who were close in age, the person with the ADHD diagnosis and the earliest cohort entry date was chosen as the exposed individual.We defined two comparison groups from the five registers. For one of the comparison groups, we selected five individuals from the general population without ADHD matched for sex, age and educational level) with no ongoing disability pension (ADHD was defined based on the diagnostic code (F90) of the International Classification of Diseases, 10th Revision (ICD-10).The cohort was followed for 5 years from the CED (2006–2016) for (1) disability pension, (2) long-term SA , (3) long-term unemployment  and (4) total LMM, defined as granting either the disability pension, long-term SA or long-term unemployment.socio-demographic factors , all measured on 31 December the year before CED, (2) work-related factors  and (3) comorbid disorders , anxiety- and stress-related disorders (ICD-10: F40-F48), autism-spectrum disorders (ICD-10: F84), substance use (ICD-10: F10-F19), behavioural and emotional disorders (ICD-10: F91-F98), schizophrenia/non-affective psychoses (ICD-10: F20-F29), mental retardation/disorders of psychological development , other mental disorders (ICD-10: Other F codes), musculoskeletal disorders (ICD-10: M01-M99), asthma (ICD-10: J45), diabetes mellitus (ICD-10: E10-E14), neoplasms (ICD-10: C00-D48), cardiovascular disorders (ICD-10: I00-I99), accidents (ICD-10: S00-S99) and other somatic disorders (ICD-10: remaining codes for somatic disorders except O.80 and Z00–99). The categorisation of all covariates is presented in Covariates in the analyses included (1) Individuals ⩾16 years of age can receive sickness benefits if they have an income from an established business or employment. During the first 14 days, except for the first day, which is a qualifying day, the employer covers these sickness benefits. From day 15, the Swedish Social Insurance Agency then takes over to continue to pay the benefits, and from that day, data are available in registers. Individuals between 19 and 29 years of age can receive a time-restricted disability pension when their work capacity is reduced or if compulsory education is not completed at the age of 19. Persons from 30 years of age can only be granted permanent disability pension. Individuals >16 years of age can be enrolled at the Swedish Public Employment Service where they can receive unemployment benefits. From age 20, basic levels of unemployment benefits can also be received without earlier income from work.Cox proportional hazard regression models with competing risks (cause-specific hazards) were applied to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) in order to determine the association between ADHD and subsequent outcomes of LMM  were performed to adjust for shared familial confounders, i.e. genetic factors and unmeasured shared confounders such as socioeconomic status, neighbourhood or stable parental factors. To assess the contribution of specific comorbid conditions a set of analyses was conducted. Here, individuals with specific comorbid disorders were excluded one at a time, with the remaining individuals compared to the matched comparison group. All analyses were carried out using SAS, version 9.4 (SAS Institute Inc.).Periods of unemployment, as well as SA in the year before the CED, were much more prevalent in young adults diagnosed with ADHD than in both comparison groups . MoreoveIn total, 21% of the young adults with ADHD were granted disability pension within 5 years after the incident diagnosis, which can be compared to just 2% in both the matched comparison group  and the Some 24% of individuals with ADHD experienced long-term SA (>90 days) during the follow-up compared to 7% in the general population. This difference translates to an HR of 4.3 for individuals diagnosed with ADHD in the crude model . The risSome 19% of young adults diagnosed with ADHD were long-term unemployed during the follow-up compared to 13% in the general population. In the crude model this equals a 70% higher risk of long-term unemployment (HR = 1.7) compared to the matched comparison group . The risConcerning the combined measure , young adults diagnosed with ADHD had almost a four times higher risk in the crude model (HR = 3.6) compared to the matched comparison group . The risWhen excluding specific mental comorbidities, other mental disorders  was exceedingly high. Two other Swedish longitudinal studies also reported high levels of both disability pension and work absence in adults diagnosed with ADHD (Edvinsson & Ekselius, v. during childhood in the other study and thus our participants may have had less severe symptoms during childhood. Another explanation could be that the measures are not comparable, as that study measured the total amount of days on work disability, whereas our study employed a dichotomised variable. One might conclude that both mental and somatic comorbidities play some role in explaining the high risk of LMM in persons diagnosed with ADHD. However, after considering comorbidities in our study, the risk of LMM was still extremely high. Accordingly, the symptomatic picture of ADHD is responsible for the problematic situation regarding work in young adults diagnosed with ADHD. Of the comorbidities, depression/bipolar disorders, and anxiety- and stress-related disorders had the highest impact on long-term SA. The most important comorbid disorders authorising disability pensions were comorbid autism-spectrum disorders, behavioural/emotional disorders, and mental retardation. Other studies confirm that individuals with these disorders have a high risk of work disability (Helgesson et al., Comorbid mental and somatic disorders are reported to be common in individuals diagnosed with ADHD (Edvinsson et al., Still, we found a substantially higher risk of LMM in individuals with ADHD compared to their siblings; however, these estimates were lower than when compared to the matched controls from the general population. This finding indicates that a part of the high risk of LMM could be attributed to familial factors. The same pattern was also seen in a study on persons diagnosed with obsessive−compulsive disorders (Pérez-Vigil, Mittendorfer-Rutz, Helgesson, Fernández de la Cruz, & Mataix-Cols, A few studies have reported sex differences in the symptomatic expression of ADHD (Gershon & Gershon, The major strengths of this study include the high quality and completeness of register data that allow individual information on many covariates over a long period. This strength includes the advantage of little loss to follow-up. The inclusion of several measures of LMM is another strength. The large population-based study also provides the possibility of investigating many comorbid disorders. Depending on welfare regime, the scope of LMM might vary between countries. Therefore, a combined measure of LMM, that captures both unemployment and work disability, was introduced. This combined measure may be comparable with the findings from other countries. Limitations of the study also warrant discussion. Both ADHD and all comorbid disorders have been measured by the information available in specialised health care, which most likely represents young adults with medical conditions of greater severity. This fact means that information on less severe conditions has not been covered. Still, estimates on ADHD might not have been strongly affected, as such patients are predominantly treated in specialised health care settings. In our study only persons diagnosed in young adult age were included. Because ADHD cannot be acquired during the life course, the population in this study may have less severe symptoms of ADHD than persons diagnosed during childhood. Moreover, the measure of SA does not include information during the first 14 days of sick leave for employees. Furthermore, information on individuals who are unemployed but who are not registered at The Swedish Employment Agency is not covered in the available dataset. Still, because our outcome variables comprise long-term measures, we are confident that this lack of information does not have a significant effect on the estimates. Finally, despite the availability of a large number of covariates, some information on e.g. body mass index was not available in the registers.Young adults with ADHD have a high risk of LMM. Of clinical importance is that comorbidity with other disorders does not play a major role in the association between ADHD and LMM. Regardless of comorbidities, establishment of early interventions targeting work capacity in young individuals with ADHD might be a clinical important intervention to prevent long-term marginalisation of these individuals.


Guanfacine  is a commonly used drug with recognized efficacy in the treatment of attention deficit hyperactivity disorder (ADHD). This study aimed to assess the effects of guanfacine on short-lasting  epileptiform discharges in cortical neurons. Moreover, we assessed the effects of guanfacine on voltage-gated sodium currents.We conducted patch-clamp recordings in prefrontal cortex pyramidal neurons obtained from young rats. Interictal epileptiform events were evoked in cortical slices in a zero magnesium proepileptic extracellular solution with an elevated concentration of potassium ions.Interictal epileptiform discharges were spontaneous depolarisations, which triggered action potentials. Guanfacine (10 and 100 µM) inhibited the frequency of epileptiform discharges. The effect of guanfacine on interictal events persisted in the presence of alpha-2 adrenergic receptor antagonist idazoxan. The tested drug inhibited neuronal excitability. Tonic NMDA currents were not influenced by guanfacine. Recordings from dispersed neurons showed that the tested drug (10 and 100 µM) inhibited persistent and fast inactivating voltage-gated sodium currents.This study shows that guanfacine inhibits interictal discharges in cortical neurons independently of alpha-2A adrenergic receptors. This effect may be mediated by voltage-gated sodium currents. Inhibition of interictal activity by guanfacine may be of clinical importance because interictal events often occur in patients with ADHD and may contribute to symptoms of this disease.The online version contains supplementary material available at 10.1007/s43440-023-00458-4. Attention deficit hyperactivity disorder (ADHD) is described as a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development . Emergin+ channels, which strengthens network connectivity and working memory . Differences between more than two groups were evaluated using one-way ANOVA for repeated measures followed by the Tukey post hoc test if the data passed the normality test. If the data did not pass the normality test nonparametric equivalent of one-way ANOVA for repeated measures (Friedman’s test) followed by Dunn’s post hoc test was used. Depending on the results of the normality test, the Students n = 6 recordings, the paired Students t test, p = 0.0362, t5 = 2.8, as shown by the black solid arrow in Fig. Interictal epileptiform discharges (IEDs) were evoked in a zero magnesium, high potassium proepileptic extracellular solution similar to our previous study . Firstlyn = 5 recordings, the paired Students t test, p > 0.05, t4 = 1.04).After a steady control frequency of the IEDs was recorded, the influence of guanfacine on the membrane potential and the frequency of interictal events were assessed. Guanfacine was applied for 7–10 min. The tested compound (100 μM) did not change the membrane potential . Guanfacine inhibited the frequency of the IEDs. Example recordings are shown in Fig. n = 6 recordings, one-way ANOVA for repeated measures, F3,17 = 324.72 (p < 0.0001) followed by Tukey’s post hoc test (p < 0.001 control vs guanfacine), Fig. n = 5 recordings, 2 animals, the paired Students t test, p = 0.0042, t4 = 5.9, Fig. The control frequency of the IEDs was 0.12 ± 0.02 Hz , Friedman’s statistic = 8, p = 0.0046 followed by Dunn’s post hoc test ). Thus, the effect of guanfacine on IEDs in PFC pyramidal neurons is independent of alpha-2A adrenergic receptors.The most commonly described mechanism of action of guanfacine is the stimulation of alpha-2A adrenergic receptors . For thin = 4 recordings, nonparametric repeated measures ANOVA (Friedman’s test), Friedman’s statistic = 8, p = 0.0046 followed by Dunn’s post hoc test ). Example recordings and averaged results are shown in Fig. In the next series of experiments, we recorded action potentials in PFC pyramidal neurons in physiological ACSF. Excitability was defined as the number of action potentials per depolarisation step lasting 3 s. We found that guanfacine 100 μM inhibited neuronal excitability  followed by Tukey’s post hoc test (p < 0.001 control vs guanfacine)). Furthermore, normalized maximal current amplitudes were 1.0 [1.0–1.0], 0.62 [0.51–65] and 0.78 [0.55–0.82] in the control, after the application of guanfacine 10 μM and after wash-out, respectively , Friedman’s statistic = 8, p = 0.0046 followed by Dunn’s post hoc test ). Normalized results are depicted as medians in Fig. We hypothesized that guanfacine may inhibit persistent sodium currents because it has been found that the inhibition of these currents substantially contributes to decreasing excitability in central neurons . Persistn = 5 recordings, one-way ANOVA for repeated measures, F3,14 = 40.95 (p < 0.0001) followed by Tukey’s post hoc test (p < 0.001 control vs guanfacine)). A lower concentration of guanfacine (10 μM) was also tested and the maximal, normalized sodium current amplitudes were 1.0 [1.0–1.0] in the control, 0.53 [0.52–0.59] after application of the tested drug and 0.66 [0.64–0.71] after wash-out , Friedman’s statistic = 8, p = 0.0046 followed by Dunn’s post hoc test , Fig. n = 4 recordings, paired t test p > 0.05, t3 = 0.6).In the next series of experiments, the influence of guanfacine on fast-activating and fast-inactivating voltage-gated sodium channels was tested. The currents were evoked once every 10 s by rectangular voltage steps to − 10 mV. Control recordings were conducted for 2 min, the tested drug was applied for 2 min and after that wash-out was recorded. Example recordings of fast inactivating sodium currents are shown in Fig. n= 4 recordings, 2 animals, Wilcoxon’s matched-pairs test, p > 0.05). It was shown in our previous study  pA, as shown by the left grey arrow in Fig. us study  that tonThe epileptiform discharges recorded in this study may be regarded as interictal events because of their short duration (less than 2 s) . They doIn this study, IEDs were recorded with the use of the patch-clamp technique in PFC pyramidal neurons in a zero magnesium, elevated potassium pro-epileptic solution. Such composition of the extracellular solution enhances the effects of glutamate on NMDA receptors/channels because magnesium ions are removed from the NMDA channels pore. Furthermore, more glutamate is released from presynaptic terminals in the presence of increased potassium concentration because presynaptic axons are depolarized. Thus, the zero magnesium, elevated potassium extracellular solution increases the glutaminergic transmission that generates IEDs , 21.We found that guanfacine blocks IEDs. The tested drug, however, did not exert this effect via the inhibition of glutaminergic NMDA receptors/channels, because we showed that they were not influenced by guanfacine. We recorded both synaptic and extra-synaptic NMDA currents simultaneously since the recordings were made from the whole plasma membrane, and NMDA was applied to the whole bath .Glutamate release is caused by the opening of presynaptic voltage-gated sodium and calcium channels , 24. ConFew reports assess guanfacine’s influence on the electrophysiological properties of neurons. It was found that guanfacine suppressed excitatory postsynaptic currents in PFC pyramidal neurons , 27. SimAs stated above guanfacine enhances neuronal activity in the PFC during the delay period of a working memory task via alpha-2 adrenergic receptors . It may The most commonly described mechanism of action of guanfacine is stimulating G-protein-coupled alpha-2A adrenergic receptors , 27, 28.IEDs occur more often in patients with ADHD and may contribute to symptoms of this disease –15. TherThis study shows that guanfacine inhibits IEDs in prefrontal cortex pyramidal neurons independently of alpha-2A adrenergic receptors. Sodium channel blockade by guanfacine is likely involved in this effect. This novel mechanism may be important clinically as inhibition of IEDs by guanfacine may reduce symptoms of ADHD.Supplementary file1 (XLSX 14 KB)Below is the link to the electronic supplementary material.


ADHD patients had significantly lower P3 NGA response compared to controls. The decrease in NGA was related to impulsivity scores as measured by the Conners’ Adult ADHD Rating Scale: patients with higher impulsivity scores had significantly lower NGA. Treatment with stimulant medication, as compared to the lack of such treatment, was associated with a correction of the lower NGA response in ADHD patients. The current study revealed a lower NGA in adult ADHD, a finding which is consistent with the inhibitory control and frontal lobe dysfunctions described in the disorder. Our finding of the inverse relationship between NGA and impulsivity suggests that clinically more severe impulsivity is linked to a more pronounced frontal dysfunction in adult ADHD subjects.Our aim was to delineate the electrophysiological basis of dysfunctional inhibitory control of adult ADHD via investigating the anteriorization of the P3 component of the event-related brain response associated with the NoGo task condition . NGA is a neurophysiological measure of brain topography for cognitive response control, which indexes an overall shift of the brain’s electrical activity in anterior direction towards the prefrontal areas. While the NoGo P3 received considerable attention in the adult ADHD literature, the brain topography of this component, which reflects the inhibitory process, remains largely unaddressed. EEG recordings were obtained during a Go/NoGo task from 51 subjects ( Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by its core symptoms of developmentally inappropriate levels of inattention and/or hyperactivity and impulsivity  electroencephalography (EEG) and event-related potential (ERP) paradigms are increasingly applied. Through their high time resolution, they have the advantage of being able to capture the exact time course of distinct neurophysiological events during the applied paradigms within the millisecond range  ADHD (and schizophrenia) , gender and level of education. Adult ADHD patients were recruited from the Adult ADHD Outpatient Clinic of the Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary. Controls were recruited from the local community, clinical staff, and their relatives.Inclusion criterion for the patient group was a diagnosis of ADHD persisting into adulthood, established by an experienced psychiatrist. All diagnoses were made in adulthood. The psychiatric evaluation consisted of (1) a structured interview for evaluating current and retrospective childhood DSM-IV-TR ADHD symptoms; (2) semi-structured and open interviews assessing background information, developmental data, functional impairment, psychiatric comorbidity; (3) medical history data obtained from medical documentation and close family members, and (4) self-rated questionnaires.Exclusion criteria for all participants included a history of severe neurological or somatic disorder or severe head trauma. Controls were also excluded in case of positive neurological or psychiatric history. No exclusions were made based on these criteria. The 90-item Symptom Checklist (SCL-90R) was used to select controls with no current psychiatric comorbidity  the morning before the investigation. The computer screen for stimuli was placed at a viewing distance of approximately 50 cm. The applied Go/NoGo paradigm was previously used and described in detail  was applied  at a sampling rate of 1024 Hz. A band-pass filter of 0.5–70 Hz was applied. Electro-oculogram (EOG) was recorded for monitoring eye movements for artifact identification and rejection. Besides eye movements, epochs with a voltage exceeding ± 90 μV on any EEG or EOG channel were excluded applying automatic artifact rejection criteria. Data were stored and analyzed off-line using the Electromagnetic Source Signal Imaging (EMSE) Suite and the Statistical Analysis System (SAS 9.4) software. The stimulus-locked data were segmented into epochs of 700 ms, including 200 ms before stimulus and 500 ms after stimulus. The threshold cut-off was 50 for the required minimum number of usable segments for the ERP analyses. Only correct trials were included in the current analyses. The stimulus-locked segments were baseline-corrected using a 200-ms pre-response window and averaged to obtain the ERP waveforms for each subject and each condition (Go/NoGo).Our EEG data analyses followed a 2-step procedure, including (1) the determination of global field power as a function of time in relation to the stimulus onset to identify a data-driven time window of interest; (2) computation of the amplitude centroid to measure the NoGo anteriorization of the ERP NoGo responses relative to the Go responses in the specified time window of interest. Below, a detailed description of the two steps is provided.Step 1. Determination of global field power. Following the procedures of Fallgatter and colleagues . Within the time window identified in Step 1, we computed the centroids based on the amplitude and topographical configuration of the respective map Lehmann . The cenTo examine the robustness of the findings, we conducted analyses by computing the NGA (centroid) measure for the full time window ; and for the peak GFP , since the latter (peak GFP) may delineate changes that are associated more specifically with the event of interest . Furthermore, we also determined the NGA (centroid) measure separately, based, respectively, on the full set of 128 electrodes; and on the anterior midline electrodes, since these sensors are considered to represent the best established region of interest for the NoGo P3 ERP component. The effect size for the group difference in NGA was characterized in terms of the Cohen D measure Cohen .The statistical analyses were based on random-regression hierarchical linear modeling (HLM) . As expected, all four CAARS symptom factors were significantly higher in the ADHD group compared to healthy controls: Hyperactivity , Inattention , Impulsivity  and Problems with self-concept subscales . All of the adult ADHD patients belonged to the combined subtype of the disorder.ADHD patients were characterized by significantly higher overall symptom severity as measured by the CAARS Total core symptom score compared to controls (n = 12). Nine patients received methylphenidate (34.6%); and 3 patients (11.5%) received antidepressants, with 1 of them receiving anxiolytics (for adjustment disorder). Regarding non-stimulant medication, bupropion was administered to 3 patients (combined with paroxetine in 1 patient and with clonazepam in another one).As for comorbidity, a total of 11 (42.3%) patients had another DSM-IV-TR psychiatric diagnosis, all of which were affective disorders (unipolar depression and anxiety). Personality disorders were not assessed in the current study. Psychopharmacological treatment was received in 46.2% of cases . ADHD patients performed significantly worse compared to control subjects .Task performance speed was comparable in the two study groups . This result was independent of age and gender.Regarding the grand average of Go–NoGo GFP amplitudes in the two groups Fig. , ADHD paIn order to visualize the NoGo anteriorization in terms of the original ERP curves, we depicted the grand average ERPs for the ADHD and the control groups from three mid-anterior electrodes  that are commonly focused on for the investigation of the NoGo P3 , with a Cohen D effect size of 1.56 for the group difference. We also investigated whether the difference in NGA between patient and control groups is also observable at the peak GFP, defined as a P3 time window of 20 ms around the GFP maximum. Our analyses for the GFP peak yielded similar findings to those observed for the full P3 time window; ADHD patients had a significantly lower NGA than controls: 5.77 vs. 13.40, F = 11.14, p = 0.0016), with a Cohen D effect size of 0.68 for the group difference. Detailed results are shown in Table 2.Using HLM analyses, we examined whether the statistical measure of the NGA, as described by the topographical centroid value, differed between the ADHD and control groups within the whole identified P3 time frame (full 280–380-ms window). Our results indicate that the NGA was significantly less pronounced in ADHD patients than in healthy controls . In addition to using medication status as covariate in the analyses, we performed a sensitivity analysis by comparing the NGA results between ADHD unmedicated and ADHD medicated groups against control subjects. We had the following findings: NGA at GFP peak of the ADHD unmedicated group (n = 14) was 2.50 (SE 2.18), while NGA of the ADHD medicated group (n = 12) was 9.57 (SE 2.36). Hence, the direct comparison between the two ADHD groups and the control group revealed consistent results with the approach we used previously with medication as a covariate: ADHD patients had lower NGA than controls. The difference between the ADHD unmedicated and control groups was significant (p = 0.0002), similar to the difference between the two ADHD groups (p = 0.0325), while the difference between the ADHD medicated and control groups did not reach significance (p = 0.1877).The difference in NGA between the ADHD and control group remained significant after correcting for medication status, even though medication use per se was associated with increased NGA . When we excluded ADHD patients who were taking stimulant medication (n = 9), the NGA was still significantly less pronounced in ADHD patients compared with healthy controls (p < 0.0001). The presence of comorbidity did not have a significant effect on our results.The use of stimulant medication in itself was also associated with a numerically more pronounced NGA, with the difference between NGA of patients receiving stimulants compared to those not taking stimulant medication approaching marginal significance  to capture the changes at the most specific scalp region within the P3 time window (280–380 ms).F = 9.39, p = 0.0059): higher Impulsivity scores were associated with lower NGA values. The relationship between NGA anteriorization and CAARS symptom dimensions is shown in Table n = 9) the results did not reach significance in the limited sample , even though the direction of the relationship between NGA values and CAARS Impulsivity scores remained the same.After corrections for multiple comparisons, we found a significant inverse relationship between NGA values and CAARS Impulsivity scores . The latter analyses were conducted using the data from patients with ADHD. Analysis of covariance was performed in the ADHD group to investigate the joint impact of behavioral variables and impulsivity, as measured by the CAARS Impulsivity subscale, on the extent of NoGo anteriorization. A separate analysis was conducted for each of the two behavioral measures (reaction time and number of correct responses). The results indicated a statistically significant interaction between the behavioral measures and impulsivity.F = 22.78, p = 0.0002). Significant changes occurred in association with high impulsivity scores. Specifically, as shown by the upper part of Table . The interaction between the rate of correct responses and impulsivity was also significant on NGA . Post hoc analyses indicated that low rate of correct responses (more commission errors) with high impulsivity (high score on the CAARS Impulsivity domain) was associated with the most pronounced decrease of NGA in the ADHD group (lower part of Table 4).After correction for multiple comparisons, we found that the interaction between reaction time and impulsivity was significant on NGA  reaction time, impulsivity and NGA and b) number of correct responses, severity on the CAARS Impulsivity subscale and NoGo anteriorization. Our results highlight that the most the prominent alterations in NGA are linked to a covariation of certain behavioral and clinical measures, such as fast reaction times and high error rates combined with high impulsivity in ADHD subjects. To our knowledge, the current study is the first to describe the relationship between behavioral and clinical variables including ADHD severity and NoGo anteriorization, making it possible to connect specific ADHD symptoms and their severity to NGA, a highly reliable electrophysiological correlate of prefrontal/cognitive response control (Fallgatter et al. Additionally, it is important to note that the large effect size that we found for NGA makes it a good candidate for a potential biomarker for ADHD. Furthermore, the fact that the P3 NoGo anteriorization is a reliable and simple-to-use measure may pave the way for a clinical application of novel neuromodulation treatments based on NGA, in order to modify altered neural activity in ADHD. These treatments, including neurofeedback or non-invasive brain stimulation are increasingly being viewed as promising in targeting the key neurobiological abnormalities associated with ADHD Rubia .Limitations of our study include the relatively small sample size, which—while similar to the sample sizes used in other ERP studies (Fallgatter et al.


Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder, and cognitive flexibility is a sub-component of executive functioning. Studies have shown impairments in cognitive flexibility in ADHD, which is affected by attentional processes. Personality, as a long-standing trait, has a profound effect on ADHD. However, previous studies have not assessed the relationship between attentional function, personality traits, and cognitive flexibility in children with ADHD. This study explored the association between attention networks, personality, and cognitive flexibility in ADHD, filling a gap in the related field. We expect our findings will provide insights into and clues for the prevention and interventional treatment of ADHD. This study primarily aimed to analyze differences in cognitive flexibility between individuals with ADHD and those without and further examine associations between attention networks, personality, and cognitive flexibility in children with ADHD. Overall, 55 children aged 7 to 11 years diagnosed with ADHD and 40 children without ADHD participated in this study. Cognitive flexibility, personality traits, and attentional networks were assessed using the Wisconsin Card Sorting Test, Eysenck Personality Questionnaire, and Attention Network Test, respectively. Additionally, the association between personality traits and strong attentional functioning and cognitive flexibility was investigated using multiple regression analysis. Children with ADHD had significant deficits in cognitive flexibility. A multiple regression analysis revealed that the alerting effect was highly associated with cognitive flexibility at high levels of neuroticism. This association was not salient in individuals with low levels of neuroticism. This study demonstrated that the ADHD group experienced lower cognitive flexibility than the control group. In addition, we showed the effect of neuroticism and alerting networks on cognitive flexibility. These findings may help psychiatrists provide intervention strategies to mitigate the impairment of social functioning in ADHD with cognitive spirituality deficits. ADHD frequently occurs in school-aged children and may be comorbid with oppositional defiant disorder and mood disorders, placing a severe burden on the affected family, life, school, and social life. A recent global meta-analysis showed a prevalence of 3.4% in children and adolescents. A Meta-analysis showed that the prevalence estimate of 5.3% of ADHD among children and adolescents in China was 6.26%.Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by persistent inattention and hyperactive or impulsive behavior that impairs children daily functioning.,6 Cognitive flexibility, the ability to transition between tasks or goals, allowing individuals to adjust their thoughts or cognitive behavior appropriately in response to changes in the environment, is considered one of the critical sub-components of executive functioning (EF).,8 In 1993, Eslinger and Grattan classified cognitive flexibility into 2 types: reactive and spontaneous. “Reactive” flexibility is the ability to change cognitive responses in reaction to changes in context. In contrast, “spontaneous” flexibility is the continuous flow of spontaneously sought ideas in response to open-ended questions.Impaired mental health and social functioning in people with ADHD are intimately associated with abnormal alterations in cognitive flexibility. Many researchers have found significant impairments in cognitive flexibility in ADHD.–18 Wixted, Sue, Dube, Potter used functional magnetic resonance imaging and found that the anterior cingulate gyrus, superior frontal gyrus, and inferior frontal gyrus, associated with cognitive flexibility, are less active in ADHD.Although previous studies have explored cognitive flexibility in ADHD, their findings are contradictory.–21 Rommelse, Altink, de Sonneville, Buschgens, Buitelaar, Oosterlaan, Sergeant showed that children with ADHD and their unaffected siblings did not exhibit cognitive flexibility deficits. The authors suggested that deficits in EF in ADHD may be due to deficits in cognitive functions, including attention, rather than directly affecting cognitive flexibility. Irwin, Kofler, Soto, Groves compared children with ADHD aged 8 to 13 years with controls and observed no significant evidence of a unique cognitive flexibility deficit. This finding suggests that cognitive flexibility in ADHD requires further investigation.However, other studies have not identified impaired cognitive flexibility in ADHD.,22 A longitudinal study of typically developing children found that children with more advanced cognitive flexibility skills exhibited lower levels of hyperactivity/inattention behavior. For those diagnosed with ADHD, attention deficits are one of the most common symptoms of ADHD; to date, this disorder remains not undefined in terms of specific cognitive deficits. It is unclear which specific attentional ability is reduced in ADHD.The primary mechanism underlying abnormal cognitive flexibility is an attention deficiency, which prevents access to accurate and continuous processing, memory, and eventual recall of information. attention comprises 3 mutually independent brain networks, each representing an attentional process. The alerting network is responsible for achieving and maintaining alertness. In contrast, the orienting network selects information from the environment by focusing on a particular input pathway, and the conflict network resolves the conflict that arises between competing stimuli. These networks are believed to be anatomically and functionally well separated. Alertness depends on the frontal, parietal, and thalamic areas and is affected by the noradrenergic system. The orienting network depends on the parietal and midbrain circuits and the pulvinar, and the cholinergic system plays a crucial role.,27 The conflict network is considered to be associated with the dorsal anterior cingulate and the lateral prefrontal cortex, which are also dopaminergic systems. Fan, McCandliss, Sommer, Raz, Posner, to independently measure the efficiency of these 3 networks through a single task, invented the attention network test (ANT), which allows the efficiency of each attention system to be simultaneously assessed. This task showed not only the specific attention-impaired system in ADHD but also the apparent correlation between behavioral measures and the neural network of attentional processes. Consequently, the ANT has been well-received in research on attention disorders in ADHD. Most studies on children with ADHD only show differences between the alerting and conflict networks compared with the control group. However, there are hardly any historical papers on the relationship between the attention network system and cognitive flexibility in ADHD. Therefore, the present study will continue to explore the relationship between attentional networks and cognitive flexibility in ADHD.According to Posner and Petersen model, proposed a 3-factor model of personality, classifying personality as psychoticism, neuroticism, and extraversion. Additionally, they developed the Eysenck personality questionnaire (EPQ) based on this model, which has been widely used in personality assessments. In studies using the EPQ, high psychoticism and neuroticism scores are strongly associated with the manifestation of ADHD symptoms. This association has also been found in adult patients with ADHD and adolescent patients with ADHD. The interaction between extraversion and ADHD has been inconsistently reported in previous reports.–35 In the study by Yin, Wu, Yu, Liu, personality factors were considered a moderating factor that moderates the effects of traumatic experience on PTSD among 457 survivors who experienced the Wenchuan earthquake. Neuroticism increases arousal symptoms of PTSD. Individuals with high neuroticism in the face of earthquake trauma are more likely to view the earthquake experience as a more negative and threatening event, and are more prone to maladaptive coping styles, and inflexible cognitions. Therefore, in the present study we presumed that personality factors may also moderate the relationship between attention and cognitive flexibility in ADHD.Personality traits are closely associated with ADHD. Eysenck, Eysenck Research in normal populations suggests that neuroticism, emotional disorders, and emotion dysregulation–41 are related. People with high neuroticism scores were less willing to modify their behavior in response to feedback. Kashdan, Rottenberg suggest that people high in neuroticism experience excessive negative emotions, are tightly wrapped up in negative thoughts and behaviors, and have difficulty tolerating them, compromising their cognitive flexibility. In counterpoint, positive emotion states facilitate increased cognitive flexibility. One hundred seventy-four undergraduates tested on the modified version of the Continuous Performance Test (AX-CPT) under positive, neutral, and negative affect. The results found that the participants performed better cognitive flexibility under the mildly positive affect. It has been reported that HPA axis activity is implicated in negative clinical outcomes, irrespective of the presence or absence of psychiatric conditions. The aberrations in HPA axis function, primarily marked by heightened HPA axis activity, appear to exert a significant modulatory influence on adverse clinical outcomes, while deficiencies in stress response mechanisms contribute significantly to the manifestation of neuroticism and risk of suicide. Furthermore, a study has demonstrated that individuals with ADHD and comorbid psychiatric conditions may have an even higher risk of developing treatment resistance. Moreover, elevated concentrations of inflammatory mediators in both the peripheral blood and brain may also contribute to treatment resistance.Neuroticism is a personality trait characteristic of a tendency to experience negative emotions more frequently, intensely, more readily, and for more extended periods. One condition that individuals often confront is the necessity to dynamically adjust their actions and thoughts to respond to the changing demands of the environment. This means that individuals must maintain their purposes and goals over time while being flexible enough to shift from one thought or behavior to another when significant changes occur.Nevertheless, the above association remains further confirmed in the ADHD population. Therefore, personality factors, as persistent long-term characteristics, probably influence cognitive flexibility in children with ADHD. Based on these findings, we assume that neuroticism might be a risk factor for cognitive flexibility in children with ADHD. Moreover, the attentional performance of children with ADHD should also be taken into account. Although the attentional process is related to cognitive flexibility, the exact mechanism by which it currently affects cognitive flexibility is unclear, and its intertwined effect on personality has rarely been explored.As mentioned earlier, a close correlation exists between personality traits, attention networks, and cognitive flexibility in ADHD. This study aimed to investigate whether children with ADHD have cognitive flexibility impairments and investigate the association between their attention networks and cognitive flexibility. Furthermore, we examined whether personality traits moderated these correlations. Additionally, we used the Wisconsin card sorting test (WCST) to measure cognitive flexibility, ANT to assess attention networks, and EPQ to measure personality dimensions and make the following hypotheses: the ADHD group showed lower cognitive flexibility compared to controls, decreased attention network effect and high neuroticism may predict cognitive flexibility, and personality traits may moderate the relationship between attention and cognitive flexibility. All potential non-ADHD were recruited through advertisements in the Ningxia area and local public primary schools.Overall, 95 children aged 7 to 11 years were included in this study. There were 55  and 40 children  included in the ADHD and the control groups, respectively. Participants were excluded if they had an intellectual disability, autism spectrum disorder, other severe developmental disorders, schizophrenia, bipolar disorder, and other severe mental illnesses, or a Full-Scale IQ lower than 70. The children in the ADHD group were recruited from referrals to Psychiatric Clinics and Pediatric Clinics at the General Hospital of Ningxia Medical University, who visited the outpatient clinics and were diagnosed with ADHD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnostic criteria (DSM-V). Of the participants, 10 were diagnosed with ADHD-H, 22 with ADHD-I and 23 with ADHD-C. Participants in the control group did not meet the current diagnostic criteria. The local institutional review board approved this study and written informed consent was obtained from all the children and their guardians before the assessment.The diagnosis of ADHD was based on multiple aspects, including an interview with a trained psychiatrist; Face-to-face clinical observation of participants performance by psychiatrists; the participants medical history provided by parents; the severity of ADHD symptoms were assessed using the Swanson, Nolan, and Pelham, Version IV- Scale (SNAP-IV) - Chinese version. with good reliability and construct validity. It includes 3 subscales: inattention, hyperactivity/impulsivity, and oppositional defiant disorder. This scale symptoms are rated as a 4-point Likert scale .The clinical severity of ADHD symptoms was assessed using SNAP-IV - Chinese version. The Chinese version SNAP-IV scale is commonly used in the clinical diagnosis of ADHD Raw scores for all indicators are recorded. All indicators were calculated using the method described by the retest reliability  of WCST ranges from 0.37 to 0.72.This study measured cognitive flexibility using a computerized version of the WCST-128. There were 3 indicators of cognitive flexibility: number of categories completed (CC), perseverative responses (PR), and perseverative errors (PRE). administration and scoring in the present study. Perseverative responses - when the dimension the participant is sorting by matches the perseverated-to-principle, the participant continually responds using an incorrect dimension even after receiving feedback that this response is incorrect. PRE - the wrong part of the PR. The Number of Categories Completed - The ten consecutive correct matching sequences completed by the participants in the test. The score ranges from 0 to 6.We implemented Heaton, Chelune, Talley, Kay, Curtiss and it is a self-report measure comprising 88 items on 4 subscales to assess personality traits. The EPQ includes 3 personality scores  and a Lie (L) score. The Chinese version of the EPQ was revised by Gong Yaoxian and has been used for at least 40 years, with good reliability and validity.The EPQ was developed by Eysenck, Eysenck, and it comprises ten core subtests that are combined to form 4 metric scores. All of these were aggregated into one full-scale intelligence quotient, ranging from a minimum of 40 points to a maximum of 160 points. The split-half reliability of each subtest of the scale ranged from 0.78 to 0.92, and the split-half reliability of each composite score ranged from 0.87 to 0.97. The retest reliability of each subtest ranged from 0.71 to 0.86, and the reliability of the composite scores were above 0.8.The WISC-IV-Chinese is commonly used to assess the intelligence of children and adolescents, The children version replaced the ANT background with blue and yellow fishes instead of the original black arrows. In the task, participants were required to identify the direction of the target arrow in a disturbance, with arrows pointing in different directions on either side.Considering the developmental characteristics of children attention, a researcher designed a child version of the ANT based on the adult version.The child version of the ANT task was performed using E-prime software on computers. Figure The ANT in this study comprised 24 practice trials and 3 experimental blocks of 48 trials each. Practice trials were not factored in the overall score since they were intended to familiarize participants with the rules. Although the practice trials provided feedback after each response, there was no feedback in the experimental area. Each trial represented 1 of the 12 conditions in equal proportions: 3 target types  × four cues . Additionally, the flanking conditions could be neutral , congruent (flanking fish pointed in the same direction), or incongruent . The participants sat at a distance of approximately 55 cm from the screen for the experiment. The participants needed to judge the direction of the centermost fish according to the instruction, pressing the “F” key when the fish was facing left and the “J” key when the fish was facing right. The response time (RT) and accuracy of each experiment trial were recorded.The 3 attention network effects were manipulated as the mean RT difference between the task-specific conditions. Alerting network effect = mean RTno cue - mean RTdouble cue; orienting network effect = mean RTcentral cue- mean RTspatial cue; and conflict network effect = mean RTincongruent - mean RTincongruent. Trials with reaction times below 200 ms and those with incorrect responses were excluded.After agreeing to participate in this study, all participants parents or legal guardians signed an informed consent form, and a clinical interview was conducted to consolidate the clinical diagnosis. The assessment protocol included demographic information, attention network measures, personality traits, and cognitive flexibility. Additionally, all the participants completed the assessment process and received a written report that included their results afterward.t test and χ2 test, respectively.Descriptive statistics are the mean ± standard deviation (SD) or frequency (percentage). The Kolmogorov–Smirnov test was used to assess the normality of the variables. The ADHD and control groups were compared using the As the influence of multiple factors on cognitive flexibility was examined in the current study, we used a 2-step statistical analysis to investigate the correlation of the attention network system with personality and cognitive flexibility. In the first step, Pearson correlations was used to select possible predictors of cognitive flexibility for further analysis, including age, attention network effects, and personalities. Additionally, the multiple regression analysis was performed to assess the correlation between attention network, personality traits, and cognitive flexibility by controlling for the effects of other factors. Factors that are significant in the first step will be entered into the second step of multiple regression analysis. moderating effect was examined. In the present study, if attention network effect and hypothesized moderators  were significantly associated with cognitive flexibility, then the interactions (attention network effects × hypothesized moderators) were further selected for multiple regression analysis to examine the moderating effects. Data analyses were performed using the SPSS software . Simple slope analyses were conducted using Process v3.3. Two-tailed P values of < .05 were considered statistically significant.According to the criteria proposed by Baron, Kenny,2 = 9.481, P = .004), with significantly more boys than girls in the ADHD group than in the healthy group. There were no differences in age , grade , full-scale intelligence quotient , EPQ-P scores , EPQ-E scores . Inattention , hyperactivity/impulsivity , and oppositional  on SNAP-IV scale were significantly different. The alerting effect , EPQ-N scores , PR , PRE , and CC  were significantly different between the 2 groups; children with ADHD had a significantly higher PR and PRE, and a significantly lower CC and alerting network effect than the controls.Table P < .05), PRE , and positively correlated with CC . The correlations between orienting and conflict effect with PR, PRE and CC were insignificant. Additionally, no significant relationship was observed between personality traits and cognitive flexibility. The conflict and orienting networks in the attention network were not significantly correlated with cognitive flexibility.Table P < .05) and PRE , and negatively associated with CC . Alerting effect was significantly associated with PR  and PRE , and positively associated with CC .Following the description in Statistical Analyses, the significant variable in the first step entered the multiple regression analysis to examine the independent factors related to cognitive flexibility. After variables including sex, age, EPQ-P, EPQ-E, Orienting, conflict, and ADHD symptoms were controlled, Table  were also entered into multiple regression analysis.Because EPQ-N and alerting effect were significantly associated with cognitive flexibility, the interaction terms of predictors and moderators (Alerting × EPQ-N) according to the criteria proposed by Baron, KennyP = .032; Table P = .008; Table P = .219; Table Table  we replaced the variable of the neuroticism in the regression model with the values of −1 SD (low EPQ-N) and + 1 SD (high EPQ-N) to examine the simple effects.To further explore the moderating effect, according to the procedures described by Aiken, West, Reno,P < .0001) and PRE  at high neuroticism levels. As the alerting efficacy of children with ADHD declined, they demonstrated increasing errors in cognitive flexibility, indicating worse cognitive flexibility. However, this association with PR  and PRE  was not significant for low neuroticism.Figures Based on our literature review, the main findings indicated that children with ADHD had significantly higher PR and PRE on the WCST and a statistically lower CC than the control group. At high levels of neuroticism, the alerting effect was strongly associated with cognitive flexibility, and this association was not salient in participants with low levels of neuroticism.,16,18,56 The findings of Rommelse, Altink, de Sonneville, Buschgens, Buitelaar, Oosterlaan, Sergeant are inconsistent with ours, which indicated that children with ADHD and their unaffected siblings did not present evidence of impairments in cognitive flexibility. This finding may be because Rommelse, Altink, de Sonneville, Buschgens, Buitelaar, Oosterlaan, Sergeant used a more straightforward experimental paradigm in their study, where the rules for the participants were known and constant throughout, rather than a complex experimental paradigm such as the WCST, which has unknown and constantly changing rules. Moreover, the author considers that the deficits in EF observed in ADHD are probably due to deficiencies in lower-order cognitive functions, including attention, and do not directly influence higher-order functions, such as cognitive flexibility. A possible explanation for these inconsistent findings is that studies on children with ADHD tend to involve those across a wide age range. Moreover, the role of the accelerated development of cognitive flexibility in early school age may be obscured by the inclusion of older children.,57As mentioned previously, we observed that the number of errors in cognitive flexibility was significantly higher in the ADHD group than in the control group, which is consistent with our hypothesis, implying a deficit in cognitive flexibility in ADHD. This finding is consistent with previous studies findings.,58 Previous studies have shown differences between alertness and control networks.,59 Such inconsistencies may be related to different ANT procedures, analysis methods, and patients inclusion criteria. In addition, we observed that the alerting effect was a predictor of cognitive flexibility after controlling for other correlates in the regression model. Alerting effect and PR or PRE were significantly and negatively correlated at high levels of neuroticism. Since a higher PR or PRE represented lower cognitive flexibility, a lower alerting effect was associated with lower levels of cognitive flexibility. This result may imply that children with ADHD have some deficits in their ability to be alert to incoming stimuli and maintain arousal to receive incoming information while processing the attentional system. It has been proposed that the primary cause of abnormal cognitive flexibility lies in attention deficits, which result in a lack of acquiring the right and consecutively to problems with processing, memorizing, and recalling information. Roshani, Piri, Malek, Michel, Vafaee argued that cognitive flexibility depends on controlling attention processes to check whether the situation has changed and whether a non-routine response is necessary. Thus, we believe that attention deficits in children with ADHD, with more appropriately impaired alertness, make them less sensitive to external stimuli and incoming information. They have challenges maintaining an alert state in response to subsequent changes, leading to abnormal cognitive flexibility. Therefore, clinicians need to provide timely emotional regulation and psychological assistance to help people with ADHD to stabilize and calm them. This measure will help improve attentional functioning and cognitive flexibility in ADHD and halt its progression.This study used a child-specific version of the ANT to measure attention network. Significant impairments in the alerting network in the ADHD group compared to the control group were observed in this study; however, no significant impairments were found in the orienting and executive networks, which is partially consistent with the findings of previous studies.,60 Contrary to our hypothesis, we found that neuroticism predicted cognitive flexibility after controlling for other correlates in the regression model. Notably, neuroticism moderating effect on the relationship between alerting and cognitive flexibility. We observed that at high levels of neuroticism, the alerting effect and PR/PRE were significantly negatively correlated, indicating that in high neuroticism ADHD, children with lower alertness had a higher PR and PRE (poorer cognitive flexibility). The moderating effect of neuroticism was not strong but significant in our analysis. Combining the results of multiple regression analysis with simple slope analyses, we deem that alerting deficit was presumably more likely to induce decreased cognitive flexibility in ADHD with high neuroticism. In other words, the failure to separate from the negative thoughts and feelings keeps ADHD with high neuroticism from connecting with the present moment, making it difficult to perceive whether a situation has changed, needs to be changed, or the requirement to respond accordingly.This present study showed that children with ADHD had higher neuroticism scores than those in the control group. One possible explanation of moderating effect is that individuals with high levels of neuroticism are emotionally unstable, have difficulty in self-emotional regulation, and have challenges controlling their attentional processes. Research in normal populations suggests that neuroticism, emotional disorders, and emotion dysregulation–41 are related. People with high neuroticism scores were less willing to modify their behavior in response to feedback. Kashdan et al suggest that people high in neuroticism experience excessive negative emotions, are tightly wrapped up in negative thoughts and behaviors, and have difficulty tolerating them, compromising their cognitive flexibility. In counterpoint, positive emotion states facilitate increased cognitive flexibility.Neuroticism is a personality trait characteristic of a tendency to experience negative emotions more frequently, intensely, more readily, and for more extended periods. One condition that individuals often confront is the necessity to dynamically adjust their actions and thoughts to respond to the changing demands of the environment. This means that individuals must maintain their purposes and goals over time while being flexible enough to shift from one thought or behavior to another when significant changes occur.,62 or the anterior cingulate cortex, improved cognitive flexibility. This is the primary focus of the ADHD etiology hypothesis. Methylphenidate, which enhances dopaminergic activity, is a common medication used to improve symptom management in ADHD. Research has indicated that appropriate doses of methylphenidate can enhance cognitive flexibility in individuals with ADHD. Rubia, Halari, Mohammad, Taylor, Brammer found that neural activation in many brain regions, including prefrontal areas and anterior cingulate cortex, was reduced in ADHD participants taking a placebo. However, ADHD participants taking methylphenidate exhibited activation levels comparable to controls, suggesting that methylphenidate treatment may normalize brain activations during cognitive tasks. The moderating role of neuroticism may be related to the activation of the dopamine system and some brain regions. The causal relationship of the effects of neuroticism on the association between alerting and cognitive flexibility requires further clarification by prospective studies. From a clinical perspective, psychiatrists should consider personality factors in the comprehensive assessment of cognitive flexibility in ADHD. Especially in individuals with high levels of neuroticism, psychiatrists should provide early psychological counseling and social support to remedy corresponding personality deficits and guide them to stabilize their emotions. In addition, whether personality factors are clinically relevant to the assessment and treatment of the ADHD population is a topical issue. Based on our findings, it would be wise for psychiatrists to consider personality traits when assessing a patient ability for cognitive flexibility.From a neuropsychological perspective, maintaining positive affect, presumably via mild increases in neurotransmitter dopamine activity in prefrontal brain areas Although an adult version of the Big Five Questionnaire is currently popular in China, the children versions have not been adopted. Hopefully, future research will continue to explore the implications of the 5-factor personality model on cognitive flexibility in ADHD once the children version of the Big Five Questionnaire is officially used in China.The current study has several limitations. First, the cross-sectional design precludes the inference of causality. Moreover, given the limited sample size included in this study, our analysis might not be sufficient to test the entire model. Therefore, further validation using more prospective studies with larger samples is needed in future research to strengthen our findings and conclusions. Second, the self-reported nature of this study has inherent limitations, including personality traits. However, the data were collected using an anonymous online questionnaire, which may have mitigated these implications. The Eysenck personality model, one of the most used models in the study of ADHD, has been widely used. The present study used the Chinese version of the EPQ to measure ADHD personality traits, which has been applied in China for many years and has excellent reliability and validity. However, many ADHD studies now prefer to use the 5-factor personality model.This study has several practical clinical implications. First, our findings confirmed that children with ADHD had lower alertness efficacy, higher neuroticism scores, and impaired cognitive flexibility than the controls. In children with ADHD and high neuroticism, alerting effect and cognitive flexibility were significantly correlated. This finding deepens our understanding of ADHD and offers novel insights into the mechanism underlying cognitive flexibility deficits in individuals with ADHD. Based on these research findings, healthcare professionals can contemplate implementing targeted intervention measures that address various contributing factors, emphasizing the enhancement of cognitive flexibility. In cases where ADHD patients exhibit distinct neurotic features, doctors can devise individual-specific intervention plans, taking into account unique differences among patients. By enhancing the cognitive flexibility of individuals with ADHD, it is anticipated that their behavioral performance and learning abilities will improve, leading to advancements in their daily social interactions. The influence of attentional function, specifically alertness and personality traits, should also be considered in subsequent assessments and measurements of cognitive flexibility. Furthermore, knowledge of the cognitive flexibility profile, attentional function, and personality traits of children with ADHD may help to identify protective factors for cognitive flexibility.We want to thank Professor Fang and Wang of Ningxia Medical University for their guidance and great support. Special thanks to all the students who participated in the study, and to Dr Wu for her support and encouragement.Conceptualization: Xiang Zhang, Jianqun Fang.Data curation: Xiang Zhang, Shaoxia Wang, Qianyun Liu, Yunyun Du.Formal analysis: Xiang Zhang, Shaoxia Wang, Qianyun Liu, Chujun Wu, Yunyun Du.Investigation: Xiang Zhang, Jianqun Fang.Methodology: Xiang Zhang, Yanrong Wang.Project administration: Xiang Zhang, Qianyun Liu, Jianqun Fang.Resources: Xiang Zhang, Shaoxia Wang, Qianyun Liu, Chujun Wu.Software: Xiang Zhang, Shaoxia Wang.Supervision: Xiang Zhang, Yunyun Du, Yanrong Wang.Validation: Xiang Zhang, Shaoxia Wang, Qianyun Liu, Chujun Wu, Yunyun Du, Yanrong Wang.Visualization: Xiang Zhang, Shaoxia Wang.Writing – original draft: Xiang Zhang, Chujun Wu, Yanrong Wang.Writing – review & editing: Xiang Zhang, Chujun Wu, Yanrong Wang, Jianqun Fang.


Attention deficit hyperactivity disorder (ADHD), one of the most common neurodevelopmental disorders in childhood, is diagnosed clinically by assessing the symptoms of inattention, hyperactivity, and impulsivity. Also, there are limited objective assessment tools to support the diagnosis. Thus, in this study, a new electrooculography (EOG) based on visual stimulus tracking to support the diagnosis of ADHD was proposed.Reference stimulus one-to-one tracking numbers (RSOT) and colour game detection (CGD) were applied to 53 medication-free children with ADHD and 36 healthy controls (HCs). Also, the test was applied six months after the treatment to children with ADHD. Parameters obtained during the visual stimulus tracking test were analyzed and Higuchi fractal dimension (HFD) and Hjorth parameters were calculated for all EOG records.The average test success rate was higher in HCs than in children with ADHD. Based on machine learning algorithms, the proposed system can distinguish drug-free ADHD patients from HCs with an 89.13% classification performance and also distinguish drug-free children from treated children with an 80.47% classification performance.The findings showed that the proposed system could be helpful to support the diagnosis of ADHD and the follow-up of the treatment. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that typically appears in early childhood, before the age of twelve . AccordiThere is no specific test for the diagnosis of ADHD. Clinicians diagnose ADHD based on symptoms of inattention, hyperactivity, and impulsivity and diagnostic procedures include the following: the assessment of DSM-V ADHD criteria, a medical examination, information from school, interviews with family members, ADHD rating scales such as the Conners Rating Scale for both parents and teachers, and neuropsychological tests. Psychometric tests could be used to support the diagnosis of ADHD, but they are not only specific to the diagnosis of ADHD –10. On tElectrooculography (EOG) is a technique to record the electrical activity of eye movements, using surface electrodes. An EOG offers crucial information on electrical activity in the eye and also provides useful data for researchers since it can be used for the detection of eye movements, visual disturbances, and sleep status ,12. In tSixty drug-free children aged 8–13 with a diagnosis of ADHD according to DSM-5 were included in the study. All children in the study were recruited in Erciyes University Faculty of Medicine, Child and Adolescent Psychiatry Outpatient Clinic, and were evaluated with KD-SADS-PL . ChildreThe treatment of the patients was determined independently of the clinicians who designed the study. The patients were selected from drug-free children who would receive OROS- Methylphenidate (MPH) treatment. Response to treatment was also evaluated 2 months after treatment onset. The patients were divided into two subgroups: responders and nonresponders, and children who were adherent to treatment were excluded from the analysis. Finally, data of 53 children were included.This study was approved by the local ethical committee of the university (2015/90) and the procedures were according to the ethical standards of the responsible committee on human experimentation. Written informed consent was obtained from both children and their parents.A power analysis was performed. The Type-I error rate was 0.05 and the Type-II error rate was 0.020 in the power analysis. The CDG parameter was utilized for power analysis, in order to establish the sample size since the CGD parameter has the highest success in ADHD/healthy classification . When the power was considered 80%, the required minimum number of subjects in each group was calculated as 19 for the recent study. In addition, when other parameters were considered, the required minimum number of subjects in each group was calculated as 31.First records were obtained from 53 children with ADHD and 36 HCs. Six months after treatment, the second set of records was obtained from 39 patients who responded to the treatment. Therefore, a total of 128 EOG records were analyzed to determine the diagnostic and treatment status.The developed visual stimulus tracking test provided the opportunity to examine horizontal and vertical eye movements under stimuli with visual and auditory distortion. The visual stimulus tracking test was prepared with C# programming language and in front of a 24” monitor, 30 cm from the screen, the eye level of the individual was set at the same level as the midpoint of the screen, as seen in EOG recordings were obtained simultaneously while children watched visual stimuli that appear on the monitor during the test. The test was carried out for a total of about 3 min.www.biopac.com/wp-content/uploads/MP150-Systems.pdf). Two EOG 100C EOG modules compatible with the MP150 physiological signal recorder were used during the test. Thus, a two-channel EOG recording was used. EOG signals were obtained via electrical potential change on noninvasive surface electrodes attached to the right, left, upper, and lower regions of the eye, based on the reference electrode. Noninvasive Ag-AgCl surface electrodes were used during the measurements. Initially, EOG records were obtained from patients with ADHD. Subsequently, after 6 months of drug treatment, the second EOG signals were obtained from the same patient group. In addition, records were obtained from HCs. The obtained EOG signals were recorded for two channels in “.txt” format, processed in MATLAB, and analyzed.The BIOPAC-MP150 developed by Biopac Systems Inc. was used for the recording of EOG signals  is a nonlinear method for the analysis of biological signals . HiguchiHjorth developed a quantitative method to conduct a continuous analysis of physiological signal activity. The three parameters proposed by Hjorth: activity, mobility, and complexity. The signal power, or variance of a time function, is represented by the activity parameter. The mobility parameter measures the power spectrum’s mean frequency or proportion of standard deviation. The frequency change is represented by the complexity parameter . In the Machine learning algorithms are used to assign a class label with examples from the research/problem domain. In this study, patients with ADHD and healthy children were classified using extracted features from EOG signals with Machine learning algorithms.Decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) were implemented as machine learning algorithms in order to classify children with ADHD and also healthy subjects . Also, tIn the visual stimulus tracking test, the number of tracking stimuli appearing at different points and the occurring time of stimuli are determined.Step 1: In the visual stimulus tracking test, we determined when and where all visual stimuli occur.Step 2: The time and position of each eye movement in the records obtained from participants were determined.Step 3: The data obtained in step 1 and step 2 were compared.Finally, the success percentages of all measurements were calculated. The percentages of success in left-right direction movements were averaged and multiplied by 16. The percentages of success in up-down movements were also averaged and multiplied by 9. A total of 25 divided and weighted success percentages were calculated using the equation below:There is a relation between stimulus colors and background color in the visual stimulus tracking test. In repeated tests, the background color was the color of the stimulus in the next period. The participants were asked if they realized there was a pattern between the background and the stimulus and their responses were evaluated as true or false.HFD and Hjort parameters  were calculated for all EOG signals obtained from patients and healthy individuals. The findings were used in the classification study.Obtained EOG signal features from patients with ADHD before and after treatment and also HCs were applied to machine learning algorithms for classification. The classification process was performed using the Matlab software package. Classification success of SVM, DT, and KNN algorithms were obtained using extracted features. Data obtained from RSOT, CGD, HFD, and Hjort parameters were used as input data in the classification. The distinction between patients healthy subjects was provided as well as drug-free/after medication.Obtained data were analyzed by the Statistical Package for Social Sciences (SPSS) for Windows version 22  program. After examining the distribution of the data with the Shapiro–Wilk test, the Independent Sample t-Test, and ANOVA were used to compare the continuous variables with normal distribution within the group, and the Mann-Whitney U test was used to compare the variables that were not normally distributed. The Pearson chi-square test and Fisher’s exact test were used to compare categorical data. Bonferroni correction was used for post hoc analyzes. The statistical significance level was taken as p < 0.05 for all analyses.In The test performed during obtaining the EOG signals was performed on 16:9 monitors. Because of this screen size, horizontal eye movements were detected more effectively and vertical eye movements were detected in smaller amplitudes. In this context, while determining the overall success percentage of the RSOT parameter, the data for horizontal movements were weighted with 16 and the data for vertical movements were weighted with 9, as shown in The practitioner asked all participants at the end of the test whether there was a relevance between the colors of the visual stimuli and the background colors. Children who explained the pattern correctly were accepted as successful. Questions that could not be answered completely were considered unsuccessful. The CGD success rate was 39.02% in HCs, 45.00% in treated patients, and 4.62% in medication-free patients  .HFD and Hjorth parameters were calculated for all parts of the test in all patients and HCs using EOG records . The average of all the data obtained as a result of the calculation is presented in All the data obtained  was used for classification. This study introduces a system to assist the diagnosis and follow-up treatment of ADHD with an EOG-based objective approach.In the previous literature, eye tracking and EOG were investigated in the field of ADHD and other psychiatric disorders. Although there is no study in the literature on the diagnostic support system for ADHD, which uses the stimulus test, Studies also focused on the distinction of ADHD from healthy comparisons, based on the eye-tracking principle. In these studies, the eye movements of individuals in different situations were analyzed –20. The On the other hand, in some studies, the classification of ADHD and healthy subjects was performed using a classical statistical approach ,22. In tThe findings of the present study showed promising results as an objective approach. When the RSOT, CGD, HFD, and Hjorth parameters were evaluated together, HCs showed a higher success rate than patients. In addition, the test performance increased considerably after treatment, even at higher rates than those seen in HCs. Therefore, clinicians also could use this system to assess the treatment response.Considering the results of our study using machine learning algorithms, the new system can be used with different features to increase the classification performance for unmedicated patients with ADHD. One-to-one (by time and location) and stimulus tracking accuracy percentages also could support the diagnosis of ADHD. The activity results obtained from HFD and Hijorth parameters could be used as a diagnostic parameter in ADHD after taking into account more parameters obtained from patients.


Neurometabolic disorders such as tyrosinemia type 1 (TYRSN1) may interfere with brain metabolism and show symptoms of attention-deficit hyperactivity disorder (ADHD) in patients treated with the enzyme inhibitor nitisinone . It has been reported that ADHD treatment improves treatment compliance, which is imperative for the long-term prognosis of patients with TYRSN1. In this study, we report the case of a male patient who was diagnosed with TYRSN1 at 3 months of age and was subsequently treated with NTBC, restricted protein intake, and amino acids supplementation. At 7 years of age, he was referred for neuropsychiatric assessment, diagnosed with ADHD, and treated with methylphenidate. The effects of the treatment were monitored via parental interviews, questionnaires covering ADHD symptoms, and a continuous performance test. A reduction in ADHD symptoms, particularly inattentiveness, was observed across all measures. The early identification of ADHD and the treatment of neurometabolic disorders, such as TYRSN1, may be important from a lifetime perspective as this may improve the prognosis of the medical condition as well. Hereditary tyrosinemia type I  is an autosomal recessive aminoacidopathy whose global incidence is estimated to be 1 in 100–120,000 live births, and its incidence in Norway is ~1 per 74,800 live births -1,3-cyclohexanedione, NTBC], and a protein-restricted diet. The drug inhibits tyrosine degradation at an earlier step, thus avoiding the production of carcinogenic metabolites; however, the levels of P-tyrosine remain elevated or even increase. This drug dramatically improves the prognosis of TYRSN1 patients , and trevice versa. Furthermore, several other medical conditions are associated with ADHD , and international normalized ratio (INR) were detected. Ultrasonography and CT scans confirmed ascites, splenomegaly, and hepatomegaly with nodular structures, signs of cirrhosis, and portal hypertension. The patient was hospitalized with liver failure, and TYRSN1 was suspected. The diagnosis was confirmed by the detection of increased urinary succinylacetone levels and disease-causing mutations in the FAH gene. Lab results also showed low plasma albumin, low phosphate, and elevated ammonia, 112 μmol/L. Treatment was initiated with enzyme inhibitor/NTBC and dietary treatments such as restricted protein intake and adjusted amino acid supplements. During this treatment, the patient was recovered from liver failure. The plasma alpha-fetoprotein (AFP) level was initially >10,000 μg/L, but after a month, it was < 1,000, and liver enzymes also gradually normalized. The plasma AFP level was within the normal range (< 10 μg/L) after 2 years of treatment.A boy who was born healthy at birth and developed normally during the first 2 months of his life became ill with a high fever after 3 months, which led to hospitalization. He was diagnosed with The patient and family managed the treatment well, and after the treatment, he showed normal physical development and plasma tyrosine levels ranging between 300 and 600 μmol/L in almost every blood test . The recAfter 2 years of commencing treatment, at the age of 2 years and 3 months, the patient experienced seizure episodes, initially with shaking movements in the arms and legs, blue lips, and a lack of consciousness for 5 min. The first EEG appeared normal, but the episodes recurred; Later, EEG showed possible epileptic activity. Treatment with topiramate was initiated from 3 years of age. Later, the patient developed absence seizures, following which lamotrigine was successfully administered in response. Although his epilepsy was well-controlled, his motor and language skills developed more slowly from the age of 3–5 years.In kindergarten, he received permanent assistance because of his low social functioning. At the age of 7 years, he presented with a variety of problems, including academic challenges that required special support in school, and was referred to the Child and Adolescent Mental Health Service. He experienced problems with inattention, planning, and the organizing of activities. He also showed problems understanding information and expressing himself. Furthermore, he described himself as being anxious in new situations; he resorted to sitting under the table for ~15 min in settings with several people. In addition, he often got into conflicts with other children during school breaks.The diagnostic procedure for ADHD was performed according to the National Guidelines for ADHD published by the Norwegian Directorate of Health and the guidelines for ADHD treatment published by the Norwegian Medical Association. The diagnostic procedure was based on a thorough evaluation of the patient's symptoms and functional impairments.The ADHD core symptoms (baseline and with treatment) were evaluated using the Clinician's Manual for the Assessment of Disruptive Behavior Disorders Rating Scale for Parents/Teachers , which iTo assess inattention, hyperactivity, and impulsivity before and after treatment, the QB test was administered . This coThe K-SADS-PL-2009 [Schedule for Affective Disorders and Schizophrenia for School-Aged Children (6–18 years)—Present and Lifetime (version 2009)]  is a semGuidelines for the treatment of ADHD, as edited by the Norwegian Medical Association (NMA)  and the The study was approved by the Regional Committee for Medical Research Ethics of Western Norway (IRB 00001872). Both the parents and the boy signed an informed consent form and received information about what would be presented in the article, and they were also provided the manuscript for approval before submission. All procedures were performed in accordance with the Declaration of Helsinki .A standard assessment of intellectual functioning was administered at the local psychological-educational service when he was 7 years of age. His intellectual functioning was below average, with the lowest score on the index measuring working memory and the highest measuring processing speed. There was no difference between verbal comprehension and perceptual reasoning. After ruling out other psychiatric disorders, it was concluded that he met the criteria for ADHD and specific phobia but no other diagnoses as per the K-SADS-PL-2009.The effect of methylphenidate was assessed after 3 weeks of treatment. The evaluation involved (1) clinical evaluations along with the mother, (2) parents' and teacher's reports on ADHD core symptoms, and (3) changes in inattention/hyperactivity (before and after treatment) measured using the QB test.His mother reported that their family had “got a new life.” She described him as more attentive, organized, and communicative. He also exhibited less anxiety in novel situations. In school, his learning capacity increased (reported after 6 months), and no adverse effects were observed.Parents and the teacher reported improvements in the core symptoms of ADHD, mostly regarding attention on the Barkley ADHD questionnaire . HoweverThe results of the continuous performance test (in percentiles) for the different measures of the QB test are presented in ® , the proprietary name for methylphenidate, 36 mg one capsule q.a.m. and 5–10 mg of short-acting methylphenidate, on demand, in the afternoon. At approximately the age of 10 years, he developed motor tics, problems with understanding, and expressing language became more prominent, and he experienced increasing problems with academic functioning. After 3 years of the first assessment, he was also diagnosed with chronic motor or vocal tic disorder, receptive language disorder, and a mixed disorder of scholastic skills.After the initial assessment, he continued with ConcertaAfter 13 years of age, his symptoms of inattention increased, and the dosage of Concerta was successively increased to 54 mg q.a.m, which reduced the inattention problems. This was also when his body mass index (BMI) was in the normal range, which gradually decreased until his 15 years; thereafter, treatment with methylphenidate was discontinued at 15.5 years of age . As the An 8-year-old boy with TYRSN1 was evaluated at the Department of Child and Adolescent Psychiatry for several neuropsychiatric symptoms, including ADHD. He was treated with methylphenidate, which had an effective response, as informed by himself, his parents, and his teacher, in addition to scores on the continuous performance test. The largest effect was observed for the symptoms of inattention.This treatment was conducted in accordance with previous findings indicating that TYRSN1 is associated with symptoms of inattention  and cognAdolescents with ADHD have increased school dropout rates, substance abuse, and injuries such as motor vehicle crashes . Early iThe term “diagnostic overshadowing” has been used to describe the underdiagnosis of comorbid psychiatric conditions in severe somatic diseases. For example, one study reported that ADHD was overshadowed by neurological disorders in 71.8% of the cases . InteresThis case also illustrates the complexity that often characterizes patients with neurodevelopmental disorders. The trajectory of diffuse problems early in life and the successive fulfillment of the criteria for different diagnoses has also been described as typical by Gillberg . This phThis case highlights the importance of identifying the symptoms of ADHD in neurometabolic disorders and the risk of diagnostic overshadowing in such complex cases. Gathering more data on the possible relationship between ADHD and neurometabolic disorders could also provide deeper insights into the etiology of ADHD and improve the patient's clinical management . HoweverThe lifestyle that sometimes accompanies untreated ADHD may be especially harmful to individuals dependent on following strict treatment regimens for their medical conditions. Early identification of ADHD and treatment of medical conditions such as TYRSN1 is important from a lifetime perspective as this may also improve the prognosis of the medical condition itself. Clinicians working both in somatic and psychiatric wards should therefore be aware of the association between ADHD symptoms in tyrosinemia type 1 and other neurometabolic conditions.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Regional Committee for Medical Research Ethics of Western Norway (IRB 00001872). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the minor(s)' legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article. Written informed consent was obtained from the participants/next of kin for the publication of this case report.RS and MF have made substantial contributions to the acquisition of data for the work. YB, EV, HB, and IE have made substantial contributions to the conception and design of the work, the acquisition and interpretation of data for the work, and drafting the work. JH has made substantial contributions to the interpretation of data of the work. All authors have revisited the work critically for important intellectual content, provided approval for publication of the content, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.