Findings of genetic overlap between Schizophrenia, Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) contributed to a renewed conceptualization of these disorders as laying on a continuum based on aetiological, pathophysiological and neurodevelopmental features. Given that cognitive impairments are core to their pathophysiology, we compared patients with schizophrenia, ADHD, ASD, and controls on ocular-motor and manual-motor tasks, challenging crucial cognitive processes. Group comparisons revealed inhibition deficits common to all disorders, increased intra-subject variability in schizophrenia and, to a lesser extent, ADHD as well as slowed processing in schizophrenia. Patterns of deviancies from controls exhibited strong correlations, along with differences that posited schizophrenia as the most impaired group, followed by ASD and ADHD. While vector correlations point towards a common neurodevelopmental continuum of impairment, vector levels suggest differences in the severity of such impairment. These findings argue towards a dimensional approach to Neurodevelopmental Disorders’ pathophysiological mechanisms. Its usual emergence in adolescence or young adulthood has been explained by an interaction between distorted and normal brain developmental processes. Supportive evidence highlights prenatal and perinatal risk factors2 and schizophrenia-related genes early in neurodevelopment3. Furthermore, premorbid neurobehavioural deficits and structural, physiological, and neurochemical brain abnormalities may date back to childhood or earlier in high-risk samples4.Schizophrenia is a complex chronic and disabling psychiatric disorder with neurodevelopmental origins5.Despite neurodevelopmental contributions to its pathophysiology, schizophrenia has so far been considered as distinct aetiologically and nosologically from Neurodevelopmental Disorders (NDD), which define disturbances in the central nervous system development, with resulting abnormalities typically manifesting early in life. Among them, Autism Spectrum Disorder (ASD) denotes social interactions and communication impairments and restricted, repetitive patterns, that manifest from early childhood, whilst Attention-Deficit/Hyperactivity Disorder (ADHD) expresses core inattention, hyperactivity and impulsivity symptoms, identifiable from childhood6 that could be associated with continuous variations in the neurodevelopmental impairment severity. Other key observations include overlapping environmental exposures affecting early brain development7, comorbidity, common motor, sensory and cognitive impairments6.Despite their current nosological distinction, there is evidence of overlapping pathophysiological mechanisms between schizophrenia and NDD. Genomic studies isolated rare copy number variants common to schizophrenia, ADHD and ASD6, according to which alterations in brain development may represent the overlapping underpinnings of different psychiatric disorders, such as schizophrenia, ADHD and ASD, which posit them along an aetiological and neurodevelopmental continuum. Within such continuum, NDD (including schizophrenia) occupy a gradient of decreasing neurodevelopmental impairment, resulting from age at onset, severity of associated cognitive impairment and persistence of functional impairment. While acknowledging that disorders differ along several other clinical dimensions Owen and O’Donovan6 hypothesised that within such continuum the degree (or gradient) of neurodevelopmental impairment is the most recognisable disorders’ feature.Such outcomes have supported the Neurodevelopmental Continuum Model of impairment9.Within this research framework, deficits in cognition have been identified across many psychiatric disorders, thus not being specific to any11. If confirmed, these results would link cognitive constructs with the manifestations of the disorder but not with the risk of its development. However, opposite results have also been reported11 and no firm conclusion can be reached so far. Similarly, investigations in unaffected first-degree relatives provided inconclusive results on depression12, are absent on anxiety. Deficits in patients with obsessive compulsive disorder and their unaffected first-degree relatives are limited to cognitive inflexibility and motor impulsivity13. Conversely, results on planning, spatial working memory and volitional saccade performance are inconclusive15.However, the still fragmentary evidence presents cognitive deficits as secondary impairments in some psychiatric disorders. Cognitive impairments in depression and anxiety seem to depend on the current symptoms’ severity, the disorder course and the medication use19. They appeared in non-affected first-degree relatives21, linking the constructs with each disorder’s genetic liability. Given the relevance of cognitive deficits to these disorders and their pathophysiologies, their investigation could shed light on the disorders’ common (and unique) pathophysiologies.By contrast, cognitive deficits in schizophrenia, ADHD and ASD (and bipolar disorder) are enduring, not state-dependent, independent of the disorder subtype and the severity of the symptomatology22. Subjects are instructed to make an eye movement to a peripheral visual stimulus in the prosaccade (PRO) paradigm, to its mirror location in the antisaccade (ANT) paradigm, towards a memorised stimulus in the memory-guided saccade (MEM) paradigm, or to refrain from making an eye movement in the fixation (FIX) paradigm. These tasks provide different operational definitions of the central constructs of processing speed, inhibition and intra-subject variability (ISV).Through eye movement recordings, many cognitive processes underlying saccade programming could be disentangled25. Several studies also confirmed prolonged memory-saccade latency in SCZ compared to TD26 and ADHD27, results are less consistent for ADHD and ASD25.Starting with processing speed, prolonged antisaccade latency is systematically documented in schizophrenia, ADHD and ASD, consistent with a fronto-striatal pathophysiology, while typical PRO latency across clinical groups suggests mature saccade generation circuits25, expressing disinhibition of a prepotent response. Similarly, frequent premature glances at memorised locations have been reported in schizophrenia or ADHD from separate26 or joint comparisons27 with controls, resulting from impaired interference control from disruption by competing events and responses. By contrast, findings have been inconsistent for ASD22.Moving to inhibition, disproportionally frequent direction errors during ANT have been documented in schizophrenia, ADHD and ASD29. Frequent intrusive saccades in schizophrenia28 could suggest difficulty in maintaining prolonged fixation in presence of distractors. Findings reported in a recent meta-analysis13 showed more intrusions during prolonged fixation without distractors in ADHD than controls, are conflicting from the condition with distractors. Results are no clearer regarding ASD31.Mixed results have been reported also for the FIX intrusive saccades rate33. ISV has been attributed to neural noise, reduced myelination34 or periodicity of the nervous system33. One line of evidence linked increased ISV to fluctuating top-down attentional processes, following findings of increased ISV in patients with prefrontal lesions35. Another line of evidence linked increased ISV to catecholaminergic system dysregulations and consequent impairment in top-down control36. While being typically assessed through the standard deviation of response time (RT), a sensitive albeit non-specific measure of ISV, recent research emphasizes the need to more thoroughly investigate RT distributions to describe the nature of increased ISV of RT in psychiatric conditions. Indeed, increased ISV has been documented for schizophrenia38 and ADHD39 across saccade tasks and manual choice RT tasks, while results in ASD are less consistent42.Ultimately, due to the repetitive presentation of analogous trials, saccade paradigms enable measurement of ISV. Corresponding to the short-term within-subject fluctuations in task performance, and, specifically, trial-to-trial variability of saccade latency, ISV can manifest through a high frequency of slow responses and fast anticipatory responses, expressing transient fluctuations and moment-to-moment variability of attention43. Results are ambiguous in ASD44 however, possibly due to the high comorbidity with ADHD that could not be diagnosed before DSM-5.Finally, the Go/NoGo (GNG) task—requiring responding to target stimuli, not to non-target stimuli—is a useful comparison in virtue of its overlap in involved processes with saccade tasks, namely speed, ISV and response inhibition. Response selection and inhibition deficits have been found in schizophrenia and ADHDThe present study investigated several cognitive trait markers in patients with schizophrenia, ADHD and ASD via basic ocular-motor tasks and a baseline manual response task, aiming to achieve the following goals: (1) to determine between-group similarities and differences on single ocular-motor and manual-motor measures, (2) to explore similarities and differences between clinical groups in their performance deviancy from healthy controls.I.Impaired Inhibition in SCZ, ADHD and ASD.Increased Intra-subject variability (ISV) in SCZ and ADHD, unimpaired in ASD.3.1Typical Processing speed in PRO in SCZ, ADHD and ASD.3.2Decreased Processing speed in ANT, MEM and GNG in SCZ, ADHD and ASD.Processing speed:Hypotheses on differences between patients and controls on single cognitive constructs:Based on above, the following hypotheses, contrasting scenarios and explorative research questions could be formulated.To test the above hypotheses, we extracted the following variables: (1) Inhibition: direction errors in ANT, intrusive saccades in FIX, anticipatory saccades across PRO, ANT and MEM, commission errors in GNG; (2) ISV: Standard deviation of RT in PRO, AND, MEM and GNG, Ex-Gaussian model parameters σ and τ for PRO and GNG; (3) Processing Speed: Mean response time in PRO, ANT, MEM and GNG, Ex-Gaussian model parameter μ for PRO and GNG.II.Following Owen and O’Donovan prediction, ASD will show the overall most impaired and SCZ the least impaired performance.Based on the literature covering the specific tests we expected SCZ to show the most impaired and ASD the least impaired performance.Contrasting scenarios on differences in the profiles of deviancy from controls:III.The three profiles of deviancy will be highly positively correlated, suggesting substantial commonalities in the quality of cognitive impairment between clinical groups.Explorative research questions about similarities between clinical groups in their profiles of deviancy:Noteworthy, while most of the hypotheses are supported by replicated findings, some studies failed to confirm atypical FIX inhibition towards distractors and prolonged MEM RT in ASD and ADHD. Similarly, results are unclear as to typical or prolonged manual RT during Go-stimuli in ASD.While eye movements have been largely investigated in each of the three clinical groups in comparison to controls, and few studies comparing pairs of groups have also been published, regarding the simultaneous comparison of the three clinical groups on basic eye movement tasks the current study is to our knowledge so far unprecedented.N = 103 participants were tested: 20 patients with schizophrenia (SCZ), from a residential home specialised in the treatment of adolescents with psychosis ; 28 with ADHD and 26 with ASD from the out-patient populations of the Department of Child and Adolescent Psychiatry, University Medical Centre Freiburg; 29 healthy controls (TD) from the departmental database, posted announcements and word of mouth (Table 45 and the Autism Diagnostic Interview-Revised (ADI-R)46. Patients from the ADHD group had a subtype diagnosis of ADHD , based on interviews with parents and children, behavioural observations and the Conner’s Rating scales47. Current symptomatology in ASD, ADHD and its absence in TD was assessed using the Social Responsiveness Scale (SRS)48 for ASD symptoms and the Conners’ Rating Scales for ADHD symptoms. Intelligence was measured across the four groups with two different instruments: using the Wechsler Intelligence Test 49 in SCZ, and using the Grundintelligenztest Skala 2-Revision (CFT-20-R)50 in patients with ASD or ADHD as well as in the TD group. In addition, in order to guarantee an adequate between-group comparison in IQ, a 9-item version of the Raven Standard Progressive Matrices (RSPM)51, correlating with the original (long) version by r = 0.98 was taken by all participants52. Participants’ written informed consent was obtained, in addition to parent consent for minors. The study was given ethical approval by the University of Freiburg Ethics Committee .Patients with schizophrenia, ASD and ADHD had previously received a clinical diagnosis determined through expert clinical judgment. Patients from the SCZ group had a diagnosis of schizophrenia, schizophreniform or schizoaffective disorder. Patients from the ASD group had a diagnosis of childhood autism, atypical autism or Asperger Syndrome, following the administration of the Autism Diagnostic Observation Schedule (ADOS)53 assessed participant’s handedness.Participants were administered a battery of eye movement tasks in counterbalanced order within each group. A dominant eye test determined ocular preference, the Edinburgh Handedness InventoryParticipants sat inside a lit cabin, in front of a flat, 24 in. LCD screen monitor with a resolution of 1920 × 1080 pixels and a refresh rate of 60 Hz, at a viewing distance of 90 cm, with stabilised head via a forehead and chin rest. The cabin’s illumination level was maintained between 70 and 80 lx. Each task started with standardised instructions by one examiner sitting next to the participant, followed by practice trials. Administration of experimental trials followed, while the experimenter moved laterally behind the participant, to ascertain participant’s stillness and appropriate task execution. A second examiner sitting in an adjacent room, monitored eye movements to ensure adequate gaze recording and participant’s alertness.While a review of the complex cognitive processes underlying task performance is beyond our scope, we would nevertheless like to highlight some cognitive aspects of the employed tasks.The performance during ANT is believed to reflect inhibition of an automatic response, sensorimotor transformation mechanisms to convert the stimulus spatial location to the opposite visual field and control mechanisms to facilitate the generation of a volitional saccade to the newly calculated location.36.In PRO, there is a direct correspondence between sensory stimulation (stimulus) and motor response (to look at the stimulus). This results in a considerably faster programming of saccades. Thus, the additional time to initiate a volitional in comparison to a visually-guided saccade represents the extra time for the sensorimotor transformation or volitional decision-making processes54. Its application to both PRO and ANT allows to test the generalisation of such processes across different task difficulty levels.Furthermore, including offsetting the central stimulus some 200 ms before presenting the peripheral stimuli allows investigating the mechanisms behind reduction in the saccade latency, which have been hypothesised to reflect release of fixation and motor preparation processes, that occur between the offset of the central fixation and the onset of the peripheral stimulus and a fixation offset effect (FOE) specific to the ocular-motor system55.MEM requires the suppression of a glance toward the presentation of the peripheral stimulus. It also activates the processes of maintenance of the visuospatial location of the stimulus and the generation of a volitional saccade to the now unmarked location in the visual field56.Furthermore, FIX activates inhibition skills while not engaging sensorimotor transformation mechanisms for the generation of a saccadeUltimately, the GNG task involves rapid response preparation following a Go-stimulus appearance, inhibition of an automatic manual response towards a NoGo-stimulus and conflict monitoring operations when response tendencies interfere with each other.All tasks were programmed using Experiment Builder . A representation and a description of the tasks are provided in Supplementary Figs. Eye movements were recorded binocularly with the EyeLink 1000 Plus system , at a sampling rate of 1000 Hz and spatial resolution of 0.01°. A nine-point manual calibration method was used to calibrate and validate the eyes’ position. Calibration was accepted with gaze inaccuracies ≤ 1° binocularly. Additionally, trials in each task were interleaved by a drift correction using a central fixation point, accepted if the gaze accuracy was ≤ 0.5° binocularly. Recordings were recalibrated whenever necessary.Eye movement recordings were analysed offline. Analyses were performed for one eye, depending on subject ocular preference and the eye that, separately for each task, produced the most stable and noise-free signal. The eye trace quality was established via comparison of the two eyes on calibration, validation and drift corrections. This was combined with information derived from the test of ocular preference and a final decision was made.Saccades and fixations were automatically detected according to a velocity-based method . Saccade onset was defined as peak velocity > 25°/s and amplitude > 1°, fixations as any period that was not a blink or a saccade. The EyeLink Host PC Software marks blink events by flagging the onset and offset of periods when the pupil is missing from the camera image, or is too small for its center to be reliably computed. The pupil size is determined by the number of pixels on the camera sensor that are below the pupil threshold .Across saccade tasks, trials in which blinks occurred between onset and offset of the first correct saccade were excluded from analysis.Based on the previously listed (i) Stated hypotheses on differences between patients and controls on single cognitive constructs.Across PRO, ANT and MEM, we extracted the direction errors, if the first saccade after appearance of the peripheral stimulus  or the disappearance of the fixation cross (MEM) was in the wrong direction .In PRO and ANT, we defined anticipatory saccades as saccades with onset after 80 ms and before 300 ms from the target onset and express saccades as saccades with onset between 81 and 130 ms. In MEM, saccades were coded as anticipatory if executed in the direction of the peripheral target during the memory phase, as correct if they followed the disappearance of the central cross (cue) and were directed towards the location of the remembered cue.In FIX, intrusive saccade errors were defined as unnecessary rapid shifts in horizontal eye position that exceeded 1° in amplitude, away from the target for the block without distractors or in the distractor’s direction for the blocks with distractors.In the GNG task error measures included percentage of omission errors (missed responses) in Go-trials and of commission errors  in No-Go trials.Across tasks, error rate was quantified as the number of error trials divided by the total number of usable trials, then multiplied by 100.ISV was defined as the standard deviation (SD) of response times of correct saccades  and correct manual responses (GNG).57, the ex-Gaussian distribution model allows to decompose the RT distribution into Gaussian and exponential components, providing a better fit to the actual RT spread58. Noteworthy, although their psychological interpretation has been questioned59 the ex-Gaussian σ and τ have been shown to reflect different underlying processes, namely sensory-motor and perceptual attentional processing (for σ) or fluctuation resulting from poor suppression of the default mode network (for τ)33.To analyse ISV further, the ex-Gaussian analysis was performed. In fact, while the RT distribution shows an asymmetrical positive skew that refutes the Gaussian assumption of normalityCoherently, the parameters sigma (σ) and tau (τ), signifying the SD of the Gaussian and of the exponential component, respectively, were extracted from PRO and GNG RTs, given the relatively high frequency of correct trials in both tasks.Processing speed was defined as mean response times of correct responses across different tasks. Saccadic response time was defined as the time between target onset  or central fixation offset (MEM) and correct saccade onset. Manual response time for Go-trials (correct responses) from the GNG task was measured as the time between onset of Go-stimuli and button press. Furthermore, the ex-Gaussian parameter mu (μ), reflecting the mean of the Gaussian component of the RT distribution, was extracted from PRO and GNG RTs of correct responses.p) reported. Following significant GROUP main effects across all dependent variables, differences between each clinical group and healthy controls for single dependent variables were tested via planned contrast for single dependent variables, adopting a significance level of α = 0.05 (two-tailed). For the remaining multivariate analyses, referring to control variables and exploration, we adopted a significance level of α = 0.01 (two-tailed) in order to reduce the probability of false positive results.A priori hypotheses were tested via repeated measures ANOVA, with the dependent variables testing different facets of each construct as the levels of the within-subject factor named after the construct. For ISV the repeated measures ANOVA were performed twice: (1) SDRT across the four tasks and (2) SDRT for ANT and MEM separately from σ and τ for PRO and GNG. Similarly, for processing speed the analyses were repeated, including (1) mean RT across the four tasks and (2) mean RT for ANT and MEM and μ for PRO and GNG. In the case of violations of the sphericity assumptions Greenhouse–Geisser corrections of degrees of freedom were applied and the corresponding test statistics  and five within-subject factors: “THREETASK” , to test saccade task main effect, GAPOVERLAP  to test the gap effect during PRO, ANT and FIX, its (in)dependency of saccade task ), TARGETLOCATION  and TARGETECCENTRICITY , to explore the main effect of target and distractor location, its interaction with GROUP, task and GAPOVERLAP, and ultimately BLOCK  to explore the main effect of FIX condition.Task parameters were grouped in two categories, being continuous and categorical variables. Continuous variables included all dependent variables . Categorical variables included the between-subject factor GROUP and all within-subject factors .60.Across variables the normality assumption was evaluated using the Levene’s test. Parametric comparisons were accomplished even following the few cases of violation of the normality assumption, as frequently reported in the literature in light of its known limited and manageable risks and the robustness of parametric tests against normality violation32.In the context of the Ex-Gaussian analyses and in order to exclude potential ISV confounders, RT of manual correct responses to Go-trials (GNG) and regular correct saccades (PRO) were “residualised” before extracting σ and τ. To do so, we ran a multivariate analysis, including “trial index” as covariate, to control for potential confounders of Type I ISV (fluctuations in performance arising purely from within an organism when presented with the same stimulus in the same situation at two points in time) effects of linear trends, and GAPOVERLAP and TARGETLOCATION from PRO as factors to control for Type III ISV effect of variations in task conditions. This residualisation procedure thus removed all components of ISV due to task, stimulus manipulations or trends61, repeating analyses by means of ANCOVAs. In the inclusion of IQ as covariate we sticked with a common statistical recommendation, which has also been discussed controversially62.Given the between-group differences on gender distributions and between SCZ and any other group on the IQ score and medication status, all variables were included as covariates after mean-centringPotential antipsychotic medication effects on the performance of SCZ were further tested via correlation analyses between Chlorpromazine equivalent (CPE) and dependent variables.First, vector levels were defined as the average of the t-values constituting a vector and were compared between clinical groups using paired-sample t-tests to quantify the relative degree of impairment of each clinical group. Second, vector correlations were obtained by correlating the profiles of deviancy (t-test-based vectors) between clinical groups  using Pearson’s r to quantify the qualitative similarity of impairment across clinical groups.Differences between each clinical group and TD were calculated for each dependent variable using independent t-tests, thus obtaining three vectors of group differences  as expressed through t-values. This procedure was accomplished for all measures of SD after controlling statistically for the corresponding means by residualisation. These difference vectors were processed further in two ways and with two complementary aims. In order to examine the potential dependencies of levels or similarities of impairments on the inclusion of IQ, vector correlations/levels were re-calculated following partialling all dependent variables for the RSPM scores. Vector correlations/levels were, furthermore, re-calculated with RSPM scores included as an additional variable. Ex-Gaussian parameters were derived using the retimes package for the statistical package RStudio (version 1.2.1335). All statistical analyses were performed with SPSS software, Version 24 . All methods were carried out in accordance with relevant guidelines and regulations.Descriptive statistics for all dependent measures are presented in Table A significant between-group difference in the proportion of correct saccades was found across tasks. Compared to TD, SCZ made less correct responses in all tasks, ADHD in ANT and MEM, never ASD  SCZ showed more anticipations than ADHD and ASD see Table. FurtherF3,99 = 3.797, p = 0.013, ηp2 = 0.103, see Table During FIX, the block with distractors elicited more intrusive saccades than the one without, differently in the four groups . Correlation coefficients between dependent variables and CPE were all statistically non-significant, and associated with a small to moderate effect size  and for SCZ and ADHD also using the proportion of GNG commission errors.F3,99 = 4.701, p = 0.004, ηp2 = 0.125) and ex-Gaussian parameters  lead to significant results .Across groups, the largest SDRT was found in MEM, the smallest in PRO . Conversely, results were not significant  for the ex-Gaussian parameter μ for PRO and GNG.When including mean RT across tasks, the GROUP main effect was significant , across all groups alike. Overlap trials also elicited larger SDRT than gap trials, more in ANT than PRO , similarly across groups  and ANT . By contrast, the gap effect on mean RT shrank  or remained significant  when co-varying for SDRT.The average RT decreased in gap compared to overlap trials, more in PRO than ANT  and even more so to ADHD , while ASD and ADHD  did not differ significantly. Despite level differences, the three profiles of deviancy from controls were highly positively correlated between the clinical groups. This held consistently for SCZ and ADHD , SCZ and ASD  and ADHD and ASD  and vector correlations  were confirmed.When controlling for IQ, results from vector levels , followed by ADHD and ASD , and finally SCZ and ASD .When including RSPM correct responses as dependent variable, results from vector levels were confirmed, those from vector correlations replicated the highest correlation between SCZ and ADHD  shared inhibition deficits across clinical groups, coherent with our predictions; (b) abnormally high ISV in schizophrenia, unexpectedly as a trend only in ADHD; and (c) prolonged RT across cognitively demanding tasks in schizophrenia. Vector correlations, assessing similarities in the performance impairment between clinical groups, resulted (d) in profiles of deviancy from controls that were highly positively correlated; while vector levels, testing each group’s degree of impairment, indicated (e) the performance of SCZ as the most impaired, followed by ASD and, immediately after, ADHD.Ad (a). Starting with inhibition, abnormally frequent MEM premature saccades and ANT direction errors aggregated SCZ, ADHD or ASD, confirming our hypotheses and replicating previous findings12.13. However, the current study differs from the ones included in the meta-analysis in various ways: participants’ ages (19- versus 8–12-year-olds), task instruction (most studies did not include a distractor condition), task duration (large variability between studies). Likely, one or more of these factors might explain the typical ADHD performance.Deficit in inhibition was the most pervasive in patients with SCZ. SCZ and ASD shared more frequent intrusive saccades towards distractors than TD, confirming the assumption of a dysregulated inhibitory system in both clinical groups. Conversely, the typical intrusive saccade frequency in absence of distractors would suggest intact sustained attention. The same pattern was found in ADHD, disconfirming results from a recent meta-analysis63.Altogether, our data argue in favour of deficient inhibition across ocular-motor and manual-motor systems in schizophrenia, ADHD and, moderately, ASD, suggesting its independence from the motor modality, and possible mediation of inhibition networks  common in generating both responses64, ADHD65 and ASD66. This frontostriatal pathophysiology, that leads to suboptimal inhibition, may underlie the overt symptoms of ADHD.FMRI studies linked inhibition deficits with disturbances in the fronto-striatal-thalamo-cortical circuits and frontoparietal circuits, previously involved in suppressing reflexive saccades in schizophrenia67, fMRI68 and pharmacological69 evidence.These findings have various implications. They suggest that inhibition is core to ADHD, as suggested by follow-up behavioural70 and schizophrenia71; they remain stable over time  and have been variously linked to distinct schizophrenia72 and ASD73 core symptoms, although the nature of such association is less clear than for ADHD symptoms.Deficits in inhibition have also been identified early on in the course of ASD63. Such evidence may suggest overlapping pathophysiologies.Following fMRI evidence, similarities in behavioural inhibition among patients with SCZ, ADHD or ASD could result from overlapping functional abnormalities73.While we present inhibition deficits as a common cognitive abnormality, similar evidence in non-affected first-degree relatives of SCZ and ADHD patients propose inhibition as candidate trans-diagnostic endophenotype and potential genetic vulnerability marker for SCZ and ADHDAd (b). Increased ISV of RT was found across tasks in SCZ. SDRT and τ best dissociated SCZ from controls with the large effect sizes. Differences in σ from PRO and GNG were associated with medium effect sizes. These results replicate previous ones38 where increased τ was thought to reflect suboptimal decision processes, while increased σ abnormal sensorimotor processes.37.Noteworthy, RT distributions differentiated SCZ from TD across all tasks, proposing ISV as a general measure of cognitive instability as has been suggested alreadyTrends of increased ISV in ADHD compared to TD were associated with small effect sizes for SDRT from PRO, ANT and τ from PRO, with medium effect sizes for SDRT from GNG, MEM and τ from GNG. Conversely, σ never dissociated ADHD from TD.39. Alternatively, they might be adduced to the milder symptom severity of our ADHD group. Replicating previous findings57, large τ in patients with ADHD and SCZ might suggest this facet of ISV as a trans-diagnostic phenotype of these disorders57.The findings in ADHD could point to a age moderating effect, as suggested elsewhere77 and ADHD79. The present findings do not provide any compelling evidence for atypically high ISV in ASD, coherently with studies showing increased ISV only in those with ASD co-morbid for ADHD80.Overall, the decomposition of ISV into several facets contributed to linking different measures of behavioural variability with different and overlapping underlying processes in SCZ81 argued that the increasing difficulty level of the RSPM items was due to the increasing number of required rules to be maintained in WM, appearing as a main mechanism underlying reasoning ability .Most of the present results remained essentially unchanged when controlling for IQ, with the exception of SDRT in MEM. The determinant of the association of WM with fluid intelligence are still unclear. Carpenter et al.62 argued against it, referring to both statistical and theoretical arguments59. Overall, while the IQ score in the study of NDD has an undoubtable value, it raises the need for an articulated model that appropriately specifies IQ as latent variable, legitimating the routinary procedure of including it in a model of outcome.More generally, the appropriateness of controlling for IQ has been questioned altogether. Dennis et al.Ad (c). Moving to processing speed, results replicated findings of deficient volitional responses initiation, but intact processes of simple saccade generation, in SCZ.Slow responding held for all—manual and ocular-motor—tasks. Furthermore, we found that delayed responses were uncorrelated across saccade and GNG tasks, which may point to distinct response selection deficits with possibly different underlying neuropathological mechanisms in SCZ.The low correlations with CPE together with the confirmed results when controlling for CPE, may altogether indicate that prolonged response initiation in SCZ is not an effect of daily antipsychotic medication.d > 0.5) and not during MEM could be explained by the highest cognitive load required in the moment of target appearance in the first task. Typical RT in ASD suggests spared visual orienting22.ADHD performance was intermediate between SCZ and TD. The descriptive ADHD slowing during ANT  with large effect sizes, which remained substantially unchanged after removing statistical dependencies between variables, such as those between means and SD, or between dependent variables and IQ, by partialling. The fact that large positive correlations resulted from heterogeneous cognitive facets, across distinct psychiatric disorders, suggests that they might reflect a similar profile of deviancy from “normality” for schizophrenia, ADHD and ASD.The three profiles of deviancy were positively correlated, as confirmed by the Noteworthy, vector correlations evidenced the highest correlation between the profiles of deviancy of SCZ and ADHD, even when including IQ, indicating that these two clinical groups share the most similar profile of impairment.Conversely, results from vector levels located SCZ closer to ASD along the continuum of impairment, calling for their higher similarity in the severity—rather than the quality—of cognitive impairment.6, if cognitive deficits are conceptualised as a qualia that manifests across cognitive sub-processes, then this qualia would result in a unique profile of deviancy from “normality” for the three clinical groups, with differences expressing a continuum of cognitive impairment, from schizophrenia as the most to ADHD as the least impaired group.Back to the neurodevelopmental continuum of impairment85 based visual search, confirming the continuum idea with schizophrenia as the group with the most deficient performance and acknowledging the location’s interchangeability of ADHD and ASD along such continuum based on group-specific strengths .Current conclusions replicate and strengthen the ones presented in another study from our group86. By contrast, the multifinality principle86 highlights how similar developmental factors  may lead to dissimilar outcomes . While we adduce the neurodevelopmental continuum as one possible explanation for overlapping findings among NDD, all cited models are naturally simplifications, likewise they may not be seen as mutually exclusive. Similarly, in the study of schizophrenia, ADHD and ASD, findings of overlapping risk factors parallel evidence of as many unique factors. In this multifactorial conceptualisation of psychiatric disorders the equifinality, multifinality and developmental issues can thus coexist and force to think in terms of complex pathways that lead to a given state87.One might argue that overlapping behavioural outcomes could result from different underlying pathophysiological mechanisms, thus pointing to developmental equifinality The present study has some limitations.First, the sample size of the clinical groups, particularly SCZ, was small, limiting generalisations.89. Ultimately, such argument would also need to explain the typical prosaccade latency in the present SCZ group. Likewise, similar prosaccade speed in patients with schizophrenia before versus after antipsychotic treatment was reported91. Antipsychotic medications seem to also have either no effect or improving effects on antisaccade latency and error rate, opening up to antipsychotics beneficial effects on cognition92. Ultimately, it cannot explain the patterns of deviances that SCZ shared with ADHD and ASD.Second, an effect of antipsychotic medications cannot be ultimately excluded. Indeed, it could be argued that prolonged RT resulted from a generalised antipsychotics’ sedative effect. However, low correlation between processing speed and CPE together with unvaried results from ANCOVAs when including CPE as covariate, pointed towards the independency of processing slowing from antipsychotics assumption. Furthermore, for second and third generation antipsychotics the sedative effect is confined to the initial dosage adjustment phase, which did not apply for any of our patients with schizophrenia. Conversely, sedation is not a problem during extended treatment for most patientsThird, ANT might be a risk marker for psychosis, rather than a risk for schizophrenia, requiring further investigation.Fourth, potential effects of fatigue cannot be excluded. However, additional and/or prolonged breaks were provided whenever necessary, while participants who did not complete the battery in one appointment due to tiredness, were invited for a second session.Fifth, the modest number of trials per task, while being necessary due to the high number of presented tasks and investigated processes, would pose a reliability issue, suggesting the need for replication including more comprehensive data aggregation.Overall, these findings emphasize the importance of extending the scope of research from disorder-specific features towards common neurocognitive deficits and their underlying pathophysiologies. Implications for future studies include the need to widen the nosological concept of NDD to include schizophrenia, in order to test the same model across other psychiatric disorders which share genetic liability with NDD and to extend the focus of attention towards intermediate phenotypes common across psychiatric disorders.Supplementary Information.


Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with onset usually in childhood characterized by inattention, impulsivity, and hyperactivity causing a functional impairment. Untreated ADHD, or treatment delay is associated with adverse outcomes and poor quality of life. Although conservative management strategies such as behavioral and psychological interventions are important, pharmacological treatment has a strong evidence base with improved outcomes. ADHD medications are broadly divided into stimulant and non-stimulant medications. Stimulant medications are generally more effective than non-stimulants. Cardiovascular safety of ADHD medication has been a matter of debate for decades. Treatment guidelines advise the careful consideration of risks and benefits in people with cardiovascular diseases such as congenital heart disease or cardiomyopathy. Although stimulants can increase systemic blood pressure and heart rate, no significant associations were found between their use and serious cardiovascular events. Concerns regarding QT effects and attendant sudden cardiac death risks deter clinicians from initiating much-needed ADHD medications in patients with heart disease. This overly cautious approach is potentially depriving low-risk individuals from significant benefits associated with timely ADHD drug treatment. This review discusses the cardiovascular risks reportedly associated with ADHD medications, the evidence base for their safe usage in persons with established cardiovascular disease, and highlights future research directions. Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder (NDD) affecting around 5% of the population . DiagnosIt is hypothesized that ADHD is associated with a decrease in pre-frontal cortex and limbic system size and function . In addic)]. Although many cardiovascular diagnoses are not contraindications to commencing ADHD medications , the management in the context of a serious cardiac disease  is still a dilemma as guidelines often vaguely recommend a risk-benefit assessment. This topic is especially relevant since ADHD is more prevalent in patients suffering from congenital heart disease or cardiomyopathy  safety of ADHD medications were raised . As a remyopathy . Treatinmyopathy , while nmyopathy . Thus, tThe current review summarizes the cardiovascular complications and risks associated with ADHD medication, discusses their use in individuals suffering from cardiomyopathy and congenital heart disease and concludes with future research directions.In order to identify manuscripts in line with our review aim, a systematic search of Embase, PubMed, and Scopus was used to identify relevant publications published up to 01/10/2022. Search items were defined using the PICO (Patient/Intervention/Comparator/Outcomes) framework: (P) = ; (I) = ; © = ; (O) = . The PICO framework categories were combined using “AND,” while we grouped the variations within categories via “OR.” Reference lists of included articles were also reviewed to identify further eligible publications.Inclusion criteria were peer-reviewed, English-written manuscripts available online through electronic indexing fulfilling the following criteria: (1) study participants had diagnosed ADHD formally diagnosed, (2) study participants received pharmacological ADHD treatment, (3) study participants had any form of cardiovascular disease, or the study aimed to highlight an association between ADHD medications and a cardiovascular complication. Non-original research  were excluded. The quality of studies  was not formally evaluated through a validated tool, but papers were critically appraised in the discussion.The cardiovascular effects of medications used to treat ADHD in the general population are summarized in Stimulant drugs and atomoxetine are positively chronotropic and inotropic, and increase the peripheral systemic vascular resistance as they block the reuptake of norepinephrine and/or dopamine. Thus, they are associated with increases in HR, cardiac contractility, and BP . On the An ECG is highly accessible screening test for CV disease. In certain conditions such as HCM, more than 95% have an ECG abnormality such as repolarization abnormalities  in 80%, left ventricular hypertrophy  in 70%, or QRS-axis deviation in less than 40% . HoweverT-wave inversions in leads III and V4-V6, incomplete right bundle branch block and sinus arrythmia are common in the pediatric population  and shouBoth dexamfetamine and methylphenidate are pro-arrhythmogenic given their direct and indirect sympathomimetic effects. The increase in β-adrenergic stimulation of the heart may cause, precipitate or worsen atrial fibrillation (AF), supraventricular tachycardia  or monomorphic ventricular tachycardia (VT) as per the American Heart Association consensus statement . The recVariable drug-response phenotypes can be explained by differences in pharmacokinetics and pharmacodynamics . The difAs dexamfetamine can increase the BP, they are relatively contraindicated in ADHD patients with moderate and severe hypertension . HoweverThe association between amfetamines and vascular accidents such as myocardial infarction (MI) and hemorrhagic stroke became apparent from population studies based on drug users hospitalized patients . AlthougCoronary failure is uncommon in children and adolescence. Often the causes include coronary artery anomalies (CAAs), acute Kawasaki disease and vasculitis . The incAortic diameter above 3.5 cm is considered to be dilated in adults, but in children normograms with height or body surface area indexation are used. The greater the dilatation, the more likely it is to rupture or to dissect . In patiExcluding sudden infant death syndrome (SIDS), the commonest causes of sudden cardiac death in children are: (1) structural cardiac disease , (2) cardiomyopathies , (3) arrhythmic cardiomyopathy (AC) and (4) channelopathies  with HCM and CAAs being the top two causes . In the HCM is relatively common affecting 1 in 500 adults and is the leading cause of SCD in the developed world. Current predictors of SCD in HCM include family history of SCD, a left ventricular outflow tract obstruction (LVOTO), high maximal wall thickness (MWT) and non-sustained VT (NSVT) . HoweverBy lowering the blood pressure, α2-agonists increase the LVOT gradient, but this is counterbalanced by their negatively inotropic effect which decreases any LVOTO . AlthougBy increasing adrenergic conduction, psychostimulants and atomoxetine can predispose to NSVT and other atrial and ventricular tachyarrhythmias, and therefore are generally avoided in HCM. Although this is the consensus, it is based mostly on biological plausibility, case reports and retrospective cohort studies  with no Although in adults, DCM is often secondary to ischemic heart disease, hypertension, and excessive alcohol , it is cc. This also holds true for the use of stimulant medications in patients with long-QT syndrome (n = 144) although higher odds for syncope was demonstrated  regarding the prospect of α2-agonists  use in those with long-QT syndrome, clinical studies concluded that they do not interfere with cardiac repolarization and are not known to be pro-arrhythmic  or significant increases in QTnstrated . Extraponstrated . Althoug channel , this isncreased  in a dosncreased . As IKr rhythmic  and hencKr channel  syndrome, but close monitoring is warranted. The prospect of developing atrio-ventricular re-entrant tachycardia (AVRT) is linked to atrio-ventricular node blockade as it is the case with β-blockers . As psycAttention-deficit hyperactivity disorder treatment has been shown to decrease antisocial behavior, poor academic performance, and mortality but to date, many individuals with cardiovascular disease continue to be deprived of ADHD medications because of overly cautious approaches unsupported by the evidence-base. Review of published evidence shows that stimulant and non-stimulant ADHD medications have generally good cardiovascular safety profiles but given their mechanisms of action, they should be used cautiously in children and adults with pro-arrhythmic cardiovascular diseases. Although α2-agonists are not as effective as stimulants, they are not pro-arrhythmic and therefore safer in those at higher risk of arrhythmias. The increase or decrease in blood pressure associated with stimulants and α2-agonists, respectively, is not usually clinically significant so clinicians should not be deterred from starting them although their abrupt withdrawal, especially of clonidine, can be problematic.All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. As the data used in this manuscript is publicly available, an ethics approval waiver was applied.C-CT guarantor of this work and attested that all listed authors meet the authorship criteria and that no others meeting the criteria have been omitted. All authors contributed significantly to the study design and implementation, data analysis and interpretation, and manuscript writing, involved in critically reviewing and revising the manuscript, approved the final version as submitted, and agree to be accountable for all aspects of the work.


The ADHD Rating Scale—5 for Children and Adolescents, School Version, has been adopted and validated to be used in assessing ADHD among school children within Western contexts. However, there are few assessment tools in use for identifying ADHD characteristics in children in Sudan. Therefore, this study aimed to investigate the psychometric properties of this rating scale in the context of Sudan. To accomplish this, data were collected on a sample of 3,742 school-aged children and adolescents as reported by their teachers. Psychometric properties can be classified as very good, with very high reliability (>0.90), and high construct validity tested by exploratory and confirmatory factor analysis. Thus, the ADHD Rating Scale—5 for Children and Adolescents, School Version, is valid, reliable, and suitable to use for assessing ADHD symptoms among children and adolescents in the Sudanese context. Diagnostic and Statistical Manual of Mental Disorders-II  in 1968, labeled as “Hyperkinetic Reaction of Childhood” (DSM-5), ADHD was henceforth fully included as a disorder of significant concern for children and adolescents (Attention-deficit hyperactivity disorder (ADHD) is a heterogeneous neuropsychiatric disorder that usually appears in childhood and is characterized by attention deficit, hyperactivity, and impulsivity . The disildhood” . With thlescents .ADHD has a generalized effect on an individual’s life and academic problems are common among students with ADHD . The negThe exact cause of ADHD is still unknown; both genetic and environmental factors are considered during diagnosis . In addiSpecialists in the field of ADHD emphasize the importance of accurate diagnosis because of the advantages this creates for the individual with the disorder on a variety of levels . For exaDSM-5, diagnosing ADHD in children depends on a set of strict criteria; specifically, at least six of the established symptoms of inattentiveness or at least six of the established symptoms of hyperactivity and impulsiveness must be recognized and persist over the course of 6 months or more  that were used to assess the psychometric properties, limited procedures used to assess the psychometric properties, and the lack of psychometric information. This study aimed to address these gaps by utilizing the ADHD Rating Scale—5 , an actuExisting theories about the factor structure behind the 18 symptom items of the ADHD Rating Scale—5 are based on the assumption of a non-hierarchical structure with two to three correlated factors for inattention, hyperactivity, and impulsivity . AdditioDSM-5  and one for hyperactivity-impulsivity (short: “Hyp.-Imp.”). The Inattention items are: (1) Attention to detail; (2) Sustaining attention; (3) Does not seem to listen; (4) Follows instructions; (5) Difficulty organizing; (6) Sustained mental effort; (7) Loses things; (8) Distracted; (9) Forgetful. The Hyperactivity-impulsivity items are: (1) Fidgets; (2) Leaves seat; (3) Runs about; (4) Playing quietly; (5) On the go; (6) Talks excessively; (7) Blurts out answers; (8) Awaiting turns; (9) Interrupts or intrudes. Each item is responded to using a four-point Likert scale, where 0 = “never or rarely”; 1 = “sometimes”; 2 = “often”; and 3 = “very often.” Total scores for (sub)scales are to be formed using summations of all associated items.The ADHD Rating Scale—5  is an 18DSM-5 . The symimpairment, again on the two subscales of Inattention and Hyperactivity-Impulsivity. These items are (1) Family/Teacher Relations; (2) Peer Relations; (3) Homework Functioning; (4) Academic Functioning; (5) Behavioral Functioning; (6) Self-Esteem. These six items were rated twice, after rating Inattention and after rating Hyperactivity-Impulsivity. Ratings and scorings followed the same procedure as for the symptom scale, however, on this four-point Likert scale, 0 = “no problem”; 1 = “minor problem”; 2 = “moderate problem”; and 3 = “severe problem.”The ADHD Rating Scale—5 also includes six items for Hyperactivity-Impulsivity; however, only marginal differences occurred in factor structures, with somewhat higher loadings in ratings by teachers compared to ratings by parents ; 833 (22.3%) obtained a high school diploma or less, 2,196 (58.7%) had a bachelor’s degree, 675 (18.0%) had a master’s degree, and 38 (1.0%) did not specify their educational background. Of these teachers, 2,887 (77.2%) taught general education and 736 (19.7%) taught special education, while others  did not specify an area of specialization. In terms of length of teaching experience, 620 (16.6%) had less than 5 years, 871 (23.3%) had between 5 and 10 years, 2,227 (59.5%) had more than 10 years, and 25 (0.6%) did not specify how much experience they had. At least one special program  has been attended by 1,010 (27.3%) of the teachers, 340 (9.1%) attended two, 91 (2.4%) attended three, and 141 (3.8) indicated attending more than three such programs.The children were assessed by their teachers, who were 3,742 professionals asked to rate Student Number 5 on the list of students in their individual class. The teachers ranged in age from 22 to 73 years old  of which had full data. There were never more than two cases of missing values per variable and only symptom items were affected.Scores from the ADHD Rating Scale—5 were not recoded, thus higher scores represent a higher frequency of symptoms or problems, with “0” as the minimum and “3” (items), 27|18  and 54|36  as the maximum. Within the recorded data, 20 datapoints gave an invalid score of “4.” It is not clear whether these were coding errors or due to incorrectly completed questionnaires. However, as these 20 datapoints came from only four different raters—who also gave some valid answers—the latter was assumed. Thus, the researchers decided to replace all scores of “4” with a “3.” Overall, however, due to the sample size, this did not cause significant deviations.α; Guttman’s 6; McDonald’s ω); for the factor analyses, the packages Lavaan  and confirmatory factor analysis (CFA)], and multiple regression analyses (MRA) were applied on the results from both the symptom and the impairment scale. In case of EFA, the researchers used maximum likelihood for factor extraction assuming multivariate normal distribution, and obliging for rotation allowing intercorrelations. CFA was run with maximum likelihood estimation (ML). Due to the extremely low percentage of missing values, the researchers dispensed with imputation procedures and used the complete case approach for the factor analyses. All analyses were done with R  and impairment items . Confidence intervals (95%) are within a maximum of one decimal place and dropping items does not result in any significant changes.Cronbach’s e manual , the scae manual , 2. As tχ2[153] = 37366.31; p < 0.001) for symptom items. The researchers found that EFA suggested a one-factor solution based on the accelerated factors approach, and a two-factor solution based on the optimal coordinates approach and Velicer’s minimum average partial (MAP) test. However, fit (RMSR = 0.04) were good for the latter but not for the former (RMSR = 0.09). Also, the two-factors solution explains an additional 10% of the total variance compared to the one-factor solution (R2 = 0.54 vs. 0.44). The three-factors solution increases the R2 to 0.61, but shows the same fits (RMSR = 0.04) as the two-factors solution. Thus, To analyze the underlying factor structure of the symptom items for Sudanese children, the researchers first ran explorative factor analysis. Assuming correlation of the extracted factors and missing normal distribution in the items, the researchers used an oblique rotation (oblimnin) and maximum likelihood (ml) for extraction. The prerequisites for this are met by the Sudanese data, with exceptional Kaiser–Meyer–Olkin (KMO) test results of 0.96 and significant Barlett-sphericities (0) argues for a rejection, with values of −58749.227. Goodness of fit indices are partly sufficient and partly insufficient, with χ2[134] = 2131.909 (p < 0.001), CFI = 0.946, TLI = 0.939, RMSEA = 0.063, and SRMR = 0.037, but marginal and to be assessed as sufficient for the overall picture. Compared to the manual  shows a value of −58691.647, but χ2[135] = 1879.287 (p < 0.001), CFI = 0.953, TLI = 0.946, RMSEA = 0.060, and SRMR = 0.035, and therefore an overall sufficient fit  for impairment items. Here, EFA suggested a one-factor solution based on the accelerated factors approach, and a two-factor solution based on the optimal coordinates approach and Velicer’s MAP. RMSR was 0.08 for the one-factor solution and 0.05 for the two-factors solution. However, RMSR was significantly better at 0.01 for six sufficient factors, as recommended by the manual , CFI = 0.932, TLI = 0.885, RMSEA = 0.112, and SRMR = 0.035, which thus, in the Sudanese case, results in their being assessed as insufficient in the overall picture. Again, λ are lower compared to the manual, at between 0.74 and 0.83 compared to 0.93 and 0.99. Moreover, fit indices were again consistently slightly better—but similar—for females than males  argues for a rejection, with a value of −35353.921 see . GoodnesDSM-5 for use in public schools. The current study aimed to examine the validity and reliability of the ADHD Rating Scale—5 for Children and Adolescents, School Version in the Sudanese context. Compared to other studies that have investigating assessing ADHD in this population in Sudan, one strength of this study is that the data were collected from a large number of children and adolescents with and without ADHD at all grade levels of public education in Sudan. This provides greater value to the results and translates to a better contribution to the understanding and evaluation of developmental symptoms in Sudanese children and adolescents with ADHD. Another strength is the demographics of the participants with and without ADHD, which represented all components of Sudanese society. Furthermore, this research involved the application of the school version of the scale as completed by Sudanese teachers. When evaluating a student, teachers could observe behaviors in the classroom as compared to those of their peers, which is not possible when evaluation is implemented by parents who do not have access to numbers of typical students with whom to compare their child. This highlights the importance of the use of teacher rating scales  could be clearly identified, but items for Behavioral functioning and Self-Esteem loaded on a common rather than two separate factors. However, both domains are defined as related to disruptive behavior, the first directly and the latter indirectly by a various level of discipline due to disruptive behavior. Therefore, it seems appropriate to distinguish between the two factors in terms of content and to load the associated items together on a common factor.In summary, the current version of ADHD Rating Scale-5 was found to have appropriate psychometric characteristics in the Sudanese educational environment, which was supported by the types of validity and reliability established in this study. The current study provided more cross-cultural evidence for the effectiveness of the ADHD Rating Scale-5. It therefore can be used to assess ADHD symptoms among Sudanese children and adolescents. It is a useful tool to assess students for identifying ADHD symptoms. The availability of such an important tool can enable teachers and caregivers to act as specialists in conducting regular assessment of children and adolescents prior to the final assessment. It can also enable school professionals to conduct initial assessments to improve their educational interventions for children and adolescents with ADHD in Sudan. Finally, it can be of great benefit in keeping track of the changes that appear on children and adolescents with ADHD and monitoring the effects of the interventions provided to them.This study involved a relatively large sample  of Sudanese children and adolescents aged from 5 to 17 years old in all school grade levels in public schools affiliated with the Ministry of Education in the country’s capital of Khartoum. The study was limited in that it was specifically focused on only establishing the validity and reliability of the ADHD Rating Scale—5 for Children and Adolescents, School Version in Sudan, rather than in a more generalized context with a more diverse sample. Despite the study’s large sample, it was limited in that only the school version of the scale was used and validated. The home version of the scale needs to be explored in future studies. It would also be useful to conduct a future study to obtain normative data for the ADHD Rating Scale—5, as well as its differential validity, since the researchers in this study only established confirmatory and exploratory factor validity.ad hoc adjustments to the models for fit-improvement, as such efforts would only have affected one of the three models and in the remaining two models, the fit indices are close to their critical value.A notable limitation of this research is the matter of the partly unsatisfactory fit indices, specifically in the single-factor model for the symptom items. This issue was striking in that it seems to have been completely caused by the male subsample, especially as this sample is somewhat larger, thus should be suspected to gives the better fit. One possible explanation is that the teachers did not rate girls and boys with equal accuracy, possibly due to cultural factors. Overall, the researchers did not make any DSM-5, among children and adolescents in Sudan.Several implications can be drawn from the findings of this study. First, it is important to make the ADHD Rating Scale—5 available in public schools in Sudan to identify those with ADHD early and accurately. Second, it is important to train teachers in how to apply the scale. Third, the scale should be used to identify the quality of educational interventions provided to students with ADHD. Finally, there is a need for a study to identify the prevalence ADHD, as described by the The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by King Saud University. The patients/participants provided their written informed consent to participate in this study.AA, AAA, DB, RA, SB, MJ, MA, NE, and NA contributed to the design and implementation of the study, to the collection and analysis of the data, to the presentation and discussion of the results, and the writing and finalizing 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls  across the lifespan (7–50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults.  •ADHD patients have a quadratic association with age in brain functional networks•Limbic system better predicts hyperactivity across the lifespan•Salient attention system better predicts the inattention across the lifespan•The predictions on symptom scores are shared in ADHD children and adults Biological sciences; Neuroscience; Developmental neuroscience; Cognitive neuroscience Attention-deficit/hyperactivity disorder (ADHD) is the most common neurological disorder in childhood  and is cIn a worldwide meta-analysis on brain anatomies across the lifespan (4–63 years) , ADHD paHyperactivity and inattention are the major clinical symptoms of ADHD, and these symptoms are thought to have different neural bases . Sudre eTo address the above questions, neural signatures that link the brain to ADHD symptoms across the lifespan need to be extracted. Normal brain functions depend not only on the sufficiently segregated processing in specialized systems but also on the effective global integration among them . FunctioTherefore, in this work, we studied hierarchical segregation and integration in brain FC networks and explored lifespan associations with distinct ADHD symptoms. Hierarchical modules in FC networks were analyzed using resting-state functional magnetic resonance imaging (fMRI) datasets of children and adults with ADHD and HCs with a wide range of ages (7–50 years). We first extracted the lifespan associations of brain FC networks with age in the ADHD and HC groups and studied the alterations of network segregation and integration related to ADHD in different age ranges. Second, we identified the dominant effects of age and ADHD on different functional systems and investigated their heterogeneous functional patterns across the lifespan. Finally, we tested whether brain systems differentially affected by ADHD or age could selectively predict distinct ADHD symptoms and whether these predictions are specific in ADHD patients relative to HCs.N = 100 regions) were constructed for each participant using the Pearson correlation coefficient . Furthermore, we divided each group into three age-binned subgroups roughly termed childhood , adulthood  and old adults . In the HCs, the OA subgroup had the highest connectivity density in FC networks, and CH and AH had nearly the same density (The likelihood ratio test (LRT) was used to identify the lifespan associations between brain network segregation/integration and age , and the density C and S2,Previous works have reported inconsistent effects of ADHD on brain network segregation and integration in children or adults . When tawithin the subnetwork related to ADHD were distributed in the control and DMN systems . The negative correlation implies higher hyperactivity for less network integration. Meanwhile, the β = 0.254, p = 0.033), which indicates higher hyperactivity for a more heterogeneous distribution of the regional integration component, matching to less network integration. Thus, it seems consistent that the limbic and visual systems dominantly affected by ADHD can better predict hyperactivity in ADHD patients.We performed multiple linear regression models while controlling for sex and age, and the beta estimation was used to represent the correlation between the ADHD scores and brain measures see . In all β = 0.368, p = 0.044) and between β = −0.416, p = 0.002). In addition, we further collected the hyperactive score of healthy children  and used different brain measures to predict it. Contrary to the ADHD children, healthy children had a positive correlation between the hyperactive score and network integration, and the best prediction was not in the limbic system  or decreased FC (6–10 years) with age . SeveralAcross the lifespan, ADHD has different effects on brain FC networks in children and adults. Generally, ADHD has been hypothesized to be a DMN-dysconnectivity disorder , which eEven though a previous study reported that children with ADHD and adults with ADHD shared altered FCs within the DMN and between the DMN and ventral attention network , we did Children/adolescents (7.2–21.8 years) with ADHD were found to have a functional maturation lag in the DMN , and youThe salient attention system  was dominantly affected by age in ADHD adults but not in ADHD children. However, this system can better predict inattention in both children and adults with ADHD and was not related to the inattentive scores in healthy children. This result indicates that the salient attention system is closely related to the inattentive score uniquely in ADHD patients rather than in HCs. Meanwhile, the predictions revealed that brains with more homogeneous integration components or more heterogeneous segregation component distributions in the salient attention system correspond to higher inattention. Indeed, to achieve task switching, the brain needs to activate certain regions of the salient attention system and suppress others , which mAccordingly, a hierarchical module analysis enabled the discovery of functional systems that revealed heterogeneous lifespan associations with age and robustly predicted the hyperactive and inattentive symptoms of ADHD patients. The identified functional circuits provide insight into the neurobiological mechanisms that support the important clinical components of ADHD shared in children and adults, which may, in turn, have implications for the development of more objective and accurate diagnostic standards and contribute to the ability to distinguish between the hyperactive and inattentive ADHD subtypes.The sample size is relative small for the lifespan (7–50 years) investigation. We found that the limbic system was significantly different between the two groups during AH (20–35 years), but this system showed a linear lifespan association with age that was insignificantly different from that in HCs. Larger sample size may contribute to identifying more significant alteration of the limbic system in ADHD patients. Meanwhile, the standard Schaefer atlas does not contain the subcortical structures of the limbic system , and this system has the smallest number of voxels and most of them lie in areas likely to be contaminated with susceptibility artifacts. This limitation may be related to the insignificant difference in the lifespan associations with age between ADHD patients and HCs. Finally, though we revealed the abnormal lifespan association of brain functional networks in ADHD patients, it is still unknown how the ADHD affects the development of brain functional organization. A longitudinal study would be necessary to address this question.cszhou@hkbu.edu.hk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Changsong Zhou  Child Study Center in the ADHD-200 project . The datPhenotypic Quick-Fix.csv and Complete Test Set Phenotypic.csv files at the following website: http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html#). Thus, the final number of ADHD children was 57.We chose all 40 ADHD adults in the UCLA dataset with the repetition time [TR] = 2 s. To control the effect of TR on the results, we first selected ADHD children data from the ADHD-200 project with the same TR = 2 s, including the datasets from Peking University, Bradley Hospital/Brown University (BBU) and New York University (NYU) Child Study Center. Then, we further filtered the data where ADHD children had clinical scores, and the BBU data were excluded because of the absence of clinical scores . The procedure allowed the eyes to be either closed or open during the resting state fMRI. In the NYU dataset, the data were acquired using a 3T MRI scanner (Siemens) in an 8-min period in which the participants were awake in the scanner. A total of 180 volumes of images were obtained . During acquisition, participants were asked simply to remain still, close their eyes, think of nothing systematically and not fall asleep. A black screen was presented to them. And in the UCLA dataset, the data were acquired on a 3T Siemens Trio scanners (Siemens) at UCLA. Functional MRI data were collected using a T2∗-weighted echoplanar imaging (EPI) sequence with the following parameters: slice thickness = 4 mm, 34 slices, TR = 2 s, TE = 30 ms, flip angle = 90°, matrix 64 × 64, FOV = 192 mm, oblique slice orientation.All resting-state fMRI data has the same TR = 2s. In the Peking dataset, the data were acquired using a 3T MRI scanner (Siemens) in an 8-min period in which the participants were awake in the scanner. A total of 240 volumes of images were obtained  and the FMRIB Software Library (FSL) (http://www.fmrib.ox.ac.uk/fsl/) were used to preprocess the resting-state fMRI data  6 rigid body motion correction parameters and 2) the signal from the white matter and the signal from a ventricular region of interest. The global whole-brain signal was not removed because the use of global signal regression as a pre-processing step in resting-state fMRI analyses remains controversial and is not universally recommended  . This resolution of the atlas has also been used in a recent ADHD study .The 12.nd mode, the regions with positive eigenvector signs were assigned to a module, and the remaining regions with negative signs formed the second module. This mode was regarded as the second level with two modules.In the 23.rd mode, each module in the second level could be further partitioned into two submodules to form the third level. Successively, the FC network could be partitioned into modules of multiple levels as the order of functional modes increased. When each module contained only a single region at a given level, the partitioning process was stopped. In addition, the regions within a module at a specific level may have the same sign of eigenvector values in the next level; then, the module was indivisible, which had no effect on the subsequent iterative process. During the partitioning process, the module number According to the positive or negative eigenvector sign of the regions in the 3The NSP method was applied to identify the segregation and integration of brain FC networks based on eigenmodes , 2021b. ures see :1.The 1sDifferent from classical segregation and integration based on modules at a single level , the hiefined as  was used to assess the alterations induced by ADHD in R.The linear regression model


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that interferes with multiple aspects of daily functioning. Malingering or feigning of symptoms can be a major challenge during ADHD assessment. Stimulant medication abuse, misuse and diversion may constitute another challenge during management. A literature search of the past 15 years on the topic continued to suggest that there are several reasons for malingering and faking ADHD symptoms. Some of the reasons include the intent to obtain prescriptions for stimulant medications for performance enhancement, to gain access to additional school services and accommodations, to use recreationally and to sell as a street drug. In some countries, patients may receive additional tax or student loan benefits. Several researchers suggested that self-report rating measures are easily simulated by patients without ADHD. They concluded that no questionnaire has proved sufficiently robust against false positives. Some clinical factors that may suggest malingering during the ADHD assessment are highlighted and some available tests to detect malingering are discussed. The concept of feigning of mental illness is not new. The feigning of mental illness for external incentive has been recognized for centuries. In 1825, some authors stated: “Diseases are generally feigned for one of three causes—fear, shame, or hope of gain.” They also noted that the most easily feigned illnesses are those with few to no physical manifestations or those based on self-report and, in particular, “insanity, epilepsy, and pain”. They suggested five elements to consider malingering: “An external incentive is present, no causative factor is present, or the illness has a sudden onset, the individual is resistant to receiving treatment, symptom complaints are inconsistent with the true illness and the course of the disorder is inconsistent with the true illness” [Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that interferes with multiple aspects of daily functioning. There is a need for a clear and concise approach to the diagnosis of a complex disorder such as ADHD. The diagnosis and management of ADHD in adults is complex and challenging for many reasons. One reason is the presence of comorbidity of other psychiatric disorders that have symptoms overlapping with those of ADHD. The presence of comorbidities can be challenging in terms of both diagnosis and management. Another challenge in ADHD assessment is malingering and feigning of symptoms. Clinicians rely on the self-report of ADHD symptoms in addition to collateral information, but the subjective report may be inaccurate or misleading. Understanding the diagnosis and management of ADHD is incomplete without understanding the full dimensions of malingering and feigning of symptoms .Misuse or nonmedical medication use (NMU) can include abuse, inappropriate use and dependence, as was described in the DSM-IV and replaced by stimulant use disorder in the DSM-5 . MedicatThe DSM-5 defines malingering as “the intentional production of false or grossly exaggerated physical or psychological symptoms, motivated by external incentives” . PrescriThere are several reasons for malingering and faking ADHD symptoms. Some of the reasons include the intent to obtain prescriptions for stimulant medications for performance enhancement. Students may gain access to additional school services and accommodations . Some individuals obtain stimulants to use recreationally and to sell as a street drug. In some countries, patients may receive additional tax or student loan reduction benefits ,5,6,7,8.Given the large body of literature and space constraints, this review is selective rather than systematic and comprehensive. It relies on other systematic reviews and meta-analysis of the past 15 years in addition to clinical data and expert opinion. The article attempts to provide guidance on several questions that are relevant to clinicians such as the prevalence of malingering, reasons for malingering, what clinical factors that may suggest malingering during the ADHD assessment should be considered and what is the current evidence of tests used to detect malingering.Some researchers suggested that life-time prevalence rates of nonmedical use of prescribed stimulant use (NMUPS) in college and university students ranges from 5% to 40%, but the exact prevalence is unknown ,10. TheyAuthors examined strategies used to fake ADHD diagnosis and concluded that specific strategies have been used to fake ADHD symptoms. They suggested that adult ADHD can be successfully faked on self-report instruments . ADHD siThe literature indicates that rating scales and questionnaires that are based on self-report are not sensitive enough to allow the detection of malingered ADHD symptoms ,18,19. RSeveral researchers who examined simulation studies on personality inventories such as the Minnesota Multiphasic Personality Inventory, Second Edition MMPI-2) and its Restructured Form (RF) concluded that they are more useful than self-report ADHD questionnaires in detecting simulated ADHD, but more research is needed  and its ,20. The Several neuropsychological tests have been used during the diagnostic assessment of ADHD: Test of Variables of Attention (TOVA) , IntegraThe term performance validity refers to validity of test performance. A performance validity test (PVT) such as the Test of Memory Malingering (TOMM) may be used to detect malingering during cognitive assessment. A performance validity test can be a separate test or part of another scale. It has high specificity when the traditional cut-off points are used to identify irregular performance in cognitive tests .Symptom validity testing (SVT) can be a separate scale or embedded in neuropsychological test batteries and used to test the validity of a symptom report. There are two general categories of SVTs: cognitive and psychological. Examples of SVTs include the Recognition Memory Test (RMT), Word Memory Test (WMT), Portland Digit Recognition Test (PDRT), Victoria Symptom Validity Test (VSVT), which originated in Canada, and Validity Indicator Profile (VIP) .It appears that SVTs can be useful in detecting malingered ADHD, but was originally designed to detect malingered cognitive symptoms. SVTs have a high specificity, but inadequate sensitivity. Combining two SVTs may significantly increase the ability to identify malingerers . A recenMarked disagreement and contradictions of the reported symptoms. Example: patient reported inability to complete tasks because of inattention, but the patient also reported finishing all the required tasks such as completing assignments on time.Marked disagreement and discrepancies between reported and observed symptoms. Example: patient reported blurting answers before the questions are complete, but during a 2 h assessment, no blurting of answers observed.Marked discrepancies between reported and actual level of function. Example: reported poor academic functioning in university, but the records show a GPA average of 4.1 each year.Lack of evidence or discrepancies between reported symptoms and psychological test results. Example: patient mentioned that he was diagnosed with ADHD in grade 5, but his family physician included in the referral letter the results of his psychoeducational assessment that shows that patient was not diagnosed with ADHD.Patient endorses improbable ADHD symptoms. Examples include forgetting the names of siblings, directions to go home and name of elementary school.Patient is behaving in an unusual way during the interview. Examples include extremely guarded, uncooperative, angry, evasive and responding with difficulty answering simple questions.Clinicians are encouraged to consider the following clinical factors that may suggest malingering during the ADHD assessment:This opinion is limited by the clinical experience in Western society and may apply differently in other countries.This article aims to underscore the importance of the problem of malingering and stimulant use disorder. Malingering for ADHD medications is difficult to detect. Stimulant medication abuse, misuse and diversion constitute a serious public health concern. These medications have major adverse effects and addictive potential. It is suggested that malingering is not an infrequent event and has been increasing overtime ,6,11. Pu


Full siblings of individuals with low SBP, low physical fitness, and obesity were more likely to receive an ADHD diagnosis compared to full siblings without those risk factors (OR ranging from 1.17 to 1.31). Additionally, analyses showed robust associations between ADHD and low SBP, low physical fitness, and obesity, even in ADHD medication‐naïve individuals.Individuals with several cardiovascular risk factors are more often diagnosed with ADHD, regardless of psychiatric comorbidity. These association are not explained by ADHD pharmacotherapy, rather, they are in part due to shared familial risk factors. We explored the potential influence of critical covariates, including family socioeconomic status and psychiatric comorbidity . To gain insight on the relationship between ADHD and cardiovascular risk factors, we additionally examined whether ADHD shared familial risk factors with SBP, DBP, RHR, PP, BMI, and physical fitness by assessing the familiar coaggregation. Lastly, in order to assess whether potential associations were due to ADHD medication (rather than the disorder itself), we examined the associations in an unmedicated sample.22.1N = 395,978). Further, using the Multi‐generation Register, we linked these individuals with their biological parents, which allowed us to identify full‐sibling relationships within the same birth cohort  in which the individual and their full sibling shared both of their biological parents. We carried out additional analysis in a unmedicated cohort (before ADHD medication was introduced in Sweden) restricted to military conscription between 1978 and 1991 .This study was based on the linkage of several Swedish population‐based registers linked by unique personal numbers: The Swedish Military Service Conscription Register (MSCR), the Multi‐Generation Register, Longitudinal Integration Database for Health Insurance and Labor Market Studies (LISA), Prescribed Drug Register (PDR), and National Patient Register (NPR). The MSCR includes extensive medical and psychological assessments for all men at 18 years of age. Military service was obligatory by law to all Swedish men until year 2010 unless individuals were in prison, presented with severe chronic medical or mental conditions or handicaps documented by a medical certificate . All individuals born between 1973 and 1991 who enlisted for military service after 1991 with information on SBP, DBP, RHR, BMI, and physical fitness were identified  or 10  or a prescription of ADHD medication  from the PDR. The NPR provides diagnoses for psychiatric inpatient care since 1973 and outpatient care since 2001 up until 2013, and the PDR includes drug prescriptions dispensations from 2005 to 2013.2.3th and 75th percentiles , and the medium class was used as the reference group.Blood pressure (BP) and RHR were measured while in a supine position after 10 min of rest during the first day of the conscription examination. Data points were regarded as outliers and potential data errors, and as such excluded from the analysis, if SBP was below 45 and above 200 mm Hg, DBP was below 40 and above 120 mm Hg, and RHR was below 35 and above 145 beats per minute (bpm). Pulse pressure was calculated as the difference between SBP and DBP. Variables were categorized into low, medium, and high based on the 252) from the military conscription register values and categorized according to WHO criteria into low weight (<18.5), normal weight , overweight (25 ≤ BMI <30), and obesity (BMI ≥30)  using the database LISA. These were all included as covariates in the analysis. Lifetime diagnoses of substance use disorder , depressive disorders , and anxiety disorders  were obtained from the NPR and were dichotomized into binary variables.2.5p < 0.05. Data preparation and analyses were performed using SAS software version 9.4  and R 4.0.2, respectively.In order to determine the relationship between ADHD and our set of risk factors , a series of logistic regression models were performed. Results were displayed as odds ratios (ORs) together with their 95% confidence intervals (CI). The statistical significance was set at 2.6The odds of having an ADHD diagnosis in individuals with our sets of predictors were estimated. In the first model, estimates were adjusted for birth year to adjust for potential cohort effects and differences in follow‐up length. In the second model, further adjustment was made for sex, family socioeconomic status, depression, anxiety, and SUD.2.7In order to investigate the presence of a shared familial component between ADHD and cardiovascular risk factors, we estimated the ORs of having ADHD in individuals whose full sibling had low or high values of our predictors compared to the reference group . Higher risk of ADHD in siblings of individuals with high or low predictor values indicates that familial  factors shared among siblings contribute to the co‐occurrence of conditions. Estimates were adjusted for birth year. In order to account for the non‐independence of family‐clustered observations, the Huber‐White cluster‐robust sandwich estimator was used allowing for intragroup correlation for the standard errors (SE).2.8N = 269,503) between 1978 and 1991, that is, before ADHD medication was introduced in Sweden, to examine whether associations were influenced by ADHD medication. Estimates were first adjusted for birth year and then, in a second model, further adjusted for sex, family socioeconomic status, depression, anxiety, and SUD.Due to the potential effect of ADHD medication on BP and heart rate  in the main sample , within‐individual analyses were performed after missing values were imputed. K‐nearest neighbor (kNN) imputation was carried out using 5 neighbors based on the Gower's distance function. Third, analyses were restricted to military conscriptions recruited after year 2000 to include individuals with in and outpatient data. Lastly, diagnoses were defined as having a diagnosis before military conscription and before age 25 .33.1Among 395,978 index individuals included in the study, the vast majority were men (97.3%) and the mean age at military conscription was 18.31 years (SD = 0.57). The estimated lifetime prevalence of ADHD was 1.3% for the index individuals and 5.2% for full siblings of individuals with ADHD, with a mean age at diagnosis of 27.7 years (SD = 5.88). The mean age difference at conscription between the index individuals and their siblings was 0.03 months (SD = 8.68). Additional descriptive information can be observed in Table 3.2Among index individuals, ADHD was positively associated with low SBP , high DBP , low PP , low physical fitness , being overweight , and obese , while adjusting for birth year , low physical fitness , and obesity  compared with individuals in the medium group. Conversely, a lower risk was observed for full siblings of cases with high SBP , high PP , and high physical fitness . The between‐siblings associations of ADHD with measures of RHR and DBP were not significant. Table 3.4N = 269,503) and after ADHD medication was introduced in 1992 . Results displayed robust patterns of associations before and after 1992, and associations remained significant before 1992 for high (negative association) and low SBP, PP, low physical fitness, and obesity with ADHD  and after medication was introduced . Finally, estimates remained unchanged regardless of diagnoses definition  and family education. Moreover, we found that full siblings of individuals with low SBP, low physical fitness, and who had obesity were also more likely to be diagnosed with ADHD compared to full siblings of individuals in the reference group. Additional analyses showed that individuals with low levels of SBP, PP, physical fitness, and obese were more likely to be diagnosed with ADHD even before ADHD medication was introduced in Sweden, which highlights that ADHD per se, rather than its pharmacologic treatment, is an important risk factor for several important indicators of cardiovascular risk. The finding that individuals with ADHD, on average, present with lower SBP is novel, with only one prior study reporting similar results  and RHR (+5.7 bpm) associated with the use of ADHD medication , a population‐based design, and the use of objective and detailed measures of cardiovascular risk factors. Moreover, the study population largely represents the male population given that only a very small amount did not qualify for military conscription . However, the study should be interpreted in light of its limitations. Given our definition of ADHD using diagnoses and medication prescriptions and the low prevalence ratio in our sample (1.3%) compared to the reported in the literature  outside the submitted work. Samule Cortese declares honoraria and reimbursement for travel and accommodation expenses for lectures from the following non‐profit associations: Association for Child and Adolescent Central Health (ACAMH), Canadian ADHD Alliance Resource (CADDRA), British Association of Pharmacology (BAP), and from Healthcare Convention for educational activity on ADHD. The remaining authors declare having no conflict of interest.Miguel Garcia‐Argibay and Henrik Larsson conceptualized and designed the study. Miguel Garcia‐Argibay conducted literature search, analyzed the data, and drafted the manuscript. Henrik Larsson and Ebba Du Rietz provided supervision. All authors contributed to the interpretation of results, reviewing, and editing of the final manuscript, and had responsibility in deciding to submit the manuscript for publication. Miguel Garcia‐Argibay attests that all listed authors meet authorship criteria and that no other individuals meeting the criteria have been omitted.The study had ethical approval from the Regional Ethical Review Board in Stockholm, Sweden (Dnr 2013/862–31/5). Requirement for informed consent was waived for the current study because it was a secondary analysis of existing data. The investigation conforms to the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Supporting Information S1Click here for additional data file.