Surface roughness and gloss were then measured. Results: For roughness, material and surface treatment were significant factors (p < 0.001) with SF = FB = EC < FU and AP < SW < HL = OS. Material and surface treatment had a significant effect also on gloss (p < 0.001), with SF > FB = EC > FU and SW > AP > HL > OS. Conclusions: The tested combinations of bulk-fill and polishing systems provided clinically acceptable results with regard to roughness, while the outcome was poor for gloss. Multistep finishing/polishing systems were able to produce smoother surfaces on full-body bulk-fill materials compared to simplified ones.Background: In this study, we assess the effect produced on roughness and gloss of full-body bulk-fill materials by different finishing and polishing systems. Methods: Four full-body bulk-fill materials were tested: SonicFill2 (SF), Filtek Bulk Fill Posterior Restorative (FB), Tetric EvoCeram bulk-fill (EC), and Fill-Up! (FU). Sixty discs per material (2 mm in thickness and 7 mm in diameter) were obtained and randomly assigned ( Resin composites (RCs) are widely used for anterior and posterior restorations, owing to their ability to mimic mechanical and optical properties of the natural tooth. As an improvement of early macro- and microfilled RCs, in the 1980s, “hybrid” RCs were developed. The initial limits in wearability and polishability were progressively overcome as a result of the increase in filler load and the reduction of filler size ,3.The first developed bulk-fill resin composites were meant as base materials to be capped with a conventional resin composite as the occlusal layer. This original category of bulk-fill composites has been classified as “base”  or “low The purpose of this study was then to evaluate surface roughness and gloss of full-body bulk-fill materials treated with different finishing and polishing systems that are not specifically developed for this category of materials. Two null hypotheses were formulated and subjected to test. The first null hypothesis was that neither the material type nor the performed finishing and polishing procedure had a significant influence on the surface roughness of currently available full-body bulk-fill RCs. The second null hypothesis was that surface gloss of these same RCs was not significantly affected either by the material type or by the followed finishing and polishing method.2 and the light tip was placed at a standardized distance of 1 mm from the specimen surface. To allow for composite postcure, specimens were left in an incubator at 37 °C and 100% humidity for 24 h before use. A #1982M medium grit Sof-Lex disc  was used for 20 s to create a standardized initial roughness on the specimen surface, simulating the one produced clinically by a red diamond bur. A new Sof-Lex disc was used for each specimen. Manual finishing and polishing procedures of all the specimens were then performed by one same operator (EM). The operator was calibrated using a precision scale before and during the procedure, considering that a 40 g force was considered light pressure. The operator calibration was repeated every 10 specimens [Four full-body bulk-fill materials were selected for the study: SonicFill2 (SF) shade A2 , Filtek Bulk Fill Posterior Restorative (FB) shade A2 , Ivoclar Tetric EvoCeram bulk-fill (EC) shade IVA  shade Universal : Sof-Lex™ Spiral Wheels , 20 s for each system step, using a low-speed handpiece in circular movements under continuous water cooling (50 mL/min);Group 2 (HL): HiLusterPLUS Polishing System  20 s for each system step, using a low-speed handpiece in circular movements under continuous water cooling (50 mL/min);Group 3 (AP): Astropol  15 s for each system step, using a low-speed handpiece in circular movements under continuous water cooling (50 mL/min);Group 4 (OS): Opti1Step  20 s for each system, using a low-speed handpiece in circular movements under continuous water cooling (50 mL/min). Chemical composition and instructions for use of the finishing and polishing systems are reported in Before testing, specimens were ultrasonically cleaned in a 95% ethanol solution for 3 min. A profilometer  set with a cutoff value of 0.8 mm, a stylus speed of 0.5 mm/s, and a tracking length of 5.0 mm was used  to assesThe gloss measurements were performed using a small-area glossmeter  with a square measurement area of 2 × 2 mm. Gloss assessment was performed at a 60° angle following the ISO 2813 specification for ceramic materials . A blackIn order to test the first formulated null hypothesis, a two-way analysis of variance (ANOVA) was applied with roughness data as the dependent variable and material type and finishing/polishing system as factors, having preliminarily verified that roughness data were normally distributed (Shapiro–Wilk test) and that they had homogeneous group variances (Levene test). The Tukey test was applied for post hoc comparisons as needed.The same statistical analysis  was applied to gloss measurements to test the second formulated null hypothesis, once it had been checked that data distribution was normal (Shapiro–Wilk test) and that group variances were homogeneous (Levene test).The statistical significance of the correlation between roughness and gloss measurements was assessed with the Pearson correlation test.p < 0.05. The SigmaPlot 11.0 software  was used for statistical calculations.In all the statistical tests, the level of significance was set at Specimens’ preparation for SEM observations involved ultrasonically cleansing in a 95% alcohol solution for 3 min and air drying with an oil-free air spray. Specimens were then secured onto SEM  slabs with gold conducting tape and gold 80%/platinum 20% coated in a vacuum sputter coater . The treated surfaces were then observed at 500× magnification .p < 0.001) and finishing/polishing system (p < 0.001) were significant factors for roughness. Particularly regarding material type, irrespective of the finishing/polishing procedure, the post hoc test highlighted that FU had a significantly higher roughness than the other materials (p < 0.05). Concerning the finishing/polishing system, it emerged from post hoc comparisons that, regardless of the material type, AP yielded the lowest roughness, and the difference was statistically significant (p < 0.05); SW produced a significantly lower roughness than HL and OS (p < 0.05).The descriptive statistics of surface roughness data are reported in p < 0.001) and finishing/polishing system (p < 0.001) were significant factors for gloss. When assessing the influence of material type per se, post hoc comparisons revealed that SF had the highest gloss, and the difference was statistically significant (p < 0.05). Furthermore, FB and EC exhibited a significantly higher gloss than FU. Concerning the finishing/polishing procedures per se, all the systems differed significantly according to the post hoc test (p < 0.05), with SW and OS yielding the highest and the lowest gloss, respectively.The descriptive statistics of surface gloss data are reported in r = −0.534). The correlation was statistically significant (p < 0.001).The Pearson correlation test revealed that a moderate negative correlation existed between roughness and gloss measurements  and other combinations with a higher degree of irregularities . Most of the observed specimens showed homogeneous surfaces with few exceptions .Numerous finishing and polishing systems for RC restorations are currently available and described in the literature ,17,18,19It might be argued that a traditional hybrid resin composite should have been tested as the control group. This was indeed considered redundant, as the study was aimed at comparing different finishing/polishing systems on several available full-body bulk-fill materials and the comparative assessment of roughness and gloss was made with reference to threshold values of these properties that have been reported in the literature ,25.Roughness and gloss are clinically relevant characteristics of restorative materials. Roughness is related to irregularities and it is usually evaluated as roughness average (Ra), which is defined as the mean arithmetical value of all the absolute distances of the profile inside of the measuring length . Gloss iAs the purpose of a finishing/polishing procedure is to provide enamel-like surfaces, ideally, the final composite roughness should be similar to enamel-to-enamel contact in occlusal areas (0.64 µm) . RoughneNo statistically significant difference in roughness emerged among SF, FB, and EC. Such an observation is in line with the outcome of the study by O’Neill et al. , where nThe finishing/polishing procedures are also aimed at providing the restoration surface with an enamel-like gloss. Mormann et al.  reportedThe present study evaluated four finishing and polishing systems. Commonly utilized finishing and polishing systems involving the sequential use of a series of discs were not included, as they are not indicated for occlusal surfaces for which the bulk-fill materials of the present study are used. While bulk-fill materials enable a time-saving filling technique, there is still no consensus as to whether they also allow a simplified time-saving finishing/polishing procedure. In the present study, one-, two-, and three-step silicon points differed significantly from a two-step wheel system. The three-step system Astropol was found to provide the average smoothest surface compared to the other systems. The combination Sof-Lex™ Spiral Wheels+FB provided the smoothest surface. No differences were found between HiLuster and Opti1Step, being both significantly different from Astropol and Sof-Lex™ Spiral Wheels. Sof-Lex™ Spiral Wheels produced significantly higher gloss than the other investigated treatments. The combination Astropol+SF provided the highest gloss value among all the composites and polishing systems, whereas the combination 1Step+FU had the worst outcome for gloss. Sof-Lex™ Spiral Wheels provided consistent results on the different materials.Irrespective of the number of steps, the chemical composition of the polishing points could account for the superiority of the three-step systems, considering that Astropol is the only one containing silicon carbide while the other two-step systems all have aluminum oxide.As a limitation of the present study and as possible matter for future investigations, it should be mentioned that only one initial finishing system was used to create a standard surface for the present study. Nevertheless, several other finishing systems, such as diamond burs and carbide burs, are available, and their use can affect the initial roughness . StabiliWithin the limitations of this in vitro study, the following conclusions can be drawn:The combination of tested bulk-fill and polishing systems provided clinically acceptable results in terms of roughness, while poor results were achieved in terms of gloss.The multistep finishing/polishing systems tested in the present study showed a higher polishing ability on full-body bulk-fill resin composite than the single step systems.The study suggested the need for developing a polishing system dedicated to full-body bulk-fill resin composite.


Background: Excessive daytime sleepiness (EDS) is central in Attention deficit hyperactivity disorder (ADHD) but its causes remain unclear. The aim of this study was to explore objective EDS and homeostatic sleep pressure buildup, evaluated by power theta–alpha frequency (PTAF), in drug-free sleepy adults with ADHD and controls.Methods: Participants were placed during a 36-h period of extended wakefulness under constant routine protocol to strictly control sleep time, sleep duration, and circadian zeitgebers.Results: Eight drug-free sleepy patients with ADHD and 7 matched controls were included. The ADHD group had significantly shorter sleep latency on the Maintenance of Wakefulness Test (MWT) throughout extended wakefulness than the control group. There was no significant difference between the groups in PTAF evolution during extended wakefulness and in kinetic sleep pressure buildup, evaluated by the time constant of saturating exponential function.Limitations: The sample was small, so the findings cannot be generalized. Moreover, psychiatric comorbidities and circadian regulation should be taken into account in future studies.Conclusion: In very controlled conditions, mean sleep latency on the MWT during the whole extended wakefulness was significantly shorter in sleepy patients with ADHD than in control subjects. However, the difficulty to remain awake during soporific circumstances observed in these patients with ADHD cannot be explained by changes in the kinetic of sleep pressure buildup.Clinical Trials Registration:www.clinicaltrials.gov/, Identifier: NCT02217371. Attention deficit hyperactivity disorder (ADHD) is the most common developmental disorder and is characterized by inappropriate levels of inattention, impulsivity, and hyperactivity . Follow-Consistent evidence for circadian rhythm disruption in ADHD is now emerging. As described by Coogan and McGowan in their recent systematic review , ADHD isMoreover, DSPS may involve changes in the circadian clock drive (Process-C) and/or the sleep homeostatic process (Process-S) . CircadiConcerning process-S, we previously demonstrated that evening chronotype feels sleepier during the daytime and that the kinetics of homeostatic sleep pressure build-up differ between morning and evening chronotypes . Our hypTo go further than the aforementioned studies, the aim of this study was to explore EDS objectified by MWT and homeostatic sleep pressure in a sleepy ADHD subgroup, and in control subjects for a 36-h period of extended wakefulness under constant routine protocol.DSM-IVR criteria   according to the criteria . The cli CAADID) . The Con CAADID) . Accordi CAADID) .DSM IV).We excluded all patients with any clinically relevant medical or psychiatric condition including current affective or psychotic disorders, substance abuse within 1 year prior to screening, and long-term treatment with benzodiazepine. Comorbid psychiatric disorders were assessed with the Mini International Neuropsychiatric Interview , sex and chronotype, as defined by the score on the Morning Evening Questionnaire (MEQ) . We exclClinicaltrials.gov (NCT02217371).For patients and controls, the presence of nocturnal sleep-disordered breathing (AHI > 10/h) and periodic limb movements (index > 15/h) were ruled out with ambulatory polygraphy. Patients and controls with restless leg syndrome and DSPS were also excluded. Subjects provided written informed consent, and the local ethics committee [consultative committee for the protection of persons participating in biomedical research (CPP Sud-Ouest et Outre-Mer III)] approved the study. The study is registered at For 4 days prior to the study, participants were asked to maintain regular bedtimes and wake-up times according to their usual preferences. During this period, compliance was checked by sleep diaries and actimetry. They performed a nocturnal polysomnography (PSG) just before the constant routine to control sleep time and duration. The next morning, 1 h after their preferential wake-up time, all volunteers underwent a 36-h period of extended wakefulness in the constant routine protocol . As sleeSleep pressure was evaluated by theta-alpha (6–9 Hz) band of frontal EEG  during tDuring PSG, KDT and MWT, EEG, EOG, and EMG were recorded on a Brainbox EEG-1042 Amplifier with coherence software . All electrophysiological signals were digitized at a sampling rate of 256 Hz. KDT and MWT were repeated every 4 h.p-Values <0.05 were considered statistically significant.Actimetry and PSG measures were compared between ADHD patients and controls using Wilcoxon rank tests. Repeated outcomes (MWT and Process S fPTAF) as well as the constant and the asymptote of the fPTAF were compared between groups using linear mixed effects models adjusted for age, gender, and Hörne and Ostberg score, and taking into account an interaction between group and time. The analyses were conducted on available data, using SAS software v9.4 . Eight ADHD patients and 9 control participants were recruited. Two control participants withdrew from the experiment during the constant routine protocol. The sample thus consisted of 15 subjects: 8 ADHD patients  and 7 control participants . The mean MEQ score in ADHD patients was 56.8 ± 12.7, demonstrating that sleepy ADHD patients were mostly intermediate chronotypes. Only one sleepy ADHD patient was classified as evening chronotype.Among the 8 patients with ADHD included, 50% presented a psychiatric comorbidity (25% had comorbid anxiety disorder (past or present) and 50% had a history of mood disorders but no current mood disorder). Fifty percent presented with ADHD of the mixed subtype and 50% with the inattentive subtype. Patients with ADHD presented the following scores on the CAARS (58.4 ± 15.7) and the Brown questionnaire (69.8 ± 26.3).p =0.41; Night 2: 453.3 ± 128.2 min vs. 437.0 ± 52.2 min, p = 0.73; Night 3: 419.8 ± 63.9 min vs. 385.3 ± 73.6 min, p = 0.39; Night 4: 429.3 ±110.5 vs. 415.7± 40.2, p = 0.73).There was no significant difference between patients with ADHD and controls in total sleep time for the four nights before the protocol  min vs. 392.9 (62.0) p = 0.013)]. This significantly shorter sleep latency on the MWT persists over time, but remains stable over the eight measures (p = 0.94) (The ADHD group had a shorter sleep latency on the MWT than the control group at T1 . The patients/participants provided their written informed consent to participate in this study.SB, PS, PP, and JT: designed this study. SB, PS, JT, ET, AB, CB, and MB: coordinated the data collection and enrolment of participants. SB, PS, and JT drafted the manuscript which was added to and modified by AB, ET, CB, MB, and PP. All authors read and approved the final manuscript.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.


Adult-attention-deficit-hyperactive-disorder (ADHD) is often unrecognized condition. FMRI examination along with neuropsychological testing might strengthen the diagnosis. We hypothesized that ADHD-adults with and without medication would show different fMRI pattern compared to healthy controls while testing tasks of motor inhibition and cognitive switching.45 subjects in three age-matched groups: (1) controls, (2) ADHD-adults under medication (ADHD+) and (3) medication-naïve adults with ADHD (ADHD−) underwent fMRI and neuropsychological testing. Group analysis and population-based statistics were performed.p < 0.05).DTVP-A, intellectual ability as well as attention capability, visual-perceptual and visual-motor abilities showed no significant differences between the groups. However, fMRI revealed statistically significant differences between the ADHD+, ADHD− and control groups on tasks of motor inhibition and cognitive switching on adults in bilateral fronto-striatal brain regions, inferior fronto-frontal, fronto-cingulate and fronto-parietal networks as well as in the parietal lobe .NCT02578342, registered at August 2015 to clinical trial registry ( Adult Attention Deficit Hyperactivity Disorder (ADHD) is a relatively common, but often unrecognized condition. ADHD is characterized by age-inappropriate symptoms of inattention, impulsiveness and hyperactivity . It affeThe most consistent neuropsychological findings in adults with ADHD are deficits in motor response inhibition and cognitive switching , 10. RecThree modified tasks of inhibitory control that have earlier been described in fMRI studies in adults with ADHD , 25: Go/Stimulant medication history is a major confounder in imaging studies, given evidence for long-term effects of stimulant medication on brain structure  and braiWe hypothesized that inattentive/hyperactive adults with ADHD have different activation patterns in fronto-striatal and fronto-parietal brain regions compared to healthy comparison adults.All participants were over 20-year old adults. They were divided into three groups: controls (n = 15), subjects with ADHD under medication  and medication-naïve ADHD subjects . All subjects had normal or corrected-to-normal visual acuity. The three groups of the study were age matched as follows: (1) controls , all right handed), (2) ADHD+ , all right handed) and (3) ADHD− , 2 left handed).The study was performed in agreement with the Declaration of Helsinki. All subjects gave written informed consent and the study was approved by the local ethical committee. Exclusion criteria for controls were present or past history of any mental disorder and substance abuse. Since the stimulant medication history is a major confounder in imaging studies , all ADHIntellectual ability (IQ) was assessed with the German version of the KAIT . Attention capability, visual-perceptual and visual-motor abilities were assessed by the d2 test and the Developmental Test of Visual Perception–Adolescent and Adult , respectively.All subjects underwent fMRI at 3T  with a 32-channel head coil. FMRI was performed utilizing an echo planar imaging sequence  = 2000 ms, echo time (TE) = 30 ms, flip angle 90°). T1-weighted anatomical, 3-dimensional, volumetric, interpolated brain examination sequence  = 4.3 min) with T2-weighted turbo spin echo  followed.fMRI tasks were built in as block-paradigms and all stimuli were presented in the center of the screen. The visual stimuli were programmed using E-Prime software . Subjects laid in the scanner and viewed the stimuli displayed on a monitor via a mirror . Participants practiced the first trial block per task to familiarize themselves with the test.Go/No-Go task”, No-Go signals are presented intermixed with Go-signals [In the “-signals , calling-signals , 19, 20.-signals , 37, letStop task” similar to Rubia et al. [In “a et al. , 22 arroSwitch task” requires cognitive switching between two spatial dimensions [The “mensions , 11, 14.p < 0.05, considering a minimum cluster of more than 20 contiguous voxels in terms of t-statistics based on BOLD signal changes.Data was analyzed individually for all three groups. Blood oxygen level dependent (BOLD) signal changes were analyzed and resulting contrast maps were created for a response to cognitive tasks versus crosshair. BOLD clusters were assessed individually with respect to the anatomical location. Data was analyzed using Brainvoyager QX software  consisting of the following steps: motion correction , co-registration of functional imaging and 3-dimensional isovoxel anatomic data and spatial smoothing ). Anatomical images were then transferred to the Talairach space. At the first level of analysis, a general linear model was computed for each experiment, applying separate predictors for each subject. Then, multi-subject analysis was applied by averaging all the data. Activation maps were corrected for multiple comparisons using false discovery rate (FDR) approach with For population-based statistics, random-effect (RFX) analysis was performed that takes into account the between- and intra-subject variability. The different subjects are treated as random samples from the possible selection of subjects. The RFX analysis calculates beta (β) weights per subject and predictor. The resulting BOLD activation map shows a comparison of the individual betas of all subjects. In this way, the variability between the different subjects can be calculated and, thus, conclusions can also be drawn for the general population. Gaussian smoothing of FWHM of 4 mm, and min. cluster of 20 voxels was used.Groups were compared by computing the mean of the summary statistic (contrast) for each group followed by a false discovery rate (FDR) corrected t-statistics (FDR < 0.05). Using FDR correction one is able to control the number of false positive voxels among the subset of voxels labeled as significant on multiple comparisons.Clinical MR images were viewed by two board certified neuroradiologists.Descriptive statistics were used to characterize the patient population. Data are presented as mean ± standard deviation (SD). To determine whether there are statistically significant differences of the behavioural data between the three groups  one-way analysis of variance ANOVA. Correlation was calculated using Sperson's linear Correlation. All statistics were performed using SPSS . Outliers were defined, in case the results would have been outside the group’s mean ± 2SD.DTVP-A, intellectual ability as well as attention capability, visual-perceptual and visual-motor abilities tested in the d2 test revealed no statistically significant differences between the three groups. Results are summarized in Table Since the stimulant medication history is a major confounder in imaging studies, all ADHD participants under medication were studied after they reached a steady state with the medication doses (min. 6 months after starting the medication). For the rest of the ADHD+ group the imaging examination was done after 1–11 years (mean 3.8 ± 3.3 year) since they got their diagnosis and started the medication. The ADHD− group had 8 newly diagnosed adults, the rest ranged between 1 to 22 years (mean 5.3 ± 7.0 year).The average response time is summarized in Table When correlating IQ, DTVP-A and motion displacement within the groups, no statistically significant correlations were found.t-values are listed in Table A list of activated brain regions of interest with respective In the Go/No-Go task ADHD+ group showed diminished activation and ADHD− group increased activation in the following anatomical regions: bilateral on inferior frontal gyrus, insula, postcentral gyrus, putamen, caudate, thalamus, anterior cingulate cortex (ACC), precuneus, globus ballidus and inferior temporal gyrus Fig. , Table 2The Stop task revealed differences on the brain activation pattern of the studies groups in inferior prefrontal cortex, inferior frontal gyrus, insula, caudate, putamen, thalamus and ACC Fig. , Table 3In Switch task ADHD− group revealed bilaterally increased activation on inferior frontal gyrus, insula, caudate, putamen, precentral gyrus and postcentral gyrus Fig. , Table 4The significant regions of BOLD contrast map of the population effect based on RFX analysis during the tasks of motor inhibition and cognitive switching are presented on Fig. When comparing Stop task activation between (1) controls and ADHD+, (2) controls vs ADHD− and (3) ADHD+ versus ADHD− groups, similar activation pattern was revealed in bilateral of inferior prefrontal cortex, inferior frontal gyrus, caudate, putamen, thalamus and ACC. Additionally, when comparing controls vs ADHD− and ADHD+ versus ADHD−, additional insula activation was shown.At Switch task comparison between (1) controls and ADHD+; (2) controls and ADHD− and (3) ADHD+ and ADHD−; bilateral activation of inferior prefrontal cortex, inferior frontal gyrus, insula, caudate, putamen, thalamus, ACC, precentral gyrus, inferior parietal gyrus, precuneus and superior parietal gyrus was detected. Additionally, between controls versus ADHD+ and controls vs ADHD− bilateral precuneus activation was detected, respectively. ADHD− group revealed hyperactivation pattern compared to the two other groups.This fMRI study demonstrated a statistically significant differences in the brain activation patterns during motor task inhibition and cognitive switching in adults with and without ADHD, as well as patients with ADHD under and without stimulant medication. These differences in activation regions of BOLD contrast maps based on the group comparison analysis were found at inferior prefrontal cortex, inferior frontal gyrus, insula, putamen and thalamus as well as at right caudate, right ACC, right pre- and postcentral gyrus. These brain regions are responsible in context of ADHD for cognitive skills such as attention, learning, regulating emotions, planning and executing motoric tasks as well as processing the information at working memory.Dopamine and noradrenaline play important roles in high-level executive functions in attention-deficit/hyperactivity disorder (ADHD) by influencing the fronto-striato-cerebellar circuits . ADHD isInhibitory control of behavior, composed of motor, emotional, cognitive, and social acts, as well as error detection are among the highest evolved human self-monitoring functions. Ontogenetically, motor response inhibition has been shown to develop progressively from childhood to adulthood . InhibitNeuroimaging studies of response inhibition have identified a set of regions, which are activated in conditions that require withholding a motor response with functional abnormalities in frontal striato-thalamic and fronto-parieto-cerebellar regions , 28, 35.Task switching is an executive function and tests cognitive flexibility that involves the ability to shift attention between one task to another. This ability allows a person to rapidly and efficiently adapt to different situations by inhibiting one response and executing an alternative one , 21. InfADHD subjects were comparable to controls in the speed of inhibiting a response and did equally correct the task performance compared to controls on fMRI Go/No-Go task. In the sub-group analysis, ADHD+ group showed generally decreased activation, whereas ADHD− group showed generally increased activation at Go/No-Go task. Also at the Switch task, ADHD− group showed hyperactivation compared to the two other groups, controls and ADHD+, respectively.The Stop signal paradigm  is a speInterestingly, behavioral parameters didn't vary statistically significantly between the three groups. However, Go/No-Go task together with Switch task produced the best contrast between the groups: medication-naïve ADHD subjects are showing hyperactivation compared to the ADHD+ and control groups, respectively. FMRI might add additional information to the brain activation pattern differences and possible treatment response of adults with ADHD. Information concerning the morphological substrate of ADHD might prove to be a toll that allows to monitor and quantify the effect of the medical treatment.Block designs have been criticized in the cognitive test literature , 29, 37.FMRI studies of motor task inhibition and cognitive switching—two important features in ADHD− demonstrate the potential to differentiate adults with ADHD and without ADHD, as well patients with ADHD under and without stimulant medication. FMRI opens possibly a new window for monitoring the therapeutic effect of ADHD medication. Further prospective validation studies are needed.


A variety of tools and methods have been used to measure behavioral symptoms of attention-deficit/hyperactivity disorder (ADHD). Missing data is a major concern in ADHD behavioral studies. This study used a deep learning method to impute missing data in ADHD rating scales and evaluated the ability of the imputed dataset  to distinguish youths with ADHD from youths without ADHD. The data were collected from 1220 youths, 799 of whom had an ADHD diagnosis, and 421 were typically developing (TD) youths without ADHD, recruited in Northern Taiwan. Participants were assessed using the Conners’ Continuous Performance Test, the Chinese versions of the Conners’ rating scale-revised: short form for parent and teacher reports, and the Swanson, Nolan, and Pelham, version IV scale for parent and teacher reports. We used deep learning, with information from the original complete dataset (referred to as the reference dataset), to perform missing data imputation and generate an imputation order according to the imputed accuracy of each question. We evaluated the effectiveness of imputation using support vector machine to classify the ADHD and TD groups in the imputed dataset. The imputed dataset can classify ADHD vs. TD up to 89% accuracy, which did not differ from the classification accuracy (89%) using the reference dataset. Most of the behaviors related to oppositional behaviors rated by teachers and hyperactivity/impulsivity rated by both parents and teachers showed high discriminatory accuracy to distinguish ADHD from non-ADHD. Our findings support a deep learning solution for missing data imputation without introducing bias to the data. Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset neuropsychiatric disorder with developmentally inappropriate inattention, hyperactivity, and impulsivity , 2. The Of the core symptoms of ADHD, hyperactivity and impulsivity are more readily observable than attention problems , 28. GivClinical interview with the child and their caregivers is the gold starndard for diagnosing ADHD. Direct observations of the child, neuropsychological and cognitive assessment , and the use of self-administered questionnaires completed by parents and/or teachers , 33 can A common methodological issue for data collection in a large survey-based or epidemiology study is missing data –43. TherMost of these proposed imputation techniques may bias study results , 52. InvDeep learning, a branch of machine learning methods based on artificial neural network (ANN), has been proposed in the early 1980s  but limiIn this study, we propose an approach based on deep learning to impute missing data in ADHD questionnaires. To the best of our knowledge, this work is the first to apply deep learning to clinical data imputation in ADHD. We combined multiple samples from our previous studies to increase the total sample size (N=1220) and used a deep learning approach to impute the missing data in parent- and teacher-rated ADHD scales. We expect deep learning to be able to impute missing values and generate a complete imputed dataset that resembles the original complete dataset (referred to as the reference dataset) as closely as possible in its ability to distinguish ADHD from TD children. In addition, through the process of this deep learning approach, we can rank the questions of the rating scales in terms of the ability of the machine to learn from the data and to predict the missing values accurately. We hypothesized that questions assessing hyperactivity-impulsivity behaviors, particularly from teacher reports, would have high imputation accuracy and discriminating ability based on previous studies suggesting that these symptoms are observable  and thatvia the help of school principals and teachers. All the participants and their parents were interviewed using the Chinese version of the Kiddie Epidemiologic Version of the Schedule for Affective Disorders and Schizophrenia  according to DSM-IV diagnostic criteria and 421 typically developing (TD) youths . The sample came from two separate studies – a longitudinal study of adolescent outcomes in children with ADHD aged 11-16 years (192 ADHD and 142 TD) conducted during 2006-2009 and a genetic, treatment, and imaging study of drug-naïve children and adolescents with ADHD aged 6-18 years (607 ADHD and 279 TD) conducted during 2007-2015. Youths with ADHD were recruited from the child psychiatric clinic in National Taiwan University Hospital (NTUH), Taipei, Taiwan. The TD youths without a lifetime diagnosis of ADHD were recruited from the same school districts as youths with ADHD rd edition (WISC-III)   and ConnISC-III) , respectISC-III) , 34 and ISC-III)  and teacISC-III) . These sISC-III) , 70–76. ISC-III) , we inclThe K-SADS-E is a semi-structured interview scale for a systematic assessment of both past and current mental disorders in children and adolescents. The Chinese version of K-SADS-E was developed by the Child Psychiatry Research Group in Taiwan , 78. To X (non-target) shows up. There are six blocks in CCPT, with three sub-blocks in each block. Each sub-block has 20-letter presentations. The sub-blocks differ in Inter-Stimulus Intervals (ISIs) of 1, 2, and 4 s, and the sequence of ISI conditions presents randomly. There are 12 indices covering different domains of CCPT performances: (1) Omission errors: the number of times the target is missed; (2) Commission errors: the number of times X is hit; (3) Variability: intra-individual variability in reaction time; (4) Perseveration: a reaction time less than 100 ms; (5) Detectability: the ability to distinguish X from non-X letters; (6) Reaction Time (RT): the period of time between the presentation of the stimulus and the response; (7) Hit RT Standard Errors (Hit RT SE): consistency of response time; (8) Response Style: a function of the ratio of hit target to hit non-target stimuli; (9) Hit RT change by blocks (Hit RT BC): the slope of change in RT over six blocks as the test progresses; (10) Standard error of Hit RT change by blocks (Hit RT SE BC); (11) Hit RT changed by ISIs (Hit RT ISI Change); (12) Standard error of Hit RT changed by ISIs (Hit SE ISI Change). Indices of CCPT can be grouped into several dimensions  FocThe Chinese SNAP-IV form is a 26-item scale rated on a 4-point Likert scale with 0 for not at all (never), 1 for just a little , 2 for quite a bit (often), and 3 for very much (very often). There are nine items for inattention (item 1–9) and nine items for hyperactivity/impulsivity (item 10-18) of the core symptoms of ADHD and eight items for the ODD symptoms according to the DSM-IV symptom criteria for ADHD and ODD . The psyThe Conners’ Rating Scales (CRS), developed in 1969, have been widely used for screening and measuring ADHD symptoms –86. We uWe combined samples from two studies on ADHD. A total of 1220 youths completed CCPT assessments, of which 787 (64.5%) had SNAP-IV parent form, 575 (47.1%) had SNAP-IV teacher form, 995 (81.6%) had CPRS-R:S, and 729 (59.8%) had CTRS-R:S, and 462 (37.9%) had all four rating scales see Table 1.The interior architecture we used here is deep neural networks (DNN), which stacked modules that have multiple hidden layers and many neurons . It is aDeep learning has raised several concerns about hyper-parameters, which affect the speed and quality of the learning process , 95. OneWe imputed missing data with the DNN analysis. After that, we conducted SVM classification  with theAt the end of each iteration, we conducted SVM classification to classify ADHD and TD and recorded the classification accuracy. The classification accuracy increased from 72% to 90% from the first iteration to the last iteration. Despite some differences in classification accuracy in the middle of the whole iteration process, the accuracy of all the models (with different hyper-parameters) increased throughout iterations and achieved similar accuracy at the end of iterations , estimated with different combinations of hyper-parameters, to classify between the ADHD vs. TD groups. We then conducted independent t-tests to compare the classification accuracy of each of these datasets to that of the reference dataset i.e., the original dataset for which all the four scales were complete . Results showed that different imputed datasets shared similar mean accuracy (0.89 to 0.90), which was not significantly different from the reference dataset (accuracy = 0.89) see Table 2.discrimination accuracy and were picked up by the machine early (35 items), (2) Bottom group: items that had low accuracy and did not become a target for imputation until other items with higher imputed accuracy were picked (35 items), and (3) Intermediate group (37 items).All the items  in the four scales were categorized into three groups by the imputation order .”Intermediate group: Most questions included in this group were about inattention e.g., “cannot pay attention,” “fails to finish work,” “disorganized,” “cannot concentrate,” “distractible,” “not reading up to par,” “poor in arithmetic,” and “forgets things he or she has already learned.” Several impulsive questions, such as “intrudes on others” and “blames others for his or her mistakes,” were also included in this group.Bottom group: Both parent and teacher reports on questions such as “talks excessively,” “only pays attention to things he/she is interested in,” “loses things,” and “makes careless mistakes” did not have high discriminatory accuracy. In contrast to the high accuracy of teacher reports of several oppositional behaviors, parent reports on these questions fell into this group.The current study, to the best of our knowledge, is the first work using deep learning to impute missing data in ADHD-related rating scales. Three main findings emerged. First, missing data can be imputed using deep learning with high accuracy and that the imputed dataset had a similar, high discriminatory ability to distinguish between the ADHD and TD groups compared to the complete original dataset. Second, our approach generated an imputation order of questions, demonstrating that teacher-reported oppositional symptoms and both teacher- and parent-reported hyperactivity-impulsivity symptoms were highly discriminating symptom clusters to distinguish between the ADHD and TD groups. Third, changing hyper-parameters in deep learning affects the analysis processes and results i.e., deep learning performance is sensitive to hyper-parameters. This study focuses on manipulating batch size, dropout rate, and early stopping. By changing these hyper-parameters, we partially verified some previous findings suggesting that batch size and early stopping have a large effect on processing time and that the dropout rate is the most relevant hyper-parameter for predictive power –99, 104.With our approach, the missing data can be imputed using deep learning, and the imputed dataset has the same high discriminatory ability as the original complete dataset . Our results provide strong evidence to support that our imputation not only generated the dataset without missing values but also kept the imputed and reference datasets consistent. Of note, one novelty of this deep learning approach is that we let the machine impute missing data for the whole sample combining the ADHD and TD groups. Previous studies typically only focus on one group imputation at a time, given concerns about biasing the subsequent analyses when combining the case and the control groups during the imputation process , 106. FoThis study imputed 45,229 missing values. Each question has a different amount of missing data. About 200 participants had one or two missing questions, and more than 600 participants had missing data in some questions of the four scales. Our result showed that there is no relation between the order of missing data imputation and the amount of missing data in the questions. Even though the original dataset has about 60% missing items across the four scales, our finding showed that the machine could still learn from this dataset. What matters more is to have questions that are highly discriminative between ADHD and non-ADHD. If a question lacks discriminative ability but has minimal amount of missing data, our algorithm would select another question that has higher discriminative ability because the machine would always pick the best feature in each iteration. Our classification accuracy between the ADHD and TD groups increased rapidly at the beginning of the iteration after the missing values for the highly-discriminative questions were imputed. At the end of the iteration, the imputed dataset had the same classification accuracy and distribution compared to the original complete dataset (reference dataset). The most critical issue of data imputation is the bias that it may introduce, ultimately affecting the inferences that can be drawn from the analysis conducted with the imputed dataset. We also compared our classification accuracy with other imputation methods . Results indicated that deep learning approach have higher accuracy than traditional statistical imputation methods . We also conducted analyses without the ODD symptoms  in deep learning may affect the performance of the algorithm. We processed with different batch sizes  to evaluate the outcomes of the discriminatory accuracy, a hot topic in the deep learning field –99, 104.There are several methodological limitations in our study. First, due to the architecture based on the deep learning approach, the larger the size of data is, the more thoroughly the machine can learn. Although our sample size is more than 1,000, this may not be sufficiently large for deep learning. However, for the clinical data with excellent quality and internal validity collected from a single site, our sample size is rather large. Second, our imputation approach combined the ADHD and TD groups, resulting in the machine having to learn more varying values in each feature with a limited sample size. Hence, future research with larger sample sizes is also warranted in this aspect. Third, although our imputed dataset had the same accuracy as the original complete data in classifying the ADHD and TD groups, there is no guarantee that the imputed values are “accurate.” Indeed, our results showed that the predictive power  decreased over time with iterations. This suggests that the machine performed poorly for some items, especially when imputing missing scores for the bottom third of the questions. Fourth, although we used the clinician’s diagnosis (which is based on observations and interviews of the patient as well as interviews with the parent/caregiver) as the outcome to evaluate the effectiveness of imputation and parent questionnaires as part of the features for imputation, the shared variance from parent reports either through answering questions from the clinician or as self-response to the questionnaires is a methodological limitation and potential confounder. Future investigation is warranted to add more features from other informants, e.g., participant’s self-reports, peers reports, or other objective measures. Lastly, although we designed flexible neuron size of each hidden layer to adapt to the number of increasing neurons needed for each input layer, as the number of hidden layers is static, it might lead the last few iterations of imputed output layer to over converge than expected.We present a novel approach to impute missing data in ADHD rating scales based on deep learning using participants’ neuropsychological data and ADHD-related behaviors assessed with four scales reported by parents and teachers. Our deep learning approach can impute missing data with both the case and control groups together in the dataset. Our findings provide evidence that our deep learning approach can impute missing data with high accuracy in an aggregated dataset from multiple samples and thus can increase the size of the dataset while maintaining the characteristics and representativeness of the data’s original distribution.The raw data supporting the conclusions of this article will be made available by the corresponding author only if this request is approved by the Research Ethics Committee of National Taiwan University Hospital, Taipei, Taiwan, according to the current regulation of patient protection in Taiwan.ClinicalTrials.gov number: NCT00529906 (NSC96-2628-B-002-069-MY3), NCT00916786 (NSC98-2314-B-002-051-MY3), and NCT00417781 (NHRI-EX94~98-9407PC).The datasets used in the analysis were collected using the grants support to the corresponding author (SS-FG) mentioned in the acknowledgments section. The data are available upon request. The studies were preregistered at The studies involving human participants were reviewed and approved by the Research Ethics Committee of National Taiwan University Hospital, Taipei, Taiwan . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.Author contributions included conception and study design (C-YC and SS-FG), data collection and acquisition (SS-FG), statistical analysis , interpretation of results , drafting the manuscript work  and revising it critically for important intellectual content , and approval of the final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neuropsychiatric disorder and may persist into adulthood. Working memory and attention deficits have been reported to persist from childhood to adulthood. How neuronal underpinnings of deficits differ across adolescence and adulthood is not clear. In this study, we investigated gray matter of two cohorts, 486 adults and 508 adolescents, each including participants from ADHD and healthy controls families. Two cohorts both presented significant attention and working memory deficits in individuals with ADHD. Independent component analysis was applied to the gray matter of each cohort, separately, to extract cohort-inherent networks. Then, we identified gray matter networks associated with inattention or working memory in each cohort, and projected them onto the other cohort for comparison. Two components in the inferior, middle/superior frontal regions identified in adults and one component in the insula and inferior frontal region identified in adolescents were significantly associated with working memory in both cohorts. One component in bilateral cerebellar tonsil and culmen identified in adults and one component in left cerebellar region identified in adolescents were significantly associated with inattention in both cohorts. All these components presented a significant or nominal level of gray matter reduction for ADHD participants in adolescents, but only one showed nominal reduction in adults. Our findings suggest although the gray matter reduction of these regions may not be indicative of persistency of ADHD, their persistent associations with inattention or working memory indicate an important role of these regions in the mechanism of persistence or remission of the disorder. It is a highly heterogeneous disorder with three clinical presentations recognized in DSM-5 edition5, and diverse neurocognitive impairments7, comorbid disorders, etc9. Predominant features of adults with ADHD also differ from that of children with ADHD. For instance, adults with ADHD were more affected by inattention than hyperactivity7, while children presented symptoms in both inattention and hyperactivity domains. Adult ADHD had a higher rate of comorbidity than children ADHD10, complicating diagnosis11. Children with ADHD were affected in more diverse cognitive domains19 than adults, whose working memory impairments were most frequently documented20. Working memory deficits in ADHD have been consistently reported for both adolescents and adults22. Yet, the underlying neural mechanisms for the different presentations of children and adults with ADHD, as well as consistent presentations, remain elusive. The focus of this study is to investigate gray matter (GM) variation in relation to attention and working memory deficits in both adolescents and adults with ADHD24.Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neuropsychiatric disorder characterized by inattention, and/or hyperactivity and impulsivity. Recently ADHD persistence into adulthood has been observed in 15–50% of cases27 and cerebellum28, followed by regions in the frontal, parietal, temporal cortex31. Studies of adult patients with ADHD, relatively sparse compared to children, have shown GM alterations in less but more specific brain regions, with the cerebellum and frontal cortex33 being reported more consistently than subcortical regions35. How these regions GM reduction links to inattention symptom or cognitive deficits is not entirely clear. Castellanos et al. showed that in children and adolescent patients with ADHD, GM volumes of frontal and temporal lobes, caudate, and cerebellum were negatively correlated with the overall score of illness severity, and particularly, attention problems31. However, Jacobson et al. showed that GM reduction in the frontal, temporal and parietal lobes were more associated with hyperactive/impulsive symptoms than inattention36. Assisted by functional MRI studies using attentional, working memory, reward and inhibition tasks, these regions have been separated into different networks, fronto-striatal, fronto-parietal and fronto-cerebellar networks8, interactively subserving functions of reward processing, working memory, orientation of attention, executive control37, lending support to the brain networks based analyses. Furthermore, the structure and function of the networks change with age38, which promotes direct comparison of the brain networks between children/adolescents and adults with ADHD. Any knowledge gained on the network characteristics and relationship of adolescent and adult ADHD might shed light on the mechanism of persistence or remission of the disorder39.Studies of children and/or adolescents have reported GM reduction in widespread brain regions, while the most common effects are in the subcortical regions39. In particular, brain networks serving ‘top-down’ cognition as working memory or attention largely experience neural normalization; i.e., early anomalies in brain structure and function disappear in remitted brains, whereas persistence is linked with persisting neural anomalies39. When comparing brain GM networks  associated with attention or working memory between adolescents and adults with ADHD, we hypothesize persistent brain network associations will present at large. As the brain changes much more dramatically in adolescents than adults, we also hypothesize that GM networks of adolescents and adults will comprise different but overlapping brain regions.As reviewed by Sudre et al., the remission (otherwise persistence) of ADHD can be modeled as three processes, including ‘neural normalization’, ‘neural reorganization’, and ‘fixed anomaly’, which are not exclusive and might occur in different brain networks42 and functional MRI data45 to extract brain networks. When applied to gray matter density maps as we did in this study, it surveys the whole brain without preselection of regions of interest, and utilizes voxels GM variation patterns across participants to group voxels with similar or related patterns into components, forming coherent GM networks. Thus, ICA is a data-driven network-based analysis. This work utilized ICA to extract GM networks of two age groups separately, reflecting inherent GM network configuration of their own. Then we tested GM networks’ associations with inattention and working memory in their own group, and cross evaluated for the other group. Our previous study has analyzed the adult group and reported five GM networks of interest24. Here we extended into the adolescent group and focused on the comparison and relationship between the two age groups. The findings of this study will answer whether persistent brain networks underlie persistent attention and working memory deficits in ADHD.To test our hypotheses, we applied independent component analysis (ICA) to GM of two age groups, adults and adolescents. ICA has been used for various types of data, including both structural46, and the NeuroIMAGE project47. The IMpACT project recruited adult participants with ADHD and healthy controls, while the NeuroIMAGE project recruited participants from ADHD families (including probands and siblings) and healthy controls in childhood and then followed them up. Data used here were from the 1st MRI scan with some participants in adolescence and some in adulthood. The inclusion and exclusion criteria were described in the original papers47. In brief, the IMpACT ADHD participants met DSM-IV-TR criteria for ADHD in adulthood as well as childhood retrospectively. The NeuroIMAGE participants met DSM-IV-TR criteria for children or adult ADHD, and adult participants also had a formal and research diagnosis in childhood. All participants had IQ ≥ 70, no diagnosis of autism, epilepsy, general learning difficulties, brain disorders, and genetic disorders (such as Fragile X syndrome or Down syndrome). The Dutch chapter of IMpACT study was approved by the regional ethics committee . Written informed consent was obtained from all participants. The NeuroIMAGE study was approved by the regional ethics committee  and the medical ethical committee of the VU University Medical Center. Written informed consent for every participant was obtained. For children 12–18 years old, both parents and children gave consent. For children younger than 12, parents gave consent for their children.This study analyzed data from two ADHD projects: the Dutch chapter of the International Multicentre persistent ADHD genetics CollaboraTion (IMpACT) consortiumN = 486, age ≥ 18, including participants of IMpACT and NeuroIMAGE) and adolescent samples . A summary of participant demographics is shown in Table In order to compare adults with adolescents, the participants of IMpACT and NeuroIMAGE projects were regrouped into adult samples . In the adult group, participants with ADHD had significantly lower scores in both forward and backward tests than controls . Similarly, adolescent participants with ADHD also presented significantly lower forward and backward scores .Two ADHD domains, inattention and hyperactivity/impulsivity, were assessed for all participants. NeuroIMAGE used the Schedule for Affective Disorders and Schizophrenia—present and lifetime version, and Conners Teacher Rating Scale—1997 Revised Version: Long Form. IMpACT used the Diagnostic Interview for Adult ADHD. The symptom scores for both domains were counted following the 18 DSM-IV questions, with the score in each domain ranging from 0 to 9. Healthy controls had scores less than 2 in either domain. Unaffected siblings from patient families had scores fewer than 5 or 6 in either domain for adult or adolescent participants, respectively. Both IMpACT and NeuroIMAGE projects conducted WAIS Digit Span test49) were generated for further quality check. All good quality images were segmented into six types of tissues using Statistical Parametric Mapping 12 , followed by normalization to the Montreal Neurological Institute space, modulated, and smoothed with a 6 × 6 × 6 mm3 Gaussian kernel. The only difference during the preprocessing of adult and adolescent data was the tissue probability map templates used for segmentation, where SPM12 templates were used for adults and age-specific tissue map templates generated by TOM tool50 were for adolescents. Further analyses on segmented GM images were done separately for adults and adolescents, including selecting individual maps that had correlations with the group mean GM hinger than 0.8, generating a GM mask for each group to include voxels with group mean GM volume larger than 0.2, and regressing out effects of age, sex, and site using a linear regression model for each included voxel. The GM mask of adults was slightly different from adolescents, and comparison analyses were applied only to the common voxels .T1-weighted MRI images were acquired with three 1.5T scanners with closely matched settings. Thorough quality control was applied to the imaging data as previously described, including selecting the better one from the two sessions. MRI quality indexes , we added medication status (used vs. not used) as a fixed effect in the linear models. Comorbidity with major depression and anxiety were also tested by adding them separately as a covariate into the linear models. We did not control for IQ, because it can remove ADHD related variance51. Note that for both directions of cross-evaluation , we applied false discovery rate (FDR) p < 0.05 to control for multiple comparisons  on the discovery results, and uncorrected p < 0.05 for evaluation of the projected results.A linear mixed model with family structure as a random effect and diagnosis as a fixed effect was applied onto GM loadings of cases and controls to test case vs. control differences . Similarly, linear mixed models with symptom score or working memory score as a dependent variable, GM loading, age, and sex as fixed effects and family as a random effect were applied to test GM association with symptoms or working memory using all participants  in the group. Given that medication can affect both brain and behavior, we first compared GM loadings between medicated cases and unmedicated cases. If there was a significant difference  and adolescents (12–17 years old). Giedd et al. have reported GM in the frontal lobe increased during preadolescence with a maximum size occurring at the age of 12.1 years for males and 11.0 years for females, followed by a decline during postadolescence52. So, we chose 12–17 years old as a homogenous adolescent group.Given the large age range of participants, particularly in adults, we performed additional analyses to enhance the stability of test results. First, we selected participants with age between 18 and 40 years old, performed a separate ICA and compared the resultant components with those derived from full adult samples reported in ref. 24. Additionally, we tested possible confounding effects from three MRI quality metrics , using two regression models: (1) working memory or symptom = GM loading + age + gender + family (random effect) + three MRI quality metrics, and (2) GM loading = case/control + family + three MRI quality metrics. The new test results agreed with previously reported association results . Given the high consistency, we continued our analyses with the full-sample derived components previously reported.In the previous study we have reported five GM components in adults significantly associated with working memory, inattention, or diagnosis. The analyses and results of our previous study are summarized in the p > 0.05), while Component 5 showed a significant medication effect. Thus, the association results of Component 5 were computed with a linear model controlling for medication. In adolescents, Components 1–4 showed significant GM increases in medicated cases compared to unmedicated cases (p < 0.05). Thus, their association results were from linear models controlling for medication. Component 5 showed no medication effect and no case vs. control difference. No significant comorbidity effect was observed for these components in both adults and adolescents.After we projected the five components into adolescents, their associations with working memory and inattention in both adults and adolescents were listed in Table p < 0.05), while adult patients showed GM reduction in two components (one passed FDR and one had p < 0.05).Across adults and adolescents, three GM components . After projection to adults, no medication effects were observed. Thus, medication effect was controlled only for adolescents. As shown in Fig. Twenty components were extracted from the adolescent data of which twelve were highly similar to those in adults . But the adolescent component had more areas in insula and inferior frontal gyrus and less in the caudate nucleus relative to the adult one. As listed in Table p < 0.05), while the adolescent Component B showed consistent significant associations with working memory in both groups and significant GM reduction in adolescents.Literatures have shown that subcortical regions like the caudate nucleus and putamen were significantly reduced in ADHD patients and the reduction seemed to diminish over time from childhood to adulthoodp < 0.05.Due to the large age range of each group, we replicated association tests for these eight components using relatively homogenous group settings . The results (Supplementary Table p > 0.05). In adults, siblings presented no clear patterns. Similar results were observed when we examined only unmedicated patients . In the 20 components extracted from adolescents, 12 were highly similar to those in adults, supporting continuation of brain structural segmentations. The differences between similar components and the totally different components likely indicate developmental effects on similar regions of brain, as well as disorder progress effect, to form the unique constellation to each group. To ensure fair comparisons between the two age groups, we conducted the network discovery-and-verification two-step approach in both directions: from adults to adolescents and from adolescents to adults. Additionally, adolescent patients with ADHD will inevitably include both those likely to persist and those likely to remit. This mixture will most likely contribute to the different GM patterns between the adolescent group and the adult group that included only persistent ADHD patients. Thus, our discussion focuses on consistent findings between the two age groups.57, involving integrating sensory information, allocating neural resources, maintaining and shifting attention, and updating and manipulating information60. Better working memory performance has been found to correlate with greater GM volume in widespread brain areas, including the superior, middle, and inferior frontal gyri62. Interestingly, a recent large sample study (1336 young adults) on the relationship between regional GM and cognition reported that the strongest association between GM and working memory was in the insula, which was the only region significantly associated with working memory after controlling for total brain volume62. The longitudinal study led by Biederman et al. suggested cognitive impairments in ADHD originating in childhood persisted into adulthood63. Our results suggest that not only working memory deficits persisted from childhood and adulthood, but also did the neural correlates of working memory deficits, highlighting the frontal regions and insula.For the four components associated with working memory (three adult components and one adolescent component passing multiple comparison correction), three components showed consistent associations across childhood to adulthood, including the superior, middle, and inferior frontal regions (adult IC 2–3), and the insula, inferior frontal, superior temporal gyri and caudate (adolescent IC-B). The prefrontal regions are well documented for their role in working memory as a central executive point66.Two components (adult component 4 and adolescent component A) were significantly associated with inattention across both age groups, with more GM volume related to lower inattentive symptom scores. Component 4 mainly consisted of bilateral cerebellar tonsil and culmen, and Component A consisted of left hemisphere cerebellar tonsil and culmen, lingual gyrus, and cuneus. These two components were spatially similar with a correlation of 0.49, overlapping mostly in left hemisphere cerebellar regions. The differences likely reflect different developmental trajectories of left and right cerebellar hemispheres in adolescence. Both adult and adolescent components support the involvement of the cerebellum in ADHD, consistent with repeatedly reported cerebellum volume reduction as the most stable brain alterations observed in ADHD patients68, supported by cognitive impairment in patients with cerebellum lesions69, prevalent activation during cognitive functions70, anatomic connections between the cerebellum and cortical regions72, and functional co-organization with cortical regions74. In ADHD patients, the functional impact of cerebellum GM alteration is less studied, with the potential to affect neurological soft signs76, attention process as part of executive function68, or both as these two could be linked78. Our findings add to the literature and emphasize a specific role of the cerebellum in the attention process, and its persistence from childhood to adulthood.The role of the cerebellum in cognitive function has been recognized38: caudate volume reduction in younger ADHD patients (age 8–15), not in middle age patients (age 15–22), and reversed in older patients (age 22–30). Our age grouping and processing approach resulted in the components that included partial caudate and interrelated insula79 and cortical regions80, not a typical whole caudate region. Nevertheless, the comparison of the two components that include the caudate nucleus advocates the role of the insula and inferior frontal regions in working memory processes, as more consistent and more significant associations with working memory were observed for the component with more insula and inferior frontal region.A detailed look at the components including the caudate nucleus appeared to show that some caudate areas were integrated together with the insula, inferior frontal, and superior temporal regions for both adults and adolescents. An additional analysis using GM data without voxel-wise regression of age effect extracted a more complete/dominant caudate component  GM reductions in adolescent patients, and only one component (IC 4) showed comparable levels of reduction in adults. Moreover, we also observed more prevalent medication effects on GM in adolescents than in adults. Among the seven components with nominal or significant GM reduction in adolescents, six showed that medication mitigated the reduction in patients. Altogether these findings agree with previous studies reporting GM reduction in ADHD patients diminished with age, particularly in subcortical regions25. Our results expand the affected regions into the frontal and cerebellum regions. One note is that medication could, at least, partially contribute to the observed diminished GM reduction with age.In contrast to the consistent results discussed above, we found that GM reductions observed in adolescent ADHD patients were largely not replicated in the adult group. The two components, IC-A and IC-B showed significant GM reductions in adolescent patients (passing multiple comparison correction), but not in adults. The other five components ICs 1–4 and 6 showed nominal  largely diminishes in adulthood. This phenomenon, beyond subcortical regions, could be partially contributable to medication and development (including disorder progress). Although the GM reduction of these regions is not indicative of persistence of impairments, their persistent associations with inattention or working memory suggest an important role of these regions in the mechanism of persistence or remission of the disorder. ADHD has been described as a disorder of executive functionFindings of this study should be interpreted with consideration of the following limitations. First, the age ranges of both groups are large. To mitigate the heterogeneity, we have tested relatively homogenous age groups (adolescents of 12–17 years old and adults of 18–40 years old). Similar findings as discussed above were observed. Second, our analytical approaches focus on the comparison of two age groups with age regression specific to each group. This approach leads to common patterns across the age range within each group, suitable for group comparisons. However, the dynamic developmental trajectories, like the one in the caudate nucleus, are missed. Third, ICA segments brain regions with coherent patterns together, including those with opposite patterns as shown in blue in Figs. Supplementary Materials


Attention Deficit Hyperactivit Disorder, more simply known as ADHD, is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. There are three major presentations: primarily hyperactive and impulsive, primarily inattentive, and combined type , a research protocol in which students are placed in a classroom environment where they are medicated and their behavior is evaluated. In her contribution, Rinker et al. postulated that ADHD affects academic functioning regardless of education level; Thus, LSPs are applicable to ADHD regardless of education level. In LSPs, medication is given at the research site to control for temporal differences in application, elucidating on the large variation in dose-response to MPH in ADHD patients. Wigal et al. reported that placebo effects are reduced when research environments mirror situations in which symptoms would be aggravated and when medication administration is tightly controlled, altogether aiding in the establishment of more objective ADHD diagnostic tests.While these disparities in dose-response are puzzling, many techniques may shed light on these issues. In her contribution, Medel et al. contributed that the differential expression of α1 and α2 norepinephrine (NE) receptors can induce heterogeneous spatial modes, a shifting of “brain states.” Thus, suggesting that there is a dynamic shaping of brain activity by neuromodulations through the heterogeneous expression of α1 and α2 NE receptors that creates spontaneous fluctuations in cognition, elucidating on data previously established by Shine et al.  as the most objective ADHD diagnostic test to date. However, promising research regarding norepinephrine receptors could lead to objective diagnosis even outside of simulated classroom environments like the LSP and exams like the PERMP. In their commentary on van den Brink et al. , Medel ee et al. . In her While MPH and behavioral therapies are available, there are prevention techniques which may help to reduce the development of ADHD altogether. Tapping the black box of ADHD on its translational face, previous reports utilizing animal models showed that the application of MPH alone and/or with enriched environment (EE) on adult offspring rescues the detrimental effects of prenatal stress attentional reactivity (Zubedat et al., Taken with these results, MPH has clinical applications outside of ADHD. Acute MPH improves cognitive ability in those with and without ADHD (Linssen et al., In essence, adherence issues with MPH treatment in those with ADHD may be rooted in acute increases in anxiety, non-responders, and misdiagnosis. These adherence issues could be mediated by assuring patients anxiogenic effects are temporary. In order to accurately diagnose patients with ADHD, we must focus on discovering and implementing objective diagnostic measures such as pupillometry. In order to decrease ADHD diagnosis overall, we can increase research on potential prevention methods such as environmental enrichment, as well as on the effects of adverse life events, such as prenatal stress, on attention.AA conceptualized and wrote the article. IM and DC consulted and edited the paper.Independent of this editorial DC has received research funding from Takeda/Shire, honoraria from Takeda/Shire, Medice and Servier. The remaining 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.


Background: Attention-deficit hyperactivity disorder (ADHD) is one of the most common behavioral disorders in childhood and adolescence associated with relevant psychosocial impairments. The basic pathophysiology of ADHD may be related, at least partly, to a deficit in autonomic arousal processes, which not only influence core symptoms of the disorder, but may also lead to blood pressure (BP) deviations due to altered arousal regulation. Objectives: This study examined long-term changes in BP in children and adolescents with ADHD up to young adulthood. Methods: In children and adolescents aged between 7 and 17 years at baseline, we compared BP recordings in subjects with  and without  ADHD over a 10-year follow-up using data from the nationwide German Health Survey for Children and Adolescents (KiGGS). Propensity score matching was used to improve the comparability between children in the ADHD and control groups with now n = 1.190 in each group. Results: The results of these matched samples revealed that study participants with ADHD showed significantly lower systolic BP  and diastolic BP  at baseline. In a sensitivity analysis with a smaller (n = 272) and more stringently diagnosed ADHD group, the significant differences remained stable with somewhat higher Cohen’s d; i.e., 0.25 and 0.27, respectively. However, these differences did not persist after 10-year follow-up in a smaller matched longitudinal sub-group , as subjects with and without ADHD had similar levels of systolic  and diastolic BP . Conclusions: At baseline, children and adolescents with ADHD had significantly lower BP  compared to the non-ADHD group, whereas this difference was no longer detectable at follow-up ten years later. These developmental alterations in BP from adolescence to early adulthood may reflect changes in the state of autonomic arousal, probably modulating the pathophysiology of ADHD. Attention-deficit hyperactivity disorder (ADHD) is one of the most common behavioral disorders of childhood and adolescence, affecting 5% of school children and 3% of adults ,2,3. InaVarious studies have shown that in ADHD both the peripheral arousal and the cortical arousal are decreased and unstable ,27,28. HThough a multifactorial etiology is suspected in ADHD , in the Using the baseline data of the epidemiological, community-based, observational German Health Interview and Examination Survey for Children and Adolescents (KiGGS) survey, we previously reported a significantly lower systolic, diastolic, and mean arterial BP in children (11–17 years) diagnosed with ADHD as compared to a control group . Furthern = 17,641 participants in 167 cities and municipalities in Germany [n = 10,937). A total of 10,853 subjects from the baseline survey cohort also participated in Wave 2, corresponding to 61.5% of the participants in the baseline study. In Wave 2, physical examinations were again performed, in addition to written and oral interviews [n = 2907).Our analysis is based on data from the KiGGS study, a nationwide and representative survey conducted by the Robert Koch Institute, Berlin, Germany, in several waves. Between 2003 and 2006, representative data on the health status of children and adolescents aged between 0 and 17 years were collected in the KiGGS baseline survey using a random sample of  Germany ,39. In t Germany ,41. KiGGterviews . For theterviews . Due to The group-related design and flowchart of our study can be seen in n = 603, 5.5%). Participants with a score of equal to or greater than seven on the hyperactivity/inattention subscale of the Strengths and Difficulties Questionnaire (SDQ) were classified as suspected cases  [n = 1219, 11.1%, including n = 272 children diagnosed with both ADHD and SDQ ≥ 7), while n = 9741 (88.9%) subjects were considered as non-ADHD controls.Participants were included in the group of confirmed ADHD cases if a corresponding pre-diagnosis, at that time in Germany on the basis of the International Classification of Diseases 10 (ICD-10)/Diagnostic and Statistical Manual of Mental Disorders 4 (DSM-4)), given by either a physician or psychologist, had been reported by the parents at the beginning of the study inclusion  ,38,44. T, 8.10%) ,46. The , 8.10%) . In our In all participants in the KiGGS study aged between three and 17 years, systolic and diastolic BP recordings, as well as arterial mean pressure and heart rate were determined at two recordings by means of an oscillometric measurement using an automatic, digital BP monitor . The measurements were performed on the right arm, the subject in a seated position, having rested for five and seven minutes, respectively. The right elbow was positioned at the level of the heart and a BP cuff was selected which covered two thirds of the unclothed upper arm of the proband .t-tests for continuous variables. Propensity score matching was used to improve the comparability between the ADHD and control group. The propensity score matching approximates the counterfactual scenario by creating statistically matched groups of individuals that differ solely in their classification to the ADHD group, while the observed covariates/confounders were kept similar [p < 0.05 in all tests. Effect sizes were calculated in the form of Cohen’s d for t-tests, Cohen’s ω for chi-square tests, and odds ratios/Exp (ß) for logistic regressions. As sensitivity analysis, we compared participants in the control group and participants with more stringent ADHD, namely who fulfilled both criteria of pre-diagnosed ADHD and SDQ-H ≥ 7, to strengthen our findings  participants diagnosed as suffering from ADHD by a clinician or psychologist, and n = 888 (8.1%) participants fell into the suspected ADHD category with their score from the SDQ hyperactivity-inattention subscale ≥ 7. The diagnosed ADHD and suspected ADHD group together consisted of the ADHD risk group in the current study , while the rest of the participants  were considered as the control group. As shown in the left panel of t(10958) = −6.997, p < 0.001, Cohen’s d = 0.21), less likely to be female  = 264.440, p < 0.001, Cohen’s ω = 0.16), from lower socioeconomic background (t(10651) = −9.528, p < 0.001, Cohen’s d = 0.29), have lower body-mass index (t(10908) = −2.676, p < 0.001, Cohen’s d = 0.08), and similar level of average heart rate (t(10905) = −0.408, p = 0.683, Cohen’s d = 0.01). The upper panel of t(2378) = −0.137, p = 0.891, Cohen’s d = 0.01), sex ratio  = 0.002, p = 0.963, Cohen’s ω = 0.002), socioeconomic background (t(2378) = −0.108, p = 0.912, Cohen’s d = 0.004), body-mass index (t(2378) = −0.253, p = 0.800, Cohen’s d = 0.01), and heart rate (t(2378) = 0.575, p = 0.565, Cohen’s d = 0.02). The distributions of the participants’ propensity scores for ADHD risks after matching are presented in the bottom panel of Among the total KiGGS study cohort aged 7 to 17 years , there were t(10935) = −8.349, p < 0.001, Cohen’s d = 0.25; for diastolic BP, t(10935) = −7.572, p < 0.001, Cohen’s d = 0.23; for pulse pressure, t(10935) = −4.533, p < 0.001, Cohen’s d = 0.14, t(1190) = −4.267, p < 0.001, Cohen’s d = 0.17; for diastolic BP, t(1190) = −3.859, p < 0.001, Cohen’s d = 0.16; for pulse pressure, t(1190) = −2.192, p = 0.028, Cohen’s d = 0.09, p < 0.001), diastolic BP (exp(ß) = 0.969, 95%-CI 0.960–0.978, p < 0.001), and pulse pressure (exp(ß) = 0.983, 95%-CI 0.974–0.992, p < 0.001) were all independently and significantly associated with ADHD in separate models. In the matched sample of 2380 participants, the differences in systolic and diastolic BP, as well as the pulse pressure, among participants in the ADHD and control group were smaller but remained statistically significant  = 0.984, 95%-CI 0.976–0.991, p < 0.001; for diastolic BP, exp(ß) = 0.979, 95%-CI 0.968–0.990, p < 0.001; for pulse pressure, exp(ß) = 0.989, 95%-CI 0.978–0.999, p = 0.029, p < 0.001), diastolic BP (exp(ß) = 0.973, 95%-CI 0.961–0.985, p < 0.001), and pulse pressure (exp(ß) = 0.985, 95%-CI 0.973–0.997, p = 0.016), whereas the majority of the confounders were no longer significantly associated with ADHD.In the KiGGS baseline cohort of 10,960 participants, systolic and diastolic BP, as well as the pulse pressure were all significantly lower among participants in the ADHD group as compared with those in the control group  of the matched sample had BP measurements taken at KiGGS follow-up ten years later. The upper panel of t(594) = 0.153, p = 0.879, Cohen’s d = 0.01), sex ratio  = 0.610, p = 0.435, Cohen’s ω = 0.04), socioeconomic status (t(594) = 0.950, p = 0.342, Cohen’s d = 0.08), body-mass index (t(594) = 0.407, p = 0.684, Cohen’s d = 0.3), and heart rate (t(594) = 1.146, p = 0.252, Cohen’s d = 0.15). At follow-up, the differences in systolic and diastolic BP, as well as the pulse pressure among those participants in the ADHD and control group were no longer significant at the descriptive level  = −1.811, p = 0.071, Cohen’s d = 0.15; for diastolic BP, t(594) = −1.927, p = 0.055, Cohen’s d = 0.16; for pulse pressure, t(594) = −0.646, p = 0.519, Cohen’s d = 0.05). In contrast, the baseline results of this reduced sample size (left panel of p = 0.005) and diastolic BP (exp(ß) = 0.967, 95%-CI 0.942–0.992, p = 0.009), respectively, and ADHD were significant when the confounders were controlled for. Hence, the associations at baseline remained significant regardless of sample size shrinkage due to technical matching or follow-up measurement , suggesting the reverse relationships between ADHD and systolic or diastolic BP, but not pulse pressure, at a younger age. The middle and bottom panels of p = 0.337), diastolic BP (exp(ß) = 0.993, 95%-CI 0.968–1.018, p = 0.567) and pulse pressure (exp(ß) = 0.991, 95%-CI 0.968–1.014, p = 0.423) were all not significant. However, there might be a tendency to a somewhat higher heart rate in ADHD at follow-up.About one-fourth of participants , which had either only an SDQ-H ≥ 7, or only an ADHD prediagnosis, was excluded and only the cases with both characteristics were considered . The control group  remained the same. Again in this calculation, systolic and diastolic BP and pulse pressure were significantly lower in participants in the ADHD group compared with the control group at baseline  = 5.0016, p < 0.001, Cohen’s d = 0.31; for diastolic BP, t(9990) = 3.9492, p < 0. 001, Cohen’s d = 0.24; for pulse pressure, t(9990) = 3.2898, p = 0.001, Cohen’s d = 0.20  = 3.1451, p = 0.001, Cohen’s d = 0.27; for pulse pressure, t(527) = 1.1904, p = 0.234, Cohen’s d = 0.10  = 0.963, 95%-CI 0.939–0.986, p = 0.002; for pulse pressure, exp(ß) = 0.987, 95%-CI 0.966–1.008, p = 0.234, p < 0.001) and the diastolic BP (exp(ß) = 0.948, 95%-CI 0.923–0.974, p < 0.001) were observed (n = 58 participants (0.5%) in the stringent ADHD group and n = 73 participants (0.67%) in the control group had available BP measures. Among this small subsample, we could not detect any significant difference in BP between participants in the stringent ADHD and control groups, as shown in In comparison to the calculation described in When children with ADHD are treated with stimulants, they often show an increase of their usually low BP concurrently with symptom improvement ,52,53,54n = 9741 controls, n = 1219 ADHD) compared to our previous study [n = 1219 controls vs. n = 667 ADHD), it was shown that systolic and diastolic BP recordings within the ADHD group were significantly below the corresponding values from the non-ADHD control group at baseline, whereas heart rate was similar. In order to improve the comparability of the two groups, we also used an additional statistical tool, namely the propensity score matching technique. The results from this statistical approach demonstrated that systolic and diastolic BP recordings were significantly lower in ADHD patients compared with non-ADHD controls both before and after matching. These findings were observed in both univariate models and multivariate models with age, gender, socioeconomic status, and BMI as confounders and were strengthened by similar results of an additional sensitivity analysis with more stringent ADHD criteria and a smaller ADHD group size (n = 272). Thus, according to the arousal hypothesis, the lower BP in ADHD should indicate a subcortical hypoarousal of their brain [Even after broadening the age spectrum to 7 to 17 years, and therefore including a larger sample size , but only of small effect sizes. The slight decrease of effect sizes after matching suggests that findings on autonomic arousal in ADHD, which did not thoroughly consider confounders, should be interpreted with caution.It is noteworthy that the differences between ADHD and controls on systolic and diastolic BP were of high statistical significance  cross-sectional meta-analysis of magnetic resonance imaging (MRI)-based brain volume differences comparing children and adults with vs. without ADHD. There were significantly smaller volumes in children (below the age of 15 years) with ADHD but not in adults with ADHD. Thus, they extended the brain maturation delay theory for ADHD to include subcortical regions [Significantly low systolic and diastolic BP values for ADHD at baseline could be revealed even after strongly matching the two samples, remaining with small effect sizes of Cohen’s d = 0.17 and Cohen’s d = 0.16, respectively. However, when more stringent ADHD criteria were applied, in a sensitivity analysis the values for Cohen’s d were 0.25 and 0.27, respectively, for a smaller matched sample. Whether this might mean that subcortical hypoarousal is merely expressed in more severe ADHD remains to be investigated. The small effect sizes are not a real surprise for subcortical parameters as can be seen from a large-scale ( regions . For the regions ) are clo regions .In contrast to the findings within the larger cohort at baseline, within the smaller ten-year follow-up cohort, no significant difference for BP could be observed. This change is not entirely due to the shrinking of the sample size, as we could replicate the association between ADHD and lower BP at baseline measurement within the smaller cohort. The lack of follow-up differences might be due to brain maturational effects. The much lower number of BP values available might have played a minor role, since the similar small sample size of ADHD at baseline still showed up with significant differences of BP. Given the higher age of the participants at follow-up, BMI and BP increased during the ten-year period. Even after checking for confounders in the multivariate logistic regression models, the associations between systolic and diastolic BP and ADHD were no longer significant at follow-up and thus a model of subcortical maturational delay might be applied as in Hoogman et al. .Probably, the increase of the BP values at the follow-up examination of this study may reflect that the hypoarousal hypothesis (valid for children; also verified with other measures of autonomous arousal ,31,32,33A strength of our population study was the large and representative sample size, as well as the possibility of longitudinal observation. Furthermore, propensity score matching enabled a higher comparability of the participants with and without ADHD and an additional sensitivity analysis underlined the robustness of our findings. As a limitation, it must be mentioned that it was not possible to include data on drug treatment of the participants. On the other hand, stimulants, as the standard medication for ADHD, could have increased BP and thus having counteracted our hypothesis. ADHD diagnosis at follow-up was not available. Due to the lack of BP values in the majority of the participants, there was a substantial reduction in sample size at the time of follow-up.In summary, significant differences of small effect sizes were found in BP recordings between children with vs. without clinical signs of ADHD, but these differences seem to disappear in young adulthood. Our analysis demonstrated that hemodynamic parameters should be considered to better understand the pathophysiology of ADHD in the developmental context of the subcortical arousal hypothesis.