The overlap/distinctiveness between Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) has been increasingly investigated in recent years, particularly since the DSM-5 allows the dual diagnosis of ASD and ADHD, but the underlying brain mechanisms remain unclear. Although both disorders are associated with brain volumetric abnormalities, it is necessary to unfold the shared and specific volume abnormalities that could contribute to explain the similarities and differences in the clinical and neurocognitive profiles between ADHD and ASD. In this voxel-based morphometry (VBM) study, regional grey matter volumes (GMV) were compared between 22 children with ADHD, 18 children with ASD and 17 typically developing (TD) children aged 8 to 12 years old, controlling for age and total intracranial volume. When compared to TD children or children with ASD, children with ADHD had a larger left precuneus, and a smaller right thalamus, suggesting that these brain abnormalities are specific to ADHD relative to ASD. Overall, this study contributes to the delineation of disorder-specific structural abnormalities in ADHD and ASD. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) allows for a comorbid diagnosis of ADHD and ASD, which was not permitted in previous editions, boosting research on the overlap and distinctiveness between these disorders [Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are two neurodevelopmental disorders with distinct core diagnostic criteria that often co-occur . ADHD isisorders . Both diisorders , and neuisorders , 5. Voxeisorders .In ADHD, reduced total brain and total grey matter volume (GMV) is a consistent finding according to meta-analyses of MRI volumetric studies –8. VolumASD has been repeatedly associated with early modest but significant macrocephaly, followed by an abnormally slow or arrested brain growth and possibly a decrease in brain volume at older ages , 15. In While brain volume abnormalities in ADHD and ASD have been mostly investigated separately, only a limited number of studies have directly compared individuals with ADHD, ASD and/or comorbid ASD and ADHD , in ordeSome studies have explored the association between ADHD or ASD symptom severity and regional grey matter volumes. O’Dwyer et al.  found thOverall, there is still an important heterogeneity and inconsistency across VBM findings, possibly explained by the heterogeneous character of the disorders themselves, but also by differences in sampling and data acquisition and analysis between studies . The ideIn this VBM study, total brain, grey matter and white matter volumes, and regional grey matter volumes were compared between children with ADHD, children with ASD and TD children, aged 8–12 years old. Based on previous literature, we hypothesized that: (1) children with ADHD would exhibit decreased total brain and grey matter volume, whereas the ASD group would exhibit slightly larger total brain and grey matter volumes; (2) the ASD group would show disorder-specific regional brain volume abnormalities in the temporal lobe and motor areas, and the ADHD group in the left inferior frontal gyrus and cerebellar regions.Seventy-one right-handed children aged 8–12 years were initially enrolled. Fourteen participants were excluded a posteriori: one after the fortuitous discovery of a brain anomaly during the MRI scan and 13 participants because of poor image quality. Exclusion criteria were: history of prematurity, neurological disorders, genetic disease, complications during labour with neonatal care unit hospitalization, disabling somatic pathology, left-handedness and contraindications to MRI. The final sample consisted of 22 children with ADHD, 18 children with ASD and 17 TD children. Children with ADHD were recruited at the Erasme Hospital Department of Neurology and PsyPluriel-Pastur in Belgium. Children with ASD were recruited in autism reference centres at the Queen Fabiola Children’s University Hospital and the SUSA Foundation in Belgium. TD children participated upon announcement or personal query. ADHD and ASD diagnoses were established by trained child psychiatrists according to the DSM-5 criteria  criteriaAll participants and their parents gave signed informed consent to participate in this study approved by the Ethics Committee of the ULB-Erasme University Hospital, Belgium , and received 50 euros to cover transportation expenses.2, matrix size = 256 x 256 x 128 and voxel size = 0.94x0.94x1.2 mm3.Brain imaging data was collected using a Discovery MR750w 3.0T scanner  at UZ Brussel. Anatomical images were obtained using a T1-weigthed sagittal 3D TFE (turbo field echo) sequence: repetition time = 8.644 ms, echo time = 3.244 ms, inversion time = 450 ms, flip angle = 12°, field of view = 240x240 mmhttp://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf), implemented in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), executed in MATLAB . CAT12 workflow was adapted for paediatric samples, using customized tissue probability maps (TPM) created with the template-o-matic toolbox  and a customized DARTEL  template.Imaging data was analysed using the Computational Anatomy Toolbox . Mean correlation between all volumes was visualized through CAT12. Volumes with a correlation below two standard deviations from the sample mean were again visually inspected for artefacts. Thirteen scans were excluded due to poor image quality. IQR did not significantly differ between the ASD, ADHD and TD children groups  = 0.35, p = 0.70).T1 images were visually inspected for motion, gross anatomical artefacts and adequate whole-brain coverage. Images were excluded if their weighted average Image Quality Rating (IQR) was lower than 80%, corresponding to a “good” image quality . Total brain volume (TBV) was calculated as the sum of total GMV and total WMV.Analysis of variance (ANOVA) was used to compare total brain volume, total GMW and total white matter volume (WMV) in cmhttp://dbm.neuro.uni-jena.de/tfce/) with 5000 permutations. Results were considered significant at TFCE-FWE-corrected p = 0.05 and the p-value was Bonferroni-adjusted for the number of planned comparisons : p = 0.05/6 = 0.008. Anatomical labelling of brain regions was performed using the Anatomy Toolbox [Individual subject smoothed grey matter volumes were entered into a second level analysis to estimate between-group differences using a one-way ANOVA. Global scaling was used with TIV and age was entered as a covariate. All analyses were conducted without IQ as a covariate . Thresho Toolbox  and the  Toolbox .http://marsbar.sourceforge.net/) and plotted using RStudio (http://www.rstudio.com/). When between-group differences in a region were shown to be disorder-specific, correlations between the ADHD RS-IV total score and mean cluster intensities in that region extracted using the marsbar toolbox (http://marsbar.sourceforge.net/) were examined. VBM results visualization was performed using MRIcroGL (http://www.cabiatl.com/mricrogl/).Extraction of mean cluster intensity was performed using the marsbar toolbox , total WMV  and TBV . Age was not significantly associated with total GMV, but there was a positive significant correlation between age and total WMV , and between age and TBV  across all participants. Age could not be entered as a covariate in this between-group comparison analysis because the data violated the ANCOVA assumption known as homogeneity of regression slopes: there was a significant interaction between diagnostic group and age on total GMV (p = 0.019), total WMV (p = 0.012) and total brain volume (p = 0.009), evidencing that the relationship between age and these volume measures differ between groups. Additional between-group analyses fractionating the ASD group in terms of the presence or absence of ADHD comorbidity are reported in Groups did not significantly differ on total brain volume and total GMV/WMV see . AlthougWhole-brain analyses disclosed larger GMV in the left precuneus and the right cerebellum (lobules IV-V) in children with ADHD compared to TD children; and in the left cerebellum (lobules IV-V) in children with ADHD compared to children with ASD . Children with ADHD displayed decreased GMV in the right parahippocampal gyrus and the right thalamus compared to the TD group . ROI analysis also revealed a decrease in GMV in the right thalamus in the ADHD group compared to the ASD group , suggesting decreased GMV in the right thalamus is specific to children with ADHD relative to ASD , and a significant positive correlation between the ADHD RS-IV total score and the mean cluster intensity in the left precuneus . However, the associations did not hold within the groups .This study contributes to the investigation of disorder-specific grey matter volume abnormalities in ASD and ADHD. Decreased GMV in the right thalamus and hippocampal gyrus, as well as increased GMV in the left precuneus and the right cerebellum were found in the ADHD group when compared to the TD group. Additionally, increased GMV in the left cerebellum was found in the ADHD group compared to the ASD group. Post-hoc analysis confirmed that decreased GMV in the right thalamus and increased GMV in the left precuneus were found in the ADHD group when compared to both the TD and the ASD groups, suggesting these volume abnormalities may be specific to ADHD.The finding of an ADHD-specific decreased GMV in the right thalamus is consistent with a previous meta-analysis of VBM studies in ADHD , and extThe ADHD-specific increased grey matter volume in the left precuneus is also consistent with Nakao et al. , who shoOther volume abnormalities were evidenced in the ADHD and the ASD groups. Nevertheless, we could not prove these were disorder-specific. Our correlational analyses support the disorder-specificity of our findings, since ADHD severity was significantly associated with mean cluster intensity in the right thalamus and the right precuneus. However, the associations did not hold within the groups. These results suggest that the reduction of the right thalamus and the enlargement of the left precuneus are associated with the categorical ADHD diagnosis rather than with symptom severity.The findings in this study should be considered in the light of some limitations. The final sample was relatively small due to the exclusion of poor-quality scans, decreasing therefore the likelihood of biased results. This exclusion comes at a cost as head motion can be associated with clinical traits and the scans of the most severely impaired participants might therefore have been excluded, possibly inducing a sampling bias. Our sample was however large enough to detect robust significant between-group differences in grey matter volumes. One of the strengths of our study is that all children with ADHD were non-comorbid and medication naïve and therefore in this study, volume abnormalities found in the ADHD group cannot be confounded with medication or comorbidity effects. However, in the ASD group, three participants had a history of medication intake or were currently under medication, potentially confounding our findings. Finally, there was a high percentage of children in the ASD group who presented an ADHD comorbidity, consistent with recent comorbidity reports , 52, butThis study contributes to the delineation of disorder-specific structural abnormalities in ADHD and ASD. Our results evidenced that children with ADHD had a disorder-specific larger grey matter volume in the left precuneus and decreased grey matter volume in the right thalamus.S1 TableADHD: Attention-Deficit/Hyperactivity Disorder; ASD: Autism Spectrum Disorder; ASD only: ASD without comorbid ADHD; ASD+ADHD: ASD with comorbid ADHD; TD: typically developing; M/F: Male/Female; χ2: Pearson’s Chi-squared test; df: degrees of freedom; p: p-value; M: mean; SD: Standard deviation; H: test statistic for the Kruskal-Wallis test; ns: not significant; ADHD RS-IV: ADHD Rating Scale-IV.(PDF)Click here for additional data file.S2 TableADHD: Attention-Deficit/Hyperactivity Disorder; ASD: Autism Spectrum Disorder; ASD only: ASD without comorbid ADHD; ASD+ADHD: ASD with comorbid ADHD; TD: typically developing; M: mean; SD: Standard deviation; F: test statistic for ANOVA; df: degrees of freedom; p: p-value; ns: not significant; GMV: grey matter volume; WMV: white matter volume; TBV: total brain volume.(PDF)Click here for additional data file.


Introduction: There is significant overlap in the type of structural language impairments exhibited by children with autism spectrum disorder (ASD) and children with attention deficit hyperactivity disorder (ADHD). This similarity suggests that the cognitive impairment(s) contributing to the structural language deficits in ASD and ADHD may be shared. Previous studies have speculated that procedural memory deficits may be the shared cognitive impairment. The procedural deficit hypothesis (PDH) argues that language deficits can be explained by differences in the neural structures underlying the procedural memory network. This hypothesis is based on the premise that the neural structures comprising the procedural network support language learning. In this study, we aimed to test the PDH in children with ASD, ADHD, and typical development (TD).Methods: One hundred and sixty-three participants (ages 10–21): 91 with ASD, 26 with ADHD, and 46 with TD, completed standardized measures of cognitive and language ability as well as structural magnetic resonance imaging. We compared the structural language abilities, the neural structures underlying the procedural memory network, and the relationship between structural language and neural structure across diagnostic groups.Results: Our analyses revealed that while the structural language abilities differed across ASD, ADHD, and TD groups, the thickness, area, and volume of the structures supporting the procedural memory network were not significantly different between diagnostic groups. Also, several neural structures were associated with structural language abilities across diagnostic groups. Only two of these structures, the inferior frontal gyrus, and the left superior parietal gyrus, are known to be linked to the procedural memory network.Conclusions: The inferior frontal gyrus and the left superior parietal gyrus, have well-established roles in language learning independent of their role as part of the procedural memory system. Other structures such as the caudate and cerebellum, with critical roles in the procedural memory network, were not associated with structural language abilities across diagnostic groups. It is unclear whether the procedural memory network plays a fundamental role in language learning in ASD, ADHD, and TD. It is estimated that 1 in every 66 children in Canada is diagnosed with autism spectrum disorder , final consonant deletions  and gliding  on standardized measures of articulation as well as production errors on nonword repetition tasks  underlying the structural language impairments are similar across the two disorders. Here, we speculate, as others have before Ullman, , that thProcedural memory is the implicit long-term memory system that stores the “blueprint” on how to perform learned motor and cognitive skills such as riding a bicycle Ullman, , 2004. TProcedural skills are a product of learning and consolidating sequence-based information Determine whether receptive and expressive structural language abilities differed between children with ASD, ADHD, and TD.(2)Determine whether the thickness, area, or volume of the neural structures underlying the procedural memory network differed between children with ASD, ADHD, and TD using a region-of-interest analytic approach.(3)Determine whether thickness, area, or volume of the neural structures underlying the procedural memory network were associated with the structural language abilities of children with ASD, ADHD, and TD using a whole-brain vertex and voxel-wise analytic approach.One hundred and sixty-three children (ages 10–21 years) participated in this study: (1) 91 with ASD; (2) 26 with ADHD; and (3) 46 with TD. Participants were recruited from the Province of Ontario Neurodevelopmental Disorders (POND) Network, a multicenter research program aimed at understanding neurodevelopmental disorders including ASD and ADHD to facilitate the development of new and effective treatments  had normal hearing; (2) scored at or above 70 on a non-verbal measure of intelligence; and (3) had a gestational age of 35 weeks or more.To confirm clinical diagnoses, a series of standardized measures were administered to participating children and their parents. To verify the ASD diagnosis, the Autism Diagnostic Observation Schedule  atlas  the inferior frontal gyrus; (2) the parietal gyrus; (3) the caudate; (4) the supramarginal gyrus; and (5) the subcomponents of cerebellum. Although the putamen is part of the procedural network, it is largely involved in motor learning Ullman, . Given tP-values for the effect of diagnosis were computed for each vertex and measures were computed and subject to FDR correction.To examine if cortical anatomy was associated with receptive and expressive structural language ability, we conducted a whole-cortex vertex-wise analysis. At each vertex in the cortical mesh identified by CIVET the following analysis was conducted for the area, volume, and thickness separately. First, scanner was regressed out of the vertex measure. Then, receptive and expressive language scores were separately regressed against residual thickness, area, or volume with diagnosis, sex, age, and whole-brain volume as covariates. P-values were subject to FDR correction.To examine if subcortical anatomy was associated with receptive and expressive structural language ability, we conducted a two-step regression analysis. These structures included the caudate and the subcomponents of the cerebellum. We first regressed scanner against subcortical structure volumes to obtain residual volumes. These residual volumes, as well as age, sex, whole brain volume, non-verbal IQ, and diagnosis, were entered as predictors into a second regression for receptive and expressive language scores. F = 11.00, p < 0.01, and expressive language scores, F = 10.53, p < 0.01, independent of age, sex and non-verbal IQ. Kruskal–Wallis post hoc comparisons revealed that the mean receptive language scores for the ASD and ADHD groups were not significantly different from one another, but were both significantly different from the TD group. Also, the mean expressive language scores for the ADHD and TD groups were not significantly different from one another but were both significantly different from the ASD group  the right frontal gyrus—superior, middle, and inferior; (2) the left superior and inferior frontal gyrus; and (3) the left superior parietal gyrus. These structures have well-established roles in language comprehension and production. The right inferior frontal gyrus is known to be involved in language processing, broadly speaking  the left angular gyrus; (2) the left superior and middle occipital gyri; and (3) the left anterior cingulate and paracingulate gyri. Some of these structures have been linked to language phenomena, namely the left angular gyrus, the left superior and middle occipital gyri. The left angular gyrus is commonly associated with semantic processing of oral speech . She has received royalties from APPI and Springer. RS has consulted to Highland Therapeutics, Eli Lilly and Co., and Purdue Pharma. He has commercial interest in a cognitive rehabilitation software company, “eHave”. RN has received research grants from Roche.


This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects  were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group . Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD. Attention deficit hyperactivity disorder (ADHD) is characterized by three major symptoms: attention deficits, hyperactivity, and impulsivity . The preWhile ADHD has a very high prevalence among children and adolescents, it is difficult to clinically diagnose precisely. ADHD has many comorbidities such as mood disorders, oppositional defiant disorder, conduct disorder, anxiety disorders, and sleep disorders, making ADHD diagnosis difficult . FurtherCurrently, there are three subtypes of ADHD classified using QEEG. The first is the maturational lag subtype characterized by increased slow wave and decreased fast wave activity ; EEG actResearch on the diagnostic accuracy of resting QEEG in ADHD has been conducted previously –12. HoweIndividuals who visited the child and adolescent psychiatric clinic at Daegu Catholic University Hospital from 2018 to 2020 were considered for the study. Participants between 7 and 12 years of age diagnosed with ADHD according to the DSM-5 criteria were included in the study. The ADHD diagnosis was based on a Korean version of the Diagnostic Interview Schedule for Children Version IV (DISC-IV), which is a structured interview tool, and these diagnoses were confirmed by multiple child and adolescent psychiatrists. If participants did not meet the ADHD diagnostic criteria of DSM-IV and DISC-IV, they were assigned to the Neurotypical (NT) group. Based on the results of the DISC-IV test, participants were assigned to the ADHD or Non-Other Specified (NOS) group. Patients who met the diagnostic criteria of ADHD in DSM-IV, but whose score did not exceed six, and had a score of more than three in the attention deficit or hyperactivity/impactivity scale of DISC-IV were included in the NOS group. Children with a history of brain damage, neurological disorders, genetic disorders, substance dependence, epilepsy, or any other mental disorder were excluded from participation. Children with an IQ of 70 or lower according to the Korean-Wechsler Intelligence Scale for Children (Fourth Edition) or who were receiving drug treatment were also excluded from this study. Based on the inclusion and exclusion criteria, 74 subjects  were enrolled in this study. Detailed information regarding the study was provided to the parents and children. Written consent for the medical use of the test results and participation of the children in this study were obtained from all of the participants’ parents. In addition, after receiving a detailed explanation of the study, all of children participated voluntarily and provided written consent for participation. This study was approved by the Institutional Review Board (IRB) of the Daegu Catholic University Medical Center  and was performed in accordance with the Declaration of Helsinki .The DISC-IV is a structured diagnostic tool that was developed for use in epidemiological studies in children and adolescents. Using DISC-IV, the presence of nine symptoms of attention/concentration issues and nine symptoms of hyperactivity–impulsivity over the past 6 months can be assessed. When symptoms were noted, detailed questions, such as whether the symptoms were observed at home or school, were asked. The DISC-IV was revised in 2000 by the U.S. National Institute of Mental Health. The present study used the Korean version of the DISC-IV, which was translated into Korean in 2007 and subsequently underwent reliability(κ = 0.67) and validity(κ = 0.65~1.00) verification [For screening purposes, the 18-item Korean version of the ADHD Rating Scale was used to assess ADHD behavior . The KARwww.braintrain.com) to obtain behavioral measures of attention. The IVA CPT results yielded standardized scores of attention and the Response Control Quotient for each visual and auditory stimulus based on normative data. Cronbach’s alpha index of that study was 0.72 and that of our study was 0.7 [For screening purposes, the CPT was used to assess the level of functioning of the attention/arousal system. We used the Intermediate Visual and Auditory Continuous Performance Test  amplifier and a 10–20 layout 64-channel Quick-Cap electrode-placement system . The EEG data were digitally recorded from 19 gold cup electrodes placed according to the international 10–20 system . The impedances were maintained below 5 kΩ, and the sampling rate was 1000 Hz. We used the linked mastoid reference and two additional bipolar electrodes to measure horizontal and vertical eye movements. During the recording, each participant laid in a dimly lit, electrically shielded, sound-attenuated room. Resting EEG recordings were recorded after three minutes with the participants’ eyes closed.We used MATLAB 7.0.1  and the EEGLAB toolbox  to pre-pFive frequency bands were defined for further analysis: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (12–30 Hz). We investigated the power spectra of the EEG data for each subject using the short-time Fourier transform ‘spectrogram.m’ function from the Signal Processing Toolbox in MATLAB. Time windows of 1,000 ms with an 800 ms overlap and Hamming window were used for the spectral analysis. Outliers that were far from the spectral value distribution of each frequency band at the 0.05 significance level were removed. Finally, the absolute powers were averaged over all the time windows and frequency bands for further analysis. The data from participants were converted to Z scores based on the means and standard deviations of the NeuroGuide normative database . The Z sThe MATLAB 7.0.1 Statistical Toolbox was used for the statistical analyses. All of the values are expressed as means and the standard deviations. First, an analysis of variance (ANOVA) was conducted on the QEEG results for the ADHD, ADHD NOS, and NT groups. Second, we used the statistical function named 'classify.m' in MATLAB to implement the classifiers of linear discriminant analysis (LDA). LDA is used to determine the linear combination of features that can better separate two or more classes . LDA entThere were 74 children  included in the analysis. There was no significant difference between the mean ages of the males and females , at 8.9±1.2 years and 8.7±1.2 years, respectively. The mean KARS score for all subjects was 29.47±12.57 for males and 23.19±14.40 for females, with no statistically significant difference between males and females . Based on the DISC-IV results, we identified the differences between the groups by categorizing them into three groups: 27 participants  in the ADHD group, 32 participants  in the ADHD Not Otherwise Specified (NOS) group, and 15 participants  in the Neurotypical (NT) group.Using ANOVA, the mean-to-mean difference between the three groups was investigated with respect to the absolute and relative power of the delta, theta, slow alpha, fast alpha and beta waves. Among the waves, the fast-alpha absolute power  and beta absolute power  showed a difference in meaningful scores between the three groups. Following the Scheffe post-hoc test, the ADHD and NOS groups were found to be significantly lower in fast alpha absolute power and beta absolute power than NT.Using cluster analysis, the QEEG results were divided into groups (a) to (d). The percentages of NT, NOS, and ADHD groups in each group were obtained and plotted on a circular graph . In addiThe topography of groups (a)-(d) divided through the cluster analysis was implemented through EEGLAB . Groups The present study classified ADHD children into NT, NOS, and ADHD groups using a structured interview (DISC-IV), and the results of their attention test and QEEG characteristics were assessed for each group. We also divided participants into four groups through cluster analysis of the QEEG results, determined the distribution of the ADHD, NOS, and NT group, and constructed topographical plot to identify the characteristics of the QEEG results according to group.The results showed that the demographic characteristics of the subjects did not differ in terms of age and gender. A gender ratio of 3:1 favoring males was found, similar to what has been observed in previous studies . Using cGroup (c) included 47% of ADHD groups and had elevated theta absolute power and low fast alpha and beta relative power . This reGroup (b), which comprised of 59% of participants from the NOS group, was characterized by the elevated alpha waves. Alpha waves are functionally associated with arousal , and eleLastly, group (d) included 62% of participants from the NT, and fast alpha and beta wave absolute power were elevated, but relative power was relatively equal compared to the other groups. These elevations in absolute power may be an error of the measurement tool employed. For EEG measurements, it is important to control for artifacts and resistance. A moderate amount of electrode paste is required to attach electrodes, and excessive amounts of paste can cause electric bridges with other electrodes, which distorts the distribution of EEG. Furthermore, using too little paste also increases and causes error in measurements. The measurement environment is also important, and if the room is too hot or humid, it may cause problems such as sweating which may affect resistance and artifacts . TherefoThis study has several limitations. First, we failed to fully consider the IQ of the subjects, although we excluded subjects below an IQ of 70. It is known that EEG can vary depending on an individual's IQ, so this variable should be controlled for. Second, the number of participants among the three groups  was inconsistent, especially the number of NT . Third, When diagnosing ADHD in clinical practice, QEEG is available as an auxiliary tool, providing additional information regarding the QEEG subtypes. An elevated delta power and decreased theta power, or an elevated theta power and decreased fast wave likely indicates a diagnosis of ADHD. On the other hand, if alpha waves are high, it is possible that attention-deficit symptoms may have been caused by childhood depression, or that a comorbidity such as childhood depression may be present rather than ADHD only. Finally, if absolute power is shown to be high overall, it is necessary to evaluate QEEG using relative power.S1 FigAbbreviations: ADHD, Attention-Deficit Hyperactivity Disorder; NT, Neurotypical; NOS, ADHD Not Otherwise Specified; K-ARS, Korean ADHD rating scale, IVA+Plus, Integrated visual and auditory test; H/I, Hyperactivity/Impulsivity; Inatt., Inattention.(TIF)Click here for additional data file.


Insects live in a complex and diverse environment, threatened by a variety of microorganisms, and the innate immunity of which plays an important role in defending the invasion of pathogens. From an evolutionary perspective, different living environments and lifestyles drive the different evolutionary patterns of immune systems of insects. Fig wasps are closely associated with the fig syconia, divided into pollinators and non-pollinators according to whether they pollinate the figs. The pollinators are all herbivorous, and fulfil their development within the fig syconia, presenting different lifestyles and diets to non-pollinators, which lead to the chances of exposure to the pathogens varying greatly. The recognition of pathogens is the first step in innate immunity. Therefore, we focused on the different evolutionary patterns of peptidoglycan recognition protein genes between pollinators and non-pollinators, and found that the number of peptidoglycan recognition protein genes was significantly smaller than that of non-pollinators, and the initiation of Toll pathway of pollinators was simpler than that of non-pollinators. All the results suggested a streamlined innate immune recognition system of pollinators, and this information will provide more insights into the adaptive evolution of innate immunity in insects of host specificity.The innate immunity is the most important defense against pathogen of insects, and the peptidoglycan recognition proteins (PGRPs) play an important role in the processes of immune recognition and initiation of Toll, IMD and other signal pathways. In fig wasps, pollinators and non-pollinators present different evolutionary histories and lifestyles, even though both are closely associated with fig syconia, which may indicate their different patterns in the evolution of PGRPs. By manual annotation, we got all the PGRP genes of 12 fig wasp species, containing seven pollinators and five non-pollinators, and investigated their putative different evolutionary patterns. We found that the number of PGRP genes in pollinators was significantly lower than in non-pollinators, and the number of catalytic PGRP presented a declining trend in pollinators. More importantly, PGRP-SA is associated with initiating the Toll pathway, as well as gram-negative bacteria-binding proteins (GNBPs), which were completely lost in pollinators, which led us to speculate that the initiation of Toll pathway was simpler in pollinators than in non-pollinators. We concluded that fig pollinators owned a more streamlined innate immune recognition system than non-pollinators. Our results provide molecular evidence for the adaptive evolution of innate immunity in insects of host specificity. Bombyx mori, can specifically identify the peptidoglycan in the cell wall of gram-positive bacteria, and eventually activate the phenoloxidase, leading to melanization [Drosophila melanogaster [B. mori [Anopheles gambiae [Apis mellifera [The peptidoglycan recognition proteins (PGRP) are recognition proteins for bacterial peptidoglycan (PGN), playing important roles in the innate immunity of animals ,2. The fnization . Thereafnogaster , 12 PGRP[B. mori , seven P gambiae , and fouellifera .2+-binding region, coordinated by three resides  [2+ binding in PGN-binding groove works as an electrophilic catalyst, which promotes the cleavage of the amide bond between MurNAc and L-Ala [Bacillus megaterium within ten minutes in the presence of Zn2+ [2+, non-catalytic PGRPs only work as receptors in the immune response, by combining with PGN, but not hydrolyzing [PGRPs contain at least one PGN-binding region, which is called the PGRP domain, with the length of approximately 165 amino acids, containing three α-helices and five β-folds, four of which are parallel, and the last one is antiparallel. The overall scaffolds of PGRPs are similar, the structures and activities of which were maintained by some conserved disulfides . PGRPs aPGRP-LB) . The hydnd L-Ala ,11. Some of Zn2+ . In compD. melanogaster, PGRP-SA and PGRP-SD, as well as the gram-negative bacteria-binding protein (GNBP), mainly recognize the peptidoglycan of most gram-positive bacteria (Lys-type PGN), activate serine protease, cleave the Spätzle precursor into mature Spätzle, and then activate the Toll pathway [D. melanogaster. The extracellular PGRP-LE forms a complex with PGRP-LC to activate the IMD pathway, while the intracellular PGRP-LE interacts directly with Imd to activate the IMD pathway in a PGRP-LC-independent way [As described above, some PGRPs can specifically recognize the peptidoglycan of bacteria and initiate corresponding innate immune signal pathways. For example, in  pathway ,14,15. T pathway . PGRP-LEdent way ,18. As iFicus, Moraceae) [Fig wasps  are the general name of all hymenopteran insects whose development and living are closely associated with the fig syconia—fruits of the fig trees (oraceae) . Accordioraceae) . The polThe difference in lifestyles presented between pollinators and non-pollinators gives us a hint that, throughout their life history, the chances and duration of exposure to the open environment vary greatly between them. The pollinators may live in a more stable environment than the non-pollinators, and this may reflect their different innate immune recognition system. Based on the genome sequences of 12 fig wasp species, containing seven pollinators and five non-pollinators , we acquThe genome sequences we used were submitted data from our lab (project accession PRJNA641212 and PRJNA494992).D. melanogaster [A. mellifera [Nasonia vitripennis [tblastn was conducted to obtain the genome segment with the best match  from the fig wasps. The complete gene structures and sequences were verified by Integrative Genomics Viewer (IGV). Softberry (http://www.softberry.com/berry.phtml?topic=fgenes_plus&group=programs&subgroup=gfs) was used to predict the gene structures for the PGRP genes, which lacked certain information from IGV.We got PGRP gene families of 12 fig wasps by manual annotation, based on the PGRP amino acid sequences of nogaster , A. mellellifera , and Nasripennis , downloahttps://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi), and presented some conserved sites using WebLogo 3 (http://weblogo.threeplusone.com/). The N-terminal signal peptides were predicted by SignelP  and the transmembrane domain were predicted by TMHMM (http://www.cbs.dtu.dk/services/TMHMM/). Mapchart was used to describe the location of PGRPs on the genome, and MCScanX was used for collinearity analysis.We identified PGRP domains by the NCBI-conserved domain database (p < 0.05) between the pollinators and non-pollinators. The MAFFT software was used for sequence alignment, and IQtree was used to predict the optimal model and construct a phylogenetic tree, and the bootstrap values were calculated with 1000 replicates. Finally, we used the Interactive Tree of Life (iTOL) (http://itol.embl.de/) to show the tree.The R software was used to test whether the number of PGRP genes has a significant difference (p < 0.01) , A. mellifera (Amel-), N. vitripennis (Nvit-), Pteromalus puparum (Pp) and the 12 fig wasps  . Specifiig wasps , which iSycophaga agraensis , in most of the genomes of the fig wasps, the PGRPs were mainly concentrated on one scaffold, presenting a sequential distribution pattern. For example, in the species of Sycophila sp.2, there were, altogether, 10 out of 13 PGRP genes located on the scaffold of No.14, divided into four clusters, with each cluster containing at least two repetitive PGRP genes. In the species of Kradibia gibbosae, there were four PGRP genes, which were altogether located on the scaffold of No.4 as one cluster. We thus speculated that the PGRP gene families of wasps had expanded mainly through gene tandem duplication.Gene family is a group of genes derived from a common ancestor, consisting of two or more copies from gene duplication or doubling. They show obvious similarities in structure and function, and code similar protein products ,23. For 2+-coordinating residues in the PGN-binding groove  , and three pollinators , and two PGRP genes from the non-pollinator species of Philotrypesis tridentata. Clade II contained one PGRP from each of the species of the A. mellifera, N. vitripennis, P. puparum, five non-pollinators, and one pollinator species of the C. solmsi. Overall, the number of catalytic PGRPs was significantly different between pollinators and non-pollinators  (P. tridentata even had three), located on each of both clades (Clade I and Clade II) in the gene tree, nevertheless four out of seven pollinators  had no catalytic PGRPs. Thus, compared to non-pollinators, the number of catalytic PGRPs in pollinators showed a decreasing trend. We further investigated the structure characteristics of the genes on both PGRPs clades, and found that genes in Clade I generally had at least four exons, while genes in Clade II had only three exons  . We consis, Cys) A. The Cl < 0.01) , becauseee exons B.In the gene tree of all PGRP genes from the studied species , we founMeanwhile, we also looked into whether the PGRP-SA of non-pollinators had complete functional structures or not. By conducting motif and domain analysis on them, we found that they all contained the same and conserved motifs, complete N-terminal signal peptides and C-terminal PGRP domains A. TherefK. gibbosae, all the fig wasps had complete PGRP-LC genes , and most of the important gene members of IMD pathway, such as Imd, FADD, Dredd, Tak1, Relish, and Pirk, were absent [K. gibbosae.Due to the major differences in the initiation of Toll pathway between pollinators and non-pollinators, we also wondered if there were differences in the gene members in the initiation of IMD pathway between both fig wasp groups. When searching for the genes of PGRP-LC that play important roles in signal transduction of IMD pathway, we found that excepting the pollinator species of LC genes , with enLC genes B. The K.e absent . TherefoPeriplaneta americana, the GNBP and Toll families show great expansion, among which the number of GNBPs is the largest among all insects to date, and PGRPs  related to the IMD pathway are also expanded [Plutella xylostella even has two PGRP-SAs in the same scaffold, serving a function in Toll pathway for the surveillance of Gram-positive bacteria, and possess a dramatic expansion of GNBPs, suggesting the function with diverse [B. mori possesses some unique recognition genes and antimicrobial peptide genes, which do not exist in Drosophila, Anopheles, Apis, and Tribolium genomes, indicating that lepidopterans have a lineage-specific genetic evolution in immune recognition [A. mellifera is small, about a third of that of the fruit fly or Anopheles, which may be the strong social barrier preventing honey bees from being invaded by microorganisms, or the result of evolution with limited pathogens [Drosophila, the decreasing diversities of PGRPs in hematophagous insects, such as the tsetse fly, reflect the relatively sterile environment during their development [Acyrthosiphon pisum have lost all of their PGRPs [Daphnia pulex, the expansion of another recognition protein GNBP may compensate for the absence of PGRPs [In the innate immune system of insects, the recognition of pathogens is the first step, in which peptidoglycan recognition proteins play important roles . The comexpanded . The dia diverse . B. moriognition . In addiathogens . Compareelopment ,29. Furtir PGRPs . The divof PGRPs . We thusK. gibbosae had relatively few PGRP gene members (four genes) in the studied fig wasp species, and it had lost many crucial genes of IMD pathway [A. pisum, has lost PGRP genes and IMD pathway, but presents other intact immune pathways  [Pediculus humanus, who lives on human blood, possesses only one PGRP gene, has lost gene members in IMD pathway, but retains other intact immune pathways [K. gibbosae is more obvious. Further comparative analysis of more species will help us unravel the mystery.It is interesting that the  pathway . Some spAK/STAT) . The bodpathways . Besidespathways . Many hepathways . In shorFig wasps are a special group of insects protected by fig syconia. Even though both pollinators and non-pollinating fig wasps are closely associated with figs, their different evolutionary histories and lifestyles may indicate different patterns in immune recognition, such as the evolution of peptidoglycan recognition proteins (PGRPs). We found that the PGRP gene family of fig wasps was mainly expanded by tandem duplication, however the number of PGRP genes of pollinators was significantly smaller than that of non-pollinators, and the catalytic PGRPs were gradually lost in pollinators. In addition, PGRP-SAs associated with the initiation of Toll pathway were all lost in pollinators, but still retained in the non-pollinators. Based on these results, we speculated that the innate immune recognition systems of pollinators were more streamlined than non-pollinators. These differences might be attributed to their longer history of adaptive evolution to the living environment within fig syconia.


Behavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder. A myriad of studies using functional magnetic resonance imaging (fMRI) have reported altered intrinsic connectivity within and between networks such as the dorsal and ventral attention, salience and default mode networks4. However, far less is known about the structural networks underpinning these functional disturbances and how they may contribute to ADHD symptomology and prognosis.Dysfunction of spatially distributed, interconnected neural systems is thought to be one of the major causes of behavioural disturbances in ADHD5. Numerous studies have reported alterations in microstructural properties within specific tracts in ADHD, predominantly those linking prefrontal, parietal, cerebellar and SC (e.g.7 see8 for a review). These anomalies have also been associated with clinical symptom severity7 and the improvement of symptoms across development9, demonstrating the relevance of white matter integrity to core symptoms of ADHD.Diffusion-weighted imaging (DWI) is a method widely used to examine microstructural brain properties and white matter connections10. Graph theory is one such method that uses an entirely data-driven approach to produce metrics able to characterise neural networks in terms of information flow and network efficiency. Global efficiency is a quantified measure of how efficiently information can be distributed through the network, with shorter paths (measured by a lower number of network links) representing faster and more efficient transfer of information11. Local efficiency of brain regions (or network nodes) essentially unpacks this global efficiency metric by measuring information flow in the vicinity of the node12. Most work thus far on brain networks in ADHD has utilised resting-state fMRI (rs-fMRI), which has typically found lower global efficiency in children with ADHD relative to typically developing children (TDC)13. White matter architecture is a crucial element underpinning functional activity via the efficient communication between disparate brain regions. There is growing evidence to suggest that structural networks of the brain to some extent shape the processes underlying brain function14.More recently, our understanding of neural connectivity has been enriched through the application of sophisticated network analysis approaches. These methods allow for the description of complex patterns of neural connectivity (i.e. the ‘connectome’) and quantification of specific network properties17. These studies are relatively consistent in their overall network descriptions, finding reduced global efficiency and increased local efficiency in ADHD relative to TDC17. This corroborates findings from rs-fMRI studies18, and indicates reduced communication between more modular, specialised subnetworks in ADHD. Hong and colleagues16 identified a network spanning frontal, striatial and cerebellar brain regions that significantly differed in ADHD, with tract integrity in specific regions correlating with attentional deficits. Cao et al.15 found increased structural connectivity in orbitofrontal–striatal circuitry and decreased prefrontal dominant circuitry. By contrast, Beare et al.17 reported a sub-network of stronger connectivity encompassing bilateral frontostriatal and left occipital, temporal, and parietal regions, with white matter microstructure within these networks being associated with ADHD symptom severity. While there are some inconsistencies in regional findings, collectively, these previous studies suggest there is pathological wiring in white matter networks in ADHD, which may be crucial structural substrates underlying behavioural impairments in ADHD.To date, only three studies have used network-based methods in children and adolescents with ADHD using DWI24. In ~30% of cases, however, it is either ineffective or causes intolerable side-effects25. This differential response to medication is inherently linked to individual variation in the neurobiology of the brain26. Diffusion imaging measures have successfully predicted pharmacological treatment outcomes in other psychiatric conditions, including major depressive disorder27, first episode psychosis28 and bipolar disorder29. Better characterisation of the biological profile associated with successful MPH therapy will help further our understanding of ADHD aetiology, and may aid in the identification of novel treatment targets for non-responders.Despite previous studies finding links between structural networks and symptoms of ADHD, no previous work has examined whether structural network topology is also linked to prognostic outcomes. MPH is the first-line medication typically used to treat the clinical and cognitive symptoms of ADHD. Its therapeutic effects in ADHD occur via blocking the reuptake of catecholamines, which restores brain activation patterns and functional connectivity towards normative levels, particularly within cortico-striato-cerebellar networksHere, we used diffusion tractography and graph theoretical analyses to compare white matter whole-brain structural networks in children and adolescents with ADHD relative to TDC. Critically, we also examined whether differences in network features were associated with better response to MPH treatment or symptom severity. Support vector models were performed for each analysis in order to assess multivariate feature contributions to group differences and outcome measures. Based on previous work, we expected to find reduced global efficiency in ADHD relative to TDC. We further hypothesised that prior to treatment, there would be reduced local efficiency in catecholamine-rich regions within cortico-striatal networks in individuals who subsequently respond best to MPH treatment.https://clinicaltrials.gov/ct2/show/NCT00863499). A detailed description of diagnosis procedure has been published previously30. Briefly, diagnoses were made by referring clinicians based on Diagnostic and Statistical Manual of mental Disorders (4th ed.: DSM-IV) criteria, and confirmed using the Mini International Neuropsychiatric Interview for children and adolescents  and the Attention Deficit/Hyperactivity Disorder Rating Scale (ADHD-RS-IV)(administered to the parent/guardian). Typically developing controls were drawn from iSPOT-A and the Limbic Maturational Changes in young Adulthood (LIMCA) study32, which used identical experimental procedures on the same scanner. They were screened for Axis 1 mental disorders using the MINI-KID or SPHERE-1233. Sample size was based on previous studies investigating whole-brain connectomics using diffusion tensor imaging in child and adolescent ADHD17.DWI data were acquired from 37 children and adolescents with ADHD (8–17 years) and 26 age and gender matched TDC’s. Children with ADHD undertook imaging as part of a baseline research session, prior to commencing 6 weeks of open-label MPH  provided written informed consent.Participants were medication-free at baseline testing and a minimum 5 half-life washout period was applied for those who took medication. ADHD participants were prescribed open-label MPH by their treating paediatrician, which they continued for 6 weeks. Dosage was titrated and optimised as required by their treating paediatrician. During this time, participants refrained from all other ADHD treatments.ADHD symptom severity was assessed using the total score on the parent-completed ADHD-RS-IV. The ADHD-RS-IV assesses symptoms according to the 18 DSM-IV criteria, each symptom rated from 0 to 3. The current analysis uses baseline imaging and clinical symptoms, as well as percent change between baseline (week 0) and Week 6 (post-treatment) clinical symptom scores.Data were acquired using an 8-channel head coil on a 3-Tesla GE Sigma HDx scanner  at the Westmead Hospital, Sydney. Details of the protocol can be found in http://www.fmrib.ox.ac.uk/fsl). Raw DWI data were corrected for head movement and eddy current distortions prior to diffusion tensor models being fitted independently for each voxel within the brain. Data were segmented into 68 cortical regions and seven subcortical structures  to generate 82 × 82 connectivity matrices. See DWI data were preprocessed and analysed using the FMRIB Software library (FSL) (v5.0.1) (http://www.brain-connectivity-toolbox.net/)34. To avoid biases associated with using a single threshold, we examined topological properties across a range of thresholds (5% < S < 30% in steps of 1%) and calculated a single area under the curve (AUC) measure over these thresholds. We calculated the following global network measures: (1) the characteristic path length (the mean number of connections on the shortest path between any two regions in the network); (2) the clustering coefficient, which quantifies the probability that two nodes connected to an index node are also connected with each other and (3) global efficiency, computed as the harmonic mean of the inverse path length. We also examined the local efficiency of individual network regions, which measures the efficiency of the subgraph defined by an index node’s neighbours after removal of that node and putatively indexes fault tolerance. To aid with interpretation, regional results were classified into functional networks defined by Yeo’s 7 network parcellation: visual, somatomotor (SM), dorsal attention (DAN), ventral attention (VAN), limbic, frontoparietal (FP) and default mode network (DMN)35, along with SC.Graph theoretic analyses were performed on the interregional connectivity matrices (weighted undirected networks) using the Brain Connectivity Toolbox  Identifying differences between diagnostic groups (ADHD vs TDC), (2) Predicting MPH treatment-related symptom change and (3) Identifying associations with ADHD symptom severity.T tests and ANCOVA’s were used first to test group differences on univariate outcome measures. Associations between graph measures and baseline ADHD symptoms were assessed using correlations, and associations between graph measures and percent change in ADHD symptoms after 6 weeks of MPH treatment were assessed using linear regressions. All analyses were conducted with and without the covariates age, gender, MPH dose (mg/kg), ADHD subtype, duration of previous stimulant treatment and stimulant use in the 6 months prior to study entry. The false discovery rate (FDR) for t tests, ANCOVAS, correlations and regression models were corrected for using the Benjamini–Hochberg36 method, where p values were adjusted to q values. The FDR was corrected for each research question  and both p and q values are provided where appropriate.k-fold cross-validation37. Model performance for classification prediction was assessed using binomial tests, where each model’s accuracy was compared to its corresponding no information rates (NIR) accuracy. Given the unbalanced nature of some data classes , we also performed permutation testing (1000 times). Model performance for regression prediction was assessed by correlating observed and predicted values. For significant SVM models, we also included covariates to establish whether including covariates improved the model’s performance over local efficiency measures alone. Variable importance in classification SVM models was determined by calculating the area under the receiver operator characteristic (ROC) curve for each variable. Variable importance in regression SVM models was determined by fitting a loess smoother between the outcome and the predictor, then calculating the r2 statistic for each model against the intercept only null model. All analyses were performed using R 3.6.138. SVM models were generated using the ‘caret'39 and ‘kernlab' packages40.Three separate SVMs were completed to test whether the 82 local efficiency measures could be combined to predict diagnostic group, treatment-related symptom change and symptom severity. Model tuning parameters cost (C) and sigma  were iteratively optimised based on the root mean standard error (RMSE). Models were validated using leave-one-out cross-validation (LOOCV) procedure due to the modest sample size and its superiority in reducing bias and variance of cross-validated accuracy over Supplementary analyses comparing TDCs vs MPH responders (>25% reduction in symptom severity) and non-responders were also conducted on any significant treatment response univariate results to aid in determining the normative benchmark.t(61) = −0.042, p = 0.673; characteristic path length, t(61) = −0.311, p = 0.757; mean clustering coefficient, t(61) = 1.035, p = 0.258) or univariate measures of nodal local efficiency after correcting for multiple comparisons. However, using linear SVM we found a significant model of combination of local efficiency measures to predict ADHD from TDC with 76.2% accuracy  = −2.76, p = 0.009, q = 0.027) and a greater percent reduction in inattention symptom severity  = −2.57, p = 0.015, q = 0.045) after 6 weeks of MPH treatment. Regionally, higher local efficiency of the right supramarginal gyrus at baseline predicted greater percent reduction in total ADHD symptoms with MPH treatment  = −3.94, p < 0.001, q < 0.001)  Fig. . Both gl Fig. . Br = 0.37, p = 0.029, permutation p = 0.036). The top ten regions contributing to this model were the right thalamus (SC), bilateral precentral gyrus (DAN), left superior frontal gyrus, right isthmus cingulate and pars triangularis (FP), right fusiform , right supramarginal gyrus (VAN) and right rostral middle frontal gyrus (DMN), all of which had greater local efficiency associated with greater improvement in symptoms. Adding the covariates age, gender, dosage previous stimulant use, duration of previous stimulant use and ADHD subtype improved the model’s performance . However, these variables did not appear in the top 30 contributing to the model.Using linear SVM, multivariate measures of local efficiency predicted change in ADHD symptom severity after 6 weeks of MPH treatment  has been one of the most consistently reported regions of structural and functional difference45. This could suggest that efficient information flow from and between structures heavily innervated by dopamine and noradrenaline is fundamental to likelihood of good treatment outcomes with MPH. Non-stimulants such as atomoxetine also act on catecholamine systems within similar regions, therefore it would be interesting for future work to examine the specificity of this result to MPH relative to catecholamine therapies more broadly.Meanwhile, the regions contributing most to predicting success of MPH treatment were spread across a range of functional networks, but particularly within the prefrontal cortex. MPH at therapeutic doses preferentially activates catecholamine neurotransmission within the PFC12. In our ADHD cohort, higher global efficiency predicted a greater percent reduction in total ADHD symptom severity with MPH treatment. Connectome organisation becomes increasingly integrated across early neurodevelopment, which co-occurs with the synaptic pruning and increased myelination that takes place around puberty47. This leads to global efficiency increases across childhood and adolescence as communication improves between more disparate subnetworks. Based on this logic, our results suggest that more developmentally advanced global organisation is associated with better MPH treatment response. Notably, this result held after adjusting for age, gender, previous stimulant use and MPH dose.Global efficiency and local efficiency measure how efficiently information is exchanged at the global and local levels, respectivelyHigher global efficiency was also predictive of greater treatment-related improvement of inattentive symptoms. This suggests that the significant association between total ADHD symptom severity change and global efficiency is predominantly driven by changes in these symptoms rather than changes in hyperactivity/impulsivity. This could reflect the more diffuse nature of networks supporting attention relative to impulse control.48, and has previously been reported as having disturbed intrinsic connectivity in ADHD4. Notably, the supramarginal gyrus is involved in polymodal sensory integration in the association cortex. The association cortex contains densely connected hubs of the connectome, and has fast rates of myelination and cortical shrinking during adolescence49. The rapid neurodevelopment of these hubs during adolescence is argued to be a genetically patterned process of consolidation49, which may provide clues to the atypical neurodevelopment seen in ADHD. Based on this one could speculate that possible alterations in gene expression directing neurodevelopment may also be pivotal to ADHD treatment prognosis, via modification of key hub development.Regionally, we found that higher local efficiency of the right supramarginal gyrus (VAN) at baseline was associated with better MPH treatment response. The right lateralised VAN is involved in monitoring the environment for behaviourally relevant stimuli50. However, both primate tracing studies and human in vivo diffusion imaging have demonstrated that the supramarginal gyrus connects with parts of the lateral frontal cortex via the superior longitudinal fasciculus III52, a tract in which previous studies have reported disturbed microstructure in children with ADHD53. Future studies could examine the potential for integrity of this tract to be associated with MPH response in children with ADHD.It is important to first note that diffusion tractography cannot determine the precise cortical origins of specific white matter tracts or whether connections are mono- or polysynaptic54.Importantly, supplementary analyses revealed that mean local efficiency of the right supramarginal gyrus was lower in MPH non-responders relative to TDCs, whereas MPH responders and TDCs did not differ. This supports the idea that successful treatment with MPH may require intact structural connectivity rather than reflecting a shift from abnormal to normal connectivity. This is a pattern reflected in other disorders and medications; we recently found that depressed patients with greater than normal functional connectivity within key networks were the most likely to benefit from antidepressant treatment17, which may indicate that tract integrity is associated with current symptomology while structural topology has more bearing on prognosis.We did not observe any significant correlation between symptom severity measures and global or local measures of efficiency. Previous studies found correlations between white matter microstructural properties and ADHD symptoms rather than graph measuresUnlike previous studies, we found lower local efficiency in all regions contributing to diagnostic prediction. Also, while we had anticipated that TDCs would have higher global efficiency relative to ADHD, this was not the case. The supplementary analysis comparing TDCs vs MPH responders and non-responders revealed that this occurred due to heterogeneity within the ADHD group. Mean global efficiency for MPH non-responders and responders groups fell below and above TDCs, respectively.55. Fortunately, however, potential errors in tractography are systematically applied, therefore this has minimal impact on our analyses. In addition, the diffusion tractography method utilised counts total streams from the seed, which does not allow for specificity in determining affected white matter tracts or directionality. Secondly, despite all participants undergoing a medication washout period, some participants had a history of stimulant use. This may have already altered structural organisation prior to study entry. While stimulant use in the past 6 months was included as a covariate and did not alter findings, future replication attempts should be completed in a medication naïve sample56. Thirdly, given the relatively modest sample size, we were limited to the leave-one-out cross-validation method. While this is superior in reducing bias and variance of accuracy in smaller cohorts, our results should be validated using an independent sample. Finally, we have made speculations on the links with functional measures. A recent multimodal imaging study did find coherence between structural and functional aberrances in a large sample of children with ADHD, as well as associations with clinical features of ADHD57. Nonetheless, our results should be validated against functional brain organisation using resting state or functional tasks.Our findings should be viewed in light of some limitations. Firstly, the DTI method has difficulty resolving crossing fibre bundles, therefore, providing reduced accuracy of tractography relative to constrained spherical deconvolution-based methodsStructural connectivity places constraints on which functional interactions occur in the network, particularly during neurodevelopment. Our findings have demonstrated that graph measures of structural topology provide valuable diagnostic and prognostic markers of child and adolescent ADHD. They also highlight that variability in structural topology plays a role in determining the efficacy of MPH response. These new insights contribute to the mechanistic understanding of this complex disorder.Supplementary Methods and ResultsSupplementary Figure 1


A decreased ability to inhibit a speeded motor response is a well-studied deficit in Attention Deficit Hyperactivity Disorder (ADHD), and has been proposed as an endophenotype. Inhibitory control has been assessed reliably with the Stop Signal Task (SST) and is associated with prior documented differences in regional brain function using f-MRI. Here, we advance on these findings by examining their structural connectivity and white matter integrity with the goal of identifying a network underlying a core cognitive deficit in ADHD.Healthy controls (N=16) and youth diagnosed with ADHD (N=60) were recruited through the Province of Ontario Neurodevelopmental Disorders Network (POND) and the Hospital for Sick Children. An f-MRI activation difference map was co-registered with each participant’s white matter imaging data, representing the specific network nodes where ADHD youth diverged significantly from controls while performing the SST. Probabilistic tractography was applied from these nodes, and white matter integrity indices such as fractional anisotropy (FA) within the tracts of interest were contrasted between the groups and correlated with SST output measures, including the measure of inhibitory control, the stop signal reaction time (SSRT).The tracts that connected the network nodes belonged primarily to the inferior fronto-occipital fasciculus (IFOF) and cingulum. ADHD subjects showed trend differences in FA compared to controls between right inferior frontal gyrus (IFG) and right superior temporal gyrus (P= 0.09), right IFG and right posterior cingulate (P= 0.01), right anterior cingulate to posterior cingulate (p= 0.08), and between left middle temporal gyrus (BA 39) and left posterior cingulate (P=0.02). A trend correlation was found between radial diffusivity within IFG to STG white matter (IFOF) and SSRT.We identified potential white matter tracts related to deficient inhibitory control, elucidating the brain mechanisms of an important cognitive deficit in ADHD. These findings could be integrated into future endophenotypic biomarker studies, incorporating altogether brain structure, function, and behavior for future studies of ADHD and other psychiatric conditions that exhibit this deficit. Attention Deficit Hyperactivity Disorder (ADHD) is a childhood onset neurodevelopmental disorder with a prevalence estimate of 5% , charactWith this extensive background of neuroimaging findings, it would be advantageous to link these with key behavioral processes, such as executive functioning in ADHD , 11. TheHere, we take advantage of a well replicated cognitive deficit in ADHD, poor inhibitory control, to specifically probe underlying white matter connectivity changes. Inhibitory control is an executive function that encompasses different cognitive components including prospective withholding, which is preparing to withhold a response such as a button press versus appropriately pressing down, and reactive inhibition, which is stopping a response quickly, and error detection –17. ContInhibitory control has often been measured with the stop signal task (SST) , 20, anda priori within complex structures like the prefrontal cortex, particularly in the developing brain, where one designated region can have varying structural topographical labels . Both anatomical tracer studies and functional connectivity MRI studies reinforce strongly that within higher order structures such as the prefrontal cortex and association cortex overall, two closely adjacent regions are connected to multiple and very distinct distributed networks , Hospital for Sick Children site. For the ADHD group, participants received a consensus diagnosis of ADHD following a multi-disciplinary and comprehensive assessment within a specialized ADHD assessment clinic, involving parent and child interviews using the Parent Interview for Child Symptoms (P.I.C.S.) , followeControl subjects aged 10-18 were recruited mainly for the diffusion imaging (DTI) component through internal hospital postings or word of mouth. They were assessed in a comparable manner  and reported no psychiatric or medical disorders, including no developmental diagnosis, and no first-degree family history of such.The SST was administered to all ADHD subjects prior to neuroimaging. The SST estimates the speed with which a person voluntarily executes and stops a speeded motor response, described previously . Particihttp://fsl.fmrib.ox.ac.uk (version 5.0.9) was used to process these data, including removal of non-brain tissue, correction of head movement and eddy currents, rotation of the B-matrix  system at the Hospital for Sick Children, using a 12-channel head coil. Anatomical scans were acquired using a three-dimensional T1-weighted MPRAGE sequence . Diffusion data were acquired using a two-dimensional diffusion-weighted echoplanar imaging sequence . FSL software : MRIB’s Diffusion Toolbox: B-matrix , and to B-matrix  includinB-matrix  using thhttp://stnava.github.io/ANTs/). Since these were derived from functional brain imaging, there would not necessarily be direct structural connectivity (e.g. IFG to ACC) within our brain map, therefore we added two waypoints with previously established structural as well as functional connectivity to our original nodes, that is, the posterior cingulate (The f-MRI brain map of choice that was used as seeds for tractography (described later) was a group contrast map from our prior study , containingulate , 51 and ingulate . Althougingulate , and repingulate .https://fsl.fmrib.ox.ac.uk, version 5.0.9 . The Benjamini-Hochberg False Discovery Rate (FDR) was applied for multiple-comparison corrections.Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were extracted from probabilistic tractography findings using the FSL software output. For tract thresholding , we used a validity cutoff of 150 streamlines passing through a voxel. The SPSSThe tracts where ADHD differentiated from controls represented our tracts of interest for correlation analyses involving the behavioral measures. Correlations were performed between SWAN scale scores, that is, both hyperactivity-impulsivity and inattention scores, with white matter indices  using Pearson’s coefficient. Correlations between the same white matter indices and inhibitory control (SSRT) were also performed for these specific tracts. Although we were only interested in the correlations implicating those tracts where ADHD differentiated from the controls, the Benjamini-Hochberg False Discovery Rate (FDR) multiple-comparison testing was done for the correlations considering all tracts. Finally, SWAN scale hyperactivity-impulsivity and inattention scores were correlated with SSRT.See via the cingulum. In the left hemisphere, valid tracts were found from insula to caudate, insula to inferior parietal (IFP), insula to middle temporal lobe (MTL), insula to posterior cingulate, ACC to caudate, caudate to posterior cingulate, caudate to MTL, and caudate to IFP. Connectivity was not found between insula and ACC. The majority of the association tracts in the left hemisphere belonged to left superior longitudinal fasciculus (SLF), left IFOF, and cingulum. The SLF tract connected insula, IFP, and MTL nodes, whereas the IFOF connected insula and posterior cingulate. Finally, the SLF tract was also found to connect the reactive inhibition seeds, these being, right middle frontal gyrus to caudate and right medial frontal gyrus to caudate.Within the inhibitory control network, there were some white matter integrity differences (i.e. in FA) in specific areas, detailed in For correlations involving inhibitory control (i.e. SSRT), we excluded the four subjects who could not withhold their ADHD medication 24 hours before study day. The mean SSRT of these four medicated children was 265 ms, which was significantly quicker (p= 0.058) compared to the rest of the group, whose mean was 408 ms ±170 ms. Notable in the medication status was that more than half of the ADHD group was not medicated for the prior six months, often due to the fact that it was their first diagnostic assessment for ADHD. We did perform t-testing to contrast if the unmedicated differed from the medicated subjects in terms of white matter indices, SWAN scores, and SSRTs, and our findings were negative. Thus, the 6-month medication status did not impact our results. Finally, given the sex differences between the groups, it was added as a co-variate in an ANOVA to assess impact and was found to not be significant.In terms of inhibitory control and ADHD severity, we found that poorer inhibitory control (i.e. slower SSRTs) correlated with the degree of hyperactivity-impulsivity SWAN scale score , rather than the inattention score . This finding subsequently led us to correlate white matter changes in our In terms of inhibitory control and white matter changes detailed in . Interestingly, although near statistical significance, IFG to STG white matter differences between ADHD and controls replicated our previous pilot work findings , one can conceptualize that it could represent a neural substrate of altered communication between ventral attentional and default mode (resting) networks, which has been proposed theoretically as a potential pathophysiological mechanism for poor inhibitory control in ADHD , 56. A rOur finding of IFOF involvement in inhibitory control is in line with previous studies. In healthy adults, whole-brain FA and RD correlated specifically with SSRT and not overall reaction time, and this was significant within the right IFOF and other specific regions like the right frontal gyrus . In our The IFOF and other white matter tracts of interest found Figure 1Although still debated, altered FA is an indicator of overall disruption in white matter structure, while increased RD and AD provide more direct indices of disturbed myelination , 63 and Another interesting finding in this study was the lack of differences in white matter indices within the fronto-striatal seeds of the prospective withholding and reactive inhibition phases. This system has been a focus of past brain function studies of SST performance in neurotypical adults as well as in ADHD pathophysiology , 69. HowIn terms of study limitations, a broader control sample, with more equivalent sex ratios, and with SST performance data, could help further elucidate the relationship between SSRT and white matter indices as well as the white matter contrasts, which remained trend differences due to the multiple-comparison effect. It should be noted that although not directly compared to a control sample, the extent of the inhibitory control deficit as measured by the SSRT within our ADHD group was pronounced and comparable to a past large community sample study correlating SSRT with ADHD symptoms . AnotherIn conclusion, the results of this experiment point to specific white matter tracts implicated in inhibitory control processing in ADHD children. These results may help direct us closer to a potential biomarker incorporating brain function, structure together with an important neurocognitive deficit. There is growing evidence that white matter changes can represent endophenotypic markers in psychiatric disorders. For example, white matter volumetric differences within the posterior portion of the right IFOF and grey matter within the right IFG have been found in adult ADHD cases and their first degree relatives, with a correlation between SSRT and degree of white matter disruption . Future The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by Research Ethics Board of Hospital for Sick Children, University of Toronto. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.LT: Lead of the project, including methodology, hypotheses, analyses, writing of manuscript. CH: Statistical and data analytics, including data pre-processing, tractography, statistical review, manuscript review/critique. SA: Diffusion Tensor Imaging expertise and oversight of current data set, overview of tractography, teaching of first author, manuscript review/critique. MB: Contributed the f-MRI data expertise, and provided f-MRI dataset used in initial pilot project, collaborator in current project, manuscript review/critique. DM: Diffusion Tensor Imaging teaching and guidance, senior scientist that reviewed the DTI aspect, manuscript review. EA: Lead in data collection for POND consortium, senior scientist, manuscript review/critique. JL: Expertise in Diffusion Tensor Imaging processing, tractography, senior scientist, manuscript review/critique. RS: Expertise in the application of Stop Signal Task, response inhibition and ADHD, data collection and review, senior scientist, manuscript review and critique. All authors contributed to the article and approved the submitted version.This research was funded by the Canadian Institute of Health Research (CIHR), MOP 82796 and the Ontario Brain Institute (OBI).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.


Emotional symptoms are increasingly considered a core feature of attention deficit/hyperactivity disorder (ADHD). We aimed to quantify the evidence of emotional dysregulation and its respective facets in individuals with adult ADHD compared to healthy controls using meta-analysis.N = 2535) to assess (1) the standardized mean difference in emotion dysregulation (ED) as a general factor and its specific facets  between adults with ADHD and healthy controls; and (2) the association between ADHD symptom severity and ED.Two electronic databases  were reviewed to identify studies. Studies were eligible for inclusion that had reports on any measure of emotion (dys) regulation in adults (> 18 years of age) in clinically diagnosed patients with ADHD as well as healthy control participants. We included a total of 13 studies . With regard to intermediate dimensions of ED, emotional lability exhibited the strongest weighted effect . Furthermore, symptom severity and general ED correlated significantly . Regarding intermediate dimensions of ED, negative emotional responses correlated closely with ADHD symptom severity  and emotional lability .Compared to healthy controls, adults with ADHD revealed significantly higher levels of general ED (Hedges’ g = 1.17, Our findings support ED symptoms as a core feature of ADHD’s psychopathology. With respect to dimensions of ED, emotional lability, and negative emotional responses play a more definitive role in the psychopathology of adults with ADHD. Due to insufficient statistical reports in the included studies, we could not perform meta-regressions to control the role of moderator variables. Attention Deficit Hyperactivity Disorder (ADHD) is characterized by its core symptoms inattention, impulsivity, and hyperactivity . The pasTwo decades after Wender  recognizEmotion regulation includes all processes that unfold over time and are related to the different emotions people have, the intensity of emotions, and how emotions are experienced and expressed . The majAmong those studies and reviews reporting on ED in ADHD, there are at present one systematic review  and one Relying on the ED facets derived by Graziano and Garcia  for chilThis study has been recorded in the international prospective register of systematic reviews (Prospero) in April 2017 with the registration number CRD42017059710. A systematic literature search was undertaken using the electronic databases PubMed and PsychINFO. The literature search was consistent with the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ (PRISMA) statement  and was [ADHD* OR “attention deficit hyperactivity disorder” OR ADD* OR “attention deficit disorder” OR hyperkinetic*] AND [“emotion dysregulation” OR “emotion regulation” OR “mood regulation” OR “mood dysregulation” OR “affect regulation” OR “affect dysregulation” OR emotion OR labil*] AND [adult*].Our search in PubMed yielded 1316 and in PsycINFO 714 abstracts. We also checked the reference lists of included studies for other studies eligible for inclusion. After removing duplicates, abstracts of all articles were screened based on pre-defined inclusion criteria independently by the first author. Inclusion criteria were: (i) report of any self- or third-party measure of emotion, affect, or mood (dys) regulation or emotional lability, (ii) inclusion of clinical samples of adults (> 18 years of age) with ADHD characterized by clinical criteria  and diagnostic procedures, (iii) inclusion of non-ADHD healthy controls. Exclusion criteria were: case reports, conference abstracts, reviews, duplicates and non-English studies. We included only randomized case-control studies that were published in peer-reviewed journals at any time from the inception of the databases. We limited our search to published studies to ensure a level of methodological adequacy and rigor among included studies and to avoid the inevitable problems with securing access to a full set of unpublished studies and the bias that would introduce .After scanning a total number of 2030 studies in order to remove the duplicates, 858 studies failed to be included. In the next step, the abstracts of the studies were checked to clarify whether the subject matter is proper to our Boolean expressions. In this step, 1109 studies were excluded since the theme of ADHD in adulthood and measurements of ED were absent. In addition, studies in language other than English as well as reviews were excluded. Next, all titles fulfilling our inclusion criteria (63 studies) were reviewed in full-text. Data were collected and extracted by two independent reviewers that included ADHD status and diagnostic procedures, emotion regulation/dysregulation as defined above, gender composition , age, comorbidity, and country. When disagreement arose, reviewers consulted with each other until coming to a consensus. Ultimately, 13 studies remained for data extraction  were contacted in order to request the necessary information to derive effect size estimates and confidence limits on the selected indices. When only the standard error of the mean (SEM) was reported, the standard deviation (SD) was calculated by multiplying the SEM by the square root of the sample size . When den < 20), effect sizes were also calculated with a correction factor converting Cohen’s d to Hedges’ g.True effect estimates were computed as adjusted standardized mean differences (Hedges’ g). Meta-analysis was carried out using random-effects models and the results are reported and graphically displayed, as that better conveys data variability . To estiMoreover, to calculate the effect sizes based on correlations, each correlation factor (r) was converted to Fischer’s z. Finally, to report all of a study’s effect sizes in a corresponding unit, Fischer’s z was converted to Hedges’ g.2 statistics were calculated, in which the adopted interpretation amounts are: zero or small heterogeneity for 0 -40%, medium heterogeneity for 40 -70% and high heterogeneity for 70 -90% [To examine the consistency of results and estimate to what degree the calculated average effect sizes of a given study are representative, Q and I 70 -90% .To run all the above-mentioned analyses and demonstrate results via forest plots, we carried out initial calculations using Cochrane RevMan 5 and then repeated the calculations using Meta-Mar (1.1.0), a free online meta-analysis service developed by the first author of this study .Our systematic literature search revealed thirteen qualifying studies. We used ten of them . Cavelti et al. [p < 0.01, for all of them). Moreover, the studies by Bisch et al. [p < 0.01 for all of them). In addition, emotional lability and negative emotional responses were significantly associated in patients with ADHD in investigations by Mitchell et al. [p < 0.01, for all of them).Regarding the studies’ results, Bodalski, Knouse & Kovalev , Caveltii et al. , Irastori et al.  and Rüfei et al.  also reph et al. , Miller h et al. , Rapporth et al.  and Rüfeh et al.  demonstrl et al. , Richardl et al. , Rüfenacl et al.  and Skirl et al.  (p < 0.0p < 0.01). Also, the study by Cavelti et al. [p < 0.001). However, the difference in emotional lability was not significant between patients with ADHD and borderline personality disorders (p = 0.81). Table Furthermore, the study by Corbisiero et al.  was the i et al.  was the g = 1.17  for general emotion dysregulation according to the random effects model , negative emotional responses , and emotion recognition . However, results of an analysis of the variance ANOVA showed that the difference between those specific dimensions was not significant .In answering our study’s first question, namely whether groups with and without ADHD differ in emotion regulation, we noted a large average effect size of r = 0.54  whereas emotional lability revealed a slightly smaller weighted effect of r = 0.52 . However, results of an analysis of the variance (ANOVA) showed that the difference between those specific dimensions was not significant .Answering our study’s second question regarding a correlation between ADHD symptoms in adults and emotion dysregulation dimensions, we found a strong correlation between the severity of ADHD symptoms and ED in general with an average effect size of 2 values are presented in Tables ITo control for any analysis bias, we used funnel plots and Fail-safe N tests. Our between-group analysis results showed that the funnel plot is asymmetric, with the smaller studies tending toward the left of the average effect size. This may indicate that there are studies missing from the right side. Consequently, were there no such probable bias, the average effect size could be larger than the aforementioned amount. In addition, our Fail-safe N test results showed that 1481 studies need to be added to our analysis to reduce the effect size to statistical non-significance , 4845, 4The present meta-analysis was conducted to establish aspects of ED in adulthood ADHD and to differentiate such aspects between ADHD and healthy control groups. Another goal of our study was to assess any association between ED’s features and ADHD symptoms. In line with these research objectives, we identified dimensions of ED based on our adopted conceptual models of ED  by two labels of between-group studies (10), in which data was reported on both groups with ADHD and health controls, and within-group studies (4), in which only data on patients with ADHD was available. At the between-group analysis level, we found that compared to a control group, emotion dysregulation is significantly more pronounced in adults with ADHD with a large effect size (Hedges’ g = 1.17). Furthermore, regarding ED’s intermediate dimensions, emotional lability revealed the largest effect size (Hedges’ g = 1.20). Previous studies demonstrated the relevance of ED for mental and somatic health in general  and andN = 2l . In terms of ED, dimensions, negative emotional responses exhibited the strongest correlation with the core ADHD symptoms (r = 0.63) - findings that concur with the literature , 26, 61,dyscontrol hypothesis and affectivity hypothesis. Based on dyscontrol hypothesis, impairments in the capacity of inhibiting the emotional responses occurs significantly more in hyperactive subgroups of ADHD. In this regard, functional neuroimaging shows anomalies within frontolimbic circuits. According to the affectivity hypothesis, negative-emotionally-responsive behavior in ADHD patients emerges through the route of dysfunctional emotional processing associated with the amygdala and medial prefrontal cortex [Our results demonstrate that emotional lability plays both a significant role in differentiating clinical groups with ADHD from healthy controls and a strong correlation between negative emotional responses and ADHD symptom severity. Referring to adopted conceptual models, these findings might be explained by considering the following: First, the literature suggests that concerning ADHD’s epidemiology in adults, the evidence that hyperactive-impulsive symptoms seem to remit in older age groups may be attributable to adaptive strategies patients develop over the life-span, while inattention symptoms seem to persist –64;). Inl cortex . Emotionl cortex  analysisMost likely, ED in adults with ADHD is a problem persisting from childhood that either was addressed with no specific intervention ), We assumed that different measures of ED are contingent, though that might not always be the case. As the adopted conceptual models of ED converged in the studies included in our meta-analysis, we did not analyze them accordingly.Moreover, reviews have shown that moderators such as gender and cognitive functions  missing studies and t hus omitted from our meta-analysis contributed to asymmetrically distributed effect sizes. In between-group analysis, missing studies would strengthen, and in within-group analysis weaken effects. In addition, the lesser degree of heterogeneity (71%) in our within-group analysis compared to the between-group analysis might be a sample-size problem.In conclusion, our results from the present meta-analysis focusing on the role of emotion dysregulation in adulthood ADHD imply that compared to a control group, ED is a distinct feature of adult ADHD. Furthermore, the severity of ADHD symptoms significantly correlates with dimensions of ED such as emotional lability, emotion recognition, and emotional responses, replicating other studies in the field . In add