Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal  adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only 8 out of 291 participants scored in the clinical range. Subject-specific brain network graphs were modeled from functional MRI resting-state data and revealed significant associations between  ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks. N = 291). Significant associations between ADHD-related behaviors and region-specific profiles of between-module and within-module connectivity were observed in brain regions associated with multiple functional systems including the default-mode network, the salience network, and the central executive system. Our results support a dimensional conceptualization of ADHD and enforce network-based models of ADHD by providing further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions.Neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. We investigate whether these network-based theories of ADHD can be generalized to ADHD-related behaviors within the normal adult population. Subject-specific brain graphs were modeled from functional MRI resting-state data of a large and representative sample ( ADHD is one of the most commonly diagnosed neurodevelopmental disorders with a worldwide prevalence of ∼5.3% . Recent studies broadened the focus toward three-network models of ADHD, proposing that stronger interactions between salience and default-mode network relative to weaker interactions between salience and central executive network reflect a deficient ability of the salience network to adaptively switch between central executive and default-mode network in response to current task demands . Experimental procedures were approved by the NKI Institutional Review Board (no. 239708), and informed written consent was obtained from all participants. Note that data acquisition, preprocessing, and graph-theoretical analyses are to a large degree identical with a previous publication from our research group, which, however, focused on a different outcome measure .The current study was conducted with data acquired at the Nathan S. Kline Institute for Psychiatric Research and online distributed as part of the 1000 Functional Connectomes Project INDI  > 0.2 mm. Thus, our final sample comprises 291 participants . ADHD symptoms were assessed with the M = 99.22, SD = 12.50) as covariate of no interest.Three hundred and one participants with complete phenotypical and neuroimaging data were selected from the Enhanced NKI Rockland sample. Two participants were excluded because of medication with methylphenidate, which can alter neural activation related to ADHD symptoms . A T2*-weighted BOLD-sensitive gradient-echo EPI sequence was measured with the following parameter: 38 transversal axial slices of 3-mm thickness, 120 volumes, field of view (FOV) 216 × 216 mm, repetition time (TR) 2,500 ms, echo time (TE) 30 ms, flip angle 80°, voxel size 3 × 3 × 3 mm, acquisition time 5.05 min. Furthermore, three-dimensional high-resolution anatomical scans were obtained for coregistration with a sagittal T1-weighted, Magnetization Prepared-Rapid Gradient Echo sequence .http://afni.nimh.nih.gov/afni) and FSL (http://www.fmrib.ox.ac.uk/fsl/). The first four EPI volumes were discarded to allow for signal equilibration. Next steps comprised slice-time correction, three-dimensional motion correction, time-series despiking, and spatial smoothing (6-mm-FWHM Gaussian kernel). Four-dimensional mean-based intensity normalization was performed, and data were temporally filtered with a band-pass filter of 0.005–0.1 Hz. Linear and quadratic trends were removed, and all individual EPI volumes were normalized to the MNI152 template (3 × 3 × 3 mm resolution) by using nonlinear transformations and each individual’s anatomical scan. Finally, nine nuisance signals were regressed out, that is, six motion parameters (rigid body transformation) as well as regressors for cerebrospinal fluid , white matter, and global mean signal that were calculated by averaging  voxel-wise BOLD time series within subject-specific masks resulting from FSL’s automatic segmentation (FAST) of the anatomical image. FD was calculated on the basis of the six motion parameters indicating translation/rotation in three directions between two consecutive volumes, FDi = |Δdix| + |Δdiy| + |Δdiz| + |Δαi| + |Δβi| + |Δγi| . In-scanner motion (mean FD) was not significantly related to the variable of interest, that is, ADHD Index . Nevertheless, to further minimize potential remaining influences of head motion on the observed effects, we added mean FD as a control variable in all individual difference analyses. For subsequent graph analyses, data were downsampled by a factor of two, resulting in individual maps of 6 × 6 × 6 mm resolution. The preprocessing scripts used in the current study were released by the 1000 Functional Connectomes Project and are available for download at http://www.nitrc.org/projects/fcon_1000.Preprocessing of neuroimaging data was conducted using the freely available software packages AFNI  were excluded, as they may result from motion artifacts and spuriously high correlations induced by shared nonbiological signals  refers to the subset of edges linking node i to other nodes within the same module m  represents the number of connections within the node’s own module , and mi) and σk(mi) are the mean and standard deviation of the within-module degree distribution of module mi  and can be used to assign each network node into one of seven different classes, which are in turn characteristic for the node’s role in within- and between-module communication  and nodes with zi < 1 were classified as nonhubs. On the basis of the participation coefficientpi, nonhubs were further classified as ultra-peripheral (pi ≤ 0.05), peripheral (0.05 < pi ≤ 0.62), nonhub connector (0.62 < pi ≤ 0.80), or nonhub kinless nodes (pi > 0.80), whereas hubs were classified as provincial (pi ≤ 0.30), connector (0.30 < pi ≤ 0.75), or kinless hubs (pi > 0.75).Functional cartography  and .007 for node-type proportions . To quantify the evidence in favor of the null hypothesis (absence of an association) for nonsignificant correlation results, we calculated Bayes factors . Substantial evidence for the null was accepted at BF01 > 3  were examined using regression models in the Statistic Parametric Mapping software , again controlling for age, sex, handedness, intelligence (FSIQ), and mean FD. The resultant p values were FWE-corrected for multiple comparisons with Monte Carlo-based cluster-level thresholding as implemented in AFNI  measures of modular network organization  is presented in global modularity Q, number of modules, average module size, and variability in module size. Mean values for global modularity Q were all greater than 0.3, indicating a clear modular organization in all participants  cortex, anterior insula, inferior frontal gyrus, superior temporal gyrus, medial temporal structures , inferior parietal lobule, posterior cingulate cortex/precuneus, and in the thalamus. Nodes with high within-module degreezi were observed in large parts of medial prefrontal cortex (again including anterior and mid-cingulate cortex), supplementary motor area, lateral superior and middle frontal gyri, anterior insula, postcentral gyrus, temporo-parietal junction, posterior cingulate cortex/precuneus, middle occipital/lingual gyrus, and in the cuneus.The group-average spatial distribution of the two nodal measures, global modularity Q, number of modules, average module size, and variability in module size) nor the whole-brain proportions of node types were significantly associated with Conners’ ADHD Index , anterior cingulate cortex, posterior-medial superior frontal gyrus (supplementary motor area), as well as in the left inferior parietal lobe . Negative associations were observed in eight clusters, including anterior cingulate gyrus, right lateral middle frontal gyrus, left supplementary motor area, left posterior fusiform gyrus, right intraparietal sulcus, right posterior cingulate cortex/precuneus, posterior middle temporal gyrus adjacent to the occipital cortex, and in the right inferior parietal lobe , in superior parts of postero-medial frontal gyrus, dorsal anterior cingulate cortex, and the precuneus. More laterally located clusters comprised nearly the entire bilateral insulae and reached laterally into the inferior frontal, superior temporal, and inferior parietal lobes , as well as medially into the putamen. Further negatively associated node clusters were observed in right middle and inferior temporal gyri, bilateral thalami, bilateral posterior fusiform gyri, left posterior cingulate cortex, and right inferior occipital cortex. In general, the spatial distribution of significantly associated network nodes showed a surprisingly high degree of interhemispheric symmetry for both measures.Concerning within-module connectivity, ADHD Index was positively associated with odes see . Two extparticipation coefficient and within-module degree. These involve nine of the above-described clusters  or any of the other global measures reported above .One of the few studies that so far investigated the association between ADHD and graph-theoretical brain network characteristics observed higher global modularity may be restricted to clinically affected subjects . However, this test relies on a comparison of 283 healthy subjects with only 8 affected patients. Thus, we finally compared also the 8 subjects with ADHD diagnosis with the 20 subjects of the first post hoc analysis (20 persons with lowest ADHD Index). Also here, there was no significant difference . Similar results were obtained for all other global network measures .To explore the possibility that alterations in FD; all r < 0.11, p > 0.056; Bonferroni corrected threshold: p = 0.0018; see Supporting Information Table S3; Hilger & Fiebach, For comparability with studies that investigated the relationship between ADHD and functional connectivity strength within or between standard  brain networks  the Euclidean distance between the two nodes of this respective edge  in our sample. This was not the case . Nevertheless, we repeated all our analyses with the number of low-motion frames as covariate of no interest (rather than mean FD) and observed that the graph-theoretical results were almost unchanged, that is, only slight changes were observed in respect to pi and zi  may have been affected by distance-dependent motion artifacts that could remain in the data even after motion correction  values of participation coefficientpi and within-module degreezi from all significant clusters , that is, not in the extremes of the whole-brain pi-distribution  in middle frontal gyrus, anterior cingulate cortex, precuneus, and in both central clusters, and very low zi values (rank position <20%) in posterior cingulate cortex, left posterior fusiform gyrus, and in both temporal clusters.Last, to further characterize the functional role of the areas significantly associated with ADHD, we examined whether (a) ADHD-related brain regions have generally higher clusters  and 4 anThe current study investigated whether ADHD-related behaviors are associated with one of the key determinants of human brain function, i.e., brain network modularity. In contrast to previous studies that relied on ADHD vs. control group comparisons, we here applied a correlative approach investigating the appearance of ADHD symptoms across a broad range of variation. These ADHD-related behaviors correlated with region-specific but not global aspects of modularity, consistent with neurocognitive models of ADHD relating intrinsic connectivity between functional brain networks to ADHD. Our results extend these models to the nonclinical range of attention and executive functions.default-mode interference hypothesis  against the presence of such associations.The global modularity in patients with psychiatric conditions , the effectiveness of specific cognitive functions may be supported by less diverse, more focused processing of information within only one or between only few circumscribed modules  other nodes within the same functional module, and is therefore thought to facilitate more segregated specific cognitive functions . ADHD-associated regions, however, do not seem to have particular node-properties . The mechanisms linking individual variations in modular brain network organization and subclinical variations in attention and executive functioning are thus not localized to particularly integrative  ADHD symptoms. Our results support a network perspective of ADHD and suggest that intrinsic functional connections between and within neuronal systems are relevant for a comprehensive understanding of individual variations in ADHD-related cognition and behavior.We demonstrate that nonclinical variations in ADHD symptoms relate significantly to the modular organization of human functional brain networks. Even though ADHD-related behaviors seem to vary independent of The authors thank Caterina Gawrilow for valuable comments on an earlier version of this manuscript. Data were provided by the Nathan S. Kline Institute for Psychiatric Research (NKI), founded and operated by the New York State Office of Mental Health. Principal support for acquisition of the data used in this project was provided by the NIMH BRAINS R01MH094639-01 grant for the principle investigator Michael Milham. Funding for the decompression and augmentation of administrative and phenotypic protocols was provided by a grant from the Child Mind Institute (1FDN2012-1). Additional personnel support was provided by the Center for the Developing Brain at the Child Mind Institute, as well as NIMH R01MH081218, R01MH083246, and R21MH084126.http://fcon_1000.projects.nitrc.org/indi/enhanced/. The code used in the current study has been deposited on GitHub at https://github.com/KirstenHilger/ADHD-Modularity (https://doi.org/10.5281/zenodo.2574588).The data used in the present work were made available by the 1000 Functional Connectomes Project INDI and can be accessed under the following link: Kirsten Hilger: Conceptualization; Formal Analysis; Project Administration; Visualization; Writing - Original Draft. Christian Fiebach: Conceptualization; Supervision; Writing - Original Draft.http://dx.doi.org/10.13039/100000025), Award ID: R01MH081218, support for generating the publicly available Enhanced NKI Rockland dataset. Francisco Xavier Castellanos, National Institute of Mental Health (http://dx.doi.org/10.13039/100000025), Award ID: R01MH083246, support for generating the publicly available Enhanced NKI Rockland dataset. Francisco Xavier Castellanos, National Institute of Mental Health (http://dx.doi.org/10.13039/100000025), Award ID: R21MH084126, support for generating the publicly available Enhanced NKI Rockland dataset. Michael P. Milham, National Institute of Mental Health (http://dx.doi.org/10.13039/100000025), Award ID: R01MH094639-01, support for generating the publicly available Enhanced NKI Rockland dataset.Christian J. Fiebach, German Research Fundation (DFG), AwardID: FI848/6-1. Michael P. Milham, Child Mind Institute, NY 10022, USA, Award ID: 1FDN2012-1, support for generating the publicly available Enhanced NKI Rockland dataset. Francisco Xavier Castellanos, National Institute of Mental Health (Click here for additional data file.


When it fixes O2, it produces 2-phosphoglycolate, which is detoxified by photorespiration and recycled to the Calvin–Benson–Bassham cycle. To complete photorespiration, metabolite transport across three organelles, chloroplasts, peroxisomes, and mitochondria, is necessary through transmembrane transporters. In rice (Oryza sativa) little is known about photorespiratory transmembrane transporters. Here, we identified the rice plastidic glycolate/glycerate translocator 1 (OsPLGG1), a homolog of Arabidopsis PLGG1. OsPLGG1 mutant lines, osplgg1-1, osplgg1-2, and osplgg1-3, showed a growth retardation phenotype, such as pale green leaf, reduced tiller number, and reduced seed grain weight as well as reduced photosynthetic carbon reduction rate due to low activities of photosystem I and II. The plant growth retardation in osplgg1 mutants was rescued under high CO2 condition. Subcellular localization of OsPLGG1–GFP fusion protein, along with its predicted N-terminal transmembrane domain, confirmed that OsPLGG1 is a chloroplast transmembrane protein. Metabolite analysis indicated significant accumulation of photorespiratory metabolites, especially glycolate and glycerate, which have been shown to be transported by the Arabidopsis PLGG1, and changes for a number of metabolites which are not intermediates of photorespiration in the mutants. These results suggest that OsPLGG1 is the functional plastidic glycolate/glycerate transporter, which is necessary for photorespiration and growth in rice.Ribulose-1,5-bisphosphate carboxylase/oxygenase, the key enzyme of photosynthetic carbon fixation, is able to accept both O After the oxygen contents of the atmosphere increased as a consequence of oxygenic photosynthesis by cyanobacteria, algae, and plants, O2 began to compete with CO2 for binding to Rubisco, which has binding affinity to both CO2 and O2. During fixation of O2 the toxic compound 2-phosphoglycolate (2-PG) is produced  fixes the carbon from COproduced , which iproduced .2O2 as a byproduct is decomposed by catalase (CAT). Then, two enzymes, serine:glyoxylate aminotransferase (SGT) and glutamate:glyoxylate aminotransferase (GGT) independently transaminate glyoxylate to glycine. As the amino donors, SGT more likely uses serine and GGT uses glutamate or alanine with a preference of glutamate over alanine. Glycine then moves to mitochondria where it is converted to serine, CO2, and ammonia through a combined reaction of deamination and decarboxylation by serine hydroxymethyltransferase (SHMT) and glycine decarboxylase (GDC). Serine is transported back to peroxisomes where the amino group is donated to glyoxylate by SGT and hydroxypyruvate is produced. The hydroxypyruvate is then reduced to glycerate by hydroxypyruvate reductase (HPR). Glycerate goes back to chloroplasts and is phosphorylated by glycerate kinase (GLYK) to produce 3-PGA, which is finally recycled to RuBP. In this pathway, one molecule of CO2 is released and three carbons are recycled to the Calvin–Benson–Bassham cycle from two molecules of 2-PG  hydrolyzes 2-PG to glycolate, which is exported to peroxisomes across the membranes. Glycolate oxidase (GOX) irreversibly oxidizes glycolate to glyoxylate in peroxisomes, whereas H of 2-PG .Arabidopsis the plastidic DiT1 and DiT2 were firstly identified as 2-oxoglutarate/malate translocator and glutamate/malate translocator, respectively, which are involved in nitrogen recycling during photorespiration  is involved in chloroplast development or functions against cell death due to the visible mutant phenotype of chlorotic leaves in which chloroplasts are destroyed  Dongjin] and all the mutant seeds were germinated and grown on 1/2 Murashige and Skoog (MS) media for 10 days, and subsequently transferred to soil. Rice plants were grown either in a greenhouse with a light/dark cycle of 14/10 h (30/20°C) or in an experimental field plot under natural environmental conditions in summer  under either ambient or 0.3% CO2 condition in an environmental chamber.n summer . To obseArabidopsis Information Resource  was used to obtain the genomic and amino acid sequences of AtPLGG1 (At1g32080). To determine the candidate genes in rice, the BLAST tool of NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi) was used. To predict cDNAs, all available EST and cDNA data from NCBI as well as MSU Rice Genome Annotation Project version 7 were used.The http://signal.salk.edu/cgi-bin/RiceGE). Lines 1A22337, 2D21473, and 3A12105 were chosen for osplgg1-1, osplgg2-1, and osplgg2-2 mutants (cv. Dongjin), respectively  was used as a loading control with primers UBQ5F/UBQ5R  gene by inserting it between the CaMV35S promoter and GFP of a binary vector pH7FWG2  program were used. Two guide RNAs (5′-CCTCTTCTACGTCCCTTCCC-3′ and 5′-ACTGTACTGGGATACATGGT-3′) for osplgg1-2 and osplgg1-3, respectively, were cloned to the pOs-sgRNA vector (Agrobacterium-mediated transformation (To determine an effective protospacer adjacent motif (PAM) and avoid any off-target for CRISPR-Cas9, the CRIPRdirect program  at 1200 µmol mIn vivo chlorophyll fluorescence kinetics were measured using dark-adapted (overnight) leaves of 1-month-old plants grown in the paddy field using an IMAGING-PAM M-Series chlorophyll fluorometer . Fo delineating the minimum fluorescence was determined after turning on the measuring light, and Fm delineating the maximum fluorescence was determined during illumination with an 0.8 s saturating pulse using dark-adapted leaves. Fm′ delineating the maximum fluorescence was measured using leaves under actinic light illumination, and F delineating the fluorescence level was measured just before the determination of Fm′. The photochemical efficiency of photosystem (PS) II or Fv/Fm = (Fm − Fo)/Fm; the effective PS II quantum yield Y(II) = (Fm′ − F)/Fm′; the quantum yield of non-regulated energy dissipation, Y(NO) = F/Fm; and the quantum yield of regulated energy dissipation, Y(NPQ) = (F/Fm′) − (F/Fm); these equations were used during analyzing  for 5 min, Pm′ is the maximal P700 signal measured during actinic light illumination with an 0.8 s saturating pulse, and Pm is the maximal P700 signal measured again after full oxidation of P700 by illuminating far-red light.P700 was determined as described by osplgg1 mutant plants grown in the paddy field were collected in the middle of the day, immediately frozen in liquid nitrogen, and stored at −80°C until further processing. Metabolites were analyzed via gas chromatography–mass spectrometry (GC–MS) using protocols adapted from 3/CH3OH/H2O  mixture including 5 µM ribitol as internal standard pre-cooled to −20°C, then mixed on a rotator for 10 min and centrifuged at 20,000 g for 2 min at 4°C. A total of 50 µl of the supernatant were dried completely in a vacuum concentrator and derivatized in two steps via an MPS-Dual-head autosampler (Gerstel): (1) with 10 µl methoxyamine hydrochloride  and shaking at 37°C for 90 min, (2) adding 90 µl N-Methyl-N-(trimethylsilyl)trifluoroacetamide  and shaking at 37°C for 30 min. After incubation for 2 h at room temperature, 1 µl of derivatized compounds was injected at a flow of 1 ml min−1 with an automatic liner exchange system in conjunction with a cold injection system (Gerstel) in splitless mode (ramping from 50 to 250°C at 12°C s−1) into the GC. Chromatography was performed using a 7890B GC system (Agilent Technologies) with a HP-5MS column with 5% phenyl methyl siloxane film . The oven temperature was held constant at 70°C for 2 min and then ramped at 12.5°C min−1 to 320°C at which it was held constant for 5 min; resulting in a total run time of 27 min. Metabolites were ionized with an electron impact source at 70 V and 200°C source temperature and recorded in a mass range of m/z 60 to m/z 800 at 20 scans per sec with a 7200 GC-QTOF (Agilent Technologies). Metabolites were identified via MassHunter Qualitative  by comparison of spectra to the NIST14 Mass Spectral Library (https://www.nist.gov/srd/nist-standard-reference-database-1a-v14). A standard mixture containing all target compounds at a concentration of 50 µM was processed in parallel to the samples as a response check and retention time reference. Peaks were integrated using MassHunter Quantitative . For relative quantification, all metabolite peak areas were normalized to the corresponding fresh weight used for extraction and the peak area of the internal standard ribitol (Sigma-Aldrich). Three biological replicates with three technical replicates each were tested for this analysis. Student's t-test was performed to show statistically significant differences. Additional experimental details are provided in Uppermost fully expanded leaves of 1-month-old WT and PLGG1 candidate genes in the rice genome, we performed a BLAST search using the previously reported amino acid sequences for the AtPLGG1 (At1g32080) in Arabidopsis. We selected two candidates, named OsPLGG1 (LOC_Os01g32830) and OsPLGG2 (LOC_Os10g42780) from the NCBI database. While the annotation of OsPLGG1 was incorrect [MSU Rice Genome Annotation (Osa1) Release 7], the cDNA sequence (EEE54674.1) from a Chinese group appeared to correctly match that from our WT (cv. Dongjin) and aligned well with its homologs from many plant species (To identify the  species . Those thttp://smart.embl-heidelberg.de/) indicated that OsPLGG1 and OsPLGG2 harbor 10 and 11 transmembrane helices, respectively. Kyoto Encyclopedia of Genes and Genomes search  also showed that like AtPLGG1, both contain LrgA and LrgB domains, components for bacterial cid/lrgAB operons which regulate bacterial programmed cell death and cell lysis (http://www.cbs.dtu.dk/services/TargetP) identified a predicted N-terminal chloroplast targeting peptide with 77 amino acids in OsPLGG1 but not in OsPLGG2. Thus, only OsPLGG1 appeared to retain the chloroplast targeting peptide that is absent in OsPLGG2  of WT (11.8) (osplgg1-1 (osplgg1-1 mimicked that of atplgg1 grown under ambient air condition (osplgg1-1 was rescued to levels comparable to WT under high CO2 (0.3%) in an environmental growth chamber  and osplgg1-3 (T deletion and TCC to CTCT substitution)  in an environmental growth chamber, whereas the pale green leaves and growth retardation remained unchanged under ambient air condition  revealed that PS IIs of OsPLGG1 mutants, osplgg1-1, osplgg1-2, and osplgg1-3, were very sensitive to high light stress, and PS IIs of the mutants were partly damaged even after overnight dark-adaptation (osplgg1 mutants showed fast Y(NPQ) development kinetics in the light and slow decay kinetics in darkness , maltose, methionine, quinate, raffinose, shikimate, succinate, and tyrosine were significantly decreased in all three osplgg1 mutants  CO2 condition . AW appreciates funding by the Deutsche Forschungsgemeinschaft  under Germany's Excellence Strategy—EXC-2048/1—Project ID: 390686111, and the Federal Ministry of Education and Research, Project FormatPlant (FKZ: 031B0194).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Attention deficit hyperactivity disorder (ADHD) is a clinically defined disorder, and inattention and hyperactivity/impulsivity are its main symptom domains. The presentation, lifelong continuation and treatment response of ADHD symptoms, however, is highly heterogeneous. To better define, diagnose, treat and prevent ADHD, it is essential that we understand the biological processes underlying all of these elements. In this review, given the high heritability of ADHD, we discuss how and why genetics can foster such progress. We examine what genetics have taught us so far with regard to ADHD definition, classification, clinical presentation, diagnosis and treatment. Finally, we offer a prospect of what genetic studies on ADHD may bring in the future. The presentation, life-long continuation and treatment response of ADHD symptoms, however, is highly heterogeneous, underscored by the wide array of psychiatric and somatic comorbidities.Attention deficit hyperactivity disorder (ADHD) is a clinically defined disorder, and inattention and hyperactivity/impulsivity are its main symptom domains2), the conceptualization of this disorder moved away from being a consequence of early brain damage to being a multifactorial phenotype, with both genetics and environment affecting its development, trajectory and outcome. Here, we discuss why and how the recent genetic findings on ADHD may shape our understanding of its definition, diagnosis, treatment and prevention.To better define, diagnose, treat and prevent ADHD, it is essential that we understand the biological processes underlying all of these elements. As family and twin studies revealed that genetics contribute to the etiology of ADHD  study that revealed the first genome-wide significant loci associated with ADHD5, offering possibilities to further our understanding of this disorder. Perhaps the most important one is that ADHD appears to be a disorder of central nervous system–specific regulatory elements.The view of ADHD as a multifactorial disorder with a genetic component comes from the clinical complexity observed in ADHD’s symptomatology. ADHD runs in families and co-occurs in identical twins at a much higher rate than in fraternal twins2. This notion has recently been further affirmed by observations of ADHD displaying genetic correlation and overlap with ASD at the levels of both common and rare genetic variation7. In addition, common genetic factors have been shown to contribute to the overall correlation between ADHD and ID (except for profound ID)8. Another observation in favor of ADHD being a neurodevelopmental disorder is the higher prevalence of ADHD among boys compared to girls9. Nonetheless, ADHD also shows genetic overlap with behavioral problems11, and recent genetic study notes that common genetic variation may not explain the sex differences in its diagnosis12, suggesting that the clear-cut classification of ADHD is still an open question.Traditionally, ADHD has been classified as an externalizing behavioral disorder. However, as genetic epidemiological studies have shown high familial overlap between ADHD and autism spectrum disorder (ASD) and between ADHD and intellectual disability (ID), the classification shifted toward neurodevelopmental disorders11, much in the way that hypertension is defined to be the extreme end of blood pressure distribution in a population. To define ADHD in that fashion, we must consider whether ADHD symptoms  display consistent co-occurrence with sufficient degree of intensity and duration to form a biologically and clinically meaningful entity of ADHD. The early (and under-powered) GWA studies of these traits revealed both unique and shared genetic influences on these dimensions of ADHD14. A more recent GWA study of these traits, in more than 37,500 children, showed high genetic correlation between continuous measures of inattention and hyperactivity in children (rg = 73%) and between those two traits and ADHD diagnosis in both children and adults . The exploration of genetic correlations of these dimensions with common psychiatric disorders and traits revealed two distinct patterns of correlations—inattention correlated more with neurodevelopmental phenotypes and hyperactivity/impulsivity correlated more with behavioral problems15—highlighting the dual nature of ADHD as it is defined today. Thus, on the basis of the recent genetic studies, ADHD may be defined not as a unitary disorder with several subtypes but rather as a spectrum disorder whose core symptoms (inattention or hyperactivity/impulsivity or both) interfere with an individual’s functioning in important life aspects. In fact, such change in conceptualization of ADHD may already be seen in the latest version of theDiagnostic and Statistical Manual of Mental Disorders (DSM), where the three ADHD “subtypes” have been substituted by three ADHD “representations” 16.Also, traditionally, ADHD has been defined as a unitary disorder with a number of subtypes . One way to evaluate such a definition is to explore the notion that ADHD cases may be defined as extremes of the distribution of ADHD symptoms (both inattention and hyperactivity/impulsivity)17) and the DSM —base a clinical diagnosis of ADHD (or hyperkinetic disorder (HKD) in ICD-10) on the two sets of symptom domains: inattention and hyperactivity/impulsivity. Although ICD-10 and DSM-5 operate with the same two symptom domains to define ADHD/HKD, the diagnosis of ASD or bipolar disorder precludes the diagnosis of HKD in ICD-10, whereas DSM-5 does allow the presence of diagnoses of both ADHD and ASD. The diagnostic criterion of ICD-10 is in direct conflict with recent findings that ADHD and ASD do have a common genetic  component7. This highlights the recent perception that the current diagnostic scheme for ADHD (and many other major psychiatric disorders) is not reflective of its underlying biological foundation and that the eventual goal is to move away from clinically defined diagnoses to molecularly defined ones19.The diagnosis of ADHD relies heavily on how we define it. The two diagnostic systems of contemporary psychiatry—the International Statistical Classification of Diseases and Related Health Problems , the diagnosis of ADHD faces the questions of which symptoms to consider and to what extent. In ICD-10, for example, the HKD is a unity of symptoms , all symptoms must be exhibited in more than one setting  and the presence of comorbidities is practically not allowed. In contrast, the DSM-5 distinguishes three different diagnostic ADHD presentations , the symptoms need to be present in only some settings and the presence of comorbidities is freely allowed (as exemplified by ASD above). Given this discordant view of ADHD diagnosis between the two major diagnostic systems and given that recent genetic studies on ADHD revealed that it exhibits an extensive genetic overlap with a wide range of psychiatric disorders20. The major obstacle to developing a more effective treatment for ADHD is our limited understanding of what causes the disorder and the mechanism (or mechanisms) through which the current pharmaceuticals are acting on ADHD. The barriers to progress are many and varied, but the inaccessibility of live human brain tissues makes progress in the neurobiological basis of ADHD particularly challenging. One option to circumvent this challenge is to use induced pluripotent stem cells that could provide a promising avenue for downstream molecular interrogation of genome-wide significant loci21. Although arguably a clinician could treat a disorder without understanding it, we must make a distinction between symptom alleviation and a cure. Currently, all of the existing treatment options for ADHD  offer symptomatic relief only22.It has been reported that the current pharmacological ADHD treatment is effective in about 70% of cases24 that may help to circumvent the current limitations of drug development and re-purposing. Using data from the first well-powered GWA study on ADHD5, the examination of the association between ADHD and the genes encoding the targets of the first-line US Food and Drug Administration (FDA)-approved pharmacological agents for ADHD treatment revealed no significant findings22, suggesting that those pharmaceuticals may act through mechanisms other than the ones underlying ADHD .With the recent technological advances and large collaborative efforts, more and more large-scale GWA studies are becoming available on a variety of somatic and psychiatric phenotypes, including ADHD. These studies are an important source of information for the rapidly evolving field of ADHD pharmacogenetics25.The current FDA-approved treatments for ADHD are primarily thought to enhance catecholamine signaling. However, such a narrow pharmacological target stands in contrast to the complexity of emerging genetic findings, which suggest that other avenues of therapeutic intervention may be possible. As we learn more about the biological basis of ADHD, these findings could enable the development of new drugs through different mechanisms of actions. Furthermore, drug re-purposing of already-approved compounds and treatments may be a faster path to improving the quality of care for patients. One way to nominate such potential treatments might be to evaluate treatment options for traits with high genetic correlation to ADHD, motivating the systematic evaluation of genetic overlap between ADHD and other phenotypes26.A potentially successful example of drug re-purposing guided by genetic studies of ADHD is the trial use of fasoracetam as a treatment for this disorder. Originally developed as pharmacotherapy for vascular dementia, fasoracetam has been successfully used in a clinical trial to treat ADHD in adolescents with disrupted glutamatergic signaling that has been shown to be associated with ADHD27.Although the re-purposing and development of new pharmacotherapeutics for ADHD takes time, it is important to note that the mere shift in understanding of ADHD as a multifactorial disorder with a genetic component may help patients in their management of the disorder5, but those estimates are not accurate enough for diagnostic purposes in clinical settings. As the power of genetic studies improves, the assessment of the number and the effect sizes of genetic variants robustly associated with ADHD will also improve, increasing the potential of common variants to become a helpful tool in a clinical setting, much in the way that polygenic risk score (PRS) is used in coronary heart disease30. In the meantime, although the diagnostic usefulness of common genetic variants is still far from reality for ADHD, the genetic profile of a cumulative number of ADHD risk alleles (PRS) can be of benefit for patients whose ADHD has already been diagnosed as, in the near future, PRS is more likely to aid the prognosis of ADHD, especially in combination with additional non-genetic information .To reliably screen individuals for ADHD on the basis of common genetic variants, we first need to establish the true effect sizes of the variants associated with the disorder. So far, only one relatively well-powered GWA study on ADHD has provided estimates of these effects31, there is little evidence to support the need for genetic testing based on rare variants, especially as none of those variants is ADHD-specific.The clinical utility of rare genetic variants, in contrast to that of common ones, tends to be stronger as their penetrance  tends to be much higher. However, despite recent studies showing genetic overlap between ADHD and neurodevelopmental disordersFollowing in footsteps of the first genome-wide significant ADHD loci discovery, we must next replicate and understand these findings. GWA studies in independent large(r) samples are expected to shed light on the validity of these loci and examination of their functionality will aid our understanding of biological processes underlying ADHD. Thus, further work at the molecular level of neural cells, systems and circuits can be anticipated from both bioinformatics and experimental systems biology.32. As large phenotypically informative and genotyped cohorts become more available, it will be possible to address questions of biological background of ADHD continuation throughout life  and determine periods critical for the development, lifelong trajectory and treatment of this disorder.There is a growing interest in investigation of ADHD across the life span as it has been noted that persistence of ADHD symptoms is associated with a high number of genetic ADHD risk variants that an individual may possessIn addition, such cohorts will allow the examination of the causal impact of loci associated with ADHD that may help elucidate the reasons behind high correlations between ADHD and a wide range of psychiatric and somatic disorders and traits.33, assumed that this disorder can be influenced only by the genetics of an individual with ADHD (direct genetic effects). However, the expression of a phenotype in an individual is influenced not only by their own genotype (direct genetic effect) but also by the genotype of people in their environment, such as their mother, father, or siblings (indirect genetic effects)34. The evaluation of environment’s role in the development of ADHD could also benefit from gene–environment interaction studies. However, probing the environmental effects in ADHD is often limited by gene–environment correlation where the association between ADHD and an environmental factor can be the result of inherited confounds35. The disentangling of these direct and indirect  effects has the potential to advance our understanding of such long-standing observations as missing heritability , sex differences in ADHD prevalence, variability in persistence of ADHD symptoms across a life span and non-Mendelian forms of ADHD inheritance and aid in ADHD prevention and treatment.One branch of genetics that has received little attention in ADHD so far is the examination of direct and indirect  genetic effects influencing ADHD. To date, all genome-wide genetic studies on ADHD, except for one carried out by Wang and colleaguesFinally, the recently evolving branches of genetics can also elucidate the pharmacology of ADHD (pharmacogenetics) and environmental effects critical for clinical aspects of ADHD .


The prevalence and functional impairment for OCD–ADHD appears substantial. A recent study has shown a prevalence rate of 11.8% for co-morbid ADHD in OCD-affected individuals . Co-morbPreviously, ADHD and OCD were viewed very distinctly on an impulsive–compulsive continuum. However, there has been increasing awareness of the cross-cutting nature of the symptoms domains of impulsivity and compulsivity . ImpulsiThere is overlap between the two conditions with features such as impulsivity observed in both groups but at markedly higher levels in those with ADHD .Frontostriatal pathophysiology appears to be involved in both disorders see . PredomiWhile ADHD is characterized with frontostriatal hypoactivity , OCD comThere are also disorder-specific structural abnormalities. Patients with OCD tend to have larger and hyperfunctioning insular-striatal regions, whereas those with ADHD have smaller and underfunctioning ventrolateral prefrontal/insular-striatal regions .The degree of frontostriatal hyperactivity or hypoactivity also appears to underpin symptoms in both OCD and ADHD. In ADHD, there is an inverse correlation between impulsive symptoms with activity in prefrontal , 18 and However, despite these differences, OCD and ADHD appear to share similar neuropsychological impairments in executive functions, particularly inhibition deficit . This suBased on current neurobiological knowledge, OCD and ADHD appear to have different and apparently opposing deficits in the frontostriatal and related areas. This may have treatment implications in co-morbid OCD–ADHD.The effectiveness of selective serotonin reuptake inhibitors (SSRIs) in the treatment of OCD has been well established . These aOn the other hand, first line treatment for ADHD has been methylphenidate or dexamphetamine for several decades. Stimulant medication increases prefrontal activation and significantly improves both clinical symptomology and neurocognitive processes through modulation of dopamine reuptake. In addition, ADHD-tailored CBT which address academic and organizational skills training, problem-solving and prioritizing tasks, as well as managing unhelpful thinking and behavioral patterns, demonstrate encouraging improvements in adolescents and adults , 32.Some literature suggests that stimulants can exacerbate and provoke OCD symptoms. While this may be consistent with the theoretical understanding of dopaminergic prefrontal hyperactivity, this evidence is mostly anecdotal and remains limited –37.In terms of co-morbid OCD–ADHD, there is evidence to support that treating OCD with SSRIs and cognitive behaviour therapy with exposure response prevention (CBT+ERP) improves attentional symptoms . This maThere is also evidence that treating ADHD with stimulants improves co-morbid obsessive–compulsive symptoms. CBT+ERP has demonstrated efficacy for children and adults with OCD, but untreated co-morbid ADHD diminishes treatment response on the OCD . StimulaLimiting treatment to only one disorder when both are present appears to be associated with poorer outcomes. A study investigating the use of paroxetine in patients with co-morbid ADHD found that the OCD response rates were significantly less than in patients with OCD alone. The presence of ADHD co-morbidity was also associated with a greater rate of OCD relapse . ConversIt would therefore seem prudent to treat both conditions concurrently when they are both present and severe enough to warrant biological and psychological treatments. Additionally, screening for disorders of addiction is important due to the heightened cumulative risk in these individuals. Despite the lack of randomized controlled trials in clinical populations, it would be reasonable to start with standard treatments for both, such as SSRIs for OCD and psychostimulants such as methylphenidate and dexamphetamine for ADHD. Pharmacological treatments should be introduced one at a time to avoid confusion around treatment response and side-effects. This is especially relevant given the possibility of worsening OCD with stimulants. Monitoring progress through routine use of established instruments such as the Yale Brown Obsessive Compulsive Scale for OCD  and ADHDTranscranial magnetic stimulation (TMS) has a limited but growing body of evidence for its efficacy in both ADHD –48 and OThe treatment of OCD–ADHD co-morbidity in children, and to an even greater extent in adults, remains challenging. ADHD and OCD are not mutually exclusive and do co-exist . They shJK, SC, and ND contributed to the conception of the paper. SC wrote the first draft of the manuscript, with contributions from ND. JK significantly revised the manuscript into its near completed form. CN provided final corrections and additions. All authors read and approved the submitted version.JK has received speaker honorarium from Servier. CN had served as a consultant for Lundbeck, Grunbiotics, Servier, Janssen-Cilag, Wyeth and Eli Lilly, received research grant support from Wyeth and Lundbeck, and speaker honoraria from Servier, Lundbeck, Bristol-Myers Squibb, Organon, Eli Lilly, GlaxoSmithKline, Janssen- Cilag, Astra-Zenaca, Wyeth, and Pfizer.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A recent study  found that attention-deficit/hyperactivity disorder (ADHD) was associated with hypo-arousal, indexed by low electrodermal activity, during a low-demand reaction-time task, which normalized in a fast-incentive condition. We now investigate if (1) autonomic arousal in individuals with ADHD changes over a long testing session and (2) across time, to clarify if arousal profiles are context-dependent. We also examine (3) how autonomic arousal relates to each ADHD symptom domain, and specificity of arousal profiles to ADHD, by controlling for oppositional defiant/conduct disorder (ODD/CD) symptoms. Skin conductance level and non-specific fluctuations were measured during four successive resting-state and cognitive conditions  from 71 adolescents/young adults with ADHD and 140 controls. Lower arousal was observed in individuals with ADHD only during a slow, low-demanding task, and more fluctuating arousal was observed towards the end of assessment. Both inattentive and hyperactive-impulsive symptoms were associated with arousal levels and fluctuations, independently from ODD/CD. Overall, we extend previous findings showing that under-arousal, but also fluctuating arousal, are context-specific rather than stable impairments in ADHD. The state regulation and cognitive-energetic accounts suggested that a sub-optimal arousal state in ADHD may lead to inconsistent cognitive performance, reflected for example by within-subject fluctuations in reaction time . Recent Skin conductance provides an objective and reliable index of arousal in the peripheral nervous system . SC is aSeveral studies have reported attenuated skin conductance level in children with ADHD, compared to controls, indicating hypo-arousal during resting-state (eyes open and eyes closed) and task conditions . Researcr = −0.45) and weakly-to-moderately correlated with hyperactive-impulsive (r = −0.23) symptoms, in individuals with and without ADHD  task condition of a four-choice reaction time called the Fast Task, but this was normalized in a more stimulating fast-incentive condition . These fIn this study we firstly aim to (1) extend initial findings from In all analyses, we also aim to investigate if associations between ADHD and arousal are independent of oppositional defiant disorder and conduct disorder (ODD/CD) symptoms, which frequently co-occur with ADHD and have previously been associated with lower skin conductance level and skin conductance responses . One sma22.1The original sample  consisted of 275 participants, followed-up on average 5.8 years (SD = 1.1) after initial assessments. At follow-up, participants were on average 18.0 years of age (age range: 11.1–25.9). 108 participants had a diagnosis of DSM-IV combined type ADHD in childhood  and 167 were controls .n = 6) on the Barkley Informant Rating Scale , gender , but did differ on IQ scores , out of our original sample of 256, as skin conductance data collection equipment did not arrive until after the initial participants had been assessed. We additionally excluded participants within each testing condition  who experienced skin conductance equipment failure or extreme drowsiness. The final sample consisted of 71 ADHD probands and 140 controls. The ADHD and control groups did not differ in age  were administered to derive an IQ estimate . The WAS2.2.4Participants were asked to keep as still as possible while resting in a chair with their eyes open before and after the cognitive assessments. They were encouraged to find a spot on the wall in front of them where they could fixate their gaze. The resting-state sessions each lasted for 3 min.2.2.5The baseline condition of the Fast Task consists of 72 trials, which followed a standard warned four-choice RT task. Four empty circles  first appeared for 8 s, after which one of them (the target) was colored in. Participants were asked to press the response key that corresponded to the target position. Following a response, the stimuli disappeared and a fixed inter-trial interval of 2.5 s followed. Speed and accuracy were emphasized equally. If participants did not respond within 10 s, the trial terminated. A comparison condition of 80 trials with a fast event rate (fore-period of 1 s) and incentives followed the baseline condition. The fast-incentive condition is always administered after the baseline condition. Owing to the longer fore-period in the slow condition, the two conditions were not matched on task length, but they were matched on the number of trials.2.2.6This CPT-OX  includes2.3Participants were re-contacted by telephone and scheduled for a follow-up clinical interview and cognitive assessments at our research Centre while electrodermal and electroencephalogram (EEG) measures were recorded. Before the cognitive assessments, participants were asked to remain still and rest with their eyes open while fixating at a point in front of them for 3 min. They then performed the CPT-OX for 11 min, followed by the Fast Task baseline condition for 13 min, and were asked to rest again with their eyes open for 3 min at the end of the testing session. A 48 h ADHD medication-free period was required and the participants were also asked to abstain from caffeine, smoking, and alcohol on the day of testing.2.4Skin conductance response was recorded using PSYCHLAB SC5 24 bit equipment system, which has an absolute accuracy of ±0.1 microsiemens. The SC5 is connected to a computer that runs the PSYCHLAB software where the data can be monitored and recorded in real time and parameters can be set. Skin conductance was measured by attaching a pair of 8 mm diameter silver-silver chloride electrodes on the palm of participants’ non-dominant hand (thenar eminence and hypothenar eminence) at the beginning of the cognitive test battery. An electrode paste, formulated with 0.5% saline in a neutral lotion/cream style base (provided by PSYCHLAB), was used to establish a stable electrical skin conductance signal. The SC5 is DC coupled (infinite time constant), and a constant imperceptible voltage (0.5 V) was applied. SC5 automatically calibrates itself when switched on and then runs at a fixed internal sample rate of 80 Hz and an additionally 10 Hz filter is applied to response signal to prevent aliasing.Skin conductance variables were calculated using a in-house system that is based on a skin conductance sigmoid-exponential model that allows the tonic measure of skin conductance level to be disentangled from phasic skin conductance fluctuations and allows the decomposition of overlapping skin conductance fluctuations . The sta2.5We ran regression models to investigate ADHD case-control group differences in skin conductance level and non-specific fluctuation measures within each testing condition . Relatedness between sibling pairs was controlled for by using the ‘robust cluster’ command in STATA . Random-intercept linear models were used to test the main and interaction effects of group  and time  on skin conductance level and non-specific fluctuations, to examine the change in autonomic arousal in ADHD and control groups over time. Random-intercept models control for clustered data, due to relatedness between siblings, and handle missing data using the maximum likelihood method, which in turn reduces the loss in power from missing data points. We re-ran all analyses controlling for ODD/CD symptoms (using DAWBA) to examine if any identified ADHD case-control differences could be explained by co-occurring ODD/CD symptoms. We did not control for internalizing symptomology based on prior work with this sample that showed that controlling for internalizing symptoms did not influence the results .We ran linear regression models to investigate the associations of skin conductance measures with inattentive and hyperactive-impulsive symptoms, respectively, and added an interaction term (skin conductance*ADHD group) to investigate if the strength of the associations were different in the ADHD and control groups. We tested these associations only in conditions that showed sensitivity to ADHD as indicated by a significant case-control difference in skin conductance measures. We re-ran all analyses controlling for ODD/CD symptoms.We re-ran the main analyses on ADHD case-control group comparisons with IQ added as a covariate to examine its potential effects. We further ran sensitivity analyses testing age and gender as covariates in the main analyses in line with previous analyses in the same sample . The eff33.1p > 0.05) between the ADHD and control groups in non-specific fluctuations or skin conductance level during the two initial testing conditions (Resting-state time 1 and CPT-OX task performance) and the Fast-Incentive condition of the Fast Task  than individuals in the control group  = 6.87, p < 0.001). After controlling for ODD/CD symptoms in the models, skin conductance level findings of case-control differences did not change but non-specific fluctuation findings during the Baseline condition of the Fast Task changed slightly, in regards of the effect size (Cohen's d: from 0.33 to 0.27) and p-value . Post-hoc analyses when controlling for ODD/CD symptoms revealed significant increases in non-specific fluctuations from resting-state time 1 to time 2 in the ADHD group , but not in the control group .The random-intercept models revealed significant main effects of time (Resting-state time 1 vs 2) on non-specific fluctuations and skin conductance level , showingAs the group-by-time interaction effect on non-specific fluctuations emerged as significant after controlling for ODD/CD symptoms, we decided to run regression analyses to explore the associations between ODD/CD symptoms and skin conductance measures, within each group  and each condition. We found no significant associations between ODD/CD symptoms and skin conductance in any of the groups or conditions (Table A.1).3.3Beta = 0.13, p = 0.01), but not in the control group  . No other interaction terms (skin conductance*group) were significant  with IQ added as a covariate (Table A.2). We found that the pattern of results remained the same for non-specific fluctuations but for skin conductance level the ADHD case-control difference during the Baseline condition of the Fast Task was no longer significant and the effect size (Cohen's d) changed from −0.49 to −0.09 , althougDue to the changes in results after controlling for IQ in the skin conductance level analyses (Table A.2), we ran additional sensitivity analyses to investigate the associations between IQ and skin conductance measures across conditions (see Table A.3 and A.5).t(34) = 0.68, p = 0.50) or non-specific fluctuations (t(35) = −0.48, p = 0.64) between unmedicated and medicated participants.We further re-ran the main pairwise comparisons of groups (ADHD vs Control) with age and gender added as covariates and found that the pattern of findings remained the same in terms of significance of findings (Table A.4). Furthermore, analyses were re-run excluding siblings, and the pattern of findings remained consistent without these cases of non-independence. We also ran skin conductance comparison tests between unmedicated and medicated participants with ADHD to investigate the long-term effects of medication. Short-term effects of medication were controlled for, as participants were asked to have a 48 h medication-free period before testing. There were no significant differences in skin conductance level .As we found significant ADHD case-control differences in skin conductance during the 13 min long Baseline condition of the Fast Task but not during the 11 min long CPT-OX, we aimed to explore whether the significant differences during the Baseline condition of the Fast Task emerged because of the longer testing session, rather than the order or nature of the task. 3.5p1) to high (pm), where m represents the total number of p-values from the main analyses (m = 28). The largest k was then identified such that pk < 0.05 * k/m and the adjusted alpha level of 0.05 * k/m was 0.011. All of the reported significant effects relating to skin conductance level had p-values below the adjusted alpha. However, several of the reported effects in relation to skin conductance fluctuations had p-values above the adjusted alpha level: the ADHD case-control difference in non-specific fluctuations in both the Baseline condition of the Fast Task (p = 0.04) and the Resting-state time 2 (p = 0.03), the Group*Time interaction effect after controlling for ODD/CD (p = 0.045) and the associations between ADHD symptoms and non-specific fluctuations during Resting-state time 2 (p = 0.02). These latter results should therefore be interpreted with caution and may represent trends rather than significant effects.A false discovery rate-controlling analysis  was cond4In this large physiological study of 211 adolescents and young adults, we found that autonomic arousal profiles of individuals with ADHD varied across testing conditions. First of all, ADHD case-control differences in tonic arousal, indexed by skin conductance level, only emerged during a slow and low-demanding cognitive task. Case-control differences in phasic arousal, indexed by non-specific fluctuations, emerged towards the end of the assessments, during the low-demanding cognitive task and the final resting-state condition (time 2). Further analyses showed that case-control differences in phasic arousal, but not tonic arousal, emerged over time from resting-state time 1 to time 2, once ODD/CD symptoms were controlled for. It is, however, important to note that after correcting for multiple testing, the findings on phasic arousal emerged as trends rather than as significant effects. Lastly, both ADHD symptom domains were significantly associated with lower levels of tonic arousal and more fluctuating arousal, independently of ODD/CD symptoms. Overall, our findings suggest that individuals with ADHD experience difficulties regulating their arousal rather than being constantly under-aroused. Inconsistent findings in the literature on autonomic arousal in ADHD might be explained by differences in experimental designs and tasks.Extending the initial report from We further found that individuals with ADHD displayed more fluctuating arousal, indexed as a higher number of non-specific fluctuations per second, compared to controls, during the baseline condition of the Fast Task and Resting-state time 2 only. These effects were however not significant when false discovery rate-controlling analysis was applied and may therefore be interpreted as trends. These findings suggest that arousal variability in ADHD, similarly to under-arousal, may become more salient during slower and low-demanding tasks, but also towards the end of assessment, over time. This is further supported by our sensitivity analysis showing that the ADHD case-control difference in non-specific fluctuations during the 13 min long Fast Task was no longer significant when we shortened the task to closer match the lengths of the CPT-OX and Fast-Incentive condition of the Fast Task. These findings indicate that more fluctuations in ADHD may become especially salient over time, possibly in combination with the low-demanding task.Our results further showed that the fluctuations in and level of arousal increased over time, from resting-state time 1 to time 2. When we tested the group-by-time interaction on fluctuations, the effect emerged as significant  once ODD/CD symptoms were controlled for and post-hoc analyses revealed that the fluctuations in arousal increased over time only in the ADHD group. These findings further suggest that fluctuating arousal profiles in ADHD, relative to controls, become more salient over time; an effect that is enhanced by controlling for other co-occurring externalizing behaviors.Non-specific fluctuations were associated with both inattentive and hyperactive-impulsive symptoms, across groups, in the testing conditions that showed case-control differences, with the only exception of the Fast Task (Baseline condition) where non-specific fluctuations and inattention were only significantly associated in the ADHD group. Overall, we found that individuals with ADHD did not show stable abnormalities in fluctuating arousal, similarly to under-arousal, which may in turn explain highly inconsistent findings in the literature where different experimental designs have been used. The direction of effects is in line with findings from one previous study that showed a trend of more non-specific fluctuations in children with ADHD compared to controls , but is This is the first larger study, to our knowledge, to investigate the specificity of both phasic and tonic arousal profiles in young adults with ADHD by controlling for ODD/CD symptoms in analyses. ODD/CD symptoms did not account for our findings on atypical tonic arousal profiles in ADHD, which is in line with previous research . For phaA limitation of this study is that we used measures of non-specific fluctuations averaged across each of the CPT-OX and Fast Task conditions, as we were unable to retrieve event codes. This means that we could not tease apart skin conductance fluctuations during stimuli presentation and response execution from skin conductance fluctuations during no task events. It would have been interesting to study both event-specific and non-specific fluctuations separately to explore how they each are implicated in ADHD, however, given that very few studies have investigated arousal variability in ADHD, we believe it is still meaningful to study average fluctuations in our rich dataset that spans across a long testing session. Another issue to highlight is that we had limited power to account for all relevant covariates in one model, however, we controlled for these variables in separate models, which allowed us to gain an understanding of their unique impact on the results. Finally, we found that the ADHD case-control difference in tonic arousal was largely accounted for by IQ, suggesting that it is important to take IQ into consideration in future studies that investigate arousal levels in ADHD.4.1p-values above the adjusted alpha level. The significant association between phasic arousal during the baseline condition of the Fast Task and ADHD symptoms, however, remained as significant after the multiple testing correction. Future replication of our results is important to validate findings before drawing firm conclusions and applying implications of findings in practice.We did not correct for multiple testing in our initial main analyses because of the exploratory nature of the investigations into context effects on ADHD case-control differences in skin conductance level and non-specific fluctuations. In our study, analyses were restricted to skin conductance measures that were expected to be sensitive to impairments in ADHD, in order to reduce the chance for false negative findings from multiple testing. Further, in the interpretation of results, the emphasis was places on both effect sizes and significance to provide a complete picture of the full impact of results. We did however run sensitivity tests where we applied a false-discovery rate-controlling analysis. While all analyses on tonic arousal remained significant, several of the analyses on phasic arousal emerged as trends rather as significant, with 5We found that adolescents and young adults with ADHD displayed lower levels of and more fluctuating autonomic arousal under certain experimental conditions. ADHD case-control differences in tonic arousal emerged only during a slow, low-demanding cognitive task. A case-control difference in phasic arousal was also observed during the low-demanding task and also towards the end of the assessment. We further found that tonic and phasic arousal were associated with both inattentive and hyperactive-impulsive symptoms, independently of ODD/CD symptoms. Our findings suggest that both tonic and phasic autonomic arousal profiles in ADHD are context-specific rather than representing stable impairments. Our findings also highlight how inconsistent findings in the ADHD literature on arousal may be explained by differences in experimental paradigms used across studies.National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, and by an NIHR senior Investigator Award (NF-SI-0616-10040). Professor Asherson has acted in an advisory role for Shire, Janssen-Cilag, Eli-Lillyand Flynn Pharma. He has received education or research grants from Shire, Janssen-Cilagand Eli-Lilly. He has given talks at educational events sponsored by the above companies. Professor Daniel Brandeis serves as an unpaid scientific advisor for an EU funded neurofeedbackstudy. Professor Jonna Kuntsi has given talks at educational events sponsored by Medice; all funds are received by King's College London and used for studies of ADHD. Dr Ebba Du Rietz and Dr Sarah Naomi James report no biomedical financial interests or potential conflicts of interest.Professor Banaschewski has served as adviser or consultant for Bristol Myers-Squibb, Develco Pharma, Lilly, Medice, Novartis, Shire, and Vifor Pharma; he has received conference attendance support and conference support or speakers honoraria from Janssen McNeil, Lilly, Medice, Novartis, and Shire and has been involved in clinical trials conducted by Lilly and Shire. Professor Asherson is supported by the


N = 134; aged 12–15) from a subsample of the Twins’ Early Development Study, concordant and discordant for ADHD symptoms, performed the flanked CPT (or CPT-OX) while electroencephalography (EEG) was recorded. ERPs were obtained for cue , go  and nogo trials . We found no phenotypic associations between CPT-derived ERPs and ADHD—the sizes of the estimated phenotypic correlations were nonsignificant and very small (r’s = -.11 to .04). Twin-model fitting analyses using structural equation modelling provided preliminary evidence that some of the ERPs were heritable (with the most robust effect for go-P3 latency), but there was limited evidence of any genetic associations between ERPs and ADHD, although with the caveat that our sample was small and hence had limited power. Overall, unlike in previous research, there was no evidence of phenotypic (nor preliminary evidence for genetic) associations between ADHD and CPT-derived ERPs in this study. Hence, it may be currently premature for genetic analyses of ADHD to be guided by CPT-derived ERP parameters . Further research with better-powered, population-based, genetically-informative and cross-disorder samples are required, which could be facilitated by emerging mobile EEG technologies.We investigated key event-related brain potential markers (ERPs) derived from a flanked continuous performance task (CPT) and whether these would show phenotypic associations with ADHD (attention-deficit/hyperactivity disorder) in a population-based sample. We further explored whether there was preliminary evidence that such ERPs could also index genetic risk for ADHD (depending on finding phenotypic associations). Sixty-seven male-only twin pairs ( Attention deficit/hyperactivity disorder (ADHD) is characterised by persistent difficulties with attention, impulsivity and hyperactivity , with brwith flankers rather than alone have shown to be more sensitive to ADHD-control differences [A neurocognitive domain frequently studied in ADHD relates to attention and inhibition processes ,8,10,11,ferences ,17.Recently, the clinical utility of CPTs for ADHD has been challenged despite their widespread use in clinical settings. A recent review found that, in children, the evidence for the use of CPTs for diagnosis of ADHD is inconsistent, and for medication management is limited, although some encouraging results suggest that CPTs may be more useful clinically when combined with measures of activity . One stuCPTs have been combined with neurophysiological techniques ,13, inclnot to be associated with reduced amplitude of the nogo-N2 (or go-N2) in the CPT—this paradigm is not thought to be sufficiently demanding to reliably elicit ADHD-control group differences in conflict monitoring [ADHD has been phenotypically associated with reduced amplitudes of the cue-P3, nogo-P3, go-P3 and CN across children and adult samples. This pattern of results has been interpreted as reflecting impaired attentional orienting, response inhibition, response execution and response preparation, respectively ,28,30–35nitoring ,17,30. Initoring ,36,37 annitoring , even thnitoring . One potnitoring  and the nitoring ,42, callnitoring  or autisnitoring , rather Putative links between CPT-derived ERPs and ADHD point to candidate brain-based markers which could reflect pathways from genes to disorder, and if so CPT-derived ERPs may have the potential to guide genetic analysis of ADHD. Some recent studies suggest shared familial effects between ADHD and reduced nogo-P3 amplitude using a sibling design , or betwIn this study we investigated key event-related brain potential markers (ERPs) derived from a flanked continuous performance task (CPT), and whether these would show phenotypic associations with ADHD (attention-deficit/hyperactivity disorder) in a population-based sample of early adolescent twins. We further explored whether there was preliminary evidence that such ERPs could also index genetic risk for ADHD (depending on finding phenotypic associations). If so, we predicted the following conditions to be met ,51. FirsParticipants were from the Neurophysiological Study of Activity and Attention in Twins (NEAAT), which has also been described elsewhere ,52–54. TNEAAT participants were selected on a latent class trajectory analysis of ADHD symptom development over three timepoints , using a DSM-IV measure of ADHD symptoms . The anaThe final NEAAT sample included participants with stably high  or stably low ADHD symptoms  of inattention and of impulsivity/hyperactivity across timepoints . Participants were as follow: 67 twin pairs in total, of which 22 pairs were concordant for high ADHD symptoms ; 8 pairs were discordant for ADHD symptoms ; 37 pairs were concordant for low ADHD symptoms . All participants were free of medication at the time of the study. The study was approved by King’s College London Psychiatry, Nursing and Midwifery Research Ethics Subcommittee (PNM/08/0-089). All participating parents gave written informed consent.The CPT-OX with flankers or flanked CPT ,27,30,36ADHD symptoms were assessed using the long version of the Parents Conner’s Rating Scale  on the dTwo web-based measures were collected as part of the ongoing TEDS study at age 14: the Wechsler Intelligence Scale for Children as a Process Instrument vocabulary multiple choice subtest as an index of verbal IQ  and RaveEEG was recorded using a 62-channel extended 10–20 system montage , with impedance kept below 5 kohm, and FCz as the recording reference electrode. Vertical and horizontal electrooculograms (EOGs) were recorded from electrodes placed above and below the left eye and at the outer canthi. The data were sampled at 500 Hz, stored and analysed offline.ERP analyses were performed using the ERPLAB package  within Ecue trials , go trials which were correctly responded to (‘OXO’ preceded by ‘XOX’), and no-go trials which were correctly not responded to: any array but ‘OXO’  that was preceded by ‘XOX’ . Averages contained at least 19 trials (as in [Stimulus-locked epochs were extracted -200 to 1650 ms) and baseline-corrected (-200 to 0 ms). Epochs were averaged for s . Such ts . Group comparisons were performed using Stata [Three participants were excluded for all analyses due to excessive artefact (us study ); or extng Stata . To accong Stata ,52,53. Wng Stata  were runng Stata  to deterThe effects of age and IQ were also regressed out of the all ERP data due to associations with ADHD grouping , before within-trait correlation (correlation between the same ERP measure between twins in the same pair), and iii) the MZ and DZ cross-twin cross-trait correlation (correlation between ADHD status in one twin and an ERP measure in the other twin in the same pair).A constrained correlational model was first fitted to the observed MZ and DZ data to estimate correlations between ADHD and key ERP measures . The constrained model considered the same phenotypic correlation between each ERP measure and ADHD; 1 MZ and 1 DZ correlation for each ERP measure; and 1 MZ and DZ cross-trait cross-twin correlation between each ERP measure and ADHD. For each ERP, estimates were produced for i) the within-twin cross-trait correlation (correlation between ADHD and each ERP measure across zygosity), ii) the MZ and DZ cross-twin Twin model-fitting was performed using structural equation modelling (SEM) to model the differences in correlations between MZ and DZ twin pairs. A biometrical genetic model assumes that MZ twins and DZ twins share 100% and 50% of their genetic influences, respectively . In a genetic bivariate liability-threshold model, the MZ:DZ ratio of the cross-twin within-trait correlations is used to decompose the variance of an ERP measure into additive genetic, common environmental and individual-specific environmental influences including measurement errors, referred to as A, C and E, respectively.The MZ:DZ ratio of the cross-twin cross-trait correlations can be used to further decompose the potential covariation between ADHD and an ERP measure into genetic (rG), common environmental (rC) and individual-specific environmental (rE) correlations . Howeverh2 = .76, c2 = .00, e2 = .24, consistent with rMZ = .76 (h2+c2) and rDZ = .38 (.5h2+c2). We have established, validated and successfully applied this model to investigate the link between EEG [The heritability of ADHD cannot be estimated due to the selected nature of our sample. Because selection is through ADHD traits but blind to ERP values, the necessary correction would depend only on the model for ADHD. Hence to obviate this correction one could alternatively fix the model parameters for ADHD  to constant values. Thus, ADHD status  was modelled using liability threshold, fixed to a population prevalence for ADHD at around 5% . This moween EEG ,52,53 anween EEG  with ADHween EEG , bipolarween EEG ,81, and ween EEG .First, analyses on group comparisons are presented between ADHD and control participants , who differed in both age and IQ . As alret(130) = 5.74, p < .001 (not controlling for IQ (d = .50) based on reported medium-to-large effect sizes from previous literature [ERPs from the flanked CPT were identified, namely cue-P3, go-P3, nogo-P3, CNV, go-N2 and nogo-N2 . In linep < .001 , but thep < .001 , even whg for IQ . We run terature ,17,35. Tterature ,84.r = .20, p = .027) and negatively correlated with nogo-P3 latency , whereas age was negatively correlated with cue-P3 amplitude, cue-P3 latency and nogo-P3 latency .In additional exploratory analyses, we found that across the whole sample regardless of ADHD grouping, IQ was positively correlated with nogo-P3 amplitude  were considered here due to the lack of power in our relatively small sample , given tcross-trait correlations, further analyses to estimate genetic correlations were not pursued.We found none of the relevant MZ correlations were significant , which swithin-trait correlations , using univariate models for each ERP measure (r > .20) and was significant when IQ was not regressed-out  appeared to have at least moderate effects.r’s = -.11 to .04). We also found preliminary evidence that these ERPs may be heritable (with the strongest evidence for go-P3 latency), although caution is required as most confidence intervals overlapped with zero due to our small sample. There appear to be no sufficient evidence for genetic correlations between ADHD and ERPs—this may again be due to low power . With phenotypic correlations of around .02 (estimated in this sample) we would need thousands of twin pairs to detect a significant effect—such a small association, even if significant, would not be of theoretical interest or useful for genetic analyses [This study used a population-based twin sample in early adolescence to test for phenotypic  associations between ERP parameters (derived from a flanked CPT) and ADHD. Critically, we found that these ERPs were not phenotypically associated with ADHD—the size of the estimated phenotypic correlations between ADHD and ERPs were nonsignificant and also very small  known to be less susceptible to noise see ; our anahttps://gtr.ukri.org/projects?ref=MR%2FN013182%2F1).As positive findings predominate the literature, we have yet to learn the true extent of similar null results. We believe that it is important to publish our null results even if somehow ‘unexpected’. This evidence base  is critical for establishing any reliable finding in science, including reliable biomarkers in psychiatry . Given th2 of an ERP measure of .40  is between 48–72 twin pairs, and to detect a rg of .40 is between 102–206 twin pairs . The phenotypic correlations associated with a rg of .40, and h2 ERP = .40 would be .26, which is way above what we observe in this sample. With this caveat in mind, we first estimated the heritability of key ERPs derived from the flanked CPT in an early adolescence, as previous studies focused mainly on late adolescence [trait correlations  could help provide a wider context in which to interpret our current null results. Using the same twin sample and the same flanked CPT, we previously found significant phenotypic and genetic correlations between ADHD and very-low frequency power (< .05 Hz) . We alsoN = 67), key CPT-derived ERPs were unexpectedly not associated with ADHD, despite previous findings indicating that such associations would have been found. Hence it may be currently premature for genetic analyses of ADHD to be guided by CPT-derived ERP measures . Taken together, our data highlight the importance of large-scale, population-based, genetically-informative and cross-disorder designs [In a population-based sample of adolescent twins Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 Data(SAV)Click here for additional data file.


In addition, in 2,2’-azino-bis (ABTS) assay, IC50 values of MAB (0.3 mg/mL) and tricin (0.3 mg/mL) was 4-fold and 9-fold greater than that of individual MB (1.3 mg/mL) or MA (2.8 mg/mL), respectively. The in vitro enzymatic assays on pancreatic elastase and tyrosinase indicated that MA and MB were potential to relief skin wrinkles and freckles. In detail, MA exerted higher inhibition on both enzymatic activities  than MB (18.5 and 12.6%) and MAB (32.0 and 19.7%) at a concentration of 2.0 mg/mL. Notably, MA and the mixture MAB exhibited stronger inhibitions on elastase and tyrosinase in comparison with tricin and vanillin. MA, MB, and tricin in rice are potential to develop cosmetics as well as supplements for skin aging treatments.We previously reported the inhibitory potentials of momilactones A (MA) and B (MB) against key enzymes related to type 2 diabetes and obesity. In this study, antioxidant and anti-skin-aging activities of MA and MB were investigated and compared with tricin, a well-known antioxidant and antiaging flavonoid in rice. MA, MB, and tricin were purified from rice husk by column chromatography and their biological activities were subsequently assayed by in vitro trials. The contents of MA, MB, and tricin of different commercial rice cultivars in Japan were quantified and confirmed by ultra-performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) and high-performance liquid chromatography (HPLC) analyses. The antioxidant assays revealed a synergistic activity of the mixture MA and MB (MAB, 1:1,  In humans, the skin is the most important organ which plays a crucial role as the first protective barrier. The skin protects our body from not only physical factors—such as collisions, clashes, UV radiation, etc.—but also from chemical agents, for instance, toxics, oxidants, or oxidative stresses. These factors may impact on many aspects of the skin function, especially on skin cells which are able to be dropped in an undesired condition of apoptosis . Among sOryza sativa) is a global crop which has a long history of safe usage as an indispensable food for human. The use of rice products in cosmeceuticals is not a novelty, of which, a diverse number of bioactive compounds has been identified as ferulic, γ-oryzanol, and members of flavonoids, flavones, etc. + were acquired and processed by using Xcalibur software integrated with the NIST database. The online database  and literature + at m/z 683, see m/z 331 presenting two major product ions at 315 and 270 . However, the mixture of MA and MB  exhibits a synergistic activity (IC50 = 0.3 mg/mL) which is significantly higher than activities of MA and MB individually and was in line with the antioxidant ability of tricin (IC50 = 0.3 mg/mL). Notably, by β-carotene bleaching assay, MA, MAB, and tricin performed stronger lipid peroxidation inhibitions  than MB did (% LPI = 61.7) at the same concentration of 1 mg/mL. By this assay, antioxidant activities of MA, MAB, and tricin were determined to be close that of the standard BHT (%LPI = 86.7).Antioxidant properties of MA, MB, and MAB are displayed in Although all samples presented a negligible antioxidant ability in terms of reducing power assay, the result showed that the mixture MAB and tricin were significantly powerful than either MA or MB . Except in vitro assays, momilactones A and B were remarkably active in the inhibition of two key enzymes elastase and tyrosinase which related to wrinkles and freckles  and tricin (15.7%) which were reported as potent tyrosinase inhibitors ,18. MeanThe quantities of MA, MB, and tricin in various common rice grains are shown in In terms of tricin content, the highest amount was quantified in Koshihikari refined grain (0.55 µg/g DW) whereas the lowest one was in KT1 (0.03 µg/g DW). The tricin content of cooked Koshihikari was decreased a half (0.23 µg/g DW) compared to that of the uncooked grain.Oryza sativa). Recently, we have revealed the potential antidiabetic and anti-obesity activities of MA and MB via in vitro enzymatic assays. In this study, we focused on the novel biological activity (anti-skin-aging) of these active metabolites and compared their capacity with known beneficial compounds in rice. In fact, Fukuta et al. [50) for MA and MB were 783.9 and 790.7 µg, respectively [v/v) was found to be more significantly potent than the single MA and MB in terms of ABTS and reducing power assays. Though the anti-ABTS radical activity of MA and MB was compared to that of BHT (IC50 = 0.080 mg/mL), a strong known antioxidant, further equivalent antioxidant capacity assays should be conducted using other reference standards such as 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), ascorbic acid, and butylated hydroxyanisole (BHA) to gauge the anti-radical capacity of MA and MB more accurately. In fact, the accretion of free radicals induced by UV exposure can provoke skin problems via a series of cell destruction mechanisms [Momilactones A and B are important compounds in the defense-mechanism pathway of rice  and HPLC is effective to identify MA, MB, and tricin in rice grains. Although MA, MB, the mixture MAB and tricin are potential inhibitors for skin aging treatments, the level of inhibitory activity of MA was stronger than either MB or tricin. The three compounds MA, MB, and tricin in rice grain are promising for development of supplements and cosmetics for skin protection and antioxidants.


There is a growing international literature investigating the relationship between attention-deficit/hyperactivity disorder (ADHD) and younger relative age within the school year, but results have been mixed. There are no published systematic reviews on this topic. This study aimed to systematically review the published studies on the relative age effect in ADHD. Systematic database searches of: Medline, Embase, PsycINFO, Web of Science, ERIC, Psychology and Behavioral Sciences Collection and The Cochrane Library were conducted. Studies were selected which investigated the relative age effect in ADHD in children and adolescents. Twenty papers were included in the review. Sixteen (of 20) papers reported a significantly higher proportion of relatively younger children being diagnosed with ADHD and/or receiving medication for this. Meta-analyses involving 17 of these 20 papers revealed a modest relative age effect in countries with higher prescribing rates, risk ratio = 1.27 (95% CI 1.19–1.35) for receipt of medication. The relative age effect is well demonstrated in countries with known higher prescribing rates. Amongst other countries, there is also increasing evidence for the relative age effect, however, there is high heterogeneity amongst studies. Further research is needed to understand the possible reasons under-pinning the relative age effect and to inform attempts to reduce it. Attention-deficit/hyperactivity disorder (ADHD) is a common childhood neuro-developmental disorder, characterised by three core symptoms: inattention, hyperactivity and impulsiveness causing an impairment in functioning . AlthougThe receipt of a clinical diagnosis of ADHD depends on evidence of symptoms affecting functioning in more than one setting, for example, both at school and at home. The diagnostic process, therefore, usually involves the collection of information from those who encounter the child in different contexts, for example, the child’s parents and school teachers, as well as the observations and interpretation of the health care professionals conducting the assessment .There is an overwhelming literature documenting neurobiological, clinical and pharmacological evidence for the validity of ADHD as a diagnosis . DespiteIn relation to ADHD, the relative age effect has usually been demonstrated in countries with high prescribing rates for ADHD , 16, wheA literature search was conducted with the assistance of an information specialist. This covered articles published from the 1st of January 2000 to the search date of the 7th September 2017. Databases searched included: MEDLINE, EMBASE, PsycINFO, Web of Science, ERIC, Psychology and Behavioural Sciences Collection and The Cochrane Library. Search keywords comprised: (1) Various terms for ADHD including: Attention Deficit Disorder with Hyperactivity, ADHD, ADDH, ADHS, hyperkinesis, hyperactive* and inattention* and (2) Relative age, relative maturity, relative immaturity, young for grade, young for year, old for grade or old for year. Additional studies were identified through checking reference lists of obtained articles. A further update search using the same search terms and databases was conducted on the 23rd of November 2017.n = 2) and assessed by JH.Abstracts were screened independently by JH and KS with 100% agreement, and then full text assessments were conducted by JH. All articles were available to download from online sources. Studies not published in English were translated by colleagues  of birth of participants, was recorded.Papers were excluded for the following reasons: were case reports or conference abstracts; only data for individuals aged over 18 were used; no chronological age by month of birth data was recorded, and/or they focused on disorders or behaviour problems but did not specifically report on ADHD. Intervention studies were excluded unless they contained relative age comparison data.Each study was assessed for bias using a modified version of the Newcastle–Ottawa assessment scale . The NOFor the studies which met the inclusion criteria, data were extracted for the total number of children within each comparison group and the number of children who received a diagnosis of ADHD or ADHD medications. If a study met inclusion criteria but did not report the data in a format which could be included in the quantitative analysis, the authors were contacted to request the required figures.Studies were divided based on country of origin, separating those from countries known to have higher rates of prescribing for ADHD , and those with lower rates . For GeDue to high heterogeneity between studies, a random effects model using the Mantel–Haenszel method was used. Risk ratios (RR) with 95% confidence intervals were presented as the effect measure as this is the most commonly presented measure in studies.A total of 123 references were retrieved through initial database searches and four through reference checking. A further two references were identified through the update search. After duplicates were removed, 63 abstracts were screened. Thirty records were excluded on the basis of: not relating to ADHD (9), response letters/reviews (4), case reports (5), no birth month information (8), adult data only (2) and tests of intervention (2).The remaining 33 full-text articles were reviewed. A further 13 of these were excluded due to: no birth month information (1), ADHD not being separate from other child mental health disorders (1) and conference abstracts (11).Twenty studies were assessed for the review and data extraction, the characteristics of these studies are shown in Table For the three studies which included measures of ADHD symptoms, two showed evidence of a relative age effect. This was from symptom reports from teachers , 35 withI2 = 97%, prescriptions I2 = 95%) to be presented.For studies investigating the proportion of children receiving a diagnosis and/or medication, data were extracted and meta-analyses were conducted, separating studies into subgroups based on the outcomes studied (diagnosis or prescription) and whether the country was known to have higher or lower rates of ADHD prescribing. However, heterogeneity estimates were too high, for an overall analysis ), as shown in Fig. 2 = 91%) and therefore is not presented here, Fig. A meta-analysis of the studies investigating the proportion of children receiving medication in higher prescribing countries showed a significant relative age effect with those younger in the academic year being more likely to receive medication for ADHD (7 1.19–1.5), as shI2 = 98% and I2 = 97%, respectively, as shown in Figs. For the other countries, heterogeneity estimates were too high for reporting of the meta-analysis for either diagnosis or medication, As shown within the risk-ratio plots, almost all studies have shown, to some extent, higher proportions of ADHD diagnosis and prescriptions amongst the youngest in the academic year. The studies which did not show a significant relative age effect were from Denmark ;  and RR 1This review has found that the majority of studies show evidence of a relative age effect influencing both the diagnosis of and receipt of medication for ADHD. This was demonstrated most clearly within studies from higher prescribing countries, with a modest pooled risk estimate of 1.27 for medication amongst the youngest in the school year compared with their older peers in the same school year. Data from the other countries were more mixed, with high levels of heterogeneity.Differences between study results might reflect methodological differences. As shown in Table First, as noted above, there are significant variations in the estimated rates of ADHD diagnosis and medication provision between countries . SchwandSecond, linked to the above, there are differences in diagnostic practice across countries, for example, which professionals are able to give a diagnosis , 17. TheThird, school entry regulations and policy may play a role. Some studies have highlighted the possibility of children being held back an academic year as a possible factor influencing the magnitude of relative age estimations and contributing to heterogeneity , 17, 29.Fourth, teacher perceptions, in particular, may play a role. Elder  demonstrTo our knowledge, this is the first systematic review of the association between relative age and ADHD. This review has identified and brought together existing research in a rigorous and systematic manner, enabling meta-analyses of the data, where possible. However, there are a number of potential limitations. First, since there are a number of different ways in which the relative age effect can be flagged within a study’s title and abstract there is a risk that some studies may have been missed. Second, some studies did not publish their data in a form which could be inputted to the meta-analysis. This meant that some large studies within the field could not be included in the meta-analysis , 24. OthThese findings have significant clinical implications. Since there is mounting evidence of a relative age effect on ADHD diagnosis and medication in most countries studied, which may imply possible misdiagnosis of relatively immature children, it is possible that some relatively young children may be unnecessarily offered and exposed to medication, the long-term effects of which are still not fully understood. When assessing for ADHD, clinicians should also bear in mind that teachers may be more likely than parents to apply same year-group peer referencing when completing rating scales .In terms of educational implications, these findings should be considered in relation to school entry regulations. It may be that through more flexible school entry criteria, relatively immature children may be allowed more time to develop prior to entering schooling and potentially avoid unnecessary diagnosis and medication.In terms of research, further work is needed to understand whether the relative age effect is due to misdiagnosis of younger children, for example through a longitudinal study showing whether these children continue to meet criteria for ADHD at later stages. Although some studies have explored the association between certain population characteristics and a relative age effect, further work is needed to explore the mechanisms under-pinning this effect. Family studies examining whether these relatively young children lack familiality of the disorder would also be useful. The literature to date has used epidemiological data. Qualitative research, in particular, could be useful in improving our understanding about the processes contributing to the relative age effect.This systematic review and meta-analysis has drawn together worldwide studies investigating the relative age effect in the symptoms, diagnosis and medication treatment of ADHD amongst children and adolescents. It has shown that the relative age effect is evident in the majority of countries, however, there is considerable variation in its magnitude. Possible explanations include: overall diagnostic rates, national differences in diagnostic practice and school entry regulations and the influence of different informant sources. In isolation, none of these theories are able to fully explain the differences shown. Further research is needed to better understand the reasons for the relative age effect. At an individual level it is crucial for clinicians to consider a child’s relative age when assessing for ADHD.