Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disease featuring executive control deficits as a prominent neuropsychological trait. Executive functions are implicated in multiple sub-networks of the brain; however, few studies examine these sub-networks as a whole in ADHD. By combining resting-state functional MRI and graph-based approaches, we systematically investigated functional connectivity patterns among four control-related networks, including the frontoparietal network (FPN), cingulo-opercular network, cerebellar network, and default mode network (DMN), in 46 drug-naive children with ADHD and 31 age-, gender-, and intelligence quotient-matched healthy controls (HCs). Compared to the HCs, the ADHD children showed significantly decreased functional connectivity that primarily involved the DMN and FPN regions and cross-network long-range connections. Further graph-based network analysis revealed that the ADHD children had fewer connections, lower network efficiency, and more functional modules compared with the HCs. The ADHD-related alterations in functional connectivity but not topological organization were correlated with clinical symptoms of the ADHD children and differentiated the patients from the HCs with a good performance. Taken together, our findings suggest a less-integrated functional brain network in children with ADHD due to selective disruption of key long-range connections, with important implications for understanding the neural substrates of ADHD, particularly executive dysfunction. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and adolescents, with a prevalence of approximately 5.3% worldwide , 2. The Considerable neurophysiological research documents that ADHD is associated with disturbed executive function, a series of higher-order, top-down cognitive control processes that allow flexible, goal-directed behavior , such asRecently, a dual-network model of human task control was proposed –18. One Here, we investigated functional connectivity patterns among these four control-related networks in 46 drug-naive ADHD children and 31 age-, gender-, and intelligence quotient (IQ)-matched healthy controls (HCs) by combining resting-state functional MRI (R-fMRI) and graph-based approaches. Specifically, 34 regions of interest comprising the four networks were defined in this work according to a previously published study . Given tA total of 69 children with ADHD (6–13 years old) and 44 HCs (6–13 years old) participated in the study. All participants were right-handed according to the Annett Hand Preference Questionnaire  and wereDiagnosis of ADHD was determined independently by two experienced clinical psychiatrists  according to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria (DSM-IV) and a semi-structured diagnostic interview, the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version. The parents of the children in the ADHD group scored their children using Conner’s Parent Symptom Questionnaire—Chinese revised version . This scThirty-six children were excluded due to excessive head motion during the functional MRI data acquisition  or IQ scores < 80 (2 children). Therefore, 46 patients  and 31 controls  were included in the final analysis. Table All MRI scans were performed on a GE signal HDx 3 T MR scanner with an eight-channel phased-array head coil . During the entire scanning procedure, all subjects were in a supine position with their heads snugly fixed by foam pads to reduce head movement. Whole-brain R-fMRI data depicting blood oxygen level-dependent (BOLD) contrast were obtained using a gradient-echo echo-planar imaging sequence with the following parameters: 31 axial slices, slice thickness = 4 mm, slice gap = 0.2 mm, repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, flip angle = 90°, matrix size = 64 × 64, and field of view (FOV) = 192 mm × 192 mm. The R-fMRI lasted 8 min in total, and 240 volumes were obtained for each participant. During the R-fMRI scans, the participants were instructed to keep their eyes closed, relax their minds, and remain as still as possible without falling asleep. It was confirmed that none of the participants fell asleep during the scan by subjective report. Individual structural images were also acquired using a 3D T1-weighted SPGR sequence with the following parameters: 176 sagittal slices, slice thickness = 1.0 mm, no gap, TR = 2,530 ms, TE = 3.39 ms, inversion time = 1,100 ms, FA = 7°, matrix size = 256 × 256, FOV = 256 mm × 256 mm.Ps > 0.241). The corrected images were then spatially normalized into standard Montreal Neurological Institute (MNI) space via segmentation and resampled to 3-mm cubic voxels. Because pediatric brains differ significantly from adult brains, we used the CCHMC Pediatric Brain TemplatesResting-state functional MRI data preprocessing was performed using the GRETNA toolbox  and SPM1A total of 34 previously published regions comprising the four functional networks  were used in this study Figure ; Table 2N), with N denoting the number of nodes  . Prior to the topological characterization of these networks, a thresholding procedure was used to convert them into binary networks, in which the inter-nodal connectivity weights were either 0 or 1, indicating the absence or presence of an edge between two nodes, respectively. In the current study, two thresholding methods were employed: a correlation thresholding approach and a sparsity thresholding approach. For the correlation thresholding method, the same correlation threshold was applied to all individual correlation matrices such that connectivity weights greater than the threshold were set to 1, and others were set to 0. This method generates networks with different numbers of edges across participants due to possible differences in their overall connectivity strength and thus allows an examination of the absolute network organization. By contrast, for the sparsity thresholding approach, a subject-specific correlation threshold was used to ensure the same number of edges  across participants. This thresholding method therefore allows an examination of the relative network organization by imposing on each network the same number of edges or wiring cost for compensatory adaptations. These two thresholding strategies are complementary and together provide a comprehensive method to test the network organization . Therefo.e., 34) ; and (2)G with N nodes is defined as follows  is the global efficiency of iG, the subgraph composed of the neighbors of node i . The local efficiency reflects how well the network exchanges information locally or how much the network is fault tolerant. The modularity, Q, for a given partition, p, of G is defined as:MN is the number of modules, L is the total number of connections in sG, l is the number of connections between nodes in module s, and sd is the sum of the nodal degree  for nodes in module s. Modularity quantifies the difference between the number of intra-module links of the actual network and that of a random network in which connections are linked at random t random . The aimlgorithm  implemenTo determine whether the brain networks exhibited significantly non-random organization, for each participant, the global network measures  were normalized by dividing them by the corresponding measures derived from 100 random networks. The random networks were generated using a topological rewiring algorithm that preserved the same number of nodes, edges, and degree distributions as the real brain networks , 46. A ni in network G, the nodal degree is defined as the number of links connected to it:ija is 1 or 0 and indicate the presence or absence of an edge between node i and node j. The nodal efficiency is defined as the average shortest path length between node i and all other nodes in the network  is the number of shortest paths from node m to node n that pass through node i.For each node, we calculated the nodal degree, nodal efficiency, and nodal betweenness to capture their roles in the brain network. Specifically, for a given node  network :ei=1N−1h node i :bi=∑m≠iThe uses and interpretations of these global and nodal network measures can be found in Ref. .t-tests or Wilcoxon rank sum tests depending on whether the data were normally distributed (Lilliefors test). Gender distribution was examined with a chi-square test.Age, IQ, hyperactivity index score and impulsive score were analyzed with two-sample t-statistic matrix was first derived by an edge-wise between-group comparison (two-sample t-test) of the functional connectivity strength  for positive connections that existed in at least 50% of the participants. A primary threshold (P < 0.05) was then applied to the t-statistic matrix to identify supra-threshold connections, within which all connected components and their sizes  were identified and determined. To estimate the significance of each component, a null distribution of the maximal connected component size was empirically derived using a permutation approach . For each permutation, all subjects were reallocated randomly into two groups, and two-sample t-tests were conducted for the same set of connections mentioned above. The same primary threshold  was then used to generate supra-threshold connections, within which the maximal connected component size was recorded. After these permutations, an empirical distribution of the maximal connected component size was obtained. Finally, for a connected component of size M found in the right grouping of controls and patients, the corrected P value was determined by calculating the proportion of the 10,000 permutations for which the maximal connected component was larger than M. The effects of age, gender, IQ, and head motion  were controlled during the NBS analysis.To localize the interregional connectivity that differed between the ADHD children and HCs, we utilized a network-based statistic (NBS) approach . Brieflyt-test). Finally, given the imbalance in the number of connections showing or not showing ADHD-related alterations  was computed as the proportion of the 10,000 times for which the mean anatomical distance was larger than the real observation.We further examined whether the ADHD-related functional connectivity alterations were dependent on anatomical distance. First, we first calculated the proportion of long-range connections that were included in the identified NBS component. For a given connection, the anatomical distance was calculated as the Euclidean distance between regional centroids of two regions linked by the connection, and connections with an anatomical distance >75 mm were regarded as long-range connections , 52. TheFor brain network measures , a nonparametric permutation test was used. In brief, for each metric, we initially calculated the between-group differences in their mean values. An empirical distribution of the differences was then obtained by randomly reallocating all values to two groups and recomputing the mean differences between the two randomized groups . The 95th percentile points of the empirical distribution were used as critical values in a one-tailed test to determine whether the observed real-group differences occurred by chance. Age, gender, IQ, and head motion  were also controlled.Rank partial correlations were used to examine the relationships between the network metrics that showed significant between-group differences and clinical variables (hyperactivity index score and impulsive score) in the ADHD patients and HCs separately. Age, gender, IQ, and head motion  were treated as confounding covariates.Using the receiver operating characteristic (ROC) curve, we performed a preliminary analysis to determine whether the identified network alterations might serve as biomarkers for diagnosing ADHD. This analysis was performed using public MATLAB codes.P = 0.247), gender (P = 0.269), or IQ (P = 0.531) between the two groups. The ADHD patients showed higher hyperactivity index (P < 0.001) and impulsive scores (P < 0.001).As shown in Table P = 0.018, corrected) . Topological analyses revealed economical small-world and modular organization in both the ADHD  and HC  groups. However, between-group comparisons showed that the local and global efficiency were significantly decreased in the ADHD children compared to the HCs  .Using the correlation thresholding approach, significantly fewer connections were found in the networks of ADHD children than in the HCs   between the ADHD children and HCs, which may confound the between-group comparisons , 54. Thu) Figure B. No sigP > 0.05, false discovery rate corrected).No significant differences were found at any node for any nodal metric of the degree, efficiency, or betweenness between the ADHD and HC groups (P > 0.05). In the ADHD children, only the mean functional connectivity strength within the NBS-based connected component exhibited significantly positive correlations with the impulsive score  and hyperactivity index score  , with a sensitivity of 84.8% and a specificity of 90.3%  for distinguishing the ADHD patients from the HCs  predominantly involved the DMN and FPN; (2) mainly involved across-network long-range connections; (3) correlated with the patients’ clinical symptoms; and (4) exhibited excellent power for disease classification. Topologically, a more segregated organization with decreased network efficiency and increased number of modules was observed in the ADHD patients. Overall, these findings indicate disrupted network organization in ADHD, which has important implications for understanding executive dysfunction in the disease.Widespread functional connectivity decreases were observed in the ADHD children that primarily involved the DMN and FPN. Fair and colleagues explored the interregional functional connectivity among 12 DMN regions and found that these regions were less integrated in ADHD children than HCs . The decThe FPN, also known as the executive control circuit, is involved in sustained attention, inhibition, working memory and goal-directed decision making. There is a wealth of neuroimaging evidence for ADHD-related abnormalities in the structure  and function  of the FPN , 61, 62.The most intriguing finding for the current study is that the reduced functional connectivity in ADHD was primarily long-range connections. Long-range connections play vital roles in guaranteeing global integration and generating cognitive function. A previous study showed that, across development, functional connectivity among regions of the FPN, CON, and CN increases for long-range connections but decreases for short-range connections . These dWe found that the decreased functional connectivity in ADHD children exhibited significantly positive correlations with the hyperactivity index and impulsive scores of the patients. This implies that the larger the impulsive and hyperactive scores are for the ADHD children, the stronger the functional interactions are for the children’s control-related brain networks. The counterintuitive brain–behavioral relationships, indeed, have been previously reported in ADHD  and otheSeveral issues should be further addressed. First, the cross-sectional design of the current study limited our ability to examine the distinct developmental trajectories of these control-related networks in ADHD children. Future follow-up longitudinal studies will aid in addressing this interesting issue. In particular, studies covering the whole life span of the same patients will be extremely informative to understand how the observed alterations emerge, develop and reorganize at different stages of life and under different conditions in ADHD. Second, ADHD patients can be divided into different subtypes with unique structural and functional connectivity patterns in the brain –73. A moBy combining R-fMRI and network approaches, the current study demonstrated a less-integrated architecture of control-related functional brain networks in children with ADHD, which may underlie executive dysfunction of the disease.This study was approved by the local ethical committee of the First Hospital Affiliated to Wenzhou Medical University. After a complete explanation of the study protocol, all children agreed to take part in this study, and written informed consent was obtained from their guardians. This study was approved by the local ethical committee of the First Hospital Affiliated to Wenzhou Medical University. Written informed consent was obtained from children’s guardians.Designed the experiments: JW, CY, JL, and MW. Performed the experiments: XW, HL, AQ, QY, JT, and HC. Analyzed the data: JT and XJ. Contributed analysis tools: JW. Wrote the paper: JT, JW, and MW.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.


CTNNA1 hypermethylation in acute myeloid leukemia (AML) patients in an attempt to improve molecular prognostic model. CTNNA1 promoter methylation levels in 319 newly diagnosed AML patients were detected using quantitative methylation-specific polymerase chain reaction (qMS-PCR). Furthermore, hematological characteristics, cytogenetic abnormalities, and genetic mutation status were analyzed, followed by assessment of clinical impact. Our findings demonstrated that CTNNA1 hypermethylation was observed in 25% AML patients. Hypermethylation of the CTNNA1 promoter was associated with unfavorable karyotype, and also possessed the higher frequency of coexisting with ASXL1 and RUNX1 mutations. Patients with CTNNA1 hypermethylation exhibited the shorter relapse-free survival (RFS) and overall survival (OS) in the whole AML and non-M3 AML patients. Moreover, patients with the higher methylation levels had more aggressive course than those with relative lower levels. In multivariate analyses, CTNNA1 hypermethylation was an independent factor predicting for poor RFS, but not for OS. In conclusion, CTNNA1 hypermethylation may be a reliable factor for improving prognostic molecular model for AML.The aim of this study is to evaluate the frequency of  CHFR gene has been detected in acute myeloid leukemia (AML) [miR-193a hypermethylation has been discovered for participating in the occurrence of t AML [Recent advances in genetics and epigenetics have improved our understanding of molecular mechanisms of hematological cell transformation and progression to a great extent –4. Numbeia (AML) , and miR;21) AML .CTNNA1 is expressed in normal hematopoietic stem cells (HSCs); however, its expression is significantly lower in human leukemia initiating cells (LICs) in AML [CTNNA1 levels have been reported in human cancer cell lines derived from various solid tumors to myeloid malignancies and have been shown to closely be involved in disease progression [CTNNA1 is a promising and essential tumor-suppressor gene involved in leukemic cell transformation, and then give a reasonable presumption that any biomedical procedure that could restore CTNNA1 expression represents a potential targeted therapeutic strategy to bring benefit to patients with myeloid malignancies.In recent years, a few studies have shown that ) in AML –16. Lossgression . This suCTNNA1 promoter using quantitative methylation-specific PCR method in bone marrow samples from 319 AML patients, with the aim of identifying a subset of patients who harbored aberrant methylation levels and comparing the clinical characteristics of these patients. We also sought to examine chromosome abnormalities and gene mutations associated with AML, for finding significant associations with CTNNA1 hypermethylation. Furthermore, with the purpose of predicting clinical impact, we would analyzed the relapse-free survival (RFS) and overall survival (OS) according to CTNNA1 methylation levels.In the present work, we examined methylation levels of the CTNNA1 were assessed in bone marrow samples from 319 AML patients and 30 healthy donors using quantitative methylation-specific PCR (qMSP). CTNNA1 was hypermethylated in 25% (79/319) AML, but not in samples from healthy donors. In positive patients, the media level was 0.8051 . Gene expression analysis showed significantly decreased expression of CTNNA1 in samples of AML patients compared to control individuals . Moreover, there was a significant difference of CTNNA1 mRNA levels between the patients with hypermethylation and that with non-methylation . Seventy nine patients with CTNNA1 hypermethylation exhibited lower mRNA transcripts than those with non-methylation. Furthermore, as shown in Figure CTNNA1 methylation levels were negatively correlated with mRNA levels .Promoter methylation levels of s Figure , P<0.001n Figure , P=0.001CTNNA1 hypermethylation, the promoter of the CTNNA1 gene was confirmed by bisulfite sequencing with methylation rates of 91.7%, 92.2%, 93.9%, 93.7% and 91.7%, respectively .To assess clinical impact of CTNNA1 gene, we analyzed the differences of various karyotype abnormalities between the two groups. As Table CTNNA1 hypermethylation demonstrated the higher frequency of unfavorable karyotype (P=0.010). Furthermore, CTNNA1 hypermethylation was found to coexist more frequently with +8, −5/5q- compared to non-methylation . Among 79 individuals with CTNNA1 hypermethylation, eight patients harbored −5/5q-, while only 7 patients were detected with chromosome 5 abnormalities in the lower methylation group. Additionally, among 31 patients carrying +8 abnormalites, Fourteen patients had CTNNA1 hypermethylation, however, 17 cases with non-methylation of the CTNNA1 gene.To further investigate cytogenetic abnormalities in AML patients with and without aberrant methylation levels of the IDH1, ASXL1, FLT3-ITD, MLL-PTD, CEBPA, NRAS, TET2, DNMT3A, KIT, TP53, UTX, SF3B1, SRSF2, SETBP1, RUNX1 and EZH2 mutations were detected in 319 individuals with AML using DNA sequencing. The spectrum of mutation status in the patients with CTNNA1 hypermethylation or non-methylation was shown in Figure CTNNA1 methylation levels had more probabilities of harboring ASXL1 and RUNX1 mutations . In the present work, thirty three patients harbored ASXL1 mutations, including 15 cases in hypermethylation group and 16 in non-methylation group. Moreover, among 26 patients with RUNX1 mutations, 14 individual demonstrated aberrant methylation levels of the CTNNA1 gene.CTNNA1 hypermethylation and non-methylation. Kaplan-Meier curves indicated that the patients with aberrant CTNNA1 methylation levels were found to present reduced RFS and OS in all 319 AML patients . Moreover, the same significant differences were also detected in non-M3 AML about RFS and OS . To further appraise the prognostic impact of different CTNNA1 methylation levels, the patients with CTNNA1 hypermethylation were divided into two groups according to the 75th percentile of the initial methylation levels. Nineteen patients were assigned to the group with the higher methylation levels and the others were assigned to the group with lower methylation levels group. We found that the patients with higher CTNNA1 methylation levels exhibited inferior RFS (P=0.014) and OS (P=0.003)  Figure .CTNNA1 gene was entered into a multivariate model in addition to factors significantly associated with prognosis in univariate analysis in our cohort . In multivariate analysis, CTNNA1 hypermethylation was an independent factor predicting for poor RFS, but not for OS  samples were taken during routine clinical care, and the samples were determined to contain more than 50% blasts by morphologic assessment. Available clinical characteristics were age, sex, French-American-British (FAB) subtype, white blood cell and platelet counts, the amount of BM blasts, and hemoglobin levels. All non-M3 AML patients received intensive induction therapy with DA (daunorubicin and cytarabine) or MA (mitoxantrone and cytarabine) or Decitabine (demethylating treatment) followed by consolidation therapy with cytarabine-based therapy. For M3 patients with t, all-trans retinoic acid and arsenic trioxide-based treatment was given for induction and consolidation therapy, of which five patients were treated with cytarabine-based therapy as part of consolidation for high-risk diagnosis. Fifty-six patients underwent allogeneic hematopoietic stem cell transplantation  and thirty-one cases received autologous hematopoietic stem cell transplantation (auto-HSCT). The clinical characteristics of the patients are described in Table Complete remission (CR) was defined as recovery of morphologically normal BM and blood counts and no circulating leukemic blasts or evidence of extramedullary leukemia. Relapse was defined as 5% or more BM blasts, circulating leukemic blasts, or development of extramedullary leukemia. OS was calculated from date of diagnosis until date of death, censoring patients alive at last follow-up. RFS was calculated from the date of CR until date of relapse or death, regardless of cause, censoring patients alive at last follow-up.DNA was isolated from bone marrow using a Genomic DNA Purification Kit , and 1 μg of genomic DNA was treated with sodium bisulfite by using a EpiTect Kit  according to the manufacturer's protocols. Modified DNA was resuspended in TE buffer and used immediately or stored at −80°C until use. Bisulfite treatment was used to convert unmethylated cytosines into uracils while leaving the methylated cytosines unaffected.CTNNA1 promoter: CTNNA1-F, 5′-TTTAGTTTATTTAGAGGAAGTT-3′, and CTNNA1-R, 5′-ACTCTCTCAAAACTCCAAAAAAACC-3′. PCR amplified region was shown in Supplementary Figure. Polymerase chain reaction (PCR) products were gel purified and cloned into the pCR2.1-TOPO vector (Life Technologies). Plasmids from single colonies were purified using a QIAprep Spin Miniprep Kit  and sequenced. Ten cones were selected randomly for sequencing from one patients.Bisulfite-treated DNA was amplified with sequencing primers targeting the CTNNA1 and MYOD1 (reference gene) specific primers and probes  with d probes . PCR ampCTNNA1) into 293 cells (fully unmethylated cell line). Sensitivity was defined as 10−5 because it was the lowest dilution with the highest Ct value (Ct 39.2). Furthermore, in the present study, the mean highest Ct value of normal BM was 39.4. In the qMSP assay, CTNNA1 hypermethylation was defined as a mean Ct value <39.2, and the detectable levels were defined as methylation levels of the CTNNA1 gene >0.007. The efficiency of our standard curves in all experiments ranged between 95% and 99%. The intra- and interassay coefficients of variation of our qMSP were <0.2.The quantitative range and the sensitivity of the assay were assessed by serially diluting HL60  and was subsequently reverse transcribed to cDNA using a reverse transcription kit (Promega). We performed qPCR to quantify IDH1, ASXL1, NPM1, FLT3-ITD, MLL-ITD, CEBPA, NRAS, TET2, DNMT3A, KIT, TP53, UTX, SF3B1, SRSF2, SETBP1, RUNX1 and EZH2 mutations were detected using DNA sequencing for hyper-frequency-mutation sequences as previously reported [reported –40, and CTNNA1 promoter methylation and clinical parameters was analyzed with Pearson's and Spearman's rank correlations. Overall survival curves were plotted using the Kaplan-Meier method and compared using the log-rank test. The median time between visits was 36 months (range: 5 to 100 months). A cox model was used to identify prognostic variables. In addition to CTNNA1 methylation levels, age, chromosome abnormalities, and mutational status were included as explanatory variables in the regression analyses. For all analyses, P-values were two-tailed, and a P-value of less than 0.05 was considered statistically significant.Statistical analysis was performed using the SPSS 18.0 software . The chi-square and Fisher's exact  tests were used to compare patient groups. The correlation between frequency of


Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous condition and identification of clinically meaningful subgroups would open up a new window for personalized medicine. Thus, we aimed to identify new clinical phenotypes in children and adolescents with ADHD and to investigate whether neuroimaging findings validate the identified phenotypes. Neuroimaging and clinical data from 67 children with ADHD and 62 typically developing controls (TDCs) from the ADHD-200 database were selected. Clinical measures of ADHD symptoms and intelligence quotient (IQ) were used as input features into a topological data analysis (TDA) to identify ADHD subgroups within our sample. As external validators, graph theoretical measures obtained from the functional connectome were compared to address the biological meaningfulness of the identified subtypes. The TDA identified two unique subgroups of ADHD, labelled as mild symptom ADHD (mADHD) and severe symptom ADHD (sADHD). The output topology shape was repeatedly observed in the independent validation dataset. The graph theoretical analysis showed a decrease in the degree centrality and PageRank in the bilateral posterior cingulate cortex in the sADHD group compared with the TDC group. The mADHD group showed similar patterns of intra- and inter-module connectivity to the sADHD group. Relative to the TDC group, the inter-module connectivity between the default mode network and executive control network were significantly increased in the sADHD group but not in the mADHD group. Taken together, our results show that the data-driven TDA is potentially useful in identifying objective and biologically relevant disease phenotypes in children and adolescents with ADHD. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood, affecting 5% of school-age children worldwide . ADHD isk-means clustering, has been widely used for this purpose. However, a major criticism of this method is that identified subgroups are locally optimized, given that the initial clusters are heuristically assigned. Furthermore, the k-means clustering would fail to detect subgroups if the shape of the data becomes complex [Recent efforts in data sharing, such as the ADHD-200 Consortium have made it possible to develop a large dataset representative of the whole spectrum of the disorder. The ADHD-200 dataset provides information on age, sex, intellectual ability, symptom severity, and neuroimaging data from hundreds of individuals with ADHD and TDC . To iden complex  and thusBecause of the above-mentioned limitations of the traditional clustering algorithm, Topological Data Analysis (TDA) is receiving increasing attention for its ability to define the shape of the data and produce outcomes in the form of an easily recognizable graph, as graphically illustrated in With regard to input features, several studies have attempted to identify ADHD subgroups by clustering them according to symptom severity , 14, reaAfter identifying subgroups within large ADHD datasets, the investigation of the neurobiological validity of the newly defined subgroups is another important issue. In prior studies, neuroimaging methods, such as resting state functional magnetic resonance imaging (rs-fMRI) connectivity strength, were used to investigate the neurobiological substrates of human characteristics, such as personality types , 26. A bOn this background, the two main objectives of this study were (1) to determine whether we can identify new clinical phenotypes based on symptom severity and IQ measurements in children and adolescents with ADHD, and (2) to investigate whether neuroimaging findings validate the identified phenotypes. In this study, we used two brain-based measures to investigate the characteristics of the brains in each subgroup identified by TDA: 1) centrality measures, and 2) modularity analysis, exploring resting-state functional connectivity in ADHD in five pre-defined neural networks, searching for the neurobiological significance of the subgroups identified by TDA.s with IQs lower than 80. The ADHD Rating Scale IV [We obtained the preprocessed rs-fMRI data from the Neuro Bureau ADHD-200 Preprocessed repository . In the Scale IV  and the Scale IV  were useScale IV  and WechScale IV  were useMij) indicates a point of data for a subject i and a variable j, where characteristic variables contained three symptom variables, such as ADHD index, inattentive score and hyperactive/impulsivity score, and three intelligence scores, such as full-scale IQ, verbal IQ and performance IQ. NTDC is the number of subjects in TDCs; XTDC is a subset of subjects in TDC. Finally, the variance normalized data matrix  was computed by Pearson’s correlation coefficients of the scrubbed time-courses between the i-th and j-th regions of interest. These functional connectivity matrices were used for further investigation of functional network properties.The datasets that we downloaded from the Neuro Bureau ADHD-200 repository were the filtered time courses files, ADHD 200_AAL_TCs_filtfix.tar.gz. A detailed description of the preprocessing steps can be found elsewhere , 29. In α to 0.85 for the evaluation of the PageRank.For the modularity optimization analysis and computation of centrality measures, we only used positive edge weights , 35. TheLouvain community detection method that uses a heuristic algorithm to compute the modularity.Aij is the connection weight of the edge between i and j; si = ∑jAij is the degree centrality; Ci is the community structure to which node i is assigned; the delta function δ is 1 if x = y and 0 otherwise. The heuristic modularity optimization algorithm produced slightly variable modular partitions and modularity (Q) run by run. Therefore, we performed 1000 independent optimization processes and found the best module partition that had the highest average value of normalized mutual information over all other optimization results [To evaluate modular organization of each group, the group representing connection matrices were computed by averaging the functional network within each group. There are numerous approaches to compute the modular architectures in a network –42. Amon results .P < 0.05, correcting for multiple comparisons using the Benjamini-Hochberg procedure [P < 0.05, Bonferroni-corrected) were conducted using a post-hoc analysis to identify differences between any combinations of the groups.The statistical comparisons of demographic variables, symptom severity, and IQ scores among subgroups identified by TDA were conducted by analysis of variance (ANOVA) methods. In addition, group level differences of graph theoretical measures and the intra- and inter-modules functional connectivity density (FCD) were investigated by analysis of covariance (ANCOVA). In addition, the statistically meaningful FCDs and centrality measures among the three groups were identified using a statistical significance of corrected rocedure . FurtherP < 0.05) in TDC subjects compared to children with ADHD.In the principal and validation datasets, no significant differences were found in age and handedness between the ADHD and TDC groups see  online. P < 0.05), whereas no significant differences in intelligence scores were observed between two ADHD subgroups (P > 0.05). The validation analysis confirmed that similar patterns in the patient-patient network were observed in the validation dataset. The shape of the data in the intelligence and symptom severity space obtained from the validation dataset was the same as the shape of the principal dataset, a three branch-shaped graph including severe and mild symptom ADHD subgroups (see oups see  online.P < 0.05). The PageRank in the bilateral caudate was significantly different among the three groups (Corrected P < 0.05). The statistical comparisons of all nodal areas are summarized in supplementary materials (see P < 0.05). Interestingly, the PageRank in the bilateral caudate was significantly increased in both the mADHD and sADHD groups relative to the TDC group (Bonferroni-corrected P < 0.05). There were no significant differences in the betweenness centrality among three subgroups  were significantly different among the three groups (Corrected ubgroups  online. ubgroups  and the ubgroups  and bilaubgroups  onto theP < 0.05). Meanwhile, the inter-module FCDs in the DMN—ECN, DMN—SN, DMN—VN, SN—BGN, and VN—BGN were also significantly different among the three subgroups (Corrected P < 0.05).post-hoc analyses. Relative to the TDCs, the intra-module FCDs within the VN and BGN, and inter-module FCD in the DMN—SN were significantly increased in the mADHD as well as the sADHD groups (Corrected P < 0.05). Meanwhile, the intra-module FCD within the DMN was significantly decreased in the sADHD group compared to the TDC groups (Corrected P < 0.05). In contrast, the inter-module FCD in the DMN—VN was significantly decreased in the mADHD and sADHD groups relative to the TDCs (Corrected P < 0.05). In addition, the inter-module FCD in the SN—BGN was significantly decreased in the sADHD relative to the TDC group (Corrected P < 0.05), and the inter-module FCD in the VN—BGN was significantly decreased in the mADHD relative to the TDC group (Corrected P < 0.05). Finally, the inter-module FCD of the DMN—ECN in the sADHD group was significantly increased relative to that of the TDC and mADHD groups (Corrected P < 0.05). These significant FCD values were mapped onto the patient-patient network, as shown in In the present study, we demonstrated that the use of common clinical phenotypes (symptom severity and IQ) and an innovative unsupervised data-driven machine learning algorithm such as TDA is an informative approach for understanding the heterogeneity of ADHD. We also examined whole-brain intrinsic functional connectivity and identified the specific brain network that shows significant differences between the mADHD group and the sADHD or the TDC groups. This result suggests that the identification of the mADHD and sADHD subgroups may be biologically meaningful when the functional connectome is examined using graph theoretical approaches.i.e., combined, inattentive, and hyperactive/impulsive presentation. As shown in Our results showed that TDA produced an easily recognizable branch-shaped graph with three progressive arms and that individuals with ADHD in the same arm shared similar characteristics to form specific subgroups of the disorder. Similar results were also replicated in the independent validation dataset. The most compelling finding of this study is the identification of two unique phenotypes (mADHD and sADHD), which overlaps with current ADHD categories identified in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) , i.e., cL-infinity centrality as the filter metric, which computes for every data point the maximal distance to any other data point in the set, and thus the extreme (highest or lowest) filter metric corresponds to flares (or branches) in the patient-patient network of the TDA output. Therefore, the current approach has an advantage in being readily translated into clinical practice. Notably, these two subgroups displayed both similar and distinct functional connectivity findings in whole-brain and subnetwork connectivity, suggesting the biological validity of our subgroups. It is worth noting that the advantage of TDA is the ease with which it can be used to visualize similarities and differences between the mADHD and sADHD groups by mapping variables of interest, as shown in In our previous study, we have failed to detect subgroups of ADHD using the functional connectome data as the input features . HealthyIn the analysis of functional network modular organization for the mADHD, sADHD, and TDC groups, we observed altered functional connectivity within several large-scale brain networks in children with ADHD compared with TDCs. Specifically, relative to the TDC group, the sADHD group displayed increased intra-module connectivity in the VN and BGN, and inter-module connectivity in the DMN—ECN, DMN—SN, DMN—VN, but decreased intra-module connectivity in the DMN and inter-module connectivity in the SN—BGN. Our results also revealed that the mADHD group generally showed similar patterns of modular architectures to that of the sADHD group. For example, intra-module connectivity in the VN and BGN, and inter-module connectivity in the DMN—SN and DMN—VN were not significantly different between the mADHD and sADHD groups, but the values were significantly different between mADHD and TDC groups. Taken together, our results suggest that both intra-module and inter-module functional connectivity are critical components of the underlying neurobiology of ADHD. In general, abnormal intra- and inter-module connectivity may limit dynamic interactions among networks, which are necessary for successful processing of complex stimuli in the real world .Regarding the hypoconnectivity within the DMN and the decreased degree centrality and PageRank in the bilateral PCC regions, our results support the idea that ADHD might be considered as a default network disorder –50. The Focusing on the subcortical system of ADHD, previous study associated the malfunction of the cognitive control in ADHD with abnormality in the striatal circuits . The mADInstead of the traditional simple models of ADHD, mainly involving DMN—ECN FC or within-DMN FC, our findings with regard to the VN and SN are consistent with the recent neurobiological formulations of ADHD , 55–57, Our study results revealed that aberrant connectivity in the DMN, VN, BGN, ECN, or SN mainly concerned our sADHD group. The mADHD group shared neural signatures with the TDC group as well as the sADHD group. First, the degree centrality and PageRank in the bilateral PCC and intra-module connectivity in the DMN of the mADHD group were found to be intermediate between the sADHD and TDC groups. In contrast, relative to the TDC group, inter-module hypoconnectivity in the DMN—VN, inter-module hyperconnectivity in the DMN—SN, and intra-module hyperconnectivity in the VN and BGN were observed in both the mADHD and sADHD groups. However, inter-module hyperconnectivity in the DMN—ECN was observed in the sADHD group, but not in the mADHD group compared to TDC group. These findings show that mADHD and sADHD are somewhat different in terms of their DMN—ECN connectivity, suggesting neurobiological differences between these two subgroups. The DMN and ECN are known as the task negative and task positive regions, respectively, indicating that they should be anti-correlated in the normal brain . Therefopost-hoc comparison and selection of 15 subjects is enough to produce statistically significant results. Our findings are somewhat dissimilar to the results from prior studies reporting reduced connectivity between the DMN and other networks of brain, such as the ECN [Despite the important findings, several limitations should be considered for the proper interpretation of our results. Our findings cannot be extended to the entire ADHD population because we only used samples from boys to avoid possible bias from sex differences . Further the ECN –49. Thes the ECN , 49. TheDespite the limitations mentioned above, we showed two notable findings in this study. First, TDA is potentially useful in defining less heterogeneous clinical subgroups in ADHD. Second, neuroimaging measurements are potential validators of phenotypic groups identified using behavioral data. Taken together, data-driven and biologically relevant disease phenotypes identified using TDA will give researchers a unique opportunity to identify objective and biologically relevant disease categories. The identification of subgroups with distinct patterns of clinical manifestation may lead to development of more effective and better-targeted intervention for individualized treatment and improved patient care.S1 FigThe patient-patient network discriminates children with ADHD into two distinct groups in terms of symptom severity. The average values of full-scale IQ and ADHD index within each node were plotted. Abbreviations: ADHD, attention-deficit/hyperactivity disorder; IQ, intelligence quotient; NYU, New York University.(DOCX)Click here for additional data file.S2 FigThe patient-patient network discriminates children with ADHD into two distinct groups regarding symptom severity. Most severe symptom ADHD (sADHD) were the clinically combined subtypes, whereas most mild symptom ADHD (mADHD) were the clinically inattentive subtype. Each node indicates a group of subjects with similar characteristics in dimensions of symptom severity and intelligence score. Node color indicates the ratio of subjects with a clinical subtype. Abbreviations: ADHD, attention-deficit/hyperactivity disorder; IQ, intelligence quotient; PKU, Peking University.(DOCX)Click here for additional data file.S1 Table(DOCX)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 TableModularity optimization analyses were conducted for the principal data set.(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 TableThe statistical significance of the intra-module and inter-module FCD were summarized.(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.


Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder, prevalent among 2-10% of the population.The objective of this study was to describe where, what, and when people search online for topics related to ADHD.Data were collected from Microsoft’s Bing search engine and from the community question and answer site, Yahoo Answers. The questions were analyzed based on keywords and using further statistical methods.Our results revealed that the Internet indeed constitutes a source of information for people searching the topic of ADHD, and that they search for information mostly about ADHD symptoms. Furthermore, individuals personally affected by the disorder made 2.0 more questions about ADHD compared with others. Questions begin when children reach 2 years of age, with an average age of 5.1 years. Most of the websites searched were not specifically related to ADHD and the timing of searches as well as the query content were different among those prediagnosis compared with postdiagnosis.The study results shed light on the features of ADHD-related searches. Thus, they may help improve the Internet as a source of reliable information, and promote improved awareness and knowledge about ADHD as well as quality of life for populations dealing with the complex phenomena of ADHD. Attention deficit hyperactivity disorder (ADHD) is a common, chronic, and pervasive pediatric neurodevelopmental psychiatric disorder that is prevalent among 2-10% of the population and has a complex etiology . ADHD isPrevious literature has indicated the mobility of the real-world daily manifestations of ADHD from early childhood to adulthood as well as gender-related differences ,6. ADHD ADHD symptoms often appear in early childhood, yet the clinical guidelines of the American Academy of Pediatrics recommend diagnosing ADHD only from the age of 4 . AlthougTherefore, from the child’s early years of development up to school years and even during adulthood, parents worldwide may search for resources and solutions related to their child’s behavior.A previous study of the information needs of parents of children with ADHD revealed that parents’ information resources were related to pediatricians (86%), books (76%), general practitioners (65%), schools (61%), the Internet (59%), and other forms of media (54%) . ParentsNowadays, the Internet is a popular source of information and medical information is one of the most popular Internet topics thus enabling children’s diagnosis or self-diagnosis as well as support at different coping stages -20. HoweIn that context, it was found that parents of children with psychiatric diagnoses respond favorably to the Internet as a source of information . More spwhere, what, and when information related to ADHD is sought after. Improving insight about people’s information-seeking behavior may have implications on improving health care literacy and health outcomes through Web-based solutions for this population.The aim of this study was to explore the dynamic process of Web-based information searches related to ADHD over a 3-month period. The specific goals were to identify Data were collected from two separate sources: (1) queries made in the United States using the Microsoft Bing search engine (following findings ) and (2)All English language queries submitted to the Bing search engine by users of desktop computers in the United States for the 3-month period between January 2015 and March 2015 were extracted. Queries were filtered to maintain only those that contained the terms “ADHD” or “attention deficit” and “disorder.” A total of 613,069 queries from 222,626 users were identified.For each query, the text of the query (without use of any automated spelling correction), time, and date were extracted, in addition to a list of pages shown to the user and the pages clicked on by the user. In order to maintain privacy, user identifiers, which were based on browser cookies, were anonymized before access by the investigators. Queries were aggregated preceding analysis preventing individual-level user datum from being examined by the researchers.R2=.83 (P=.001). Furthermore, an analysis of behavioral data collected from an opt-in consumer panel recruited by an Internet analytics company comScore, which includes age (in 5-year increments) and gender, shows a correlation of .79 (P=.004) between the fraction of users at each age and gender between Bing and Google users. Thus, it is estimated that Bing users are a representative sample of the US population.We note that the market share of Bing in the United States is around 19%, according to recent estimates . The corQuestions about ADHD posted on Yahoo Answers, an online community question and answer website, were collected. Bing was used to query questions from Yahoo Answers that contained the term “attention deficit hyperactivity disorder” or the term “ADHD.” This resulted in 1609 unique questions.The questions extracted from Yahoo were then each labeled by at least three human assessors from the crowdsourcing platform CrowdFlower, who were asked to label the question according to whether the question refers primarily to a person who (1) was diagnosed with ADHD, (2) is suspected of having ADHD, and (3) none of the above.As presented in The authors examined a sample of the questions and identified four main categories. Two MSc in occupational therapy research assistants further classified the questions into one of the following four groups:Personal accountGeneral question about ADHDQuestion about ADHD among their petsOtherA question was defined as “personal” if it was asked in the first person form and included words such as “I” or “my son” or “daughter” or “we” or “my wife,” which reflects the asker’s personal involvement within the question being asked.A total of 979 personal account type of questions were found and analyzed by the two research assistant raters. Each question was examined to detect any additional information about the person discussed in the question, including the age and gender, whether the person was diagnosed with ADHD or not. Furthermore, the raters analyzed whether the question addressed problems in (1) daily function, related to activities that children do throughout the day, including activities of daily living , social activities, and play; (2) behavioral manifestations, related to how the child interacts with people in his or her environment at home, school, or at social events; and finally, (3) academic performance, related to issues pertaining to school or spontaneous learning, based on predetermined keywords and examples see . The keypersonal account or the general questions participant’s category were extracted, resulting in 12,310 questions from 1130 users.All other ADHD-related questions that were posted by anyone in the what and where queries. Namely, what is the main concept that people use for the search and where or in which sites do they search for answers to their inquiries. Initially, the 613,069 queries made on Bing were classified into whether they referred to individual persons if they contained one of the following phrases: “my son,” “my daughter,” “my child,” or “my kid;” approximately 0.4% of the queries contained these phrases, and thus are referred to henceforth as “Individual Persons Query” (IPQ). People, who made at least one IPQ made an average of 5.4 queries, compared with an average of 2.7 queries among people who did not make an IPQ. Accordingly, users who referred to individual persons tended to search for significantly more information about ADHD. The 10 most popular ADHD-related queries submitted to Bing that included specific search terms are presented in Bing queries were analyzed while focusing on the th most popular site displayed in response to an ADHD-related query, but is not among the 20 sites most clicked in response to ADHD-related queries. Both general information websites and those specializing in ADHD are present in both rankings.The 20 most popular websites displayed as a result of the ADHD queries, and their rank in website popularity displayed to the users as a result of their search in the Bing search result engine, are presented in The Health on the Net (HoN) foundation is an organization that certifies websites that provide health-related information and certification if they meet specific reliability standards. Interestingly, although 45% of the 20 popular websites that appear in th most popular website displayed to users in response to ADHD queries on Bing and the 12th most popular site clicked by them. However, Yahoo Answers is also the only question-answering site on the list.As presented in People who made at least one IPQ on Bing also differed in the association of their browsing behavior with Yahoo Answers. The average number of Bing queries made by people in the IPQ population who had a Yahoo Answers result displayed to them was 5.7, compared with 5.4 in the remaining population. However, people in the IPQ population who clicked on a Yahoo Answers result made an average of 14.5 queries on Bing compared with 7.2 made by other users in this sample. Thus, people who have a strong personal interest in ADHD tend to search more on Bing and click more on Yahoo Answers results. Consequently, the following sections will focus on questions from Yahoo Answers.when people ask questions concerning what trouble them, and whether the main issue that troubles them is associated with the need to ask more questions.Analysis of the questions in Yahoo answers focused on In total, 979 personal questions were found in Yahoo Answers in Phase A, as presented in P<.001).Moreover, children who were diagnosed with ADHD were significantly older than those suspected of having ADHD. The average age for the latter was 5.1 years , compared with 7.9 years for diagnosed children , stratified by age is presented in In order to gather more information about the query process, a Cox hazard regression model  was consdemographics: age and genderwhether the question discussed functional, behavioral, or academic issues related to ADHDdiagnosis status: whether the question discussed a child who was suspected to have ADHD, or already diagnosed with ADHDwhether the person inquiring appeared to be emotional, helpless, or positivewhether the person inquiring asked for informationwhether the question mentioned that the child was receiving medication and whether the child had additional diagnosesP<.05) were the diagnosis status, meaning whether a query appeared in children suspected of ADHD or after diagnosis , and whether the asker revealed helplessness . Thus, additional ADHD-related questions are correlated with the child being before diagnosis (hazard lower than 1) and with the person inquiring feeling helpless .For this analysis, only 97.2% (952/979) of questions in which the diagnosis status was known were considered. The statistically significant variables  of ADHD-related questions by users in our sample were extracted from all 12,310 Yahoo Answers questions. The question was labeled as ADHD-related if the text contained the word ADHD, the name of a drug used to treat ADHD , or common comorbidities of ADHD . Of the entire set of questions, 53.79%  of questions were asked by people in the labeled set of 979 personal account questions. Among the 979 personal questions, there were 13.0% (127/979) of questions labeled as ADHD-related in this set by people who asked a question about a child prediagnosis, and 33.6% (329/979) questions about children postdiagnosis.Considering all the 6621 questions, the ratio between the probability of asking an ADHD-related question, divided by the probability of asking any type of question, as a function of time is presented in R2=.30, P=.005), indicating that people are less likely to ask an ADHD-related question after diagnosis, but also shows a peak around approximately 2 years, as for the undiagnosed situation.The questions were stratified by whether they were asked by a user who discussed a child prediagnosis in the labeled set, or a child postdiagnosis. As presented in where, what, and when people seek Web-based ADHD-related information. The results indicate that the Internet indeed constitutes a source of information, as reflected by 613,069 queries about ADHD by 222,626 Bing users and 1609 questions in Yahoo Answers in the defined time of the study. These results are supported by prior studies, which suggest that the Internet is a source of information for people concerned with mental health and developmental disabilities. People, including 96% of parents, tend to seek out information on incidence, recurrence, or increased severity of the disability )which, when, where, and what, our purpose was to identify who are the people that ask additional questions following their first question. The results showed that a question asked among those with no declared diagnosis and included signs of helplessness, is associated with asking more questions online. As mentioned above, parents’ daily confrontations with the disorder serves as the motivation for Web-based use. Therefore, it is understandable that parents, who are more worried and more stressed as reflected in their first question, continue to search for additional assistance.Further to achieving insight about the When analyzing the phenomena of whether people whose children were diagnosed asked more questions, it was found that these people are less likely to continue to ask questions online. Perhaps this tendency occurs because at this stage, parents refer their questions to their doctor in light of the findings of preference of this source among this population . AnotherIn summary, our results indicate that the Internet is indeed a source of information about ADHD and tracking the search process performed by the users on the Web sheds light on people’s needs. Besides the high priority given by parents of adolescents with ADHD to receiving information from the doctor, regardless the time constrains , they inThus, as ADHD is a chronic long-life phenomenon, expert professions may use the Web as a useful source of information for supplying accurate information over time according to this population’s daily needs . AcquiriDespite the interesting results, this study has several limitations. As far as known, this is the first study in which a quantitative analysis of the search extent and content was performed. The classification criteria as well as interrater validity need to further be discussed and improved.Further studies that include more accurate criteria as well as a qualitative analysis of the queries made within international Internet forums are required to achieve better insight about this population’s needs and the existing barriers to seeking help eg, ,61)..61]).


The aims of our study were to: (i) determine the prevalence of children aged 4 to 6 years with probable Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms in the Spanish population; and (ii) analyse the association of probable ADHD symptoms with sex, age, type of school, origin (native or foreign) and socio-economic status in these children.This cross-sectional study included 1189 children (4 to 6 years-old) from 21 primary schools in 19 towns from the Ciudad Real and Cuenca provinces, Castilla-La Mancha region, Spain. The ADHD Rating Scales IV for parents and teachers was administered to determine the probability of ADHD. The 90th percentile cut-off was used to establish the prevalence of inattention, hyperactivity/impulsivity and combined subtype.The prevalence of children with probable ADHD symptoms was 5.4% . Children aged 4 to 5 years showed a higher prevalence of probable ADHD in the inattention subtype symptoms and in total of all subtypes than children aged 6 years, and children with low socio-economic status reported a higher prevalence of probable ADHD symptoms  than those with medium and high socio-economic status.Early diagnosis and an understanding of the predictors of being probable ADHD are needed to direct appropriate identification and intervention efforts. These screening efforts should be especially addressed to vulnerable groups, particularly low socio-economic status families and younger children. Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children , 2. AccoSeveral studies confirm that ADHD symptoms cause a significant impairment in school tasks  and in tIn epidemiological studies on the prevalence of ADHD it is necessary to distinguish various strategies: first, clinical (based on the assessment of an expert) and second, psychometric  . There aEstimates of the prevalence of ADHD in Spanish children and adolescents range between 4.9% and 8.8% . SeveralThe aims of this study were two-fold: (i) to determine the prevalence of Spanish children aged 4 to 6 years with probable ADHD symptoms in the region of Castilla-La Mancha (Spain); and (ii) to analyse the association between that prevalence of children with probable ADHD symptoms with age, sex, type of school, origin (native or foreign) and SES.This was a cross-sectional analysis of data (collected from September–November 2013) from a randomised cross-over cluster trial aimed to assess the effectiveness of a physical activity intervention (MOVI-KIDS) in preventing obesity and improving academic achievement in preschoolers with or without ADHD . The MOVn = 1189).Participants who had valid data on ADHD-RS-IV , completFrom the Regional Department of Education and Science of Castilla-La Mancha, Spain, a letter was sent to each of the selected schools to inform of the purpose of our study. Subsequently, the researchers explained the objectives and methods of the study to the management of the school to obtain the consent of the school board. With the help of the teachers, a letter was sent to all the parents inviting them to a meeting at the school, the objectives, measures and procedures of our study were explained, solving the questions and doubts of the parents. Signed informed consent was obtained from all parents for the participation of their children in the study and in addition children gave their verbal consent. Later, a researcher distributed the rating scales (parents’ and teachers’ versions) in the schools. Parents and teachers completed the questionnaire and 1 week later returned them to the research team. A total of 1604 closed packets were distributed, and 1437 parents and 1515 teachers returned them (89.6% and 91.4% respectively). The study protocol was approved by the Clinical Research Ethics Committee of the Virgen de la Luz Hospital in Cuenca and the General University Hospital in Ciudad Real and by the Ministry of Education and Science of the Regional Government of Castilla-La Mancha, Spain (FIS PI12/00761).The parents’ and teachers’ versions of the ADHD-RS-IV  were useThe 90th percentile cut-off was used to establish the prevalence of inattention, hyperactivity/impulsivity and combined subtype symptoms, by age groups: 4 and 5  and 6 yeAge, sex, school (public and private), origin (native-children born in Spain- or foreign - children or one of their parents born outside of Spain) and SES were collected from a questionnaire for parents.Data regarding family SES were gathered by using self-reported occupation and education questions completed by either parent. Paternal and maternal education were classified separately as was primary education , middle education , and university education (university degree or PhD). Parental occupation was classified into five categories as follows: (i) supervisor/manager or freelance with ten employees or more; (ii) supervisor/manager or freelance with less than ten employees; (iii) freelance with no staff; (iv) non-qualified staff and unskilled worker; and (v) household chores, unemployed and others. An index of SES was calculated using the items regarding parents’ education and occupation . AccordiThe associations of probable ADHD and subtypes symptoms with age-groups (4 and 5 or 6 years-old), sex, school, family SES and origin of participants were assessed using the Chi-squared test. In addition, the agreement between the two informants (parents and teachers) was evaluated for each disorder subtype using the Cohen kappa coefficient. Kappa values 0–.20 were considered slight, .21–.40 fair, .41–.60 moderate, .61–.80 substantial and .81–1 excellent . StatistSD = .60) and 18.8% lived in the provincial capitals. No differences in age, sex and family SES were found between children who agreed to participate and those who did not. Distribution of preschoolers according to age, sex, school type, origin and family SES are depicted in Table We invited 1604 children to participate in the study and 1189 had valid data (74.0%), of which 575 (48.3%) were girls. The age of participants ranged from 4 to 6 years-old , inattention symptoms (κ = .221), and total prevalence (κ = .162). Thus, the estimates for hyperactivity/impulsivity symptoms could be considered as poor agreement, and for inattention symptoms as fair agreement.p < .01) were found, with the highest difference in the hyperactivity/impulsivity subtype symptoms . Regarding age, children aged 4 and 5 years showed a higher prevalence of probable ADHD symptoms in inattention subtype symptoms and total  than children aged 6 years. Also, children with low family SES reported higher prevalence of probable ADHD symptoms than medium and high family SES in all subtypes symptoms and total .The associations of probable ADHD and subtypes symptoms with age-groups (4 and 5 or 6 years-old), sex, school, family SES and origin of participants from 90th percentile are shown in Table Our findings suggest that the prevalence of probable ADHD symptoms in children among aged 4 to 6 years was 5.4% . Also, younger children and those with low family SES reported a higher prevalence of probable ADHD symptoms than older children and those with medium and high family SES.According to parents, 23.9% of children were with probable ADHD symptoms. By contrast, teachers reported a value of 12.9%. The significant difference in the values of prevalence given by parents and teachers may be due to environmental expectations, behavioural differences in children in different contexts and the possibility of comparisons with classmates. The poorer health status perceived by mothers of ADHD-diagnosed children, as of social dysfunction and anxiety, can be related to the clinical manifestations of hyperactivity which are more frequently reported by the family . In lineThe reviews that have examined the prevalence of children with probable ADHD symptoms have reported large differences in their estimates among countries , 16, 36.In line with our results, several studies have reported a higher prevalence of inattention subtype symptoms than the other ADHD subtypes , 38. HowIn comparison to earlier studies , 18, 41,The extent to which the prevalence of ADHD symptoms and its subtypes varies by family SES is also unclear. Although an elevated ADHD symptoms prevalence is described in lower SES populations , 41, 43,Although the prevalence of probable ADHD symptoms in boys is usually higher than in girls , 41, ourAs far as we know, several studies that compared both types of schools (public and private) , 25, 47,This is the only study that analysed differences between nationalities (native versus foreign samples) and no difference was reported . ConfirmThere are a number of strengths to this study as compared to others published: (i) the prevalence of children with probable ADHD symptoms was calculated through the two versions (parents and teachers) of a validated scale, according to children’s age and percentile; (ii) we obtained a high rate of response from parents and teachers; and (iii) this is the first study that measured the prevalence of probable ADHD symptoms in 4–5 years-old Spanish children in the region of Castilla-La Mancha, Spain.Potential limitations should be taken into consideration: (i) we did not record whether children were taking medication for ADHD; (ii) we do not know whether there are any pre-existing diseases such as learning disabilities or global delays, or if participants do aerobic exercise that could affect the prevalence of being probable ADHD symptoms ; (iii) wThe prevalence of probable ADHD symptoms in Spanish children aged 4–6 years is 5.4%. Children aged 4–5 years and those who belong to low SES have a higher prevalence of probable ADHD symptoms than children aged 6 years and a medium-high family SES.Our findings suggest that a significant percentage of preschoolers are with probable ADHD symptoms, thus an early identification and an understanding of the predictors of being probable ADHD symptoms are needed to direct appropriate identification and intervention efforts. These efforts should be especially addressed to vulnerable groups, particularly low SES families and younger children.