Bartonella infections were investigated in seven species of bats from four regions of the Republic of Georgia. Of the 236 bats that were captured, 212 (90%) specimens were tested for Bartonella infection. Colonies identified as Bartonella were isolated from 105 (49.5%) of 212 bats Phylogenetic analysis based on sequence variation of the gltA gene differentiated 22 unique Bartonella genogroups. Genetic distances between these diverse genogroups were at the level of those observed between different Bartonella species described previously. Twenty-one reference strains from 19 representative genogroups were characterized using four additional genetic markers. Host specificity to bat genera or families was reported for several Bartonella genogroups. Some Bartonella genotypes found in bats clustered with those identified in dogs from Thailand and humans from Poland. Bartonella parasitize erythrocytes and endothelial cells of a wide range of mammals and recently were reported in bats from Africa, Asia, America, and northern Europe. A human disease case in the USA was associated with a novel Bartonella species, which later was identified in bats in Finland. This human case has demonstrated the zoonotic potential of bat-borne Bartonella and underscores the need for extended surveillance and studies of these pathogens. The present work assesses prevalence and diversity of Bartonella in bats in the country of Georgia (southern Caucasus), characterizes reference strains representing diverse genogroups by variation of genetic loci, and evaluates the links between identified Bartonella genogroups and bat hosts. Importantly, some Bartonella genotypes found in bats were close or identical to those identified in dogs and humans. The data indicate that the public health impact of Bartonella carried by bats should be investigated.Bacteria of the genus  Bartonella are small and slow-growing Gram-negative aerobic bacilli. These bacteria parasitize erythrocytes and endothelial cells of a wide range of mammals. During the last six years, diverse Bartonella strains were identified in bats from Europe [Bartonella, demonstrating that strains of Bartonella in bats tend to cluster according to bat families, superfamilies, and suborders [Bartonella strains [Leptospira strains [Bartonella spp. can spillover into phylogenetically distant hosts, including humans [Bartonella species  are hosts of a wide range of zoonotic pathogens. Their significance as reservoirs of emerging infectious diseases, predominantly of viral origin, has been increasinglyecognized during recent decades ,2. In com Europe –6, Africm Europe –12, Asiam Europe ,14, and m Europe –19. Receuborders ,21. Host strains ,22 and b strains . Despiteg humans ,25. A reonensis; ), which Bartonella in bats in theRepublic of Georgia (southern Caucasus) with the following objectives: 1) to compare prevalence of Bartonella infection in diverse bat species from different geographic locations within Georgia; 2) to determine the genotypes of obtained strains by variation in gltA sequences, a gene commonly used for discrimination of Bartonella species; 3) to characterize reference strains representing diverse genogroups by variation of multiple genetic loci; and 4) to evaluate the links between identified Bartonella genogroups and bat hosts.The goal of the present work was to identify prevalence and diversity of All animal work has been conducted according to relevant NCDC, national, and international guidelines.Bats were collected from two distinct parts of Georgia in June 2012. Four locations are situated in Eastern Georgia: three sites in the Kakheti region near Davit Gareja, one site in the Kvemo Kartli region in Gardabani district. The other four locations are in Western Georgia: two sites in the Samegrelo-Zemo Svaneti region (Martvili district and Chkhrotsku district) and two sites in the Imereti region . The number of captured bats from each site is shown in Bats were captured manually or using nets from different roosts in caves and buildings . The list of bat species and the number of animals per roost or colony availablefor sampling was approved by the Ministry of Environmental and Natural Resources Protection ofGeorgia. Species of captured bats were identified based on external morphological characteristics. Captured bats (n = 236) were delivered to the processing site in individual cotton bags where they were processed. Bats were anesthetized with the use of ketamine (0.05–0.1 mg/g body mass) and exsanguinated by cardiac puncture. All bats were sexed and measured. The procedures of handling animals were performed in compliance with the protocol approved by the CDC Institutional Animal Care and Use Committee (protocol 2096FRAMULX-A3). Blood specimens were transported on dry ice to the NCDC Laboratory, Tbilisi where they were stored at -80°C until they were shipped on dry ice to the CDC’s laboratory, Fort Collins, Colorado. Upon arrival at CDC, the samples were stored at -80°C until they were analyzed.Bartonellawere passaged onto a new plate to obtain pure cultures. In an attempt to capture possible Bartonella coinfections, all morphologically unique colonies growing from a single sample were sub-passaged and sequenced. All resulting isolates were collected in a 10% glycerol solution. Crude DNA extracts were obtained from isolates by heating a heavy suspension of themicroorganisms for 10 minutes at 95°C. Polymerase chain reactions (PCR) with the gltA primersBhCS781.p (5’-GGGGACCAGCTCATGGTGG-3’) and BhCS1137.n (5’-AATGCAAAAAGAACAGTAAACA-3’) [B. doshiae) and negative (nuclease free water) control samples were included in each PCR assay to evaluate the presence of appropriately sized amplicons and to rule out contamination of reagents, respectively. Positive PCR products were purified using QIAquick PCR purification Kit  and sequenced with an ABI 3130 Genetic Analyzer . Forward and reverse reads were assembled into consensus sequences with the SeqMan Pro program in Lasergene v. 11 .Bat blood was diluted 1:4 in Brain Heart Infusion (BHI) with 5% Fungizone (amphotericin B), and 100μl of the sample was placed on a chocolate agar plate following the protocol published previously . InoculaAACA-3’)  were perhttp://blast.ncbi.nlm.nih.gov/Blast.cgi) search of the GenBank database was performed with all assembled gltA sequences to verify their Bartonella origin. Positive sequences were aligned with Bartonella reference sequences available in GenBank which included sequences obtained from various bats in previous studies. Brucella abortus sequence was used as outgroup. Alignment was performed with MAFFT v7.187 using the local, accurate L-INS-i method [Bartonella sequences from Georgian bats were all isolated at the same date and thus could not be used for calibration of another clock model; furthermore, our analysis did not seek to accurately deduce branch times, and the strict clock was adequate. No codon partitioning was used due to the fact that gltA sequences represent only a 367 base pair fragment of the entire gene; codon partitioning with limited genetic information can substantially reduce the effective sample size of estimated parameters forseparate codon positions [Bartonella species [A BLAST  that have been previously used for bartonellacharacterization [gltA gene. Genogroups Vesp-7, Vesp-13, and Rhin-3 were not analyzed by MLST, while three isolates of Vesp-6 were selected for analysis to examine within-genogroup variation. The primers and cycle conditions used to generate sequences for each loci have been previously published [Bartonella species and other Bartonella sequences obtained from bats with MAFFT v7.187 using the L-INS-i method [gltA tree as described above. Separate maximum likelihood gene trees were generated using the GTRCAT model in RAxML [Five genetic loci , were collapsed into a single principlecomponent that explained 95% of variation in size. However, bat size was strongly predicted by batspecies  and sex , so size was not included as acovariate in the global model. Model selection was based on AICc [Bartonella prevalence among bat species, capture locations, and bat sexes wereapproximated with the Agresti-Coull method [A logistic model was used to examine important predictors of  on AICc . Additio on AICc ,49. Binol method . All stal method  andvalueEptesicus serotinus ; Schreibers's long-fingered bat, Miniopterus schreibersii sensu lato  [Myotis blythii ; Geoffroy's myotis, Myotis emarginatus ; whiskered myotis, Myotis mystacinus ; soprano pipistrelle, Pipistrellus pygmaeus ; Mediterranean horseshoe bat, Rhinolophus euryale ; and greater horseshoe bat, Rhinolophus ferrumequinum . The number of species and specimens obtained varied per site  ; lesser per site .Bartonella testing. The amount of blood from thesingle captured My. mystacinus was not sufficient for culturing. Except for this, bartonellae weresuccessfully cultured from all bat species tested . All the harvested colonies were confirmed as Bartonella by PCR and sequencing of gltA fragments. The overall prevalence of Bartonella in bats by culturing was 49.5% (105/212). Bartonella isolates were obtained from each of the eight sites. The prevalence of culture-positive bats varied from 16.7% at the Lavra site in Davit Gareja to 64.6% at Gliana Cave in Tskaltubo.A total of 212 bats of seven species were available for s tested . BartoneP. pygmaeus to 88.9% in Mn. schreibersii. The best model based on AICc included bat species only with a good amount of predictive power(AUC = 0.71) [Bartonella prevalence. Prevalence of Bartonella in My. blythii , Mn. schreibersii , and R. euryale  was significantly higher.The range of prevalence varied from 16.7% in  = 0.71) . Based oBartonella-like colonies were observed with morphologies that visually varied by size  and/or texture . The number of visually different colonies per plate varied from one unique colony among TNTC similar colonies to an equal number of two unique colony morphologies. We did not attempt to estimate colony forming units (CFU) for individual bats suspected of coinfection. Sequencing analysis confirmed a coinfection with two different Bartonella sequences from these 16 samples , but from distant locations. Other sequences, notably from genogroups Mini-1.1, Mini-3, and Vesp-6, clustered with bartonella sequences identified in dogs from Thailand [In some cases, Georgian bat sequences matched very closely with other Thailand  and in hThailand .ftsZ, gltA, nuoG, rpoB, and groEL) confirmed that most Bartonella genogroups from Georgian bats formed well-supported clades (posterior probability > 90%) with other Bartonella genogroups identified in bats. (The phylogeny based on concatenated sequences from five genetic loci (in bats. Bartonella genogroups found in African pteropodid (Eidolon helvum andRousettus aegyptiacus) [Hipposideros sp. and Triaenops persicus) [Coleura afra) [Bartonella genogroup found in Miniopterus schreibersii from Taiwan [Candidatus Bartonella naantaliensis found in Myotis daubentonii from Finland [Genogroups Mini-1, Mini-1.1, Mini-2, Mini-3, Rhin-2, Rhin-4, Rhin-5, and Vesp-10 formed a well-supported clade with other ptiacus) ,9, hippoersicus) , andembara afra)  bats. Gem Taiwan . GenogroEw from Eidolon helvum [Bartonella mayotimonensis isolated from a human endocarditis patient [Eptesicus nilssonii and Myotis daubentonii) [Bartonella vinsonii subspecies, with low posterior probability support (50%). Finally, genogroup Vesp-12 clustered with genogroup E4 from Eidolon helvum [Bartonella clarridgeiae and Bartonella rochalimae. The network phylogeny  where high prevalence and diversity of Bartonella strains have been reported [Mn. schreibersii and below 17% in P. pygmaeus) as well as between study sites. The difference inprevalence between locations can be likely explained by bat community composition  Mn. schreibersii tested were coinfected with two different Bartonella genotypes  from Mn.schreibersii aligned with genogroups Mini-1, Mini-1.1., Mini-2, or Mini-3  from Madagascar [Rhinolophus spp.  belonged to genogroupsRhin-1, Rhin-2, Rhin-3, or Rhin-4. Genogroups Rhin-2 and Rhin-3 cluster with Bartonella sequences identified in R. acuminatus and R. sinicus from Vietnam [Eptesicus, Myotis, and Pipistrellus spp.) were members of genogroups Vesp-1 to Vesp-12 with closely matching sequences found in other vespertilionid bats [We observed several coinfections among sampled bats. The phenomenon of coinfections with two or three different  rodents . Intereshe world ,16,20. Fr Mini-3 . Based odagascar . Thirty- Vietnam . Most isnid bats ,56.Bartonella into atypical hosts. For example, isolates of Bartonella from genogroup Mini-1 were found in E. serotinus, My. blythii, and P. pygmaeus, and isolates of Bartonella from genogroups Rhin-1 and Rhin-3 were found in My. emarginatus and My. blythii, respectively  has been collected from E. serotinus, My. blythii, P. pygmaeus, and R. ferrumequinum [Cimex pipistrelli (Hemiptera: Cimicidae) has been reported parasitizing E. serotinus, My. blythii, My. emarginatus, P. pygmaeus, and R. ferrumequinum [Spinturnix myoti (Mesostigmata: Spinturnicidae) has been recorded on E. serotinus, Mn. schreibersii, My. blythii, R. euryale, and R. ferrumequinum [Bartonella genogroups in some bat hosts can potentially be explained by sharing of ectoparasites. Future analyses exploring the influence of ectoparasite distributions on sharing of Bartonella genogroups among bats are in progress.Despite these general host associations, specificity of genogroups at the genus or family levelwas not strict, with some instances of apparent spillover of ectively . Though ectively , whereinhsteinii , Basiliaattereri , Nycterieibersii , and Phthus spp. . Neverthrginatus , Basiliaerotinus , Nycterimequinum ,63, Peni blythii , and Phtrginatus ,64. Othemequinum ,65,66. Cmequinum ,68. Addimequinum –71. ThisftsZ, gltA, nuoG, rpoB, and groEL) along with the network phylogenetic analysis strongly indicated that many genogroups characterized in our study can be segregated into new Bartonella species according to established demarcationcriteria considering loci separately [Bartonella genogroup. The host associations observed for most of identified genetic clusters also supports the biological basis for discrimination of the species. As was reasoned previously [Bartonella species compared to sequence analysis alone. For example, genogroups Vesp-1, Vesp-2, and Vesp-3 share 92%, 93%, and 92% nucleotide identity, respectively, with Bartonella mayotimonensis, the bacterial species discovered in a human patient in the United States [B. mayotimonensis is closest (95%) at the gltA locus to a sequence identified in a bat fly Anatrichobius scorzai taken from a bat Myotis keaysi in Costa Rica [B. mayotimonensis species, but using the gltA locus alone creates an artifactual split among the genogroups. When all five concatenated loci were considered, genogroups Vesp-1, Vesp-2, and Vesp-3 shared pairwise sequence identities between 96.9–98.11%. Considering their relatedness and apparent specificity to vespertilionid bats  [B. mayotimonensis ranged 95.1–95.5%, which is near the previously established minimum threshold for distinguishing between Bartonella species (95.4% for rpoB sequences [Eptesicus and Myotis) [Bartonella species. This is also true for genogroups Vesp-4 and Vesp-5 found in one bat species, My. blythii (96.3% sequence identity) and genogroups Mini-1 and Mini-1.1 found in Mn. schreibersii (96.6% sequence identity).The sequence characterization of five house-keeping genes  and we closest 9% at the Bartonella, which are similar to Bartonella strains previously reported in humans and in dogs. Thepublic health relevance of bat-borne Bartonella infection has been discussed since the identification ofsuch bacteria in bats from Kenya [Bartonella surveillance inbats, as it can help to identify potential wildlife reservoirs of human cases. Although some sequences of Bartonella found in Georgian bats clustered with B. mayotimonensis, the genetic distances were relatively long, as noted above. We might speculate that Bartonella more closely related to thishuman case are circulating in vespertilionid bats in the North and South America rather than in Europe. Even more unexpected was the discovery of Bartonella strains in Georgian bats which wereidentical or very similar to ones reported in forest workers from Poland. The study in Poland wasconducted to evaluate the level of exposure of 129 forest workers to diverse tick-borne pathogens [Bartonella antibodies were reported in about 30% of tested individuals, but more importantly, threeserologically-positive samples were also positive for Bartonella nucleic acids by PCR and sequencing. The gltA sequences identified in that study were distinct from all previously reported. They were closest (90% similarity) to B. koehlerae, B. clarridgeiae and a genotype from an arthropod from Peru. They were deposited in GenBank  as uncultured Bartonella spp. [gltA sequences to the HM116785 sequence. Vesp-6 is the largest genogroup found in bats from Georgia containing 18 sequences from My. blythii (n = 15), My. emarginatus (n = 2), and E. serotinus (n = 1). All of these bat species are listed as occurring in southern Poland where the investigation of forest workers was conducted [The most intriguing and important results from this study is the identification of bat-borneom Kenya . Our resathogens .Bartonellla spp. . All stronducted –75.Bartonella strains observed in this study were closely related to those identified in stray dogs from Thailand., Bai et al. [Bartonella infections and diverse Bartonella species in the blood of stray dogs from Bangkok and Khon Kaen (northeastern province of Thailand). Besides two Bartonella species (B. elizabethae and B. taylorii) detected in stray dogs from Khon Kaen, the authors also reported two genotypes (KK20 and KK61) that could potentially represent a new species [Bartonella strains found in bats from Georgia, we found that sequences of the strains from genogroup Mini-1.1 obtained from Mn. schreibersii (n = 7) and R. euryale (n = 1) were 99% similar to those dog sequences from Thailand . Likewise, seven sequences from Mn. schreibersii (genogroup Mini-3) were 99% similar to the sequences of the strain KK20 from stray dogs from Khon Kaen, Thailand (GenBank accession FJ946854). Bat species belonging to the genus Miniopterus  are present in Thailand [Another surprising discovery was that i et al.  provided species . PerformThailand .Bartonella strains in Georgian bats, which are identicalor similar to the strains previously described in humans and in companion animals in other geographic areas grants special attention in future studies to evaluate their role as potential zoonotic agents. Aparticular question remains regarding the route of transmission of bat-associated Bartonella to people. Itis easier to speculate how stray dogs, which may scavenge for grounded bats, can become infected withbat-associated Bartonella, but the question concerning transmission of bat-borne strains to humans ismore challenging [Bartonellaspecies [Argas vespertilionis and Cimex pipistrelli [Bartonella surveillance should include not only mammals, but also their vectors whenever possible to better understand the risks of disease transmission.The identification of diverse llenging . Howeveraspecies ,26 suggeistrelli –80. ThusS1 Appendix(DOCX)Click here for additional data file.


This study aims to investigate the prevalence, correlates and treatment seeking behavior related to ADHD among adolescents from Lebanon.Five hundred and ten adolescents were recruited through multistage stratified cluster sampling of households in Beirut, and separately interviewed along with one parent/legal guardian, using the DAWBA. All adolescents completed the PRQ and the SDQ; the parent/legal guardian also completed the SDQ and provided basic demographic information, including attitudes towards seeking mental health services.10.20% of the adolescents were diagnosed with ADHD. Having ADHD was associated with having academic difficulties and being involved in bullying. Adolescents with ADHD also had higher odds of drinking alcohol, smoking cigarettes, and having comorbid emotional and conduct disorders (compared to those without ADHD). Adolescents with ADHD and their parents reported a higher burden of illness and were more likely to consider seeing a mental health professional than healthy adolescents and their parents.ADHD among adolescents in Lebanon warrants closer attention, mainly increased awareness in the larger public, and stronger commitment to increase treatment resources to the community. Diagnostic and Statistical Manual—5th edition (DSM-V) recently updated the cut-off age of onset to be below 12 years of age .Current (within the last 6 months) and past attitudes towards seeking mental health services  as well as any history of psychiatric treatment in any member of the family were also assessed using a questionnaire with open and closed-ended questions.BEI-PSY was approved by the Institutional Review Board (IRB) at the American University of Beirut. Written informed consents from participating parents/legal guardians and assents from adolescents were obtained. All participating families were given a referral list with addresses of mental health centers in the city. Participants were given a stationary kit at the end of their participation.To answer the first aim, sociodemographic characteristics, PRQ scores, SDQ Total Impact scores and treatment-seeking attitude measures were compared across three subgroups: adolescents with ADHD (ADHD), adolescents with a psychiatric diagnosis other than ADHD (psychiatric controls) and adolescents with no psychiatric disorders . Pearson’s Chi square test and Fisher’s exact test were used to assess the bivariate association between two categorical variables. The Kruskal–Wallis H test and the Mann–Whitney U test were used to compare non-normally distributed continuous variables across all three subgroups.statistical package for the social sciences (SPSS) [version 22.0].To address the second aim, and specifically assess the medical and psychiatric correlates of a positive diagnosis of ADHD versus not having ADHD (whether diagnosed with another disorder or not), we used a multivariate logistic regression model adjusting for age, gender, nationality, household income, parental educational level and relationship status, as well as variables found to be statistically significant in bivariate analyses. The threshold for statistical significance was set at α = 0.05 based on two-tailed tests. When pairwise comparisons were done, the Bonferroni method was used to adjust for multiple comparisons and α was set accordingly. Analysis was conducted using the Among the total sample of 510, 52 (10.20%) were diagnosed with ADHD, of which around 77% had the combined type and 6% had the inattentive type. Table Adolescents and parents in the ADHD subgroup were more likely than adolescents and parents in the healthy subgroup to consider seeing a mental health professional [ for adolescents and  for parents respectively] but not more so than adolescents and parents in the psychiatric controls subgroup  and Victim subscale scores . In pairwise comparisons, differences were significant between the ADHD and the healthy subgroups on the Bully subscale  and the Victim subscale  while the ADHD and the psychiatric controls subgroups did not differ on these two subscales  or by the adolescents : scores were highest in the ADHD subgroup, followed by the psychiatric controls, then lowest among the healthy subgroup. Specifically, adolescents in the ADHD subgroup scored on average 2.08 on the parent’s report, compared to 0.89 in the psychiatric controls subgroup  and 0.09 in the healthy subgroup . On the adolescent’s report, the average total impact score in the ADHD subgroup was significantly higher compared to the healthy subgroup’s score  while it did not significantly differ with the psychiatric controls’ score  had a comorbid emotional disorder, 12 (23.08%) had oppositional defiant disorder (ODD) and 7 (13.46%) had CD, while 14 (26.92%) were reported to have a chronic medical condition. Cigarette smoking was more prevalent in the ADHD subgroup than in the psychiatric controls subgroup (19.23 vs 8.30%) as well as alcohol use (23.08 vs 10.26%).After adjusting for age, gender, nationality, mean household income, parental educational level and relationship status, family psychiatric history, school attendance, repeating grades and receiving home tutoring in the multivariate logistic regression analysis, adolescents diagnosed with ADHD were found to be significantly more likely to have any emotional disorder  and ODD  and to drink alcohol  than adolescents not diagnosed with ADHD . Our sample, however, reliably reflected the diversity of the socioeconomic strata of the Lebanese society as a whole . In addiDespite these limitations, this study is the first to investigate ADHD in adolescents in Lebanon and the first in the Arab region to uncover the significant medical, academic and functional burden as well as important correlates. Our findings highlight the need for relevant governmental authorities and mental health advocates to further develop public awareness about the symptoms of ADHD and the functional impact of the illness and the availability of resources for treatment. These efforts should specifically focus on schools as teachers and counselors can be educated to detect possible symptoms and discuss with the parents referrals for an assessment. Finally, mental health specialists should be sensitized to extensively discuss treatment options and their benefits and risks not just with the parents, but also with the affected adolescent.


Multiple studies have reported that individuals with low birth weights  have a lower intelligence quotient (IQ) than those with normal birth weights . Based on 57 eligible individual studies including 12,137 participants, we performed a meta-analysis to estimate the association between low birth weight and individuals’ IQ scores (IQs). The pooled weight mean difference (WMD) in IQs between NBW and LBW individuals was 10 (95% CI 9.26–11.68). The WMD was stable regardless of age. No publication bias was detected. The mean IQs of the extremely low birth weight , very low birth weight , moderately low birth weight  and NBW individuals were 91, 94, 99 and 104, respectively. Additionally, the WMD in IQs with NBW were 14, 10 and 7 for ELBW, VLBW, and MLBW individuals, respectively. Two studies permitted estimates of the influence of social determinants of health to the discrepancy in IQs, which was 13%. Since IQ is inherited and influenced by environmental factors, parental IQs and other factors contribute to residual confounding of the results. As the conclusion was based on population studies, it may not be applicable to a single individual. With the development of perinatal care and neonatal medicine, the survival rates of LBW infants are greatly improved5, followed by an increasing number of LBW individuals with cognitive deficit9, which has become a serious public health burden10.Infants with low birth weight (LBW), very low birth weight (VLBW) and extremely low birth weight (ELBW) are considered to be at a high risk of cognitive dysfunction11. More than 50% of VLBW children required special education services, and approximately 20% of VLBW children repeated at least one grade12. ELBW individuals without major disabilities 5 had subtle neurodevelopmental disabilities  in the school and teenage years14. Evidence from cohort studies in four western countries showed that more than 50% of adolescents with ELBW had learning difficulties 16. The effect of LBW accounted for a 0.4 standard deviation (SD) decrease in math and a 0.25 SD decrement in reading17. Those cognitive disadvantages would lead to low school achievements and persist into early adulthood21, thus resulting in low socio-economic status (SES) in the future3.Numerous studies have focused on the cognitive outcomes of VLBW individuals in recent decades22. The IQ is relatively stable and can be easily measured23. Additionally, there are some internationally recognized assessment scales which make it possible to compare the IQs in different populations. The consistent finding was that LBW individuals had lower IQs than those with normal birth weights (NBW)24. The size of this discrepancy varied across studies, ranging from 3 to 23 points25, and the discrepancy was directly proportional to their birth weight20 2. Some studies found that a gradient relationship existed, in which lower birth weight was associated with lower IQs26. In other words, the ELBW individuals’ IQs were the lowest, followed by those with VLBW and moderately low birth weight (MLBW)28. However, most of the previous individual studies were based on a small number of participants, so it was necessary to use meta-analysis to enlarge the sample size and assess the gradient relationship.The IQ score (IQs) is often used to indicate individuals’ cognitive outcomes worldwide29 found that LBW individuals scored an average of 8 IQ points lower than NBW individuals. As is already known, there have been more relevant studies focusing on preschool and school-aged children. We integrated those studies into our meta-analysis to identify the age-related change in IQs between LBW and NBW individuals.A recent meta-analysis containing 15 individual studies on the relationship between LBW and IQs in adolescent and early adulthood (age ≥ 13)29. Using 27 eligible individual studies published between 1980–2009, Kerr-Wilson et al.30 performed a meta-analysis on preterm delivery and intelligence, which showed that the preterm children had significantly lower IQs compared with term children. The weighted mean difference (WMD) was 12 [95% confidence interval (CI) 10.47–13.42]. The group’s analysis included duplicated populations , and some control groups were used more than once in the model, which may enlarge the weight of some individual studies. Despite the overlap of LBW and prematurity, they may have different relationships with IQs29. To more specifically reflect on the relationship between LBW and IQs, we performed this meta-analysis on LBW and IQs.Data from the US Centres for Disease Control showed that 45% of babies born preterm were < 2500 gIn this meta-analysis, we aimed to use 57 eligible individual studies to estimate the pooled discrepancy in IQs between LBW individuals and NBW individuals and the changes in discrepancy across age. We also used subgroup analysis to assess the gradient relationship with IQs for the different levels of LBW.The search strategy generated a total of 3,124 potentially relevant papers. After reviewing the title and abstract, 2,548 papers were excluded because of irrelevance. Another 281 articles were also excluded because they were reviews (n = 40) or intervention studies (n = 14). Furthermore, 225 studies focusing on relevant factors for LBW and 2 studies in other than English were also excluded. Thus, we reviewed 295 articles with full text. Among them, 238 were excluded because they did not meet the inclusion criteria. The flow chart for exclusion/inclusion of individual studies is presented in Fig. 34 had two pairs of groups in the study population. Therefore, the meta-analysis included 61 study groups with 6,683 LBW individuals and 5,454 NBW comparisons. The participants included both children and adults, with ages ranging from 4 to 26. These studies were performed in 21 countries, including 18 developed countries, where most of the studies were conducted (n = 53). Forty-four studies used different versions of the Wechsler scale to measure IQ. Five studies used the K-ABC (Kaufman Assessment Battery for Children), whereas three used the Stanford-Binet intelligence scale. The MIQS  and BAS  were used in other studies. Most studies (n = 50) were cohort studies, and 7 were case-control studies. The descriptive information of the included studies is shown in Table There were 57 eligible studies published over 36 years based on our search strategies, four of whichP < 0.001), with a 95% CI of 9.26–11.68, which means that the LBW individuals’ IQs were significantly lower than those of the NBW controls  and I2 = 79.9% (P < 0.001)]. The mean IQs of the ELBW, VLBW, LBW and NBW individuals were 91, 94, 99 and 104, respectively. A gradient relationship was observed between birth weight and IQ.All studies revealed that the LBW individuals had lower IQs compared with the NBW group. The pooled WMD was 10 . Figure After excluding one study at a time, the sensitivity analysis confirmed the significant association between LBW and IQs (with 95% CI ranging from 0.68 to 0.76) Figure . No publ2 = 13.22%, P =0.003). Other variables did not reach the significance level  with the highest WMD may be the main cause of high heterogeneity. After excluding these two studies, the adjusted pooled WMD was 10 . Approximately 15.6% of the heterogeneity was attributable to these two studies.We used meta-regression models to probe the source of heterogeneity. The variables included sample size, birth year, age of assessment, and the birth weight of LBW individuals. The model showed that the birth weight of the LBW participants was associated with an IQ difference between NBW and LBW individuals  and 11 (95% CI 9.31–13.53) for social determinants between matched groups and non-matched groups Figure , respect29. The lower birth weight categories had lower IQs on average. The average IQs of ELBW individuals were the lowest, followed by VLBW individuals and those with MLBW. Specifically, low birth weight individuals had approximately 10–11 points lower IQs than NBW individuals from childhood to adulthood (4–26 in age). There was a gradient relationship between low birth weight and the discrepancy in IQs between LBW and NBW individuals, with the WMDs from large to small being 14 (ELBW), 10 (VLBW), and 7 (MLBW). In addition, social determinants of health were associated with individuals’ IQs, which explained approximately 13% of the IQ difference between LBW and NBW individuals.Our study supported the evidence that individuals’ low birth weight had a negative association with IQ29, while the VLBW and ELBW infants tended to be less than 32 weeks in gestational age3. Because of the high degree of immaturity of respiratory organs and the nervous system, they were susceptible to bronchopulmonary dysplasia35, neonatal brain injury 37, and other medical complications, which may result in cognitive impairment. Additionally, children born with low birth weight had less connected and less complex brain networks38, smaller brain volumes41, and less cortical surface area42 compared with NBW children. The different degree of neonatal immaturity in LBW infants is considered to be associated with cognitive outcomes43.The gradient relationship obviously depicted the IQ gap between individuals with different levels of LBW and those with NBW. The M LBW infants were closer to preterm (<37 weeks)29. Some LBW individuals may have cognitive catch-up growth44, but it is not a universal rule among those with LBW. A long-term follow-up study on a population sample aged from 5 months to 26 years showed that the IQs were more stable in very preterm (VP)/VLBW individuals than in term-born individuals across all time points45. However, this conclusion was based on the entire LBW group and may not be applicable to a single individual.We also found a stable difference in IQs between LBW individuals and NBW individuals. The discrepancy was approximately 10–11 points regardless of the age of assessment. This finding was inconsistent with previous reports that showed that the discrepancy would decrease over time21 and that the risk of impaired cognitive development increases with decreasing SES46. A study by Sommerfelt et al. reported that 23% of the variance in child’s IQ at age 5 could be attributed to parental and family variables in Norway. Our results showed that social determinants of health explained approximately 13% of the lower IQ values. Because of the diversity of social determinants in different societies and the variations in study design, the common practice of simply matching social determinants of health  may result in an underestimation of cognitive impairment caused by social determinants of health or other similar risk factors.Social determinants of health, such as social class, parental/maternal education and occupation, marital status, etc., are known to contribute to suboptimal cognitive development of LBW children. Previous studies have indicated that LBW continues to be associated with cognitive disadvantage at each SES level47.Genetic variation is the main cause of individual differences in IQ48. Previous studies have reported that the “heritability” (h2) for IQ ranges from 20% in infancy, to 40–50% by late adolescence and to 60–80% in adulthood49. Environmental factors, such as perinatal factors50, schooling, family environment, nutrition and so on49 also contribute to individuals’ IQs. The aetiology of LBW individuals’ lower IQs is complex and unclear. Various adversities occur among LBW infants, such as preterm birth, the stress of intensive care and more frequent morbidities, which may also affect individuals’ IQs. It may be that low birth weight is an event along this causal pathway. However, two cohort studies from Denmark51 and Estonia52 demonstrated the associations between birth weight and IQs, and the associations remained significant after controlling for a wide range of confounders. These correlations were modest, ranging from 0.05 to 0.1353.Intelligence is a product of genetic and environmental variables54, inferior SES5, an unhealthy lifestyle5, and even some chronic diseases56, improving the cognitive outcome of LBW infants is essential and urgent. Previous evidence showed that the LBW individuals can benefit from early interventions for cognitive outcomes9. Some randomized controlled trials, such as the Newborn Individualized Developmental Care and Assessment Program (NIDCAP)57 and a sensitizing parental intervention programme58, showed that breastfeeding59 and kangaroo care had beneficial effects on LBW infants’ cognitive outcomes. It is recommended to assess the cognitive ability of LBW individuals first in order to determine the need for interventions. Periodic cognitive assessment of LBW children can evaluate the intervention’s effectiveness, thus providing more accurate interventions for each individual60. The cognitive benefits from early intervention may persist into preschool age or adolescence58. Therefore, long-term interventions may play a role in the long run. Although there were few long-term intervention programmes reported, it is necessary for child care centres and parents to offer long-term neuropsychological rehabilitation to LBW individuals even if they do not suffer from severe cognitive disabilities.As poor cognitive outcomes may be related to lower school achievementsCompared with previous meta-analyses of LBW/preterm individuals’ IQs, we included more eligible and recent individual studies, with a total of 12,137 participants and without a duplicated study population. We conducted subgroup analyses to show the gradient in the IQ gap between individuals with different levels of LBW and those with NBW, as well as the stability of the difference in IQs between LBW and NBW individuals. Although the selected studies used different cognitive tests to measure individuals’ IQs, each test/scale had similar normative data , which made the results from different studies comparable.We tried to include all relevant studies, but some studies may be missed in this meta-analysis due to our search strategies or incomplete databases. Additionally, grey literature publications were not included. However, the large sample size of this study made the results more stable and credible.According to individual studies, parental/maternal education was either a variable of socio-economic status or an independent social determinant. Three individual studies only matched by parental/maternal education were also included in the social determinant-matched group. Since there is not a perfect fit between education and socio-economic status, residual confounding may exist in the subgroup analysis based on social determinants of health.45, medical complications61, early home environment, schooling, and so on62. We didn’t take this residual confounding into account. These factors may also contribute to the heterogeneity. The association between these factors and LBW IQs will be explored in a further study.IQ is a complex trait that is influenced by genetic and environmental factors, such as parental IQsWe searched the PubMed and the Embase databases for full-text articles in English published between January 1980 and November 2016. The following terms were used to perform the literature search: “low birth weight” or “preterm” or “premature”, and “intelligent quotient” or “IQ” or “cogni*” or “neuro*” or “mental” or “psycho*” or “outcome”.Participants with LBW (< 2500 g) were compared with those with NBW (≥ 2500 g).The individuals’ ages were ≥ 4 years.Full-scale IQ was measured by a standardized and global scale with the mean and standard deviation of the IQs listed.Full-text articles were available from the two databases.Each study should meet all of the inclusion criteria.We excluded reviews, studies of the non-LBW group, and those without NBW individuals as a control group. If more than one study was based on the same cohort, only the study with the larger sample was included in the meta-analysis. When the study had two or more LBW groups, we calculated the weighted mean and deviation to represent the LBW individuals’ IQs in the meta-analysis Figure . For theThe following information was extracted from each study:(1) first author’s name; (2) year of publication; (3) country of origin; (4) birth year of the participants; (5) size of study population; (6) birth weight; (7) gestational age; (8) measurement tools; (9) age at assessment; and (10) mean and standard deviation of the IQs.P < 0.05 was considered statistically significant). To assess the heterogeneity, we consulted the Cochrane Q test and I2 statistics. Publication bias was assessed by Begg’s test and Egger’s test. We also used a funnel plot to depict the potential publication bias. We constructed meta-regression models and a Galbraith radial plot to probe the source of heterogeneity.A random-effects meta-analysis was performed using the WMD in IQs between LBW and NBW individuals. The significance of the WMD was determined using a Z test , very low birth weight  and extremely low birth weight . Then, we grouped individual studies into three groups by the subjects’ age at assessment  in the second subgroup analysis. Because social determinants of health are associated with individuals’ IQs, we compared the social determinant-matched group with the social determinant-unmatched group to evaluate how much of the lower IQ values were due to social determinants of health. All analyses were conducted using STATA version 11 .Individuals with LBW had lower IQs compared to those with NBW, and the discrepancy was approximately 10–11 points from childhood to adulthood (4–26 in age). We also demonstrated a gradient relationship between different levels of LBW and IQs. The social determinants of health explained approximately 13% of the IQ difference. These findings contribute to our understanding of the association between LBW and IQs. Our results will help physicians and parents to pay more attention to regular cognitive assessment and early intervention, as well as to long-term neuropsychological rehabilitation for LBW infants.supplementary information


There has been little success in attempts to reduce the proportion of births with low birth weight (LBW). However, deaths associated with LBW may be prevented with extra attention to warmth, feeding, and prevention or early treatment of infections. There are few studies on this in Nepal and in many other developing countries. This is a cohort study to evaluate the risk of deaths among LBW infants who received FCHV follow up visit for home-based care compared to those who did not receive in Rural Nepal.A cohort study design was used with data from the Morang Innovative Neonatal Intervention (MINI) program in Nepal. Relative Risk (RR) is calculated to compare LBW neonates who received FCHV follow up visit as compared to LBW neonates who did not receive visit.P < 0.001). The relative risk of death in LBW infants who received FCHV follow up visit was 84% less as compared to LBW infants who did not receive .Out of 51,853 newborn infants recorded in the MINI database, 2229 LBW neonates were included in the analysis. The proportion of deaths among those who received FCHV follow up visit and those who did not receive were 2%  and 11%  respectively(The current study indicates that to save the lives of LBW young infants simple home-based measures implemented through trained health volunteers within the existing government health system may be effective when technically more sophisticated measures such as tertiary health centers, pediatricians, and expensive technology are limited. Low Birth Weight (LBW) is defined by the World Health Organization (WHO) as weight at birth of less than 2500 g . LBW whiThere has been little success in the attempts to reduce the proportion of LBW births . HoweverDespite the importance of the adverse impact of LBW on child survival, there have been few prospective studies evaluating effectiveness of community based intervention through trained volunteers for LBW infants in Nepal and in other developing countries, largely because of the difficulties inherent in community based data collection and the higher proportion of births occurring at home  13]. As. As13]. FCHVs are local women with limited formal education, who are serving voluntarily within the government system. They receive basic training (18 days) and periodic refresher and program-specific training. Approximately 50,000 FCHVs work actively in all villages of Nepal and are supported by Facility-Based Community Health Workers . Their cNepal is a low-income country of 26.5 million inhabitants, 78% of which live on less than two dollars a day and 83% of which live in rural and often remote areas , 17. ItsMorang Innovative Neonatal Intervention (MINI) program focused on community based management and treatment of illnesses of newborn children in the Morang district of southeastern Nepal from 2005 to 2009 . Morang MINI was a prospective intervention study aimed at managing newborn’s illnesses at household and community level. MINI began in 21 randomly selected VDCs in Morang, but due to initial successes and demand from local government from the VDC level and the District Public Health Office (DPHO) level, it was rapidly scaled-up to the entire Morang district covering all 65 VDCs. This is a cohort study utilizing the data from the MINI program to evaluate the risk of deaths in LBW infants who received follow up FCHV visit or not.FCHVs were trained to measure birth weight on their initial visit and to do follow-up visits to advise on care of LBW babies. The choice to do a follow-up visit was voluntary and they did not receive any incentive for doing such follow-up visits. During the first visit, FCHVs recorded date of birth, birth place, birth condition, weight and assessed for any infection, as well as providing counseling on essential newborn care. For LBW babies, they conducted four additional follow-up visits on a weekly basis. On each follow up visit, they reassessed the young infant for signs of Possible Severe Bacterial Infection (PSBI) , 23, advDuring training of FCHVs in MINI, they were provided with Salter scales and trained to measure the weight of newborns. Given the varying levels of literacy among FCHVs  , MINI moRegardless of FCHVs follow up visits, the FCHV conducted a two-month (in 60 or more days) visit to determine the infant’s survival status at 60th day. MINI’s trained field supervisors collected and consolidated data from FCHVs on a regular basis. Standard forms and formats were developed and used for this purpose.n = 97), who were referred and taken to health facility received advanced care from referral hospital, were excluded from analysis as including them would over-estimate the effect of FCHVs visit. Ethnicity classification was done based on the Health Management Information System (HMIS) of the Government of Nepal, which categorizes ethnicity in six categories namely, (i) Dalits, (ii) Disadvantaged Janajatis, (iii) Disadvantaged Non-Dalit Terai Caste Groups, (iv) Religious Minorities, (v) Relatively Advantaged Janajatis, and (vi) Upper Caste Groups [P-values <0·05 were considered statistically significant.Any infants with birth weight measured three days after birth, weight not-taken or survival status not known at the two-month were excluded from the analysis in order to maintain data consistency. Similarly, for comparing infants who received no follow visit and those who received at least one follow up visit by the FCHV for LBW infants, neonatal deaths within one week of birth were excluded as these population were not able to receive intervention from FCHVs. Furthermore, those infant  (WIRB Pro No. 20031870).The exclusion and inclusion criteria for this study are presented in Fig. P = 0·09), illness (P = 0·37), first place of treatment (P = 0·59) and ethnicity (P = 0·48). However, these two groups were s significantly different by birth-place (p = 0.03)  Table .Table 1BP < 0.001). Further disaggregation of data showed the highest difference was observed for Dalits (27% vs 1·1%) and Disadvantaged Non-Dalit Terai Caste ethnic groups (27·0% vs 2·0%). The relative risk of death in LBW infants who received FCHV follow up visit was 84% less as compared to LBW infants who did not receive .Mortality status at 60th day in LBW infants are shown in Fig. The results demonstrated that community based management of low birth weight infants within an existing government health system of Nepal is effective in reducing risk of deaths in LBW infants. The study showed that the LBW newborns who received at least one follow up visit by FCHVs had lower risk of mortality at two months of age compared to those who received no follow up visits. It appears that MINI’s home-based care by FCHVs which included a combination of weighing neonates within three days of birth, assessing neonates for danger signs, treating and referring sick neonates, encouraging exclusive breastfeeding, promoting skin-to-skin contact, counseling on recognition of danger signs and prompt care seeking in the event of PSBI or LBI reduces mortality among LBW infants. It has been demonstrated in other studies that promoting immediate drying and wrapping of the baby, hygienic cord tying and cutting, skin-to-skin contact with the mother, and initiation of breastfeeding within the first hour of birth are critical for improving the survival of newborn infants, especially those who have a low birth weight .Concerns raised about the home-based or community based management of newborns often point to the risks involved in treating critical infants in settings so far removed from tertiary health facilities. While the ideal scenario would allow mothers to easily access these types of facilities, the reality in rural and low-resource settings is that the distance, cost and access to such services is out of reach for most families. Given these circumstances, services such as those provided by FCHVs in Nepal are critical for bringing essential newborn care closer to the home where more than 60% of deliveries occur . AnotherThe integration of essential newborn care practices such as newborn weighing and follow-up visits are an important component for reducing young infant mortality. In the implementation process, it was observed that in addition to early antenatal contact through existing government health programs, weighing the newborn was an important entry point for identifying danger signs in infants. Mothers notified birth to FCHVs when they were aware that part of the first postnatal care visit would involve weighing of the newborn. By identifying births early and providing extra visits for LBWs, there was also an increased opportunity to identify PSBI cases in the home. This identification is critical for intervening through early treatment since neonatal sepsis often progresses rapidly and has a very high mortality .There are several limitations to the current study. As the program was implemented in all VDCs of Morang district with no control group, the internal comparison between receiving at least one visit and no visit may not be representative to the population of infants who could benefit from this study. The visit by an FCHV may not be the sole factor contributing to a reduction in mortality in neonates. We do not have documented information on why the non-exposed did not get follow up-visits. However, the opportunities for receiving care by FCHVs for those who received care and those who did not receive care was comparable because FCHVs were distributed homogeneously in their community. The date of the follow-up visits is not available as the FCHVs just circled the number of the visit. Furthermore, limited data collection restricted conclusions on whether or not visits had a direct impact on increased weight at the end of the two-month period since young infants were not reweighed after they were identified as LBWs.Because of the observational nature of this study, data on reasons for no follow up visits are unavailable. However, we can speculate that no follow-up visit may be related to geographical obstacles and long distances which prevented FCHVs from frequently visiting clients, cultural elements such as postpartum practices that lead a new mother to move from her in-laws’ home to her paternal home, and/or mothers’ education and awareness of LBW as a neonatal health issue .Despite uncertainties related to the characteristics of both those receiving care and providing care for LBWs, MINI showed that LBW infants who received an FCHV visit experienced a significantly reduced risk of death compared to those who did not. The current study suggests that, to save the life of LBW infants simple home-based measures implemented through trained health volunteers within the existing government health system is effective when technically more sophisticated measures such as tertiary health centers, pediatricians, and expensive technology are scarce.Findings from the community based management and treatment of illness of newborns showed that there was lower risk of death among LBW infants who received home-based care from FCHVs. This indicates that home-based newborn care by trained health volunteers might have a significant impact in reducing young infant mortality in resource poor settings such as Nepal.


Numerous studies indicate that attention deficit/hyperactivity disorder (ADHD) is related to some developmental trends, as its symptoms change widely over time. Nevertheless, the etiology of this phenomenon remains ambiguous. There is a disagreement whether ADHD is related to deviations in brain development or to a delay in brain maturation. The model of deviated brain development suggests that the ADHD brain matures in a fundamentally different way, and does not reach normal maturity at any developmental stage. On the contrary, the delayed brain maturation model assumes that the ADHD brain indeed matures in a different, delayed way in comparison to healthy age-matched controls, yet eventually reaches proper maturation. We investigated age-related changes in resting-state EEG activity to find evidence to support one of the alternative models. A total of 141 children and teenagers participated in the study; 67 diagnosed with ADHD and 74 healthy controls. The absolute power of delta, theta, alpha, and beta frequency bands was analyzed. We observed a significant developmental pattern of decreasing absolute EEG power in both groups. Nonetheless, ADHD was characterized by consistently lower absolute EGG power, mostly in the theta frequency band, in comparison to healthy controls. Our results are in line with the deviant brain maturation theory of ADHD, as the observed effects of age-related changes in EEG power are parallel but different in the two groups. Attention deficit/hyperactivity disorder (ADHD) is one of the most commonly diagnosed neurodevelopmental disorders, with reported prevalence rates of 3–7% in school-aged children . ADHD isA growing body of research on the causes of behavioral symptoms in ADHD provides evidence for impairments on genetic, neurotransmission, neuroanatomic and functional levels  studies indicate persistent abnormalities in ADHDs’ EEG activity  with a broad age range (9–16 years), allowing us to consider age as a continuous predictor in linear regression models. Linear regression analysis has been previously used to detect EEG developmental tendencies , howeverMage = 13.11 ± 2.06; 12 females). The healthy control group was age- and sex- matched to the clinical group .A total of 150 children and teenagers (Caucasian) aged 9–16 participated in the study. Due to technical issues (five ADHD participants) and excessive motion artifacts , we included 141 participants in the final analysis. The clinical group consisted of 67 participants with an ADHD diagnosis , inattention , hyperactivity , impulsivity . The comorbidity diagnosis was based on the diagnostic criteria for ICD-10 (n = 47) or predominantly inattentive ADHD subtypes (n = 20). The comorbidity distribution was as follows: 66% of participants manifested comorbidities, 47% of which were diagnosed with ODD/ODC+CD, and 25.7% had learning difficulties.The clinical group was recruited among ADHD outpatients under the supervision of the Psychiatry Clinic at Public Pediatric Teaching Hospital in Warsaw, Poland. The diagnosis was performed according to diagnostic criteria of the DSM-IV TR  with ADHD/ADD diagnosis.The study was approved by the local Ethics Committee at the SWPS University of Social Sciences and Humanities and the Medical University of Warsaw. All participants provided assent, and parents gave informed written consent in accordance with the Declaration of Helsinki.Resting-state EEG was recorded using a 64-channel EGI HydroCel Geodesic System . The signal was sampled at 250 Hz and referenced to the Cz electrode. Electrode impedances were kept below 20 kΩ. We recorded 5 min of resting-state EEG with eyes closed (3 min) and open (2 min), alternating between the two with 1 min long sequences. The eyes open condition was introduced to prevent participants from drowsing. Following analyses were performed on signal registered while the participants had their eyes closed only. EEG data was processed offline using EEGlab software  and custpop_eegfiltnew function. One dataset was then divided into 1-s epochs prior to independent component analysis (ICA) and visually inspected for artifacts. We removed epochs with large irregular events. Noisy electrodes (large visible drifts or net noise) were interpolated. Such short (1 s) epochs allowed for keeping more data to perform the ICA on, but were not ideal for our planned spectral analysis. Therefore, we ran the ICA on the first dataset and subsequently applied the obtained ICA weights to the second dataset, which we later epoched into 3 s long epochs. We used the runica algorithm as implemented in the eeglab pop_runica function. We identified ICA components related to eye blinks and horizontal eye movements by visual inspection, based on their scalp topography, power spectra, time course, and variance among trials (We created two datasets for each EEG file. Both datasets were 45 Hz low pass and 1 Hz high pass filtered using a Hamming windowed sinc FIR filter (transition bandwidth: 1 Hz) implemented in EEGLAB’s g trials . These epop_spectopo function. Absolute EEG power was log transformed in order to normalize the distribution of the data for statistical analysis. All further analyses were conducted using We calculated a mean power spectrum for every participant using EEGLAB’s Figure 1): three anteriori , three central , and three posterior . Each participant’s power estimates were averaged across electrodes within each cluster and across frequencies within the following four frequency bands: delta (1–3 Hz), theta (3–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz). Before computing the aggregates, values differing more than 2.24 median absolute deviations from the median were removed  as moderators of age effects. To correct for multiple comparisons, we employed a false discovery rate (FDR)   within efunction  with thep-values for the interaction term > 0.14, all ΔR2 < 1.3%). Therefore, we decided to report detailed results of the simpler models, including only main effects of age and ADHD (Table 1). As the effects of both predictors were additive, we discuss them in turn.None of the regression models estimated for all participants revealed a significant interaction effect of Age and ADHD diagnosis . Age-related changes in absolute power were much more pronounced in slow-wave frequencies, i.e., delta and theta , than in beta. The increase in the proportion of explained variance attributable to the effects of age varied across frequencies and clusters. For delta and theta ΔR2 was within 9.3–23.3% range, whereas for beta frequency it was more than five times smaller, ranging from 1.3 to 6.2%. The alpha absolute power was not significantly related to age.The obtained results revealed a typical developmental effect of decreasing absolute EEG power with increasing age. Absolute EEG power was found to decrease linearly for delta, theta, and beta frequency bands across each of the scalp regions: anterior, central, and posterior in both groups , with the most pronounced difference in lower theta absolute power.Additionally, we observed the main effect of diagnosis, indicating differences between groups. The ADHD group had significantly lower absolute power in all frequency bands across centro-posterior clusters .Additional analysis of the moderating effects of symptoms intensity on power spectra age-related changes in the ADHD group did not reveal any significant effects involving ADHD-RS scores in none of the models . Most of the previous research adopted 20 min-long recordings.Our analysis did not reveal any significant effects of interaction. Instead, we observed two main effects of development and group differences. The lack of interaction  speaks for the presence of a permanent abnormality of EEG, at least within the examined time range. We observed the same pattern of age-related decreases in EEG power, particularly in slow wave activity, in both groups, whereas the difference between groups was stable in time; the EEG power of ADHD participants was always lower.Taking into consideration the EEG-heterogeneity of ADHD and the structural differences reported in MRI studies, we assume that the lower EEG power may result from abnormally diminished gray matter volume in ADHD. Different trajectories of age-related decrease in cortical thickness in control and ADHD youths were reported where, at some point of time, results of older controls match the thickness observed in younger ADHD patients . This miSeveral alternatives to the explanations of decreasing EEG power with age based on reductions in cortical gray matter density have been proposed previously. For example, absolute metabolic rates were found to parallel gray matter maturation and maturational decrease in electrical activity . Also, iSeveral limitations of this study should be noted. Firstly, the cross-sectional character of this experiment restricts the possibility of obtaining a more dynamic overview of developmental changes in EEG activity. The younger group of subjects in our study might possibly be more heterogeneous than the older group, which may influence the results. Older ADHD children presented a persistent neurodevelopmental deficit in our study, whereas some of the younger participants might be expected to remit. Certainly, the cause and effect pathway could be established more reliably in a longitudinal study. There is a need to conduct longitudinal studies on EEG activity in healthy as well as clinical cohorts, which may provide valuable information on the dynamics of the alternations observed in ADHD and may have an impact on treatment approaches, e.g., stimulant and neurofeedback therapies. Secondly, the clinical group consisted of both combined and predominantly inattentive subtypes of ADHD. We found it highly representative of a typical ADHD population. The distribution of the ADHD subtypes and comorbidities in our sample was similar to that previously reported for ADHD cohorts. However, the “inattentive” subgroup was significantly smaller which did not allow us to investigate it in comparison to the “combined” and control groups. Also, the number of the female participants was too small to obtain valuable information on sex-differences.This study revealed that the resting-EEG developmental pattern was similar in ADHD and healthy controls. Even so, the ADHD group had consistently lower absolute EGG power, mostly in the theta frequency band. Our results are in line with deviant brain maturation hypothesis, as ADHD brain activity would not be considered the same as in healthy controls at any developmental stage, at least within the studied age range (9–16 years).KG recruited control group of participants, carried out all of the study, performed the recordings, analyses, wrote the manuscript, prepared table and figures. MZ had substantial contribution to the analysis of the data and figures preparation. MB had substantial contribution to the statistical analyses of the data. ER-P recruited and diagnosed the ADHD participants. MK made comments and suggestions on the manuscript. AC-K had contribution to the conception of the work, interpretation of the data, and in writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent clinically relevant applications such as fMRI-based diagnostic classification or neuromodulation therapies targeting fMRI deficits with neurofeedback (NF) or brain stimulation. Meta-analyses of fMRI studies of executive functions (EFs) show that ADHD patients have cognitive-domain dissociated complex multisystem impairments in several right and left hemispheric dorsal, ventral and medial fronto-cingulo-striato-thalamic and fronto-parieto-cerebellar networks that mediate cognitive control, attention, timing and working memory (WM). There is furthermore emerging evidence for abnormalities in orbital and ventromedial prefrontal and limbic areas that mediate motivation and emotion control. In addition, poor deactivation of the default mode network (DMN) suggests an abnormal interrelationship between hypo-engaged task-positive and poorly “switched off” hyper-engaged task-negative networks, both of which are related to impaired cognition. Translational cognitive neuroscience in ADHD is still in its infancy. Pattern recognition analyses have attempted to provide diagnostic classification of ADHD using fMRI data with respectable classification accuracies of over 80%. Necessary replication studies, however, are still outstanding. Brain stimulation has been tested in heterogeneously designed, small numbered proof of concept studies targeting key frontal functional impairments in ADHD. Transcranial direct current stimulation (tDCS) appears to be promising to improve ADHD symptoms and cognitive functions based on some studies, but larger clinical trials of repeated stimulation with and without cognitive training are needed to test clinical efficacy and potential costs on non-targeted functions. Only three studies have piloted NF of fMRI-based frontal dysfunctions in ADHD using fMRI or near-infrared spectroscopy, with the two larger ones finding some improvements in cognition and symptoms, which, however, were not superior to the active control conditions, suggesting potential placebo effects. Neurotherapeutics seems attractive for ADHD due to their safety and potential longer-term neuroplastic effects, which drugs cannot offer. However, they need to be thoroughly tested for short- and longer-term clinical and cognitive efficacy and their potential for individualized treatment. Attention Deficit Hyperactivity Disorder (ADHD) is characterized by symptoms of age-inappropriate inattention, hyperactivity and impulsivity , that are mediated by late developing fronto-striato-parietal and fronto-cerebellar networks Rubia, . The mosSince the advent of fMRI, several hundreds of fMRI studies have been published in ADHD children and adults over the last two decades, the majority of them targeting cognitive functions. The first fMRI studies conducted in very small numbers of ADHD patients found reduced inferior fronto-striatal activation in ADHD children relative to age-matched healthy controls during motor inhibition /anterior insula, the supplementary motor area (SMA), anterior cingulate cortex (ACC), left striatum and right thalamus . Thus, ADHD patients showed enhanced activation in typical regions of the DMN such as in rostromedial prefrontal cortex during interference inhibition  or orbitofrontal cortex (OFC) and striato-limbic regions during tasks that tap into “hot” EF such as reward-related decision making or temporal discounting tasks. One of the most consistent findings is reduced ventral striatum activation during reward anticipation, as shown in a recent meta-analysis of eight fMRI studies of a monetary reward anticipation task using region of interest analysis in 340 ADHD patients and healthy controls , which are hypothesis-driven and measure changes in interactions across brain activations. In children and adults with ADHD, reduced functional connectivity has been observed between task-relevant regions during cool EF tasks, suggesting dysfunction of entire networks and not just regions.In ADHD children, during motor response inhibition and WM tasks reduced functional connectivity has been reported relative to healthy controls between the right IFC and basal ganglia, parietal lobes and cerebellum, and between cerebellum, parietal and striatal brain regions during sustained attention  and transcranial direct current stimulation (tDCS) have found successful applications in other psychiatric disorders. Pioneering applications of these techniques to ADHD over the past decade targeting IFC or DLPFC have been mixed, but revealed some promising findings of improving cognition and clinical behavior. The following sections will review these clinical applications of neuroimaging in ADHD.Despite the fact that ADHD is a neurodevelopmental disorder with consistent evidence for brain structure and function deficits, currently ADHD is diagnosed solely on the basis of subjective clinical and self-rating measures, which are often unreliable, leading to diagnostic variability between clinicians, cultures and countries  and can make predictions  for individual subjects as opposed to group-level inferences. These methods have been shown to provide sensitive and specific diagnostic indicators for individual patients with other pathologies in particular for neurological disorders such as Alzheimer’s disease but also for autism and depression  diagnosis or prognosis of individual patients and build the path for brain function (or brain structure)-based patient stratification and personalized medicine. Multimodal multivariate approaches including several imaging modalities, including functional and structural imaging data as well as non-imaging data such as cognitive and genetic measures are likely to achieve superior classification accuracy than univariate approaches  rTMS promotes cortical excitability, while low frequency (1 Hz) rTMS inhibits cortical excitability  or sham dTMS (N = 13). dTMS is a modification of standard TMS that enables deeper non-invasive cortical stimulation at an effective depth of approximately 3 cm depending on the coil’s design and the stimulation intensity. Stimulation was applied over bilateral prefrontal cortex in 20 daily sessions over 4 weeks, each session consisting of 55 trains of pulses at 18 Hz . The study found an improvement in ADHD symptoms in both groups suggesting a placebo effect and no cognitive improvements in either group  or decreasing  the excitability of neurons via the generation of subthreshold (stimulation-polarity dependent) alterations of membrane potentials that modify spontaneous discharge rates, thus increasing/decreasing cortical function and synaptic strength  relative to sham stimulation (N = 8); furthermore, all symptom improvements were still significant 2 weeks after stimulation, suggesting longer-term effects —that has been shown to interact with physiological slow oscillatory activity, which is typically abnormal in ADHD—over lateral prefrontal cortex during deep sleep in 12/14 ADHD adolescents compared to sham stimulation in a double-blind within patients design with 1 week break in between conditions. The outcome measures were slow oscillatory power during deep sleep in the non-stimulation periods and cognitive function improvements relative to sham tDCS in the next morning. Slow oscillatory power during deep sleep was increased, indicating an enhancement of endogenous oscillatory activity with the intervention. Cognitive improvements were in declarative memory  had to learn to upregulate the rIFC, while the control group (N = 13) had to upregulate a control region, the left parahippocampal gyrus (lPHG). Both groups were tested before and after treatment in clinical and cognitive measures as well as in an fMRI Stop task and were followed up a mean of 11 months later in the main ADHD clinical outcome measure. Participants were trained to enhance activation of the target/control regions in 11 sessions of 8.5 min of rtfMRI-NF over four scan hours over 2 weeks. They were trained on a computer game where a rocket moved towards the sky, passing through clouds, and ultimately reaching some planets every time they managed to increase the activation of the target/control region. The fMRI data showed significantly enhanced activation as well as enhanced linear activation increase in two regions of the rIFC across all 11 sessions in the active relative to the control group and enhanced linear activation increase in three regions of the lPHG in the control relative to the active group  and electromyography-NF (N = 9). Only NIRS-NF resulted in significant improvements in clinical ADHD symptoms and in cognitive inhibition and attention functions after 11 h sessions over 4 weeks, which was, however, not superior to EEG-NF or electromyography-NF  of the left DLPFC in nine ADHD children, compared to EEG-NF  neurobiological basis of different ADHD subtypes such as inattention without hyperactivity, or ADHD with emotional dysregulation. Furthermore, more understanding is needed on comorbid cases with other disorders such as autism, anxiety and affective disorders as well as on females with ADHD and gender differences. Future studies therefore ideally should be longitudinal, multimodal and tied to epidemiological samples.Clinical translation of neuroimaging is still in its infancy in the field of ADHD. Pattern recognition analyses applied to functional  imaging data to make individual predictions on diagnostic status are promising, but more so for homogenous subtypes that likely share the same “biotype” rather than heterogenous large groups of ADHD patients with different comorbidities or medication status. They will need to show replicability and clinical utility which will be the challenge over the next decades.Several brain stimulation studies with heterogeneous study designs have been conducted in small groups of ADHD children and adults, most of them using tDCS in either single or five sessions targeting mostly DLPFC or IFC based on the fMRI studies conducted in ADHD over the last two decades. The findings show some improvements on clinical symptoms or selective cognitive functions, with, however, also negative findings. Larger sham-controlled studies are needed to further test the efficacy of tDCS and potential costs on non-targeted cognitive or behavioral functions. In addition, far more knowledge is needed on the optimal stimulation protocols for different age and patient subpopulations . It is likely that brain stimulation combined with cognitive training has a larger potential to enhance brain plasticity in ADHD than brain stimulation alone. This will also require the development of good cognitive training tasks that target ADHD-relevant functions to be used in combination with brain stimulation techniques. Given minimal side effects, tDCS is a promising tool for the treatment of childhood onset psychiatric disorders, since it provides the opportunity to positively influence atypical brain development early and persistently , potential costs that may accompany the benefits, and their potential for individualized treatment (which ADHD subtype responds to which neurotherapy and why). It is likely that different subgroups of ADHD patients will benefit from either NF, brain stimulation or medication and establishing this knowledge will be crucial to the benefit of individual patients.The author confirms being the sole contributor of this work and approved it for publication.The author has received grants from Lilly and Shire and speaker’s honoraria from Shire, Lilly and Medice.


Due to the high intra-individual variability in attention deficit/hyperactivity disorder (ADHD), there may be considerable bias in knowledge about altered neurophysiological processes underlying executive dysfunctions in patients with different ADHD subtypes. When aiming to establish dimensional cognitive-neurophysiological constructs representing symptoms of ADHD as suggested by the initiative for Research Domain Criteria, it is crucial to consider such processes independent of variability. We examined patients with the predominantly inattentive subtype  and the combined subtype of ADHD (ADHD-C) in a flanker task measuring conflict control. Groups were matched for task performance. Besides using classic event-related potential (ERP) techniques and source localization, neurophysiological data was also analyzed using residue iteration decomposition (RIDE) to statistically account for intra-individual variability and S-LORETA to estimate the sources of the activations. The analysis of classic ERPs related to conflict monitoring revealed no differences between patients with ADD and ADHD-C. When individual variability was accounted for, clear differences became apparent in the RIDE C-cluster . While patients with ADD distinguished between compatible and incompatible flanker trials early on, patients with ADHD-C seemed to employ more cognitive resources overall. These differences are reflected in inferior parietal areas. The study demonstrates differences in neuronal mechanisms related to response selection processes between ADD and ADHD-C which, according to source localization, arise from the inferior parietal cortex. Importantly, these differences could only be detected when accounting for intra-individual variability. The results imply that it is very likely that differences in neurophysiological processes between ADHD subtypes are underestimated and have not been recognized because intra-individual variability in neurophysiological data has not sufficiently been taken into account. Attention deficit/hyperactivity disorder (ADHD) is a multi-faceted developmental disorder  = 0.08, p = 0.93) or IQ (t(57) = −1.8, p = 0.08). In the ADHD Symptom Checklist  = −2.3, p = 0.03), hyperactivity  = −7.8, p ≤ 0.001) and impulsivity  = −9.6; p ≤ 0.001), thus confirming ADHD symptomatology and the two different subtypes. All subjects and their parents or legal guardians provided written informed consent in accordance with the Helsinki Declaration of 1975, as revised in 2008. The study was approved by the local ethics committee of the Medical Faculty of the TU Dresden.Only patients in whom ADD or ADHD-C diagnoses had been determined according to standard clinical procedures  were included in the study. All participants fulfilled criteria for ADHD according to ICD-10 criteria  or F98.8 (ADD)). All participants were regular patients of the outpatient clinic of the Department of Child and Adolescent Psychiatry, University Hospital Dresden. Patients were excluded if additional severe or acute psychiatric  or somatic comorbidities had been diagnosed. For the descriptive biographical and clinical data, the mean and the standard deviation are given. Overall, 34 patients with ADHD-C  and 25 patients with ADD  participated in the study. The two groups were recruited simultaneously and did not differ regarding age  or opposite (incompatible) direction as the target stimulus. These flanker stimuli precede the target by 200 ms. To adapt the paradigm to children, the stimulus onset asynchrony had been prolonged by 100 ms compared to the adult version  using the BrainVision Recorder software. All electrode impedances were kept below 5 kΩ. The BrainVision Analyzer II was used for data pre-processing and analysis. Offline, the EEG was down-sampled to 256 Hz. Data were manually inspected to remove technical artifacts. A band-pass filter (0.5–20 Hz at 48 db/oct each) was applied. To correct for periodically recurring artifacts , an independent component analysis  was applied to the unepoched data sets. Components that revealed horizontal and vertical eye movements and blinks were visually identified by means of recurrent similar waveform and by means of scalp topography. ICA components reflecting the above-mentioned artifacts were then discarded. The EEG was segmented into compatible and incompatible trials under the constraint that the response given in the trials was correct. Trial segmentation was target-locked and began 2000 ms before the target presentation of the respective trial and ended 2000 ms after its presentation. Afterwards, an automated artifact rejection procedure using a maximum/minimum amplitude of 100/−100 μV as well as an activity below 0.5 μV in a 200 ms period as rejection criteria was applied. A current source density (CSD) transformation , the N1Flanker (−10 ms to 10 ms), the P1Target (115–135 ms) and the N1Target (260–280 ms) were quantified at electrodes P9 and P10. The N2 was measured at 280–320 ms over electrodes FCz and Cz and the P3 was examined at electrode Pz at 300–320 ms. This choice was validated using a statistical approach outlined in Mückschel et al. (p = 0.0007) was used. Only electrodes at which recorded activation was significantly stronger  when compared to that at other electrodes were chosen. This procedure revealed the same electrodes of interest as those chosen by visual inspection.The EEG was recorded from 60 Ag/AgCl electrodes arranged in equidistant positions . Full details on the RIDE method can be found in Ouyang et al. , the RIDE-N1Flanker (−10 ms to 10 ms), the RIDE-P1Target (115–135 ms) and the RIDE-N1Target (260–280 ms) at electrodes P9 and P10. In addition, the S-cluster RIDE-N2 (N2S) was examined over electrodes FCz and Cz at 280–320 ms. As reported previously  was quantified in the time window of 370–390 ms over electrodes FCz and Fz. The C-cluster RIDE-P3 was measured over electrode Pz. Time windows  were adapted based on the observed peak latencies. As it was to be expected . In our case, the R-cluster was examined at electrodes C3 and C4 in the time window of ±10 ms around the average reaction times (RTs) in the compatible and incompatible trials. In the S-cluster, we quantified the RIDE-P1. In our p < 0.01, corrected for multiple comparisons) between contrasted conditions were located in the MNI-brainFor the source localization analysis, standardized low resolution brain electromagnetic tomography  as the between-subject factor. As a within-subject factor, Compatibility  was modeled to account for the two different flanker-target combinations (not applicable to (RIDE-)P1Flanker and (RIDE-)N1Flanker). Where necessary, Electrode was modeled as an additional within-subject factor and is only reported when a relevant interaction with Group was observed. Greenhouse-Geisser correction was applied for all ANOVAs. Post hoc tests were Bonferroni-corrected when necessary. All variables included in the analysis were normally distributed, as indicated by Kolmogorov-Smirnov tests .Descriptive statistics are shown as mean ± standard error. The data was analyzed using mixed effects analysis of variance (ANOVAs) using Compatibility  = 65.9; p ≤ 0.001; F ≤ 0.48; p ≥ 0.49; Compatibility was significant  = 64.6; p ≤ 0.001; F ≤ 0.5; p ≥ 0.48; p < 0.001). Regarding intra-individual variability, we calculated the within-subject standard deviation of the RTs at the single subject level and analyzed this data using mixed ANOVAs. The results show only a main effect Compatibility  = 5.86; p = 0.019; Group .As groups were compiled specifically to not display any differences on the behavioral level, there was only a main effect of Flanker  = 6.8; p = 0.01; 2) than in compatible trials  = 4.8; p = 0.03; post hoc testing did not withstand Bonferroni-corrections  = 34.6; p ≤ 0.001; 2) than for compatible trials (26.5 ± 4.4 μV/m2). Furthermore, there was a significant main effect of Group  = 6.4; p = 0.014; 2) than the ADD group (27.2 ± 6.1 μV/m2). Interestingly, there was also a significant interaction of Compatibility*Group  = 4.9; p = 0.03; Post hoc tests showed that the compatibility (conflict) effect was larger (29.7 ± 6.7 μV/m2) in the ADD group than in ADHD-C group  = 2.2; p = 0.016). This effect was mainly driven by the compatible trials. There, the patients with ADD group showed a smaller RIDE-P3 (12.2 ± 7.1) than those with ADHD-C  = −3.23; p = 0.001). There was no group difference on incompatible trials (t(57) = −1.36; p = 0.18; ADD: 42.0 ± 6.2; ADHD-C 54.3 ± 6.2). Correlational analyses revealed no connection between RIDE-P3 and severity of ADHD symptoms . The sLORETA analysis shows that the modulation in the conflict effect was related to activity modulations in the inferior parietal cortex and the temporo-parietal junction  in particular. Thus, the results of the RIDE analysis differ significantly from those of the traditional ERP analysis.Regarding the C-cluster RIDE-N2 . This shows that the SNR does not pose a systematic bias in the data analysis regarding any main effects or interactions with the factor Group. This underlines the reliability of the obtained results.To control whether the specificity of effects obtained for the C-cluster data is an effect of a systematic bias in the signal-to-noise ratio (SNR) attributable to one specific group, we calculated the SNR for each condition, electrode and RIDE-cluster. We calculate the SNR as implemented in the Brain Vision Analyzer II as previously done in other studies  are not connected to differences in basic sensory or attentional processing. This was the case independent of the applied methodology (standard ERPs vs. RIDE-ERPs), showing that this finding does not seem to be influenced by the characteristic high intra-subject variability. Also, due to the close proximity to the common locking point, the effects of variability are generally rather small in this time window  did indeed show significant differences between subgroups in conflict-related neurophysiological processes. This effect was not biased by issues relating to differences in SNRs. The fact that these results did not become apparent in the standard ERP analysis very likely reflects the strong intra-individual variability inherent to ADHD  including the TPJ. The parietal cortex has been shown to reveal structural and functional abnormalities in ADHD . The low accuracy rates overall can thus not simply be attributed to the ADD/ADHD-C diagnoses. Lastly, no patients with the predominantly hyperactive/impulsive ADHD subtype were included in the study. Doing so would allow a better differentiation of the effects of inattentiveness and hyperactivity/impulsivity on executive functions generally and on conflict processing in particular.Of course, this study also is not without limitations. First, no correlations were evident between the neurophysiological and clinical measures. In this regard it needs to be noted that the reliability of the different correlated measures is an issue that needs to be considered. However, the current study shows that some differences between ADD and ADHD-C subtypes might not be reflected in routinely used clinical measures and therefore may remain unnoticed. Second, it may be argued that parts of the above interpretation concerning the nature of processes differentially modulated in ADD and ADHD-C are based on reverse inference (Poldrack, In summary, the study shows that there are differences in neuronal mechanisms related to response selection processes between ADD and ADHD-C. Importantly, these differences in neuronal processes were only detected when methodologically accounting for intra-individual variability. We were thus able to show that differences between the examined ADHD subtypes not only occur on the symptom level, but are also clearly reflected on the neuronal level. These findings illustrate the need to changes perspectives, to look beyond pure behavioral performance and to take alterations in intra-individual variability into account when interpreting data. An unbiased consideration of underlying neuronal processes adds crucial information concerning inter-individual differences within one diagnostic category, just like suggested by the RDoC initiative (Insel et al., AB and CB conceived and designed the study, analyzed the data and wrote the article. AB, MM and WXC analyzed data and wrote the article. VR conceived the study and wrote the article.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) has been associated with obesity in children. Lifestyle-related behaviors  are well known to be associated with increased risk of obesity, but their associations with ADHD are unclear. The objectives of this study were to clarify the associations between ADHD symptoms in children and their associated lifestyle. A cross sectional study was carried out with a total of 785 primary students aged 9 to 13 years old and their parents were recruited by stratified random sampling from primary schools of China. The Cochran-Mantel-Haenszel (CMH) test was used to examine the relationships between ADHD symptoms and health related behaviors. We found that children with ADHD symptoms were likely to spend more time using a computer during school days; they were also more likely to eat while using a computer. These children were also more likely to eat while seated in a car, using a smart phone, using a computer at bedtime, and snacking before going to sleep than children without ADHD symptoms. An increased risk of obesity in children with ADHD symptoms was associated with the overuse of electronic devices, eating while using electronic devices, and delaying bedtimes to snack and use electronic devices. Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common childhood psychiatric disorders, affecting 5–10% of school-aged children worldwide . ADHD isA number of potential mechanisms, some physiological and behavioral, have been hypothesized to explain the associations between lifestyle behaviors and obesity in people with ADHD. From a behavioral perspective, population survey data have suggested that ADHD is individually associated with lifestyle-related behaviors such as eating behaviors, screen time, and physical activity , 16. It Additionally, high screen time exposure might increase the risk of obesity in both of normal children and children with ADHD. Previous studies examined the association between screen time and ADHD, but the results are contradictory . A longiIn addition to external eating and extended screen time, physical inactivity is another factor predictive of child obesity. Understandably, people have assumed that children with ADHD would lose weight easily due to a high level of physical activity, since hyperactivity would seem to increase daily energy expenditure. However, studies showed that children with ADHD were involved in few physical activities. A study found that children with ADHD, regardless of medication status and gender, are less likely to participate in vigorous physical activity and organized sports compared to those without ADHD . PreviouFurthermore, children whose free time occurs prior to bedtime are at increased risk of excess food intake and screen-based sedentary behavior. An evening offering few chances for physical activity and relatively more opportunities for television viewing and other sedentary behaviors is known to be associated with increased snacking behavior  and expoIn the light of contradictory findings above and high relevance of lifestyle-related behaviors, it suggested that these behaviors should be considered as a whole, especially for children with ADHD. Because children with ADHD usually have multiple psychological comorbidities , these rThree primary schools were recruited by stratified and cluster sampling from Shanghai, China. These three schools are locating in central urban or suburb areas of Shanghai and represent different socioeconomic levels. As ADHD symptoms were assessed by the parent-report version of ADHD Rating scale-IV (ADHDRS-IV) . The ADHExternal Eating behaviors: External eating in this study was defined as eating accompanied with sedentary behavior, such as using electronic devices, doing homework, and snacking before bedtime. Because there is no validated questionnaire to measure the eating behaviors examined in this study, the self-designed questions were used. The option is a 4-point Likert response scale to evaluate how often the behavior occurs  which referred to 6-point Likert response scale of Child Eating Behaviour Questionnaire (CEBQ) [e (CEBQ) . Child pScreen time: The participants were asked, “How much time did you spend using the following electric devices after school: (1) computer, (2) smart phone, (3) television.” The response options included: less than 30 minutes, 30–60 minutes, 1–2 hours, 2–3 hours, and more than 3 hours. This measure was based on a scale originally developed by Robinson [Robinson . ChildreBedtime activities: Bedtime activities are defined here as the activities occurring after dinner and before bedtime, including eating, using electronic devices, and physical activities. For example, participating children were asked to report how many times they ate at bedtime in a week. Each question was rated on a five-point scale ranging from never to 7 days.Physical Activity: Child participants were asked to report their physical activity by Physical Activity Questionnaire for Older Children (PAQ-C). The PAQ-C was developed to assess general levels of physical activity throughout the elementary school year for students in grades 4 to 8 and approximately 8 to 14 years of age. The validation and reliability of PAQ-C had been confirmed in a previous study [us study . The PAQSleep environment: Parents were asked, “Are any of these electronic devices in your child’s bedroom?” Multiple options can be selected, such as televisions, video game systems, computers, the Internet, or none. The total number of electronic devices located in the child’s bedroom was calculated for analysis; that total ranged from 0 to 4. Additional survey items used to capture demographic information were completed by the children’s parents. One question asked of parents was, “Who shares a bedroom with your child?” Four options were available for this question: an individual bedroom, a room shared with siblings, a room shared with parents, or a room shared with other. The raw data was shown in 2MH) is reported in this study, since both variables are ordinal values. In addition, logistical regression analysis was carried out to examine the associations between ADHD symptoms and lifestyle-related behaviors, e.g. screen time, external eating behaviors and bedtime activities. For each behavior, a total score was summed up with a series of variables.The data was analyzed by Statistic Analysis System (SAS) 9.3 . A Chi-square test was used to investigate the relationship between ADHD symptoms, demographic information, and sleep environment. The Cochran-Mantel-Haenszel (CMH) test was used to examine the relationship between ADHD symptoms and health-related behaviors. CMH was often used to examine a linear association between two variables that lie on an ordinal  scale . A paramχ2= 7.8840, p < .0001), especially in when the child shared a bedroom with his or her parents . The proportion of single children was similar in the ADHD symptomatic group and the non-symptomatic group. There was no difference in annual household income  between children in the ADHD symptomatic group and the non-symptomatic group. In 256 bedrooms (29.6%), there were computers, and in 220 (28.1%) of the bedrooms were Internet-accessible computers. Televisions were found in 404 (51.5%) of the bedrooms, but only 24 (3.1%) bedrooms had video game consoles. There were 439 (55.9%) bedrooms with two or more than two devices.2MH = 7.191, P<0.01). However, this difference was not statistically significant after adjusting for gender . The results showed that boys spent more time than girls using the computer during school days , on weekends , and at bedtime . Boys spent more time than girls watching TV on school days , on weekends , and at bedtime . They also used smart phones more frequently on weekends than girls .2MH = 10.332, P<0.01) or sitting in a car . In addition, children with ADHD symptoms tended to eat snacks , consume soft drinks , and use smart phones  and computers  at bedtime more frequently than children without ADHD symptoms. Moreover, children with more ADHD symptoms had more electronic products in their bedrooms after adjusting for gender . In the present study, the physical activity level was not found to be associated with ADHD symptoms either at bedtime  or during the previous week . The mean activity score of PAQ-C was 2.65 (SD = 0.70) for girls and 2.70 (SD = 0.69) for boys.As mentioned above, there was a gender difference in the total time children spent using electronic devices. However, there was no gender difference in the association between eating behaviors combined with screen time and ADHD symptoms. Specifically, after adjusting for gender, children with ADHD symptoms were more likely to eat when they are using a computer Click here for additional data file.