Attention-deficit/hyperactivity disorder (ADHD) is a developmental disorder characterized by difficulty to control the own behavior. Neuroimaging studies have related ADHD with the interplay of fronto-parietal attention systems with the default mode network DMN; . However Topological data analysis (TDA) investigates the topology of interacting nodes. It may model the connectomes as a topological process instead of a static graph, exploring the transition of all nodes being isolated to binding together, as a function of the connectivity threshold. Here, we explored three parameters to characterize the algebraic topology of individual connectomes using four different brain atlases, further exploring the subnetwork levels. Our findings showed that the area under the curve (AUC) robustly differentiates children with attention-deficit/hyperactivity disorder (ADHD) and typically developing children (TDC), suggesting decreased functional segregation, with the greatest effects on the frontal lobe and the default-mode network. Overall, these results support the use of the proposed methods to robustly explore topological differences in the brain connectome.Current neuroimaging technology allows the exploration of the human brain as a network of structurally and/or functionally connected constituents, i.e., voxels or regions of interest. In particular, functional connectivity is defined as the synchrony of neuronal activity patterns of anatomically separated brain regions  and manyOne of the most commonly used frameworks to explore the functional brain network is graph theory, which provides a theoretical basis to describe and characterize complex networks . In thisRecently, topological data analysis (TDA), has been adopted in neuroimaging as a tool to quantify and visualize the evolution of the brain network at different thresholds . The maiIn this work, TDA was applied to explore individual brain networks based on the resting state functional MRI (rsfMRI) of children diagnosed with ADHD and typically developing children (TDC), obtained from the publicly available ADHD-200 database . First, https://fcon_1000.projects.nitrc.org/indi/adhd200/). Subjects reported with a secondary diagnosis and/or not medication-naive status were discarded. Only those with good imaging quality and complete phenotypic information were used for subsequent analysis, resulting in a total of 182 children. Study protocols were approved by the New York University Institutional Review Boards, and after an explanation of study procedures a written informed consent from parents and assent from children were required.Imaging and phenotypic data from 263 participants corresponding to the New York University Child Study Center dataset were obtained from the ADHD-200 database  and 96 TDC  and the Conners’ Parent Rating Scale-Revised, Long version (CPRS-LV). Moreover, IQ was measured with the Wechsler Abbreviated Scale of Intelligence (WASI). Inclusion in the ADHD group was based on the parent and child responses to KSADS-PL and obtaining a t-score greater or equal than 65 in any of the ADHD related indices of the CPRS-LV. TDC had ADHD summarized t-scores below 60, and lack of any DSM-IV axis-I disorders. Exclusion criteria were an IQ below 80 or any chronic medical conditions. However, phenotypic data of three ADHD datasets showed full intelligence scores below 80, while two TDC showed *-weighted echo planar imaging interleaved sequence  with a scan duration of 6 min. Participants were instructed to remain still, close their eyes, think of nothing systematically and not fall asleep. In order to obtain an anatomic reference, high-resolution structural T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) images were acquired .Magnetic resonance images were acquired with a Siemens Magnetom Allegra 3T scanner . Whole-brain fMRI volume images were obtained using a T23 children-specific template, the 4.5–18.5 years old NIHPD atlas  were computed based on different brain atlases: AAL , P264 P, CC200, For each subject and atlas, the average fMRI signal of every defined region was extracted and then the functional connectome was computed as the Pearson’s cross-correlation between all possible pairs of regions. The reliability of the explored TDA variables along the atlases was assessed by the Kendall’s concordance coefficient (KCC).0) accounts for the number of components, i.e., the number of isolated nodes or sets of nodes connected by a sequence of edges; Betti-1 number refers to the number of cavities in the two-dimensional space between nodes, and so on , which is a collection of nodes (V) and edges (E). Nodes usually represent regions of interest, while edges represent structural or functional connections between those nodes. Nevertheless, this graph can be represented as a topological space as well, in particular, the Rips complex, denoted by Rips, where F represent the nodes (same as V) and ε, the filtration value, which is a positive number that states if two nodes in F are connected (if their distance is lower than ε). Algebraic properties extracted from this topological space are called Betti numbers, particularly, the Betti-0 number (BB0 curve .j) = 1 − r, where r is the Pearson’s correlation between the pair of nodes ξ and xj. The B0 curves are computed with the TDA package in R (https://cran.r-project.org/web/packages/TDA/index.html), and characterized in terms of the area under the curve (AUC), slope, and kurtosis. The AUC accounts for the overall transition from all nodes being isolated to being connected into a single component, with smaller areas suggesting that B0 decreases with smaller filtration values. The slope accounts for the rate of change, being all negative, lower values mean a faster transition to a single component. Finally, the kurtosis accounts for how “tailed” the distribution is with respect to the average value, with higher values meaning faster transition to a single component.Here, the distance between nodes is defined as in 0 curves are features of the brain topology, observed values were compared with those generated from a random distribution. A weighted null distribution was calculated based on the rewiring of the original connectivity matrices  approach based on clusters of edges  was set for individual edges, and a null distribution of clusters was computed with 10,000 permutations.In addition, the same logistic regression model  was implhttps://github.com/BrainMapINB/TDA_ADHD. Also, the code is available as The code/software described in the paper is freely available online at 10.1523/ENEURO.0543-19.2020.ed1Extended Data 1Extended Data 1, ZIP file.Code applied in the present article. Download 0 curves showed a generalized sample agreement along the four brain parcellations. Significant agreement was found considering every atlas and each feature: area , slope , and kurtosis . Moreover, pairwise concordance coefficient was significant for every pair of atlases and every TDA metric .When comparing observed results against those generated from the null model, Bnal data . Regardi0 curves showed significantly lower odds for the ADHD group, no matter the brain atlas , which means that the ADHD group has smaller AUC compared with the TDC group  or the nodes of two networks (internetwork), according to the lobular and the functional parcellation of the AAL and P264 atlases, respectively. Resulting patterns of the AUC were significant after FWE correction,  the TDC . These r the TDC .0 curves were characterized in terms of three parameters: AUC, slope, and kurtosis; and compared between ADHD and TDC. The application of this model to a pediatric sample showed that the AUC was significantly lower for the ADHD group, both at the whole-brain and at the subnetwork level. These results showed decreased functional segregation in the ADHD group, mainly involving the frontal lobe and the DMN.In this work, methods from TDA were applied to explore the topology of the brain network as a function of the filtration value . Resulting B0 curves were characterized in terms of three parameters, the AUC, slope, and kurtosis. Pairwise agreement between brain parcellations was high for the AUC (KCC range: 0.84–0.97) and the slope (KCC range: 0.69–0.9), and medium to low for the kurtosis (KCC range: 0.59–0.66). These results suggest that this methodology is consistent among different parcellation schemes, especially for the area under the B0 curve. In addition, considering that the B0 features do not depend on a particular connectivity threshold, but instead explore all the filtration values with a change in the topology of the network, this methodology contributes to provide a complete picture of the brain network, overcoming one of the main limitations of other approaches. Taken together, these are potentially important advantages that may complement other methods applied to brain networks, such as graph theory, which has been shown to be highly dependent on the brain parcellation scheme (The Bn scheme , and on n scheme .0 curve was significantly lower for the ADHD group, both at the whole-brain network and at the subnetwork level, being strikingly significant for the interactions involving the frontal lobe and the DMN. As mentioned above, the AUC accounts for the overall transition from all nodes being isolated to being connected into a single component, with smaller areas suggesting that B0, the number of components, decreases faster as the filtration value increases. Such differences in B0 for a given filtration value are mediated by edges that bind together previously split components, which results from increased connectivity in some edges mediating the integration into larger components. Taken together, the results here presented can be interpreted as higher functional connectivity within the connectome and specific subnetworks in the ADHD group, especially those involving the frontal lobe and the DMN. Previous evidence has also suggested increased functional connectivity in a variety of regions of the frontal lobe in ADHD (The area under the B in ADHD , as well in ADHD  and fron in ADHD , particu in ADHD . Our res in ADHD . These r in ADHD , particu in ADHD .0 curves could be interpreted as higher integration and lower segregation of isolated components, our results seem to be contradictory to the aforementioned ones. Nevertheless, the previous studies explored connectivity costs higher than 10%, which according to 0 curves consider a wider range of connectivity thresholds, rarely explored with graph theory. Indeed, when exploring the edge-wise proportions between groups at different connectivity thresholds (Previous studies have reported a myriad of differences in network properties between ADHD and TDC participants. At the whole-brain level, higher functional segregation and lower functional integration in ADHD subjects compared with controls have been reported , althougresholds , the ADHresholds . These r0 in ADHD brain networks (As far as we are concerned, only two previous studies have explored Bnetworks , 2012, uIn summary, the present study showed a robust and informative implementation of TDA in functional connectomics. The results exhibited significant differences for the brain topology of children with ADHD, both at the whole-brain network and at the functional subnetwork level, particularly involving the frontal lobe and the DMN. Therefore, this approach may contribute to identifying the physio-pathology of neurodevelopmental disorders, complementing other connectomics methods by exploring a larger connectivity range and reducing the bias of selecting a fixed threshold.


Bartonella henselae DNA was detected in one captured animal. This is the first report of the presence of B. henselae DNA in rodents in Italy. B. henselae is the causative agent of cat scratch disease, and the detection of this bacterium in rodents might have public health implications. Invasive rats and field mice were captured under the project RESTO CON LIFE “Island conservation in Tuscany, restoring habitat not only for birds”, aiming to improve habitats preservation on the Italian islands of Pianosa, Elba, Montecristo, and Giannutri. Bartonella species that cause human bartonellosis. The aim of this study was to assess the presence of Bartonella spp. DNA in wild rodents in Pianosa island, Italy. Rats  and field mice  were captured and spleen DNA tested for the presence of Bartonella spp. by means of an initial screening using a qPCR amplifying a short segment of the 16S-23S rRNA gene intergenic transcribed spacer region  followed by conventional PCR amplification of a longer ITS fragment (~600 bp) and of a citrate synthase  gene segment. A total of 25 spleen DNA samples obtained from 31 rodent carcasses (81%) yielded positive qPCR results. Bartonella genus was confirmed by amplicon sequencing. By conventional PCR, eight out of 25 samples (32%) yielded bands on gels consistent with ITS segment, and 6/25 (24%) yielded bands consistent with the gltA locus. Amplicon sequencing identified B. henselae and B. coopersplainsensis in 1/25 (4%), and 4/25 (16%) samples, respectively. Moreover, 5/25 (20%) of Bartonella spp. positive samples showed gltA sequences with about 97% identity to B. grahamii. These results provide support to recently published observations suggesting that B. henselae circulates in wild rodent populations.Wild rodents are reservoirs of several  Bartonella includes Gram-negative pleomorphic bacteria infecting endothelial cells and erythrocytes of mammalian hosts [Bartonella have been described [B. quintana, the agent of trench fever, and B. henselae, the cause of the commonly diagnosed cat-scratch disease. Other species are considered emerging pathogens associated with a broad range of clinical conditions, including endocarditis [The genus an hosts . More thescribed , and thecarditis .Bartonella transmission to humans is widely believed to be mediated by haematophagous arthropods [Bartonella species [B. henselae [B. henselae is identified in humans and cats and their arthropods; however, two reports of the molecular identification of this species in rodents were previously described. In particular B. henselae was identified in Apodemus sylvaticus in Denmark [Rattus rattus in New Zealand [Bartonella spp. DNA in wild rats and mice from Pianosa Island in the Tuscan Archipelago National Park of Central Italy is described. To the authors’ best knowledge, this is the first report of the presence of Bartonella spp. in rodents from Italy, and the third report, worldwide, of the detection of B. henselae in these hosts. thropods , and an  species . Yet, rohenselae ,6. Usual Denmark , and in  Zealand . Here, aRattus spp.) and 16 field mice (Apodemus spp.) trapped using T-Rex Snap Rat and Sherman Traps placed all along Pianosa Island, Italy, as part of a rodent control program . Traps were emptied within 24 h of setting and the carcasses preserved at −20 °C. The carcasses were thawed overnight at room temperature and the spleens collected by incision of the left abdominal wall changing scalpel and gloves between carcasses. The collected spleens were re-frozen at −20 °C until analyzed. This study used carcasses of 15 rats  designed to amplify a ~200 bp segment of the Bartonella spp. 16S-23S rRNA intergenic transcribed spacer (ITS), using previously described primers [TM Real-Time PCR Detection System , in a PCR mixture consisting of 10 µL of iTaq Universal SYBR® Green Supermix , 300 nM of each primer, 1 µL of DNA to a final volume of 20 µL with nuclease-free water. The protocol included a 4-min step at 94 °C followed by 45 cycles at 95 °C for 5 s and 60 °C for 20 s. Samples with a quantification cycle (Cq) less than 35 were considered positive, and the amplicons were sent for bidirectional sequencing to a commercial sequencing provider  using the same primers.The spleens were thawed at room temperature and DNA was extracted using kits , according to the manufacturer’s instructions. Initial screening for  primers . AmplifiBartonella spp. were further analyzed by conventional PCR (cPCR) at two loci: ~340 bp segment of citrate synthase gene (gltA), and ~600 bp- segment of ITS locus. cPCRs were performed using previously described primers [gltA gene fragment was amplified by heating to 95 °C for 15 min, followed by 45 cycles at 95 °C for 5 s and 60 °C for 45 s. PCR products were electrophoresed in 2% agarose gels, stained by GelRed Nucleic Acid Gel Stain  and visualized under UV. Bartonella sp. FG4-1 DNA was included as positive control and nuclease-free water as negative control in each PCR batch testing. Amplicons showing bands on gels consistent in size with the amplified locus were sequenced as above, and the raw sequences edited using Geneious Prime version 2019.0.4 [https://blast.ncbi.nlm.nih.gov/Blast.cgi). GltA and ITS amplicon sequences identified in this study and from GenBank were aligned with the MUSCLE alignment algorithm and phylogenetic relations were estimated using Tamura-Nei as genetic distance model and Neighbor-Joining as tree building method. Bootstrap calculation was carried out with 1000 resamplings with support threshold ≥70%. These analysis were conducted using Geneious Prime software version 2019.0.4 [All the samples yielding ITS sequences consistent with  primers ,10,11, u2019.0.4  and comp2019.0.4 Bartonella genus (data not shown). A total of 10/25 (40%) yielded Bartonella DNA by means of cPCR. GltA amplicons were obtained by cPCR in 6/25 (24%) qPCR-positive samples, and 600bp-ITS amplicons were obtained in 8/25 samples. Four samples yielded amplicons at both loci. The results of the survey are summarized in gltA sequence was identical (100%) to the sequence of Bartonella henselae strain Houston-I (GenBank accession number CP020742.1). This gltA sequence was deposited in the GenBank as accession number MK562498.In particular, in one animal, a 346 bp gltA amplicons were identical with each other and shared 96–97% sequence similarity with the gltA gene sequence of B. grahamii (accession number CP001562.1), and other Bartonella spp. (B. coopersplainsensis (accession number EU111770.1) and were deposited in the GenBank as accession numbers MK562489, MK562490, MK562492 and MK562494. The other four ITS sequences were unique and shared 96-97% similarity with the Uncultured Bartonella sp. Clone MS_Seville-07021427 (GenBank accession number EU218552.1). These four sequences were deposited under accession numbers MK562487, MK562488, MK562491, MK562493, as Uncultured Bartonella sp. 16S-23S ribosomal RNA intergenic spacer, partial sequence. The sequences of the other five lla spp. . These sgltA and ITS sequences of Bartonella identified in the present study were compared to related gltA and ITS Bartonella sequences from GenBank, showing that both gltA and ITS novel sequences were closely related to B. grahamii, B. elizabethae, B. tribocorum, and B. queenslandensis (andensis .Bartonella DNA in carcasses of wild rodents collected by means of traps in the isle of Pianosa, Italy. Initial screening by qPCR suggested the presence of Bartonella DNA in ~80% of the samples, and subsequent cPCR identified Bartonella DNA in 40% of the qPCR positive samples. Only four samples were PCR-positive at both loci. These results suggest a greater sensitivity of the qPCR used for screening, as compared with the cPCRs. Many samples were positive at one locus only, highlighting the utility of multiple-locus PCR as a means to increase overall testing sensitivity.We performed a molecular survey of the occurrence of B. coopersplainsensis in four rats. B. coopersplainsensis was first identified in blood of Rattus leucopus in Australia [B. coopersplainsensis has yet to be established. We identified ITS sequences of ustralia , and latustralia ,15,16,17gltA and ITS sequences in five mice (gltA sequences, excluding the possibility of PCR artifacts. This sequence had not been reported previously in the GenBank and had high degree of identity to B. grahamii gltA locus. This Bartonella species can cause zoonotic infection, supporting the possible role of wild rodent as reservoir of human infections [Bartonella species infections, and infections with B. elizabethae, B. grahamii, and B. vinsonii have been observed [We found novel ive mice . Interesfections . Exposurobserved .Bartonella sp. [B. elizabethae and was identified in rodents from Spain. The phylogenetic tree built with gltA and ITS cPCR products and compared to other Bartonella sequences deposited in GenBank allowed to evidence that the five Bartonella spp. identified in Apodemus might belong to a novel species closely related to B. grahamii, B. elizabethae, B. queenslandensis, and B. tribocorum, usually identified in rats and mice, and confirming these mammalian as reservoirs of zoonotic Bartonella spp. [Bartonella identified in rats are very close to B. henselae and B. coopersplainsens species.We also identified four novel 600 bp-ITS sequences that showed a high degree of similarity to an Uncultered ella sp.  which islla spp. . MoreoveB. henselae was identified in one rat, corroborating previous observations in New Zealand [gltA locus, which has the largest number of sequences in the GenBank. The cPCR of the 600bp-ITS of this sample did not produce an amplicon, perhaps due to DNA degradation. This is the third report of the presence of B. henselae DNA in rodents, worldwide. Hence, rodents might have a greater role in the eco-epidemiology of cat scratch disease than previously thought. The circulation of B. henselae in rodents would need to be corroborated by culture [Bartonella culture from wild rodent carcasses is often hindered by the decomposition of the carcasses and the in vitro overgrowth of fast growing post-mortem contaminants. Interestingly, DNA of  Zealand  and Denm Zealand . This wa culture . It shouB. henselae, B. coopersplainsensis, and of a potential novel Bartonella species in spleen of rodents in the isle of Pianosa, Italy. Rodents may play a previously underestimated reservoir role in the eco-epidemiology of cat scratch disease.We identified DNA of


Aim: Attention-deficit hyperactivity disorder (ADHD) is a common childhood neuropsychiatric disorder that affects 6.1 million US children. The mechanism of ADHD is currently unclear. Differences in ADHD presentations between boys and girls are well-established. In the present study, we used quantitative electroencephalography (EEG) to investigate the brain area and EEG bands of boys with ADHD.Methods: This study enrolled 40 boys with ADHD and 40 age-matched controls without ADHD. Low-resolution electromagnetic tomography (LORETA) and instantaneous frequency were used to analyze EEG data to reveal the mechanisms underlying ADHD in boys.Results: We found that the instantaneous frequencies in the T3 and T4 EEG channels in boys with ADHD were significantly higher than those in the controls. The beta band showed significant difference in current density between the ADHD and control groups. In the entire brain area, the bilateral inferior and middle temporal gyrus exhibited the most significant difference between the ADHD and control groups in the EEG beta band. Connectivity analysis revealed an increase in connectivity between the left middle frontal gyrus and fusiform gyrus of the temporal lobe in boys with ADHD.Conclusions: LORETA is a promising tool for analyzing EEG signals and can be used to investigate the mechanism of ADHD. Our results reveal that the inferior temporal gyrus, middle temporal gyrus, and fusiform gyrus of the temporal lobe are potentially involved in the pathogenesis of ADHD in boys. In comparison with other imaging methods, such as magnetic resonance imaging, EEG is easy to perform, fast, and low cost. Our study presents a new approach for investigating the pathogenesis of ADHD in boys. Attention-deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder diagnosed in childhood. An estimated 6.1 million US children 2–17 years of age (9.4%) have received an ADHD diagnosis  to investigate the mechanisms of ADHD  criteria and the Child Behavior Checklist (CBCL) scales, and the Swanson, Nolan and Pelham Teacher and Parent Rating Scale (SNAP-IV) was used to evaluate ADHD severity. The SNAP-IV consists of 26 items that are rated on a 4-point scale . The items are divided between three subscales: inattention (nine items), hyperactivity/impulsivity (nine items), and opposition (eight items). Subscale scores are calculated according to the average ratings. The items for inattention and hyperactivity/impulsivity can be combined to also create a “combined ADHD score” . This study enrolled only boys to reduce the confounding effect of different sexes in ADHD. The mean age of patients in the ADHD group was 7 years, 7 months , and that of participants in the control group was 7 years, 11 months . No significant difference in age distribution was observed between the groups. All children underwent an examination and a detailed clinical interview with a pediatric neurologist or psychiatrist in addition to receiving an EEG evaluation. None of the boys were on any ADHD medication before the time of testing. Children with histories of epilepsy, intellectual disability, drug abuse, head injury, and psychotic disorders were excluded. ADHD was diagnosed according to Identical equipment and procedures were used for EEG recordings for all patients. Patients with ADHD underwent EEG examinations for 20 min with eyes closed. Patients were tested in a quiet, air-conditioned room. All recordings were made during daylight hours (between 08:00 a.m. and 05:00 p.m.). EEG data were digitally obtained using 19 electrodes at a sampling rate of 256 Hz . Amplifier characteristics were bandpass filtered between 0.5 and 60 Hz with 10,000 times gain, and electrodes were arranged according to the International 10–20 System.The 80 boys enrolled in this study were divided into two groups: ADHD and control groups. For each boy, artifact-free EEG segments were acquired. Eighteen channels of monopolar montage were adopted for analysis. To ensure unbiased comparison, all EEG segments were acquired from artifact-free sections of EEG recordings conducted when participants were awake.z scores with zero mean and a standard deviation (SD) of 1  is a real-time series measured from an EEG channel, its corresponding Hilbert transform signal can be calculated as follows:If PV represents the Cauchy principal value denoting the following operation  and Subsequently, where the instantaneous amplitude of the analytical signal isand its instantaneous phase isAfter applying signal processing, Ville formulated a time–frequency distribution of the EEG signal, which is now commonly referred to as the Wigner–Ville distribution , we can calculate the instantaneous frequency of each participant in the ADHD and control groups . A two-sample To analyze the intracortical distribution of the electrical activity from the surface EEG data, LORETA employs a discrete, three-dimensionally distributed, linear weighted minimum norm inverse solution. Intracerebral volume was partitioned into 6,239 voxels with a spatial resolution of 5 mm. In total, 20 artifact-free 5-s EEG epochs were randomly selected from the 300 s recorded EEG for each participant. For statistical neuroimaging analysis of source current density, LORETA applies a statistical non-parametric mapping (SnPM) method  was applied for functional connectivity analysis. With emphasis on the temporal lobe, 70 seeds located around the entire cortical area were selected to compare the functional connectivity between the two groups . Moreover, the LORETA non-parametric randomization procedure based on the “maximal statistic” was used to correct for multiple comparisons  was above the critical threshold tcrit for p = 0.05, as determined through 5,000 data randomizations  and T4 (p = 0.013) channels (p < 0.05). In the entire brain area, the inferior temporal gyrus (Brodmann area 20) and middle temporal gyrus (Brodmann area 21) exhibited the largest difference between the ADHD and control groups -20150052]. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.C-TC contributed to the study conception and design, contributed to acquisition and interpretation, and drafted and critically revised the manuscript. C-SO and R-CY contributed to the study conception and design, contributed to analysis and interpretation, and critically revised the manuscript. R-CW contributed to the study conception and design, contributed to acquisition and interpretation, and critically revised the manuscript. L-CL contributed to the study conception and design, contributed to analysis and interpretation, drafted and critically revised the manuscript, and provided final approval. All authors accept accountability for all aspects of the work and ensuring its integrity and accuracy.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.


APOB and LPAR5 genes were associated with ADHD persistence compared to ADHD remittance ; both genes are involved in cholesterol signaling. Both findings appeared to be linked to genetic variation in cis. We found neither significant epigenome-wide single CpG sites nor regions associated with impulsive and callous traits; the top-hits from these analyses were annotated to genes involved in neurotransmitter release and the regulation of the biological clock. No link to genetic variation was observed for these findings, which thus might reflect environmental influences. In conclusion, in this pilot study with a small sample size, we observed several DNA-methylation–disorder/trait associations of potential significance for ADHD and the related behavioral traits. Although we do not wish to draw conclusions before replication in larger, independent samples, cholesterol signaling and metabolism may be of relevance for the onset and/or persistence of ADHD.Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood. ADHD and related personality traits, such as impulsivity and callousness, are caused by genetic and environmental factors and their interplay. Epigenetic modifications of DNA, including methylation, are thought to mediate between such factors and behavior and may behave as biomarkers for disorders. Here, we set out to study DNA methylation in persistent ADHD and related traits. We performed epigenome-wide association studies (EWASs) on peripheral whole blood from participants in the NeuroIMAGE study (age range 12–23 years). We compared participants with persistent ADHD (n = 35) with healthy controls (n = 19) and with participants with remittent ADHD (n = 19). Additionally, we performed EWASs of impulsive and callous traits derived from the Conners Parent Rating Scale and the Callous-Unemotional Inventory, respectively, across all participants. For every EWAS, the linear regression model analyzed included covariates for age, sex, smoking scores, and surrogate variables reflecting blood cell type composition and genetic background. We observed no epigenome-wide significant differences in single CpG site methylation between participants with persistent ADHD and healthy controls or participants with remittent ADHD. However, epigenome-wide analysis of differentially methylated regions provided significant findings showing that hypermethylated regions in the  Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with a prevalence of 5% in children . Of thesOne of the core symptoms of ADHD is an increased level of the behavioral trait impulsivity. Another trait associated with ADHD , callousEnvironmental adversities may act on behavior through epigenetic mechanisms . EpigeneTargeted DNA methylation studies, mostly performed in children, reported on genes in the dopaminergic, adrenergic, and serotonergic system associated with ADHD . Only feIn the current pilot study, we aimed to identify methylation differences linked to ADHD persistence, impulsivity, and callousness in the NeuroIMAGE cohort . We hypoA subset of 72 independent individuals  were selected based on ADHD symptoms (i.e. sampling groups to cover the extremes of the distribution) from the longitudinal NeuroIMAGE cohort  . ParticiADHD diagnosis was made in accordance with the DSM-IV  based onThe scores derived from the PACS and K-SADS were combined with the Conners Teacher Rating Scale: Long version for children <18 years  and the To measure impulsivity, the raw scores of the Global restless–impulsive scale I of the Conners Parent Rating Scale (CPRS) were used . In totaTo measure callous traits, participants completed the Inventory of Callous-Unemotional Traits . We crea® MethylationEPIC BeadChip  by Life & Brain GmbH, Bonn, Germany. The readEPIC function of the wateRmelon package was used to import methylation data and to compare intensities of methylated (M) and unmethylated (U) DNA (https://dnamage.genetics.ucla.edu/home) the age of the subjects at the time of sampling was calculated  DNA . Bisulph (U) DNA . Probes lculated . Of the Since smoking is known to affect DNA methylation , and parvia the “sva” package in R (Surrogate variable (SV) analysis is a method to capture heterogeneity across the data not caused by the variables of interest and is sensitive enough to detect possible differences of blood cell composition (SV 1 showed a Pearson's correlation = 0.9 with the most abundant blood cell type), ethnical descent, and chip position . SVs werage in R , and all−8  in all analyses . A regiovia the genome browser of iMETHYL, based on experiments in neutrophils isolated from 100 healthy Japanese subjects  were assessed for all nominally significant CpG sites from the former analyses. Databases on meQTLs in peripheral blood during adolescence  and in tsubjects .The characteristics of the study sample are displayed in −10) , and this was strongly correlated with actual age   and healthy controls . While no individual probes passed the experiment-wide significance threshold, seven CpG sites showed differential methylation at our more lenient, suggestive significance threshold of p < 5 * 10PPT2 gene (65 bp containing four probes) to be significantly hypomethylated in participants with persistent ADHD compared to healthy controls   identify differences in peripheral blood DNA methylation profiles linked to the persistence of ADHD, and 2) find DNA methylation levels correlated with impulsive and callous traits. To our knowledge, this is the first study in which differences in DNA methylation levels in participants with persistent and remittent ADHD were investigated. This study showed global DNA methylation differences between participants with persistent ADHD compared to participants with remittent ADHD. Two DMRs, in We found that global methylation differences were lower in participants with persistent ADHD than in healthy controls. It is known that DNA methylation gradually decreases during aging , and it We expected to find biomarkers of ADHD persistence and associated traits in the DNA isolated from peripheral blood. Such biomarkers do not necessarily reflect brain DNA methylation status, and we have no information on any causal relationships with disease. Some of our findings did show a relatively high correlation with methylation levels in different brain regions. The lack of blood–brain correlation in methylation observed for many of our findings is not necessarily due to the limited sample size of our study. A recent methylome-wide association study for schizophrenia (N = 1448) found that most of their top-findings were also specific for blood, but that half of their sites were located in genes that were enriched in brain-specific GO-terms . These fPrevious studies investigating the epigenome in ADHD in a candidate gene-based manner have reported differential methylation in the serotonin and dopamine system , which hBased on genetic, rather than epigenetic studies, we did find support for the observed differential methylation sites associated with ADHD. Our most significant finding hints toward fatty acid metabolism in persistent ADHD, and genes involved in fatty acid metabolism and fatty acid oxidation pathways have also been indicated to be differentially methylated in participants with ADHD compared to healthy controls .APOB and LPAR5 to be differentially methylated between participants with persistent and remittent ADHD. Hypermethylation of APOB in participants with persistent ADHD compared to those with remittent ADHD implies lower APOB expression in those with persistent ADHD, but was not validated. APOB is the primary lipoprotein found on the surface of low density lipoproteins (LDL), which transports fatty acids and cholesterol throughout the body (−/−) show hyperactivity , and from the European Community's Horizon 2020 Programme (H2020/2014–2020) under grant agreement n° 728018 (Eat2beNICE). BF was also supported by a personal grant from the Netherlands Organization for Scientific Research (NWO) Vici Innovation Program (grant 016-130-669). The NeuroIMAGE project was supported by NIH Grant R01MH62873, NWO Large Investment Grant 1750102007010, and grants from Radboud University Medical Center, University Medical Center Groningen and Accare, and VU University Amsterdam. This work was also supported by grants from NWO Brain & Cognition (433-09-242 and 056-13-015) and from ZonMW (60-60600-97-193).PH has received a research grant from and served on the advisory board for Shire. JB has served as a consultant, advisory board member, and/or speaker for Eli Lilly, JanssenCilag, Medice, Roche, Shire, and Servier. BF received educational speaking fees from Medice.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We recently hypothesised that increased spontaneous mind wandering (MW-S) reflects a core process underlying attention-deficit/hyperactivity disorder (ADHD). Previous studies show that individuals with ADHD and neurotypical individuals with increased MW-S display similar cognitive-performance and electrophysiological (EEG) impairments in attentional processes. However, the cognitive-EEG markers associated with increased MW-S in ADHD remain poorly understood. We therefore investigated such markers in a sample of 69 sex- and age-matched adults with ADHD and 29 controls during the Sustained Attention to Response Task. We compared task performance and EEG measures  of attentional processes between groups, and examined their association with a validated self-report questionnaire of MW-S. Finally, we tested the hypothesis that MW-S and ADHD diagnosis relate to the same cognitive-EEG impairments using a hierarchical regression model. Compared to controls, adults with ADHD showed attenuations in P3, event-related alpha and beta suppression during response inhibition , and theta power activations during response execution , as well as increased reaction time variability and more commission/omission errors. MW-S was also continuously associated with most cognitive-EEG measures related to ADHD. The hierarchical regressions on measures associated with both ADHD diagnosis and MW-S showed that MW-S did not explain additional variance in the cognitive-EEG markers (except for beta suppression) beyond ADHD diagnosis, and vice versa. These findings are consistent with our hypothesis that ADHD diagnosis and MW-S share common neural deficits, and that MW-S may reflect a core symptom of the disorder. HoweverReduced P3 has also been consistently found in individuals with ADHD during attentional tasks ,18. FurtBeside investigations of ERPs, other studies have examined EEG spectral power during MW episodes in population-based samples. Using thought probes and quantitative EEG (qEEG) analyses, parietal alpha power increased during episodes of MW during vigilance , Stroop Alterations in brain oscillations have also been reported in ADHD samples. Using q EEG analyses, posterior alpha power, thought to reflect attentional selection and activation , was inThe present study investigates whether cognitive-EEG measures relevant to attentional processes during a task probing inhibitory control and sustained attention with Go and No-Go conditions  are impaired in adults with ADHD, and are significantly associated with severity of MW-S. Firstly, we compare adults with and without ADHD on ERP and finer-grained time-frequency indices of brain oscillations that were sensitive to ADHD-control differences or associated with MW in population samples in the aforementioned studies (Aim 1). Based on this previous literature, we predicted that the ADHD group would show poorer task performance, reduced P3, event-related theta power, alpha and beta power suppressions, and increased theta phase variability. Secondly, we examine the association between these cognitive-EEG measures and self-reported MW-S (using a self-reported questionnaire of MW-S in ADHD) (Aim 2), predicting that MW-S would be continuously associated with the same cognitive-EEG measures sensitive to ADHD-control differences. Finally, to formally test the hypothesis that the same neural deficits underlie increased MW-S and the ADHD diagnosis , we anal22.1The total sample consisted of 69 adults with ADHD and 29 controls. These were selected from a study of 111 adult participants (81 adults with ADHD and 30 control adults) who took part in a randomised placebo-controlled trial of a fatty acid supplements . The remIndividuals with ADHD were recruited from South London and Maudsley NHS Trust ADHD clinics, online advertisements via adult ADHD networks and primary care physicians. Controls were recruited via recruitment advertisements in the local community. Participants in both groups were excluded if they had a current or past diagnosis of major neurological disorders , severe recurrent mental health problems other than ADHD , current or past substance abuse , or an IQ < 80.All the ADHD participants met DSM-5 criteria for ADHD. Participants in the ADHD group were either on stable treatment with ADHD medication  or no medication (N = 18). Some ADHD participants (N = 15) were taking a low dose of concomitant medication for depression or anxiety disorders. All control participants screened below threshold for ADHD on the Adult Self Rating Scale for ADHD  and were2.2All participants underwent an in-person assessment lasting around 4 h 30 min, which involved a diagnostic interview, a cognitive-EEG assessment, IQ testing  and ADHD-related self-report questionnaires. Participants on medications for ADHD were asked to stop taking their medication for 48 h before the research assessment. All participants were asked to refrain from drinking caffeine or smoking on the day of assessments and the preceding evening.2.32.3.1MW-S was measured using the Mind wandering Excessively Scale (MEWS), a 12-item self-report measure reflecting descriptions of MW in ADHD. We previously found that the MEWS was highly correlated with the spontaneous mind wandering scale  used by r = .77), hyperactivity/impulsivity (r = .69), and ADHD-related functional impairment (r = .81) [r = 0.76, p < 0.001), but not MW-D , indicating that MW-S (and not MW-D) is captured by the MEWS in both individuals with ADHD and controls.The scale shows excellent internal consistency (α > .90), as well as high sensitivity and specificity  to discr = .81) . The scar = .81)   but to respond with a button press after all other digits . Participants completed the SART over three blocks, each lasting approximately 5 min. Individual blocks consisted of 225 digits, with each digit presented 25 times and an inter-stimulus interval of 1000 ms.The following performance indices were measured: commission errors  omission errors , mean reaction time (MRT) and reaction time variability . MRT and RTV were computed on trials with correct responses to Go stimuli only.2.4The EEG was recorded from a 62-channel DC-coupled recording system (extended 10–20 montage), using a 500 Hz sampling rate, impedances under 10 kΩ, and FCz as the recording reference. The electro-oculograms were recorded from electrodes above and below the left eye and at the outer canthi. The EEG data were analysed using EEGLAB . The rawOcular artefacts , clearly isolated heartbeat, line noise and muscle artefacts were identified using independent component analysis (ICA) with the AMICA (Adaptive Mixture ICA) algorithm . ICA allOnly participants with at least 20 artefact-free EEG segments in each condition were included in ERP/EEG analyses, since at least 20 artefact-free EEG segments are required to observe reliable neural effects and obtain valid ERP/ERSP indices . From thFor ERP analyses, stimulus-locked epochs (stimulus window from −500 to 1000 ms) were averaged based on Go trials with correct responses and No-Go trials with no responses . Baseline correction was performed using a 500-ms pre-stimulus period. This window was chosen for consistency with the pre-stimulus used for ERSP analyses, where a −500 pre-stimulus baseline correction was chosen for normalisation of ERSPs to capture two full cycles at the lowest frequency of interest (4 Hz theta). ERP measures were identified within the selected electrodes and latency windows for which effects were expected to be largest, based on previous ADHD or MW studies ,37,47 anTime-frequency analyses were used to investigate changes in power and phase related to the Go, correct and incorrect No-Go trials. Power changes were quantified as an event-related spectral perturbation (ERSP) index  in a 250Stimulus-locked ERSP in the theta (3−7 Hz), alpha (8−13 Hz) and beta 14−30 Hz) bands were extracted in the 1000 ms window. Consistent with previous literature ,30,53, w Hz bands2.5Analysis 1: To test aim 1, differences between individuals with ADHD and controls on performance measures  were investigated using independent sample t-tests. RTV and MRT showed skewed distributions and were log-transformed before analyses, while CE and OE were normally distributed. Group differences on EEG variables, all showing normal distributions, were investigated with general linear models testing main effects of group (ADHD vs control), condition (No-Go vs Go) and two-way group x condition interactions. Since ERSP variables were investigated across two separate windows, three-way group x condition x time window interactions were also investigated. Each frequency band measure was tested in a separate model. When the three-way interaction was not statistically significant, it was dropped from the model and only statistically significant main effects and two-way interactions were included. Significant group and interaction effects were followed up with post-hoc tests.Analysis 2: To test aim 2, we investigated continuous associations between MW-S (measured with the MEWS) and all the investigated cognitive-EEG measures. In order to report standardised beta coefficients, all measures were first standardised. Cognitive variables were analysed with linear regressions, using MW-S as the independent variable and each of the cognitive variables separately as dependent variables. P3 and ERSP variables measured over different conditions and time windows were investigated with the same general linear models used in analysis 1, but using the MEWS as the independent variable instead of group.Analysis 3: To address aim 3, we examined the shared and unique variance explained by ADHD and MW-S on the cognitive-EEG measures that showed significant impairments in the ADHD group  and were also significantly associated with the MEWS . We ran hierarchical linear regression models with each cognitive-EEG variable (dependent variable); first, entering as independent variables ADHD group in the first block and MEWS in the second block, and, secondly, entering MEWS first and ADHD second.p-values for analysis 1 were equal or lower than 0.035 in analysis 1 and equal or lower than 0.010 in analysis 2. Analyses addressing aim 3 were restricted to measures showing ADHD-control differences and significantly associated with the MEWS based on these FDR thresholds, therefore further multiple-testing corrections were not applied.Given the large number of hypotheses tested in analyses 1 and 2, results were corrected for multiple testing using a false discovery rate (FDR) threshold based on the total number of comparisons. Significant We have also carried a number of sensitivity analyses: i) comparisons between controls and individuals with ADHD who were not taking medication for anxiety or depression on cognitive-EEG measures, to ensure group differences were not driven by participants taking these medications ; ii) a comparison between individuals with ADHD and controls on ERSP variables over the whole 1 s trial time window ; iii) an analysis of theta ITC with theta ERSP (0−500 ms) as a covariate, to account for potential effects of power on the phase of theta ,  and showed significantly greater RTV . There w3.1.2P3. A significant main effect of condition (p < 0.0001), group (p < 0.0001) and condition-by-group interaction (p = 0.001) was observed for the P3. Post-hoc analyses found that individuals with ADHD had a lower P3 amplitude in both Go and No-Go conditions compared to controls (p = 0.001).controls , with diAlpha ERSP. A significant main effect of condition (p < 0.001), group (p = 0.008) and a group-by-condition-by-time interaction (p = 0.003) emerged for alpha ERSP. In the 200−600 ms post-stimulus window, there was a main effect of condition (p < 0.001), but no significant group-by-condition interaction (p = 0.13), and main group effect did not reach statistical significance or survive correction for multiple comparisons (p = 0.06). Both groups had lower alpha ERSP (i.e. greater suppression) in the No-Go compared to the Go condition in the 200−600 ms window (p < 0.001). In the 600–1000 ms window, there were significant effects of condition (p < 0.001), group (p < 0.001) and condition-by-group interaction (p < 0.001). Alpha ERSP was higher (i.e. suppression was lower) in the ADHD group compared to the control group in the No-Go condition, but not in the Go condition (ondition .Beta ERSP. A significant main effect of condition (p < 0.001), but no effects for group (p = 0.63) or group-by-condition-by-time (p = 0.49) emerged for beta ERSP over central regions. Both in the 200−600 ms and in the 600−1000 ms window there was a main effect of condition (both p < 0.001), but no significant effect of group (p = 0.99 and p = 0.20 respectively) or condition-by-group interaction . Both groups had lower central beta ERSP in the No-Go compared to the Go condition in both time windows (p < 0.001).p < 0.001), but no significant group-by-condition-by-time interaction (p = 0.54), and the main group effect did not reach statistical significance or survive correction for multiple comparisons (p = 0.06). In the 200−600 ms window there was a significant main effect of condition (p < 0.001) and a significant group-by-condition interaction (p = 0.03), but no significant effect of group (p = 0.17). The ADHD group showed higher beta ERSP (i.e. lower suppression) in the in the No-Go condition compared to the control group, although this difference did not survive correction for multiple comparisons; no group differences emerged in the Go condition (p < 0.001), group (p = 0.03) and group-by-condition interaction (p = 0.03) emerged. The ADHD group showed greater parietal beta ERSP (lower suppression) in the 600−1000 ms compared to the control group in the No-Go condition, while the groups did not differ in the Go condition  and a group-by-condition-by-time interaction (p = 0.01), but no main effect of group (p = 0.23), emerged for theta ERSP. In the 0–500 ms time window, there was a significant effect of condition (p < 0.001), but no statistically significant main effect of group (p = 0.08) or condition-by-group interaction (p = 0.60). Theta ERSP was higher during the No-Go compared to the Go condition (p < 0.001). In the 500−1000 ms time window, there was a significant group-by-condition interaction (p = 0.035), but no main effect of group (p = 0.68), and the main group effect reached statistical significance (p = 0.048), but did not survive correction for multiple comparisons. Post-hoc analyses revealed that the ADHD group had lower theta ERSP in the Go condition in the 500−1000 ms compared to controls, but there was no difference in the No-Go condition (ondition .Theta ITC. A significant main effect of condition (p < 0.001), but no main effect of group (p = 0.95) emerged for theta ITC. The group-by-condition interaction (p = 0.04) reached statistical significance, but did not survive correction for multiple comparisons. Phase consistency was greater in the No-Go compared to the Go condition in both groups (p < 0.001).Sensitivity analyses removing ADHD participants who were taking medication for anxiety or depression yielded results comparable to those of Analysis 1 in the full sample  = 10.87, p = 0.001. The MEWS score added in block 2 did not significantly increase the variance explained  = 0.16 p = 0.69). Similarly, the MEWS entered into block 1 explained 27% of the variance, F (190) = 6.84, p = 0.01. ADHD status entered in block 2 did not produce a significant increase in variance explained  = 3.86, p = 0.053).Go P3. ADHD status was entered into block 1 explained 9% of the variance in the Go P3, F (189) = 8.68, p = 0.004. The MEWS score entered in block 2 did not significantly increase the variance explained  = 2.29, p = 0.13). Similarly, the MEWS entered into block 1 explained 10% of the variance, F = 10.23, p < 0.02. ADHD status entered in block 2 did not produce a significant increase in variance explained  = 0.88, p = 0.35).No-Go P3. ADHD status entered in block 1 explained 15% of the variance in the No-Go P3 (F (189) = 6.59, p = 0.012). The MEWS score entered in block 2 did not significantly increase the variance explained  = 1.82, p = 0.18). Similarly, the MEWS entered in block 1 explained 16% of the variance  = 16.61, p < 0.0001., ADHD status entered in block 2 did not produce a significant increase in variance explained  = 2.91, p = 0.09).Alpha ERSP. ADHD status entered into block 1 explained 12% of the variance in No-Go Alpha ERSP (600–1000 ms), F (189) = 12.02, p = 0.001. The MEWS total score entered in block 2 did not significantly increase the variance explained  = 1.43, p = 0.24). Similarly, the MEWS entered in block 1 explained 12% of the variance, F (189) = 12.60, p = 0.001. ADHD status in block 2 did not produce a significant increase in variance explained  = 3.09, p = 0.08).Beta ERSP. ADHD status entered in block 1 explained 6% of the variance in parietal No-Go Beta ERSP (600–1000 ms), F (189) = 5.30, p = 0.024. The MEWS total score entered in block 2 produced a significant increase of 5% in variance explained  = 4.60, p = 0.035). The MEWS entered in block 1 explained 10% of the variance, F (189) = 10.22, p = 0.002. ADHD group in block 2 did not produce a significant increase in variance explained  = 0.003, p = 0.10).Theta ERSP. ADHD group status entered in block 1 explained 7% of the variance in Go Theta ERSP (500–1000 ms), F (189) = 6.59, p = 0.012. The MEWS total score in block 2 did not significantly increase the variance explained  = 0.78, p = 0.38). Similarly, the MEWS entered in block 1 explained 7% of the variance, F (189) = 6.89, p = 0.01. ADHD status in block 2 did not produce a significant increase in variance explained  = 2.07, p = 0.15).4Previous research has shown that ADHD is associated with measures of MW-S, and that both ADHD and MW-S are associated with similar cognitive and neural measures. This led to the hypothesis that the same cognitive-EEG correlates showing differences between individuals with ADHD and controls, and associated with MW-S in population samples, will also be associated with MW-S in individuals with ADHD . Yet, toOur analyses comparing adults with and without ADHD (aim 1) on EEG measures of attentional processes primarily focused on event-related modulations in brain oscillations derived through time-frequency EEG analyses, which provide fine-grained information on brain functioning . In lineImpairments in time-frequency EEG measures in adults with ADHD were accompanied by an attenuated P3 amplitude to Go and No-Go stimuli, in line with previous findings in adult ADHD samples using the SART and other similar attentional tasks . During Our second analysis testing aim 2 further showed that the same indices of alpha suppression, beta suppression, theta power in the later window and P3, as well as of RTV, that were impaired in adults with ADHD were also continuously associated with self-reported MW-S. Since reduced alpha suppression is a marker of attention selection (i.e. the ability to filter out task-irrelevant information) , this EEFinally, our analysis testing aim 3 involved hierarchical regressions to examine whether the cognitive-EEG measures sensitive to ADHD-control differences and associated with MW-S represented shared processes, potentially underlying both ADHD and MW-S, or whether MW-S has a unique relationship with these measures beyond ADHD or vice versa. The results indicate that MW did not have a unique contribution to most of the examined cognitive-EEG measures  beyond ADHD diagnosis, nor did ADHD beyond MW-S. Therefore, RTV,  and highly engaging due to fast presentation rate and high rate of targets compared to slower and unpredictable stimuli presentation and longer task duration (30−45 min) in previous mind wandering studies ,14. The Future work could therefore use a SART with a slower presentation rate and longer duration, potentially at least 30 min. Second, due the lack of a direct measure of MW-S, we were also not able to explore neural impairments during episodes of MW-S. Such an approach has been adopted in previous brain oscillatory work on MW in the general population, which reported higher theta and lower alpha and beta activations around 300−400 ms after a MW episode compared to episodes of task focus ,29. ThisIn conclusion, these findings provide the first evidence for a close relationship between increased MW-S and neural markers of attention allocation, attention selection, motor response activity and response execution/inhibition that are impaired in individuals with ADHD. Our findings further suggest that that self-reported MW-S and ADHD diagnosis might largely be linked to the same electrophysiological processes. Future studies should focus on investigating these neural indices sensitive to both ADHD and MW-S in individuals with and without ADHD during paradigms explicitly designed to measure MW-S and applying causal modelling to provide more direct evidence for this relationship as well as in relation to MW-S assessed with different types of assessments, such as cognitive, online and experience-sampling measures.Vifor Pharma (PADWUDB), awarded to Philip Asherson with King’s College London as sponsor. His research is supported by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, NIHR/BRC (14/23/17) and an NIHR Senior Investigator award (NF-SI-0616-10040)The OCEAN study was funded by 10.13039/501100007155Medical Research Council, as part of a doctoral training programme (DTP).Natali Bozhilova’s research is supported by a studentship awarded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.Dr Giorgia Michelini was in receipt of a fellowship funded by the The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Professor Jonna Kuntsi has given talks at educational events sponsored by Medice: all funds are received by King’s College London and used for studies of ADHD. Professor Philip Asherson has received honoraria for consultancy to Shire/Takeda, Flynn-Pharma, Eli-Lilly, Janssen, Novartis, Lundbeck and Medice; educational/research awards from Janssen, Shire, Lilly, Novartis, Flynn Pharma, Vifor Pharma, GW Pharma and QbTech; speaker at sponsored events for Shire/Takeda, Lilly, Novartis, Medice, Janssen-Cilag and Flynn Pharma


Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition affecting between 5 and 8% of all children and adolescents, characterized by impairing levels of inattention, hyperactivity, and impulsivity. Existing cognitive models of ADHD have placed working memory (WM) deficits at the core of ADHD and suggest that primary WM deficits may also underlie the additional deficits associated with the condition. Although not all children with ADHD show WM deficits, those with such deficits have been found to have worse functional outcomes when compared to their diagnosed peers with typical WM functioning. Even so, contributors to the variability of impaired WM functioning seen within this population remain unknown. In the present study, we examined the association between two known prenatal and perinatal risk factors for impaired cognitive functioning – gestational smoking and hypertension – in three independent samples of children and adolescents with ADHD (samples varied with respect to sample size and WM measurement procedures). Contrary to hypotheses and existing literature, presence of high blood pressure during pregnancy was unexpectedly found to be a positive predictor of offspring WM capacity in one of three samples . Implications and considerations for future research are discussed. Attention-deficit/hyperactivity disorder (ADHD) is a commonly occurring behavioral condition characterized by developmentally extreme patterns of inattention, hyperactivity, and impulsivity . IndividADHD is a complicated constellation of commonly co-occurring and covarying symptoms, and efforts to optimize prevention and intervention in the absence of known biological mechanisms have focused on elucidating underlying cognitive mechanisms. This search has led to the development of several cognitive models of ADHD, including a functional working memory (WM) model. WM is the ability to store and manipulate mental information . The WM Despite the known negative outcomes associated with impaired WM functioning in ADHD, much less is known regarding risk factors for WM impairment itself. Expanding the understanding of predictors of WM deficiency is an issue of important public health concern, given research demonstrating relations between WM deficits and negative social outcomes such as reduced school achievement  and incrApproximately 16% of women smoke while pregnant, making tobacco the most frequently used teratogen believed to impair fetal brain development . EvidencUp to 22% of women suffer from high blood pressure while pregnant, making hypertensive disorders of pregnancy one of the most common pregnancy complications . DespiteMechanisms explaining the relation between hypertensive disorder of pregnancy and impaired fetal neurodevelopment are not yet well understood, but may include fetal malnutrition and intrauterine growth restriction, both of which can occur as a result of placental dysfunction associated with maternal hypertension. Fetal exposure to hypoxia occurring as a result of gestational hypertension has also been suggested to affect fetal brain development . Animal We seek to expand understanding of the etiology of WM deficits within ADHD through different samples. Extant literature has shown the relation between maternal smoking and hypertension with ADHD diagnosis, specifically, and WM, broadly. However, we know of no work examining predictors of WM impairment within the diagnostic category. To this end, we examine the association between these candidate prenatal risk factors and the later WM functioning of ADHD-diagnosed offspring. We hypothesize that presence of these risk factors during the prenatal period will predict decreased offspring WM capacity.N = 228) investigating the longitudinal developmental trajectory of girls with and without ADHD. Given limited attention given in research to girls with ADHD (N = 140) ranged from 6 to 12 years old  at baseline (the sample was ascertained from 1997 to 1999). The sample is ethnically diverse, with 56% Caucasian, 28% African American, 11% Latina, 4% Asian American, and 1% Native American, with household incomes ranging from less than $10,000 to greater than $75,000, with an average of $50,000 to $60,000  participating in the Berkeley Girls with ADHD Longitudinal Study (BGALS), a broader study . This variable was calculated and used instead of the working memory index (WMI) provided by the WASI in order to avoid confounding WM with sustained attention. That is, the WMI includes a summation of performance on the Digit Span Forward (DSF) task plus performance on LNS and DSB. Given literature demonstrating that DSF is a measure of sustained attention, separable from WM  Letter Number Sequence (LNS) and Digit Span Backward (DSB) scores . First, an independent-samples t(101) = −2.1, p = 0.038, d = 0.54] , but SES, race/ethnicity, and maternal age during pregnancy did not significantly contribute. We, therefore, reconducted regression analysis using the most efficient model, testing the relation between high blood pressure during pregnancy and offspring WM capacity, adjusting for offspring PIQ  of girls with combined-type ADHD and 8% of girls with inattentive-type ADHD (n = 5) had mothers who endorsed experiencing hypertension during pregnancy. Diagnostic status did not significantly contribute to variance in offspring WM capacity (p = 0.10), and post hoc t-tests did not reveal significant differences in WM capacity or presence of high blood pressure during pregnancy by group.Children of hypertensive pregnancies had significantly increased WM capacity  compared to children of nonhypertensive pregnancies  [ = 0.54] . Given tt(109) = 0.027, p = 0.979, d = 0.01]  did not differ significantly from children whose mothers did not report smoking during pregnancy  [ = 0.01] . We did post hoc analyses using two large and independent samples of children with ADHD in order to guard against reporting incidental, spurious, or sample-specific findings. Thus, we examined associations between previously identified prenatal risk factors (PTE and blood pressure status) and offspring WM capacity in two additional longitudinal samples. These two samples – Halperin Research Group sample . Diagnostic status was determined  Version . All par Version . IndividMirroring study 1 procedures, WM capacity was determined by summing the standardized LNS and DSB scores from the Wechsler Intelligence Scale for Children (WISC-IV). This information was collected when participants were aged 7–9 years . Mean WM capacity of offspring was −0.0026 (SD = 1.69).via retrospective maternal report data collected during baseline evaluation. Mothers were asked to indicate whether they smoked cigarettes or had preeclampsia while pregnant.Predictor variables were assessed t-tests were again utilized to determine group differences in WM capacity based on presence or absence of candidate prenatal factors. Effect size was calculated using Cohen d. When significant differences were found, regressions were performed to assess individual contribution of predictor variables to WM capacity.All statistical analyses were performed with SPSS, version 25 . Independent-samples t(93) = 1.24, p = 0.219, d = 0.81. Results also did not show a significant difference in WM capacity between children whose mothers reported smoking tobacco (27.4%) and children whose mothers did not report smoking tobacco during pregnancy: t(92) = 0.81, p = 0.42, d = 0.19.Results did not show a significant difference in WM capacity between children of hypertensive pregnancies (4.2%) and children of nonhypertensive pregnancies: This sample consists of 579 individuals with ADHD  participating in the MTA. Individuals participating in this study  were recruited from areas around the United States and Canada for a randomized treatment study. ADHD diagnostic status was determined using the Diagnostic Interview Schedule for Children, Parent Report, version 3.0 DISC-P; . The samWISC-III Digit Span scores collected during screening were used to measure WM capacity. In this sample, Digit Span data were entered as a cumulative score  and were not separated by subtest. As such, separating DSF (sustained attention) performance from backward and sequencing (WM) performance was not possible. The mean Digit Span scaled score was 8.81 (SD = 3.16).via retrospective maternal report data collected during initial/baseline evaluation. Mothers were asked to indicate whether they did or did not have high blood pressure during pregnancy and whether they did or did not smoke during pregnancy.Predictor variables were assessed t-tests were again utilized to determine group differences in WM capacity based on presence or absence of prenatal factors (high blood pressure or tobacco use). Effect size was calculated using Cohen d. When significant differences were found, regressions were performed to assess individual contribution of predictor variables to WM capacity.All statistical analyses were performed with SPSS, version 25 . Independent-samples t(320) = 0.88, p = 0.38, d = 0.17. Results also did not show a significant difference in WM capacity between children whose mothers reported PTE (23.9%) and children whose mothers did not report PTE: t(330) = 1.32, p = 0.187, d = 0.18.Results did not show a significant difference in WM capacity between children of hypertensive pregnancies (12.5%) and children of nonhypertensive pregnancies: n = 4), even though the effect size was large (d = 0.8). This combination of low frequency (with limited ability to detect significant effects) and high effect size suggests that a significant relation between gestational hypertension and offspring WM may be detected in an adequately powered sample.Our aim was to examine the relation between two core prenatal risk factors – maternal high blood pressure and smoking during pregnancy – and WM functioning in offspring diagnosed with ADHD. We predicted that the presence of these risk factors would be independently associated with reduced offspring WM capacity. Findings in an initial, all-female sample did not support either hypothesis. In fact, contrary to our hypothesis, presence of high blood pressure during pregnancy predicted better offspring WM capacity, an association that remained significant after adjusting for relevant covariates such as offspring IQ. ADHD subtype was not found to affect this finding. It is important to note, however, that frequency of high blood pressure in our inattentive sample was very low, possibly leading to inability to reliably detect group differences. PTE was not predictive of offspring WM capacity. Given these unexpected findings, we examined the relation between prenatal risk factors and offspring WM capacity in two additional and independent samples of children diagnosed with ADHD. Neither maternal high blood pressure during pregnancy nor PTE was predictive of offspring WM capacity in either additional sample. However, frequency of high blood pressure was extremely low in one sample  on later cognitive function (Presence of high blood pressure during pregnancy was found to be a positive predictor of offspring WM capacity in one of our three samples (BGALS sample), explaining 16.1% of the overall variance in WM capacity. This high function . High blBGALS = 19.7 years, mean ageHalperin = 8.6 years, mean ageMTA = 8.5 years) may partially contribute to observed differences in results. WM capacity is a frontal lobe function known to continue developing until approximately the age of 25 years . Wave 1 results mirrored results found in wave 3 . Although the mean age of participants in wave 1 is much closer to that of the Halperin and MTA samples, the difference in age still falls at a critical developmental point. Given literature suggesting differentiable WM performance between ages 6–8 and ages 9–10 years  measures of WM functioning. Additional future directions include investigating the mechanisms by which these prenatal conditions may affect cognitive outcomes.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board at the University of California, Berkeley. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.EM: study design, data organization, data analysis, and drafting of manuscript. MO: data organization, data analysis, and drafting of manuscript. SH: data acquisition, data analysis tools, and drafting of manuscript. All authors contributed to the article and approved the submitted version.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.


Whilst cognitive bias modification was initially used in the treatment of anxiety disorders, it is also currently being used for the treatment of other psychopathologies. In fact, cognitive bias modification has been especially well-investigated amongst children and adolescents. A recent review suggests some evidence for the modification of interpretative biases amongst children with neurodevelopment disorders. There have since been other studies reporting of the existence of other cognitive biases, such as emotional biases, amongst individuals with attention deficit hyperactivity disorder (ADHD). This perspective article will discuss the epidemiology of ADHD and the nature of emotional biases that are present amongst individuals with ADHD. This perspective article also reviewed some of the studies that have assessed and modified emotional biases in individuals with ADHD. A total of three studies have been identified from the published literature that provide evidence for targeting emotional biases amongst individuals with ADHD. These studies provide us with preliminary evidence for the effectiveness of modifying emotional biases and how it could help in ameliorating symptoms related to emotional dysregulation. There needs to be future research in this area with further evidence supporting the effectiveness of modifying emotional biases. It is also crucial for future research to determine which of these tools is best at detecting such biases, and which of these tools are versatile enough and non-invasive that they could safely be implemented for both research and clinical needs. Psychological therapies such as cognitive behavioral therapy (CBT) have been the mainstay approach used in the treatment of children and adolescents with psychiatric disorders . To dateWhilst CBM was initially being used in the treatment of anxiety disorders, it is currently also being used for the treatment of other psychopathologies such as depressive disorder , substanMost of these prior studies have been conducted amongst typical development individuals with psychopathologies such as anxiety disorder. A recent review by Schmidt et al.  sought tIn this perspective article, the focus is on ADHD given that it is a common neurodevelopmental disorder, with a worldwide prevalence rate of 5% . IndividIndividuals afflicted with ADHD tend to have difficulties with emotional regulation. Prior research has suggested that emotional biases are present amongst these individuals. Emotional biases refer to the preferential allocation of attention towards emotional stimuli . EmotionTo identify studies that have been published on emotional biases, articles were identified using a search through the following databases: PubMed, MEDLINE (Ovid), Cochrane Library, PsycINFO and Scopus. A library information specialist from the Lee Kong Chian School of Medicine, Nanyang Technological University Singapore was involved in the refinement of the search strategy. The following search terminologies were used: “cognitive bias modification”, “attention bias modification”, “interpretation bias modification”, “affect processing bias”, “emotional bias” and “ADHD” or “attention deficit disorder”, “attention deficit disorder with hyperactivity”, or “attention deficit hyperactivity disorder”. The search strategy was modified to suit the different databases. The databases were searched from inception through to 18 February 2020. With this, a total of 183 citations were identified, of which 127 were duplicates. In this article, we have decided to review only articles that are in English and if they included (a) children and adolescents with ADHD and (b) mentioned how emotional or affective biases are being assessed or modified. Based on the search strategy, a total of three articles were identified. Pishyareh et al. . undertoThese studies provide us with preliminary evidence for the effectiveness of modifying emotional biases and how it could help in ameliorating symptoms related to emotional dysregulation. Emotional biases could therefore be a tangible target in the treatment of children and adolescents with marked emotional dysregulation. A recent meta-analysis by Beheshti, Chavanon, and Christiansen  reviewedHence, a better understanding of emotional bias modification provides a tool to assess and treat individuals with ADHD to decrease the severity of the ADHD symptoms. However, there needs to be future research in this area, with further evidence supporting the effectiveness of modifying emotional biases. From our knowledge, there has been previous research work targeting other types of cognitive biases amongst individuals with ADHD, primarily that of attentional biases ,27. It iFrom these studies, it is very evident that research into emotional biases is still in its infancy, with only three articles describing how they have investigated for such biases. Nevertheless, these three studies ,23,24 hiThis article has highlighted the potential of assessing and modifying emotional biases, which three prior studies have documented to be present amongst children with ADHD. There is a need for future research in determining the effectiveness of this and in determining the most appropriate tools that could be used for assessment and modification. Targeting emotional biases might help the amelioration of impulsive symptoms in children and children could potentially benefit further if such forms of therapy are combined with conventional psychotherapies.


In children around age 12, academic achievement was associated with the transmitted EA PGSs (R2 = 5.7%), but we found no support for genetic nurturing (R2 ~ 0.1%). The ADHD PGSs were not significantly associated with academic achievement (R2 ~ 0.6%). ADHD symptoms in children were only associated with transmitted EA PGSs and ADHD PGSs (R2 = 1–2%). Based on these results, we conclude that the associations between parent characteristics and offspring outcomes in childhood are mainly to be attributable to the effects of genes that are shared by parents and children.It remains a challenge to determine whether children resemble their parents due to nature, nurture, or a mixture of both. Here we used a design that exploits the distinction between transmitted and non-transmitted alleles in genetic transmission from parent to offspring. Two separate polygenic scores (PGS) were calculated on the basis of the transmitted and non-transmitted alleles. The effect of the non-transmitted PGS is necessarily mediated by parental phenotypes, insofar as they contribute to the rearing environment of the offspring (genetic nurturing). We calculated transmitted and non-transmitted PGSs associated with adult educational attainment (EA) and PGSs associated with childhood ADHD in a general population sample of trios, i.e. child or adult offspring and their parents (N = 1120–2518). We tested if the EA and ADHD (non-)transmitted PGSs were associated with childhood academic achievement and ADHD in offspring. Based on the earlier findings for shared environment, we hypothesized to find genetic nurturing for academic achievement, but not for ADHD. In adults, both transmitted (R Research on the influences of the family environment on children’s behavior is complicated by the fact that parents provide their offspring with both the family environment and genes, leading to intertwined effects and possible gene–environment correlation . Scarr and McCartney distinguished three types of gene–environment correlations. Passive gene–environment correlation arises when the rearing environment that parents created is related to the parents’ and hence to the children’s genotypes. Evocative gene–environment correlation arises when children’s heritable behavior evokes responses from others in their environment. Active gene–environment correlation arises when children actively seek out environments that fit their genotypes. Passive gene–environment correlation may contribute to the well-established associations between home characteristics and childhood outcomes. Examples include the association between the number of books in the home and children’s reading skills  is in part a function of the parental genotype. Hence, Kong et al. termed this phenomenon ‘genetic nurturing’, which will induce a (passive) gene–environment correlation. The transmitted alleles, present in both the parents and the offspring, have a direct genetic effect on offspring’s behavior, and an indirect genetic effect that is mediated by the environment that parents create depending on their EA. Therefore, transmitted alleles have both genetic nurturing effects and direct genetic effects, while non-transmitted alleles only have genetic nurturing effects.Ignoring genetic influences in studying the rearing environment can lead to erroneous inference concerning the role of the environment  gene–environment correlation, and if this correlation is not modeled it contributes to common environmental variance Purcell . GeneticWith respect to academic achievement and ADHD, we entertained opposing hypotheses regarding genetic nurturing of EA and ADHD. In the Netherlands, academic achievement in primary school is substantially heritable (~ 75%) with modest common environmental influences (~ 10%) , as demonstrated in both case–control and general population samples (Faraone and Larsson r = 0.44)  was established around 1987 by the Department of Biological Psychology at the Vrije Universiteit Amsterdam and recruits approximately 40% of new-born twins or higher-order multiples in the Netherlands for longitudinal research. Parents of twins receive a survey about the development of their children every 2 to 3 years until the twins are 12 years old. Starting at age 7, parents are asked consent to also approach the primary school teacher(s) of their twin and other children. The survey sent to mothers, fathers and teachers includes the age and context appropriate version of the Achenbach System of Empirically Based Assessment (ASEBA)  their own, as well as the genotype data of both of their parents were available. Genotyping in the NTR has been carried out in subsamples that are, in general, unselected for phenotype. Data were excluded if an individual had a non-European ancestry (n = 472). In this group of families, information on EA was available in people over 25 years of age for 1931 adult offspring  from birth cohorts 1946–1991. Childhood academic achievement was assessed around age 12 and available for 1120 offspring (509 boys and 611 girls) from birth cohorts 1983–2002. ADHD symptoms were assessed at age 10 and 12. If available, age 12 data were analyzed, otherwise age 10 data. Data on ADHD symptoms at home were available for 2518 children (1202 boys and 1316 girls) from birth cohorts 1986–2008. Data on ADHD symptoms at school were available for 1969 (968 boys and 1001 girls) children from birth cohorts 1986–2011.EA in adults was measured by means of a self-report on highest obtained degree. The responses were recoded into four categories: primary education (level 0), lower secondary education (level 1), higher secondary education (level 2) and tertiary education (level 3).Academic achievement in children was assessed by a nationwide standardized educational achievement test Cito . The resADHD symptoms were assessed with the ASEBA system empirically based syndrome Attention Problems scale , Illumina 660 (n = 1323), Illumina Omni Express 1 M (n = 234), Affymetrix 6.0 (n = 7086), Affymetrix Axiom (n = 2665) and Illumina GSA (n = 5195). Genotype calls were made with the platform specific software  following manufacturers' protocols. For the Affymetrix-Perlegen and Illumina 660 platforms, the single nucleotide polymorphisms (SNPs) were lifted over to build 37 (HG19) of the Human reference genome.–5 or the call rate of the SNP was < 95%. Subsequently, only SNPs were selected if the allele frequency of the SNP deviated < 0.10 as compared to the GONL data. All palindromic SNPs with a MAF > 0.40 were also removed. The individual platform data were then merged into a single dataset. In this dataset, the sample IBD was re-compared with their expected familial relations and samples were removed if these did not match. Because the number of completely overlapping SNPs within this combined set off platforms is too small (~ 70 K) for imputation against 1000G, the data were first phased and imputed with Mach-admix, using GONL as a reference panel. This was done for those SNPs that survived quality control and were present on at least one platform, forcing missing genotype imputation for all SNPs. Best guess genotypes were generated from these data, and the following SNPs were selected: SNPs with a R2 > 0.90, with HWE p > 10–5, with a Mendelian error rate < 2%, and if the association of one platform = case vs. the other platforms = controls p-value > 10–5 (applied for each platform) resulting in a genetic backbone of 1.2 M SNPs. After this step, 3017 DNA confirmed monozygotic twin samples were returned into the dataset by duplicating the SNP data of their co-twin. Another 364 DNA samples were added, 335 out of the original 349 samples, plus 29 of their confirmed monozygotic twins, of the NTR that were also sequenced in the GONL reference population. The resulting was a final dataset of 21,001 individuals from 6671 families with 1.2 M SNPs. The cross-chip imputed data were used to calculate genetic principal components using SmartPCA software, and the PCAs were subsequently used to determine if a person was from non-European descent . Allele and strand alignment of SNPs was done against the Dutch Genome of the Netherlands (GONL) reference panel for each platform , resulting in the non-transmitted alleles dataset. Both datasets were then used to calculate PGSs.In total there were 2649 families having two genotyped parents, with 5900 offspring, including 1245 MZ twin pairs, for which allele transmission could be calculated on the 1000G imputed data. Before this calculation, the genotype data were filtered using the following criteria: only ACGT SNPs on the autosomes, no SNPs with duplicate positions, no SNPs with 3 or more alleles, MAF > 0.01, HWE p > 10For the EA PGS calculation we used the GWA summary statistics from the EA meta-analysis  weighted Beta's were calculated using the LDpred package with different cut-offs of the fraction of SNPs with a causal effect /(1 + ICC*(M−1)) in which N = the number of families, M = the number of individuals in a family and ICC = the (average) phenotypic correlation within a family. We applied this separately for MZ and DZ (and siblings) families, given the expected differences in ICC. The power to detect a particular effect size (i.e. percentage of phenotypic variance explained) of the non-transmitted PGS was based on the non-central F-distribution. Power equals the percentage of significant tests of the regression coefficient given an alpha level of 0.01.The sample included twin pairs and their siblings, which meant that observations were not independent. To facilitate the power analysis, we used the effective sample size, i.e. Nr = − 0.271) and non-transmitted (r =  − 0.234) PGSs, indicating that genes involved in EA and ADHD partly overlap. Note that this correlation cannot be interpreted as a genetic correlation in the traditional sense. The correlation between the transmitted and non-transmitted PGSs was low for both EA (r = 0.090) and ADHD (r = 0.033). PGSs and phenotype data were available for adult EA , and childhood academic achievement , ADHD symptoms at home  and ADHD symptoms at school .The EA and ADHD PGSs correlated moderately for both the transmitted  and non-transmitted  PGSs based on the EA meta-analysis  on ADHD symptoms at home were both diminished. The effects of the transmitted EA PGS  and ADHD PGS  on ADHD symptoms at school are also somewhat lower , ADHD symptoms at home  and ADHD symptoms at school  Table  was not 2 explained by the non-transmitted PGS of 1.6%, 0.7% and 0.9% for, respectively, academic achievement, ADHD symptoms at home and ADHD symptoms at school  of non-transmitted alleles. The effect of genetic nurturing on ADHD was non-significant and close to zero (R2 ~ 0.1%). This supports our hypothesis concerning ADHD that genetic nurturing is not important. However, our hypothesis concerning academic achievement, namely that genetic nurturing would play a role in childhood academic achievement, was not supported. Regarding academic achievement, the twin literature shows a small, but significant, effect of common environment in the Netherlands , but present in adulthood (R2 = 1.7%)? First, we note that academic achievement scores at age 12 and the highest obtained degree in adulthood are different traits. We used PGSs for EA, i.e., the trait in adulthood. Hence, the effect of the transmitted alleles is expected to be somewhat higher for education in adulthood (7.6%) than childhood (5.7%). Nevertheless, this does not explain the difference in the effects of the non-transmitted alleles.How can we reconcile the effect of genetic nurturing on education being absent in childhood  on EA, in addition to the direct genetic effect, in adulthood. For academic achievement and ADHD in childhood, we only found evidence for direct genetic effects. We can thus conclude that reported associations between home characteristics related to parental EA and ADHD and child outcomes seem to be mainly a marker of genetic effects shared by parents and children.